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Abstract Schizophrenia (SCZ) is a highly heterogeneous disorder with remarkable intersubject variability in clinical presentations. Previous neuroimaging studies in SCZ have primarily focused on identifying group-averaged differences in the brain connectome between patients and healthy controls (HCs), largely neglecting the intersubject differences among patients. We acquired whole-brain resting-state functional MRI data from 121 SCZ patients and 183 HCs and examined the intersubject variability of the functional connectome (IVFC) in SCZ patients and HCs. Between-group differences were determined using permutation analysis. Then, we evaluated the relationship between IVFC and clinical variables in SCZ. Finally, we used datasets of patients with bipolar disorder (BD) and major depressive disorder (MDD) to assess the specificity of IVFC alteration in SCZ. The whole-brain IVFC pattern in the SCZ group was generally similar to that in HCs. Compared with the HC group, the SCZ group exhibited higher IVFC in the bilateral sensorimotor, visual, auditory, and subcortical regions. Moreover, altered IVFC was negatively correlated with age of onset, illness duration, and Brief Psychiatric Rating Scale scores and positively correlated with clinical heterogeneity. Although the SCZ shared altered IVFC in the visual cortex with BD and MDD, the alterations of IVFC in the sensorimotor, auditory, and subcortical cortices were specific to SCZ. The alterations of whole-brain IVFC in SCZ have potential implications for the understanding of the high clinical heterogeneity of SCZ and the future individualized clinical diagnosis and treatment of this disease. resting-state fMRI, functional connectivity, connectome, individual difference, heterogeneous Introduction Schizophrenia (SCZ) is a highly prevalent psychiatric disorder affecting over 20 million people worldwide,1 and it is accompanied by heterogeneous clinical manifestations, including positive symptoms, negative symptoms, and cognitive deficits.2 Growing evidence suggests that SCZ is a brain connectome disorder with dysfunctions spanning from primary to heteromodal areas.3–5 Nonetheless, findings are often inconsistent across studies, hampering the discovery of validated biomarkers that can guide clinical diagnosis and optimize treatment strategies. A critical reason for this situation is that most prior connectome studies have utilized case-control designs to evaluate the group-averaged differences between patients and healthy controls (HCs). The intersubject differences in the connectome architecture among patients with SCZ have been largely understudied. The resting-state functional magnetic resonance imaging (R-fMRI) technique provides an unprecedented opportunity to noninvasively investigate the intrinsic connectome architecture of the brain in vivo.6,7 In particular, recent progress in R-fMRI has moved towards characterizing the intersubject variability of the functional connectome (IVFC). The IVFC of each network node was estimated by subtracting the averaged intersubject similarity of the nodal functional connectivity profiles from one. In the healthy population, the whole-brain IVFC pattern exhibits a nonuniform distribution, with higher variability in the heteromodal cortices and lower variability in the primary cortices.8–11 Such a pattern is compatible with our understanding of individual differences in cognitive functions. In SCZ, although a few studies have found high heterogeneity of functional connections in focal regions (eg, the dorsolateral prefrontal cortex)12 and functional components (eg, sensorimotor, visual, and auditory components),13,14 the whole-brain IVFC pattern remains largely unclear. More importantly, it is of great interest whether the IVFC pattern is altered in SCZ patients relative to healthy subjects, and if so, whether these alterations are specific to SCZ or are general to other psychiatric disorders such as bipolar disorder (BD) and major depressive disorder (MDD). To address these issues, in this study, we collected R-fMRI data from 121 SCZ patients and 183 matched HCs. Using a voxelwise whole-brain connectivity analysis, we identified alterations in IVFC in SCZ, and we further examined the relationship of these alterations with the clinical variables in the patients. To determine the specificity of the connectivity alterations, we also analyzed the IVFC in BD and MDD. Methods Participants Three R-fMRI datasets were included in this study. The principal dataset (dataset 1) included 139 patients with SCZ and 189 HCs. The validation datasets included 108 patients with BD (dataset 2) and 114 patients with MDD (dataset 3). All patients were enrolled from the inpatient and outpatient services at Shenyang Mental Health Center and the Department of Psychiatry, First Affiliated Hospital of China Medical University, Shenyang, China. HCs were recruited via local advertisements. The diagnosis of patients was performed together by 2 experienced psychiatrists. Patients aged 18 years and older were diagnosed using the criteria from the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders Fourth Edition, whereas patients under 18 years old were diagnosed using the criteria supplied by the semistructured diagnostic interview for the Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version. All patients met the diagnostic criteria for SCZ, BD, or MDD and had no other Axis I disorder. The HCs did not have a current or lifetime history of an Axis I disorder or a history of psychotic, mood, or other Axis I disorders in first-degree relatives. The exclusion criteria for all participants included MRI contraindications, a history of drug or alcohol abuse, concomitant major medical disorder, a history of head trauma with consciousness disturbances or any neurological disorders, and suboptimal imaging data quality. Finally, 121 patients with SCZ, 183 HCs, 100 patients with BD, and 108 patients with MDD were included. The clinical symptoms and cognitive measures were assessed using the Brief Psychiatric Rating Scale (BPRS), Hamilton Depression Rating Scale (HAMD), Hamilton Anxiety Rating Scale (HAMA), Young Mania Rating Scale (YMRS), and Wisconsin Card Sorting Test (WCST). Disease duration, medication status, episode status, and onset age were collected for each patient. This study was approved by the Institutional Review Board of China Medical University. Written informed consent was obtained from all participants. The datasets have previously been used in the graph-theory analyses of functional brain networks and brain modules.15,16 Data Acquisition R-fMRI data were collected on a GE Signa HD 3.0T scanner (General Electric) with a standard 8-channel head coil at the First Affiliated Hospital of China Medical University, Shenyang, China (supplementary material). The scan lasted for 6 minutes and 40 seconds, resulting in 200 volumes. The participants were instructed to rest and relax with their eyes closed, but to keep from falling asleep during scanning. Data Preprocessing R-fMRI data were preprocessed using SPM12 and DPARSF,17 including removal of the first 10 volumes, correction for slice-timing and head motion, spatial normalization with 3 mm isotropic voxels, spatial smoothing, linear detrending, regression of nuisance signals (Friston-24 motion parameters, white matter, and cerebrospinal fluid signals) and temporal band-filtering (0.01–0.1 Hz). The global signal was not regressed out in our main analysis because of its neurobiological significance in SCZ.18 Volume censoring movement correction19 was finally performed that volumes with a framewise displacement exceeding 0.5 mm and their adjacent volumes were replaced with the linear interpolated data. Whole-Brain IVFC Pattern and Group Differences We assessed the IVFC patterns in SCZ and HCs using the procedure proposed by Mueller and colleagues.8 For each participant, we first calculated the functional connectivity profiles by performing a voxelwise whole-brain connectivity analysis. Then, for each voxel, the intersubject similarity was assessed by computing the Pearson correlation of functional connectivity profiles between any 2 participants and the IVFC was estimated by subtracting the averaged intersubject similarity from one (figure 1A and supplementary material). Thus, the whole-brain IVFC map can be obtained for each group. To test whether the IVFC patterns were significantly different between SCZ and HCs, we compared the global measures of the IVFC maps (the mean and standard deviation) and identified SCZ-related alteration at voxel level by performing a non-parametric permutation test with significant level of P < .01 for voxel and P < .05 for cluster size (10 000 times, supplementary material). Fig. 1. Open in new tabDownload slide Disrupted intersubject variability architecture of the brain functional connectome in SCZ. (A) Flow chart for the calculation of intersubject variability. (B) Intersubject variability pattern in the SCZ and HC groups. (C) Comparison of the density distribution, mean value and standard deviation of whole-brain intersubject variability between the SCZ and HC groups. (D) SCZ-related regional alterations in intersubject variability. SCZ: schizophrenia; HC: healthy control; Sub: subject; Vox: voxel; FC: functional connectivity; IVFC: intersubject variability of the functional connectome. Fig. 1. Open in new tabDownload slide Disrupted intersubject variability architecture of the brain functional connectome in SCZ. (A) Flow chart for the calculation of intersubject variability. (B) Intersubject variability pattern in the SCZ and HC groups. (C) Comparison of the density distribution, mean value and standard deviation of whole-brain intersubject variability between the SCZ and HC groups. (D) SCZ-related regional alterations in intersubject variability. SCZ: schizophrenia; HC: healthy control; Sub: subject; Vox: voxel; FC: functional connectivity; IVFC: intersubject variability of the functional connectome. Edgewise Contribution to SCZ-Related Alterations in IVFC To explore the critical connections that predominately contributed to the IVFC alterations in SCZ, we performed an edgewise variability quantification analysis. We first identified local peaks in clusters exhibiting significant between-group IVFC differences. For each peak, we extracted its functional profiles and estimated the weight of each connection on intersubject similarity by decomposing the standard Pearson’s correlation formula in the SCZ and HC groups. Finally, a between-group difference contribution (DC) of each connection was defined as the difference between the reversed intersubject similarity weight (figure 2A and supplementary material). Thus, a DC map representing the contribution to the IVFC differences between SCZ and HC was obtained for each peak. Fig. 2. Open in new tabDownload slide Edgewise contributions to intersubject functional variability. (A) Flow chart for the calculation of difference contribution. (B) Hierarchical clustering results of difference contribution maps. (C) Mean difference contribution maps of 4 typical categories. SCZ: schizophrenia; HC: healthy control; Sub: subject; Vox: voxel; FC: functional connectivity; DC: difference contribution; HES: Heschl gyrus; STG: superior temporal gyrus; PCL: paracentral lobule; CAL: calcarine fissure and surrounding cortex; LING: lingual gyrus; PoCG: postcentral gyrus; AMYG: amygdala; THA: thalamus. Fig. 2. Open in new tabDownload slide Edgewise contributions to intersubject functional variability. (A) Flow chart for the calculation of difference contribution. (B) Hierarchical clustering results of difference contribution maps. (C) Mean difference contribution maps of 4 typical categories. SCZ: schizophrenia; HC: healthy control; Sub: subject; Vox: voxel; FC: functional connectivity; DC: difference contribution; HES: Heschl gyrus; STG: superior temporal gyrus; PCL: paracentral lobule; CAL: calcarine fissure and surrounding cortex; LING: lingual gyrus; PoCG: postcentral gyrus; AMYG: amygdala; THA: thalamus. To further classify the critical connections, we performed agglomerative hierarchical clustering analysis on these DC maps (supplementary material). The optimal number of categories was determined as the minimum value of the maximum mean silhouette value.20 The representative maps were obtained by averaging the contribution maps of all peaks within each category. We demonstrated the 90th percentile of connections with the top contributions for each category. A cluster-level permutation test with a significance level of P < .05 was performed to control the false positive rate (2 other thresholds, the 95th and 85th percentiles, were also calculated; see supplementary material). Relationship Between IVFC Patterns and Clinical Variables in SCZ To investigate the relationship between IVFC and clinical variables in SCZ group, we applied a multiple regression model and bootstrap sampling. In each sampling, 50 subjects (sizes 30 and 70 were also used for validation) were randomly selected, and their IVFC for each local peak and the mean clinical variables, including age of onset, duration, and BPRS, were calculated. This sampling was repeated 10 000 times, and the regression model was built with sampled IVFC as the dependent variables, mean clinical variables as independent variables, and age and gender as covariates. The significance of each dependent variable in the model was determined using permutation test (supplementary material). Furthermore, to characterize the linkage between the heterogeneity of clinical symptoms and functional connectivity, we also calculated the intersubject variability within the 18-item BPRS score and assessed its relationship to IVFC by performing a similar bootstrap sampling analysis (supplementary material). To correct for multiple comparisons, the Bonferroni correction at P < .05 across all peaks and clinical variables was applied. Disorder Specificity of IVFC in SCZ Common alterations in functional connectivity have been reported in patients with SCZ and other psychiatric disorders such as MDD and BD.16,21–28 To assess the specificity of the IVFC alterations in SCZ, we computed the IVFC maps in the MDD and BD groups, followed by permutation tests, as described above. Validation Analysis To assess the reliability of our results, we examined the influences of the clinical and demographic variables (ie, participants’ age, medication status, and episode status of the disorder) and estimated the effect of image preprocessing (ie, global signal removal and head motion). To reduce the confounding factor of intrasubject variance in functional connectivity profiles, we also re-estimated IVFC patterns for each group by splitting the participants’ time points into 2 halves (supplementary material). Results Demographics and Clinical Characteristics There were no significant differences in age, gender, handedness, or smoking history among the 4 groups. Age onset also did not differ among the 3 patient groups. However, significant differences were observed in illness duration, first-episode status, medication status, HAMD, HAMA, YMRS, and BPRS scores among 3 patient groups (P < .001). A direct comparison between HC and SCZ groups revealed no significant differences in age, gender, handedness, or smoking history. However, patients with SCZ had significantly higher scores in HAMD, HAMA, YMRS, and BPRS than the HCs (P < .001, table 1). Table 1. Demographics and Clinical Characteristics of the Participants . Datasets 1 . . Datasets 2 . Datasets 3 . . . . Healthy Control (n = 183) . Schizophrenia (n = 121) . Bipolar Disorder (n = 100) . Major Depressive Disorder (n = 108) . Statistical Test F/χ 2 (P) . HC vs SCZ Statistical Test T/χ 2 (P) . Demographic characteristics Age at scanning, years 26.62 (8.00) 24.74 (9.03) 25.81 (8.31) 25.62 (8.43) 1.26 (0.288) 1.85 (0.065) Gender (male/female) 73/110 54/67 48/52 35/73 6.07 (0.108) 0.67 (0.412) Handedness (Right/Left/Bilateral)a 172/0/8 100/2/10 95/1/2 96/2/3 10.23 (0.115) 5.76 (0.056) Smoking history (smoking/no smoking)a 29/128 12/76 21/62 22/67 5.12 (0.163) 0.95 (0.331) Clinical characteristics Age at illness onset - 22.82 (8.89) 21.72 (7.24) 23.57 (8.20) 1.15 (0.318) - Illness duration, months - 21.87 (36.15) 41.48 (56.18) 20.58 (31.00) 7.23 (0.001) - First episode, yesa - 86 (74%) 52 (57%) 85 (88%) 34.78 (<0.001) - Medication, yesa - 71 (59%) 65 (65%) 43 (40%) 172.36 (<0.001) - Antidepressants, yesa - 4 (4%) 28 (29%) 35 (36%) 32.68 (<0.001) - Antipsychotics, yesa - 59 (61%) 35 (36%) 1(1%) 78.54 (<0.001) - Mood stabilizer, yesa - 4 (4%) 52 (53%) 0 116.53 (<0.001) - HAMD-17 (n = 165) 1.17 (1.67) (n = 86) 8.12 (6.96) (n = 99) 11.77 (9.48) (n = 107) 21.16 (8.77) 186.93 (<0.001) −9.12 (<0.001) HAMA (n = 164) 0.77 (1.76) (n = 69) 6.80 (7.26) (n = 96) 8.52 (8.80) (n = 93) 16.32 (9.49) 103.51 (<0.001) −6.80 (<0.001) YMRS (n = 158) 0.15 (0.57) (n = 60) 2.20 (4.50) (n = 95) 8.07 (10.05) (n = 89) 1.47 (2.87) 45.29 (<0.001) −3.51 (0.001) BPRS (n = 96) 18.30 (0.68) (n = 116) 35.59 (14.32) (n = 60) 25.83 (8.38) (n = 46) 25.41 (6.08) 57.01 (<0.001) −12.99 (<0.001) Cognitive function WCST (n = 110) (n = 58) (n = 58) (n = 66) - - Correct responses 29.13 (12.66) 18.14 (11.78) 23.47 (12.31) 23.27 (11.91) 10.80 (<0.001) 5.48 (<0.001) Categories completed 3.81 (2.28) 1.57 (1.80) 2.69 (2.07) 2.83 (2.04) 14.93 (<0.001) 6.50 (<0.001) Total errors 19.06 (12.74) 29.86 (11.78) 24.02 (12.41) 24.80 (11.91) 10.24 (<0.001) −5.36 (<0.001) Perseverative errors 7.36 (7.92) 13.36 (12.52) 10.03 (10.28) 10.83 (9.60) 5.05 (0.002) −3.32 (0.001) Non-perseverative errors 11.56 (7.08) 16.50 (8.80) 14.36 (7.72) 13.97 (6.53) 5.93 (0.001) −3.70 (<0.001) Head motion parameters Max translation, mm 0.56 (0.39) 0.67 (0.49) 0.66 (0.47) 0.68 (0.58) 1.89 (0.130) −1.81 (0.072) Max rotation, degree 0.55 (0.42) 0.64 (0.51) 0.61 (0.44) 0.66 (0.58) 1.64 (0.180) −1.67 (0.096) Mean FD, mm 0.11 (0.05) 0.12 (0.08) 0.12 (0.06) 0.11 (0.06) 2.12 (0.096) −1.86 (0.064) FD _ Percentage 0.01 (0.03) 0.02 (0.05) 0.02 (0.04) 0.02 (0.04) 2.25 (0.081) −1.98 (0.049) . Datasets 1 . . Datasets 2 . Datasets 3 . . . . Healthy Control (n = 183) . Schizophrenia (n = 121) . Bipolar Disorder (n = 100) . Major Depressive Disorder (n = 108) . Statistical Test F/χ 2 (P) . HC vs SCZ Statistical Test T/χ 2 (P) . Demographic characteristics Age at scanning, years 26.62 (8.00) 24.74 (9.03) 25.81 (8.31) 25.62 (8.43) 1.26 (0.288) 1.85 (0.065) Gender (male/female) 73/110 54/67 48/52 35/73 6.07 (0.108) 0.67 (0.412) Handedness (Right/Left/Bilateral)a 172/0/8 100/2/10 95/1/2 96/2/3 10.23 (0.115) 5.76 (0.056) Smoking history (smoking/no smoking)a 29/128 12/76 21/62 22/67 5.12 (0.163) 0.95 (0.331) Clinical characteristics Age at illness onset - 22.82 (8.89) 21.72 (7.24) 23.57 (8.20) 1.15 (0.318) - Illness duration, months - 21.87 (36.15) 41.48 (56.18) 20.58 (31.00) 7.23 (0.001) - First episode, yesa - 86 (74%) 52 (57%) 85 (88%) 34.78 (<0.001) - Medication, yesa - 71 (59%) 65 (65%) 43 (40%) 172.36 (<0.001) - Antidepressants, yesa - 4 (4%) 28 (29%) 35 (36%) 32.68 (<0.001) - Antipsychotics, yesa - 59 (61%) 35 (36%) 1(1%) 78.54 (<0.001) - Mood stabilizer, yesa - 4 (4%) 52 (53%) 0 116.53 (<0.001) - HAMD-17 (n = 165) 1.17 (1.67) (n = 86) 8.12 (6.96) (n = 99) 11.77 (9.48) (n = 107) 21.16 (8.77) 186.93 (<0.001) −9.12 (<0.001) HAMA (n = 164) 0.77 (1.76) (n = 69) 6.80 (7.26) (n = 96) 8.52 (8.80) (n = 93) 16.32 (9.49) 103.51 (<0.001) −6.80 (<0.001) YMRS (n = 158) 0.15 (0.57) (n = 60) 2.20 (4.50) (n = 95) 8.07 (10.05) (n = 89) 1.47 (2.87) 45.29 (<0.001) −3.51 (0.001) BPRS (n = 96) 18.30 (0.68) (n = 116) 35.59 (14.32) (n = 60) 25.83 (8.38) (n = 46) 25.41 (6.08) 57.01 (<0.001) −12.99 (<0.001) Cognitive function WCST (n = 110) (n = 58) (n = 58) (n = 66) - - Correct responses 29.13 (12.66) 18.14 (11.78) 23.47 (12.31) 23.27 (11.91) 10.80 (<0.001) 5.48 (<0.001) Categories completed 3.81 (2.28) 1.57 (1.80) 2.69 (2.07) 2.83 (2.04) 14.93 (<0.001) 6.50 (<0.001) Total errors 19.06 (12.74) 29.86 (11.78) 24.02 (12.41) 24.80 (11.91) 10.24 (<0.001) −5.36 (<0.001) Perseverative errors 7.36 (7.92) 13.36 (12.52) 10.03 (10.28) 10.83 (9.60) 5.05 (0.002) −3.32 (0.001) Non-perseverative errors 11.56 (7.08) 16.50 (8.80) 14.36 (7.72) 13.97 (6.53) 5.93 (0.001) −3.70 (<0.001) Head motion parameters Max translation, mm 0.56 (0.39) 0.67 (0.49) 0.66 (0.47) 0.68 (0.58) 1.89 (0.130) −1.81 (0.072) Max rotation, degree 0.55 (0.42) 0.64 (0.51) 0.61 (0.44) 0.66 (0.58) 1.64 (0.180) −1.67 (0.096) Mean FD, mm 0.11 (0.05) 0.12 (0.08) 0.12 (0.06) 0.11 (0.06) 2.12 (0.096) −1.86 (0.064) FD _ Percentage 0.01 (0.03) 0.02 (0.05) 0.02 (0.04) 0.02 (0.04) 2.25 (0.081) −1.98 (0.049) Note: Data are presented as either n (%) or means (SDs). HAMD-17, Hamilton Depression Scale; HAMA, Hamilton Anxiety Scale; YMRS, Young Mania Rating Scale; BPRS, Brief Psychiatric Rating Scale; WCST, Wisconsin Card Sorting Test; FD, framewise displacement. aInformation was missing for some participants. Open in new tab Table 1. Demographics and Clinical Characteristics of the Participants . Datasets 1 . . Datasets 2 . Datasets 3 . . . . Healthy Control (n = 183) . Schizophrenia (n = 121) . Bipolar Disorder (n = 100) . Major Depressive Disorder (n = 108) . Statistical Test F/χ 2 (P) . HC vs SCZ Statistical Test T/χ 2 (P) . Demographic characteristics Age at scanning, years 26.62 (8.00) 24.74 (9.03) 25.81 (8.31) 25.62 (8.43) 1.26 (0.288) 1.85 (0.065) Gender (male/female) 73/110 54/67 48/52 35/73 6.07 (0.108) 0.67 (0.412) Handedness (Right/Left/Bilateral)a 172/0/8 100/2/10 95/1/2 96/2/3 10.23 (0.115) 5.76 (0.056) Smoking history (smoking/no smoking)a 29/128 12/76 21/62 22/67 5.12 (0.163) 0.95 (0.331) Clinical characteristics Age at illness onset - 22.82 (8.89) 21.72 (7.24) 23.57 (8.20) 1.15 (0.318) - Illness duration, months - 21.87 (36.15) 41.48 (56.18) 20.58 (31.00) 7.23 (0.001) - First episode, yesa - 86 (74%) 52 (57%) 85 (88%) 34.78 (<0.001) - Medication, yesa - 71 (59%) 65 (65%) 43 (40%) 172.36 (<0.001) - Antidepressants, yesa - 4 (4%) 28 (29%) 35 (36%) 32.68 (<0.001) - Antipsychotics, yesa - 59 (61%) 35 (36%) 1(1%) 78.54 (<0.001) - Mood stabilizer, yesa - 4 (4%) 52 (53%) 0 116.53 (<0.001) - HAMD-17 (n = 165) 1.17 (1.67) (n = 86) 8.12 (6.96) (n = 99) 11.77 (9.48) (n = 107) 21.16 (8.77) 186.93 (<0.001) −9.12 (<0.001) HAMA (n = 164) 0.77 (1.76) (n = 69) 6.80 (7.26) (n = 96) 8.52 (8.80) (n = 93) 16.32 (9.49) 103.51 (<0.001) −6.80 (<0.001) YMRS (n = 158) 0.15 (0.57) (n = 60) 2.20 (4.50) (n = 95) 8.07 (10.05) (n = 89) 1.47 (2.87) 45.29 (<0.001) −3.51 (0.001) BPRS (n = 96) 18.30 (0.68) (n = 116) 35.59 (14.32) (n = 60) 25.83 (8.38) (n = 46) 25.41 (6.08) 57.01 (<0.001) −12.99 (<0.001) Cognitive function WCST (n = 110) (n = 58) (n = 58) (n = 66) - - Correct responses 29.13 (12.66) 18.14 (11.78) 23.47 (12.31) 23.27 (11.91) 10.80 (<0.001) 5.48 (<0.001) Categories completed 3.81 (2.28) 1.57 (1.80) 2.69 (2.07) 2.83 (2.04) 14.93 (<0.001) 6.50 (<0.001) Total errors 19.06 (12.74) 29.86 (11.78) 24.02 (12.41) 24.80 (11.91) 10.24 (<0.001) −5.36 (<0.001) Perseverative errors 7.36 (7.92) 13.36 (12.52) 10.03 (10.28) 10.83 (9.60) 5.05 (0.002) −3.32 (0.001) Non-perseverative errors 11.56 (7.08) 16.50 (8.80) 14.36 (7.72) 13.97 (6.53) 5.93 (0.001) −3.70 (<0.001) Head motion parameters Max translation, mm 0.56 (0.39) 0.67 (0.49) 0.66 (0.47) 0.68 (0.58) 1.89 (0.130) −1.81 (0.072) Max rotation, degree 0.55 (0.42) 0.64 (0.51) 0.61 (0.44) 0.66 (0.58) 1.64 (0.180) −1.67 (0.096) Mean FD, mm 0.11 (0.05) 0.12 (0.08) 0.12 (0.06) 0.11 (0.06) 2.12 (0.096) −1.86 (0.064) FD _ Percentage 0.01 (0.03) 0.02 (0.05) 0.02 (0.04) 0.02 (0.04) 2.25 (0.081) −1.98 (0.049) . Datasets 1 . . Datasets 2 . Datasets 3 . . . . Healthy Control (n = 183) . Schizophrenia (n = 121) . Bipolar Disorder (n = 100) . Major Depressive Disorder (n = 108) . Statistical Test F/χ 2 (P) . HC vs SCZ Statistical Test T/χ 2 (P) . Demographic characteristics Age at scanning, years 26.62 (8.00) 24.74 (9.03) 25.81 (8.31) 25.62 (8.43) 1.26 (0.288) 1.85 (0.065) Gender (male/female) 73/110 54/67 48/52 35/73 6.07 (0.108) 0.67 (0.412) Handedness (Right/Left/Bilateral)a 172/0/8 100/2/10 95/1/2 96/2/3 10.23 (0.115) 5.76 (0.056) Smoking history (smoking/no smoking)a 29/128 12/76 21/62 22/67 5.12 (0.163) 0.95 (0.331) Clinical characteristics Age at illness onset - 22.82 (8.89) 21.72 (7.24) 23.57 (8.20) 1.15 (0.318) - Illness duration, months - 21.87 (36.15) 41.48 (56.18) 20.58 (31.00) 7.23 (0.001) - First episode, yesa - 86 (74%) 52 (57%) 85 (88%) 34.78 (<0.001) - Medication, yesa - 71 (59%) 65 (65%) 43 (40%) 172.36 (<0.001) - Antidepressants, yesa - 4 (4%) 28 (29%) 35 (36%) 32.68 (<0.001) - Antipsychotics, yesa - 59 (61%) 35 (36%) 1(1%) 78.54 (<0.001) - Mood stabilizer, yesa - 4 (4%) 52 (53%) 0 116.53 (<0.001) - HAMD-17 (n = 165) 1.17 (1.67) (n = 86) 8.12 (6.96) (n = 99) 11.77 (9.48) (n = 107) 21.16 (8.77) 186.93 (<0.001) −9.12 (<0.001) HAMA (n = 164) 0.77 (1.76) (n = 69) 6.80 (7.26) (n = 96) 8.52 (8.80) (n = 93) 16.32 (9.49) 103.51 (<0.001) −6.80 (<0.001) YMRS (n = 158) 0.15 (0.57) (n = 60) 2.20 (4.50) (n = 95) 8.07 (10.05) (n = 89) 1.47 (2.87) 45.29 (<0.001) −3.51 (0.001) BPRS (n = 96) 18.30 (0.68) (n = 116) 35.59 (14.32) (n = 60) 25.83 (8.38) (n = 46) 25.41 (6.08) 57.01 (<0.001) −12.99 (<0.001) Cognitive function WCST (n = 110) (n = 58) (n = 58) (n = 66) - - Correct responses 29.13 (12.66) 18.14 (11.78) 23.47 (12.31) 23.27 (11.91) 10.80 (<0.001) 5.48 (<0.001) Categories completed 3.81 (2.28) 1.57 (1.80) 2.69 (2.07) 2.83 (2.04) 14.93 (<0.001) 6.50 (<0.001) Total errors 19.06 (12.74) 29.86 (11.78) 24.02 (12.41) 24.80 (11.91) 10.24 (<0.001) −5.36 (<0.001) Perseverative errors 7.36 (7.92) 13.36 (12.52) 10.03 (10.28) 10.83 (9.60) 5.05 (0.002) −3.32 (0.001) Non-perseverative errors 11.56 (7.08) 16.50 (8.80) 14.36 (7.72) 13.97 (6.53) 5.93 (0.001) −3.70 (<0.001) Head motion parameters Max translation, mm 0.56 (0.39) 0.67 (0.49) 0.66 (0.47) 0.68 (0.58) 1.89 (0.130) −1.81 (0.072) Max rotation, degree 0.55 (0.42) 0.64 (0.51) 0.61 (0.44) 0.66 (0.58) 1.64 (0.180) −1.67 (0.096) Mean FD, mm 0.11 (0.05) 0.12 (0.08) 0.12 (0.06) 0.11 (0.06) 2.12 (0.096) −1.86 (0.064) FD _ Percentage 0.01 (0.03) 0.02 (0.05) 0.02 (0.04) 0.02 (0.04) 2.25 (0.081) −1.98 (0.049) Note: Data are presented as either n (%) or means (SDs). HAMD-17, Hamilton Depression Scale; HAMA, Hamilton Anxiety Scale; YMRS, Young Mania Rating Scale; BPRS, Brief Psychiatric Rating Scale; WCST, Wisconsin Card Sorting Test; FD, framewise displacement. aInformation was missing for some participants. Open in new tab Abnormal Whole-Brain IVFC Pattern in SCZ The whole-brain IVFC pattern in the SCZ group was remarkably similar to that in the HCs (r = .93, P < .0001), with higher IVFC in the heteromodal association cortex, including the lateral prefrontal, parietal, and temporal cortices, and lower IVFC in the primary sensorimotor and visual areas (figure 1B). The SCZ group showed a significantly higher global mean variability (P < .001) and a lower standard deviation of variability (P = .003) than the HC group (figure 1C). This result suggests that the SCZ group tended to have higher across-subject heterogeneity and simultaneously reduced across-regional diversity in the whole-brain functional connectome than the HC group. Regionally, the SCZ group had a significantly higher IVFC in the bilateral sensorimotor, visual, auditory, and subcortical regions (P < .05, 10 000 permutations corrected, figure 1D and supplementary table S1). Edgewise Contributions to SCZ-Related Alterations in IVFC We identified the critical connections that contributed to alterations of IVFC in SCZ (supplementary figure S1), which were mainly classified into 4 categories (figure 2 and supplementary figure S2): (1) connections of the bilateral Heschl gyrus (HES), the left superior temporal gyrus (STG), and the right paracentral lobule (PCL) (P < .05, 10 000 permutations corrected, figure 2 and supplementary table S2), which are mainly related to auditory perception and language processing; (2) connections of the bilateral calcarine (CAL), the lingual gyrus (LING), and the postcentral gyrus (PoCG) (figure 2 and supplementary table S3), which are primarily involved in sensorimotor and visual processing; (3) connections of the right amygdala (AMYG) (figure 2 and supplementary table S4), which is dominantly related to emotional and social cognition regulation; (4) connections of the bilateral thalamus (THA) (figure 2 and supplementary table S5), which is involved in sensory information integration and mental coordination. The validation test showed that the number of connections extracted did not change the significant regions with edgewise contributions to the IVFC for each category (supplementary figure S3). Clinical Relevance of Intersubject Variability The IVFC was negatively correlated with the age of onset (t = −10.52 ± 5.21, P < .001 Bonferroni-corrected) in the bilateral CAL, LING, and PoCG; right HES, PCL, and AMYG; and left THA and STG, with the disease duration (t = −9.58 ± 3.64, P < .001 Bonferroni-corrected) in bilateral CAL and LING; right HES, PCL, and AMYG; and left STG and PoCG, and with the BPRS (t = −13.85 ± 6.00, P < .001 Bonferroni-corrected) in bilateral CAL, THA, and LING; right AMYG; and left PoCG and STG (figure 3A and supplementary table S6). Further analyses showed that IVFC was positively correlated with the within-BPRS heterogeneity (t = 12.52 ± 3.91, P < .001 Bonferroni-corrected) in the bilateral HES, CAL and LING; right AMYG and PCL; and left PoCG and STG (figure 3A and supplementary table S7). The sample size of bootstrapping did not change the relevance (supplementary tables S8–S10). Moreover, when dividing patients into subgroups according to their clinical status (ie, medication and episode), we found that the non-medicated and first-episode subgroups had an additionally broader disrupted variability than the medicated (figure 3B) and recurrent subgroups (figure 3C), respectively. Fig. 3. Open in new tabDownload slide The clinical relevance of intersubject variability. (A) Relationship between intersubject variability and clinical variables. A non-white grid indicates a significant correlation with a Bonferroni-corrected P < .05. (B) Effects of the medication status of SCZ patients on the calculation of intersubject functional variability. (C) Effects of the episode status of SCZ patients on the calculation of intersubject functional variability. HC, healthy control; HES: Heschl gyrus; THA: thalamus; CAL: calcarine fissure and surrounding cortex; LING: lingual gyrus; PoCG: postcentral gyrus; AMYG: amygdala; STG: superior temporal gyrus; PCL: paracentral lobule; BPRS: Brief Psychiatric Rating Scale. Fig. 3. Open in new tabDownload slide The clinical relevance of intersubject variability. (A) Relationship between intersubject variability and clinical variables. A non-white grid indicates a significant correlation with a Bonferroni-corrected P < .05. (B) Effects of the medication status of SCZ patients on the calculation of intersubject functional variability. (C) Effects of the episode status of SCZ patients on the calculation of intersubject functional variability. HC, healthy control; HES: Heschl gyrus; THA: thalamus; CAL: calcarine fissure and surrounding cortex; LING: lingual gyrus; PoCG: postcentral gyrus; AMYG: amygdala; STG: superior temporal gyrus; PCL: paracentral lobule; BPRS: Brief Psychiatric Rating Scale. Disorder Specificity of IVFC in SCZ Both the BD and MDD groups exhibited similar spatial patterns to the HC group (BD: r = .96, MDD: r = .97, both P < .0001) (figure 4). Voxelwise comparison showed that the medial occipital cortex exhibited significantly higher IVFC in the BD and MDD groups than in the HC group, and this region was also the altered region shared with the SCZ group (P < .05 permutation-corrected) (figure 4, supplementary tables S11 and S12). However, there was a broader regional higher variability in SCZ, including the bilateral sensorimotor, auditory, and subcortical regions, indicating the relative specificity of SCZ-related alterations of IVFC (figure 4). Fig. 4. Open in new tabDownload slide Disorder specificity of intersubject functional variability in SCZ. Columns 1 to 3 are the spatial patterns of the intersubject variability in the MDD, BD, and SCZ groups. Columns 4 to 6 are the MDD-, BD-, and SCZ-related alterations in intersubject functional variability. Column 7 is the comparison of the altered intersubject variability of MDD, BD, and SCZ. The yellow patches indicate the SCZ-specificity alterations, and the red patches indicate the overlapped alterations of 3 disorders. SCZ: schizophrenia; BD: bipolar disorder; MDD: major depressive disorder. Fig. 4. Open in new tabDownload slide Disorder specificity of intersubject functional variability in SCZ. Columns 1 to 3 are the spatial patterns of the intersubject variability in the MDD, BD, and SCZ groups. Columns 4 to 6 are the MDD-, BD-, and SCZ-related alterations in intersubject functional variability. Column 7 is the comparison of the altered intersubject variability of MDD, BD, and SCZ. The yellow patches indicate the SCZ-specificity alterations, and the red patches indicate the overlapped alterations of 3 disorders. SCZ: schizophrenia; BD: bipolar disorder; MDD: major depressive disorder. Validation Results Generally, the altered IVFC pattern in SCZ remained largely unchanged in the validation, including the participants’ age (supplementary figure S4), global signal removal (supplementary figure S5), intrasubject variance (supplementary figure S6), and head motion (supplementary figures S7, S8, and table S13). However, the global signal and intrasubject variance regression led to SCZ-related higher IVFC in the lateral frontal cortex and anterior cingulate gyrus (supplementary figures S5 and S6). Discussion In this study, we systematically investigated the whole-brain voxelwise IVFC pattern in patients with SCZ. Specifically, we found that the spatial distribution of the IVFC pattern was similar between the SCZ and HC groups. However, the SCZ group exhibited significantly higher IVFC than the HC group in the sensorimotor, visual, auditory, and subcortical cortex, and the altered IVFC was significantly correlated with clinical scores. The alteration of IVFC was relatively specific to SCZ, while the visual cortex was a shared altered region among SCZ, BD, and MDD, and the sensorimotor, auditory, and subcortical cortices were only altered in SCZ. Together, these findings provide mechanic insights into the observed inconsistent dysconnectivity results in previous studies and inspire imaging-derived candidate phenotypes for the guidance of individualized clinical diagnosis and treatment evaluations. Disrupted Whole-Brain IVFC in SCZ The higher whole-brain mean variability and lower standard deviation of variability indicated a higher global individual variability across patients and lower heterogeneity across brain regions in SCZ. These results are largely comparable with the previous findings of randomized functional connectivity16 and dedifferentiation across whole functional systems in SCZ.15 Our additional statistical analysis showed that IVFC was negatively correlated to the clustering coefficient, characteristic shortest path length, and modularity in SCZ (supplementary table S14). These findings indicate that the functional connectivity became more heterogeneity among patients as their brain connectome became more randomized. Regionally, higher IVFC of the sensorimotor, visual, auditory, and subcortical cortex were found in SCZ. Altered functional connectivity of these regions has generally been reported and is related to bottom-up processing and integration, emotional dysfunction, and social cognition impairments in SCZ.4,5,16,29–35 The greater IVFC of these regions might imply that the decoding and integrating processing of primary sensory input are different among patients before higher-level processes, which could partially explain the high heterogeneity of clinical symptoms and the diverse dysconnectivity observed across studies in SCZ.5,36–39 Moreover, physiological changes in these regions were also found in previous studies.40–44 Participants with SCZ or at high risk for SCZ showed significant elevations in glutamate, glutamine or Glx (glutamate + glutamine) in the basal ganglia, thalamus, and medial temporal lobe. These abnormal metabolites were related to the illness phase42 and indicated different potential physiological factors across SCZ patients. Higher heterogeneity of abnormal structural measures were also found in SCZ.45–48 Our findings of higher IVFC in sensorimotor, visual, auditory, and subcortical regions are largely comparable with the structural observations. The inconsistency between the 2 modalities occurred mainly in the frontal cortex. It is consistent with finding that structure-function coupling is weaker in the transmodal than unimodal regions.49 However, the correspondence between functional and structural variability in SCZ remains largely unknown, and further research may provide a structural basis for the IVFC alteration. Critical Contribution Connections to SCZ-Related IVFC Alterations We found that the connections with the top contributions to IVFC in SCZ involved several different pathways. Specifically, the temporoparietal junction (TPJ) is one of the most involved regions connecting to the auditory, sensorimotor, and visual cortices, as well as the AMYG. Previous studies have shown that the connections between the TPJ and these areas are related to perceptual abnormalities and external/inner voices differentiate inability in SCZ.50–55 The increased IVFC of TPJ might thus point to different forms of self-consciousness delusions, auditory-verbal hallucinations, and social responses among patients. Moreover, our findings provide an interpretation of the mixed results for dysconnectivity in the TPJ with both hyper- and hypo-connectivity reported in previous studies.51 THA connections, involving primary, limbic, and subcortical areas, also contribute largely to the high variability in SCZ. The cortico-striatal-thalamic-AMYG pathway is a classical reward/punishment model related to motivation and goal-directed behavior31,56,57 and is involved in emotional memory and effective perception of fear signals.58–60 Impaired functional connectivity of this pathway has been reported in the SCZ, which is most pronounced in patients with negative symptoms.31,61 This pathway belongs to the substantia nigra-based subcortical-cortical motor circuit62–64 and closely related to the disrupted serotonin (5-HT), dopamine, and glutamate activity.63,65–68 Considering the different findings for dysconnectivity and the correlation between dysconnectivity and clinical scores among studies,25,60,69–72 our results suggest that the intersubject difference in functional connectivity may contribute to the different negative symptoms and task responses among patients. Relationship Between IVFC and Clinical Variables We found that the IVFC is negatively correlated with age onset. This is consistent with previous finding that childhood-/early-onset SCZ patients exhibit severer functional connectivity abnormalities and clinical manifestations than adult-onset patients.73–75 A potential explanation may be due to the high plasticity of brain development in childhood.76 Evidence has shown that a longer disease duration and higher clinical severity are positively correlated with the functional connectivity abnormalities in SCZ.71,72,77–80 The negative correlation between IVFC and these 2 clinical variables observed herein indicates that the altered brain architecture of patients becomes more convergent as the duration and clinical severity increases. Furthermore, the positive correlation between IVFC and the intersubject variability of 18-items BPRS scores highlights the correspondence between clinical heterogeneity and functional connection heterogeneity, indicating the potential implications of our findings for understanding the high clinical heterogeneity of SCZ. Future analysis based on clinical information in single symptom dimensions, such as the Positive and Negative Syndrome Scale, may reflect more factors related to IVFC across SCZ patients. The difference between IVFC of medicated and non-medicated subgroups is consistent with previous evidence that non-medicated patients exhibit broader abnormalities in functional connectivity.78,81 Studies also showed that functional connectivity of the medial prefrontal cortex, anterior cingulate, and THA can be largely improved after antipsychotic medications.82–85 Altogether, these results validate that non-medicated patients exhibit more severity and differences among patients. Besides, our results of the first-episode subgroup exhibited more alteration than the recurrent subgroup (figure 3C), potentially because most of the patients in the first-episode subgroup were non-medicated. Disorder Specificity of IVFC in SCZ MDD, BD, and SCZ are 3 complex brain dysconnectivity disorders characterized by various clinical symptoms.86–88 Because of the overlapping clinical symptoms across these 3 disorders, clinical differential diagnosis has been a major challenge.89 Recently, increasing neuroimaging studies have focused on the transdiagnostic common and specific alterations in functional brain networks among psychiatric disorders.15,16,90 A brain structure study showed that the distribution of reduced gray matter among SCZ patients had wide intersubject variability involving frontal, temporal, and cerebellar regions, while that among BD patients was only found in cerebellar regions.48 Comparable to the previous study, our results provide novel evidence of SCZ-specific broader alterations in IVFC compared with BD and MDD from a functional connectome perspective. The specific IVFC alterations in sensorimotor, auditory, and subcortical areas might indicate a more complex heterogeneous pathology in SCZ and provide insight into the search for disease-specific biomarkers. Limitations and Future Directions Several issues of the current study need to be further addressed. First, all participants were instructed to keep awake during scanning. Although all participants reported being awake after scanning, we cannot ensure the validity. Further studies using equipment that monitors eye movements or records physiological signals during the R-fMRI scan can better address this issue. Second, studies have shown that a resting-state scan longer than 6 minutes can increase the reliability of measuring individual differences in terms of strength of connectivity.91,92 Further analysis based on fMRI data longer than 6 minutes may have more stable results in the assessment of intersubject variability. Third, several different methods have been used to study heterogeneity in functional connectivity in SCZ, including regional seed-based connectivity,12 independent vector analysis,13 and group independent component analysis.14 Interestingly, all these methods showed higher individual variability in SCZ, although the measurements and observation levels differed. Besides demonstrating the whole-brain IVFC pattern at a finer voxel-level, the method of the current study also can identify critical pathways that contribute to the IVFC in a data-driven manner. Nonetheless, novel methods and models are required to delineate inter-subject variability in SCZ from multiple perspectives. Fourth, the high heterogeneity of the brain functional connectome among SCZ patients might suggest the presence of different pathologies in SCZ. A recent study has shown potential genetic associations to brain functional component variability,14 clarification of the neurophysiological subtypes in SCZ combining the individual functional connectome and biological data (eg, gene expression) could highly contribute to our understanding of SCZ pathology. Finally, our results also provide an explanation for the currently unsatisfactory imaging-based individual diagnosis, treatment prediction, and stimulation target optimization in SCZ. Developing biomarkers based on an individualized connectome analysis framework is becoming a valuable direction in developing precision medication for the treatment of SCZ. Acknowledgments The authors have declared that there are no conflicts of interest in relation to the subject of this study. Funding National Natural Science Foundation of China (81671767, 82071998 to M.X. and 81620108016 to Y.H.), Beijing Nova Program (Z191100001119023 to M.X.), Changjiang Scholar Professorship Award (T2015027 to Y.H.), Beijing Municipal Science & Technology Commission (Z161100004916027 to Y.H.), Fundamental Research Funds for the Central Universities (2020NTST29 to M.X.). References 1. James SL , Abate D, Abate KH, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017 . The Lancet 2018 ; 392 ( 10159 ): 1789 – 1858 . Google Scholar Crossref Search ADS WorldCat 2. Owen MJ , Sawa A, Mortensen PB. Schizophrenia . Lancet. 2016 ; 388 ( 10039 ): 86 – 97 . Google Scholar Crossref Search ADS PubMed WorldCat 3. Fornito A , Zalesky A, Pantelis C, Bullmore ET. Schizophrenia, neuroimaging and connectomics . 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Google Scholar Crossref Search ADS PubMed WorldCat Author notes These authors are co-corresponding authors who jointly directed this work. © The Author(s) 2020. 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)
Guinart, Daniel; de Filippis, Renato; Rosson, Stella; Patil, Bhagyashree; Prizgint, Lara; Talasazan, Nahal; Meltzer, Herbert; Kane, John M; Gibbons, Robert D
doi: 10.1093/schbul/sbaa168pmid: 33164091
Abstract Objective Time constraints limit the use of measurement-based approaches in research and routine clinical management of psychosis. Computerized adaptive testing (CAT) can reduce administration time, thus increasing measurement efficiency. This study aimed to develop and test the capacity of the CAT-Psychosis battery, both self-administered and rater-administered, to measure the severity of psychotic symptoms and discriminate psychosis from healthy controls. Methods An item bank was developed and calibrated. Two raters administered CAT-Psychosis for inter-rater reliability (IRR). Subjects rated themselves and were retested within 7 days for test-retest reliability. The Brief Psychiatric Rating Scale (BPRS) was administered for convergent validity and chart diagnosis, and the Structured Clinical Interview (SCID) was used to test psychosis discriminant validity. Results Development and calibration study included 649 psychotic patients. Simulations revealed a correlation of r = .92 with the total 73-item bank score, using an average of 12 items. Validation study included 160 additional patients and 40 healthy controls. CAT-Psychosis showed convergent validity (clinician: r = 0.690; 95% confidence interval [95% CI]: 0.610–0.757; self-report: r = .690; 95% CI: 0.609–0.756), IRR (intraclass correlation coefficient [ICC] = 0.733; 95% CI: 0.611–0.828), and test-retest reliability (clinician ICC = 0.862; 95% CI: 0.767–0.922; self-report ICC = 0.815; 95%CI: 0.741–0.871). CAT-Psychosis could discriminate psychosis from healthy controls (clinician: area under the receiver operating characteristic curve [AUC] = 0.965, 95% CI: 0.945–0.984; self-report AUC = 0.850, 95% CI: 0.807–0.894). The median length of the clinician-administered assessment was 5 minutes (interquartile range [IQR]: 3:23–8:29 min) and 1 minute, 20 seconds (IQR: 0:57–2:09 min) for the self-report. Conclusion CAT-Psychosis can quickly and reliably assess the severity of psychosis and discriminate psychotic patients from healthy controls, creating an opportunity for frequent remote assessment and patient/population-level follow-up. schizophrenia, schizoaffective disorder, psychosis, bipolar disorder, testing Introduction Psychotic disorders rank among the most debilitating disorders in all areas of medicine.1 Clinical outcomes for people with schizophrenia and other psychotic disorders leave much to be desired,2 due to a complex and multifactorial etiology3 and pathophysiology.4 Furthermore, unlike other areas of medicine, psychiatry is highly dependent on patient/family reports to assess the presence and severity of disease.5 Not only the experience of a subjective event such as psychopathological phenomena but also the recollection, report, and interpretation by the rater of such an experience can be subject to multiple biases.6Hence, achieving further objectification has become a challenge and a goal for evidence-based mental health research. While measurement-based approaches are frequently used in schizophrenia research, they are not widely used in the routine clinical management. Time constraints and lack of familiarity with and/or training in validated assessment tools limit their frequent clinical use.7 Unfortunately, the measurement of psychopathology does not utilize many recent technological innovations that could help increase its clinical application. So far, the presence or severity of symptoms on a given assessment tool is determined by a total score, which requires that the same items be administered to all respondents.8 One alternative to traditional assessments is adaptive testing, in which a person’s initial item responses are used to determine a provisional estimate of his or her standing on the measured trait, which is then used for the selection of subsequent items,8 allowing a precise measurement while only requiring the administration of a small subset of questions targeted to the person’s specific severity or impairment level.9 When computational algorithms automatically match questionnaire takers with the most relevant questions for them, this is called computerized adaptive testing (CAT). CAT relies on item response theory (IRT),10 which models the relationship between a patient’s responses to a series of items in terms of one or more latent variables that the test was designed to measure.11 Hence, CAT automatically selects the items which are the most informative for the candidate taking the assessment, increasing measurement precision and efficiency, thus allowing assessments to be considerably shorter than traditional, fixed-length assessments. Evidence suggests that one can create item banks with a large item pool, a small relevant set of which can be administered for a given individual with no or minimal loss of information, yielding a dramatic reduction in patient and clinician burden while maintaining high correlation with well-established symptom severity assessments, as well as high sensitivity and specificity for discriminating patients from healthy individuals.11–15 CAT tools can be, therefore, used as dimensional symptom severity measures to inform stepped-based care and provide measurement-based care assessments not only in the clinic and remotely but also potentially as first-stage symptom screeners.16 However, such an assessment tool is currently lacking in psychosis. Thus, this study aimed to develop and calibrate a CAT-Psychosis assessment battery, both self-administered and rater-administered versions, and test its psychometric properties and capacity to measure the severity of psychotic symptoms as well as to discriminate patients from healthy controls. Methods Development We developed the CAT-Psychosis scale using the general methodology introduced by Gibbons and coworkers.12 This methodology has 5 stages: (1) item bank development, (2) calibration of the multidimensional IRT model using complete data from a sample of subjects, (3) simulated CAT from the complete item response patterns, (4) development of the live-CAT testing program, and (5) validation. In this section, we describe the first 4 steps used in this process. Item Bank Development The item bank was constructed from clinician-rated items on the Schedule for Affective Disorders and Schizophrenia (SADS),17 the Scale for the Assessment of Positive Symptoms (SAPS),18 the Brief Psychiatry Rating Scale (BPRS),19 and the Scale for the Assessment of Negative Symptoms (SANS).20 Items were classified into the positive, negative, disorganized, and manic subdomains for analysis using the bifactor IRT model.21,22 This yielded 144 items for which existing clinician ratings were available for 649 subjects (535 schizophrenia, 43 schizoaffective, 54 depression, and 17 mania) drawn from an independent sample from inpatient and outpatient units treating psychotic disorders at Case Western Reserve University. Items were reworded to make them appropriate for patient self-report in addition to clinician administration. Calibration. A full-information item bifactor model21,22 was fitted to the 144 items from the 649 subjects. The bifactor model is the first confirmatory item factor analysis model and one of the first examples of a multidimensional IRT (MIRT) model. All items load on the primary dimension (which is the focus of the adaptive test) and one subdomain (the subdomain from which the item was drawn, which is determined in advance by clinicians). If the choice is not obvious, then different models can be fitted to the data using different subdomain structures and model selection criteria (Bayesian Information Criterion23) can be used to select the best-fitting structure. Gibbons and Hedeker21 developed the bifactor model for binary item response data, and Gibbons22 generalized it for ordinal item response data. The bifactor model provides a parameter related to each item’s ability to discriminate high and low levels of the underlying primary and secondary latent variables and severity parameters for the k-1 thresholds between the k ordinal response categories. The bifactor model produces a score and uncertainty estimate on the primary dimension for each subject, that is informed by items drawn from each of the subdomains. Items with loadings on the primary dimension of <0.3 were removed due to lack of discrimination. Computerized Adaptive Testing. Once we have calibrated the entire bank of items, we have estimates of each items’ associated severity and we can adaptively match the severity of the items to the severity of the person. We do not know the severity of the person in advance of testing, but we learn it as we adaptively administer items. Beginning with an item in the middle of the severity distribution, we administer the item, obtain a categorical response, estimate the person’s severity and the uncertainty in that estimate, and select the next maximally informative item.12 This process continues until the uncertainty falls below a predefined threshold, in our case, 5 points on a 100-point scale. The CAT has several tuning parameters,12 which we select by simulating CAT from the complete response patterns; 1200 simulations are conducted, and we select the tuning parameters that minimize the number of items administered and maximize the correlation with the total item bank score. Development of the Live-CAT Testing Program. Once the item bank has been calibrated and the CAT tuning parameters have been selected, a graphical user interface is developed and the CAT is added to a library of CATs, the CAT-Mental Health24 in a cloud computing environment for a routine test administration on internet-capable devices, such as smartphones, tablets, notebooks, and computers. To accommodate literacy issues, audio is added to the self-report questions. Audio can be muted anytime during the administration. For the clinician version, questions are drawn from the same item bank, but the wording is adapted for a third person to rate. Additionally, we developed an indexed semi-structured script containing suggested optional wording to help inquire about the most common symptoms (supplementary material 1). Due to the adaptive nature of the test, the script was not designed as a structured guideline to follow sequentially, but rather as a tool to assist the rater in gathering information to rate the most common symptoms. Validation After the assessment tool was developed and calibrated, we designed a validation study in an independent sample. To be considered for inclusion in the validation study, patients had to be English-speaking, 18–80 years of age, and with a diagnosis of schizophrenia, schizoaffective disorder, schizophreniform disorder, delusional disorder or brief psychotic disorder, as well as bipolar disorder, major depressive disorder, and healthy controls. Patients were recruited from inpatient and outpatient units from The Zucker Hillside Hospital, a large, private, non-for-profit semi-urban, psychiatric hospital providing both tertiary and routine care services that draws a representative racial/ethnic and sociodemographic mixture of eligible patients. Healthy controls were referred from other active research studies in our department. This investigation was carried out in accordance with the Declaration of Helsinki,25 and all participants provided written informed consent after reviewing of the procedures as approved by the local Institutional Review Board (IRB#180626). Participants who did not understand English or were unable to sign informed consent, as well as patients with an active substance use disorder, structural brain disease, or any serious, acute, or chronic medical illnesses that could pose a danger to self and others and could interfere with the patient’s ability to comply with the study procedures, were excluded. Basic demographic information, family history, current treatment, and diagnoses assigned by the current clinical treatment team were obtained from the patient and from the medical record. The CAT-Psychosis battery was administered using tablet computers with touch screens. Patients rated themselves with the self-administered CAT-Psychosis. Research staff were available to assist the participants if they had difficulty answering the questions. Psychometric Properties Convergent validity of CAT-Psychosis, both for self and rater-administered versions, were tested comparing severity scores obtained with the CAT-Psychosis battery to the Brief Psychosis Rating Scale in its anchored version (BPRS-A),26 a rater-administered 18-item scale ranging from 1 (“absent”) to 7 (“very severe”). Assessment order (BPRS first vs CAT-Psychosis first) was assigned in an alternating manner. To assess inter-rater reliability (IRR), subjects were requested to be consecutively but independently assessed by 2 raters using the clinician version of the CAT-psychosis. To assess test-retest reliability, both self-administered and rater-administered versions of CAT-Psychosis were administered again within 1–7 days of the initial administration. Psychosis discriminant validity was tested against chart diagnosis and the Structured Clinical Interview (SCID),27 conducted by trained departmental raters at the time of the CAT-Psychosis assessment, and validated in diagnostic consensus conferences. SCID was not readministered if it had been conducted during the previous 12 months. Statistics The bifactor IRT models were fitted with the POLYBIF program that is freely available at www.healthstats.org. Generalized linear mixed-effects regression models and Pearson product-moment correlation coefficients were used to examine associations between continuous measures (eg, CAT-Psychosis severity scores and the BPRS). Intraclass correlation coefficients (ICCs) computed using linear mixed-models were used to test for inter-rater and test-retest reliability.28 Mixed-effects logistic regression was used to estimate psychosis discrimination capacity (lifetime ratings) and to estimate sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve with 10-fold cross-validation. We also tested the extent to which gender modified psychosis discrimination capacity. With a sample size of 160 schizophrenia spectrum and mood disorder patients with psychotic symptoms, we expected to have more than 80% power (2-tailed α = .05) to determine a Pearson’s r of at least 0.22 for continuous scores and an AUC with 95% confidence interval (CI) with a width of 10% assuming a null hypothesis value of AUC = 0.8 and power of 80%. The data were analyzed with SuperMix Version 2.1, Stata Version 15.1, and JMP software v.14 (JMP, Version 14, 1989–2007). P-values lower than .05 were considered statistically significant. Results Development Data from 649 subjects were used to calibrate the 144 items. Following the removal of items with poor discrimination on the primary dimension, 73 items remained (supplementary material 2). The bifactor model significantly improved the fit over a unidimensional IRT alternative (chi-square = 2204, df = 73, P < .00001). Simulated adaptive testing from the clinician ratings (n = 420 patients with <10% missing data) revealed that using an average of 12 items per subject (range 3–21 items), we maintained a correlation of r = .92 with the 73-item total bank score. Validation In total, 200 subjects were enrolled in the validation study and completed the assessment. Of those, 160 subjects were affected by a affective and non-affective psychoses (n = 76 schizophrenia, n = 35 schizoaffective disorder, n = 26 bipolar disorder, n = 11 major depressive disorder, n = 7 psychosis not otherwise specified, and n = 5 schizophreniform disorder) and 40 were healthy controls. Consolidated Standards of Reporting Trials (CONSORT)29 flow diagram is available in figure 1. Sociodemographic characteristics of the sample are described in table 1. Table 1. Sociodemographic Characteristics of the Validation Sample . Total (n = 200) . Psychosis (n = 160) . Healthy Controls (n = 40) . P-value . Age; median (Q1, Q3) 30 (25;42.8) 30 (24;42) 31.5 (27,45.8) .1908 Sex .0458 Male, n (%) 113 (56.5) 96 (60.0) 17 (42.5) Female, n (%) 87 (43.5) 64 (40.0) 23 (57.5) Race .5484 White, n(%) 62 (31.0) 48 (30.0) 14 (35.0) Black/African American, n (%) 71 (35.5) 60 (37.5) 11 (27.5) Asian, n (%) 34 (17.0) 25 (15.6) 9 (22.5) Mixed/Other, n (%) 33 (16.5) 27 (16.9) 6 (15.0) Marital status .0610 Single, n (%) 152 (76.0) 123 (76.9) 29 (72.5) Married, n (%) 23 (11.5) 15 (9.4) 8 (20.0) Divorced/separated, n (%) 8 (4.0) 5 (3.1) 3 (7.5) Widowed, n (%) 3 (1.5) 3 (1.9) 0 (0) Unknown/unreported, n (%) 14 (7.0) 14 (8.8) 0 (0) Adopted .1658 Yes, n (%) 8 (4.0) 7 (4.4) 1 (2.5) No, n (%) 180 (90.0) 141 (88.1) 39 (97.5) Unknown/unreported, n (%) 12 (6.0) 12 (7.5) 0 (0) English as a primary language .1639 Yes, n (%) 161 (80.5) 128 (80.0) 33 (82.5) No, n (%) 27 (13.5) 20 (12.5) 7 (17.5) Unknown/unreported, n (%) 12 (6.0) 12 (7.5) 0 (0) Education level <.0001 Less than high school, n (%) 15 (7.5) 15 (9.4) 0 (0) High school diploma, n (%) 37 (18.5) 34 (21.3) 3 (7.5) Some college/Associate’s, n (%) 50 (25.0) 41 (25.6) 9 (22.5) College graduate, n (%) 49 (24.5) 40 (25.0) 9 (22.5) Doctorate/PhD, n (%) 11 (5.5) 10 (25.0) 1 (0.6) Unknown/unreported, n (%) 21 (10.5) 21 (13.1) 0 (0) Current occupation <.0001 Homemaker/unemployed, n (%) 97 (48.5) 92 (57.5) 5 (12.5) Unskilled employment, n (%) 53 (26.5) 40 (25.0) 13 (32.5) Skilled employment, n (%) 26 (13.0) 5 (3.1) 21 (52.5) Unknown/unreported, n (%) 24 (12.0) 23 (14.4) 1 (2.5) . Total (n = 200) . Psychosis (n = 160) . Healthy Controls (n = 40) . P-value . Age; median (Q1, Q3) 30 (25;42.8) 30 (24;42) 31.5 (27,45.8) .1908 Sex .0458 Male, n (%) 113 (56.5) 96 (60.0) 17 (42.5) Female, n (%) 87 (43.5) 64 (40.0) 23 (57.5) Race .5484 White, n(%) 62 (31.0) 48 (30.0) 14 (35.0) Black/African American, n (%) 71 (35.5) 60 (37.5) 11 (27.5) Asian, n (%) 34 (17.0) 25 (15.6) 9 (22.5) Mixed/Other, n (%) 33 (16.5) 27 (16.9) 6 (15.0) Marital status .0610 Single, n (%) 152 (76.0) 123 (76.9) 29 (72.5) Married, n (%) 23 (11.5) 15 (9.4) 8 (20.0) Divorced/separated, n (%) 8 (4.0) 5 (3.1) 3 (7.5) Widowed, n (%) 3 (1.5) 3 (1.9) 0 (0) Unknown/unreported, n (%) 14 (7.0) 14 (8.8) 0 (0) Adopted .1658 Yes, n (%) 8 (4.0) 7 (4.4) 1 (2.5) No, n (%) 180 (90.0) 141 (88.1) 39 (97.5) Unknown/unreported, n (%) 12 (6.0) 12 (7.5) 0 (0) English as a primary language .1639 Yes, n (%) 161 (80.5) 128 (80.0) 33 (82.5) No, n (%) 27 (13.5) 20 (12.5) 7 (17.5) Unknown/unreported, n (%) 12 (6.0) 12 (7.5) 0 (0) Education level <.0001 Less than high school, n (%) 15 (7.5) 15 (9.4) 0 (0) High school diploma, n (%) 37 (18.5) 34 (21.3) 3 (7.5) Some college/Associate’s, n (%) 50 (25.0) 41 (25.6) 9 (22.5) College graduate, n (%) 49 (24.5) 40 (25.0) 9 (22.5) Doctorate/PhD, n (%) 11 (5.5) 10 (25.0) 1 (0.6) Unknown/unreported, n (%) 21 (10.5) 21 (13.1) 0 (0) Current occupation <.0001 Homemaker/unemployed, n (%) 97 (48.5) 92 (57.5) 5 (12.5) Unskilled employment, n (%) 53 (26.5) 40 (25.0) 13 (32.5) Skilled employment, n (%) 26 (13.0) 5 (3.1) 21 (52.5) Unknown/unreported, n (%) 24 (12.0) 23 (14.4) 1 (2.5) Note: N, number; %, percentage; Q1, first quartile; Q3, third quartile. Open in new tab Table 1. Sociodemographic Characteristics of the Validation Sample . Total (n = 200) . Psychosis (n = 160) . Healthy Controls (n = 40) . P-value . Age; median (Q1, Q3) 30 (25;42.8) 30 (24;42) 31.5 (27,45.8) .1908 Sex .0458 Male, n (%) 113 (56.5) 96 (60.0) 17 (42.5) Female, n (%) 87 (43.5) 64 (40.0) 23 (57.5) Race .5484 White, n(%) 62 (31.0) 48 (30.0) 14 (35.0) Black/African American, n (%) 71 (35.5) 60 (37.5) 11 (27.5) Asian, n (%) 34 (17.0) 25 (15.6) 9 (22.5) Mixed/Other, n (%) 33 (16.5) 27 (16.9) 6 (15.0) Marital status .0610 Single, n (%) 152 (76.0) 123 (76.9) 29 (72.5) Married, n (%) 23 (11.5) 15 (9.4) 8 (20.0) Divorced/separated, n (%) 8 (4.0) 5 (3.1) 3 (7.5) Widowed, n (%) 3 (1.5) 3 (1.9) 0 (0) Unknown/unreported, n (%) 14 (7.0) 14 (8.8) 0 (0) Adopted .1658 Yes, n (%) 8 (4.0) 7 (4.4) 1 (2.5) No, n (%) 180 (90.0) 141 (88.1) 39 (97.5) Unknown/unreported, n (%) 12 (6.0) 12 (7.5) 0 (0) English as a primary language .1639 Yes, n (%) 161 (80.5) 128 (80.0) 33 (82.5) No, n (%) 27 (13.5) 20 (12.5) 7 (17.5) Unknown/unreported, n (%) 12 (6.0) 12 (7.5) 0 (0) Education level <.0001 Less than high school, n (%) 15 (7.5) 15 (9.4) 0 (0) High school diploma, n (%) 37 (18.5) 34 (21.3) 3 (7.5) Some college/Associate’s, n (%) 50 (25.0) 41 (25.6) 9 (22.5) College graduate, n (%) 49 (24.5) 40 (25.0) 9 (22.5) Doctorate/PhD, n (%) 11 (5.5) 10 (25.0) 1 (0.6) Unknown/unreported, n (%) 21 (10.5) 21 (13.1) 0 (0) Current occupation <.0001 Homemaker/unemployed, n (%) 97 (48.5) 92 (57.5) 5 (12.5) Unskilled employment, n (%) 53 (26.5) 40 (25.0) 13 (32.5) Skilled employment, n (%) 26 (13.0) 5 (3.1) 21 (52.5) Unknown/unreported, n (%) 24 (12.0) 23 (14.4) 1 (2.5) . Total (n = 200) . Psychosis (n = 160) . Healthy Controls (n = 40) . P-value . Age; median (Q1, Q3) 30 (25;42.8) 30 (24;42) 31.5 (27,45.8) .1908 Sex .0458 Male, n (%) 113 (56.5) 96 (60.0) 17 (42.5) Female, n (%) 87 (43.5) 64 (40.0) 23 (57.5) Race .5484 White, n(%) 62 (31.0) 48 (30.0) 14 (35.0) Black/African American, n (%) 71 (35.5) 60 (37.5) 11 (27.5) Asian, n (%) 34 (17.0) 25 (15.6) 9 (22.5) Mixed/Other, n (%) 33 (16.5) 27 (16.9) 6 (15.0) Marital status .0610 Single, n (%) 152 (76.0) 123 (76.9) 29 (72.5) Married, n (%) 23 (11.5) 15 (9.4) 8 (20.0) Divorced/separated, n (%) 8 (4.0) 5 (3.1) 3 (7.5) Widowed, n (%) 3 (1.5) 3 (1.9) 0 (0) Unknown/unreported, n (%) 14 (7.0) 14 (8.8) 0 (0) Adopted .1658 Yes, n (%) 8 (4.0) 7 (4.4) 1 (2.5) No, n (%) 180 (90.0) 141 (88.1) 39 (97.5) Unknown/unreported, n (%) 12 (6.0) 12 (7.5) 0 (0) English as a primary language .1639 Yes, n (%) 161 (80.5) 128 (80.0) 33 (82.5) No, n (%) 27 (13.5) 20 (12.5) 7 (17.5) Unknown/unreported, n (%) 12 (6.0) 12 (7.5) 0 (0) Education level <.0001 Less than high school, n (%) 15 (7.5) 15 (9.4) 0 (0) High school diploma, n (%) 37 (18.5) 34 (21.3) 3 (7.5) Some college/Associate’s, n (%) 50 (25.0) 41 (25.6) 9 (22.5) College graduate, n (%) 49 (24.5) 40 (25.0) 9 (22.5) Doctorate/PhD, n (%) 11 (5.5) 10 (25.0) 1 (0.6) Unknown/unreported, n (%) 21 (10.5) 21 (13.1) 0 (0) Current occupation <.0001 Homemaker/unemployed, n (%) 97 (48.5) 92 (57.5) 5 (12.5) Unskilled employment, n (%) 53 (26.5) 40 (25.0) 13 (32.5) Skilled employment, n (%) 26 (13.0) 5 (3.1) 21 (52.5) Unknown/unreported, n (%) 24 (12.0) 23 (14.4) 1 (2.5) Note: N, number; %, percentage; Q1, first quartile; Q3, third quartile. Open in new tab Fig. 1. Open in new tabDownload slide CONSORT diagram. Fig. 1. Open in new tabDownload slide CONSORT diagram. Psychometric Properties CAT-Psychosis showed convergent validity against total BPRS scores for both clinician-administered (r = 0.690; 95% CI: 0.610–0.757; marginal maximum likelihood Estimate [MMLE] = 0.057, standard error [SE] = 0.004, P < .00001) and self-administered (r = .690; 95% CI: 0.609–0.756; MMLE = 0.061, SE = 0.004, P < .00001) versions (figure 2). We also found that CAT-Psychosis clinician version and CAT-Psychosis self-rated version showed a significant positive correlation (r = .594, 95% CI: 0.495, 0.667). CAT-Psychosis clinician version’s IRR was strong (ICC = 0.733; 95% CI: 0.611–0.828), and test-retest reliability was strong for both self-report (ICC = 0.815; 95% CI: 0.741–0.871) and clinician (ICC = 0.862; 95% CI: 0.767–0.922) versions. Fig. 2. Open in new tabDownload slide Predicted probability of diagnosis based on severity score for clinician-assessed (above) and self-assessed (below) CAT-Psychosis. Pr(dx), probability of a positive diagnosis; dx, Diagnosis of psychosis. Fig. 2. Open in new tabDownload slide Predicted probability of diagnosis based on severity score for clinician-assessed (above) and self-assessed (below) CAT-Psychosis. Pr(dx), probability of a positive diagnosis; dx, Diagnosis of psychosis. Psychosis Discrimination The CAT-Psychosis clinician version was able to successfully discriminate psychosis vs healthy controls (area under the ROC curve [AUC] = 0.965, 95% CI: 0.945–0.984; MMLE = 1.42, SE = 0.12, P < .00001), with an effect size (total variance) of 2.23 SD units. The CAT-Psychosis self-report version yielded similar results (AUC = 0.850, 95% CI: 0.807–0.894; MMLE = 1.28, SE = 0.16, P < .00001), with an effect size (total variance) of 1.60 SD units (figure 3). We conducted an additional analysis including only patients with a SCID (n = 79), showing similar results despite the smaller sample size. CAT-Psychosis clinician version was able to successfully discriminate psychosis vs healthy controls (AUC = 0.968, 95% CI: 0.943–0.993; MMLE = 1.33, SE = 0.103, P < .00001), with an effect size (total variance) of 2.522 SD units. The CAT-Psychosis self-report version yielded similar results (AUC = 0.796, 95% CI: 0.730–0.862; MMLE = 1.125, SE = 0.157, P < .00001), with an effect size (total variance) of 1.535 SD units. Males and females showed no significant differences (CAT-Psychosis by gender interaction: clinician Odds Ratio (OR) = 1.43, 95% CI: 0.52, 3.94, P < .48; self-report OR = 1.11, 95% CI: 0.02, 55.65, P = .93). Fig. 3. Open in new tabDownload slide Receiver operating characteristic (ROC) curve for clinician-assessed (above) and self-assessed (below) CAT-Psychosis, with estimated area under the curve. The 45-degree line represents an chance association between the continuous test score and the binary psychosis diagnosis. Fig. 3. Open in new tabDownload slide Receiver operating characteristic (ROC) curve for clinician-assessed (above) and self-assessed (below) CAT-Psychosis, with estimated area under the curve. The 45-degree line represents an chance association between the continuous test score and the binary psychosis diagnosis. Testing Times and Number of Items The median length of assessment was 5 minutes and 2 seconds (interquartile range [IQR]: 3:23—8:29 min) for the clinician-administered version. Since we used a script that includes a brief introduction (supplementary material 1), an additional 3–8 minutes were used before the computerized assessment started. The median length of assessment of the self-administered CAT-Psychosis was 1 minute and 20 seconds (IQR: 0:57—2:09 min). The median number of questions needed to elicit a valid severity rating using the clinician-administered version was 12 (IQR: 8–15 items), same as for the patient self-report version (IQR: 8–13). Table 2 presents an example of a CAT-Psychosis self-assessment in a patient with severe psychotic symptoms and an example of a CAT-Psychosis rater-administered assessment in a patient with moderate psychotic symptoms. Table 2. Examples of CAT-Psychosis Administrations Questions . Replies . Severity Score . Precision . (A) CAT-Psychosis self-assessment I feel that I am a particularly important person or that I have special powers or abilities. Not at all 41.5 14.7 I feel that I have committed a crime or have done some terrible things and deserve punishment. Sometimes 53.5 12.3 I feel emotionally withdrawn, lack spontaneity, and am isolated from others. Rarely 52.2 12.0 I have trouble communicating with others. Always 81.2 7.2 I am having trouble concentrating on this interview. Not at all 77.8 6.8 I am confused, disconnected, disorganized, and/or disrupted. Rarely 73.6 6.2 I say things that are unrelated to each other, for example, “I’m tired. All people have eyes.” Sometimes 73.4 5.7 When I speak, my ideas slip off track into unrelated topics. Always 79.4 4.9 (B) CAT-Psychosis rater-assessment Claims power, knowledge or identity beyond the bounds of credibility. Absent 41.5 14.7 Severity of delusions of any type—consider conviction in delusion, preoccupation, and effect on her actions. Mild 39.3 12.4 Lack of spontaneous interaction, isolation, deficiency in relating to others. Moderate 43.0 12.0 Extent to which the patient’s ability to communicate is affected. Mild 53.2 6.4 This rating should assess the patient’s overall lack of concentration, clinically and on tests. Questionable 51.1 6.5 Thought processes confused, disconnected, disorganized, disrupted. Mild 51.1 6.5 Repeatedly saying things in juxtaposition, which lack a readily understandable relationship (eg, “I’m tired; all people have eyes”). Mild 53.2 6.1 The patient appears uninvolved or unengaged. She may seem “spacey.” Moderate 55.4 5.6 A pattern of speech in which conclusions are reached that do not follow logically. Mild 58.5 5.0 Questions . Replies . Severity Score . Precision . (A) CAT-Psychosis self-assessment I feel that I am a particularly important person or that I have special powers or abilities. Not at all 41.5 14.7 I feel that I have committed a crime or have done some terrible things and deserve punishment. Sometimes 53.5 12.3 I feel emotionally withdrawn, lack spontaneity, and am isolated from others. Rarely 52.2 12.0 I have trouble communicating with others. Always 81.2 7.2 I am having trouble concentrating on this interview. Not at all 77.8 6.8 I am confused, disconnected, disorganized, and/or disrupted. Rarely 73.6 6.2 I say things that are unrelated to each other, for example, “I’m tired. All people have eyes.” Sometimes 73.4 5.7 When I speak, my ideas slip off track into unrelated topics. Always 79.4 4.9 (B) CAT-Psychosis rater-assessment Claims power, knowledge or identity beyond the bounds of credibility. Absent 41.5 14.7 Severity of delusions of any type—consider conviction in delusion, preoccupation, and effect on her actions. Mild 39.3 12.4 Lack of spontaneous interaction, isolation, deficiency in relating to others. Moderate 43.0 12.0 Extent to which the patient’s ability to communicate is affected. Mild 53.2 6.4 This rating should assess the patient’s overall lack of concentration, clinically and on tests. Questionable 51.1 6.5 Thought processes confused, disconnected, disorganized, disrupted. Mild 51.1 6.5 Repeatedly saying things in juxtaposition, which lack a readily understandable relationship (eg, “I’m tired; all people have eyes”). Mild 53.2 6.1 The patient appears uninvolved or unengaged. She may seem “spacey.” Moderate 55.4 5.6 A pattern of speech in which conclusions are reached that do not follow logically. Mild 58.5 5.0 Note: (A) shows a self-administered test for a patient with severe psychotic symptoms (category = severe, severity = 79.4, and precision = 4.9). (B) shows a rater-administered test for a patient with moderate psychotic symptoms (category = moderate, severity = 58.5, and precision = 5.0). In both cases, the CAT terminated when the uncertainty was at or below 5 points on the 100-point scale. Open in new tab Table 2. Examples of CAT-Psychosis Administrations Questions . Replies . Severity Score . Precision . (A) CAT-Psychosis self-assessment I feel that I am a particularly important person or that I have special powers or abilities. Not at all 41.5 14.7 I feel that I have committed a crime or have done some terrible things and deserve punishment. Sometimes 53.5 12.3 I feel emotionally withdrawn, lack spontaneity, and am isolated from others. Rarely 52.2 12.0 I have trouble communicating with others. Always 81.2 7.2 I am having trouble concentrating on this interview. Not at all 77.8 6.8 I am confused, disconnected, disorganized, and/or disrupted. Rarely 73.6 6.2 I say things that are unrelated to each other, for example, “I’m tired. All people have eyes.” Sometimes 73.4 5.7 When I speak, my ideas slip off track into unrelated topics. Always 79.4 4.9 (B) CAT-Psychosis rater-assessment Claims power, knowledge or identity beyond the bounds of credibility. Absent 41.5 14.7 Severity of delusions of any type—consider conviction in delusion, preoccupation, and effect on her actions. Mild 39.3 12.4 Lack of spontaneous interaction, isolation, deficiency in relating to others. Moderate 43.0 12.0 Extent to which the patient’s ability to communicate is affected. Mild 53.2 6.4 This rating should assess the patient’s overall lack of concentration, clinically and on tests. Questionable 51.1 6.5 Thought processes confused, disconnected, disorganized, disrupted. Mild 51.1 6.5 Repeatedly saying things in juxtaposition, which lack a readily understandable relationship (eg, “I’m tired; all people have eyes”). Mild 53.2 6.1 The patient appears uninvolved or unengaged. She may seem “spacey.” Moderate 55.4 5.6 A pattern of speech in which conclusions are reached that do not follow logically. Mild 58.5 5.0 Questions . Replies . Severity Score . Precision . (A) CAT-Psychosis self-assessment I feel that I am a particularly important person or that I have special powers or abilities. Not at all 41.5 14.7 I feel that I have committed a crime or have done some terrible things and deserve punishment. Sometimes 53.5 12.3 I feel emotionally withdrawn, lack spontaneity, and am isolated from others. Rarely 52.2 12.0 I have trouble communicating with others. Always 81.2 7.2 I am having trouble concentrating on this interview. Not at all 77.8 6.8 I am confused, disconnected, disorganized, and/or disrupted. Rarely 73.6 6.2 I say things that are unrelated to each other, for example, “I’m tired. All people have eyes.” Sometimes 73.4 5.7 When I speak, my ideas slip off track into unrelated topics. Always 79.4 4.9 (B) CAT-Psychosis rater-assessment Claims power, knowledge or identity beyond the bounds of credibility. Absent 41.5 14.7 Severity of delusions of any type—consider conviction in delusion, preoccupation, and effect on her actions. Mild 39.3 12.4 Lack of spontaneous interaction, isolation, deficiency in relating to others. Moderate 43.0 12.0 Extent to which the patient’s ability to communicate is affected. Mild 53.2 6.4 This rating should assess the patient’s overall lack of concentration, clinically and on tests. Questionable 51.1 6.5 Thought processes confused, disconnected, disorganized, disrupted. Mild 51.1 6.5 Repeatedly saying things in juxtaposition, which lack a readily understandable relationship (eg, “I’m tired; all people have eyes”). Mild 53.2 6.1 The patient appears uninvolved or unengaged. She may seem “spacey.” Moderate 55.4 5.6 A pattern of speech in which conclusions are reached that do not follow logically. Mild 58.5 5.0 Note: (A) shows a self-administered test for a patient with severe psychotic symptoms (category = severe, severity = 79.4, and precision = 4.9). (B) shows a rater-administered test for a patient with moderate psychotic symptoms (category = moderate, severity = 58.5, and precision = 5.0). In both cases, the CAT terminated when the uncertainty was at or below 5 points on the 100-point scale. Open in new tab Discussion We demonstrate that the CAT-Psychosis battery, not only in its rater-assessed but also, notably, in its self-report version, can accurately measure the severity of psychosis in less than 2 minutes, in the case of the self-administered version, or 5 minutes, in the case of the rater-assessed version. Further, CAT-Psychosis can discriminate psychotic patients from healthy controls. A fundamental advantage of the rater version of the CAT-Psychosis battery is the rapid administration time. Since questions are tailored to every patient and the test is dynamically modified based on the answers provided, the time of administration is dramatically reduced. To our knowledge, there are currently no available assessment tools that can reliably allow a rater to measure the severity of psychotic symptoms in such a brief administration time. Some tools, such as the 4-item Negative Symptom Assessment (NSA-4),30 a brief version of the NSA-16, a validated tool for evaluating negative symptoms in schizophrenia,31 as well as the Brief Negative Symptom Scale (BNSS)32 lasting about 15 minutes, have proven to be quick and reliable, but they are limited to negative symptoms. When positive symptoms are included in the assessment, administration time and rater and patient burden increase substantially. The most commonly used rater-based assessment tools, such as the Positive and Negative Syndrome Scale (PANSS), the SAPS, or the BPRS, require a variable but significant amount of time to complete, ranging from 20 to 50 minutes.18,19,33,34 Recently, a valid and scalable 6-item version of the PANSS was developed,35 but it appears to require the guidance of the Simplified Negative and Positive Symptom Interview (SNAPSI) a 15-to-25-minute structured interview36 that, despite significantly optimizing the 30-item PANSS, may still not be rapid enough for routine clinical use. CAT-Psychosis rater-assessed version, however, generates a quick assessment that could facilitate routine screening and measurement of psychotic symptoms and whose potential integration with electronic health records would allow its widespread use in mainstream clinical care, thus generating reliable, real-world data at a scale that is currently not imaginable, while aiming to keep clinician burden to a minimum. Unlike in other areas of mental health, self-report measures in psychosis are rare, and their use in normal clinical care is unusual,37 probably due to concerns that self-report questionnaires may not be appropriate for evaluating psychotic disorders because of a risk of minimization and/or denial of symptoms due to stigma and/or impaired awareness of the illness.38,39 However, the advantages of self-assessments over rater-based evaluations could be substantial, as assessments are not limited to the availability of highly trained interviewers, eliminating interviewer bias and reducing costs, thus enhancing scalability. In a context of greater availability of digital tools and devices that allow collecting information from patients remotely and repeatedly, the need for valid, rapid, reliable, and easy-to-administer self-report tools for psychosis is greater than ever. Some self-reports for psychosis have been made available,40–43 either circumscribed to a specific area of evaluation, such as hallucinations,40 apathy,41 and negative symptoms,42 or smartphone based,43 all of which rely on traditional scoring systems. Unfortunately, the repeated administration of fixed-length, identical tests in short time spans may lead to response bias, limiting the ability of such a test to be used regularly in longitudinal studies. CAT-Psychosis, however, produces a tailored set of questions at each administration, allowing for frequent and even remote longitudinal monitoring of symptom severity, which could have very relevant implications in the field of mental health research, shortening assessments significantly, minimizing patient burden, and allowing for more frequent evaluations. More importantly, the availability of a valid self-assessment tool that can be used to quickly measure the severity of symptoms not only in the clinic but also remotely can assist in determining the effectiveness of interventions or detecting clinical worsening. Bridging the current information blackout between clinical visits with frequent adaptive tests that can be administered electronically could help bring significant advances to routine clinical care. Nonetheless, the optimization of measurement-based care aims to assist clinicians and does not call into question the need for trained psychologists and psychiatrists. This study has some limitations. First, the CAT-Psychosis battery provides the investigator with an overall severity score, including all symptom domains, and is not designed to monitor specific individual symptoms (eg, auditory hallucinations), to the extent that they may not always be adaptively administered. While it is possible to develop an adaptive test to score the primary dimension and each of the subdomains, this would increase the number of items adaptively administered and add to the subject burden. In this study, our objective is to adaptively score the primary severity dimension, to which all of the subdomains contribute. In future work, we will explore the adaptive scoring of subdomain scores. Second, the validation sample size did not allow for specific diagnostic predictions within all the psychoses that we included as well as the detailed study of the influence of sociodemographic factors. Future studies, including larger samples, will address these issues. Third, this study was conducted exclusively in English. Independent replication of our findings in other patient populations, settings, devices, and languages is needed and will be the subject of further research. Once developed, the CAT-Psychosis scale can be tested for suitability in different populations, using differential item functioning (DIF).44 As an example, CATs developed for depression, anxiety, mania/hypomania, suicidality, and substance use disorder have been studied for DIF in diverse Spanish-speaking populations,45 perinatal women,46 emergency department patients,47 and in subjects within the criminal justice system.48 Fourth, the adoption of an exclusively digital self-assessment tool may be limited in non-digital native populations. However, while some studies have suggested that younger patients would be more willing to participate in digital-based interventions,49 others show opposite results,50 and rates of access of schizophrenia patients to technology and smartphones have progressively merged with that of the general population.51 In fact, in our validation sample, n = 32 subjects were between the ages of 50 and 70 years old and no test was interrupted or incomplete. Until recently, the use of a valid self-administered psychopathology assessment tool has been missing in schizophrenia and other psychoses research, partly because of lack of trust in the ability of patients to self-report psychotic symptoms. Recent literature, including our study, demonstrates that this is not the case. In fact, we show that self-reports of psychotic symptoms made by psychotic patients can lead to rapid, reliable, and valid measurement of psychotic symptoms, which can have relevant implications for research and clinical care. Conclusion CAT-Psychosis, both rater-administered and as a self-report, provides valid psychosis severity scores and can also discriminate psychotic patients from healthy controls, requiring only a brief administration time. Funding None. Acknowledgments Dr Guinart has been a consultant for and/or has received speaker honoraria from Otsuka America Pharmaceuticals and Janssen Pharmaceuticals. Dr Kane has been a consultant and/or advisor for or has received honoraria from Alkermes, Allergan, LB Pharmaceuticals, H. Lundbeck, Intracellular Therapies, Janssen Pharmaceuticals, Johnson and Johnson, Merck, Minerva, Neurocrine, Newron, Otsuka, Pierre Fabre, Reviva, Roche, Sumitomo Dainippon, Sunovion, Takeda, Teva, and UpToDate and is a shareholder in LB Pharmaceuticals and Vanguard Research Group. Dr Gibbons has been an expert witness for the US Department of Justice, Merck, Glaxo-Smith-Kline, Pfizer, and Wyeth and is a founder of Adaptive Testing Technologies, which distributes the CAT-MH battery of adaptive tests in which CAT-Psychosis is included. Drs de Filippis, Rosson, and Patil, as well as Nahal Talasazan and Lara Prizgint, have nothing to disclose. References 1. Salomon JA , Vos T, Hogan DR, et al. Common values in assessing health outcomes from disease and injury: disability weights measurement study for the Global Burden of Disease Study 2010 . 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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)
Webb, Rebecca; Bartl, Gergely; James, Bryony; Skan, Rosie; Peters, Emmanuelle; Jones, Anna-Marie; Garety, Philippa; Kuipers, Elizabeth; Hayward, Mark; Greenwood, Kathryn
doi: 10.1093/schbul/sbaa173pmid: 33215190
Abstract The original CHoice of Outcome In Cbt for psychosEs (CHOICE) measure was designed in collaboration with experts by experience as a patient-reported “Psychological Recovery” outcome measure for cognitive-behavioral therapy for psychosis (CBTp). A short version (CHOICE-SF) was developed to use as a brief outcome measure, with a focus on sensitivity to change, for use in future research and practice. CHOICE-SF was developed and validated using 3 separate samples, comprising 640 service users attending 1 of 2 transdiagnostic clinics for (1) CBTp or (2) therapies for voice hearing or (3) who took part in the treatment as usual arm of a trial. In the initial subsample of 69 participants, items from the original CHOICE measure with medium to large effect sizes for change pre- to post-CBTp were retained to form the CHOICE-SF. Internal consistency, construct validity, and sensitivity to change were confirmed, and the factor structure was examined in 242 participants. Specificity was confirmed by comparison with 44 participants who completed CHOICE at 2 time points but did not receive therapy. Validation of CHOICE-SF was carried out by confirming factor structure and sensitivity to change in a new sample of 354 and a subsample of 51 participants, respectively. The CHOICE-SF comprised 11 items and 1 additional personal goal item. A single-factor structure was confirmed, with high internal consistency, construct validity, and sensitivity to change. The CHOICE-SF is a brief, psychometrically robust measure to assess change following psychological therapies in research and clinical practice for people with psychosis and severe mental illness. psychosis, patient-reported outcome measurement, psychological therapy, cognitive therapy Introduction One of the key interventions for psychosis is cognitive-behavioral therapy.1 Previous research examining the effectiveness of cognitive-behavioral therapy for psychosis (CBTp) has mainly focused on the reduction of psychosis symptoms using clinician-rated scales.2 However, it has been argued that CBTp focuses on alternative outcomes that are not well captured by current measures.3,4 Specifically, CBTp targets symptom-related distress and the impact on functioning, associated unhelpful thinking biases, and psychological and emotional recovery as opposed to symptom reduction.5,6 Furthermore, the goals of CBTp are agreed upon in collaboration with service users who have reported that other issues, such as empowerment, choice, control, and personal fulfillment, are of central concern.7 CBTp, therefore, may address a variety of issues, such as negative beliefs about psychosis, self-esteem, self-stigma, self-confidence, and empowerment.8–11 As a result of this mismatch between CBTp aims and available measures, a new outcome measure of CBTp was developed and was designed to reflect both the priorities of CBTp and those of the service user (CHoice of Outcome In Cbt for psychosEs [CHOICE]).12 The initial measure was developed in collaboration with service user experts by experience through qualitative analysis and Delphi consultation to reach a consensus on the questionnaire items. The final measure was found to have 1 single factor, with good test–retest reliability, face and construct validity, and sensitivity to change.12 Whilst constituting a single factor, the measure included both generic recovery-related items, such as “feeling happy” and “a sense of being in control of my life,” and CBT-specific cognitive and coping items, such as “positive thinking,” “ways of dealing with unpleasant feelings and emotions,” and “ways of dealing with distressing experiences.” Since publication, the original CHOICE measure has been used in at least 4 randomized trials and 3 observational studies to assess the effectiveness of various psychological interventions, including mindfulness, structured communication, and relating therapy, as well as group-based, self-help and one-to-one CBT in individuals with psychosis experiences.13–19 It has been referenced in recent policy documents20 and has been adopted by routine clinical services in at least 10 National Health Service (NHS) trusts around the United Kingdom, as well as by UK clinical psychology training departments, and international services in Asia, America, and Europe, where it has been translated into other languages.21 It has been described as an important measure of “self- defined recovery that may be valued more highly than symptom reduction alone by many service users.” 4 The original CHOICE questionnaire comprised 24 items, covering a range of issues, each rated on 2 separate subscales of severity and satisfaction. Participants first rated the severity of each item on a Likert scale from 0 (worst) to 10 (best) and then rated their level of satisfaction in relation to each item on a separate Likert scale from 0 (not at all satisfied) to 10 (very satisfied). However, the length of the original CHOICE measure was a potential drawback, as participants were required to make 48 individual ratings, which may be a challenge for some people with psychosis where characteristic difficulties with concentration, motivation, and cognition may hamper completion.22,23 Furthermore, there was a specific need for a measure that could be used to measure change over short time frames, including on a session-by-session basis, as part of the Improving Access to Psychological Therapies in Severe Mental Illness (IAPT-SMI) program. This study, therefore, aimed to develop a brief, psychometrically robust and clinically valuable version of CHOICE that would also be highly sensitive to change over a short (1 week) time frame. Initial development commenced, at the start of the IAPT-SMI program in 2013, and validation has subsequently been conducted across several distinct phases in a large sample of participants receiving psychological therapies. Methods A total of 640 patients with psychosis experiences and severe mental illness (in receipt of secondary care services) contributed data in one of 2 phases: (1) the initial development or (2) the secondary validation of the CHOICE short form (CHOICE-SF). Development Phase Participants The initial participants were 69 psychosis service users from the Psychological Interventions Clinic for outpatients with Psychosis (PICuP) run by the South London and Maudsley NHS Foundation Trust. They received CBTp according to NICE guidelines and provided full choice data at 2 time points: (1) an initial screening assessment (either prior to joining the waiting list [N = 45] or immediately pretherapy [N = 24]) and (2) post-CBTp. See Peters et al for further details about assessment procedures and therapy in PICuP.24 A larger sample of 242 individuals with psychosis from PICuP, which included these 69 participants, completed the original full CHOICE measure on at least 1 of 5 time points (initial screening, immediately pretherapy, mid-therapy, posttherapy, or follow-up) and provided data for the initial factor analysis. Measures Service users were asked to complete 5 measures as part of routine data collection by the clinic. All measures have good reliability and validity in a psychosis sample. The original CHOICE measure12—a 24-item self-report questionnaire, which provides mean scores for severity and satisfaction with a range of aspects of psychological well-being, as well as additional self-reported items or therapy goals that are unique to the individual. Each item is reported on an 11-point scale from 0 (worst) to 10 (best). The mean score is used. The Beck Depression Inventory (BDI)25—a 21-item measure of depressive symptoms and cognitions, on which each item is rated on a 4-point scale from 0 to 3 to produce a total score, from 0 to 63, where higher scores reflect more severe symptoms. The Beck Anxiety Inventory (BAI)26—a 21-item measure of physiological anxiety symptoms, on which each item is rated on a 4-point scale from 0 (not at all) to 3 (severely) to produce a total score, from 0 to 63, where again high scores reflect more severe symptoms. The Manchester Assessment of Quality of Life (MANSA)27—a 16-item measure that assesses satisfaction with life across 12 subjective measures and 4 objective measures, including areas, such as employment, finances, leisure, friendships, relationships, personal safety, accommodation, and physical and mental health. Each item is rated on a 7-point scale from 1 (could not be worse) to 7 (could not be better). The total score was used. The Psychotic Symptoms Rating Scale (PSYRATS)28—which comprises a 17-item, 5-point (0–4) scale, multidimensional measure. The delusion scale comprises 6 items that capture distress, disruption, preoccupation, and conviction (scores range from 0 to 24). The Hallucinations Scale has 11 items that include frequency, duration, loudness, location, intensity, distress, disruption, negative content, and beliefs about origin (scores range from 0 to 44). A comparison group of 44 participants with a diagnosis of schizophrenia completed the full CHOICE at 2 time points 16 weeks apart as part of the treatment as usual control arm of a randomized trial conducted separately by our group.16 Statistical Analysis The severity and satisfaction scales of the original CHOICE measure are highly correlated. In the interests of developing a much briefer scale, only the CHOICE severity scale was selected as it is a simpler construct than satisfaction, which is impacted conceptually by the knowledge of opportunities and expectancies and methodologically by overlaps with health outcomes.29,30 Cohen’s d effect sizes were calculated to reflect the change on each of the individual items of the CHOICE measure using the pre-CBTp SD and mean difference pretherapy and posttherapy for the 69 participants who provided data pre- and post-CBTp. Items where the effect size was medium large (Cohen’s d > 0.55) were retained, whilst those with smaller effect sizes (0.28 to 0.54) were omitted. Using this same sample of 69 participants, internal consistency (Cronbach’s alpha) and sensitivity to change (one-way ANOVA) were also calculated, and specificity was confirmed by comparison with the 44 participants who provided full CHOICE data at 2 time points, from which the short form was derived, but who did not receive therapy. Construct validity was assessed by correlating pretherapy scores from the CHOICE-SF with the BDI, BAI, MANSA, and PSYRATS. The reliable change index (RCI)31–33 and factor structure were also calculated in the whole sample of 242 participants, the latter using principal axis factoring. The Kaiser–Meyer–Olkin (KMO) value was 0.91, and the Bartlett’s Test of Sphericity reached significance, thus supporting the suitability of the data for factor analysis. Sample sizes were large and greater than those reported for reliability and factor analysis in the original CHOICE paper. The single-factor, high-loading items, and communalities in the 0.5 range, together with >20–30 participants per item, provide sufficient power and a robust factor structure for both exploratory factor analysis (EFA) and Confirmatory factor analysis (CFA).34–36 For the sensitivity analysis, a sample size calculation based on a medium pre–post effect size consistent with published literature (Cohen’s d = 0.4) using a 2-sided paired t-test at the 5% alpha level and 80% power resulted in a minimum required sample size of N = 59. Secondary Validation Phase Participants The factor structure of the CHOICE-SF was further examined using data provided by a further 354 transdiagnostic voice-hearing participants who attended an initial assessment at the Sussex Voices Clinic (a specialist outpatient service offered by Sussex Partnership NHS Foundation Trust) between May 2014 and June 2018 and completed the CHOICE-SF. Sensitivity to change of the CHOICE-SF was examined in 51 of these 354 participants who also completed the CHOICE-SF following at least 2–4 sessions of Coping Strategy Enhancement for voices37 delivered by therapists with a range of expertise and either 8–12 sessions of group person-based cognitive therapy for distressing voices,16 6–16 sessions of individual relating and assertiveness therapy for voices,18 or 6–8 sessions of individual guided self-help cognitive-behavioral intervention for distressing voices19 delivered by expert therapists. Analysis In the second validation phase, an a priori model of the questionnaire data was tested using confirmatory factor analysis based on a sample of N = 354 participants. The analysis was performed in the R software environment,38 using the lavaan package,39 and plots were created using lavaanPlot.40 Full information maximum likelihood estimation was used in order to handle missing data points whilst improving bias and efficiency.41 In addition to the chi-square test of exact fit, model fit was assessed using global and local fit indices: comparative fit index (CFI),42 Tucker–Lewis index (TLI),43 and goodness of fit index (GFI), as well as root mean square error of approximation (RMSEA)44 and standardized root mean square residual (SRMR).45 The magnitude and significance of factor loadings of the resulting model were also evaluated. Two paired t-tests were then conducted to test the measure’s sensitivity to change between baseline and follow-up: the first using the mean score of the 11 CHOICE-SF items and the second using the personal goal item alone. The personal goal item was analyzed separately as this item is personal to the individual and is not necessarily completed by all service users. Bayes factors were also calculated where possible as they enable a distinction between results that refute a hypothesis and those that reflect insufficient evidence, from which no conclusions can be drawn.46,47 Results Development Phase Participants Characteristics of the participants who took part in the development of the CHOICE-SF can be found in table 1. Table 1. Characteristics of participants who took part in measure development and initial validation . Initial item selection, sensitivity, and construct validity N = 69 . Initial factor structure N = 242 . Comparison sample N = 44 . Age (mean and SD) . 39 (8.29) . 38 (9.44) . 40 (10.87) . . N (%) . N (%) . N (%) . Gender Male 38 (55%) 142 (59%) 23 (52%) Female 31 (45%) 100 (41%) 20 (46%)d Marital status Single 55 (81%) 185 (82%) 25 (57%) Married 6 (9%) 22 (10%) 8 (18%) Divorced 4 (6%) 7 (3%) 6 (14%) Other 3 (4%) 12 (5%) 4 (9%)e Ethnicity White British/ English 37 (54%) 118 (49%) 40 (91%) White Other 7 (10%) 19 (8%) 1 (2%) Asian 2 (3%) 9 (4%) 1 (2%) Black 16 (23%) 64 (27%) 2 (5%) Chinese 0 (0%) 1 (0.4%) — Mixed 0 (0%) 0 (0%) — Other 7 (10%) 19 (8%) — Missing 0 (0%) 12 (5%) — Clinical characteristics Mean (SD; range) MANSA 48.1 (9.5; 23–68) — BDIa 23.7 (12.9; 3–53) — BAIa 19.2 (12.5; 0–50) — PSYRATS delusionsb 14.6 (5.5; 0–22) — 13.6 (5.5; 0–21)f PSYRATS voicesc 27.3 (9.3; 0–39) — 29.8 (7.5; 0–38)g . Initial item selection, sensitivity, and construct validity N = 69 . Initial factor structure N = 242 . Comparison sample N = 44 . Age (mean and SD) . 39 (8.29) . 38 (9.44) . 40 (10.87) . . N (%) . N (%) . N (%) . Gender Male 38 (55%) 142 (59%) 23 (52%) Female 31 (45%) 100 (41%) 20 (46%)d Marital status Single 55 (81%) 185 (82%) 25 (57%) Married 6 (9%) 22 (10%) 8 (18%) Divorced 4 (6%) 7 (3%) 6 (14%) Other 3 (4%) 12 (5%) 4 (9%)e Ethnicity White British/ English 37 (54%) 118 (49%) 40 (91%) White Other 7 (10%) 19 (8%) 1 (2%) Asian 2 (3%) 9 (4%) 1 (2%) Black 16 (23%) 64 (27%) 2 (5%) Chinese 0 (0%) 1 (0.4%) — Mixed 0 (0%) 0 (0%) — Other 7 (10%) 19 (8%) — Missing 0 (0%) 12 (5%) — Clinical characteristics Mean (SD; range) MANSA 48.1 (9.5; 23–68) — BDIa 23.7 (12.9; 3–53) — BAIa 19.2 (12.5; 0–50) — PSYRATS delusionsb 14.6 (5.5; 0–22) — 13.6 (5.5; 0–21)f PSYRATS voicesc 27.3 (9.3; 0–39) — 29.8 (7.5; 0–38)g Note: BAI, Beck Anxiety Inventory; BDI, Beck Depression Inventory; PSYRATS, Psychotic Symptoms Rating Scale. aN = 68. bN = 45. cN = 30. d N = 43. eN = 43. fN = 25. gN = 43. Open in new tab Table 1. Characteristics of participants who took part in measure development and initial validation . Initial item selection, sensitivity, and construct validity N = 69 . Initial factor structure N = 242 . Comparison sample N = 44 . Age (mean and SD) . 39 (8.29) . 38 (9.44) . 40 (10.87) . . N (%) . N (%) . N (%) . Gender Male 38 (55%) 142 (59%) 23 (52%) Female 31 (45%) 100 (41%) 20 (46%)d Marital status Single 55 (81%) 185 (82%) 25 (57%) Married 6 (9%) 22 (10%) 8 (18%) Divorced 4 (6%) 7 (3%) 6 (14%) Other 3 (4%) 12 (5%) 4 (9%)e Ethnicity White British/ English 37 (54%) 118 (49%) 40 (91%) White Other 7 (10%) 19 (8%) 1 (2%) Asian 2 (3%) 9 (4%) 1 (2%) Black 16 (23%) 64 (27%) 2 (5%) Chinese 0 (0%) 1 (0.4%) — Mixed 0 (0%) 0 (0%) — Other 7 (10%) 19 (8%) — Missing 0 (0%) 12 (5%) — Clinical characteristics Mean (SD; range) MANSA 48.1 (9.5; 23–68) — BDIa 23.7 (12.9; 3–53) — BAIa 19.2 (12.5; 0–50) — PSYRATS delusionsb 14.6 (5.5; 0–22) — 13.6 (5.5; 0–21)f PSYRATS voicesc 27.3 (9.3; 0–39) — 29.8 (7.5; 0–38)g . Initial item selection, sensitivity, and construct validity N = 69 . Initial factor structure N = 242 . Comparison sample N = 44 . Age (mean and SD) . 39 (8.29) . 38 (9.44) . 40 (10.87) . . N (%) . N (%) . N (%) . Gender Male 38 (55%) 142 (59%) 23 (52%) Female 31 (45%) 100 (41%) 20 (46%)d Marital status Single 55 (81%) 185 (82%) 25 (57%) Married 6 (9%) 22 (10%) 8 (18%) Divorced 4 (6%) 7 (3%) 6 (14%) Other 3 (4%) 12 (5%) 4 (9%)e Ethnicity White British/ English 37 (54%) 118 (49%) 40 (91%) White Other 7 (10%) 19 (8%) 1 (2%) Asian 2 (3%) 9 (4%) 1 (2%) Black 16 (23%) 64 (27%) 2 (5%) Chinese 0 (0%) 1 (0.4%) — Mixed 0 (0%) 0 (0%) — Other 7 (10%) 19 (8%) — Missing 0 (0%) 12 (5%) — Clinical characteristics Mean (SD; range) MANSA 48.1 (9.5; 23–68) — BDIa 23.7 (12.9; 3–53) — BAIa 19.2 (12.5; 0–50) — PSYRATS delusionsb 14.6 (5.5; 0–22) — 13.6 (5.5; 0–21)f PSYRATS voicesc 27.3 (9.3; 0–39) — 29.8 (7.5; 0–38)g Note: BAI, Beck Anxiety Inventory; BDI, Beck Depression Inventory; PSYRATS, Psychotic Symptoms Rating Scale. aN = 68. bN = 45. cN = 30. d N = 43. eN = 43. fN = 25. gN = 43. Open in new tab Development of the Short Form Item Selection Twelve out of 24 items had medium-large effect sizes (Cohen’s d ranging from 0.55 to 0.77) for change with CBTp in the full (N = 69), and even larger effect sizes (Cohen’s d ranging from 0.64 to 0.98) in the smaller subsample (N = 24/69) with immediate pretherapy and posttherapy data. One of these items, “The ability to question the way I look at things,” overlapped conceptually with another, “The ability to see things from another point of view,” and internal consistency was found to be marginally greater (Cronbach’s α = .93) with the former item. Therefore, only the first was included to provide a final CHOICE short-form measure (see supplementary materials for the final CHOICE-SF) comprising 11 items and 1 blank item for recording a personal goal. Internal Consistency, Construct Validity, and Sensitivity to Change The final CHOICE-SF had high internal consistency (Cronbach’s α = .93) and was highly sensitive to change with CBTp (df[68], t = −6.08, P < .001 [CI(95) = −2.19 to −1.10]) with a large effect size for change (Cohen’s d = −0.84 [CI(95) = −1.15 to −0.54]). The same sensitivity and effect size calculations for the original full CHOICE in the same 69 participants yielded a slightly lower effect size (df[68], t = −5.56, P < .001 [CI(95) = −1.98 to −0.93]; Cohen’s d = −0.73 [CI(95) = −1.03 to −0.44]). In contrast, and in support of the specificity of change to the intervention, the comparison group who received no intervention experienced no significant change on the CHOICE-SF (df[43], t = −1.09, P = .28 [CI(95) = −1.09 to 0.20]; Cohen’s d = −0.15 [CI(95) = −0.57 to 0.27]). Construct validity was good, with the CHOICE-SF mean score correlating positively with the MANSA (r = .70, P < .001) and negatively with the BDI (r = −.70, P < .001, N = 68) and BAI (r = −.52; P < .001; N = 68). However, the CHOICE-SF did not correlate with the PSYRATS delusions (r = −.277, P = .069, N = 45) or voices scales (r = −.313, P = .098, N = 30). These findings are consistent with and stronger than those for the original CHOICE measure where the severity scale correlated significantly with the MANSA (r = .52), BDI (r = −.70), BAI (r = −.48), distress/disruption items on the PSYRATS (r = −.26–.28) but not with traditional PSYRATS symptom measures of conviction with delusions (r = .11) or frequency, location, or beliefs about voices (r = .01–.10).12 Initial Factor Analysis Following item selection, internal consistency, validity, and sensitivity analysis, a principal axis factoring analysis was conducted on a larger sample of 242 participants. This revealed a single factor with an eigenvalue exceeding 1, which explained 52.3%, of the variance. The factor loadings are presented in table 2. Table 2. Factor structure item loadings Item . Initial sample factor loadings (N = 242) . 1. The ability to approach problems in a variety of ways 0.59 2. Self-confidence 0.64 3.Positive ways of relating to people 0.69 4. The ability to question the way I look at things 0.64 5. Ways of dealing with everyday life stresses 0.67 6. Ways of dealing with a crisis 0.73 7. Facing my own upsetting thoughts and feelings 0.71 8. Peace of mind 0.71 9. Understanding myself and my past 0.70 10. Understanding my experiences 0.73 11. Positive ways of thinking 0.81 Item . Initial sample factor loadings (N = 242) . 1. The ability to approach problems in a variety of ways 0.59 2. Self-confidence 0.64 3.Positive ways of relating to people 0.69 4. The ability to question the way I look at things 0.64 5. Ways of dealing with everyday life stresses 0.67 6. Ways of dealing with a crisis 0.73 7. Facing my own upsetting thoughts and feelings 0.71 8. Peace of mind 0.71 9. Understanding myself and my past 0.70 10. Understanding my experiences 0.73 11. Positive ways of thinking 0.81 Open in new tab Table 2. Factor structure item loadings Item . Initial sample factor loadings (N = 242) . 1. The ability to approach problems in a variety of ways 0.59 2. Self-confidence 0.64 3.Positive ways of relating to people 0.69 4. The ability to question the way I look at things 0.64 5. Ways of dealing with everyday life stresses 0.67 6. Ways of dealing with a crisis 0.73 7. Facing my own upsetting thoughts and feelings 0.71 8. Peace of mind 0.71 9. Understanding myself and my past 0.70 10. Understanding my experiences 0.73 11. Positive ways of thinking 0.81 Item . Initial sample factor loadings (N = 242) . 1. The ability to approach problems in a variety of ways 0.59 2. Self-confidence 0.64 3.Positive ways of relating to people 0.69 4. The ability to question the way I look at things 0.64 5. Ways of dealing with everyday life stresses 0.67 6. Ways of dealing with a crisis 0.73 7. Facing my own upsetting thoughts and feelings 0.71 8. Peace of mind 0.71 9. Understanding myself and my past 0.70 10. Understanding my experiences 0.73 11. Positive ways of thinking 0.81 Open in new tab The RCI The RCI31–33 for CHOICE-SF was calculated for items 1–11 only (as the goal item is not used by all service users) using the SD (1.983) from the full 242 participant sample. The RCI was found to be 1.45, indicating that a change in the mean score of 1.45 points or more is considered a statistically reliable change. This compares to the RCI derived from the SD (1.93) and reliability (0.83) of the original CHOICE questionnaire12 of 2.2 points. With regards to the initial development sample (N = 69), 82% of this sample demonstrated some improvement, ie, an increase in total score on the CHOICE-SF following CBTp and over half of the participants (53%) showed a statistically reliable improvement of at least 1.45 points on the CHOICE-SF. Secondary Validation of the Short Form Participants The characteristics of participants who took part in the secondary validation can be found in table 3. Table 3. Characteristics of participants who took part in the secondary validation . Baseline sample used for confirmatory factor analysis (N = 354) . Completers of therapy (N = 51) . . M (SD: range) . M (range) . Age . 37 (13.1: 13–69)a . 41 (11.5: 19–67)* . . N (%) . N (%) . Gender Male 164 (46%) 25 (50%) Female 191 (53%) 26 (50%) Other 2 (1%) Employment Employed 64 (18%) 7 (14%) Unemployed 240 (67%) 39 (76%) Student 28 (8%) 4 (8%) Missing 26 (7%) 1 (2%) Ethnicity White British 293 (82%) 39 (76%) White Other 21 (6%) 6 (11%) Asian 9 (3%) Black 8 (2%) 3 (5%) Chinese 2 (1%) 1 (2%) Mixed 12 (3%) 2 (5%) Other 8 (2%) Missing 4 (1%) Diagnosisb Schizophrenia 105 (29%) 19 (36%) Schizoaffective 16 (5%) 5 (11%) Borderline/ unstable personality disorder 62 (17%) 8 (16%) Complex trauma 1 (0.1%) 0 (0%) Depression 20 (6%) 7 (13%) Mixed 70 (20%) 9 (18%) Other 44 (12%) 1 (2%) . Baseline sample used for confirmatory factor analysis (N = 354) . Completers of therapy (N = 51) . . M (SD: range) . M (range) . Age . 37 (13.1: 13–69)a . 41 (11.5: 19–67)* . . N (%) . N (%) . Gender Male 164 (46%) 25 (50%) Female 191 (53%) 26 (50%) Other 2 (1%) Employment Employed 64 (18%) 7 (14%) Unemployed 240 (67%) 39 (76%) Student 28 (8%) 4 (8%) Missing 26 (7%) 1 (2%) Ethnicity White British 293 (82%) 39 (76%) White Other 21 (6%) 6 (11%) Asian 9 (3%) Black 8 (2%) 3 (5%) Chinese 2 (1%) 1 (2%) Mixed 12 (3%) 2 (5%) Other 8 (2%) Missing 4 (1%) Diagnosisb Schizophrenia 105 (29%) 19 (36%) Schizoaffective 16 (5%) 5 (11%) Borderline/ unstable personality disorder 62 (17%) 8 (16%) Complex trauma 1 (0.1%) 0 (0%) Depression 20 (6%) 7 (13%) Mixed 70 (20%) 9 (18%) Other 44 (12%) 1 (2%) aN = 349 for age. bN = 352 for diagnosis. *Significant difference in age between those who completed therapy and the remainder of the baseline sample (df[347], t = −2.0, P = .044 [CI(95) = 7.93 to −0.11]). There were no other significant differences in clinical or demographic characteristics between groups. Open in new tab Table 3. Characteristics of participants who took part in the secondary validation . Baseline sample used for confirmatory factor analysis (N = 354) . Completers of therapy (N = 51) . . M (SD: range) . M (range) . Age . 37 (13.1: 13–69)a . 41 (11.5: 19–67)* . . N (%) . N (%) . Gender Male 164 (46%) 25 (50%) Female 191 (53%) 26 (50%) Other 2 (1%) Employment Employed 64 (18%) 7 (14%) Unemployed 240 (67%) 39 (76%) Student 28 (8%) 4 (8%) Missing 26 (7%) 1 (2%) Ethnicity White British 293 (82%) 39 (76%) White Other 21 (6%) 6 (11%) Asian 9 (3%) Black 8 (2%) 3 (5%) Chinese 2 (1%) 1 (2%) Mixed 12 (3%) 2 (5%) Other 8 (2%) Missing 4 (1%) Diagnosisb Schizophrenia 105 (29%) 19 (36%) Schizoaffective 16 (5%) 5 (11%) Borderline/ unstable personality disorder 62 (17%) 8 (16%) Complex trauma 1 (0.1%) 0 (0%) Depression 20 (6%) 7 (13%) Mixed 70 (20%) 9 (18%) Other 44 (12%) 1 (2%) . Baseline sample used for confirmatory factor analysis (N = 354) . Completers of therapy (N = 51) . . M (SD: range) . M (range) . Age . 37 (13.1: 13–69)a . 41 (11.5: 19–67)* . . N (%) . N (%) . Gender Male 164 (46%) 25 (50%) Female 191 (53%) 26 (50%) Other 2 (1%) Employment Employed 64 (18%) 7 (14%) Unemployed 240 (67%) 39 (76%) Student 28 (8%) 4 (8%) Missing 26 (7%) 1 (2%) Ethnicity White British 293 (82%) 39 (76%) White Other 21 (6%) 6 (11%) Asian 9 (3%) Black 8 (2%) 3 (5%) Chinese 2 (1%) 1 (2%) Mixed 12 (3%) 2 (5%) Other 8 (2%) Missing 4 (1%) Diagnosisb Schizophrenia 105 (29%) 19 (36%) Schizoaffective 16 (5%) 5 (11%) Borderline/ unstable personality disorder 62 (17%) 8 (16%) Complex trauma 1 (0.1%) 0 (0%) Depression 20 (6%) 7 (13%) Mixed 70 (20%) 9 (18%) Other 44 (12%) 1 (2%) aN = 349 for age. bN = 352 for diagnosis. *Significant difference in age between those who completed therapy and the remainder of the baseline sample (df[347], t = −2.0, P = .044 [CI(95) = 7.93 to −0.11]). There were no other significant differences in clinical or demographic characteristics between groups. Open in new tab Secondary Factor Analyses For the CFA model of a single “Psychological Recovery” factor with 11 indicators, the chi-square test was significant, χ 2 = 175.408, df = 44, P < .001, as can occur in smaller data sets. Other fit indices suggested a good or acceptable level of approximate fit: CFI = 0.942, TLI = 0.942, GFI = 0.953, RMSEA = 0.092, CI(90) = 0.076; 0.106, and SRMR = 0.042. In order to evaluate potential errors of model specification, the standardized residuals matrix was evaluated as suggested by Kline.48 Two adjacent items (Q9: understanding myself and my past; Q10: understanding my experiences) were found to have a high (>|.10|) residual. The CFA model allowing for correlated errors between Q9 and Q10 improved model fit resulting in a lower but significant chi-square value, χ 2 = 92.124, df = 43, P < .001. Other indices also suggested an improved fit compared to the previous model, CFI = 0.978, TLI = 0.972, GFI = 0.975. RMSEA = 0.057, CI(90) = 0.041; 0.073 allowing the rejection of the poor-fit hypothesis (<.10). The SRMR also decreased and was below the value of 0.08, SRMR = 0.029. All factor loadings (ranging from 0.667 and 0.815) were significant indicators of psychological recovery as displayed below in table 4. Table 4. Unstandardized (est) and standardized (std) estimates of factors loadings, with SE, significance (P), and variance explained (var), for the confirmatory factor analysis Questionnaire item . Est . SE . P . Std . 1. The ability to approach problems in a variety of ways 1 0.731 2. Self-confidence 1.052 0.076 <.001 0.771 3. Positive ways of relating to people 1.002 0.082 <.001 0.681 4. The ability to question the way I look at things 1.123 0.085 <.001 0.730 5. Ways of dealing with everyday life stresses 1.062 0.072 <.001 0.815 6. Ways of dealing with a crisis 1.191 0.083 <.001 0.794 7. Facing my own upsetting thoughts and feelings 1.023 0.077 <.001 0.740 8. Peace of mind 0.938 0.075 <.001 0.700 9. Understanding myself and my past 1.118 0.092 <.001 0.676 10. Understanding my experiences (eg, beliefs, thoughts, voices, and related feelings) 1.007 0.084 <.001 0.667 11. Positive ways of thinking 1.018 0.075 <.001 0.753 Questionnaire item . Est . SE . P . Std . 1. The ability to approach problems in a variety of ways 1 0.731 2. Self-confidence 1.052 0.076 <.001 0.771 3. Positive ways of relating to people 1.002 0.082 <.001 0.681 4. The ability to question the way I look at things 1.123 0.085 <.001 0.730 5. Ways of dealing with everyday life stresses 1.062 0.072 <.001 0.815 6. Ways of dealing with a crisis 1.191 0.083 <.001 0.794 7. Facing my own upsetting thoughts and feelings 1.023 0.077 <.001 0.740 8. Peace of mind 0.938 0.075 <.001 0.700 9. Understanding myself and my past 1.118 0.092 <.001 0.676 10. Understanding my experiences (eg, beliefs, thoughts, voices, and related feelings) 1.007 0.084 <.001 0.667 11. Positive ways of thinking 1.018 0.075 <.001 0.753 Open in new tab Table 4. Unstandardized (est) and standardized (std) estimates of factors loadings, with SE, significance (P), and variance explained (var), for the confirmatory factor analysis Questionnaire item . Est . SE . P . Std . 1. The ability to approach problems in a variety of ways 1 0.731 2. Self-confidence 1.052 0.076 <.001 0.771 3. Positive ways of relating to people 1.002 0.082 <.001 0.681 4. The ability to question the way I look at things 1.123 0.085 <.001 0.730 5. Ways of dealing with everyday life stresses 1.062 0.072 <.001 0.815 6. Ways of dealing with a crisis 1.191 0.083 <.001 0.794 7. Facing my own upsetting thoughts and feelings 1.023 0.077 <.001 0.740 8. Peace of mind 0.938 0.075 <.001 0.700 9. Understanding myself and my past 1.118 0.092 <.001 0.676 10. Understanding my experiences (eg, beliefs, thoughts, voices, and related feelings) 1.007 0.084 <.001 0.667 11. Positive ways of thinking 1.018 0.075 <.001 0.753 Questionnaire item . Est . SE . P . Std . 1. The ability to approach problems in a variety of ways 1 0.731 2. Self-confidence 1.052 0.076 <.001 0.771 3. Positive ways of relating to people 1.002 0.082 <.001 0.681 4. The ability to question the way I look at things 1.123 0.085 <.001 0.730 5. Ways of dealing with everyday life stresses 1.062 0.072 <.001 0.815 6. Ways of dealing with a crisis 1.191 0.083 <.001 0.794 7. Facing my own upsetting thoughts and feelings 1.023 0.077 <.001 0.740 8. Peace of mind 0.938 0.075 <.001 0.700 9. Understanding myself and my past 1.118 0.092 <.001 0.676 10. Understanding my experiences (eg, beliefs, thoughts, voices, and related feelings) 1.007 0.084 <.001 0.667 11. Positive ways of thinking 1.018 0.075 <.001 0.753 Open in new tab Secondary Sensitivity to Change The t-tests assessing sensitivity to change again revealed a significant difference between baseline and posttherapy mean score for the CHOICE-SF (11 items; (df[50], t = 5.34, P < .001 [CI(95) = 2.04–0.92], Bayes factor = 38 614). A larger significant difference was found for the personal goal item (df[53], t = 7.93, P < .0001, [CI(95) = 2.19–3.67], Bayes factor = 846). Bayes factors were greater than 100, which demonstrates that these effects are strong evidence of change over time with CBTp.47 Discussion We aimed to develop and validate a short version of the CHOICE measure for use in research and clinical practice, with a specific view to it being sensitive and suitable for use over short time scales and on a sessional basis. A 1-page version (CHOICE-SF) was developed from the original severity scale, containing 11 items + 1 personal goal, which retained a clear single-factor structure, was highly sensitive to change and showed high internal consistency, validity, and utility for research and clinical practice as evidenced by its already rapid adoption in a broad range of contexts.11,49–64 The CHOICE-SF continues to incorporate both cognitive and coping outcomes that are amenable to change with CBTp, as well as well-being outcomes, such as peace of mind. The single-factor structure was stable across 2 data sets from different clinics in different mental health services. It correlated closely with affect and quality of life but not delusions and voices measures and, as such, supports its discriminant validity from positive symptom measures. Since the development of the CHOICE-SF, it has already been used in 6 recent or on-going trials,11,49–53 3 pilot-feasibility studies,54–56 2 case studies,57,58 1 case series,59 and 5 observational studies.60–64 However, as noted by Stevens et al,64 no psychometric properties have been available for this short form. Fornells-Ambroio et al61 used the CHOICE-SF at every session in an IAPT-SMI demonstration site for people with psychosis. The authors found that the CHOICE-SF was well received, with 71% (N = 64) of clients surveyed at the end of therapy reporting it to be actively helpful. Qualitative analysis found that using the CHOICE-SF was helpful for monitoring improvements, although it could be less helpful when progress was not being made. Furthermore, the goal-setting item in the CHOICE-SF was particularly valued by service users.61 In a further paper from the same IAPT-SMI site, the authors reported that paired completion rates, ie, a minimum of 2 CHOICE-SF being completed over the course of therapy, were high at 97%.60 Interestingly, this paper also showed that 77% of patients showed some improvement in CHOICE score following CBTp and 55% showed significant reliable change. These figures are highly consistent with those reported in the current paper where 82% of participants showed some improvement and 53% showed reliable change. These studies show that the CHOICE-SF can be implemented and is acceptable and successful in demonstrating reliable change as a routine outcome measure. It can be used within clinical services, including in an IAPT-SMI demonstration site, and in research trials that evaluate psychological therapies for a range of issues, including paranoia, sleep, worry, and self-confidence. The study has several strengths. We have validated the psychometric properties in 2 transdiagnostic participant populations, one incorporating psychosis symptoms and the other being voice hearers specifically, with different gender distribution. This demonstrates that the measure is valid and sensitive to change in a mixed sample of people with severe mental illness, including borderline and emotionally unstable personality disorders, complex trauma, and depression, as well as psychosis. Furthermore, people from black Caribbean or African populations are 2.4–14.4 times more likely to develop psychosis compared to other ethnic groups.65,66 This ethnic variation is captured in the initial sample where 26% of service users were black Caribbean or African, suggesting that the CHOICE-SF can be used across a mixed, heterogeneous population. It has similar internal consistency and validity, but enhanced sensitivity to change compared to the original measure, and facilitates a focus on other aspects of change besides positive symptom. It retains a personal goal item, which is liked by service users, valuable in shaping therapy focus, and particularly sensitive to change. Although we have not directly compared acceptability and ease of use between CHOICE and CHOICE-SF, the latter is much shorter and simpler to complete and score, comprising only one subscale, making it highly valuable for use in research and routine clinical services. In terms of limitations, the initial item selection, internal consistency, and construct validity of the CHOICE-SF were calculated in a comparatively small sample. Item selection was informed by effect sizes, which are not influenced by sample size, and, although small samples can lead to biases in sample selection, the current study utilized an unselected heterogenous clinical sample, with high ecological validity. The sample size may have contributed to the lack of a significant correlation with psychosis symptoms in contrast to the quality of life and emotional symptoms. The low correlation values with psychosis symptoms are, however, in line with the recovery and CBTp literature and with our previous study, where psychosis symptoms are not necessarily related to well-being or to important CBTp outcomes. Black African and Caribbean service users were slightly underrepresented in the secondary validation sample (2% compared to 3% in the general population)67 with some underrepresentation of Asian populations in both samples (general population = 6.9% vs initial validation = 2.90% and baseline secondary validation sample = 4%). Conclusions The CHOICE-SF is an 11-item patient-reported service-user-led psychological recovery outcome measure with high validity, internal consistency, and sensitivity to change, with an additional item for a personal goal, which has been found to be highly regarded by service users.61 It is applicable to a broad and heterogeneous service user population and can be employed on a session-by-session basis to evaluate psychological therapy outcomes for severe mental illness. Acknowledgments The authors have declared that there were no financial conflicts of interest in relation to the subject of this study. Funding This research received no specific grant from any funding agency, commercial or not-for-profit sectors. Ethical Standards The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975 as revised in 2008. References 1. 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Kirkbride JB , Barker D, Cowden F, et al. Psychoses, ethnicity and socio-economic status . Br J Psychiatry. 2008 ; 193 ( 1 ): 18 – 24 . Google Scholar Crossref Search ADS PubMed WorldCat 67. Office for National Statistics . 2011 Census: Ethnic Group, Local Authorities in the United Kingdom . London, UK : Office for National Statistics ; 2013 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC © The Author(s) 2020. 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)
Du, Yang; Chen, Lei; Li, Xue-Song; Li, Xiao-Lin; Xu, Xiang-Dong; Tai, Shao-Bin; Yang, Geng-Lin; Tang, Quan; Liu, Hua; Liu, Shu-Han; Zhang, Shu-Yao; Cheng, Yong
doi: 10.1093/schbul/sbaa166pmid: 33159208
Abstract Exosomes have been suggested as promising targets for the diagnosis and treatment of neurological diseases, including schizophrenia (SCZ), but the potential role of exosome-derived metabolites in these diseases was rarely studied. Using ultra-performance liquid chromatography-tandem mass spectrometry, we performed the first metabolomic study of serum-derived exosomes from patients with SCZ. Our sample comprised 385 patients and 332 healthy controls recruited from 3 clinical centers and 4 independent cohorts. We identified 25 perturbed metabolites in patients that can be used to classify samples from patients and control participants with 95.7% accuracy (95% CI: 92.6%–98.9%) in the training samples (78 patients and 66 controls). These metabolites also showed good to excellent performance in differentiating between patients and controls in the 3 test sets of participants, with accuracies 91.0% (95% CI: 85.7%–96.3%; 107 patients and 62 controls), 82.7% (95% CI: 77.6%–87.9%; 104 patients and 142 controls), and 99.0% (95% CI: 97.7%–100%; 96 patients and 62 controls), respectively. Bioinformatic analysis suggested that these metabolites were enriched in pathways implicated in SCZ, such as glycerophospholipid metabolism. Taken together, our findings support a role for exosomal metabolite dysregulation in the pathophysiology of SCZ and indicate a strong potential for exosome-derived metabolites to inform the diagnosis of SCZ. exosome, schizophrenia, metabolomics, biomarker Introduction Schizophrenia (SCZ) is a severe neuropsychiatric disease that affects approximately 1% of the population worldwide1 and is characterized by positive symptoms (delusions and hallucinations), negative symptoms (apathy and social withdrawal), and cognitive impairment.2,3 Currently, diagnosis relies solely on a somewhat lengthy subjective evaluation by a clinician. Because earlier identification and treatment of SCZ are known to improve clinical outcomes, the last 2 decades have seen rapidly growing interest in identifying molecules associated with SCZ, in the hope of finding biomarkers that could inform an early and objective diagnosis.4 Researchers have applied “omics” methods, including proteomics and metabolomics, to the search for SCZ biomarkers, leading to several candidate molecules being proposed.5,6 However, an objective and reliable diagnosis of SCZ based on biomarkers remains elusive, and achieving a better understanding of the etiology of SCZ and subsequently developing new biomarkers for the diagnosis and prognosis of this disease remain important goals. Exosomes are small vesicles with a typical diameter of 50–150 nm, which are widely present in the blood, urine, and cerebrospinal fluid.7 They are released into circulation from multiple cell types and reach both distant and neighboring cells, where their contents of nucleic acids, proteins, and lipids regulate the phenotypes of the recipients.8 The roles of exosome-derived microRNAs (miRNAs) in intercellular communications have attracted great attention in recent years, and they have been found to regulate physiological functions and the pathological processes of many diseases, including angiogenesis, inflammation, aging, and major depression.9–11Moreover, blood exosomal miRNAs are reportedly promising biomarkers for various diseases, such as breast cancer,12 atherosclerosis,13 and SCZ.14 More recently, metabolomic data have suggested that exosome-associated metabolites can potentially serve as biomarkers for the diagnosis of pancreatic cancer15 and premalignant liver disease.16 However, until now, this “omics” method has not been applied in the investigation of exosomal metabolite aberrations in neuropsychiatric diseases. Here, we used ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) to study the exosome metabolomic profile of patients with SCZ and healthy control (HC) participants. We recruited 4 sets of participants from 3 clinical centers to provide 1 training set for predictor discovery and 3 test sets for class prediction. We found a cluster of 25 exosome-derived metabolites with good to excellent performance in differentiating patients with SCZ from HCs in all 4 participant sets. Materials and Methods The training participant set included 78 patients with SCZ and 66 HCs, recruited from Huangshan Second People’s Hospital. The first test set included 107 patients with SCZ and 62 HCs, recruited from Urumqi Fourth People’s Hospital. The second test set included 104 patients with SCZ and 142 HCs, from the Third People’s Hospital of Foshan. The third test set included 96 patients with SCZ and 62 HCs, also from the Third People’s Hospital of Foshan. All patients with SCZ had been diagnosed by experienced psychiatrists according to the International Classification of Diseases-10 and the Structured Clinical Interview of DSM-IV. The patients’ psychiatric symptoms were evaluated according to the Positive and Negative Syndrome Scale (PANSS). Exclusions were any medical illness, including cancer, hypertension, diabetes, mental retardation, autoimmune diseases, substance abuse, and infectious diseases. Furthermore, the HC subjects were recruited from similar communities from the patients with SCZ. The demographic and clinical characteristics of the study participants are presented in supplementary table 1. All participants or their relatives signed an informed consent form. The research plan was reviewed and approved by the Ethics Committee of Huangshan Second People’s Hospital, the Ethics Committee of the Third People’s Hospital of Foshan, and the Ethics Committee of Urumqi Fourth People’s Hospital. The experiments were conducted in accordance with the Declaration of Helsinki. Exosome Isolation and Validation We collected blood samples from patients and HCs after overnight fasting. The samples were allowed to clot at room temperature for 1 h and the serum obtained by centrifugation at 3000g for 10 min. Serum exosomes were isolated on a qEV column according to the manufacturer’s protocol (Izon). Then, the exosomes were concentrated at 10 000g for 60 min using a protein concentrator based on a polyethersulfone membrane having a 30-kDa molecular-weight cutoff (Vivaspin). The concentrated exosomes were resuspended in 200 μl of phosphate-buffered saline for further analysis. The exosome validation methods such as negative-staining electron microscopy, nanoparticle tracking analysis, and Western blotting were applied, as described in our recent article.14 Metabolite Measurements Widely targeted metabolomics of blood exosome samples from patients and HCs were acquired using an UPLC (Shim-pack UFLC SHIMADZU CBM30A system) and tandem mass spectrometer (MS/MS) (4500 QTRAP; Applied Biosystems) equipment. Qualitative analysis of the first- and second-order mass spectra employed a public database of metabolite information and metabolomics data curation environment (MetWare). Multiple-reaction monitoring in triple quadrupole mass spectrometry was used for the quantitation of metabolites. The detailed assay procedure, including quality control, is described in a recent article.17 The metabolomics data are presented in supplementary tables 2–5. Bioinformatics Analysis To understand the biological functions of the differentially expressed metabolites, they were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (the major public database on metabolic pathways), then mapped to the KEGG pathway database by the MetaboAnalyst software.18 Significant enrichments were defined as pathways with P < .05. To further explore the biological function, metabolite-gene interaction analysis was performed on 264 SCZ risk genes identified by genome-wide association studies and the differentially expressed metabolites identified here, using MetScape,19 a Cytoscape plug-in. Statistical Analysis Unsupervised principal components analysis (PCA) was performed by the statistical function prcomp in R. Based on the detected metabolites, an orthogonal partial least-squares-discriminant analysis (OPLS-DA) model was constructed to assess differentially expressed metabolites, and variable importance in projection (VIP) was extracted from this model.17 Differentially expressed metabolites were defined as VIP > 1.520 and P < .05 by a Mann–-Whitney U test. The potential of blood exosomal metabolites to discriminate between patients with SCZ and HCs was evaluated in terms of a receiver operating characteristic (ROC) curve generated by the SPSS 22.0 statistical analysis software.21 Results Differential Expression of Serum Exosomal Metabolites in the Training Set We isolated serum exosomes from patients with SCZ and controls (supplementary figure 1), and the nanoparticle tracking analysis showed that the size and number of serum exosomes from patients with SCZ and controls were not significantly different (supplementary figure 2). We then used UPLC-MS/MS to analyze differentially expressed metabolites in serum exosomes between patients with SCZ and controls. Our widely targeted UPLC-MS/MS analysis detected hundreds of metabolites, including phospholipids, amino acids, organic acids, and lipid oxidation products. Plots of PCA scores showed a separation of metabolite profiles between patients and controls (figure 1A), suggesting the presence of an SCZ signature in the blood exosomal metabolite concentrations. To identify exosomal metabolites differentially expressed between SCZ patients and controls, an OPLS-DA model was constructed (figure 1B), which identified 25 metabolites by the criteria: VIP score > 1.5 and P < .05 (figures 1C and 1D). Of these, 10 were upregulated and 15 were downregulated (figures 1C and 1D). Significant KEGG pathway enrichment was found for the differentially expressed metabolites in pathways related to glycerophospholipid metabolism and the biosynthesis of phenylalanine, tyrosine, and tryptophan (figure 1E). Fig. 1. Open in new tabDownload slide Bioinformatic screening for blood exosomal metabolites differentially expressed in schizophrenia (SCZ). (A) Principal components analysis (PCA) and (B) an orthogonal partial least squares-discriminant analysis (OPLS-DA) model diagram based on the metabolites detected in the training participant set. (C) Volcano plot displaying metabolite differences between patients with SCZ and healthy controls (HCs) for the training participant set. (D) Illustration of the 25 differentially expressed blood exosomal metabolites in 78 patients with SCZ compared with 66 HCs. (E) KEGG enrichment pathways for the 25 differentially expressed metabolites. Fig. 1. Open in new tabDownload slide Bioinformatic screening for blood exosomal metabolites differentially expressed in schizophrenia (SCZ). (A) Principal components analysis (PCA) and (B) an orthogonal partial least squares-discriminant analysis (OPLS-DA) model diagram based on the metabolites detected in the training participant set. (C) Volcano plot displaying metabolite differences between patients with SCZ and healthy controls (HCs) for the training participant set. (D) Illustration of the 25 differentially expressed blood exosomal metabolites in 78 patients with SCZ compared with 66 HCs. (E) KEGG enrichment pathways for the 25 differentially expressed metabolites. Exosomal Metabolites as Biomarkers for SCZ To investigate whether the 25 differentially expressed exosomal metabolites that we had identified could serve as biomarkers for differentiating between patients with SCZ and controls, we initially used the training set as the test set. An ROC curve (figure 2A) yielded a sensitivity of 85.9 %, a specificity of 95.5%, and an area under the curve (AUC) of 95.7 % (95% CI = 92.6%–98.9%), suggesting that blood exosomal metabolites had potential as biomarkers for SCZ. Fig. 2. Open in new tabDownload slide Biomarker performance for the set of 25 metabolites differentially expressed in schizophrenia (SCZ). Receiver operating characteristic (ROC) curves showing the accuracy of the metabolites in discriminating between patients with SCZ and HCs in (A) the training participant set, (B) the first test participant set, (C) the second test participant set, and (D) the third test participant set. Fig. 2. Open in new tabDownload slide Biomarker performance for the set of 25 metabolites differentially expressed in schizophrenia (SCZ). Receiver operating characteristic (ROC) curves showing the accuracy of the metabolites in discriminating between patients with SCZ and HCs in (A) the training participant set, (B) the first test participant set, (C) the second test participant set, and (D) the third test participant set. To validate these results, we applied the 25 metabolites to 3 additional test sets for class prediction. The first test set included 107 patients with SCZ and 62 HCs and yielded a sensitivity of 86.9%, a specificity of 87.1%, and an AUC of 91.0% (95% CI = 85.7%–96.3%) (figure 2B). The second test set included 104 patients with SCZ and 142 HCs and yielded a sensitivity of 73.1%, a specificity of 80.3%, and an AUC of 82.7% (95% CI = 77.6%–87.9%) (figure 2C). The third test set included 96 patients with SCZ and 62 HCs and yielded a sensitivity of 97.9%, a specificity of 95.2%, and an AUC of 99.0% (95% CI = 97.7%–100%) (figure 2D). These results demonstrated that blood exosomal metabolites had good to excellent performance in differentiating between patients with SCZ and HCs. We then used the multi-correlation coefficient analysis to assess the relationships of the 25 metabolites to potential confounders, including age and disease severity. The results suggested that age was not a confounding factor for the levels of the 25 metabolites (supplementary figure 3). Additionally, the 25 metabolites were not significantly associated with the PANSS total score, PANSS positive score, and PANSS negative score in the training set and the first test set (supplementary figure 3). However, the 25 metabolites showed a signification association with the PANSS negative score in both the second and third test sets (supplementary figure 3). Biomarker Performance Stratified by Medication Status and Sex We next investigated whether these biomarker results were influenced by the atypical antipsychotics prescribed to patients with SCZ. Because the second and third test sets included a large proportion of first-episode, drug-free patients, we tested the performance of the 25 metabolites in differentiating between first-episode, drug-free patients with SCZ and HCs. In subgroups drawn from the second test set, which included 40 first-episode, drug-free patients with SCZ and 142 HCs, these metabolites yielded a sensitivity of 77.5%, a specificity of 85.9%, and an AUC of 86.4% (95% CI = 80.3%–92.6%) (figure 3A). In subgroups drawn from the third test set, which included 49 first-episode, drug-free patients with SCZ and 62 HCs, the 25 metabolites yielded a sensitivity of 100%, a specificity of 100%, and an AUC of 100% (95% CI = 100%–100%) (figure 3B). These results demonstrated that the biomarker performance of the 25 metabolites was not reduced in unmedicated patients with SCZ. Fig. 3. Open in new tabDownload slide Biomarker performance of exosomal metabolites stratified by medication status and race. Receiver operating characteristic (ROC) curves showing the accuracy of the differentially expressed metabolites in discriminating between first-episode, drug-free patients with schizophrenia (SCZ) and healthy controls (HCs) in (A) the second test participant set and (B) the third test participant set. (C) Evaluation of the accuracy of the 25 metabolites in discriminating between Uyghur Chinese patients with SCZ and HCs in the first test participant set. Fig. 3. Open in new tabDownload slide Biomarker performance of exosomal metabolites stratified by medication status and race. Receiver operating characteristic (ROC) curves showing the accuracy of the differentially expressed metabolites in discriminating between first-episode, drug-free patients with schizophrenia (SCZ) and healthy controls (HCs) in (A) the second test participant set and (B) the third test participant set. (C) Evaluation of the accuracy of the 25 metabolites in discriminating between Uyghur Chinese patients with SCZ and HCs in the first test participant set. We then tested whether sex affected the differentiation accuracy of the 25 metabolites, drawing ROC curves separately for female and male participants. Analyses of all 4 sets of participants revealed that these metabolites had good to excellent performance in discriminating between female or male patients with SCZ and their respective controls (supplementary table 6). The participants were all Han Chinese except in the first test set, which included 36 Uyghur Chinese patients. The blood-exosome biomarker panel also showed excellent performance in discriminating between these patients and HCs, yielding a sensitivity of 92.9%, a specificity of 88.7%, and an AUC of 93.7% (95% CI = 88.9%–98.6%) (figure 3C). We further analyzed whether there were metabolites from serum exosomes to differentiate between first-episode, drug-free, and chronically treated patients with SCZ. We identified 6 metabolites (supplementary figure 4) which had VIP score > 1.5 in both the second and third test sets. The 6 metabolites distinguished these subsets of SCZ patients, with 73.8% accuracy for the second test set and 100% for the third test set (supplementary figure 4). Metabolite-Gene Molecular Network in SCZ To further explore the biological roles of the differentially expressed exosomal metabolites in the pathophysiology of SCZ, we constructed a metabolite-gene molecular network specific to SCZ. From among the 264 SCZ risk genes reported by genome-wide association studies and the differentially expressed blood exosomal metabolites reported by the present study, 38 genes and 8 metabolites were found in the database. Analysis of these suggested that 4 of the risk genes (nitric oxide synthase 1 [NOS1], dipeptidase 2 [DPEP2], 5-aminolevulinic acid synthase 1 [ALAS1], and glutamic acid decarboxylase 1 [GAD1]) and 2 of the differentially expressed metabolites (l-arginine and taurine) were functionally connected (figure 4). Fig. 4. Open in new tabDownload slide Molecular network associations. The network associations among 2 differentially expressed metabolites found here (L-arginine and taurine) and 4 previously known schizophrenia risk genes. Fig. 4. Open in new tabDownload slide Molecular network associations. The network associations among 2 differentially expressed metabolites found here (L-arginine and taurine) and 4 previously known schizophrenia risk genes. Discussion Our metabolomics data demonstrated a profile of differentially expressed metabolites in the blood exosomes of patients vs HCs, and 25 differentially expressed metabolites showed excellent performance in distinguishing patients with SCZ from HCs. The potential of the differentially expressed metabolite cluster as a biomarker for SCZ was validated in 3 additional test sets of participants from 2 other clinical centers. Further analysis suggested that the unique SCZ signatures were not due to medication effects. Sex did not affect the classification accuracy of the biomarkers. Bioinformatics analysis suggested that the 25 differentially expressed metabolites were significantly enriched in the pathways related to phenylalanine, tyrosine, and tryptophan biosynthesis and glycerophospholipid metabolism. Taken together, data from the present study revealed for the first time the dysregulation of blood exosomal metabolite contents in patients with SCZ and provided novel insights into the roles of metabolites in SCZ pathogenesis and diagnosis. The membrane phospholipid hypothesis of SCZ, which postulates membrane phospholipid dysfunction as a biochemical basis for the neurodevelopmental concept of SCZ, was proposed by Horrobin in 1998.22 The initial rationale was the discovery of the critical roles that phospholipids play in normal brain development and in adult brain functioning, and the discovery of aberrations in essential fatty acid levels in the peripheral blood of patients with SCZ.22,23 Individuals at ultra-high risk of psychosis were later found to have the dysregulation of essential fatty acid metabolism.5 Here, we found that several fatty acids were downregulated in the blood exosomes of patients with SCZ, consistent with a recent postmortem study showing decreased glycerophospholipid metabolism in the prefrontal cortex of patients with SCZ.24 Moreover, the differentially expressed metabolites that we found here included 2 products of lipid peroxidation that were upregulated in patients. These results agree with previous reports of increased malondialdehyde levels in the blood of patients with SCZ,25,26 suggesting that lipid peroxidation plays a role in SCZ pathogenesis. The potential role of phospholipid metabolism in SCZ was further supported by a recent preclinical study, which showed that a deficiency of 2 phospholipids—arachidonic acid and docosahexaenoic acid—during the early neurodevelopmental period caused SCZ-like phenotypes in adult mice through the epigenetic regulation of nuclear receptor genes.27 Moreover, studies by Pawełczyk et al28,29 suggested that dietary supplementation with n-3 polyunsaturated fatty acid had beneficial effects in patients with SCZ. Interestingly, Amminger et al30 demonstrated that dietary supplementation with n-3 polyunsaturated fatty acids reduced the rate of progression to first-episode psychosis in individuals at ultra-high risk of psychotic disorder. Taken together, these results indicate that disturbances of lipid metabolism are critical for the onset and/or development of SCZ, and more investigations into lipid metabolism as a therapeutic target for SCZ are warranted. In addition to the dysregulation of lipid metabolism, the present study found upregulation of the amino acid l-arginine in patients with SCZ, in agreement with 2 previous reports that showed elevated plasma l-arginine levels in patients with SCZ,31,32 but inconsistent with the findings of He et al.33 Our metabolite-gene network analysis showed that l-arginine is related to 4 SCZ risk genes, NOS1, DPEP2, ALAS1, and GAD1, and that taurine is related to GAD1. The metabolite-gene connection in SCZ is supported by reports of altered NO metabolism in patients with SCZ,34 it being well known that NO metabolism impacts synaptic transmission and vesicular release.35 Furthermore, abundant evidence from postmortem studies shows that GAD1 is downregulated in multiple regions of the SCZ brain, including the prefrontal and medial temporal cortex, at both transcriptional and translational levels.36 Since approximately 80%–90% of brain GABA is synthesized by the GAD1 enzyme, the altered expression of GAD1 was considered at least in part to contribute to the GABAergic neuron dysfunction found in SCZ.36 However, the reasons for the functional involvement of DPEP2 and ALAS1 in SCZ are unclear; further studies are necessary to better understand the metabolite-gene molecular network in SCZ. The field of blood-biomarker discovery for neurological diseases has exploded over the last 2 decades,4 due to the easy accessibility of blood samples and the influence of the “peripheral as a window on the brain” hypothesis.37 Although these studies so far have not resulted in breakthroughs such as international acceptance of blood biomarkers capable of informing diagnosis, prognosis, or treatment response, potential blood biomarkers for SCZ have been proposed. These potential biomarkers implicate processes such as inflammatory response, neurotrophin deficit, and epigenetic regulation in the etiology of SCZ.14,38,39 In recent years, the field has grown to encompass metabolomics approaches to biomarker discovery in neuropsychiatric disease. Although several studies have found differentially expressed metabolites in the serum or plasma of patients with SCZ, findings have been largely inconsistent across studies, as summarized by Davison et al5 in a systematic review. Most of these studies had a small sample size and did not test the accuracy of the metabolites as biomarkers for SCZ. Therefore, the usefulness of these metabolites for the diagnosis of SCZ has been uncertain. Nevertheless, a few studies have identified metabolite biomarker panels that differentiate between SCZ patients and controls with reasonable to excellent performance. However, these results were limited by sample size and/or single-center data.20,33,40 In the present study, we discovered a panel of biomarkers consisting of 25 blood exosomal metabolites that could discriminate between patients with SCZ and HCs. The strength of our study is that this panel showed good to excellent performance consistently across 4 independent cohorts from 3 clinical centers, located in the Eastern, Central, and Western parts of China. One possible reason for this consistency was the large sample size of our study. It is also very likely that exosomal metabolites reflect the pathophysiology of the brain, since it is known that exosomes cross the blood-brain barrier easily. Furthermore, our analysis demonstrated that the SCZ signature of the biomarker panel was present at the early stages of the disease and prior to the initiation of antipsychotic treatment, suggesting that this set of 25 blood exosomal metabolites can potentially aid in the diagnosis of SCZ. A limitation of this study was that some potential confounding factors were not considered in this study, such as smoking status and socioeconomic status, which may affect the levels of blood exosomal metabolites. An example is that the disease severity may have moderating effects on the 25 metabolites in blood exosomes of patients with SCZ. Another limitation of this study was the ethnic homogeneity of the participants, who were mostly Han Chinese, although we also obtained data with a relatively small sample size on Uyghur Chinese patients with SCZ, which also suggested the reliability of the biomarker panel. However, due to the multifactorial nature of SCZ etiology, there is increasing recognition that the integration of different biomarkers reflective of the various etiological pathways relevant to SCZ should lead to better diagnosis. Our previous study showed that blood exosomal miRNAs had approximately 90% (training samples) and 75% (testing samples) accuracies in differentiating between patients with SCZ and controls.14 It is likely that a combination of miRNAs and metabolites from blood exosomes could improve the diagnostic accuracy for SCZ. The limitation of this study was that only a small proportion of samples were overlapping between the 2 studies, and the usefulness of combination of miRNAs and metabolites to diagnose SCZ is unclear. Therefore, more studies are needed, hopefully with international cooperation, to translate our findings on blood exosomal metabolite biomarkers into benefits for patients with SCZ. Funding This study was supported by the National Natural Science Foundation of China (82071676 and 81703492), the Beijing Natural Science Foundation (7182092), the High-Level Hospital Development Program for Foshan “Climbing” Project, the Minzu University Research Fund (2018CXTD03), and the MUC 111 Project. Acknowledgment The authors have no biomedical financial interests or potential conflicts of interest to declare. Authors Contributions Y.C. conceived and designed the study; X.-S.L., X.-L.L., X.-D.X., S.-B.T., and G.-L.Y. diagnosed the patients and collected the samples; Y.D., L.C., Q.T., and H.L. performed the experiments; Y.D., S.-H.L., and S.-Y.Z. did statistical analyses; all the authors analyzed and interpreted the data. Y.C. drafted the manuscript with critical revisions from all the authors. References 1. Cassoli JS , Guest PC, Santana AG, Martins-de-Souza D. Employing proteomics to unravel the molecular effects of antipsychotics and their role in schizophrenia . Proteomics Clin Appl. 2016 ; 10 ( 4 ): 442 – 455 . Google Scholar Crossref Search ADS PubMed WorldCat 2. Rodrigues-Amorim D , Rivera-Baltanás T, López M, Spuch C, Olivares JM, Agís-Balboa RC. Schizophrenia: a review of potential biomarkers . J Psychiatr Res. 2017 ; 93 : 37 – 49 . Google Scholar Crossref Search ADS PubMed WorldCat 3. Zhang X , Zhang Y, Liao J, et al. 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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)
doi: 10.1093/schbul/sbaa140pmid: 33174600
Chris is Mr. A in the following accounts from jail, this is his history. The school called my son Chris “emotionally disturbed” from the time he was 4 years old, but we never received a clear diagnosis. In class, he struggled terribly in class and needed to be refocused all the time. Once he was treated with Ritalin for ADHD—it made him quiet in the classroom but did not help him focus, and at the end of the day, he’d become terribly hyperactive, moody, and unable to sleep. Around the age of 11, he became depressed, staying in the house and not eating. Treated with Prozac and Lithium, he improved much, joining the varsity hockey team and rising through the Boy Scouts to Life Scout, graduating 12th grade when he was 18. After High School, he stopped the medicines, and in his early 20s, he showed some odd behaviors: his face would become completely blank, and he would stare but be unaware of his surroundings. Sometimes at night, we heard him sobbing, when voices he was hearing wouldn’t let him sleep. He became paranoid, convinced that someone was listening to us, or would kill his dog or would kill us in our home, and he hid knives in the house and in his truck. He moved into his truck, parking it near his job, until we convinced him to return home to help us. We begged him to get treatment, but he thought that he could fight this without medication. Mr A was first arrested in 2006 when he was 24 years old. In a 6 AM raid, the police found child pornography on his laptop. This was discovered by an internet conversation he had with an undercover officer. A doctor who was the author of Megan’s Law tested Chris and thought his collecting child pornography might be related to his auditory hallucinations. Chris was convicted of Child Endangerment and sentenced to 5 years of probation, court-mandated medication, and therapy. He was diagnosed with Schizoaffective Disorder and treated by a psychiatrist, whom he came to trust. For several years, Chris was compliant with his medication, Lexapro and Risperdal, but when the dose was increased, he became lethargic, very overweight, and was losing his short-term memory. He worried about losing his job as an HVAC service technician, and he stopped taking the medications. When his psychiatrist died suddenly, Chris had trouble trusting a new doctor. He admitted to hearing voices again but refused to resume medication. He described 3 distinct voices: mostly a harsh voice telling him to do things and that he is a bad person and doesn’t deserve to live, a more nurturing and softer voice, and one voice screaming above the others. In 2018, he was arrested again and charged with trading videos of 10- to 13-year-old girls. He was fully psychotic then, and in jail, he was placed on suicide watch—he attempted suicide 3 times in the past, but was never hospitalized. Since August 27, 2018, he has been in the Forensic Unit in the Essex County, NJ jail. In jail, his medications—Risperdal, Wellbutrin, and gabapentin—are working much better than his old medications. Chris now understands that he must continue taking the medication for the rest of his life. He is awaiting sentencing, which has been delayed by the pandemic. If found guilty, he could be sentenced to 15 years in prison. Mr A—Diagnosed with Schizoaffective Disorder in 2007 at the age of 24 Written in pencil from the Essex County Correctional Facility, Newark, NJ In here, I am locked in my little room alone for 22 hours a day. I do things to keep myself occupied and my mind stimulated. I do a lot of Origami and display it on my door. The Corrections Officers and other inmates comment on them and some inmates have asked me to make them for them to display or send home to loved ones. My family and friends send me a lot of books to read and do puzzles like Sudoku and crosswords. These things help me to focus and I feel badly for the ones in here who don’t have anyone, so I lend my books out and share my commissary goods. Since being here, I have been taking my meds on a regular schedule. I now realize how important that is and how much more in control of my thoughts I feel. I see what happens to the people who don’t take them. There is a guy here who doesn’t take his medication, he fakes it in front of the C.O.s and nurses and they don’t ever really check or care most of the time. This guy then acts up and causes everyone problems. Once he started a fight with another guy because he said he was “taking in his health.” I feel badly for him because the other inmates in Protective Custody in this part of the jail who are in this section by choice (to keep out of the general population) and who don’t have mental health issues mess with him because they think it’s funny to watch him go off. For the most part in this section, the other inmates try to encourage or sometimes pressure him and others to take their medications. No one wants to be punished for someone else’s bad behavior and lose their free time. Sometimes people act out or fight against the C.O.s. For those things, they bring in what we call the “Turtle Squad” we call them that because of all the protective gear that they wear. They bring in paintball guns loaded with Mace balls. One day one of the inmates (who doesn’t take his meds) wanted to use the computer, the C.O.s told him no. He got mad and refused to lock into his room. He started yelling and being loud saying he was going to “fight everyone” or “die trying.” The staff gave him time to cool down and talk to the mental health people, but he refused. They ended up just shooting him with the paintball guns like over 100 times. That shut him up… Sometimes It is entertaining to see the Turtles in action, but when it does happen, you know you are going to lose your time out of your cell. It takes a while for the mace to dissipate and get cleaned up. When I was home, I used to carry at least 2 knives with me all of the time and had knives stashed all over my house. I have always been paranoid that someone was going to hurt me or my family. It is exhausting to live like that and always be looking around and be prepared. I always feel like someone is plotting to hurt me or my family and always feel like someone is following me. I always felt like I had to be prepared for the worst. When I arrived at the jail I was scared, the voices in my head (there are 3 of them) were loud and yelling at me. I wanted to die and was so stressed out. I felt like everyone hated me and I would die alone, I hadn’t taken my medications for a long time because of the horrible side effects. I was extremely depressed, and the voices and paranoia were all really bad and had been for a while before this. They took all of my clothes away, gave me a smock and a blanket, and put me on a suicide watch for days or weeks. I’m really still not sure how long it was because it was kind of a blur. It was humiliating to be almost naked and on camera 24/7. Once the medications kicked in and I got my clothes, I felt a little bit better but slept almost all of the time. We are all locked in for 22 hours a day in our rooms except for the worker inmates who clean and/or serve food. I asked to be a worker, but because of my mental health issues they couldn’t let me. We all get recreation time for about 2 hours a day. The guys all get along for the most part because no one wants to lose those 2 hours out. Everyone talks to each other and it can get loud when they shout from room to room. This makes it hard to hear if you are on the phone or watching TV while you’re out. The phone system sucks because there is only one and it imprints your voice, but it doesn’t always work correctly, especially with a lot of background noise. Making calls can be very frustrating, but many times if my account isn’t working the other guys will help by letting me use their accounts. If someone needs to make an emergency call while not out on their rec time, one of the guys that are out on their rec will bring that person a tablet so that they can make their call, someone will usually help if needed. There are a few guys who don’t always take their medications, they act out usually by starting a fight and end up getting punished by being moved from this area, which has a TV. I have seen fights start with one guy spraying water on another and sometimes they throw pee or poop at other inmates. Some of the calmer inmates will try to stop this behavior before the C.O.s notice, so we don’t all lose our rec time. Rec time means phone calls to loved ones, TV and entering commissary orders so everyone values it highly. © The Author(s) 2020. 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)
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