Schizophrenia Bulletin

Mittal, Vijay, A;Walther,, Sebastian

doi: 10.1093/schbul/sby176pmid: 30496514

Kendler contrasted early and more modern depictions of schizophrenia and observed that while abnormal motor behaviors were prominently featured in the diagnostic conceptions in the first half of the 20th century, these signs are all but entirely neglected in more modern operationalized systems.1 This change was likely due to the mid-century advent of antipsychotic medication when characteristic motor side effects began to mask “spontaneous” movement abnormalities and investigators and clinicians turned to extrapyramidal symptoms (EPS) and tardive dyskinesia instead. As a result, motor pathology vanished as a core feature of psychosis for more than 50 years. However, a new generation of research, focusing on biological relatives, medication-naïve patients, and prospective high-risk research designs has renewed the mechanistic focus on motor function.2 Dovetailing these advancements is accumulating evidence which suggests that although some motor abnormalities may prove to be unique to psychosis, that movement phenomena can be highly relevant even if they are not specific to a particular disorder.3–5 For example, the NIMH Research Domain Criteria (RDoC) has just now released a sixth domain devoted to motor symptoms: https://grants.nih.gov/grants/guide/notice-files/NOT-MH-18-053.html. Further, Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) assigned a separate diagnostic category for catatonia and emphasized association with multiple disorders. However, while mechanistic and dimensional understanding of motor behaviors has received increased attention, a focus on relevant clinical considerations has been lacking. This is unfortunate as motor phenomena have significant intervention as well as unique clinical research implications that are not addressed in other domains of psychopathology. This list ranges widely from the direct treatment of motor symptoms such as catatonia and minimization of motor medication side effects to the broader applications of driving prediction, precision medicine and highlighting novel treatment target mechanisms as well. One prominent application relates to staging and predicting clinical outcomes. Studies in the 1990s first highlighted that abnormal and delayed premorbid period motor development characterized infants and toddlers who later developed schizophrenia.6 Following this work, investigators evaluated clinical high-risk individuals and observed unusual motor activity (eg, spontaneous dyskinesias) is characteristic and further, predictive of eventual onset.7 Along this line, recent advances in instrumental or automated motor assessments promise to dramatically improve the precision and scope of motor abnormality assessments. One excellent example of this application is the current paper of Apthorp and colleagues (in this issue)8 who tested postural sway with instrumental measures in schizotypal personality disorder, schizophrenia, and controls. Abnormal sway indicating poor cerebellar function was detected in both patient groups (who performed comparably, despite the fact that schizotypal individuals are typically less impaired than individuals with schizophrenia on any number of indices), suggesting that discrete disturbances of postural control are closely linked to the constitutional vulnerability underlying psychotic disorders. This type of breakthrough, which has numerous implications for informing neurodevelopmental conceptions, also highlights the promise of instrumental measures that allow for precise quantification of abnormal movements. With respect to treatment applications, this approach is likely to inform on any number of future studies designed to employ the method for detecting psychosis-risk populations as well. An additional application of instrumental motor measures is to inform on underlying neurocircuitry. Neuroimaging studies applied these measures to identify components of the motor circuit, which is distinct from circuits involved in other psychosis dimensions.3 For example, the SyNoPsis project focuses on 3 separate key brain circuits relevant to psychosis, ie, motor circuitry in abnormal motor behavior, associative circuit in formal thought disorder, and a limbic circuit in persecutory or grandiose delusions.9 Knowledge on the underlying network dysfunction will be critical in developing new treatment options including targets for non-invasive brain stimulation. Another promising clinical application relates to harnessing assessment of motor phenomena in improving treatment and promoting individualized medicine. As indicated above, in addition to “spontaneous” movement symptoms, motor abnormalities may occur as a side effect of antipsychotic medication. Investigators have been working to employ sensitive instrumental assessments (eg, such as handwriting kinematic analysis) to carefully monitor medication side-effects in a quick, standardized, and convenient fashion.10 Further, having mobile platform for motor assessments (handwriting on a tablet computer), would allow for non-specialists to incorporate motor assessments in clinical settings. In addition, by the time we can assess behaviors with portable wearable technology, we will be able to monitor these types of side effects across all settings. Another possibility is that given that some motor signs are distinctly associated with negative symptoms or disorganization,11 tracking abnormalities could indicate clinically relevant changes. In this way, motor assessments may inform the treatment strategy (eg, adapt medication, schedule appointments). Likewise, motor abnormalities may reflect who might respond to a particular treatment,12 or indicate distinct trajectory in a high-risk context. Recently Dean and colleagues stratified a sample of clinical high-risk subjects according to their motor performance on instrumental measures identifying 3 vulnerability subtypes with distinct cognitive and clinical profiles, as well as different transition risks.13 Finally, a body of emerging evidence suggests that the motor behavior themselves may serve as promising clinical targets. Complex motor-related behaviors such as the perception, interpretation, and performance of gesture are impacted in psychosis, and given the critical role these nonverbal behaviors play in normative everyday communication, it is not surprising that respective deficits significantly contribute to poor functional outcome and disability in schizophrenia.14 This is particularly relevant as gesture may be responsive to remediation. For example, Schülke and Straube (in this issue)15 examined abnormalities in the semantic processing of co-verbal gestures in psychosis, and observed that reducing frontal excitability (with cathodal stimulation) normalized patient group performance on a task where participants determined if the content of a storied segment matched or did not match with the gesture displayed by the actor. This type of approach, employing stimulation and targeting motor-related abnormalities and deficits, is showing promise in treating motor-related phenomena across the psychosis spectrum (eg, motor learning with cerebellar stimulation).16 Finally, ambulatory movement itself could be a target. Feedback on ambulatory movements and instructions could be given by the same mobile devices. This field of ambulatory exercise interventions is rapidly evolving and mobile devices may be an important advantage in this process. Psychotic disorders are characterized by numerous transdiagnostic and unique movement abnormality subtypes, including those discussed presently, as well as others (eg, grip strength, neurological soft signs, eye movements).2,3,17 We argue these behaviors may hold significant clinical relevance. In contrast to several prominent symptoms in psychosis, abnormal motor behaviors are readily observable markers, adding rigor to traditional clinical assessment and providing a potentially valuable tool for efforts to disseminate broader assessment and monitoring while staying closely tied to underlying mechanisms. In addition, the availability of novel sensitive and mobile technologies is likely to continue to improve our ability to monitor these behaviors in-depth, and across an array of settings, with minimal cost or inconvenience. Finally, attending to motor abnormalities may inform on individual risk, course, and treatment effects in psychosis, potentially directing targeted behavioral interventions or neurostimulation. As motor dysfunction continues to benefit from increased attention in our field, clinical application should be at the forefront of these efforts. References 1. Kendler KS . Phenomenology of schizophrenia and the representativeness of modern diagnostic criteria . JAMA Psychiatry . 2016 ; 73 : 1082 – 1092 . Google Scholar Crossref Search ADS PubMed 2. Walther S , Mittal VA . Motor system pathology in psychosis . Curr Psychiatry Rep . 2017 ; 19 : 97 . Google Scholar Crossref Search ADS PubMed 3. Mittal VA , Bernard JA , Northoff G . What can different motor circuits tell us about psychosis? An RDoC perspective . Schizophr Bull . 2017 ; 43 : 949 – 955 . Google Scholar Crossref Search ADS PubMed 4. Hirjak D , Meyer-Lindenberg A , Fritze S , Sambataro F , Kubera KM , Wolf RC . Motor dysfunction as research domain across bipolar, obsessive-compulsive and neurodevelopmental disorders . Neurosci Biobehav Rev . 2018 ; S0149–7634 : 30214 – 30218 . 5. Walther S , Mittal VA , Bernard JA , Shankman SA . The utility of an RDoC motor domain to understand psychomotor dysfunction in depression . Psychol Med . 1 – 5 . doi: 10.1017/S0033291718003033. 6. Walker E , Lewine RJ . Prediction of adult-onset schizophrenia from childhood home movies of the patients . Am J Psychiatry . 1990 ; 147 : 1052 – 1056 . Google Scholar Crossref Search ADS PubMed 7. Mittal VA , Neumann C , Saczawa M , Walker EF . Longitudinal progression of movement abnormalities in relation to psychotic symptoms in adolescents at high risk of schizophrenia . Arch Gen Psychiatry . 2008 ; 65 : 165 – 171 . Google Scholar Crossref Search ADS PubMed 8. Apthorp D , Bolbecker A , Bartolomeo L , O’Donnell BF , Hetrick W . Postural sway abnormalities in schizotypal personality disorder . Schizophr Bull . 2019 ; 45 : 512 – 521 . 9. Strik W , Stegmayer K , Walther S , Dierks T . Systems neuroscience of psychosis: mapping schizophrenia symptoms onto brain systems . Neuropsychobiology . 2017 ; 75 : 100 – 116 . Google Scholar Crossref Search ADS PubMed 10. Caligiuri MP , Teulings HL , Dean CE , Niculescu AB III , Lohr JB . Handwriting movement kinematics for quantifying extrapyramidal side effects in patients treated with atypical antipsychotics . Psychiatry Res . 2010 ; 177 : 77 – 83 . Google Scholar Crossref Search ADS PubMed 11. Walther S , Stegmayer K , Horn H , Razavi N , Müller TJ , Strik W . Physical activity in schizophrenia is higher in the first episode than in subsequent ones . Front Psychiatry . 2014 ; 5 : 191 . Google Scholar PubMed 12. Murck H , Laughren T , Lamers F , et al. Taking personalized medicine seriously: biomarker approaches in phase IIb/III studies in major depression and schizophrenia . Innov Clin Neurosci . 2015 ; 12 : 26S – 40S . Google Scholar PubMed 13. Dean DJ , Walther S , Bernard JA , Mittal VA . Motor clusters reveal differences in risk for psychosis, cognitive functioning, and thalamocortical connectivity: evidence for vulnerability subtypes . Clin Psychol Sci . 2018 ; 6 : 721 – 734 . Google Scholar Crossref Search ADS PubMed 14. Walther S , Eisenhardt S , Bohlhalter S , et al. Gesture performance in schizophrenia predicts functional outcome after 6 months . Schizophr Bull . 2016 ; 42 : 1326 – 1333 . Google Scholar Crossref Search ADS PubMed 15. Schulke R , Straube B . Transcranial direct current stimulation improves semantic speech-gesture matching in patients with schizophrenia spectrum disorder . Schizophr Bull . 2019 ; 45 : 522 – 530 . 16. Gupta T , Dean DJ , Kelley NJ , Bernard JA , Ristanovic I , Mittal VA . Cerebellar transcranial direct current stimulation improves procedural learning in nonclinical psychosis: a double-blind crossover study . Schizophr Bull . 2018 ; 44 : 1373 – 1380 . Google Scholar Crossref Search ADS PubMed 17. Firth J , Stubbs B , Vancampfort D , et al. Grip strength is associated with cognitive performance in schizophrenia and the general population: a UK biobank study of 476559 participants . Schizophr Bull . 2018 ; 44 : 728 – 736 . Google Scholar Crossref Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Newhill, Christina, E

doi: 10.1093/schbul/sby043pmid: 29660097

One evening my telephone rang, and it was my mother. She didn’t sound like herself; she sounded sad… “Christina, I’m afraid I have some bad news. Julie Sipowicz died earlier this week … her family hadn’t heard from her in a while, and when the police checked on her welfare, she was found dead, alone in her apartment. They think she killed herself.” “What?” I said, “Oh no … poor Julie.” I sat down; feeling like someone had sucker punched me. I thought back to the first day of class two years prior when I was a junior professor of social work. The class was a course on working with individuals with serious and persistent mental illness and their families. I noticed a young woman sitting in the first-row center who looked very familiar although I couldn’t quite place her. During the mid-class break, the young woman approached me “Professor Newhill, do you remember me from high school?” I took a closer look at her and then exclaimed “Oh my gosh—Julie? It’s you!” Julie had been our star oboe player, embarking on a successful music career after high school. “What are you doing here?” I asked inanely. “Well, it’s a long story,” Julie said, “Maybe we could have coffee sometime.” We didn’t have coffee until after the semester was over—it was awkward being Julie’s professor and also her former high school friend. But one day the following January, we met for coffee and caught up on each other’s lives. Our conversation drifted back to high school, and I commented on how successful Julie had been then: National Honor Society, class treasurer, varsity volleyball player and star oboist who went all the way to State Orchestra two years in a row. “I hated high school,” Julie said. My mouth dropped open. “Why?” I asked, “I certainly couldn’t wait to graduate but you had everything going for you.” Julie looked over her shoulder and then whispered “It may have looked that way but things weren’t as they seemed. You see … my mother tried to poison me all through high school.” A chill went up my spine and I thought “uh oh.” We talked a little about Julie’s music career teaching private lessons and then she told me why she decided to pursue social work. “I was having some problems and went to a clinical social worker for counseling and she helped me so much that I knew I wanted to be a social worker.” “Ah,” I said, “so, um, did things get better with your mother?” “No!” Julie said sharply, “She committed me to the hospital not once but three times. I try to stay away from her but I know she has bugged my house and she talks to me sometimes through the walls.” My thoughts were swirling as we finished our coffee. That evening, I called my mother and told her what Julie had said. “Oh yes,” my mother said, “It has been just awful for Julie’s mother. Julie is very paranoid, you know, and takes everything out on her mother—claims her mother poisons her and she has even threatened her—after that, she was committed to the hospital.” It was hard for me to get my mind around all this. How could such a brilliant, accomplished, nice person like Julie end up paranoid and threatening? I understood it all intellectually but emotionally it was hard to reconcile. Julie and I had coffee a couple more times, and she revealed to me that she had been diagnosed with paranoid schizophrenia after being misdiagnosed as having bipolar disorder. With the hand tremors due to medication side effects, she couldn’t play her oboe anymore but hoped she could help others through social work. She had good days and bad days but was a top student throughout our program and graduated with her MSW. After not having seen Julie for several months, I ran into her one evening as I was coming down the stairs in the university library. Apparently, she had come to the library hoping to find me. She looked terrible: hands shaking, glassy-eyed and confused, her face broken out in a rash. In a shaky voice, Julie asked me to help her. I was shocked by her appearance and asked her to call her doctor, but she refused. I offered to call her doctor, but she said no. Finally, I said that I would like to take her to the emergency room, but she refused that too, although she allowed me to accompany her to the bus stop to go home. As a social worker, I wanted to respect and support her self-determination, but as her friend, I wanted her to get help. We sat together until the bus arrived. I called Julie the next day, and she sounded better but said she planned to stop taking her medication. I urged her to at least talk with her psychiatrist first that there are many medication options; she said she would think about it. I called her again a couple of weeks later to check on her, but her phone had been disconnected. It was several weeks later when she took her own life. Afterwards, I wrote a letter to Julie’s mother expressing not just my heartfelt sympathy but also sharing with her happy memories of playing music together with Julie in high school orchestra, discussing issues in class when she was my student, and of our friendship. What happened to Julie had a great impact on me personally and professionally in ways profoundly different from my previous formal clinical training and research experience. Although I had always empathized with the challenges my clients with serious mental illness and their families faced, I had never before completely grasped the deep losses incurred when serious mental illness hits someone’s life. Julie was a highly accomplished individual, and yet she lost almost all of her accomplishments as a result of her illness including close family relationships, her music career, her marriage and, eventually, her life. This insight led to my developing an assignment for my serious mental illness class which asks students to select a serious mental illness and write a paper describing how their actual life would have been impacted and changed if they had developed the particular disorder. Would their family relationships be different? Would they be able to pursue graduate school? Would they have challenges making and maintaining friendships? Would they have been able to work in competitive employment? I also realized that although Julie was a top student academically in our social work program, the knowledge and skills she gained did not help her to help herself. Knowledge about mental illness did not facilitate her own journey toward recovery, including developing insight that might have led to receptiveness to treatment. In fact, her condition only worsened. As a long-time advocate for the importance of psychoeducation for individuals with schizophrenia and their families, I realized now that education alone might not be sufficient for some individuals. The last issue that became vividly apparent to me was how I personally struggled with conflicting feelings about my various roles with Julie during the latter period I knew her before her death. I was Julie’s friend and her professor but I was not her therapist, and I felt that this limited how I could help her or should attempt to help her at that time. I think we academic educators, particularly those of us who are former clinicians, can struggle with figuring out what constitutes appropriate roles and boundaries when encountering troubled students and my situation was complicated further by my prior friendship with Julie. After she took her life, I kept thinking about how I might have or should have managed things differently, and I felt great guilt about that. Today, looking back, I feel sadness, regret, and the guilty feelings still linger. I feel sad not only because Julie’s life had reached a point where she decided to end it, but sadness for all who cared about her and their profound loss. This essay has helped me deal with my sadness by writing about Julie and, through that, paying tribute to her and who she was. My regret and guilt relate to the decisions I made before her death. Looking back to the day I saw her in the library, I believe I should have set my strong commitment to client self-determination aside (along with my ambivalence over role conflict), called the mobile crisis team and asked them to come out and evaluate Julie for involuntary hospitalization even if she would have seen that as a betrayal of trust. Just as a person has the right to be ill and refuse treatment, doesn’t he/she also have the right to be rescued?1 Clinicians, family members, and consumers continue to struggle to find a balance between client civil rights and the reality that serious mental illness can affect one’s ability to make the best decisions for oneself, and so sometimes others must step in. Furthermore, the consequences of such decisions can include irreversible outcomes such as suicide which affect not just the ill individual, but those who care about him/her. Schizophrenia alone affects approximately 1 out of 100 people; I had 250 people in my high school graduating class. Odds are, then, at least two would develop schizophrenia and, in fact, two people did including Julie. I think we read these statistics but don’t think it could happen to those we know personally, those we care about, especially someone as highly successful as my friend who at one time appeared to have such a bright future. Reference 1. Treffert DA . Dying with their rights on . Am J Psychiatry . 1973 ; 130 : 1041 . Google Scholar Crossref Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Meijer,, May-May

doi: 10.1093/schbul/sby022pmid: 29546321

Introduction I was working as an assistant professor at the working group on Philanthropy at the Vrije Universiteit in Amsterdam, active in foreign politics, and advisor for an endowment on microcredits, when I got suspicious in 2005. This lead to forced hospitalization for 6 months in 2009, a short stay in solitary and the diagnosis “schizophrenia.” My husband divorced from me, I hardly saw my son, I quit my job and felt lonely. I also suffered heavily from the side effects of the medicines. In the second phase of my psychoses, I was in (forced) hospitalization for 4 months and had a short stay in solitary that followed in October 2013 till the beginning of February 2014. My diagnosis was changed. I have a vulnerability for psychoses and mania. In a previous article, “Mum you will get better” for Schizophrenia Bulletin, I elaborated upon what helped me deal with it. In this article, I stress the need for good triage at mental health hospitals. It is based on my experiences with my mental health hospital in the Netherlands. I realize the drawbacks of having experiences with one hospital only. Nevertheless, I hope that I can help fellow patients and psychiatrists by sharing my experiences. My Experiences with Triage at My Psychiatric Hospital Usually, the experiences with the receptionists of my psychiatric hospital were fine. However, two incidents alarmed me, which I elaborate upon below. Case 1 In July 2016, I had lowered my medication. During the night I had the idea that I wanted to make love to people to contribute to a more peaceful world. They were primarily politicians. I could not sleep and e-mailed this quite embarrassing story to my psychiatrist. This was very different from my first psychoses because then, I was completely locked up in myself and did not say anything to anyone. I had started to trust my psychiatrist and knew that I should reach out to her. When I emailed my imaginary love affairs to her in the middle of the night, I looked at a news website. I saw that a truck drove into a group of people in France, Nice, killing 84 people. I felt as if I was responsible for that attack that was claimed by ISIS. Nevertheless, I did not feel tears well up in my eyes. I was afraid that I started to lose my emotions and had become a monster.(My psychiatrist of the hospital department mentioned that the loss of feeling did not belong to my personality, but was caused by the mania. I was happy that he shared his knowledge with me. I was not a monster. We agreed upon to higher the dose of my mood stabilizer. I recovered soon and stayed for 6 days in the hospital.) In addition, I had read that the Dutch minister of Internal Affairs suffered from heart disease. I felt that I had contributed to his illness because he is responsible for the secret service and I had unraveled the system of the secret service via my psychoses, although I did not know exactly what was real and what was not. Coincidently, I had been in the manifesto committee of the Dutch Labour Party with him for the 2006 national Dutch elections. The next day I felt horrible because I felt that I was responsible for the horrible attack in Nice, despite the fact that I was at that time in my bed at home in the Netherlands. As in earlier psychoses, I had the feeling that the secret service was tapping my phone. I contacted my psychiatrist, and she said that she understood my suffering and that if I wanted I could be taken into voluntary hospitalization. I thought about it for a while. There were voices in my head—one of a Dutch psychiatrist I deeply trust—saying that I was suffering so much and that I had a burden on my shoulders that normal persons could not bear. The voices suggested to me that it might be better to be hospitalized. Therefore, I called the telephonist of my mental health hospital. She said to me something like: “I do not know anything about your hospitalization.” I mentioned that I had agreed with my psychiatrist that I could be taken into voluntary hospitalization when I wanted to. She continued to say that she did not know anything about my hospitalization. I felt ill and desperate. Making phone calls is an impossible thing to do for me when I am ill, as they exhaust me enormously. When I am ill, I am afraid that the secret service will tap my phone and that I will get exposed to radiation from the phone. It made it even worse that in this situation, I had to my very best to convince someone about something that I already agreed upon with my psychiatrist. I do not remember how I ended up in the hospital, but I think that my sister finally arranged my hospitalization. Case 2 Recently, I wanted to go to Israel to participate in public peace negotiations at Rabin Square in Tel Aviv. People from Israel and Palestinian territories were invited. My family and my ex-husband did not like the idea that I would go abroad because they knew that I had been very ill. Nevertheless, two of my friends encouraged me. They mentioned that it is normal for family to be anxious, but that I should not let my life ruled by fear and that I am a grown-up woman who knows when she is ill. I agreed with them. I was also looking for freedom, so I decided to go. I wanted to prepare myself well. Therefore, I called the mental health hospital to ask for enough medicines. The telephonist mentioned that my psychiatrist was on holiday and that I should contact her when she was back, which would be on Monday. I replied to her that this was too short in advance because I would leave Wednesday morning. The telephonist insisted that I had to wait for my own psychiatrist to return. Finally, I said to the telephonist that my psychiatrist had mentioned to me that I could always contact her deputies and I insisted to talk to them. She tried to connect me with one of them during office hours. However, the deputy psychiatrist did not pick up the phone. I called another time during office hours, but again she did not pick up the phone. In the afternoon I called again, and this time the telephonist emailed the deputy, but I never received a call. I called again just before the end of the working day, but the two deputy psychiatrists had already gone home. Giving Feedback to My Psychiatric Hospital and the Reaction I was frustrated about these two cases. Coincidently, I had recently called the general practitioner for an issue with my 11-year old son. The doctor’s assistant asked me lots of questions if I had measured his temperature and so on. It occurred to me that general practitioners and somatic hospitals have triage protocols. Therefore, I emailed my psychiatric hospital to ask about their triage protocols. I mentioned that I had the feeling that they did not have them and that telephonists just did their best to do what they thought was reasonable. They lack medical knowledge however. When my own psychiatrist came back, she mentioned that the telephonists are administrative assistants. On the one hand, this confirmed my experiences with them, on the other hand, I was very surprised to find out that there were indeed no triage protocols at my mental health care hospital. I had also emailed the director of my mental health care hospital. Luckily, she appreciated my feedback, and she mentioned that she would discuss it in the meeting with the heads of the departments. For those who are interested: my visit to Israel was a success. It was very interesting to attend the public peace negotiations, to visit the Church of Nativity with a Palestinian peace activist in Bethlehem and to meet a peace activist in Jerusalem and walk the Via Dolorosa. I could combine my peace work with my love for God, meet people searching for peace as well and enjoy wonderful surroundings. Conclusion Although this article is based only on my experiences with my own mental health hospital in the Netherlands, I stress the need for triage at mental health care hospitals. Suffering from a psychiatric illness is not different from suffering from a somatic illness, and therefore there should not be any difference in triage. Telephonists of psychiatric hospitals should possess medical knowledge. © The Author(s) 2018. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Jepson, Jason A

doi: 10.1093/schbul/sbx186pmid: 29301036

Looking back there are steps in my recovery from mental illness that I was totally unaware of as I was going through them. Recently, I’ve been thinking about the positive steps in my recovery. It began when I finally accepted my mental illness and became consistent with my medication. I realized that this was how life was going to be. I would have this illness for the rest of my life. It would be up to me to have a voice in how my future would develop. After I left the hospital for the second time, I went into a homeless respite for a period of time. After being confined to a hospital I enjoyed this newfound freedom and the prospects of another step in a forward direction. I was a 3 pack a day smoker, and then I was able to smoke whenever I wanted. I also had an hour each day when I could walk or be picked up by a family member to go to a nearby lunch counter where I enjoyed their homemade German chocolate cake. I began to feel slightly normal again. I could have been anyone eating at that café. No one knew me, and no one could make a judgment about me. Gradually, I moved to a rooming house where I had my own room and my own TV that I could watch whenever I pleased. I took a major step when my Veterans benefits came through and I finally moved into my own apartment. Words could not explain how happy I was. I finally felt independent again. I was beginning to manage my mental illness. I was responsible again for my own space in the world. Over time, I realized that my 3 pack a day cigarette habit was having a negative effect on my life and my health. I was missing out on some parts of life because I had to go on a smoke break about every 15 min. With the help of God and Nicorette lozenges, I was able to quit. I had been drinking alcohol since high school. When I wanted to plan an outing, it involved alcohol. Yes, I was an alcoholic. Drinking was more important to me than eating. I finally decided to quit drinking for a number of reasons: the calories involved were making me unhealthy; I was spending a lot of money; and more importantly, the alcohol was affecting my medication. I enrolled in a Substance Abuse class at my Veteran’s hospital where I learned about being good when no one is watching. Stopping smoking and drinking were some of the most important changes that I have made in my life since my initial mental health diagnosis. There have been other changes that may seem smaller, but have been helpful to my positive state of mine. I started exercising and trying to eat healthier. I am also taking responsibility for my health by taking advantage of other benefits at my V-A, such as dental care. For the first time in my life, I can say I am truly satisfied. This would not be so without the steps that helps me to change my life. Today with the help of my veteran’s benefits and social security, I live in a very nice apartment. There is a grocery store across the street, and a Costco a block away. I have an outdoor porch where I can enjoy sitting in the sunshine and a fitness center where I can work out. I don’t take any of these amenities for granted because I know that my life would spiral downward without my medication and my support system at the veteran’s hospital and also my family. What is the point of this reflection on the changes in my life? It’s because I want everyone to know that things can get better. My experiences have made me more religious now but I will not preach, however, I do feel a since of pride in what I have been able to accomplish. I have found my voice as a writer, and my desire is to bring hope to anyone who might be facing mental illness. My advice for anyone is keep moving forward, avoid negativity, and don’t ever give up. Remember that exercise helps, set goals. Find a release whether it is physical, mental, or emotional. Everyone will have bad days, but make sure you stand up to them, and don’t be afraid to ask for help. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center 2018. This work is written by (a) US Government employee(s) and is in the public domain in the US. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center 2018.

Poletti,, Michele;Gebhardt,, Eva;Kvande, Marianne, N;Ford,, Judith;Raballo,, Andrea

doi: 10.1093/schbul/sby100pmid: 30007369

Abstract The motor system in its manifold articulations is receiving increasing clinical and research attention. This is because motor impairments constitute a central, expressive component of the mental state examination and a key transdiagnostic feature indexing disease severity. Furthermore, within the schizophrenia spectrum, the integration of neurophysiological, developmental, and phenomenological perspectives suggests that motor impairment is not simply a generic, extrinsic proxy of an altered neurodevelopment, but might be more intimately related to psychotic risk. Therefore, an increased understanding, conceptualization, and knowledge of such motor system and its anomalies could empower contemporary risk prediction and diagnostic procedures. corollary discharge, developmental psychotic risk, sense of agency, self-disorders, psychosis, vulnerability, endophenotypes The motor system in its manifold articulations is receiving increasing clinical and research attention, partly as a consequence of the empirical impulse induced by the research domain criteria (RDoC) initiative, with the related emphasis on multiple levels of explanation and dimensional approaches.1 Indeed, as well known to many clinicians in their daily practice, most of the neuropsychiatric disorders such as schizophrenia, obsessive compulsive disorders, autism, and mood disorders, and neurodegenerative conditions are characterized by various degrees of motor impairment. Those motor impairments constitute a central, expressive component of the mental state examination, and a key transdiagnostic feature indexing disease severity.2 Motor impairment plays a special role in schizophrenia spectrum disorders, because early motor manifestations emerge already in premorbid and prodromal stages of the neurodevelopmental trajectory leading to overt and syndromic psychotic states. This is the case of later achievement of motor milestones in infancy, poor motor coordination, dyskinesia, and neurological soft signs.2 Furthermore, besides motor delays and dys-coordination, several subtypes of motor abnormalities, including athetosis, chorea, dystonia, bradykinesia, tics, and stereotypies, are elevated in psychotic disorders, even among drug-naïve individuals. However, whereas, other motor symptoms clusters, such as catatonic symptoms, dystonia, and stereotypies might become more pronounced in later clinical stages; dys-coordination and motor lags emerge relatively early in development.3 This is probably related to the different underlying neuropathological mechanisms subtending movement abnormalities in psychosis. Accordingly given the continuous, gradient-like distribution of motor impairment along the neurodevelopmental progression to psychosis, motor impairment clearly warrants more prominence in contemporary psychosis prediction frameworks (eg, within the ultrahigh-risk paradigm), which mostly incorporate nonmotoric features. Needless to say, increased understanding, conceptualization, and knowledge of motor impairment could empower contemporary diagnostic procedures of psychosis. Indeed, motor impairment profiling could complement other clinical assessment procedures (eg, interviews addressing the patient’s psychological processes)4 with psychomotor proxies amenable to innovative, digitally enabled tracking (eg, digital phenotyping of motoric patterns through accelerometer-based smart wearables).5,6 Furthermore, motoric performances are likely to point to more specific and circumscribable functional circuits at a neurobiological level, whose neurodevelopmental pathways might be more easily mapped than those associated to neuropsychological performances (eg, social cognition and theory of mind). However, although current empirical research provides extensive descriptions of specific motor circuits involved in distinct aspects of motor impairment within the schizophrenia spectrum (eg, basal ganglia in excitation/inhibition abnormalities, cerebellar-subcortical circuits in sensorimotor dynamics, and corticomotor circuits in psychomotor organization and speed),7 the nature of the relationship between motor impairment and psychotic risk (or broadly speaking psychosis-proneness) is still unclear and perhaps underconceptualized. Crucially, is motor impairment just an indirect phenotypic proxy of broad neurodevelopmental alterations putatively associated with prospective risk8 of developing psychosis (ie, an epiphenomenic flag)—or, rather, is motor impairment a pathogenetically central kernel (eg, a direct manifestation of a latent pathophysiological mechanism causally involved in the neurodevelopment of psychosis)?9,10 Recent neuroscientific research in the field seems to confer further plausibility to the pathogenetic relevance. For example, Feinberg11 suggested that impairments in corollary discharges (CDs) may underpin psychotic experiences, and more recently, we suggested that they may represent a specific pathophysiological link between motor impairment and longitudinal psychotic risk.12 Across the animal kingdom, this basic neural mechanism allows all species to distinguish between sensations coming from external sources (eg, pressure on a nematode’s head from an approaching predator) and self-generated sensations (eg, pressure on its head from swimming forward).13 It both tags sensations as coming from “self” and minimizes the resources needed to process the sensations in multiple sensory domains (eg, visual, auditory).13,14 Vocalization studies in primates show that responses in auditory cortex are relatively inhibited during self-initiated vocalizing and excited during passive listening,15–17 likely reflecting the successful action of the CD mechanism. Also, the CD contributes to perceptual stabilization, and at the motor level, it enables fluid motor sequencing and motor learning, contributing to the subliminal scaffolding of the experiential field (see figure 1).18 Fig. 1. View largeDownload slide Corollary discharge (CD), sensorimotor physiology, and anomalies of the sense of agency. CD is a basic neurophysiologic architecture enabling the dynamic processing of the sensory consequences of embodied, situated actions. It contributes to the coherent organization of the experiential field in a unique sensing and acting flow. When altered, broad and pervasive impairments of the sense of agency might emerge. (See figure 2 for the developmental articulation of related psychopathological vulnerability.) Fig. 1. View largeDownload slide Corollary discharge (CD), sensorimotor physiology, and anomalies of the sense of agency. CD is a basic neurophysiologic architecture enabling the dynamic processing of the sensory consequences of embodied, situated actions. It contributes to the coherent organization of the experiential field in a unique sensing and acting flow. When altered, broad and pervasive impairments of the sense of agency might emerge. (See figure 2 for the developmental articulation of related psychopathological vulnerability.) From a developmental perspective, a pivotal pathophysiological role of CD in linking motor impairment to psychotic risk, is not implausible. Indeed, first, neonates already by 2 months of age are able to discriminate precisely between self- and exogenous-stimulations19: as the integrity of CD mechanisms is crucial for such discrimination, CD mechanisms are likely to have an early onset in human development. Second, motor coordination impairment in childhood (ie, dyspraxia) is presumed to be subtended by co-potentiating impairments in two key basic processes involving movement circuits: CD mechanisms and the sensory feedback to estimate actual body states.20 The sensorimotor remapping in dyspraxic children is marked by a larger discrepancy between sensory and motor signals in order to maintain continuous learning and adaptation; ie, these children have difficulties in processing error signals used for adjusting action that arise from comparing sensory feedback to CD. The deficiencies in error signal processing may be due to noisier/inefficient sensory feedback and impaired CD mechanisms, both of which have been directly documented in this condition.21–23 Another cognitive model of developmental coordination disorder, the internal deficit model,24 suggests that these children have difficulties in generating or using predictive estimates of body position as a means of correcting actions in real time: this would also affect their ability to learn new internal models or modify existing ones. Thus, even within the internal deficit model, impaired CD mechanisms may be causally involved. Third, childhood motor impairment is longitudinally associated with an increased risk of psychosis25–28 and schizotypy,29 and the anamnesis of psychotic subjects is characterized by later achievement of motor milestones and subsequent motor impairment.2,3 Moreover, genetic risk for schizophrenia spectrum disorder is associated in childhood with phenotypic impairments at the motor level, as highlighted by familial high-risk studies on offspring of subjects with diagnosis of schizophrenia.30 Importantly, clinical-high risk youth have a documented abnormality in suppressing cortical responsiveness to sensations resulting from their own motor actions, specifically during talking.31,32 This has also been seen in people with schizotypy,33 further underscoring the sensitivity of CD abnormalities to psychosis across the wellness spectrum. Finally, CDs are altered in psychosis in multiple sensory systems34,35 and CD impairments may be involved in the pathogenesis of specific psychotic states.11,12,36 This is due to the pivotal role of CD for the primitive, immediate experience of self-agency (ie, the direct, implicit sense of being the author/volitional agent of an ongoing action). That is, when a predicted sensation matches an actual sensation it contributes to the concrete subjective experience of volitionally controlling our own acts. However, psychotic states may be associated with the external misattribution of self-generated actions.11 In the emergence of prototypical schizophrenia-spectrum psychotic phenomena, misattributions of self-generated actions may take place in terms of delusional-hallucinatory agency disturbances. This might be the case of (1) passivity delusions (eg, experiencing one’s own thoughts, feelings, or actions as under external control), and (2) auditory verbal hallucinations, such as hearing one’s thoughts spoken aloud or externalized commenting “voices.” In sum, CD is likely a key domain—or at least an informative neurophysiological window—to better understand the link between some features of childhood motor impairment and developmental liability to psychosis. Such connection might be mechanistically due to an early onset of CD abnormality in neonates and to its snow-ball-like progressive interference early in development (eg, childhood motor impairment and subtle trait-like anomalies of the sense of self-agency), slowly progressing toward more characteristic vulnerability features (eg, schizotaxic-schizotypal traits), ultimately increasing the chance of incurring in a psychotic state. Indeed, moving bottom-up from a neurophysiological to a cognitive/subjective level, the contribution of CD mechanisms to self-agency and to the perceived continuity of the experiential stream could explain why their alterations may play a role in the development and gradual consolidation of a multidimensional vulnerability to psychosis. CD mechanisms enable sophisticated sensorimotor and neurocognitive operations (eg, perceptual stabilization, motor sequencing, and sensorimotor learning) and contribute to the subliminal scaffolding of the experiential field18,37; at a neural level they enable the implicit sense of mineness of psychomotor experience and lend coherence and fluidity to our immediate interaction with the surrounding world, which are often compromised in schizophrenia and related vulnerability states. Therefore, early CD mechanism impairments unavoidably reverberate into subtle, inchoate distortion of the sense of agency: this might prompt the emergence of those subtler, subclinical modes of altered subjective experience (aka, self-disorders)38–40 that precede (often by several years) the onset of positive symptoms. Self-disorders are trait-like, nonpsychotic anomalies of subjective experience that have been recursively corroborated as schizophrenia spectrum vulnerability phenotypes. They encompass varieties of depersonalization, derealization, and similar distortions of the subjective experience, characterized by a diminished sense of existing as an embodied, coherent subject, vitally immersed in the world and author of his own actions. Self-disorders are clinically closer to initial disturbances of the sense of agency than overt psychotic symptoms and might constitute a more robust (and developmentally earlier) phenotype to anchor the investigation of basic physiological processes as CD mechanisms conferring premorbid (schizotaxic) vulnerability to psychosis. Therefore, closer attention to early alteration of CD mechanism and to their longitudinal, neurodevelopmental impact, might illuminate possible pathogenetic and pathophysiological connections between motor development (and its impairments) and broad vulnerability to mental disorders (eg, psychotic risk) (see figure 2). For example, an early CD mechanism impairment would interfere with the processing of error signals for the adjustments of ongoing motor actions, phenotypically resulting in a childhood clinical picture of motor coordination impairment. Along development, the early alteration of CD mechanism may longitudinally impact the ontogenetic development of the sense of agency, subjectively experienced as fleeting, yet disturbing self-disorders. Therefore, the same mechanism that is early involved in motor coordination impairment, in a more long-term perspective, is also involved in the development of those trait-like subjective experiences indexing the longitudinal liability to psychosis. Moreover, we should also consider possible developmental changes in CD mechanisms, which could capture brain maturational trajectories associated to age-specific windows of vulnerability to schizophrenia spectrum disorders. In this perspective, the early alteration of CD mechanisms presumably present since childhood could be worsened by additive alterations in age-specific processes of brain development, as detected, for example, by oculomotor control tasks.41 Abnormal synaptic pruning42 and myelination43 during adolescence could cause further delay of already altered CD system that, interacting with other possible neural alterations,44 may contribute to explain the peak of psychotic risk during peri-adolescence. Fig. 2. View largeDownload slide Putative developmental progression from early anomalies of sensorimotor integration to clinical phenotypes of increasing severity. Initial anomalies of sensorimotor integration interfere since early developmental phases with the ontogenesis of the sense of self, potentially increasing lifetime liability to schizophrenia spectrum and related psychotic conditions. (Modified from Poletti et al.12.) Fig. 2. View largeDownload slide Putative developmental progression from early anomalies of sensorimotor integration to clinical phenotypes of increasing severity. Initial anomalies of sensorimotor integration interfere since early developmental phases with the ontogenesis of the sense of self, potentially increasing lifetime liability to schizophrenia spectrum and related psychotic conditions. (Modified from Poletti et al.12.) However, given that childhood motor impairment is not necessarily associated with adult psychosis (and, conversely, adult psychosis is not systematically preceded by childhood motor impairment), other physiologically and temporally intermediate mechanisms need to be considered in the developmental cascade from early motor impairment to later psychotic risk. In this sense, developmental psychopathology framework could empower the heuristic resolution of the multiple levels of enquiry thematized in the RDoC approach.1,7 Indeed, in line with the multifinality principle of developmental psychopathology,45 several protective/risk factors as well as other variables may morph the developmental trajectory leading to increased cumulative risk of psychosis over time. Overall, this coheres with some degree of motor impairment described in other adult psychopathological conditions outside the psychosis spectrum2 (although further investigation is needed to establish their possible developmental origins). In any case, even if childhood motor impairment is to be considered a simple, distal risk factor for psychosis, it is crucial to realize that its prognostic value increases along the premorbid and early clinical risk stages. That is, the presence of motor impairment in at-risk/prodromal phases has an increased prognostic weight than in previous, premorbid ones.46,47 This clearly strengthens the rationale for including developmental motor impairment features in multivariate models of psychotic risk calculators.48 In conclusion, the integration of neurophysiological, developmental, and phenomenological perspectives suggests that motor impairment is not simply a generic, extrinsic proxy of altered neurodevelopment, but might be more intimately related to psychotic risk. In this respect, the potential impact of altered CD systems for the early development of an unstable sense of self-agency (ie, a perturbation of basic self-awareness and my-ness of experience that might confer liability to schizophrenia spectrum conditions) is an attractive direction of research.12 This conception may fit alternative (although not necessarily mutually exclusive) extant models of motor dysfunction in psychosis,7,49 and strongly suggests investigating clinical implications of motor impairment outside the motor domain. Furthermore, current technology-enabled tools for laboratory (eg, eye tracking)41 and daily life settings (eg, accelerometers and other tracking sensors built into ubiquitous portable devices) make the prospect of generating multimodal, high-resolution motoric profiling concrete and unobtrusive,5,6 with the ultimate goal of defining gradients along a number of kinematic dimensions. This might be the enabling step for an improved stratification and subtyping of subtle motor anomalies, including those—so-called micro-movements50—that may elude traditional clinical observation. Finally, given their central adaptive function and long-evolutionary history across species, these sensorimotor mechanisms (such as CD systems), provide a unique translational bridge for the neurophysiological exploration of their alterations. References 1. Garvey MA , Cuthbert BN . Developing a motor systems domain for the NIMH RDoC program . Schizophr Bull . 2017 ; 43 : 935 – 936 . Google Scholar Crossref Search ADS PubMed 2. Peralta V , Cuesta MJ . Motor abnormalities: from neurodevelopmental to neurodegenerative through “Functional” (Neuro)psychiatric disorders . Schizophr Bull . 2017 ; 43 : 956 – 971 . Google Scholar Crossref Search ADS PubMed 3. Fatemi SH , Folsom TD . The neurodevelopmental hypothesis of schizophrenia, revisited . Schizophr Bull . 2009 ; 35 : 528 – 548 . 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Nat Rev Neurosci . 2008 ; 9 : 587 – 600 . Google Scholar Crossref Search ADS PubMed 14. Straka H , Simmers J , Chagnaud BP . A new perspective on predictive motor signaling . Curr Biol . 2018 ; 28 : R232 – R243 . Google Scholar Crossref Search ADS PubMed 15. Eliades SJ , Wang X . Sensory-motor interaction in the primate auditory cortex during self-initiated vocalizations . J Neurophysiol . 2003 ; 89 : 2194 – 2207 . Google Scholar Crossref Search ADS PubMed 16. Eliades SJ , Wang X . Dynamics of auditory-vocal interaction in monkey auditory cortex . Cereb Cortex . 2005 ; 15 : 1510 – 1523 . Google Scholar Crossref Search ADS PubMed 17. Eliades SJ , Wang X . Neural substrates of vocalization feedback monitoring in primate auditory cortex . Nature . 2008 ; 453 : 1102 – 1106 . Google Scholar Crossref Search ADS PubMed 18. Sun LD , Goldberg ME . Corollary discharge and oculomotor proprioception: cortical mechanisms for spatially accurate vision . Annu Rev Vis Sci . 2016 ; 2 : 61 – 84 . 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Schizophrenia, myelination, and delayed corollary discharges: a hypothesis . Schizophr Bull . 2012 ; 38 : 486 – 494 . Google Scholar Crossref Search ADS PubMed 44. Whitford TJ , Oestreich LKL , Ford JM , et al. Deficits in cortical suppression during vocalization are associated with structural abnormalities in the arcuate fasciculus in early illness schizophrenia and clinical high risk for psychosis . Schizopr Bull . 2019 ; 45 : 522 – 530 . 45. Franklin JC , Jamieson JP , Glenn CR , Nock MK . How developmental psychopathology theory and research can inform the research domain criteria (RDoC) project . J Clin Child Adolesc Psychol . 2015 ; 44 : 280 – 290 . Google Scholar Crossref Search ADS PubMed 46. Hirjak D , Meyer-Lindenberg A , Kubera KM , Thomann PA , Wolf RC . Motor dysfunction as research domain in the period preceding manifest schizophrenia: a systematic review . Neurosci Biobehav Rev . 2018 ; 87 : 87 – 105 . Google Scholar Crossref Search ADS PubMed 47. Dean DJ , Walther S , Bernard JA , et al. Motor clusters reveal differences in risk for psychosis, cognitive functioning, and thalamocortical connectivity. Evidence for vulnerability subtypes [published online ahead of print May 31, 2018]. Clin Psychol Sci . doi: https://doi.org/10.1177/2167702618773759 . 48. Cannon TD , Yu C , Addington J , et al. An individualized risk calculator for research in prodromal psychosis . Am J Psychiatry . 2016 ; 173 : 980 – 988 . Google Scholar Crossref Search ADS PubMed 49. Walther S , Mittal VA . Motor system pathology in psychosis . Curr Psychiatry Rep . 2017 ; 19 : 97 . Google Scholar Crossref Search ADS PubMed 50. Nguyen J , Majmudar U , Papathomas TV , Silverstein SM , Torres EB . Schizophrenia: the micro-movements perspective . Neuropsychologia . 2016 ; 85 : 310 – 326 . Google Scholar Crossref Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Annette,, Schaub;Peter,, Falkai

doi: 10.1093/schbul/sbz003pmid: 30721994

Abstract The Sixth Kraepelin Symposium was held at the Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich in October 2018, covering reports from 12 working groups (Keith H. Nuechterlein, Ph.D., University of California, Los Angeles; Kim T. Mueser, Ph.D., Center for Psychiatric Rehabilitation, Boston University, U.S.A.; Dominic Dwyer, Ph.D, Hospital LMU, Munich; David Fowler, Ph.D. University of Sussex, Brighton, U.K.; Martin Hautzinger, Ph.D., University of Tübingen; Nikolaos Koutsouleris, M.D., Hospital LMU, Munich; Stephan Leucht, M.D., Technical University Munich, Munich; David Miklowitz, Ph.D., UCLA School of Medicine, Los Angeles. U.S.A.; Cornelius Schüle, M.D., Hospital LMU, Munich; Florian Seemüller. M.D., kbo-Lech-Mangfall Clinics for Psychiatry and Psychotherapy, Garmisch Partenkirchen; Carla Torrent, Ph.D., Institute of Neuroscience, University of Barcelona, Barcelona, Spain.) from the United States and Europe on understanding and treating cognitive impairment and depression in schizophrenia and affective disorders. Current psychological interventions to improve outcome in schizophrenia and affective disorder such as cognitive remediation, illness management, psychoeducational and cognitive therapy were focused on, as were evidence-based psychological and pharmacological treatment options, guidelines for treating cognitive deficits and depression in schizophrenia, Cochrane-meta-analysis of acute therapies, relapse prevention as well as supported withdrawal from medication. Prevention of cognitive dysfunction and symptom exacerbation was approached in terms of machine learning methods to revisit Kraepelin’s illness distinctions, application of new strategies in order to increase the rate of social recovery in patients with first-episode psychosis as well as in terms of state of the art psychosocial interventions for bipolar disorder in adolescents. Finally, the dissemination of information to consumers and the contribution to the reduction of stereotypes in psychiatry was also part of the symposiums aims. schizophrenia, affective disorders, aetiopathological models, cognitive dysfunction, depression, cognitive remediation, cognitive behavior therapy, illness management, cognitive psychoeduc ational therapy, early recognition Emil Kraepelin1 founded the hospital in the 19th century, today known as the Department of Psychiatry and Psychotherapy of the University of Munich, where the symposium was held. It was led by Peter Falkai and Annette Schaub and divided into 4 parts. The symposium was based on the reports of 12 working groups covering pharmacotherapy and psychological interventions related to cognitive dysfunction and depression. It also focused on the available knowledge as well as gaps in their respective fields and shows ideas for future research. Kraepelin developed psychopathological categories in schizophrenia, which he considered a strictly biological disease with an invariably downward trajectory; however, his understanding was challenged by progress in basic research, pharmacotherapy, psychosocial interventions, and the empowerment of relatives. He doubted that cognitive restructuring could modify delusions, while research in cognitive therapy on changing dysfunctional thoughts and responses to them now stresses their importance.2–4 Cognitive remediation aims to remediate basic information-processing skills that serve as vulnerability markers for further episodes.5 Psychoeducational interventions6 cover information about the illness and its treatment as well as strategies for preventing relapses and for dealing with stress and persistent symptoms. The first part of the symposium covered severe mental illnesses in Europe and the United States. Falkai7 outlined the burden of the illness due to disability to work and high financial costs about 152 billion Euro per year in Europe. Whereas pharmacotherapy and psychotherapy are effective in addictive and personality disorders, new effective therapeutics aside from new glutamatergic antidepressants are missing in psychotic and affective disorders. Aerobic exercise gained more importance due to effects on cognition in schizophrenia.8 The gap between schizophrenia and bipolar disorders has narrowed, stressing distinguishing features as well as similarities. Nuechterlein9 focused on the vulnerability-stress-coping model, emphasizing the interplay of enduring personal vulnerability factors (eg, dopaminergic anomalies, cognitive deficits) and personal and environmental protective factors that precede psychosis.10 The second part covered psychological interventions including psychoeducation, functional remediation, illness management, and cognitive behavior therapy to improve outcome in schizophrenia and affective disorders. Torrent11 stressed that 40%–60% of patients with bipolar disorder experience functional impairment during euthymia. Functional remediation shows positive effects on daily functioning and reduces costs and social burden.12 TMS, physical exercise, and enhancement of cognitive reserve are possible new treatments. Illness management programs set up by Mueser13 providing information about the illness, relapse prevention as well as medication and symptom management showed feasibility and effectiveness. Cognitive behavior therapy for psychosis overlaps with these programs, while it tends to focus less on skills training and more on cognitive restructuring. Schaub14,15 evaluated a psychoeducational and a cognitive-program in schizophrenia and depression including psychoeducation about the illness, building up positive activities, cognitive restructuring, and relapse prevention. The third part described recent developments in evidence-based psychological and psychopharmacological options to improve outcome in affective disorders. Hautzinger showed a successful development over the last 5 decades in psychotherapy of unipolar depression and bipolar disorder, including overlapping theoretical rationale, strategies, and techniques to treat acute, recurrent and chronic depression and to prevent relapse.16 Current guidelines include those from NICE, CANMAT, and WFSBP; however, despite progress in the development of newer generation antidepressants, a significant portion of patients show an inadequate treatment response including latency of onset of action.17,18 Leucht19,20 asks for compelling evidence that schizophrenia long-term treatment should be changed and focuses on Cochrane-meta-analyses covering acute therapy, relapse prevention and supported withdrawal from medication. For cognitive deficits and depression in schizophrenia, multiple new psychosocial intervention directions are being pursued that may lead to greater improvements than pharmacotherapy, including cognitive remediation in combination with aerobic exercise. The fourth part covered prevention of cognitive dysfunction and symptom exacerbation in schizophrenia. Dwyer21 revisited Kraepelin’s idea of using a full clinical assessment to categorize individuals into subtypes within high-risk populations and first-episode psychosis. Subgroups were strongly influenced by sociodemographic, clinical data and transdiagnostic factors, except for the severe psychosis group. Despite early intervention for psychosis, about 40% of young people do not make a social recovery due to problems in childhood. Social Recovery therapy (SRT) is a new approach combining cognitive behavior therapy with systemic formulation and case management set up by Fowler. The SUPEREDEN322 trial addressed the problems of the treatment-resistant subgroup of first-episode psychosis showing a definitive superiority at 9 months with a clinically significant increase in structured activity of over 8 hours and promising support for effects at follow-up. Two-year follow-up from the ISREP study showed gains on work and education.23 The PRODIGY trial24 focuses on At-Risk Cases with social disability and severe and complex mental health problems completing in 2018. The period in which children develop prodromal signs of bipolar disorder is often a good time to initiate preventative interventions according to Miklowitz25: He presented a family-focused therapy to enhance the outcomes of young patients with high risk conditions. A recent trial conducted in 3 sites26 indicates that children at high risk who received 4 months of family-focused treatment have longer times to depressive recurrence and less severe manic symptoms than those who received a comparison intervention consisting of 4 months of psychoeducation and support. Carpenter27 outlines clinical high risk controversies and challenges for the experts. Summary Substantial progress in psychopharmacological and psychological interventions occurred during the last 20 years. Many psychosocial and pharmacological interventions have demonstrated meaningful clinical impact. However, unsolved problems remain in limited treatment response and remission rates, high relapse rates, limited long-term outcome impact, and unclear mechanisms of action. Mediators of change and of successful outcome need to be identified. In psychopharmacology, additional information is needed on the comparative efficacy and tolerability of second-generation antidepressants, on clinically relevant drug-drug interactions, as well as on the possibility that antidepressants sometimes trigger suicidality. Funding The international meeting Sixth Kraepelin Symposium—Understanding and Treating Cognitive Impairment and Depression in Schizophrenia and Affective Disorders held in Munich on October 6, 2018, was funded by the German Research Foundation DFG (SCHA 1942/2-1). Acknowledgments Special thanks go to Keith Nuechterlein and Kim T. Mueser for their additional input and support in preparing this report. The authors have declared that there are no conflicts of interest in relation to the subject of this study. References 1. Kraepelin E. Psychiatrie. Ein Lehrbuch für Studierende und Ärzte. 7. Aufl., Bd. II . Leipzig, Germany : Barth ; 1903 . English translation: Diefendorf AR. Clinical psychiatry: a textbook for students and physicians. New York: MacMillan; 1923. 2. Kuipers E , Yesufu-Udechuku A , Taylor C , Kendall T. Management of psychosis and schizophrenia in adults: summary of updated NICE guidance . BMJ . 2014 ; 348 : g1173 . Google Scholar Crossref Search ADS PubMed 3. Beck AT , Rector NA , Stolar N , Grant P. Schizophrenia: Cognitive Therapy Research and Therapy . New York, NY : Guilford Press ; 2009 . 4. Jones C , Hacker D , Xia J , et al. Cognitive behavioural therapy plus standard care versus standard care for people with schizophrenia . Cochrane Database Syst Rev . 2018 ; 12 : CD007964 . Google Scholar PubMed 5. Wykes T , Huddy V , Cellard C , McGurk SR , Czobor P. A meta-analysis of cognitive remediation for schizophrenia: methodology and effect sizes . Am J Psychiatry . 2011 ; 168 : 472 – 485 . Google Scholar Crossref Search ADS PubMed 6. Xia J , Merinder LB , Belgamwar MR. Psychoeducation for schizophrenia . Cochrane Database Syst Rev . 2011 ; 15 : CD002831 . 7. Falkai P. Bedeutung psychischer Erkrankungen für das Gesundheitssystem in Deutschland . Drug Res 2017 ; 67 : S3 – S4 . Google Scholar Crossref Search ADS 8. Falkai P , Malchow B , Schmitt A. Aerobic exercise and its effects on cognition in schizophrenia . Curr Opin Psychiatry . 2017 ; 30 : 171 – 175 . Google Scholar Crossref Search ADS PubMed 9. Nuechterlein KH , Dawson ME , Gitlin M , et al. Developmental processes in schizophrenic disorders: longitudinal studies of vulnerability and stress . Schizophr Bull . 1992 ; 18 : 387 – 425 . Google Scholar Crossref Search ADS PubMed 10. Nuechterlein KH , Barch DM , Gold JM , Goldberg TE , Green MF , Heaton RK. Identification of separable cognitive factors in schizophrenia . Schizophr Res . 2004 ; 72 : 29 – 39 . Google Scholar Crossref Search ADS PubMed 11. Torrent C , Bonnin Cdel M , Martínez-Arán A , et al. Efficacy of functional remediation in bipolar disorder: a multicenter randomized controlled study . Am J Psychiatry . 2013 ; 170 : 852 – 859 . Google Scholar Crossref Search ADS PubMed 12. Sanchez-Moreno J , Bonnín C , González-Pinto A , et al. ; CIBERSAM Functional Remediation Group . Do patients with bipolar disorder and subsyndromal symptoms benefit from functional remediation? A 12-month follow-up study . Eur Neuropsychopharmacol . 2017 ; 27 : 350 – 359 . Google Scholar Crossref Search ADS PubMed 13. Monroe-DeVita M , Morse G , Mueser KT , et al. Implementing illness management and recovery within assertive community treatment: a pilot trial of feasibility and effectiveness . Psychiatr Serv . 2018 ; 69 : 562 – 571 . Google Scholar Crossref Search ADS PubMed 14. Schaub A , Goldmann U , Mueser TK , et al. Efficacy of extended clinical management, group CBT, and group plus individual CBT for major depression: results of a two-year follow-up study . J Affect Disord . 2018 ; 238 : 570 – 578 . Google Scholar Crossref Search ADS PubMed 15. Schaub A , Mueser KT , von Werder T , Engel R , Möller HJ , Falkai P. A randomized controlled trial of group coping-oriented therapy vs supportive therapy in schizophrenia: results of a 2-year follow-up . Schizophr Bull . 2016 ; 42 ( Suppl 1 ): S71 – S80 . Google Scholar Crossref Search ADS PubMed 16. Leuzinger-Bohleber M , Hautzinger M , Fiedler G , et al. Outcome of psychoanalytic and cognitive-behavioural long-term therapy with chronically depressed patients: a controlled trial with preferential and randomized allocation [published online ahead of print November 1, 2018]. Can J Psychiatry . doi: https://doi.org/10.1177/0706743718780340 . 17. Bayes AJ , Parker GB. Comparison of guidelines for the treatment of unipolar depression: a focus on pharmacotherapy and neurostimulation . Acta Psychiatr Scand . 2018 ; 137 : 459 – 471 . Google Scholar Crossref Search ADS PubMed 18. Schüle C. Chronische depression . Fortschr Neurol Psychiatr . 2014 ; 82 : 155 – 173 . Google Scholar Crossref Search ADS PubMed 19. Leucht S. Is there compelling evidence that schizophrenia long-term treatment guidelines should be changed ? World Psychiatry . 2018 ; 17 : 166 – 167 . Google Scholar Crossref Search ADS PubMed 20. Leucht S , Tardy M , Komossa K , et al. Antipsychotic drugs versus placebo for relapse prevention in schizophrenia: a systematic review and meta-analysis . Lancet . 2012 ; 379 : 2063 – 2071 . Google Scholar Crossref Search ADS PubMed 21. Dwyer DB , Cabral C , Kambeitz-Ilankovic L , et al. Brain subtyping enhances the neuroanatomical discrimination of schizophrenia . Schizophr Bull . 2018 ; 44 : 1060 – 1069 . Google Scholar Crossref Search ADS PubMed 22. Fowler D , Hodgekins J , French P , et al. Social recovery therapy in combination with early intervention services for enhancement of social recovery in patients with first-episode psychosis (SUPEREDEN3): a single-blind, randomised controlled trial . Lancet Psychiatry . 2018 ; 5 : 41 – 50 . Google Scholar Crossref Search ADS PubMed 23. Fowler D , Hodgekins J , French P. Social recovery therapy in improving activity and social outcomes in early psychosis: current evidence and longer term outcomes . Schizophr Res . 2019 ; 203 : 99 – 104 . Google Scholar Crossref Search ADS PubMed 24. Fowler D , French P , Banerjee R , et al. Prevention and treatment of long-term social disability amongst young people with emerging severe mental illness with social recovery therapy (The PRODIGY Trial): study protocol for a randomised controlled trial . Trials . 2017 ; 18 : 315 . Google Scholar Crossref Search ADS PubMed 25. O’Donnell LA , Ellis AJ , Van de Loo MM , et al. Mood instability as a predictor of clinical and functional outcomes in adolescents with bipolar I and bipolar II disorder . J Affect Disord . 2018 ; 236 : 199 – 206 . Google Scholar Crossref Search ADS PubMed 26. Perlick DA , Jackson C , Grier S , et al. Randomized trial comparing caregiver-only family-focused treatment to standard health education on the 6-month outcome of bipolar disorder . Bipolar Disord . 2018 ; 20 : 622 – 633 . Google Scholar Crossref Search ADS PubMed 27. Carpenter W. Clinical high risk controversies and challenges for experts . Schizophr Bull . 2018 ; 44 : 223 – 225 . Google Scholar Crossref Search ADS PubMed © The Author(s) 2019. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Apthorp,, Deborah;Bolbecker, Amanda, R;Bartolomeo, Lisa, A;O’Donnell, Brian, F;Hetrick, William, P

doi: 10.1093/schbul/sby141pmid: 30376125

Abstract Motor abnormalities are among the most robust findings in schizophrenia, and increasing evidence suggests they are a core feature of the disorder. Postural sway during balance tasks is a highly sensitive probe of sensorimotor systems including the cerebellum, basal ganglia, and motor cortices. Postural sway deficits are present in schizophrenia as well as groups at high risk for psychosis, suggesting altered postural control may be sensitive to the pathophysiological processes associated with risk and expression of schizophrenia spectrum disorders. This study examined postural sway performance in schizotypal personality disorder (SPD). Individuals with SPD have attenuated psychotic symptoms and share genetic risk with schizophrenia but are usually free from antipsychotic medication and other illness confounds, making SPD useful for assessing candidate biomarkers. We measured postural sway using force plates in 27 individuals with SPD, 27 carefully matched controls, and 27 matched patients with schizophrenia. It was predicted that postural sway in the SPD group would fall intermediate to schizophrenia and controls. In all conditions (eyes open and closed, with feet together or apart), the SPD group swayed significantly more than the controls, as measured by path length and sway area. Moreover, the magnitude of the sway deficit was comparable in the SPD and schizophrenia groups. These findings suggest that postural sway measures may represent a sensorimotor biomarker of schizophrenia spectrum disorders. schizotypal personality disorder, postural sway, cerebellum, biomarkers Introduction Since its earliest conceptualization, schizophrenia has been associated with motor anomalies.1–3 The advent of antipsychotic medication shifted focus away from movement abnormalities in the disorder because these medications both decreased manifestation of the most obvious motor symptoms of schizophrenia, ie, catatonia, and complicated direct studies of neuromotor symptoms in schizophrenia, because the drugs were themselves associated with medication-related movement abnormalities.4,5 In the last several decades, there has been renewed focus on motor symptoms as core features of the disorder.6 For example, motor symptoms have been consistently identified in individuals at high risk for schizophrenia, including increased rates of neurological soft signs (NSS),7,8 abnormal involuntary movements,9 and significant delays in motor development.10 Moreover, early evidence indicates that abnormal involuntary movements are predictive of psychosis risk in children and adolescents in the general population.11 Motor deficits have also been observed in never medicated, first-episode schizophrenia; these include NSS7 and abnormal involuntary movements.12,13 Likewise, NSS,10 movement abnormalities,13 and reductions in gross motor skills14 have been found in first-degree relatives of individuals with schizophrenia. Individuals with schizotypal personality disorder (SPD) experience attenuated symptoms of schizophrenia, including positive, negative, and cognitive symptoms, and show similarities in brain structure and function.15,16 In addition, relatives of probands with SPD have elevated risk for schizophrenia spectrum disorders.17 Differentiating features of SPD, compared with schizophrenia, are that individuals with SPD have less functional impairment, usually live and work unassisted, and are usually able to manage symptoms without antipsychotic medication.18 Overall, neurobehavioral, neurophysiological, and neuroimaging studies have documented abnormalities in SPD that are similar in kind to observations in schizophrenia, but they are usually reduced in magnitude. These characteristics make SPD a useful model for studying the etiology of neural dysfunction in schizophrenia because these individuals are relatively free of confounding factors such as antipsychotic medication, inactivity due to social isolation, comorbid drug abuse, and medical illnesses secondary to chronic psychosis. Following the pattern of results seen in schizophrenia and motor control, signs of abnormal motor control are also seen in SPD. SPD has been associated with excessively increased force and variability in a motor stability task in adults19 and adolescents,16 decreased performance on motor learning tasks,20 and increased white matter volume in motor pathways,15 as well as movement abnormalities.19,21,22 Individuals with schizophrenia have increased postural sway, indicating impaired postural control23–31; this has also been shown in individuals at risk for psychosis.32,33 This likely reflects abnormalities in brain structures critical for postural control, the cerebellum, and basal ganglia. Cerebellar abnormalities in functional activation, volume, white matter integrity, functional connectivity, cerebellar metabolism, gyrification, and neuropathological alterations in Purkinje cell morphology and protein expression have been observed in schizophrenia spectrum disorders.34–38 A recent meta-analysis showed reduction in basal ganglia activation in schizophrenia across a wide variety of cognitive and motor tasks.39 A meta-analysis of magnetic resonance spectroscopy studies in schizophrenia has shown increased glutamate levels in the cerebellum.40 However, it is unknown whether postural sway is impaired in SPD. Measurement of postural sway offers opportunities to quantify motor functioning more objectively, as well as studying sensory integration by manipulating vestibular, visual, and proprioceptive inputs. In addition, theoretically driven analysis techniques offer some insight into the potential origin of postural sway abnormalities. This study aimed to determine whether postural sway deficits were apparent in SPD. We hypothesized that the SPD group would demonstrate impaired postural control compared with controls, but that deficits would be most severe in the schizophrenia group. Materials and Methods Subjects Subjects were 27 individuals diagnosed with SPD but mainly not medicated, with no other neurological disorders or substance abuse; the healthy normal control group (HC) consisted of 27 age-matched subjects, also matched as closely as possible on height and weight; and 27 patients who had been diagnosed with schizophrenia (n = 17) or schizoaffective disorder (n = 10)—the schizophrenia group. Patients with schizophrenia were recruited from psychiatric clinics. Each participant was evaluated for Axis I disorders using the Structured Clinical Interview I (SCID-I).41 Participants with SPD were recruited through an advertisement in a local newspaper that invited individuals with special abilities such as “extrasensory perception, sixth sense, perception of auras, or clairvoyance” to participate in a research investigation. The diagnosis of SPD using Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV)42 criteria was determined on the basis of the Structured Clinical Interview II (SCID-II;43). The SCID-I for the DSM-IV41 was also used to exclude participants with SPD who presented with Axis I disorders. All diagnoses were made by a clinician based on DSM-IV criteria, supplemented by chart and record information. Exclusion criteria included current drug or alcohol abuse or dependence, and report of loss of consciousness greater than 5 min. Control participants were excluded for any Axis I disorder(s), including past drug or alcohol dependence. For schizophrenia and SPD groups, inclusion criteria were that they had been free from alcohol abuse or dependence for at least 6 months. No participants in the study were related to each other. In the control group, no participants were taking any form of psychoactive medication; 5 were taking blood pressure medication, and 2 lipid-lowering medication. In the SPD group, 1 patient was taking typical antipsychotics, no patients were taking atypical antipsychotics, 1 was taking anticholinergics, 4 were taking antidepressants, 1 was taking anticonvulsants, 3 were taking narcotic drugs (eg, oxycodone), and 19 were medication free. (Note that there is some overlap in the medications.) In the schizophrenia group, 5 were taking typical antipsychotics, 20 were taking atypical antipsychotics, 4 were taking anticholinergics, 11 were taking antidepressants, and 6 were taking anticonvulsants, whereas 2 were medication free. Control participants were recruited using advertisements in a local community newspaper. All control participants were interviewed using the SCID-NP (non-patient edition)44 to exclude individuals with psychiatric diagnoses. The participants with SPD were also interviewed with the SCID-NP. Intelligence quotient (IQ) was determined using the Full-Scale Intelligence Quotient [2-component version] (FSIQ-2) (vocabulary and matrix reasoning). There were no significant differences between the groups in age, F(2,78) = .036, P = .965, η2 = .001, weight, F(2,78) = 2.54, P = .085, η2 = 0.061, height, F(2,76) = 0.482, P = .619, η2 = 0.013 or gender, χ2(2) = 0.652. There was a significant group difference in IQ, F(2,78) = 5.86, P = .004, η2 = 1.31, with the control group significantly higher than the schizophrenia group, t(78) = 3.29, P = .004, but not the SPD group, t(78) = 0.837, P > .5; the SPD group also scored higher than the schizophrenia group, t(78) = 2.46, P = .049. See table 1 for the mean values on these variables. Diagnostic status for the schizophrenia group was determined using the SCID for DSM-IV Axis I disorders (SCID-I)41 sections for mood disorders, psychotic disorders, and substance abuse disorders, as well as chart review. Kappa inter-rater reliability in our laboratory has been 0.95 for schizophrenia compared with mood disordered, or other diagnoses in patients who have been pre-screened for showing psychosis. Exclusion criteria for all participants included a history of neurological or cardiovascular disease, clinically documented hearing loss, head injury resulting in loss of consciousness, electroconvulsive therapy, diagnosis of alcohol, or other substance dependence within 3 months, and IQ below 70. Procedure Each participant was required to stand as still as possible while barefoot on an AMTI’s AccuSway force platform, with as sampling rate of 200 Hz. Participants stood with eyes either open or closed, and base (foot position) either open or closed, leading to 4 separate conditions for each participant. Order of trials was counterbalanced. During the open base conditions, feet were placed shoulder width apart; during the closed base conditions, participants stood with their feet together (approximately 1 in apart). Each trial lasted 2 min. Postural Sway Measures Sway Path. Sway path length was calculated as a simple trigonometrical analysis of the length of the path of the center of pressure (CoP) during a given time period, given by the following equation: PL=∑n−1N[x(n)−x(n−1)]2+[y(n)−y(n−1)]2 where x indicates the mediolateral (M/L) direction and y the anteroposterior (A/P) direction. This measure is one of the most widely used measures of postural sway.45,46 Sway Area. Sway area was computed using principal components analysis to derive the 2 major axes of body sway trajectory, and then computing the area of a 95% confidence ellipse computed around the data points.46,47 In this method, the 2 principal axes are derived by calculating the eigenvalues of the covariance matrix between the mediolateral (M/L) and anteroposterior (A/P) data, each of which was a vector of 6000 data points in this study. MATLAB code for this is provided in the appendix of Duarte and Zatsiorsky.47 This is illustrated for an individual subject in figure 1. Fig. 1. View largeDownload slide Example data from a single subject (from the schizophrenia group) showing ellipse fits for each of the 4 conditions. Eyes open data are plotted in blue, eyes closed in red, and open-base conditions are on the right whereas closed are on the left. Fig. 1. View largeDownload slide Example data from a single subject (from the schizophrenia group) showing ellipse fits for each of the 4 conditions. Eyes open data are plotted in blue, eyes closed in red, and open-base conditions are on the right whereas closed are on the left. Sway Conditions. Sway was measured under conditions designed to vary the demands on the sensorimotor systems that maintain upright posture. Sway was measured with the eyes closed and open, and with the base (foot position) either open or closed. It was expected that group differences would be larger in conditions that were more demanding (eyes closed, base closed). Data Analysis The data were initially stored as text files, then analyzed in MATLAB 2015b, using custom software, for the signal processing, and then R statistical software for the statistical analyses. All code and raw data are available on figshare (doi:10.6084/m9.figshare.7165118). For the sway data, the raw CoP data were first downsampled from 200 Hz to 50 Hz using the MATLAB function “decimate,” which first smooths the raw data using an eighth order Chebyshev Type I low-pass filter with a cutoff frequency of .8*(Fs/2)/R before resampling. Next, path length and sway area were calculated as detailed earlier. Statistical analyses for the main analyses were mixed ANOVA analyses with base (open/closed) and eye (open/closed) as within-subjects factors, and group (HN/SPD/schizophrenia) as between-subjects factors, with weight (mean centered) as a covariate where it was found to correlate with one or more of the sway conditions. All statistical analyses were carried out using R statistical software. The raw data and code are available on figshare (doi:10.6084/m9.figshare.7165118). Results Demographics The groups were matched for age and gender, and were also screened for substance abuse disorders and alcohol use. Demographics are presented in table 1. Table 1. Demographic Variables for the Three Groups; Means Are Shown Here, With SDs in Brackets. Significant Differences From the Control Group Are Indicated by Asterisks HC SPD SZ Age (SD) 41.5 (10.60) 42.2 (11.30) 41.6 (9.91) Gender (M/F) 13/14 14/13 13/14 Height (in) 66.88 (4.58) 67.1 (3.39) 66.1 (3.83) Weight (lb) 178 (43.0) 201 (50.2) 179 (34.6) WASI IQ (SD) 111 (15.7) 107 (15.3) 97.6 (13.2)** SPQ (SD) 8.19 (7.77) 29.07 (12.18)*** 41.15 (20.64)*** HC SPD SZ Age (SD) 41.5 (10.60) 42.2 (11.30) 41.6 (9.91) Gender (M/F) 13/14 14/13 13/14 Height (in) 66.88 (4.58) 67.1 (3.39) 66.1 (3.83) Weight (lb) 178 (43.0) 201 (50.2) 179 (34.6) WASI IQ (SD) 111 (15.7) 107 (15.3) 97.6 (13.2)** SPQ (SD) 8.19 (7.77) 29.07 (12.18)*** 41.15 (20.64)*** Note: M, male; F, female; SPD, schizotypal personality disorder; SZ; schizophrenia, SPQ, Schizotypal Personality Questionnaire; HC, Healthy Control; WASI IQ, Wechsler Abbreviated Scale of Intelligence IQ. * P < .05; ** P < .01; *** P < .001. View Large Table 1. Demographic Variables for the Three Groups; Means Are Shown Here, With SDs in Brackets. Significant Differences From the Control Group Are Indicated by Asterisks HC SPD SZ Age (SD) 41.5 (10.60) 42.2 (11.30) 41.6 (9.91) Gender (M/F) 13/14 14/13 13/14 Height (in) 66.88 (4.58) 67.1 (3.39) 66.1 (3.83) Weight (lb) 178 (43.0) 201 (50.2) 179 (34.6) WASI IQ (SD) 111 (15.7) 107 (15.3) 97.6 (13.2)** SPQ (SD) 8.19 (7.77) 29.07 (12.18)*** 41.15 (20.64)*** HC SPD SZ Age (SD) 41.5 (10.60) 42.2 (11.30) 41.6 (9.91) Gender (M/F) 13/14 14/13 13/14 Height (in) 66.88 (4.58) 67.1 (3.39) 66.1 (3.83) Weight (lb) 178 (43.0) 201 (50.2) 179 (34.6) WASI IQ (SD) 111 (15.7) 107 (15.3) 97.6 (13.2)** SPQ (SD) 8.19 (7.77) 29.07 (12.18)*** 41.15 (20.64)*** Note: M, male; F, female; SPD, schizotypal personality disorder; SZ; schizophrenia, SPQ, Schizotypal Personality Questionnaire; HC, Healthy Control; WASI IQ, Wechsler Abbreviated Scale of Intelligence IQ. * P < .05; ** P < .01; *** P < .001. View Large Schizotypal Personality Questionnaire All participants completed the Schizotypal Personality Questionnaire (SPQ).48 Results are shown in figure 2. A 1-way ANOVA showed a significant effect of group, F(2, 78) = 35.5, P < .001. Post hoc comparisons showed significant differences between controls and participants with SPD, t(78) = 5.28, P <.001, and between controls and participants with schizophrenia, t(78) = 8.33, P < .001, and also a difference between SPD and schizophrenia, t(78) = 3.05, P = .009, with SPD scoring lower than schizophrenia. All P values are Bonferroni corrected. The results are illustrated in figure 2. Fig. 2. View largeDownload slide Boxplot showing results of the questionnaire measure, the Schizotypal Personality Questionnaire (SPQ). Means are shown by black horizontal bars. Individual scores are represented by black circles, slightly jittered for clarity; colored areas represent 95% Highest Density Intervals (HDIs), calculated using R’s BEST (Bayesian Estimation Supersedes the T-Test) package, and vertical bars represent the 10th and 90th quantiles. Fig. 2. View largeDownload slide Boxplot showing results of the questionnaire measure, the Schizotypal Personality Questionnaire (SPQ). Means are shown by black horizontal bars. Individual scores are represented by black circles, slightly jittered for clarity; colored areas represent 95% Highest Density Intervals (HDIs), calculated using R’s BEST (Bayesian Estimation Supersedes the T-Test) package, and vertical bars represent the 10th and 90th quantiles. Sway Path Individual data for one of the conditions (eyes closed, base open) showing all the sway paths, arranged by sway magnitude, are shown in figure 3. Notably, the sway for individuals with SPD is much greater than for the controls, and comparable to the schizophrenia group. This was borne out in our statistical analyses, detailed later. Fig. 3 View largeDownload slide Individual sway data (stabilograms) for all of the healthy control participants (a), participants with schizotypal personality disorder (SPD) (b) and participants with schizophrenia (c) in the eyes closed, base open condition. These are arranged in order of sway magnitude as calculated by the 90% confidence ellipse (see “Materials and Methods” section for details). Fig. 3 View largeDownload slide Individual sway data (stabilograms) for all of the healthy control participants (a), participants with schizotypal personality disorder (SPD) (b) and participants with schizophrenia (c) in the eyes closed, base open condition. These are arranged in order of sway magnitude as calculated by the 90% confidence ellipse (see “Materials and Methods” section for details). For sway path, the data were heavily skewed, thus were log transformed before analysis. Because weight was significantly correlated with some of the conditions, we included weight as a covariate in the analysis, after mean-centering. The results are illustrated in figure 4. A mixed model ANOVA, with base (open/closed) and eye (open/closed) as within-subjects factors, group as a between-subjects factor and weight (mean centered) as a covariate, showed a significant main effect of group, F(2,77) = 7.48, P = .001, η2 = 159, but no main effect of weight, F(2, 77) = 2.23, P = .139, η2 = .024. There were also significant within-subjects effects of eye, F(1, 77) = 397.75, P < .001, η2 = .81, and base, F(1,77) = 61.53, P < .001, η2 = .415 and a significant eye × base interaction, F(1,77) = 88.72, P < .001, η2 = .533, as well as a small but significant base × weight interaction, F(1,77) = 4.90, P = .03, η2 = .033. There were no interactions between group and any of the other variables, indicating that SPD and schizophrenia groups swayed more than controls in all of the conditions. Supplementary analysis without weight as a covariate showed similar results (see supplementary information for details). Fig. 4. View largeDownload slide Results for sway path (log transformed to correct skew) for all participants. Means are shown by black horizontal bars. Individual scores are represented by black circles, slightly jittered for clarity; colored areas represent 95% Highest Density Intervals (HDIs), calculated using R’s BEST (Bayesian Estimation Supersedes the T-Test) package, and vertical bars represent the 10th and 90th quantiles. The thin gray lines show the full densities for each group. Fig. 4. View largeDownload slide Results for sway path (log transformed to correct skew) for all participants. Means are shown by black horizontal bars. Individual scores are represented by black circles, slightly jittered for clarity; colored areas represent 95% Highest Density Intervals (HDIs), calculated using R’s BEST (Bayesian Estimation Supersedes the T-Test) package, and vertical bars represent the 10th and 90th quantiles. The thin gray lines show the full densities for each group. Post hoc comparisons for group (averaging across all eye and base conditions) showed that the SPD group swayed significantly more than controls, t(75) = 3.254, P = .005, corrected, as did the schizophrenia group, t(75) = 3.78, P < .001, corrected, but the schizophrenia and SPD groups did not differ, t(75) = .478, P > .5. All comparisons were corrected using the Bonferroni method. Sway Area Sway area values were also heavily right-skewed, and were log transformed before analysis. For this analysis, we also included weight (mean-centered) as a covariate. Again, there was a significant between-subjects effect of group, F(2,77) = 9.04, P < .001, η2 = .181, and of weight, F(1,77) = 4.58, P = .036, η2 = .046 and significant within-subjects main effects of eye, F(1,77) = 109.11, P < .001, η2 = .567, and base, F(1,77) = 101.33, P < .001, η2 = .548, as well as a significant eye × base interaction, F(1,77) = 15.95, P < .001, η2 = .171. There was also a small but significant base × group interaction, F(1,77) = 3.19, P = .047, η2 = .035. There were no other within-subject or between-subject interactions. Post hoc pairwise comparisons for group, averaging across all conditions, showed that sway area for SPD was significantly greater than for controls, t(75) = 3.1, P = .008, corrected, and for schizophrenia compared with controls, t(75) = 4.0, P < .001, corrected, but SPD and schizophrenia groups did not differ, t(75) = .842, P > .5, corrected. The results are illustrated in figure 5. Fig. 5. View largeDownload slide Results for sway area (log transformed to correct skew) for all participants. Individual scores are represented by black circles, slightly jittered for clarity; colored areas represent 95% Highest Density Intervals (HDIs), calculated using R’s BEST (Bayesian Estimation Supersedes the T-Test) package, and vertical bars represent the 10th and 90th quantiles. The thin gray lines show the full densities for each group. Fig. 5. View largeDownload slide Results for sway area (log transformed to correct skew) for all participants. Individual scores are represented by black circles, slightly jittered for clarity; colored areas represent 95% Highest Density Intervals (HDIs), calculated using R’s BEST (Bayesian Estimation Supersedes the T-Test) package, and vertical bars represent the 10th and 90th quantiles. The thin gray lines show the full densities for each group. Additional Analyses Interestingly, there were no correlations between the SPQ and any of the sway variables, either within the subgroups or for a combined sample of the SPD and schizophrenia groups (all P’s > .3, uncorrected; full analyses are available on figshare—doi:10.6084/m9.figshare.7165118). Nor did the sway measures correlate with any other cognitive variables (Wechsler Abbreviated Scale of Intelligence [WASI IQ] or any of its subscales). We also examined scores based on the negative symptoms represented by the SPQ (scales of social anxiety, no close friends, restricted affect and suspiciousness)49; these also did not correlate with any of the sway measures. To ensure the robustness of our analyses against potential confounds, we carried out several additional analyses, which are reported in the supplementary materials. To test whether the subset of participants with SPD who were on some type of psychoactive medication (antipsychotic, opioid, antidepressant, or anticonvulsant; n = 8) might have affected the results, we repeated the ANOVA analysis without these participants. All of the results remained significant in the same direction as reported earlier, for both sway path and sway area. In addition, a separate analysis including medication status as a covariate (0 = not medicated, 1 = medicated) showed no effect of medication on sway, and no interactions with any of the other factors, for both sway path and sway area. To check whether the nonsignificant differences between SPD and schizophrenia might have resulted from a lack of power, we carried out additional Bayesian analyses comparing mean sway measured for the 2 groups. These analyses (Bayesian independent-samples t tests) showed 3.5 to 4 times more evidence for the null than for the alternative hypotheses for both sway path and sway area (supplementary analyses). An additional robustness check showed that this evidence was moderate to strong. Because anticholinergic medication may have interfered more strongly with sway than other medications, we also repeated the analyses without the patients on anticholinergic medication, and these analyses also showed the same pattern of results (see supplementary analyses). Thus, we are confident that the impairments seen in SPD were not impacted by any artifact due to medication, and that the study was adequately powered. Discussion As we predicted, individuals with SPD showed increased postural sway compared with the control group. Notably, the postural sway abnormalities observed in SPD were similar in magnitude to the schizophrenia group. This finding is significant because, while individuals with SPD show attenuated symptoms of schizophrenia and carry some risk for conversion to this more serious and debilitating disorder, the functional impairment in this group on most neurobehavioral measures is usually smaller or absent compared with those with schizophrenia.18 Although a minority of our SPD sample were on some form of medication, our analysis was robust to the removal of these participants, as well as the inclusion of medication status as a covariate (see supplementary data for details). An additional Bayesian analysis comparing the SPD and schizophrenia groups showed moderate to strong evidence for the null (no difference between the groups in either sway path or sway area). Together, these additional analyses strengthen our conclusion that sway is equally affected in unmedicated SPD and schizophrenia. This similarity in level of impairment magnitude between SPD and schizophrenia suggests that alterations in the neurobiological substrates underlying postural control are biomarkers of illness risk, possibly genetic, which may interact with developmental factors for conversion to schizophrenia. Previous studies have also found evidence of motor abnormalities in SPD that are of a similar magnitude to those in schizophrenia: specifically, cerebellar-dependent delay eyeblink conditioning.20 First-degree relatives of individuals with schizophrenia also show strikingly similar levels of impairment to individuals who are actually diagnosed with the disorder on this task.50 These findings are consistent with the cognitive dysmetria theory of schizophrenia,51 and consistent with the interpretation that these abnormalities may signal some underlying risk factor related to cerebellar impairment. The lack of correlation of postural sway with SPQ or with cognitive measures adds further weight to the argument that sway reflects underlying vulnerability or risk factors, rather than clinical severity. A previous study of a group of individuals at ultra-high risk of schizophrenia (UHR),32 who were also not medicated, found not only increased postural sway, but decreased cerebellar-cortical connectivity as evidenced by resting-state fMRI. Further, these deficits in connectivity were correlated with increased postural sway indices, as well as with negative symptom severity. A more recent study found that postural sway in an UHR group predicted negative symptom progression.33 It would be most informative to examine cortical connectivity in SPD in conjunction with postural sway; another group of interest would be first-degree relatives who do not show symptoms of SPD. This finding of impaired postural control in SPD is consistent with a large body of literature documenting motor abnormalities in both SPD16,19,52 and psychometrically defined schizotypy53–56), as well as in schizophrenia.1,2,23,26,27 To the best of our knowledge, this is the first examination of postural sway in SPD or any other intermediate phenotype of schizophrenia. The finding of this deficit and its large magnitude suggests that postural sway may be a biological marker of schizophrenia and may have a relationship to biological mechanisms of the disorder that confer risk rather than artifacts or confounding variables. Future studies incorporating imaging techniques would be helpful in delineating the role of different components of the motor system, including the cerebellum, basal ganglia, and frontal motor and premotor cortex that contribute to these deficits. The present findings add to the robust evidence for disrupted cerebellar circuitry in the pathophysiology of schizophrenia, which is present across the schizophrenia spectrum and stages of illness progression. The clinical implications of such findings are promising for earlier detection of psychosis lability, and hopefully prevention of illness onset.57 Cerebellar-related motor abnormalities have been observed as early as the first 2 years of life in individuals who later develop schizophrenia and SPD,58–60 and reduced cerebellar gray matter has been shown to predict conversion to psychosis in high-risk individuals.61 Supporting the diathesis-stress model of schizophrenia, prospective studies demonstrate that differences in motor function are most pronounced during key neurodevelopmental periods (eg, group differences are easily detected during infancy and become gradually less pronounced throughout childhood until again being triggered during adolescence), and also dramatically increase with age.62 Future Directions Although neuroimaging methods provide profound insight into the neural correlates of psychosis and risk, they are expensive and therefore poorly suited for regular monitoring. Alternatively, behavioral tasks like postural sway that are efficient and pose minimal burden on participants may have increased cost-effectiveness. Future research should explore the feasibility and clinical utility of this approach, for which preliminary studies are already promising. For example, a recent longitudinal study examining self-reported motor dysfunction, as measured by the Structured Interview for Psychosis-Risk Syndromes ,63 found that baseline scores were higher among converters than non-converters in a high-risk sample.64 Combining subjective and objective data, such as postural sway, may prove beneficial for assessing psychosis risk, as well as identifying moderating factors that may differentiate the development of SPD compared with schizophrenia. In addition, future research should should focus on dissociating familial risk (ie, close family members with schizophrenia) from those who may have sustained some neurodevelopmental risk that contributed to their schizotypy, but do not have genetic risk. Funding This study was supported by the National Institute of Mental Health (R01 MH074983B) to WPH. Conflict of Interest Statement The authors have declared that there are no conflicts of interest in relation to the subject of this study. References 1. Bleuler E , Zinkin J. Dementia Praecox or the Group of Schizophrenias . New York : International University Press ; 1911 . 2. Kraepelin E. Dementia Praecox and Paraphrenia . Chicago: Chicago Medical Book Co . 1913 . 3. Kendler KS . Phenomenology of schizophrenia and the representativeness of modern diagnostic criteria . JAMA Psychiatry . 2016 ; 73 : 1082 – 1092 . Google Scholar Crossref Search ADS PubMed 4. Whitty PF , Owoeye O , Waddington JL . 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Schülke, Rasmus; Straube, Benjamin

doi: 10.1093/schbul/sby144pmid: 30304518

Abstract Background Patients with schizophrenia spectrum disorders (SSD) have severe deficits in speech and gesture processing that contribute considerably to the burden of this disorder. Brain imaging shows left inferior frontal gyrus involvement for impaired processing of co-verbal gestures in patients with schizophrenia. Recently, transcranial direct current stimulation (tDCS) of the left frontal lobe has been shown to modulate processing of co-verbal gestures in healthy subjects. Although tDCS has been used to reduce symptoms of patients with SSD, the effects of tDCS on gesture processing deficits remain hitherto unexplored. Objective Here we tested the hypothesis that inhibitory cathodal tDCS of the left frontal lobe decreases pathological dysfunction and improves semantic processing of co-verbal gestures in patients with SSD. Methods We measured ratings and reaction times in a speech–gesture semantic relatedness assessment task during application of frontal, frontoparietal, parietal, and sham tDCS to 20 patients with SSD and 29 healthy controls. Results We found a specific effect of tDCS on speech–gesture relatedness ratings of patients. Frontal compared to parietal and sham stimulation significantly improved the differentiation between related and unrelated gestures. Placement of the second electrode (right frontal vs parietal) did not affect the effect of left frontal stimulation, which reduced the preexisting difference between patients and healthy controls. Conclusion Here we show that left frontal tDCS can improve semantic co-verbal gesture processing in patients with SSD. tDCS could be a viable tool to normalize processing in the left frontal lobe and facilitate direct social communicative functioning in patients with SSD. co-verbal gestures, gesture processing, left inferior frontal gyrus (IFG), left frontal lobe, tDCS Introduction Gestures are an integral part of human communication.1,2 In real life, gestures usually occur in the context of spoken language. These co-verbal gestures accompany speech and thereby improve understanding,3,4 learning,5,6 memory performance,5,7,8 and reduce processing during communication.7 Gesture deficits are very characteristic of schizophrenia,9–12 present at all stages of the disorder,13–15 play an important role for social dysfunction,16 and are a predictive marker of poor outcome.17 Regarding gesture production, patients’ ability to imitate gestures is markedly impaired.15,18–21 Concerning gesture perception and interpretation, patients show severe gesture recognition deficits.9,21,22 They do not only have difficulties at correctly identifying meaningful gestures, but also tend to perceive incidental movements as meaningful gestures, to perceive neutral gestures as conveying an insulting meaning10 and to perceive gestures as self-referential.23 Generally, overactivation of the superior temporal sulcus (STS) and the temporoparietal junction seems to be at the core of social communication deficits characteristic of the schizophrenic syndrome.24 Functional magnetic resonance imaging (fMRI) research investigating the brain regions involved in perception of co-verbal gestures has shown more activation in bilateral frontal structures for patients with schizophrenia compared to control subjects.25 Moreover, connectivity between the left STS and the left inferior frontal gyrus (IFG) seems to be impaired, especially for metaphoric gestures.26 Another recent study linked poor performance during gesture planning and execution in patients with schizophrenia spectrum disorders (SSD) to reduced right dorsolateral prefrontal cortex and increased inferior parietal lobe activity.27 In sum, the neural correlates of gesture processing in schizophrenia point to a specific involvement of the frontal cortex and dysfunctional connectivity between frontotemporal brain regions. Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique that makes use of electrical currency to stimulate and inhibit brain regions. Anodal stimulation is generally thought to increase cortical excitability, whereas cathodal stimulation usually leads to a decrease in excitability.28,29 tDCS has repeatedly been tested as a possible clinical treatment tool for schizophrenia.30–35 So far, the effects of tDCS on deficient semantic speech–gesture matching in patients with SSD have not been investigated. In a recent study, we explored the effects of left frontal tDCS on semantic speech–gesture matching in healthy subjects.36 We found that anodal compared to cathodal stimulation of the left frontal lobe decreased reaction times and relatedness assessments for metaphoric gestures, demonstrating that tDCS may influence speech–gesture matching in healthy subjects.36 Another recent study showed that transcranial magnetic stimulation over the left frontal cortex disrupts speech–gesture integration.37 However, until now no study has looked at the effects of tDCS on speech–gesture processing in patients with SSD. In this study, we investigated the effects of tDCS on speech–gesture relatedness assessment of patients with SSD. We hypothesized that left frontal tDCS would modulate impaired speech–gesture relatedness assessment of patients with SSD. fMRI evidence suggests both a general overactivation of the left IFG in schizophrenia38 and a specific imbalance of left IFG activation for processing co-verbal gestures (decrease in ventral activation/increase in dorsal activation).25 We therefore assumed that reducing excitability of the left frontal area using cathodal tDCS would normalize patients’ assessments of speech–gesture relatedness, ie, result in higher relatedness ratings for related stimuli and more critical assessment of unrelated stimuli. Because a single tDCS condition may be difficult to interpret, as stimulation effects may be due to stimulation at the anodal site, inhibition at the cathodal site, or both electrodes (see Reinhart et al),39 we opted for a comprehensive design that would allow us to disentangle the effects of anode and cathode. To test our hypothesis of facilitated gesture processing by left frontal tDCS in patients with SSD, we performed exclusively frontal (LFC-RFA; left frontal cathodal and right frontal anodal) and frontoparietal (LFC-RPA; left frontal cathodal and right parietal anodal) stimulation. In addition, we included exclusively parietal (LPC-RPA; left parietal cathodal) and sham stimulation as control conditions, which we assumed not to lead to facilitation in speech–gesture matching. Methods Participants All subjects were right-handed, native-level German speakers with normal or corrected-to-normal vision, no hearing deficits, and no electric implants. All subjects gave written informed consent prior to participation and received an expense allowance. The local ethics committee approved the study. Patients Twenty patients with SSD were recruited at the Department of Psychiatry and Psychotherapy, Philipps-University, Marburg, Germany (18 male, 2 female; mean age = 38.70 years, SD = 11.70, range = 41; mean level of education as measured by the Comparative Analysis of Social Mobility in Industrial Nations (CASMIN) classification = 5.55, SD = 1.96, range = 7). Thirteen patients were diagnosed with paranoid schizophrenia (International Classification of Diseases, Tenth Revision [ICD-10] GM F20.0), 4 patients were diagnosed with schizoaffective disorder (ICD-10 GM F25.0), 1 patient was diagnosed with residual schizophrenia (ICD-10 GM F20.5), 1 patient was diagnosed with prodromal schizophrenia (ICD-10 GM F21.0) and 1 patient was diagnosed with acute and transient psychotic disorder (ICD-10 GM F23.0). All patients were under stable medication when undergoing the study and symptom severity was relatively low (mean Scale for the Assessment of Positive Symptoms = 11.17, SD = 12.91, range = 50; mean Scale for the Assessment of Negative Symptoms = 17.50, SD = 17.67, range = 57; clinical ratings were missing for 2 patients). Healthy Controls Twenty-nine healthy subjects served as a control group (18 male, 11 female; mean age = 36.52 years, SD = 13.23, range = 40; average level of education as measured by the CASMIN classification = 5.97, SD = 2.11, range = 6) and were matched to patients based on age and education. As a result, groups did not differ significantly in age (P = .24) and education (P = .74). All healthy controls fulfilled the following inclusion criteria: history free of mental or neurologic illness and alcohol or drug abuse. Data of a subsample of 17 healthy controls have already been published elsewhere.36 Transcranial Direct Current Stimulation We used a direct current stimulator from neuroConn GmbH. Frontal electrodes were positioned at F3/F4 and parietal electrodes were positioned at C3-P3/C4-P4 (between C3 and P3/between C4 and P4), according to the 10–20 electroencephalography (EEG) system,36 for further details. A current of 1.5 mA was applied to the head using saline-soaked sponges (0.9% NaCl, to minimize side effects,40,41 5 cm × 7 cm) placed on rubber electrodes, resulting in a current density of 0.043 mA/cm2. Stimulation duration was 10 min plus 10 s fade in/fade out. All parameters complied with tDCS safety guidelines.42–44 Sessions were performed at least 20 h apart to ensure that tDCS effects had completely faded away by the beginning of each new session. Sham stimulation was performed using the sinus (half wave) mode for a duration of 30 s.45 Experiment Design We applied anodal, cathodal, and sham stimulation to the left and right frontal (F3/F4) and parietal (CP3/CP4) areas (see figure 1).36 Each patient took part in 4 independent tDCS sessions and underwent 4 different stimulation conditions, 1 on each day (L = left; R = right; F = frontal; P = parietal; C = cathode; A = anode): (1) frontal condition LFC-RFA, (2) frontoparietal condition LFC-RPA, (3) parietal condition LPC-RPA, and (4) sham condition. To control for effects of order and repetition, order of stimulation conditions was pseudorandomized and counterbalanced across subjects. Healthy controls underwent 3 additional inverse stimulation conditions.36 Fig. 1. Open in new tabDownload slide Study design and speech–gesture relatedness assessment task. (A, top) Study design. Each subject underwent four stimulation sessions (L = left; R = right; F = frontal; P = parietal; C = cathode; A = anode) on 4 days. The colors indicate electrode polarization (orange = right anodal stimulation; blue = left cathodal stimulation). (B, bottom): Speech–gesture relatedness assessment task, performed during stimulation. Example clips for each of the 4 gesture types presented, from right to left: metaphoric related, iconic related, metaphoric unrelated, and iconic unrelated. Figure adopted from Schülke and Straube.36 Speech–Gesture Relatedness Assessment Task During stimulation, subjects were continuously presented with video clips of an actor saying a concrete (eg, “The house is located on a mountain.”) or abstract sentence (eg, “The conversation is at a high level.”) accompanied by a hand gesture that was either semantically unrelated or related to the sentence content (see figure 1). For each co-verbal gesture, subjects rated relatedness of sentence content and gesture. They were instructed to rate on a scale from 1 (sentence content and gesture matches very badly) to 7 (sentence content and gesture matches very well) and pressed the respective button on the keyboard. Reaction times were measured from video onset. We used 2 different sets of stimuli (80/set) to counterbalance related and unrelated counterparts of speech–gesture pairs across subjects. Each set included 20 metaphoric related (abstract sentence + related gesture), 20 metaphoric unrelated (abstract sentence + unrelated gesture), 20 iconic related (concrete sentence + related gesture), and 20 iconic unrelated (concrete sentence + unrelated gesture) clips. We presented the video clips in pseudorandomized order. The stimulus set presented to the participant was identical in each experiment session, to maximize comparability across stimulation sessions. Thus, each subject saw only a related or unrelated version of any given sentence–gesture pair. However, across the full body of subjects, both versions were presented. Stimulus Material The stimuli have been extensively validated and successfully made use of in other studies.7,8,25,26,46–49 The videos looked as natural as possible and differed only in type of co-verbal gesture and relatedness. Iconic and metaphoric gestures were chosen in concordance with McNeill’s definitions, illustrating form, size or movement of something concrete the speaker is referring to (iconic gestures), or being speech–related on an abstract semantic level (metaphoric gestures).50 Sentences were of similar length (5–8 words) and grammatical form (subject–predicate–object). Unrelated gestures were not too obviously unrelated to speech and matched related gestures in terms of complexity (gesture direction and extent), smoothness, and vividness. Extensive rating proved that unrelated gestures did not contain any clear-cut semantic information and differed significantly in semantic strength from iconic47 and metaphoric gestures.7 Each clip had a length of 5 s. For additional information on the stimuli and their creation, see Kircher et al46 and Green et al.47 Assessment of Side Effects After each session, subjects filled out a questionnaire that consisted of 28 items (eg, headache, itching sensation, difficulty concentrating) to assess any perceived side effects. Data Analysis We performed generalized estimating equations (GEE) for relatedness ratings and reaction times as implemented in SPSS Statistics 19 for Windows by IBM. We chose GEE because they work well even in cases of unmeasured dependence between outcomes and were thus useful for our complex, repeated-measures design.36 We used an AR (1) working correlation structure and robust (sandwich) covariance estimators for the regression coefficients. The identity link function was selected for both reaction times and ratings. We included the following predictors in our model: Main effects: group (healthy controls, patients with SSD), stimulation (frontal, parietal, frontoparietal), gesture type (metaphoric, iconic), and relatedness (related, unrelated). Factorial interactions: We used a comprehensive model including all factorial interactions of the aforementioned factors. However, on the basis of our hypotheses of significant differences between healthy controls/patients and frontal/parietal stimulation, we were particularly interested in whether there would be group- and stimulation-dependent effects on gesture type and relatedness (ie, significant effects for the interactions group × stimulation × gesture type, group × stimulation × relatedness, and group × stimulation × gesture type × relatedness). After running our main analysis including all 4 stimulation conditions, we performed different post hoc tests to explore the importance of electrode position: (1) frontal against parietal stimulation, to test our main hypothesis; (2) frontal against frontoparietal and frontoparietal against parietal stimulation, to elucidate which electrode might be relevant for the effects of frontoparietal stimulation; and (3) each stimulation against sham. Finally, we analyzed the patient group separately, to check whether effects are in fact due to improvements in patients. As all post hoc tests reveal different aspects of the main analyses and as we only interpret post hoc tests of significant factorial interactions of the main analyses, post hoc tests are not corrected for multiple comparisons. Results Side Effects In sum, tDCS was well tolerated. No significant discomfort was observed during or after the experiment. There was no difference in reported side effects (rated on a scale from 1 to 5) between patients and healthy controls (overall mean for patients = 1.42, SE = 0.07; overall mean for healthy controls = 1.53, SE = 0.07; P = .256) and no difference between the different real stimulation conditions. However, reported side effects differed slightly but significantly between sham and real stimulation (overall mean for real stimulation conditions = 1.51, SE = 0.06; overall mean for sham stimulation = 1.43, SE = 0.05; P = .038). Perceived stimulation intensity was also higher for real compared to sham stimulation (mean for real stimulation = 2.27, SE = 1.5; mean for sham stimulation = 1.76, SE = 1.4). Ratings The overall analysis showed that patients rated related gestures as relatively more unrelated than healthy controls, whereas they rated unrelated gestures as relatively more related (table 1 and figure 2; interaction group × relatedness, P = .032), indicating reduced discrimination between conditions and an impairment of evaluating the relation between speech and gesture semantics. Table 1. Results of Main Analysis Source . df . Test of Model Effects Rating . Test of Model Effects Reaction Time . Wald Chi-Square . Sig. . Wald Chi-Square . Sig. . (Intercept) 1 1670.141 <0.001 6245.191 <0.001 Group 1 .348 .555 15.830 <0.001 Stimulation 3 3.603 .308 2.447 .485 Gesture type 1 4.926 .026 155.487 <0.001 Relatedness 1 412.948 <0.001 14.870 <0.001 Group × Stimulation 3 6.413 .093 3.862 .277 Group × Gesture type 1 .375 .540 5.196 .023 Group × Relatedness 1 4.577 .032 .922 .337 Stimulation × Gesture type 3 6.791 .079 3.127 .372 Stimulation × Relatedness 3 4.238 .237 5.204 .157 Gesture type × Relatedness 1 32.558 <0.001 9.997 .002 Group × Stimulation × Gesture type 3 1.294 .731 .333 .954 Group × Stimulation × Relatedness 3 9.099 .028 .783 .854 Group × Gesture type × Relatedness 1 4.974 .026 3.164 .075 Stimulation × Gesture type × Relatedness 3 3.146 .370 10.101 .018 Group × Stimulation * Gesture type × Relatedness 3 4.085 .252 4.795 .187 Source . df . Test of Model Effects Rating . Test of Model Effects Reaction Time . Wald Chi-Square . Sig. . Wald Chi-Square . Sig. . (Intercept) 1 1670.141 <0.001 6245.191 <0.001 Group 1 .348 .555 15.830 <0.001 Stimulation 3 3.603 .308 2.447 .485 Gesture type 1 4.926 .026 155.487 <0.001 Relatedness 1 412.948 <0.001 14.870 <0.001 Group × Stimulation 3 6.413 .093 3.862 .277 Group × Gesture type 1 .375 .540 5.196 .023 Group × Relatedness 1 4.577 .032 .922 .337 Stimulation × Gesture type 3 6.791 .079 3.127 .372 Stimulation × Relatedness 3 4.238 .237 5.204 .157 Gesture type × Relatedness 1 32.558 <0.001 9.997 .002 Group × Stimulation × Gesture type 3 1.294 .731 .333 .954 Group × Stimulation × Relatedness 3 9.099 .028 .783 .854 Group × Gesture type × Relatedness 1 4.974 .026 3.164 .075 Stimulation × Gesture type × Relatedness 3 3.146 .370 10.101 .018 Group × Stimulation * Gesture type × Relatedness 3 4.085 .252 4.795 .187 Note. Sig., significance. Open in new tab Table 1. Results of Main Analysis Source . df . Test of Model Effects Rating . Test of Model Effects Reaction Time . Wald Chi-Square . Sig. . Wald Chi-Square . Sig. . (Intercept) 1 1670.141 <0.001 6245.191 <0.001 Group 1 .348 .555 15.830 <0.001 Stimulation 3 3.603 .308 2.447 .485 Gesture type 1 4.926 .026 155.487 <0.001 Relatedness 1 412.948 <0.001 14.870 <0.001 Group × Stimulation 3 6.413 .093 3.862 .277 Group × Gesture type 1 .375 .540 5.196 .023 Group × Relatedness 1 4.577 .032 .922 .337 Stimulation × Gesture type 3 6.791 .079 3.127 .372 Stimulation × Relatedness 3 4.238 .237 5.204 .157 Gesture type × Relatedness 1 32.558 <0.001 9.997 .002 Group × Stimulation × Gesture type 3 1.294 .731 .333 .954 Group × Stimulation × Relatedness 3 9.099 .028 .783 .854 Group × Gesture type × Relatedness 1 4.974 .026 3.164 .075 Stimulation × Gesture type × Relatedness 3 3.146 .370 10.101 .018 Group × Stimulation * Gesture type × Relatedness 3 4.085 .252 4.795 .187 Source . df . Test of Model Effects Rating . Test of Model Effects Reaction Time . Wald Chi-Square . Sig. . Wald Chi-Square . Sig. . (Intercept) 1 1670.141 <0.001 6245.191 <0.001 Group 1 .348 .555 15.830 <0.001 Stimulation 3 3.603 .308 2.447 .485 Gesture type 1 4.926 .026 155.487 <0.001 Relatedness 1 412.948 <0.001 14.870 <0.001 Group × Stimulation 3 6.413 .093 3.862 .277 Group × Gesture type 1 .375 .540 5.196 .023 Group × Relatedness 1 4.577 .032 .922 .337 Stimulation × Gesture type 3 6.791 .079 3.127 .372 Stimulation × Relatedness 3 4.238 .237 5.204 .157 Gesture type × Relatedness 1 32.558 <0.001 9.997 .002 Group × Stimulation × Gesture type 3 1.294 .731 .333 .954 Group × Stimulation × Relatedness 3 9.099 .028 .783 .854 Group × Gesture type × Relatedness 1 4.974 .026 3.164 .075 Stimulation × Gesture type × Relatedness 3 3.146 .370 10.101 .018 Group × Stimulation * Gesture type × Relatedness 3 4.085 .252 4.795 .187 Note. Sig., significance. Open in new tab Fig. 2. Open in new tabDownload slide Group dependence of relatedness ratings. Mean relatedness ratings (relatedness rated on a scale from 1 = very low to 7 = very high relatedness). SSD = schizophrenia spectrum disorder. Error bars indicate the standard error of the mean (SEM). Most importantly, the interaction group × stimulation × relatedness was significant (P = .028), indicating that stimulation influenced group differences (table 1 and figure 3). Post hoc tests resulted in a clear pattern: Frontal and frontoparietal stimulation alike differed significantly from parietal and sham stimulation (frontal vs sham, P = .031; frontal vs parietal, P = .021; frontoparietal vs sham, P = .034; frontoparietal vs parietal, P = .034). The interaction was not significant for comparing frontal against frontoparietal stimulation. Likewise, the contrast of parietal against sham stimulation was not significant. Fig. 3. Open in new tabDownload slide Stimulation, group, and relatedness dependence of ratings. (A, top) Mean ratings (relatedness rated on a scale from 1 = very low to 7 = very high relatedness). (B, bottom) tDCS improvement in differentiation between related and unrelated gestures: Difference between real stimulation conditions and sham condition, regarding the difference in ratings between related and unrelated gestures of each condition. L = left; R = right; F = frontal; P = parietal; C = cathode; A = anode; eg, LFC-RFA = left frontal cathodal and right frontal anodal stimulation; LFC-RPA = left frontal cathodal and right parietal anodal stimulation; LPC-RPA = left parietal cathodal stimulation. Electrode positions illustrated by head drawings above (blue = cathode; orange = anode). Error bars indicate the standard error of the mean (SEM). Frontal and frontoparietal stimulation significantly improved discrimination between related and unrelated gestures in patients (see figure 3B). Thus, frontal and frontoparietal stimulation reduced group differences by improving patients’ performance in evaluating the relationship between speech and gesture. Moreover, we found that patients rated unrelated iconic and unrelated metaphoric gestures similarly, whereas healthy subjects rated unrelated metaphoric stimuli more critically (interaction group × gesture type × relatedness, P = .026). Even though there was an interaction of gesture type × relatedness, indicating that metaphoric-related gestures were rated as being relatively less related to speech content (interaction gesture type × relatedness, P < .001), the interactions of gesture type with group and/or stimulation did not reach significance. Reaction Times Although we found no effects of stimulation on group differences regarding reaction times (table 1 and figure 4), we found that: Fig. 4. Open in new tabDownload slide Stimulation, gesture type, and relatedness dependence of mean reaction times across the entire group of patients and healthy controls. Light red: iconic unrelated. Dark red: iconic related. Light green: metaphoric unrelated. Dark green: metaphoric related. Electrode positions illustrated by head drawings above (blue = cathode; orange = anode). Error bars indicate the standard error of the mean (SEM). First, patients responded generally more slowly than healthy controls (mean reaction time = 4599 ms for patients, mean reaction time = 4158 ms for healthy controls, P < .001). Second, patients and healthy subjects were both faster at responding to iconic gestures in comparison to metaphoric gestures. The advantage in reaction times for iconic gestures, however, was relatively smaller for patients (group × gesture type, P = .023; difference metaphoric – iconic for healthy controls = 279 ms, difference metaphoric – iconic for patients = 193 ms). Third, the advantage (faster reaction times) for iconic compared to metaphoric gestures was significantly bigger for related compared to unrelated gestures (gesture type × relatedness, P = .002; difference metaphoric unrelated – iconic unrelated = 196 ms vs difference metaphoric related – iconic related = 277 ms). Finally, stimulation influenced the interaction between gesture type and relatedness (figure 4, stimulation × gesture type × relatedness, P = .018). Post hoc tests were significant only for contrasting frontoparietal against frontal (P = .008), parietal (P = .004), and sham stimulation (P = .009), indicating that frontoparietal stimulation increased the difference in reaction times between related and unrelated metaphoric gestures and facilitated processing of related metaphoric gestures. When analyzing healthy controls and patients separately, this interaction is significant only for patients (P = .047), indicating that the effect is driven mainly by the patient group. Results of our patient-only analysis were consistent with results of the main model (ratings: stimulation × relatedness: P = .032, gesture type × relatedness: P = .016; reaction times: gesture type × relatedness: P = .001, stimulation × gesture type × relatedness: P = .047), indicating that stimulation influenced the evaluation of speech–gesture relatedness in patients. Discussion In this study, we tested the hypothesis that cathodal tDCS of the left frontal cortex can influence dysfunctional co-verbal gesture processing. We found that frontal and frontoparietal stimulation did in fact significantly improve the differentiation of related and unrelated speech–gesture conditions in patients, reducing the difference in rating behavior between patients and healthy controls. Results We could show that patients have substantial gesture deficits, by demonstrating for the first time that their ability to discriminate between related and unrelated co-verbal gestures is reduced. Patients tended to rate related co-verbal gestures as less related and unrelated co-verbal gestures as more related than healthy controls. Using tDCS, we were able to normalize this speech–gesture matching deficit. We found a specific stimulation effect on ratings for related, compared to unrelated, co-verbal gestures, confirming the importance of the left frontal region for assessing semantic relatedness.51 In normal communication, gestures are usually related to speech, so it is promising for possible clinical applications that the observed effect is mainly driven by related gestures. The left frontal inferior gyrus has been identified as an area of major overactivation in schizophrenia38 and seems to be particularly relevant for gesture deficits.25 Furthermore, in schizophrenia the functional connection between left IFG and left STS is weakened, especially for metaphoric gestures.26 It is likely that cathodal tDCS has modulated pathological processing in left frontal areas and/or influenced the connectivity between the left IFG and the left STS. This would be in line with a recent review that concluded that both local excitability changes (induced by radial currents) and synaptic changes (induced by tangential currents) in the frontoparietal network are relevant for tDCS effects in patients with schizophrenia.52 In healthy subjects, left frontal anodal stimulation specifically decreased reaction times and ratings for metaphoric co-verbal gestures.36 In this study, we did not include a condition with anodal stimulation of the left frontal cortex, which could be the reason that we did not find a gesture type dependent effect on ratings. A recent study with high temporal resolution due to a combined EEG-fMRI approach suggests an important involvement of the left IFG even for the processing of intrinsic meaningful gestures53; this could also explain why left frontal stimulation had no differential effect on ratings between metaphoric and iconic gestures. Of course, differences in gesture processing between healthy controls and patients with SSD might play a role as well. Moreover, the decrease in reaction times for related metaphoric gestures during frontoparietal stimulation in patients and across groups indicated at least some gesture-type-specific improvement. Limitations Despite the encouraging finding of improved semantic processing after left frontal tDCS, we need to interpret our results cautiously. We did not directly compare left frontal cathodal against left frontal anodal stimulation. To confirm that the improvement in relatedness assessment was indeed due to left frontal cathodal stimulation (and not to contralateral anodal stimulation), further studies should replicate our results using a left frontal cathode/anode and a relatively inactive reference electrode (placed in an area such as the cheek). In addition, the application of other brain stimulation methods such as transcranial magnetic stimulation or transcranial alternating current stimulation could also be useful to corroborate and expand our present findings. More generally, due to the limitations of tDCS as a research tool, our study is limited with regard to elucidating the precise brain regions and mechanisms influenced by stimulation. Outlook Here, we showed that tDCS can improve gesture processing during stimulation (online). It should be probed if and for how long tDCS effects on gesture processing last after stimulation (offline). Moreover, as gesture perception and gesture performance are closely related, it seems likely that tDCS may also improve gesture performance. In the future, tDCS may be a useful tool for improving semantic processing and thereby possibly improve social functioning of patients with SSD. However, many tDCS studies in patients with schizophrenia conducted so far have applied anodal stimulation to the left dorsolateral prefrontal cortex (eg, to improve auditory hallucinations30,54 or working memory32–35). Before using any tDCS protocol in clinical practice, its effects on a wide range of brain functions need to be assessed thoroughly. Eventually, optimization of stimulation duration, strength, and repetition would be necessary to establish an effective tDCS protocol for improving clinically relevant parameters of social cognition in schizophrenia. Conclusion Here we show for the first time that tDCS can improve semantic speech–gesture matching in patients with SSD. However, before clinical application can be considered, further research is needed to understand the mechanisms behind this effect, to examine possible side effects of stimulation, and to explore whether tDCS can be used to improve social communication and gestural processing in patients over the long term. Funding University Medical Centre Giessen and Marburg (UKGM; grant number 25/2015MR); German Research Foundation (grant numbers Ki588/6-2, STR 1146/11-2, STR 1146/8-1, and STR 1146/9-1 to B.S.). Acknowledgments We thank Alexei Sirbu, Caro Wittke, Christina Schmitter, and Leona Hömberg for help with data collection. Furthermore, we thank Bjarne Schülke for technical assistance extracting raw data from log files. The authors have declared that there are no conflicts of interest in relation to the subject of this study. References 1. Goldin-Meadow S . The role of gesture in communication and thinking . Trends Cogn Sci . 1999 ; 3 ( 11 ): 419 – 429 . Google Scholar Crossref Search ADS PubMed WorldCat 2. Goldin-Meadow S , Alibali MW. Gesture’s role in speaking, learning, and creating language . Annu Rev Psychol . 2013 ; 64 ( 1 ): 257 – 283 . Google Scholar Crossref Search ADS PubMed WorldCat 3. Kelly SD , Barr DJ, Church RB, Lynch K. 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This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected] © The Author(s) 2018. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center.

Moussa-Tooks, Alexandra, B;Kim,, Dae-Jin;Bartolomeo, Lisa, A;Purcell, John, R;Bolbecker, Amanda, R;Newman, Sharlene, D;O’Donnell, Brian, F;Hetrick, William, P

doi: 10.1093/schbul/sby064pmid: 29800417

Abstract Prominent conceptual models characterize schizophrenia as a dysconnectivity syndrome, with recent research focusing on the contributions of the cerebellum in this framework. The present study examined the role of the cerebellum and its effective connectivity to the cerebrum during sensorimotor synchronization in schizophrenia. Specifically, the role of the cerebellum in temporally coordinating cerebral motor activity was examined through path analysis. Thirty-one individuals diagnosed with schizophrenia and 40 healthy controls completed a finger-tapping fMRI task including tone-paced synchronization and self-paced continuation tapping at a 500 ms intertap interval (ITI). Behavioral data revealed shorter and more variable ITIs during self-paced continuation, greater clock (vs motor) variance, and greater force of tapping in the schizophrenia group. In a whole-brain analysis, groups showed robust activation of the cerebellum during self-paced continuation but not during tone-paced synchronization. However, effective connectivity analysis revealed decreased connectivity in individuals with schizophrenia between the cerebellum and primary motor cortex but increased connectivity between cerebellum and thalamus during self-paced continuation compared with healthy controls. These findings in schizophrenia indicate diminished temporal coordination of cerebral motor activity by cerebellum during the continuation tapping portion of sensorimotor synchronization. Taken together with the behavioral finding of greater temporal variability in schizophrenia, these effective connectivity results are consistent with structural and temporal models of dysconnectivity in the disorder. psychosis, cerebellum, finger tapping, effective connectivity, basal ganglia, fMRI Introduction Schizophrenia has long been identified as a dysconnectivity syndrome, beginning with Bleuler’s conceptualization of a “fragmented phrene”1 and Stransky’s intrapsychic ataxia theory,2 both of which theorized a dyscoordination of the motor and cognitive processes in the disorder. Andreasen’s3 cognitive dysmetria theory extended these conceptualizations by suggesting a neural mechanism by which such processes occur, namely disruption to the cortico-cerebellar-thalamic-cortical circuit (CCTCC). In this circuit, the cerebellar node plays a primary coordinative role.4 Explicit examination of the function of the cerebellum in this circuit is important because many large-scale studies of neural connectivity exclude the cerebellum,5,6 which is known to be interconnected with cerebrum7,8 and contains an upward estimate of 80% of the brain’s total neurons.9 Calls from the NIMH to add a “motor systems” domain to the Research Domain Criteria (RDoC) matrix underscore the importance of understanding neural mechanisms contributing to motor impairment in psychopathology.10 The present study assessed neural dysconnectivity in schizophrenia within the CCTCC during a sensorimotor task. Sensorimotor timing tasks are well suited to investigations of neural dyscoordination, and the processing circuits involved have been well characterized.11,12 Cerebellum, cortical (primary motor [M1], supplementary motor, and prefrontal cortices), and subcortical (caudate, putamen, and thalamus) structures have been implicated broadly in motor and perceptual timing and integration.13–15 Lesion studies point to critical contributions of the cerebellum and basal ganglia14 as a distributed timing network for subsecond, discrete, rhythmic timing. Moreover, basal ganglia and cerebellar projections converge on subregions of the thalamus, before projecting to M1.16 In sensorimotor synchronization finger-tapping—wherein participants synchronize tapping with a tone and then attempt to maintain the same pace after the tone discontinues—the cerebellum is more activated during the self-paced continuation portion compared to the stimulus-cued synchronization portion. Such findings demonstrate the cerebellum’s function as a timekeeper or internal “clock,”17 in which cerebellar-generated temporal representations putatively drive cerebral motor areas to maintain internal temporal representations, thereby maximizing task performance. Research on the neural substrates of this form of sensorimotor synchronization has shown predominant cerebellar involvement, and the cortico-cerebellar circuit, in tasks of automatic, subsecond timing.18–21 The basal ganglia, specifically the putamen, is also a key structure in interval timing tasks, including sensorimotor synchronization,11,13 and is heavily integrated with the cerebellum during time estimation and motor output in both the subsecond and second range.11,22 The thalamus is also crucial for sensorimotor integration.23 Involvement of subcortical structures in this timing circuit has been likened to a “coincidence detection” system24 that integrates sensory inputs and motor outputs and facilitates coordinated communication between cerebellar and cerebral structures. In addition to their critical roles in the timing circuit, deficits in the cerebellum, basal ganglia, and thalamus, and the interconnectivity between these regions have been identified in schizophrenia. Cerebellar volume, symmetry, and function are abnormal in schizophrenia.4,25–29 Cerebellar soft signs have been reported in schizophrenia,30 including medication naïve participants,26,31 suggesting these deficits are features of the underlying disorder rather than effects of medication. Moreover, neurological soft signs predict abnormal white matter development in the cerebellar-thalamic tract in individuals at ultra-high risk for schizophrenia.32 Findings of dopamine-dysregulation and motor dysfunction in schizophrenia also implicate dysfunction in the basal ganglia and its connections.33 In schizophrenia, studies have revealed decreased basal ganglia activation that was associated with both positive symptoms and motor deficits.34 Altered functional connectivity during motor tasks, where decreased posterior putamen activation was associated with decreased thalamic activation, has also been reported.35 During time estimation and frequency discrimination tasks, thalamic and putamen activation was decreased in individuals with schizophrenia, with hypoactivity in thalamic and striatal regions observed with increased task difficulty.36 Finally, disturbances in thalamocortical connectivity have been reported between the motor and sensory regions in individuals with schizophrenia37,38 and those at high risk for the disorder.39 Taken together, there is evidence that sensorimotor synchronization and continuation finger tapping engages discrete, rhythmic timing processes involving cerebellum, basal ganglia, and thalamus13,14,40 and that these same regions are implicated in schizophrenia. However, despite evidence of robust behavioral impairments in schizophrenia on this sensorimotor synchronization task, the neural correlates of these deficits have not been investigated. The present study utilized a sensorimotor synchronization task to, for the first time, determine the neural correlates of sensorimotor timing deficits in schizophrenia, including the connectivity of the cerebellum to the cerebrum. The following predictions were made: First, that individuals with schizophrenia would exhibit increased tapping variability and a shorter ITI, consistent with previous findings.41 Second, that the task itself would activate M1, cerebellum, basal ganglia, and thalamus, as evidenced by blood-oxygen-level dependent (BOLD) response.13,14 Third, that individuals with schizophrenia would show decreased cerebellar activation compared with the healthy control group, which would further be correlated with their task performance. Fourth, the schizophrenia group would exhibit impairments in effective connectivity during the task within the theorized timing circuit, with hypoconnectivity between the cerebellum and cerebral motor regions and subcortical structures (ie, thalamus and basal ganglia), suggesting temporal dyscoordination. Finally, exploratory analyses examined neural, cognitive, and symptom correlates. Materials and Methods Participants Participants were recruited from local community and inpatient clinics in Bloomington and Indianapolis, IN. All procedures were approved by the Indiana University Institutional Review Board. Written, informed consent was provided by 42 healthy controls and 33 individuals with schizophrenia or schizoaffective disorder (SZ) as diagnosed by DSM-IV. Forty controls and 31 SZ participants were retained for analyses (see table 1 and exclusion information below). Table 1. Values for Sex and Ethnicity Reflect Frequency; Values for Age, WASI, and WAIS Represent Mean and SD Participant Demographics Healthy (n = 40) Schizophrenia (n = 31) Statistics (t or χ2) P-value Sex (male/female) 19/21 20/11 2.043 .153 Ethnicity (C/A/H/O) 33/5/0/2 13/15/1/2 13.884 .003 Age (years) 38.9 (9.4) 36.7 (10.7) 1.073 .340 WASI42  FSIQ 115.2 (10.9) 100.3 (16.8) 4.222 <.001  Vocabulary 59.0 (7.7) 48.9 (12.7) 3.820 <.001  Matrix reasoning 59.5 (12.7) 51.3 (9.7) 2.951 .004 WAIS43  Digit-symbol44 12.5 (2.8) 7.8 (2.6) 7.122 <.001 Participant Demographics Healthy (n = 40) Schizophrenia (n = 31) Statistics (t or χ2) P-value Sex (male/female) 19/21 20/11 2.043 .153 Ethnicity (C/A/H/O) 33/5/0/2 13/15/1/2 13.884 .003 Age (years) 38.9 (9.4) 36.7 (10.7) 1.073 .340 WASI42  FSIQ 115.2 (10.9) 100.3 (16.8) 4.222 <.001  Vocabulary 59.0 (7.7) 48.9 (12.7) 3.820 <.001  Matrix reasoning 59.5 (12.7) 51.3 (9.7) 2.951 .004 WAIS43  Digit-symbol44 12.5 (2.8) 7.8 (2.6) 7.122 <.001 Note: WASI, Wechsler Abbreviated Scale of Intelligence; FSIQ, Full Scale Intelligent Quotient; WAIS, Wechsler Adult Intelligence Scale; C, Caucasian; A, African American; H, Hispanic; O, Other. Italics indicate P-values that met a significance threshold of P < 0.05. View Large Table 1. Values for Sex and Ethnicity Reflect Frequency; Values for Age, WASI, and WAIS Represent Mean and SD Participant Demographics Healthy (n = 40) Schizophrenia (n = 31) Statistics (t or χ2) P-value Sex (male/female) 19/21 20/11 2.043 .153 Ethnicity (C/A/H/O) 33/5/0/2 13/15/1/2 13.884 .003 Age (years) 38.9 (9.4) 36.7 (10.7) 1.073 .340 WASI42  FSIQ 115.2 (10.9) 100.3 (16.8) 4.222 <.001  Vocabulary 59.0 (7.7) 48.9 (12.7) 3.820 <.001  Matrix reasoning 59.5 (12.7) 51.3 (9.7) 2.951 .004 WAIS43  Digit-symbol44 12.5 (2.8) 7.8 (2.6) 7.122 <.001 Participant Demographics Healthy (n = 40) Schizophrenia (n = 31) Statistics (t or χ2) P-value Sex (male/female) 19/21 20/11 2.043 .153 Ethnicity (C/A/H/O) 33/5/0/2 13/15/1/2 13.884 .003 Age (years) 38.9 (9.4) 36.7 (10.7) 1.073 .340 WASI42  FSIQ 115.2 (10.9) 100.3 (16.8) 4.222 <.001  Vocabulary 59.0 (7.7) 48.9 (12.7) 3.820 <.001  Matrix reasoning 59.5 (12.7) 51.3 (9.7) 2.951 .004 WAIS43  Digit-symbol44 12.5 (2.8) 7.8 (2.6) 7.122 <.001 Note: WASI, Wechsler Abbreviated Scale of Intelligence; FSIQ, Full Scale Intelligent Quotient; WAIS, Wechsler Adult Intelligence Scale; C, Caucasian; A, African American; H, Hispanic; O, Other. Italics indicate P-values that met a significance threshold of P < 0.05. View Large Clinical Assessment Participants were administered Structured Clinical Interviews for DSM-IV Criteria for Axis I and Axis II disorders (SCID-I Patient45 or Non-Patient Version46 and SCID-II47) and other clinical measures to establish the diagnosis and clinical state (supplementary material). A urine drug screen confirmed that participants were not using illicit substances. Participants completed cognitive measures shown in table 1. Sensorimotor Synchronization Finger-Tapping Task Participants underwent three consecutive sessions of a 6-minute functional magnetic resonance imaging (fMRI) scan during a sensorimotor synchronization finger-tapping task, including 6 blocks of the following sequence: 6-second synchronization tapping, 20-second continuation tapping, 6-second listen, and 15-second rest periods. Participants tapped with their right index finger (SZ: 7 left-handed, 2 ambidextrous; HC: 2 left-handed, 3 ambidextrous) on a handheld tapping pad with a 1.5 cm diameter force sensor. Handedness did not impact behavioral or neuroimaging findings (supplementary material). Behavioral Analyses Mean and variability of consecutive tapping intervals, and tapping force were analyzed. Variability was analyzed as the coefficient of variation (ie, SD divided by the mean within subjects). Participants with at least 8 synchronization ITIs and 30 continuation ITIs were included in the analysis to ensure adequate statistical power. Accordingly, 2 SZ and 18 controls were excluded, for a final sample of 31 SZ and 24 controls for these analyses. Wing–Kristofferson48 (W-K; cf,41) analysis was performed to parse clock timing variance and motor execution variance from total behavioral tapping variance (supplementary material). A threshold of at least 10 consecutive error-free taps in 10 consecutive trials within the continuation-tapping block resulted in 6 SZ and 8 controls being excluded from this analysis. Magnetic Resonance Imaging (MRI) Acquisition Data acquisition was carried out on a Siemens 3T Magnetom Trio-Tim Scanner. Functional scans were acquired using a single-shot echo-planar-imaging (SS-EPI) sequence with a 12-channel head coil [repetition time (TR) = 2500 ms; echo time (TE) = 30 ms; 40 transverse slices; slice thickness 3.2 mm; field of view (FOV) = 220 × 220 mm2; imaging matrix = 96 × 96; in-plane voxel size = 2.3 × 2.3 mm2]. T1-weight anatomical scans were acquired with a 32-channel head coil, using an 8-minute magnetization prepared rapid gradient echo (MP-RAGE) sequence [TR = 1800 ms; TE = 2.67 ms; TI = 900 ms; FOV = 256 × 256 mm2; 160 slices in sagittal plane; flip angle 9°; voxel size = 1 × 1 × 1 mm3]. MRI Preprocessing and Analysis Preprocessing of T1-weighted MRI was performed using FSL toolbox (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki). T1-weighted MRIs were normalized to Montreal Neurological Institute (MNI) space, in which the linear transformation was conducted using FLIRT49,50 and nonlinear transformation using FNIRT.51,52 Structural scans were resampled to MNI152 space using FSL (function applywarp). Data preprocessing and analysis of functional data was done with SPM12 (http://www.fil.ion.ucl.ac.uk/spm/software/spm12). Preprocessing included slice timing correction, motion correction, coregistration, segmentation, spatial normalization, and spatial smoothing [6 mm full width at half maximum (FWHM)]. First-level, whole-brain analysis followed a block-design general linear model (GLM) including 36 regressors–5 block (eg, synchronization, continuation, rest) and 6 motion parameters for each scan session plus 3 total session parameters. Covariates of age and sex were added to this model because of known effects on the cerebellum and brain size. The output of this model was a BOLD map. Activation was defined as the t-values of this BOLD map. Two main contrasts were computed: Synchronization (Tone-Paced Tapping minus Rest) and Continuation (Self-Paced Tapping minus Rest). An additional 2 post hoc contrasts were also computed (supplementary material). Generated contrast maps were used to compute 2-sample t-tests (control vs SZ) for second level analyses. Motion was assessed using methods described by Power and colleagues53; a threshold of frame-wise displacement of 2 mm in 10% or more scans was set, and 2 subjects per group were excluded for a final sample of 31 SZ and 40 controls. Contrast weights, or beta weights from the continuation contrast, were extracted for individual subjects in regions of interest (ROIs) defined by a 3 mm sphere around maximum peak voxels of interest (VOIs) in cerebellar lobule V, thalamus, putamen, and M1 (coordinates in table 3). ROIs were selected based on (1) previous findings and (2) theoretical models of timing and sensorimotor synchronization. Specifically, data suggest the critical role of the cerebellum in generating self-paced, subsecond timing cues17–20 and M1 serves as a primary generator of the output response.13,14 Crucial for the synchronization of these cues is the thalamus, through which cerebellum and M1 are anatomically connected.16,23,24 Finally, putamen serves as a relay station within this circuit to modulate thalamic signals.11,13,54,55 Relevance of these structures in the schizophrenia literature4,26,33,36,38,39,56–58 and observed activations from the whole brain analysis further confirmed the suitability of these ROIs for these analyses. Extracted weights were used in correlational and path analyses, performed using SPSS (24.0, IBM Corporation) and AMOS (23.0, IBM Corporation). Effective Connectivity Models, Analyses, and Correlations Extracted continuation contrast weights, as defined above, were used to assess effective connectivity. Effective connectivity was defined, according to Lindquist,59 as the change in activity in one ROI as influenced by another ROI, averaged across a given time interval. Though these models indicate a causal relationship between these ROIs, the directionality is determined a priori and is not derived from the data itself. Individual subjects’ beta weights from these ROIs were input into a predefined model (figure 2). Multigroup path analysis was used to generate path coefficients, or “connectivity,” between ROIs of the tested models and to compare models between groups. Two models were evaluated: a conceptual, causal model of covariation between (A) cerebellum, M1, and thalamus and (B) cerebellum, thalamus, and putamen. The directionality of model A was determined based on the functional conceptualization of the cerebellum as an oscillatory pacemaker, which prompts M1 with timing cues during continuation tapping.11,13,18,60 This model allowed us to test the hypothesis that cerebellar activation during continuation tapping would be more closely related to the intended target of the timing signal (M1) than to the relay node (thalamus), for these signals as they travel to cortex61 in healthy compared to schizophrenia participants. Model B allowed us to test the hypothesis that in healthy controls compared to the schizophrenia group cerebellar activation during continuation tapping would be more closely related to activation within the “coincidence detection loop,” thalamus and putamen, hypothesized to regulate the integration of motor and clock processes and facilitate error detection.13,14,60,62 Exploratory correlational analyses were conducted between the following 6 variables given their centrality to the critical constructs of interest in this article: cerebellar activation; tapping variability and force; Digit-symbol as a measure of visuomotor coordination and processing speed (ie, Digit-symbol task); and the PANSS standard negative subscore and disorganized factor score63 to assess severity of psychopathology. Results Behavioral Findings Individuals with schizophrenia exhibited shorter ITIs [t(46.396) = 2.151, P = .037, d = 0.562] and more variable tapping [t(53) = −2.414, P = .019, d = 0.655] during continuation only compared with controls (table 2). The schizophrenia group tapped with greater force in the synchronization [t(52) = −3.667, P < .005, d = 0.972] and continuation [t(51.518) = −3.720, P < .005, d = 0.985) portions of the task. No group differences were observed in variability or coefficient of variation of tapping force (table 2). Table 2. Behavioral Analyses ITI Mean (ms) CV Synchronization Continuationa Synchronization Continuationa HC (N = 24) 478.41 (26.77) 509.22 (26.85) 0.1036 (0.034) 0.080 (0.046) SZ (N = 31) 476.09 (37.10) 485.50 (53.29) 0.1173 (0.042) 0.110 (0.045) Force Mean CV Synchronizationa Continuationa Synchronization Continuation HC (N = 24) 703.3 (357.7) 666.2 (355.9) 0.481 (0.148) 0.487 (0.142) SZ (N = 31) 1146.4 (536.0) 1123.0 (551.3) 0.500 (0.197) 0.462 (0.162) W-K Mean Motora Clockb Totala Ratio (motor/clock) HC (N = 16) 1359.9 (783.1) 4215.6 (2457.1) 5575.5 (3239.7) 0.756 SZ (N = 25) 2179.0 (1468.1) 6671.9 (4450.2) 8850.9 (5917.9) 0.754 ITI Mean (ms) CV Synchronization Continuationa Synchronization Continuationa HC (N = 24) 478.41 (26.77) 509.22 (26.85) 0.1036 (0.034) 0.080 (0.046) SZ (N = 31) 476.09 (37.10) 485.50 (53.29) 0.1173 (0.042) 0.110 (0.045) Force Mean CV Synchronizationa Continuationa Synchronization Continuation HC (N = 24) 703.3 (357.7) 666.2 (355.9) 0.481 (0.148) 0.487 (0.142) SZ (N = 31) 1146.4 (536.0) 1123.0 (551.3) 0.500 (0.197) 0.462 (0.162) W-K Mean Motora Clockb Totala Ratio (motor/clock) HC (N = 16) 1359.9 (783.1) 4215.6 (2457.1) 5575.5 (3239.7) 0.756 SZ (N = 25) 2179.0 (1468.1) 6671.9 (4450.2) 8850.9 (5917.9) 0.754 Note: Coefficient of variation (CV) = standard deviation/mean; ITI, intertap interval; W-K, Wing-Kristofferson; HC, healthy control; SZ, schizophrenia spectrum. aSignificant difference between groups. bTrending difference between groups. View Large Table 2. Behavioral Analyses ITI Mean (ms) CV Synchronization Continuationa Synchronization Continuationa HC (N = 24) 478.41 (26.77) 509.22 (26.85) 0.1036 (0.034) 0.080 (0.046) SZ (N = 31) 476.09 (37.10) 485.50 (53.29) 0.1173 (0.042) 0.110 (0.045) Force Mean CV Synchronizationa Continuationa Synchronization Continuation HC (N = 24) 703.3 (357.7) 666.2 (355.9) 0.481 (0.148) 0.487 (0.142) SZ (N = 31) 1146.4 (536.0) 1123.0 (551.3) 0.500 (0.197) 0.462 (0.162) W-K Mean Motora Clockb Totala Ratio (motor/clock) HC (N = 16) 1359.9 (783.1) 4215.6 (2457.1) 5575.5 (3239.7) 0.756 SZ (N = 25) 2179.0 (1468.1) 6671.9 (4450.2) 8850.9 (5917.9) 0.754 ITI Mean (ms) CV Synchronization Continuationa Synchronization Continuationa HC (N = 24) 478.41 (26.77) 509.22 (26.85) 0.1036 (0.034) 0.080 (0.046) SZ (N = 31) 476.09 (37.10) 485.50 (53.29) 0.1173 (0.042) 0.110 (0.045) Force Mean CV Synchronizationa Continuationa Synchronization Continuation HC (N = 24) 703.3 (357.7) 666.2 (355.9) 0.481 (0.148) 0.487 (0.142) SZ (N = 31) 1146.4 (536.0) 1123.0 (551.3) 0.500 (0.197) 0.462 (0.162) W-K Mean Motora Clockb Totala Ratio (motor/clock) HC (N = 16) 1359.9 (783.1) 4215.6 (2457.1) 5575.5 (3239.7) 0.756 SZ (N = 25) 2179.0 (1468.1) 6671.9 (4450.2) 8850.9 (5917.9) 0.754 Note: Coefficient of variation (CV) = standard deviation/mean; ITI, intertap interval; W-K, Wing-Kristofferson; HC, healthy control; SZ, schizophrenia spectrum. aSignificant difference between groups. bTrending difference between groups. View Large Increased total tapping variance was observed in schizophrenia compared with controls [t(39) = −2.022, P = .050]. When total variance was decomposed into clock and motor variance using the W-K model the schizophrenia group had significantly higher motor variance [t(39) = −2.321, P = .026, d = 0.696) and a trend toward higher clock variance [t(39) = −2.014, P = .051, d = 0.683] compared with controls. Clock variance was higher than motor variance for both groups; the ratio of clock to motor variance did not statistically differ between diagnostic groups, with clock variance accounting for approximately 75% of the total (table 2). Functional MRI Findings Whole brain analyses were corrected to family-wise error (FWE) rate P <.05 with an extent threshold of 20 voxels (2 × 2 × 2 mm3). Whole brain analysis revealed significant BOLD activation of M1 during the synchronization compared to rest portion of the task (figure 1; table 3). During continuation, the cerebellum, thalamus, supplementary motor area,27 putamen, and inferior parietal cortex were also significantly activated compared to rest periods. Significant activation in the cerebellum was observed in lobules identified in both “motor” (IV, V, and VIIIa) and “cognitive” (VI) cerebellar regions (cf,40,64). No group differences were found between SZ and HC regarding whole-brain activation. Table 3. Whole Brain Analysis Activation Coordinates and Cluster Size Healthy Control (N = 40) Schizophrenia Spectrum (N = 31) xa y z kEb xa y z kEb Synchronization  Primary motor cortex −32 −26 56 862 −32 −30 58 508  Supplementary motor area −6 −2 54 84  Premotor cortex −58 6 30 26  Auditory cortex −44 −28 8 58  Occipital cortex −32 −88 −10 30 38 −84 −8 61  Striatum −24 10 −6 102 Continuation  Primary motor cortex −34 −26 56 4163 −40 −22 46 2112  Finger 60 8 20 100  Supplementary motor area −4 −8 50 431 4 0 64 725  Premotor cortex −10 −22 48 47 −56 4 26 156 66 −30 18 69 60 −30 20 47 54 −2 46 160 54 2 44 113 56 8 24 88  Somatosensory −50 −24 18 703 56 −16 20 22 54 −18 20 59  Thalamus −14 −20 2 — −14 −20 6 394  Putamen 22 6 0 202 24 0 0 103  Cerebellum −28 −60 −22 128 −24 −58 −20 49 12 −52 −18 1965 18 −52 −18 1300 12 −64 −46 392  Insula −28 24 10 34 −40 0 2 1135 48 12 0 453 40 2 8 203  Occipital cortex −16 −98 −2 377 26 −96 10 334 32 −92 8 80 10 −88 −10 44 16 −88 −16 63  Cingulate 6 18 30 33 Healthy Control (N = 40) Schizophrenia Spectrum (N = 31) xa y z kEb xa y z kEb Synchronization  Primary motor cortex −32 −26 56 862 −32 −30 58 508  Supplementary motor area −6 −2 54 84  Premotor cortex −58 6 30 26  Auditory cortex −44 −28 8 58  Occipital cortex −32 −88 −10 30 38 −84 −8 61  Striatum −24 10 −6 102 Continuation  Primary motor cortex −34 −26 56 4163 −40 −22 46 2112  Finger 60 8 20 100  Supplementary motor area −4 −8 50 431 4 0 64 725  Premotor cortex −10 −22 48 47 −56 4 26 156 66 −30 18 69 60 −30 20 47 54 −2 46 160 54 2 44 113 56 8 24 88  Somatosensory −50 −24 18 703 56 −16 20 22 54 −18 20 59  Thalamus −14 −20 2 — −14 −20 6 394  Putamen 22 6 0 202 24 0 0 103  Cerebellum −28 −60 −22 128 −24 −58 −20 49 12 −52 −18 1965 18 −52 −18 1300 12 −64 −46 392  Insula −28 24 10 34 −40 0 2 1135 48 12 0 453 40 2 8 203  Occipital cortex −16 −98 −2 377 26 −96 10 334 32 −92 8 80 10 −88 −10 44 16 −88 −16 63  Cingulate 6 18 30 33 Note: Bold coordinate values indicate extracted voxel of interest (VOI) coordinates for path analysis; SMA, supplementary motor area. ax-Coordinate differentiates the left and right hemispheres of the brain, with positive values indicating the right (ipsilateral to the tapping hand) hemisphere and negative values indicating left (contralateral to the tapping hand) hemisphere. bKE is the cluster size, or number of voxels contributing to the area of activation meeting FWE P <.05 threshold criteria. View Large Table 3. Whole Brain Analysis Activation Coordinates and Cluster Size Healthy Control (N = 40) Schizophrenia Spectrum (N = 31) xa y z kEb xa y z kEb Synchronization  Primary motor cortex −32 −26 56 862 −32 −30 58 508  Supplementary motor area −6 −2 54 84  Premotor cortex −58 6 30 26  Auditory cortex −44 −28 8 58  Occipital cortex −32 −88 −10 30 38 −84 −8 61  Striatum −24 10 −6 102 Continuation  Primary motor cortex −34 −26 56 4163 −40 −22 46 2112  Finger 60 8 20 100  Supplementary motor area −4 −8 50 431 4 0 64 725  Premotor cortex −10 −22 48 47 −56 4 26 156 66 −30 18 69 60 −30 20 47 54 −2 46 160 54 2 44 113 56 8 24 88  Somatosensory −50 −24 18 703 56 −16 20 22 54 −18 20 59  Thalamus −14 −20 2 — −14 −20 6 394  Putamen 22 6 0 202 24 0 0 103  Cerebellum −28 −60 −22 128 −24 −58 −20 49 12 −52 −18 1965 18 −52 −18 1300 12 −64 −46 392  Insula −28 24 10 34 −40 0 2 1135 48 12 0 453 40 2 8 203  Occipital cortex −16 −98 −2 377 26 −96 10 334 32 −92 8 80 10 −88 −10 44 16 −88 −16 63  Cingulate 6 18 30 33 Healthy Control (N = 40) Schizophrenia Spectrum (N = 31) xa y z kEb xa y z kEb Synchronization  Primary motor cortex −32 −26 56 862 −32 −30 58 508  Supplementary motor area −6 −2 54 84  Premotor cortex −58 6 30 26  Auditory cortex −44 −28 8 58  Occipital cortex −32 −88 −10 30 38 −84 −8 61  Striatum −24 10 −6 102 Continuation  Primary motor cortex −34 −26 56 4163 −40 −22 46 2112  Finger 60 8 20 100  Supplementary motor area −4 −8 50 431 4 0 64 725  Premotor cortex −10 −22 48 47 −56 4 26 156 66 −30 18 69 60 −30 20 47 54 −2 46 160 54 2 44 113 56 8 24 88  Somatosensory −50 −24 18 703 56 −16 20 22 54 −18 20 59  Thalamus −14 −20 2 — −14 −20 6 394  Putamen 22 6 0 202 24 0 0 103  Cerebellum −28 −60 −22 128 −24 −58 −20 49 12 −52 −18 1965 18 −52 −18 1300 12 −64 −46 392  Insula −28 24 10 34 −40 0 2 1135 48 12 0 453 40 2 8 203  Occipital cortex −16 −98 −2 377 26 −96 10 334 32 −92 8 80 10 −88 −10 44 16 −88 −16 63  Cingulate 6 18 30 33 Note: Bold coordinate values indicate extracted voxel of interest (VOI) coordinates for path analysis; SMA, supplementary motor area. ax-Coordinate differentiates the left and right hemispheres of the brain, with positive values indicating the right (ipsilateral to the tapping hand) hemisphere and negative values indicating left (contralateral to the tapping hand) hemisphere. bKE is the cluster size, or number of voxels contributing to the area of activation meeting FWE P <.05 threshold criteria. View Large Fig. 1. View largeDownload slide Task activation for a contrast of continuation tapping minus rest periods for HC (left, N = 40) and SZ (right, N = 31). Color bar represents t-value for activation (FWE corrected P < .05, extent threshold 20 voxels (2 × 2 × 2 mm3). FWE, family-wise error; HC, healthy control; SZ, schizophrenia spectrum. Fig. 1. View largeDownload slide Task activation for a contrast of continuation tapping minus rest periods for HC (left, N = 40) and SZ (right, N = 31). Color bar represents t-value for activation (FWE corrected P < .05, extent threshold 20 voxels (2 × 2 × 2 mm3). FWE, family-wise error; HC, healthy control; SZ, schizophrenia spectrum. Effective Connectivity In the first path analysis model (figure 2A) evaluating connections between the cerebellum and M1 directly or through the thalamus, the path fit between the cerebellum and M1 was significantly decreased in the SZ group (χ2 = 7.262, P = .007). There were no significant differences in cerebellum–thalamus or thalamus–M1 paths between groups (figure 2A). A model of the groups in which the nonsignificant cerebellum–thalamus and thalamus–M1 paths are constrained (ie, set equal) between groups exhibits good fit of the data (χ2 = 3.464, P = .177, comparative fit index [CFI] = 0.963, Bollen’s parsimonious fit index [BFI] or incremental index of fit [IFI] = 0.966). The second model (figure 2B) evaluating a loop between cerebellum and thalamus and putamen revealed significantly increased connectivity between cerebellum and thalamus in the schizophrenia group (χ2 = 4.042, P = .044). No significant group differences were observed in thalamus–putamen or putamen–cerebellum paths. Good model fit was observed when nonsignificant thalamus–putamen and putamen–cerebellum paths are constrained between groups (χ2 = 1.280, P = .527, CFI = 1.000, BFI/IFI = 1.020). Fig. 2. View largeDownload slide Unstandardized path coefficients for 2 models of interest (thick arrow = increased effective connectivity in SZ [schizophrenia spectrum; right side] compared to HC [healthy control; left side]; dotted arrow = decreased effective connectivity in SZ compared to HC). HC, healthy control; SZ, schizophrenia spectrum. *Significant group difference for indicated path. Fig. 2. View largeDownload slide Unstandardized path coefficients for 2 models of interest (thick arrow = increased effective connectivity in SZ [schizophrenia spectrum; right side] compared to HC [healthy control; left side]; dotted arrow = decreased effective connectivity in SZ compared to HC). HC, healthy control; SZ, schizophrenia spectrum. *Significant group difference for indicated path. Exploratory Correlations of Neural Activation With Cognitive and Symptom Measures In the schizophrenia sample, PANSS negative symptom scores were inversely correlated with tapping force during synchronization (r = −.454, P = .010) and continuation (r = −.434, P = .015). PANSS disorganized factor scores were inversely correlated with continuation tapping force (r = −.385, P = .043). Digit-symbol scores negatively correlated with cerebellar activation (continuation-rest contrast) (r = −.487, P = .022) in the nonpatient control group only. Discussion The present study was the first to examine the cerebellum’s role as a temporal pacemaker driving M1-generated motor behavior during tapping continuation of a cerebellar-dependent sensorimotor synchronization task, using effective connectivity analysis in persons with schizophrenia spectrum disorders. As predicted, SZ was associated with aberrant connectivity in the cerebellar-thalamocortical and cerebellar-thalamo-basal ganglia loops. Moreover, individuals with schizophrenia exhibited temporal dysfunction indicated by faster and more variable tapping. Contrary to predictions, no differences in whole-brain activation during the task were observed, suggesting aberrant functional connectivity as a source of the group differences in sensorimotor timing. Consistent with the only other previous report of sensorimotor synchronization in schizophrenia,41 impaired timing was observed in SZ. Specifically, shorter and more variable ITIs were observed during the continuation phase (table 2). These findings are consistent with suggestions that individuals with SZ have a sped up internal clock, which has been associated with hyper-dopaminergic states.65 Moreover, timing deficits in schizophrenia may be associated with, and contribute to, widely observed impairments of learning, memory, and perception in the disorder.66 For example, a meta-analysis showed that select neural regions—putamen, inferior parietal cortex, insula—were activated during both interval timing and cognitive (ie, working memory, executive functioning) tasks.67 Finally, timing deficits have been associated with errors in misattribution, self-monitoring, and top-down processing, leading to hypotheses that timing deficits may underlie hallucinations.68 The major new finding from the current study was that individuals with schizophrenia showed aberrant effective connectivity between cerebellum and M1 (figure 2A) during task performance compared with controls. In this statistical path model, allowing cerebellum to functionally co-vary with M1 and thalamus, it is interesting that functional covariation in controls shows increased cerebellum–M1 association compared with cerebellum–thalamus given that anatomically these regions are connected via thalamus. Assuming cerebellum provides a pacemaker signal, the functional co-variation should be highest between cerebellum (source of temporal coordination signal) and M1 (source of motor tapping execution), where activation may be more relevant to cerebellar oscillations than modulatory processes occurring elsewhere in the circuit. Aberrations in this path in the SZ group point to impairments synchronizing the cerebellar-generated pacemaker signal and motor output processed in M1 (evidenced by a weaker cerebellum–M1 relationship in SZ), thereby producing inaccurately timed behavioral responses. Alternatively, the cerebellum may be sending improper timing signals to M1 via the thalamus. Accordingly, a second possible explanation is that connectivity deficits within the basal ganglia (figure 2B) are structurally hindering cerebello-cerebral associations (evident from weaker thalamus–putamen and putamen–cerebellum connections in schizophrenia), disrupting coincidence detection. Increased activation in the SZ group between cerebellum and thalamus (figure 2B) may represent an attempt to compensate for the failure of the thalamus to engage in other regulatory processes, thereby increasing its association with the cerebellum during this active, highly cerebellar task. Broadly, these findings are consistent with the theory that in SZ the cerebellum and its associated temporal processing regions (ie, basal ganglia) fail to play their usual coordinative roles within the CCTCC. These results complement recent findings of aberrant cerebello-cortical and cortico-striatal connectivity in schizophrenia. For example, abnormalities in cerebellar-cortical connectivity were recently found in clinical high-risk and early schizophrenia groups,39,69 unaffected siblings,70 and individuals with schizophrenia during a working memory task.71 Further, disrupted cerebellar-thalamic functional connectivity and fractional anisotropy relationships in schizophrenia are associated with decreased cerebellar-cortical function connectivity and integrity.72 Moreover, abnormal resting-state functional connectivity between the cerebellum and motor cortex in schizophrenia was recently linked to abnormal spontaneous motor activity,73 which together with the present findings may suggest a mechanism underpinning increased motor variability observed during sensorimotor synchronization tasks. The fact that the schizophrenia and control groups did not differ in whole-brain BOLD activation suggests that it is not the nodes in the network that are impaired per se; rather it is the connectivity between those nodes that are impaired during task performance. For example, as predicted, whole-brain analysis exhibited significant activation of M1 during synchronization in both groups as well as activation of M1, the cerebellum, thalamus, putamen, and other regions (figure 1, table 3) known to be associated with motor timing.11–13,42,74 Activation of these regions during continuation tapping suggests that substantial cross-region communication is necessary for integrating the tapping “motor” component (ie, M1 identified during synchronization) and the timing “clock” component (ie, recruitment of cerebellum, thalamus, and basal ganglia during continuation) of the task. However, as shown by Kim and colleagues,28 schizophrenia is associated with a disruption of the modular architecture of the cerebellum, which could result in unusual patterns of cerebello-cerebral connectivity. Taken together, this suggests aberrant organization and connectivity between cerebellum and cerebrum in schizophrenia, rather than impaired cerebellar functional integrity alone, during sensorimotor synchronization. Increased tapping force in the SZ group may indicate deficits in sensorimotor feedback (ie, decreased integration of proprioceptive input) or differences in mechanical tap execution in the schizophrenia group, both of which may suggest further impairment within the CCTCC. A negative correlation, accounting for 21% of the variance between tapping force and negative symptoms assessed by the PANSS, points to a testable hypothesis for future studies. Specifically, decreased motor engagement (ie, force) in this task is associated with increased negative symptoms. In fact, motor dysfunction in the form of akinesia is significantly positively correlated with negative symptoms in schizophrenia,67 including drug-naïve patients.43,44 Likewise, force in the continuation condition was correlated with PANSS disorganized symptom factor scores. Such relationships between motor75 or sensorimotor76 abnormalities and PANSS disorganized factor scores have been previously observed in schizophrenia patients. Moreover, though not observed in the current study, it has been shown that accelerated tapping is negatively correlated with PANSS negative symptoms.15 Interestingly, studies have observed a positive correlation between negative symptoms and increased M1-striatal connectivity.77 Altogether, these findings underscore the association of motor aberrations and symptomology in schizophrenia. Other exploratory analyses indicated that cerebellar activation was positively correlated with Digit-symbol performance in the HC group only (accounting for 24% of shared variance in controls vs 7% in SZ). Due to their exploratory nature, these correlations should be interpreted with caution, but suggest the testable hypotheses that the cerebellum plays a coordinative role in visuomotor performance, which is impaired in SZ.78 There are limitations and potential confounds that warrant consideration and further study. First, the use of antipsychotic medications, most of which alter dopamine signaling, in the schizophrenia group may have affected the current findings. It is well-established that increases in dopamine, particularly within basal ganglia, can alter movement and timing.13 However, any anticipated effects of dopamine blockers would have been to slow the “internal clock,”79,80 rather than accelerate it; thus, the present study may have underestimated clock speed. Alternatively, antipsychotic use could be responsible for connectivity or downstream neural signaling aberrations within dopamine-rich subcortical structures (cf,81). Medication assessed by chlorpromazine-equivalent doses82 was not correlated with behavioral or neuroimaging measures in the current study (supplementary material). Nonetheless, assessing sensorimotor synchronization in a medication-naïve population of individuals with schizophrenia or unaffected first-degree relatives would be informative. Second, groups significantly differed on cognitive functioning (table 1). Though cognitive deficits are common in schizophrenia, intelligence alone has been shown to account for upwards of 15% of the variance on a sensorimotor synchronization task in healthy individuals.83,84 Differences in IQ may be associated with core differences in timing ability or strategies, which may be reflected in these group differences independent of diagnostic status. Third, the effective connectivity models evaluated in this study were limited by their unidirectionality. Anatomical tract tracing studies indicate that many of these regions are bidirectionally connected; ie, they form closed-loops as opposed to sending information exclusively in one direction.22 Fourth, the correlational analyses, while theoretically guided, were exploratory and must be replicated in subsequent studies. Finally, sample size limited the number of variables that could be included in the path analysis and precluded the use of other analyses (ie, dynamic causal modeling); therefore, it was not possible to study a larger, more realistic functional-anatomical circuit of the behavior. Larger sample sizes would allow more power to detect relationships between multiple brain regions simultaneously, and more extensive examination of symptom correlates. Conclusions This is the first study to investigate the potential neural source of sensorimotor synchronization behavioral deficits in a schizophrenia population. As predicted, individuals with schizophrenia exhibited accelerated, more variable timing during sensorimotor synchronization. These deficits appeared to be associated with aberrations in cerebellar-cortical (M1) and cerebellar-subcortical (basal ganglia and thalamus) effective connectivity. These findings are consistent with conceptualizations of schizophrenia as a dysconnectivity syndrome, and they directly implicate the cerebellum in a fundamental coordinative process that has been hypothesized to be a cardinal feature of schizophrenia (cf, Andreasen’s cognitive dysmetria model and Bleuler’s “fragmented phrene”1,3). Funding This work was supported by the National Institutes of Health (T32 MH103213 to W.P.H., A.B.M., and J.R.P.; R01 MH074983 to W.P.H.), Indiana Clinical and Translational Sciences Institute award (TL1 TR001107 and UL1 TR001108 to A.B.M. and J.R.P. and R21 MH091774 to B.F.O.), and National Alliance for Research on Schizophrenia and Depression (NARSAD) Young Investigator Award to A.R.B. Acknowledgments We wish to thank the patients and their families for participation as well as support from the staff and administration of Larue D. Carter Memorial Hospital. We also thank the IUB Imaging Research Facility staff and Psychological and Brain Sciences technical support group, especially Jeffrey Sturgeon and Alex Shroyer. All authors declare that they have no conflicts of interest. References 1. Bleuler E. Dementia Praecox or the Group of Schizophrenias , translated by J. Zinkin. New York : International Universities Press, Inc .; 1911/1950. 2. Stransky E . Zur Lehre der dementia praecox . 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Servaas, Michelle, N;Kos,, Claire;Gravel,, Nicolás;Renken, Remco, J;Marsman, Jan-Bernard, C;, van Tol, Marie-José;Aleman,, André

doi: 10.1093/schbul/sby108pmid: 30053198

Abstract The aim of this study was to investigate whether apathy in schizophrenia is associated with rigidity in behavior and brain functioning. To this end, we studied associations between variability in dynamic functional connectivity (DFC) in relevant functional brain networks, apathy, and variability in physical activity in schizophrenia. Thirty-one patients with schizophrenia, scoring high on apathy, were included and wore an actigraph. Activity variability was calculated on the activity counts using the root of the Mean Squared Successive Difference (MSSD). Furthermore, we calculated DFC on resting-state data as phase interactions between blood oxygen-level dependent (BOLD) signals of 270 brain regions per volume. Variability (MSSD) in DFC was calculated for 3 networks, including the default-mode network (DMN), frontoparietal network, and salience-reward network (SRN). Finally, we calculated correlations between these DFC estimates and apathy and activity variability. First, lower activity variability was associated with higher levels of apathy. Second, higher levels of apathy were associated with lower variability in DFC in the DMN and SRN. Third, higher activity variability was associated with higher variability in DFC in the SRN. In conclusion, patients with schizophrenia and more severe levels of apathy showed less variability in their physical activity and more rigid functional brain network behavior in the DMN and SRN. These networks have been shown relevant for self-reflection, mental simulation, and reward processing, processes that are pivotal for self-initiated goal-directed behavior. Functional rigidity of these networks may therefore contribute to reduced goal-directed behavior, which is characteristic for these patients. apathy, schizophrenia, dynamic functional connectivity, brain networks, motor behavior, rigidity Introduction Flexibility in cognition and behavior is important for psychological health.1 Individuals, who are more flexible, are better able to modulate behavior to changing environmental demands; possess more characteristic features such as being vital, curious, explorative, and productive; and are more successful in areas such as work and social functioning.2,3 In contrast, rigidity in cognition and behavior has been shown to be increased in psychopathology.1 This may be specifically the case in patients with schizophrenia who suffer from apathy. Apathy is regarded as a core negative symptom of schizophrenia and is associated with a loss of motivation, goal-directed cognitive activity, goal-directed behavior, and emotions.4 Often, the days of these patients consist of doing basic tasks and nonproductive activities (eg, lying in bed or watching television), instead of, eg, working, going to school, or undertaking social activities.5 Apathy is important to assess because it is burdensome for patients and related to poorer functional outcome.6,7 Moreover, adequate treatment options for apathy are lacking, which signifies the need for a deeper understanding of the neurobiological basis of this debilitating symptom.8 In this study, we aim to investigate whether apathy in schizophrenia is associated with rigidity in motor behavior and brain functioning. First, we operationalize rigidity in motor behavior as less variable motor activity. Actigraphy can be used to objectively quantify motor activity using a wrist worn motion watch.9 This method has multiple advantages, specifically it is well tolerated, feasible, ambulatory, and noninvasive.9 A limited number of studies have investigated the association between apathy and activity level in patients with schizophrenia and found a negative correlation between the two variables (e.g.10–12). Another study found that activity level at baseline predicts the course of negative symptoms with treatment within a psychotic episode.9 However, no study to date has investigated variability in motor behavior in relation to apathy. Second, we operationalize rigidity in brain functioning as less variable functional connectivity (FC). In the dysconnection hypothesis, abnormal FC is suggested to underlie symptomatology in schizophrenia.13–15 This hypothesis states that a failure of neuromodulatory mechanisms may lead to the formation of false inferences (eg, hallucinations and delusions) and maladaptive behavior.16 FC is defined as the temporal relationship between time series of different brain regions across the entire scan duration, thereby assuming stationarity (ie, static functional connections). However recently, it has been shown that the strength and directionality of functional connections changes over the course of minutes or even seconds, suggesting that dynamic functional connectivity (DFC; for a review, see Hutchison et al17) may be a more accurate measure to capture brain functioning underlying cognition and behavior. From a theoretical perspective, it would be of interest to investigate whether apathy, conceptualized as a rigid state of inactivity (ie, lack of self-initiated goal-directed behavior), is related to less variable DFC. Although no studies have investigated the association between apathy and DFC, there are a number of studies that have investigated DFC in patients with schizophrenia and supported the suggestion of a dynamically more rigid brain in these patients. First, in patients with schizophrenia compared with healthy controls, less variance was found in DFC-based graph metrics, representing variation in global connectivity strength and functional integration and segregation of information processing.18 Second, it was observed that patients with schizophrenia, compared with healthy controls, dwell longer in a connectivity state characterized by weaker functional connections between networks and switch less between connectivity states.19 Third, two studies, using higher dimensional analysis and computational modeling, showed less DFC and weaker coupling between brain regions impacting DFC in patients with schizophrenia compared with healthy controls, respectively.20,21 In addition, the former study showed a reduction of DFC in patients with schizophrenia who experience more psychotic symptoms, specifically hallucinations.21 Previous research has shown a relationship between variation in brain signals and behavior. Studies have provided preliminary support for an inverted U-shaped curve of brain signal variability across the lifespan (ie, lower variability in infancy and older adulthood and higher variability in young adulthood) and a positive linear relationship between brain signal variability and cognitive performance (for a review, see Garret et al22,23). Furthermore, differences in brain signal variability have been found between individuals with brain disease/injury and healthy controls.22,24,25 Thus, there seems to be an optimal level of variability in the brain that facilitates neural efficiency, a greater dynamic range and a greater ability to transition between brain states.22,23 To conclude, we propose that apathy in schizophrenia is related to rigidity in motor behavior and that this is reflected in rigidity in brain functioning of brain networks, which are related to apathy and subserve self-initiated goal-directed behavior. Networks that have been shown to be affected in patients with schizophrenia, scoring higher on apathy, are the default mode network (DMN), frontoparietal network (FPN) and salience-reward network (SRN).26–29 To estimate DFC, we used a relatively novel method named phase synchronization, which has a higher temporal resolution than correlation-based sliding window analysis (SWA).30 In this article, we hypothesized to find (1) lower variability in activity and lower variability in DFC in the DMN, FPN, and SRN in patients with schizophrenia scoring higher on apathy, (2) lower variability in activity to be related to lower variability in DFC in the DMN, FPN, and SRN in patients with schizophrenia that score high on apathy. Methods Participants Data were available for 31 patients with schizophrenia, who scored high on apathy (study inclusion criterion: a score of ≥27 on the apathy subscale of the Apathy Evaluation Scale [AES]31). All patients are part of an ongoing multicenter treatment trial aimed to improve apathy with neurostimulative treatment (trialregister.nl: NTR3805). Participants (in- and outpatients) had a diagnosis of schizophrenia or schizoaffective disorder according to DSM-IV-TR criteria and were forwarded by their clinicians. Inclusion criteria were (1) age more than 18 years, (2) Dutch proficiency, (3) stability of antipsychotic medication use for at least 4 weeks before inclusion, and (4) eligibility for magnetic resonance imaging (MRI) and neurostimulative treatment. Exclusion criteria were (1) a history of neurological disorders or head injury, (2) a current diagnosis of an alcohol/substance dependence disorder, and (3) visual and auditory problems that cannot be corrected. All patients gave informed consent after explanation of the experimental procedure. The study was approved by the medical ethics committee of the University Medical Center Groningen and performed according to the Declaration of Helsinki (World Medical Association Inc, 2009). For demographics of the sample, see table 1. Table 1. Demographics (n = 31) Variables Mean/distribution SD Min Max Diagnosis, SZ/SZ-A 22/9 Age 33.68 8.65 19 54 Sex, M/F 25/6 BMI 26.39 5.39 17.36 38.20 Handednessa 2/28/1 Education levelb 0/0/0/5/14/9/3 Living circumstancesc 9/1/21 Number of psychoses 2.77 1.75 1 9 Illness duration 8.19 4.27 2 16 Age at onset 23.61 7.21 11 50 Dopamine equivalent dose, mg 63.98 22.62 0 96.44 AES-C 46.87 6.45 31 60 SANS—apathy 14.42 3.05 7 20 SANS—total 55.74 11.78 29 78 PANSS—positive 13.45 4.86 7 23 PANSS—negative 18.16 3.87 10 24 PANSS—general 34.55 7.72 25 51 PANSS—total 66.16 13.89 43 95 CDSS 4.39 3.22 0 11 Activity variabilityd 744.22 381.61 90.88 2287.33 Variables Mean/distribution SD Min Max Diagnosis, SZ/SZ-A 22/9 Age 33.68 8.65 19 54 Sex, M/F 25/6 BMI 26.39 5.39 17.36 38.20 Handednessa 2/28/1 Education levelb 0/0/0/5/14/9/3 Living circumstancesc 9/1/21 Number of psychoses 2.77 1.75 1 9 Illness duration 8.19 4.27 2 16 Age at onset 23.61 7.21 11 50 Dopamine equivalent dose, mg 63.98 22.62 0 96.44 AES-C 46.87 6.45 31 60 SANS—apathy 14.42 3.05 7 20 SANS—total 55.74 11.78 29 78 PANSS—positive 13.45 4.86 7 23 PANSS—negative 18.16 3.87 10 24 PANSS—general 34.55 7.72 25 51 PANSS—total 66.16 13.89 43 95 CDSS 4.39 3.22 0 11 Activity variabilityd 744.22 381.61 90.88 2287.33 AES-C, Apathy Evaluation Scale, clinician rated; BMI, body mass index; CDSS, Calgary Depression Scale for Schizophrenia; F, female; M, male; mg, milligram; PANSS, Positive and Negative Syndrome Scale; SANS, Scale for the Assessment of Negative Symptoms; SZ, schizophrenia, SZ-A, schizoaffective disorder. aHandedness: left, right, ambidexter. bLevel of educational attainment.32 Levels range from 1 to 7 (1 = primary school not finished, 7 = pre-university/university degree). cLiving circumstances: sheltered living, admitted, other (independent/with parents) dn = 30. View Large Table 1. Demographics (n = 31) Variables Mean/distribution SD Min Max Diagnosis, SZ/SZ-A 22/9 Age 33.68 8.65 19 54 Sex, M/F 25/6 BMI 26.39 5.39 17.36 38.20 Handednessa 2/28/1 Education levelb 0/0/0/5/14/9/3 Living circumstancesc 9/1/21 Number of psychoses 2.77 1.75 1 9 Illness duration 8.19 4.27 2 16 Age at onset 23.61 7.21 11 50 Dopamine equivalent dose, mg 63.98 22.62 0 96.44 AES-C 46.87 6.45 31 60 SANS—apathy 14.42 3.05 7 20 SANS—total 55.74 11.78 29 78 PANSS—positive 13.45 4.86 7 23 PANSS—negative 18.16 3.87 10 24 PANSS—general 34.55 7.72 25 51 PANSS—total 66.16 13.89 43 95 CDSS 4.39 3.22 0 11 Activity variabilityd 744.22 381.61 90.88 2287.33 Variables Mean/distribution SD Min Max Diagnosis, SZ/SZ-A 22/9 Age 33.68 8.65 19 54 Sex, M/F 25/6 BMI 26.39 5.39 17.36 38.20 Handednessa 2/28/1 Education levelb 0/0/0/5/14/9/3 Living circumstancesc 9/1/21 Number of psychoses 2.77 1.75 1 9 Illness duration 8.19 4.27 2 16 Age at onset 23.61 7.21 11 50 Dopamine equivalent dose, mg 63.98 22.62 0 96.44 AES-C 46.87 6.45 31 60 SANS—apathy 14.42 3.05 7 20 SANS—total 55.74 11.78 29 78 PANSS—positive 13.45 4.86 7 23 PANSS—negative 18.16 3.87 10 24 PANSS—general 34.55 7.72 25 51 PANSS—total 66.16 13.89 43 95 CDSS 4.39 3.22 0 11 Activity variabilityd 744.22 381.61 90.88 2287.33 AES-C, Apathy Evaluation Scale, clinician rated; BMI, body mass index; CDSS, Calgary Depression Scale for Schizophrenia; F, female; M, male; mg, milligram; PANSS, Positive and Negative Syndrome Scale; SANS, Scale for the Assessment of Negative Symptoms; SZ, schizophrenia, SZ-A, schizoaffective disorder. aHandedness: left, right, ambidexter. bLevel of educational attainment.32 Levels range from 1 to 7 (1 = primary school not finished, 7 = pre-university/university degree). cLiving circumstances: sheltered living, admitted, other (independent/with parents) dn = 30. View Large In this study, baseline data were used before start of the neurostimulative treatment (on a Monday). In the week prior to the start of treatment, participants took part in a baseline measurement that consisted of interviews, neuropsychological testing and a functional magnetic resonance imaging (fMRI) session. After these measurements, the actigraph was handed to the participants (mostly on a Thursday or Friday) with the instruction to wear it continuously during the neurostimulative treatment until the post measurement (weekend days before neurostimulative treatment, median = 2, range = 2; weekdays before neurostimulative treatment, median = 2, range = 0–4). Questionnaires The Mini-International Neuropsychiatry Interview Plus (MINI-Plus) was used to confirm diagnosis of schizophrenia or schizoaffective disorder.33 Subsequently, the following symptoms were assessed: (1) apathy with the Apathy Evaluation Scale, clinician rated (AES-C34), (2) negative symptoms with the Scale for the Assessment of Negative Symptoms (SANS35), (3) positive, negative and generalized psychopathology with the Positive and Negative Symptom Scale (PANSS36), and (4) depression with the Calgary Depression Scale for Schizophrenia (CDSS37). In case of missing items, which was uncommon (one item on the SANS), a hot-deck imputation was performed.38 Actigraphy Participants wore an actigraph (Actical Step, FG, FCC version; Respironics, Inc) in their natural environment. The actigraph was continuously worn around the wrist of the nondominant hand. For this study, 2 full weekend days (48 h) were selected for the analyses, because behavior has been shown to be more self-initiated and less externally driven during the weekend by, eg, school, work, supervised daytime activities, or therapy.10,39,40 Activity counts were recorded with a 1-min time interval and the data were extracted by means of Actical software (Respironics, Inc; for a technical explanation of the workings of activity monitors, see John and Freedson41). To obtain a dynamic measure of activity, we calculated activity variability using the root of the Mean Squared Successive Difference (MSSD; variability measure42). The root was taken for comparability to previous research.43 This measure was calculated on the 10 most active hours per participant per weekend day to take into account that some participants may sleep more than others. Subsequently, a sum was calculated across the 2 weekend days (ie, 20 h in total). One participant was excluded for the analyses including activity variability as a variable, because this individual had been using construction equipment while wearing the actigraph. This resulted in a relatively high MSSD value, which could be designated as an outlier (>5 SD). Medication Effects of antipsychotic medication are largely mediated by blockade of dopamine D2-receptors, a neurotransmitter that is—among others—important for motor functioning.44 In this study, 30 of 31 patients used antipsychotic medication. To take into account the effect of medication type and dosage, we estimated the dopamine D2-receptor occupancy for each participant (see Supplementary Information S1). Other types of medication were less frequently used (selective serotonin reuptake inhibitor: 4 patients; tricyclic: 0 patients; tetracyclic: 2 patients; benzodiazepines: 7 patients). For this reason, we were unable to take into account the effect of medication type and dosage of these latter types of medication. Image Processing and Time Course Extraction For the image acquisition parameters, see Supplementary Information S2. Image processing and analysis were performed using SPM12 (v6470; http://www.fil.ion.ucl.ac.uk), implemented in MATLAB 7.8.0 (The MathWorks Inc). Preprocessing of the data was performed (see Supplementary Information S3 for details). Furthermore, scrubbing parameters were used to interpolate measurements on time points, which are possibly affected by motion artifacts (see Supplementary Information S4). Finally, we extracted the regional mean time series of 219 regions of interest (ROIs; Supplementary Information S5). Phase Synchronization as a Measure of DFC Phase synchronization was selected as a measure of DFC, instead of correlation-based SWA, because it has been shown that results based on SWA are dependent on window length and that it gives a poor estimation of the actual underlying correlations between brain areas within a window.45 Furthermore, phase synchronization has a higher temporal resolution than SWA.30 Phase synchronization was calculated by applying the Hilbert transform, which is a mathematical transform that is used to obtain the instantaneous phase and amplitude of a narrowband signal per time point (ie, volume).30 In this analysis, only the instantaneous phase was used and phase differences were calculated for each ROI pair per time point. The first 13 time points were discarded to ensure that at least one cycle has passed to obtain a reliable estimate of the instantaneous phase (1/0.04 [lowest frequency of the band-pass filter] = 0.25/2 TR = 12.5 time points). Phase differences approach zero, when phase synchronization is higher. The end result is a phase difference matrix per time point per subject. Definition of Brain Networks A data-driven, iterative module decomposition procedure was applied to achieve an optimal modular structure using a proportional threshold of 1% (Supplementary Information S6). Input for this procedure was the binarized phase difference adjacency matrix averaged across subjects. First, nodes were partitioned into modules using the algorithm of Blondel et al,46 wherein nodes are divided into groups with a maximum number of within-group edges (ie, connections) and a minimum number of between-group edges. Second, the statistic was further optimized by applying the modularity fine-tuning algorithm of Sun et al,47 wherein nodes are randomly assigned to other modules until modularity no further improves. The end result is a module decomposition index that provides information on which node belongs to which module. In total, 5 networks were derived: somatosensory–motor network (SMN), DMN, visual network, FPN, and SRN (figure 1). Fig. 1. View largeDownload slide Module decomposition. Nodes could be partitioned in 5 functional brain networks with a maximum number of within-group edges and a minimum number of between-group edges. Colors indicate the different networks that nodes belong to: SMN, somatosensory-motor network (yellow); DMN, default mode network (red); VN, visual network (purple); FPN, frontoparietal network (blue); SRN, salience-reward network (green). Nodes are pasted on a surface template of the human brain using BrainNet Viewer.48 Top: lateral view; middle: cerebellum, coronal view; bottom: medial view. Fig. 1. View largeDownload slide Module decomposition. Nodes could be partitioned in 5 functional brain networks with a maximum number of within-group edges and a minimum number of between-group edges. Colors indicate the different networks that nodes belong to: SMN, somatosensory-motor network (yellow); DMN, default mode network (red); VN, visual network (purple); FPN, frontoparietal network (blue); SRN, salience-reward network (green). Nodes are pasted on a surface template of the human brain using BrainNet Viewer.48 Top: lateral view; middle: cerebellum, coronal view; bottom: medial view. Calculation of DFC Estimates and the Association With Apathy and Activity Variability First, we calculated a proxy for the level of phase synchrony, based on the order parameter in Ponce-Alvarez et al49, across nodes belonging to the DMN, FPN, and SRN separately (using the module decomposition index, see “Definition of Brain Networks”) per time point (see Supplementary Information S7; step 1). Second, we calculated the MSSD on this order parameter to obtain variability of phase synchrony per network, resulting in 3 DFC estimates per subject (step 2). Third, we calculated Spearman correlations between these estimates and (1) apathy scores (AES) and (2) activity variability (1-tailed test, see the last paragraph of the “Introduction” section for the specific hypotheses; step 3). Fourth, to observe whether the associations found in the former step are possibly affected by confounding variables, we calculated Spearman correlations between the DFC estimates, apathy and activity variability, and a number of possible confounding variables, ie, age, Body Mass Index (BMI), illness duration, medication (ie, dopamine equivalent dose), positive symptoms (PANSS), and depression scores (CDSS) (2-tailed test; see Supplementary Information S8). When (trend) significant associations were found (P ≤ .10), we calculated semi-partial Spearman correlations to control for the effect of confounding variables using the package ppcor in R (v1.1; step 4). A semi-partial correlation is the correlation between 2 variables of interest with variation from a third variable removed only from the second variable. Results Associations Between DFC Estimates, Apathy, and Activity Variability First, a significant negative correlation was found between apathy and activity variability. Second, significant negative correlations were found between apathy and variability in DFC in the DMN and SRN, but not in the FPN. However, the significant negative correlation between apathy and variability in DFC in the DMN became trend significant, after controlling for positive symptoms. Third, a trend significant positive correlation was found between activity variability and variability in DFC in the SRN. Furthermore, a significant positive correlation was found between activity variability and variability in DFC in the DMN after controlling for BMI, but not age. No significant correlations were found between activity variability and variability in DFC in the FPN. See table 2 for the original and corrected correlation values and see figure 2 for scatter plots of the results. As a validity check, we also investigated the association between apathy and activity variability, and variability in DFC between the DMN and SRN. A moderate negative correlation was observed between apathy and variability in DFC between the DMN and SRN. A weak positive correlation was observed between activity variability and variability in DFC between the DMN and SRN (Supplementary Information S9). To avoid circular analysis, we refrained from statistical testing in this latter analysis (only). To be able to compare our findings to studies using a static approach, we also calculated associations between the mean level of DFC (mean over time points), apathy, and mean level of activity (mean over hours) (Supplementary Information S10). Furthermore, because variability in activity is possibly associated with DFC in the SMN, we investigated the association between variability in DFC in the SMN, apathy and activity variability. No significant results were found (Supplementary Information S11). Moreover, because motor symptoms are possibly associated with our variables of interest (DFC estimates, apathy, and activity variability), we investigated the confounding effect of motor retardation (PANSS item G7) on the current findings. We observed that all found associations remained (trend) significant (Supplementary Information S12). Finally, we repeated the analyses excluding participants who were scanned with a different echo planar imaging (EPI) sequence. The findings remained significant or became significant (Supplementary Information S13). Table 2. Associations Between DFC Estimates and Apathy and Activity Variability DFC Apathy (n = 31) Activity variability (n = 30) r r† P P† Confounding variable(s) r r† P P† Confounding variable(s) DMN variability −.325 −.350 −.294 −.345 .037** .029** .057* .031** Depression1 Positive symptoms2 BMI .226 .264 .339 .114 .145 .036** Age BMI SRN variability −.386 −.421 −.369 −.446 .016** .010** .022** .007** Depression Positive symptoms Illness duration .222 .375 .266 .119 .051* .082* Age Illness duration FPN variability −.062 −.176 −.123 −.065 .371 .175 .258 .366 Depression Positive symptoms Medication3 .035 −.003 .075 .426 .493 .349 Age Medication Activity variability (n = 30) −.359 −.323 −.333 .026** .044** .039** Depression Age DFC Apathy (n = 31) Activity variability (n = 30) r r† P P† Confounding variable(s) r r† P P† Confounding variable(s) DMN variability −.325 −.350 −.294 −.345 .037** .029** .057* .031** Depression1 Positive symptoms2 BMI .226 .264 .339 .114 .145 .036** Age BMI SRN variability −.386 −.421 −.369 −.446 .016** .010** .022** .007** Depression Positive symptoms Illness duration .222 .375 .266 .119 .051* .082* Age Illness duration FPN variability −.062 −.176 −.123 −.065 .371 .175 .258 .366 Depression Positive symptoms Medication3 .035 −.003 .075 .426 .493 .349 Age Medication Activity variability (n = 30) −.359 −.323 −.333 .026** .044** .039** Depression Age DFC, dynamic functional connectivity; DMN, default mode network; SRN, salience-reward network; FPN, frontoparietal network; r, Spearman correlation value; P, P value. †Values corrected for confounding variables. 1As measured with the Calgary Depression Rating Scale (CDSS). 2As measured with the positive symptom scale of the Positive and Negative Symptom Scale (PANSS). 3As measured by the dopamine equivalent dose in milligrams. *P ≤ .10. **P ≤ .05. View Large Table 2. Associations Between DFC Estimates and Apathy and Activity Variability DFC Apathy (n = 31) Activity variability (n = 30) r r† P P† Confounding variable(s) r r† P P† Confounding variable(s) DMN variability −.325 −.350 −.294 −.345 .037** .029** .057* .031** Depression1 Positive symptoms2 BMI .226 .264 .339 .114 .145 .036** Age BMI SRN variability −.386 −.421 −.369 −.446 .016** .010** .022** .007** Depression Positive symptoms Illness duration .222 .375 .266 .119 .051* .082* Age Illness duration FPN variability −.062 −.176 −.123 −.065 .371 .175 .258 .366 Depression Positive symptoms Medication3 .035 −.003 .075 .426 .493 .349 Age Medication Activity variability (n = 30) −.359 −.323 −.333 .026** .044** .039** Depression Age DFC Apathy (n = 31) Activity variability (n = 30) r r† P P† Confounding variable(s) r r† P P† Confounding variable(s) DMN variability −.325 −.350 −.294 −.345 .037** .029** .057* .031** Depression1 Positive symptoms2 BMI .226 .264 .339 .114 .145 .036** Age BMI SRN variability −.386 −.421 −.369 −.446 .016** .010** .022** .007** Depression Positive symptoms Illness duration .222 .375 .266 .119 .051* .082* Age Illness duration FPN variability −.062 −.176 −.123 −.065 .371 .175 .258 .366 Depression Positive symptoms Medication3 .035 −.003 .075 .426 .493 .349 Age Medication Activity variability (n = 30) −.359 −.323 −.333 .026** .044** .039** Depression Age DFC, dynamic functional connectivity; DMN, default mode network; SRN, salience-reward network; FPN, frontoparietal network; r, Spearman correlation value; P, P value. †Values corrected for confounding variables. 1As measured with the Calgary Depression Rating Scale (CDSS). 2As measured with the positive symptom scale of the Positive and Negative Symptom Scale (PANSS). 3As measured by the dopamine equivalent dose in milligrams. *P ≤ .10. **P ≤ .05. View Large Fig. 2. View largeDownload slide Scatter plots of the results (A–D). The images were created using the package ggplot2 in R (Version 0.98.1062). The added line in the scatter plots is based on linear regression, including a 95% confidence region. For r-values and P values, see table 2. AES: Apathy Evaluation Scale; DFC: dynamic functional connectivity; DMN, default mode network; SRN, salience-reward network. Fig. 2. View largeDownload slide Scatter plots of the results (A–D). The images were created using the package ggplot2 in R (Version 0.98.1062). The added line in the scatter plots is based on linear regression, including a 95% confidence region. For r-values and P values, see table 2. AES: Apathy Evaluation Scale; DFC: dynamic functional connectivity; DMN, default mode network; SRN, salience-reward network. Discussion The aim of this study was to investigate whether apathy in schizophrenia is associated with rigidity in motor behavior and brain functioning. To this end, we studied, for the first time, associations between variability in DFC in relevant functional brain networks, variability in motor behavior, and apathy in schizophrenia. First, we observed lower variability in activity and lower variability in DFC in the DMN and SRN in patients with schizophrenia scoring higher on apathy. Second, we observed higher variability in DFC in the SRN in patients with schizophrenia, who showed more variability in their activity. The results indeed seem to suggest that flexibility in brain functioning (in particular brain networks) and motor behavior is compromised in patients with schizophrenia and higher levels of apathy, possibly impacting goal directed behavior. As mentioned earlier, 2 networks seem to play a role in relation to apathy and show lower variability in DFC, ie, the DMN and SRN. However, after controlling for positive symptoms, the relationship with variability in DFC in the DMN became trend significant. This means that, although the relationship with DFC in the DMN is stronger in patients with schizophrenia and apathy, it seems not to be specific for patients with this symptom. The DMN has been reported to be more active during rest and to subserve functions, such as self-reflection, introspection, autobiographical memory, envisioning the future, mental simulation, and emotion regulation.50–55 In line with our results, a previous study has shown that DFC between subsystems of the DMN is weaker in patients with schizophrenia compared with healthy controls, specifically in patients reporting more negative symptoms.56 Furthermore, decreased variability in DFC in several DMN brain regions was found in patients with schizophrenia compared with healthy controls.57 One of the functions of the DMN, which has specifically been mentioned in the literature on goal directed behavior and apathy in schizophrenia, is episodic future thinking.51–53,58 This process is the ability to mentally simulate possible future events (ie, “pre-experiencing”59). The mental simulation of desired future events may benefit goal attainment by increasing expectation of success, motivation and, effort and by facilitating the formation of concrete plans and problem-solving.59 It seems that motivation and effort to pursue goals are specifically increased when future-event simulations are related to personal goals.60 In line with this, D’Argembeau et al61 suggest that the function of the DMN comprises the localization of future-event simulations on a continuum of self-relevance, thereby prioritizing future-events that are important for personal goal attainment.61,62 Indeed, it has been shown that patients with schizophrenia, who scored higher on apathy, had more difficulties to mentally simulate self-referential information for pleasant future events.58 Thus, lower variability in DFC in the DMN in patients with schizophrenia scoring higher on apathy (and those who have more positive symptoms) may be related to impairments in self-reflection and mental simulation while thinking about pleasant future events. However, more research is needed to investigate the role of the DMN in episodic future thinking in patients with schizophrenia and high levels of apathy. The SRN has been shown to subserve functions related to reinforcement learning and reward processing,6,63,64 the identification and appraisal of salient affective stimuli and the production of affective states,63,65–67 and the detection of emotional or novel stimuli.68–70 In contrast to our results, a previous study has found increased DFC in the SRN and between the DMN and SRN in patients with schizophrenia compared with healthy controls.71 It is possible that lower variability in DFC in and between these networks is specific for patients with schizophrenia and higher levels of apathy. One of the functions of the SRN, which has specifically been mentioned in the literature on goal directed behavior and apathy in schizophrenia, is effort-based decision-making.72 This process incorporates how much effort an individual is willing to exert for a given level of reward.73 Effort and motivation are multifaceted constructs that consist of several subprocesses.74 Two of these subprocesses have been proposed as correlates of apathy, ie, reward processing (including reward learning, anticipation, and simulation) and effort discounting (ie, the tendency to devalue rewards that require more effort to obtain).72 A review of studies investigating effort-based decision-making in schizophrenia reported that 5 of 8 studies found an association between lower task performance on several effort tasks and higher negative symptoms.73 Brain systems and regions that are part of the SRN and DMN have been related to effort-based decision-making, such as the striatum, amygdala, insula, orbital frontal cortex (OFC), and anterior cingulate cortex.73,74 Notably, a recent study has found associations between apathy and activity and FC in and between the striatum and OFC during an effort-based reinforcement task.75 Thus, lower variability in DFC in and between the SRN and DMN in patients with schizophrenia and higher levels of apathy may be related to impaired reward processing while making decisions to undertake (pleasant) activities. However, more research is needed to investigate the role of the SRN in effort-based decision-making in patients with schizophrenia and high levels of apathy. Regarding activity variability, we found lower variability in activity in patients with schizophrenia scoring higher on apathy. As mentioned in the “Introduction” section, this is the first time, to our knowledge, that the association between variability in activity and apathy is investigated. However, there is a study that investigated the association between predictability of movements and negative symptoms and found no relationship.76 There are a number of discrepancies between the two studies (Walther et al76 vs our report): type of patients (only inpatients vs in- and outpatients; patients with schizophrenia vs patients with schizophrenia and schizoaffective disorder scoring high on apathy), sampling frequency (2 s vs 1 min), total duration of activity monitoring selected for data analysis (two 60-min periods vs 10 most active hours per weekend day), type of time periods (structured time periods wherein individuals undertake single or group activities vs leisure), different methods (partial autocorrelation function vs root of the MSSD; linear regression model vs semi-partial Spearman correlation, including controlling for confounding variables), and different symptom measures (PANSS negative symptom subscale vs AES). Due to these discrepancies, it is difficult to draw conclusions on why the results of the two studies are different. Further research is needed to investigate variability in activity as measured with actigraphy in patients with schizophrenia and negative symptoms. In this study, we observed that apathy was associated with more rigid motor behavior and DFC as well as that more variability in motor behavior was associated with more flexible DFC. Although we cannot make causal statements, it would be interesting to investigate whether treatments that intervene on the level of behavior (ie, activity variability) or the brain (ie, variability in DFC) have an impact on apathy. For instance, Behavioral Activation Therapy (BAT) in depression (a treatment that is also offered to patients with schizophrenia) has been shown to alter connectivity between brain regions that are part of the DMN and SRN.77,78 Furthermore, neurostimulative treatments, such as transcrianial magnetic stimulation and transcranial direct current stimulation, have been shown to modulate oscillatory activity, thereby impacting interactions between brain networks (for a review, see Dayan et al79 and Muldoon et al80). Several limitations of our study should be noted. First, we had to use a narrowband signal to obtain the instantaneous phase using the Hilbert transform. However, we selected the frequency band that has the highest functional relevance and is the least affected by noise (eg, cardiac and respiratory effects).30 Second, actigraphy only captures motor behavior and not cognitive behavior, such as reading, studying, or following a lecture. A recent study has investigated actigraphy in combination with ecological momentary assessment in patients with schizophrenia and high levels of apathy to examine whether these patients engage in less daily activities. The authors found a negative association between apathy and motor activity, but no associations between apathy and daily activities. One of the reasons for this null result is that the classification of self-reported behavior in goal-directed and nongoal-directed behavior is very challenging (for further discussion of these findings, see Kluge et al12). Third, only patients with schizophrenia and high levels of apathy were investigated in this study. The reason for this is that we investigated baseline measurements of a study exploring the effects of neurostimulative treatment on apathy in schizophrenia, wherein only patients with schizophrenia and clinical levels of apathy were recruited and no healthy controls. Although there was considerable variation in apathy scores and these were approximately normally distributed, future research is necessary to confirm our findings for a larger range of apathy scores and in other populations than patients with schizophrenia. Fourth, we investigated associations between variability in DFC in specific functional brain networks, apathy, and activity variability during resting state. Although brain network functioning during resting state has been shown to exhibit experience-dependent changes over time,81 it would be interesting to replicate and confirm our findings in a task targeting goal-directed behavior specifically. Notably, it is important that such a task contains lengthy enough trials over which DFC can be properly estimated. Fifth, we performed our analyses based on actigraphy data gathered during the weekend, because behavior has been shown to be more self-initiated and less externally driven during the weekend by, eg, school, work, supervised daytime activities, or therapy. However, it would also be important to replicate and confirm our findings with actigraphy data gathered during weekdays. Possibly, the latter corresponds more to clinically evaluated apathy, specifically in inpatients with schizophrenia In conclusion, the aim of this study was to investigate associations between variability in DFC in relevant brain networks, variability in motor behavior and apathy in patients with schizophrenia. We observed that patients with schizophrenia and severe levels of apathy showed less variability in activity and less variability in DFC in networks involved in self-reflection, mental simulation, and reward processing. This may possibly affect processes such as episodic future thinking and effort-based decision-making. Consequently, this may help to explain the reduction of goal-directed behavior in these patients. Future research should evaluate whether treatments that intervene on the level of behavior or the brain, such as BAT or neurostimulation, may reduce rigidity, thereby facilitating a shift toward more psychological flexibility and a reduction of apathy. Funding This work was supported by a VICI grant from the Netherlands Organisation for Scientific Research (NWO) grant no. 453.11.004 and an ERC Consolidator grant, project no. 312787 to A. Aleman. M. J. van Tol was supported by a VENI grant from the Netherlands Organisation for Scientific Research (NWO) grant no. 016.156.077. Acknowledgments The authors would like to thank L. van Asperen for her help in participant recruitment and assessments, and A. Sibeijn-Kuiper and J. Streurman-Werdekker for their assistance with MR scanning. 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Cambridge, Massachusetts : The MIT Press ; 2012 . © The Author(s) 2018. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)