Abnormal alpha band power in the dynamic pain connectome is a marker of chronic pain with a neuropathic component

Abnormal alpha band power in the dynamic pain connectome is a marker of chronic pain with a... NeuroImage: Clinical 26 (2020) 102241 Contents lists available at ScienceDirect NeuroImage: Clinical journal homepage: www.elsevier.com/locate/ynicl Abnormal alpha band power in the dynamic pain connectome is a marker of chronic pain with a neuropathic component a a,b a,b a,b a,b Lee B. Kisler , Junseok A. Kim , Kasey S. Hemington , Anton Rogachov , Joshua C. Cheng , a a,b f,g,h b,d,i Rachael L. Bosma , Natalie R. Osborne , Benjamin T. Dunkley , Robert D. Inman , a,b,c,e, Karen D. Davis Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada Institute of Medical Science, University of Toronto, Toronto, ON, Canada Department of Surgery, University of Toronto, Toronto, ON, Canada Department of Medicine, University of Toronto, Toronto, ON, Canada Surgery, University of Toronto, Toronto, ON, Canada Neurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, ON, Canada Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, Canada Department of Medical Imaging, University of Toronto, Toronto, ON, Canada Arthritis Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada ARTIC L E I NF O ABSTRAC T Keywords: We previously identified alpha frequency slowing and beta attenuation in the dynamic pain connectome related Chronic pain to pain severity and interference in patients with multiple sclerosis-related neuropathic pain (NP). Here, we Neuropathic pain determined whether these abnormalities, are markers of aberrant temporal dynamics in non-neuropathic in- Magnetoencephalography flammatory pain (non-NP) or when NP is also suspected. We measured resting-state magnetoencephalography Default mode network (MEG) spectral density in 45 people (17 females, 28 males) with chronic back pain due to ankylosing spondylitis Salience network (AS) and 38 age/sex matched healthy controls. We used painDETECT scores to divide the chronic pain group into Somatosensory cortex those with only non-NP (NNP) and those who likely also had a component of NP in addition to their in- flammatory pain. We also assessed pain severity, pain interference, and disease activity with the Brief Pain Inventory and Bath AS Disease Activity Index (BASDAI). We examined spectral power in the dynamic pain connectome, including nodes of the ascending nociceptive pathway (ANP), default mode (DMN), and salience networks (SN). Compared to the healthy controls, the AS patients exhibited increased theta power in the DMN and decreased low-gamma power in the DMN and ANP, but did not exhibit beta-band attenuation or peak-alpha slowing. The NNP patients were not different from HCs. Compared to both healthy controls and NNP, NP pa- tients had increased alpha power in the ANP. Increased alpha power within the ANP was associated with reduced BASDAI in the NNP group, and increased pain in the mixed-NP group within the DMN, SN, and ANP. Thus, high theta and low gamma activity may be markers of chronic pain but high alpha-band activity may relate to particular features of neuropathic chronic pain. 1. Introduction pain connectome (DPC) and include nodes in the ascending (ANP) and descending nociceptive pathways, the default mode (DMN) and salience People who have chronic pain exhibit functional abnormalities in (SN) networks (Kucyi and Davis, 2015, 2017). Findings of abnormal brain areas that are associated with attention, salience, and pain pro- activity in the DPC in chronic pain is mostly based on data acquired cessing and modulation (Baliki et al., 2008; Davis and Moayedi, 2013; using slow, hemodynamic-based fMRI that has a temporal resolution of Hemington et al., 2016; Kaplan et al., 2019; Kim et al., 2019; seconds and so cannot capture faster occurring brain activity (Kucyi and Porreca et al., 2002). These areas are collectively known as the dynamic Davis, 2015, 2017). However, aberrant activity within the DPC that Corresponding author at: Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, Krembil Research Institute, University Health Network, 399 Bathurst St, Room MP12-306, Toronto, ON M5T 2S8, Canada. E-mail address: karen.davis@uhnresearch.ca (K.D. Davis). https://doi.org/10.1016/j.nicl.2020.102241 Received 29 December 2019; Received in revised form 25 February 2020; Accepted 10 March 2020 Available online 13 March 2020 2213-1582/ © 2020 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/). L.B. Kisler, et al. NeuroImage: Clinical 26 (2020) 102241 occurs in the frequency range of 1–100 Hz that is associated with and alcohol for at least 1 and 8 h, respectively, before testing. chronic pain can be examined using techniques that have a temporal The inclusion criteria for participants with AS was a diagnosis of AS resolution of milliseconds, such as EEG or magnetoencephalography based on the modified New York criteria (Van Der Linden et al., 1984), (MEG). Using these methods, slower resting peak alpha frequency that includes low back pain that improves with exercise, impaired (PAF) in healthy individuals predicted greater pain sensitivity during mobility of the spine and sacroiliitis finding at radiography. This was pain (Furman et al., 2018). Beta and gamma power has also been shown verified in the Spondylitis clinic at the Toronto Western Hospital. Ex- to be related to the intensity of ongoing chronic pain (May et al., 2019). clusion criteria for all participants were: (1) the presence of acute pain Furthermore some studies have reported that patients with chronic pain or a history of chronic pain (other than AS in the patient group), (2) any exhibit PAF slowing (Boord et al., 2008; Vries et al., 2013; Lim et al., diagnosis of neurological or psychiatric disorder, (3) any diagnosis of a 2016; Sarnthein et al., 2005; Stern et al., 2006; Wydenkeller et al., major health issue (e.g. diabetes), and (4) taking medications on a 2009) and increased alpha and theta power oscillations (Pietro et al., regular basis, except for the AS-related treatments. 2018; Kim et al., 2019; Lim et al., 2016; Meneses et al., 2016; There were 83 (45 AS, 38 HCs) participants in this study that were Pinheiro et al., 2016; Sarnthein et al., 2005; Stern et al., 2006; included in different analyses: The main analysis of the AS vs HCs Broeke et al., 2013; Walton et al., 2010) that can normalize following groups comprised 38 AS and 38 age/sex-matched HCs. However, we treatment (Sarnthein et al., 2005; Stern et al., 2006). Reduced beta also performed analyses on several subgroups. We selected participants (Kim et al., 2019) and increased gamma (Lim et al., 2016) brain activity to ensure age/sex-matching between groups and for subgroup analyses have also been found in chronic pain conditions and in some cases in- (see Section 2.2) that resulted in: (1) 12 AS and 12 age/sex-matched HC creased band power was linked to pain (Pietro et al., 2018; Kim et al., (for the comparison of mixed-NP vs HCs groups), (2) 26 AS and 26 age/ 2019; Lim et al., 2016; Schmidt et al., 2012). sex-matched HCs (for the comparison of NNP vs HCs groups), and (3) Despite the evidence that there can be aberrant brain activity in 12 NP and 12 age/sex-matched NNPs (for the comparison of mixed-NP patients with chronic pain, it is unclear whether these dysfunctions are vs NNP groups). ubiquitous across pain conditions. Notably, one EEG study failed to identify any temporal abnormalities in chronic pain (Schmidt et al., 2.2. Clinical assessment and questionnaires 2012) and in another study, abnormalities were only seen in patients with neuropathic pain (NP) (Vuckovic et al., 2014). Our recent MEG Key AS symptoms were evaluated by the Bath Ankylosing study of multiple sclerosis (MS) (Kim et al., 2019) also showed that Spondylitis Disease Activity Index (BASDAI) (Garrett et al., 1994), ob- abnormalities in activity differed between the patients who had non-NP tained at the most recent visit of the patient to the AS clinic. The and those with mixed-NP. As a chronic pain group, these MS patients BASDAI index includes 6 items that assess fatigue, joint swelling and had increased alpha power in nodes of the ANP and the SN. However, pain, local tenderness and morning stiffness. The composite BASDAI the subgroup of patients with mixed-NP had slowing of the PAF in score ranges from 0 to 10, with 10 indicative of high disease activity. various DPC nodes and reduced beta-band power in nodes of the ANP Patients were also asked to verbally rate their current pain (typically and these abnormalities were associated with pain interference. Thus, localized to the lower back/buttock area) and the average general pain temporal abnormalities seem to be more evident when NP is likely in the last 4 weeks, on a scale that ranged from 0 (“none”)to10(“max”) (Boord et al., 2008; Pietro et al., 2018; Sarnthein et al., 2005; using the painDETECT questionnaire that also asses pain severity Stern et al., 2006; Walton et al., 2010; Wydenkeller et al., 2009), which (Freynhagen et al., 2006). AS can include a NP component (Wu et al., occurs in up to 20% of the general population (Bouhassira, 2019; 2013), and to assess this we used the painDETECT questionnaire Bouhassira et al., 2008; Fayaz et al., 2016; Harifi et al., 2013; (Freynhagen et al., 2006). The painDETECT scores range from 0–38 and Toth et al., 2009). These findings raise the question of how aberrant patients rate how much they suffer from different neuropathic com- neural dynamics relate to disease and pain measures and whether these ponents (e.g. burning, tingling, electric shocks). Scores of 12 and under relationships are a general marker of chronic pain or if they are specific indicate that NP is unlikely; patients with these scores were deemed to to certain attributes such as NP. have inflammatory non-neuropathic pain (NNP). Scores of 19 and Thus, the aim of this study was to determine whether abnormalities greater indicate a high likelihood of NP. A score of 13–18 indicates that in resting state MEG spectral density and their association with disease there is a likelihood of some NP and so patients with these scores were activity and clinical pain differ for those with only inflammatory pain categorized as having mixed inflammatory-NP. In this study we con- (non-NP, NNP) and those who are also likely to have a NP component. sidered patients with a score of 13 or higher as having mixed in- We hypothesized that abnormalities would be specific to the ankylosing flammatory-NP (NP) to be consistent with our previous studies spondylitis (AS) patients who have a component of NP. Towards this (Bosma et al., 2018; Cheng et al., 2018; Kim et al., 2019; Wu et al., goal, we focussed on patients with AS because this patient population 2013). All participants also completed the Beck Depression Inventory has relatively few co-morbidities and affects adults much younger than (BDI) questionnaire. The BDI evaluates the cognitive and affective most other types of chronic pain (Braun and Sieper, 2007). These pa- symptoms of depression in the past week and consists of 21 questions, tients suffer from spondyloarthritis that mostly affects the spine and each rated from 0 to 3. The total score thus ranges from 0 to 63; scores causes chronic pain that can be mixed neuropathic and inflammatory of 0–9 classified as none to minimal depression, and scores of 10–18, pains (Pathan and Inman, 2017; Wu et al., 2013), which allowed us to 19–29, and 30–63 considered as mild-moderate, moderate-severe, and examine the effect of a NP component. severe depression, respectively (Beck et al., 1988). 2. Methods 2.3. Magnetoencephalography and MRI acquisition 2.1. Participants All participants underwent a 5 min resting state MEG scan acquired with a 306 channel Elekta Neuromag TRIUX system, with a sampling Participants with AS were recruited from Toronto Western Hospital rate of 1000 Hz and a DC bandpass of 330 Hz. Prior to the MEG scan, Spondylitis clinic in collaboration with staff rheumatologists. Age/sex- the nasion and bilateral pre-auricular positions were marked for each matched healthy control (HCs) participants were recruited through participant. These locations were used as fiducial reference points that advertisements that were posted at the University Health Network and later served for motion correction and registration to the MRI anato- through word of mouth. The study was approved by the Research Ethics mical scan (Velmurugan et al., 2014). Participants were scanned sitting Board of the University Health Network and all participants signed an in an upright position with their eyes open and fixated on a cross on a informed consent form. Participants were asked to refrain from caffeine screen in front of them with the room lights off. Participants were 2 L.B. Kisler, et al. NeuroImage: Clinical 26 (2020) 102241 instructed to avoid structured thinking and to let their mind wander. 2.5. Power spectra analysis The position of the participant's head was monitored continuously through head position indicator coils affixed to the participant's head. The output time series from beamforming analysis was normalized The spatiotemporal signal space separation (tSSS) algorithm (Gonzalez- (z-score) within subject and as previously described (Kim et al., 2019). Moreno et al., 2014; Taulu and Simola, 2006), implemented in the Specifically, after obtaining each individual's resting state time-series MaxFilter program, was used for artifact and head movement correc- from beamforming (a “virtual electrode”), the power spectrum density tion. Following the MEG scan, a 3T MRI (GE) high resolution T1 ana- (PSD) was obtained using Welch's Power Density. The PSD values were tomical image was obtained with the following acquisition parameters: then normalized because each individual will have different raw mag- voxel size = 1 mm , matrix = 256 × 256, 180 axial slices, TR = 7.8 s, nitude of relative power spectra thus it must be normalized before TE = 3 ms, inversion time = 450 ms. group level inferences could be made. Thus z-scoring was used to normalize each individual's resting state timer-series before calculating the PSD used for group level statistics. The Welch power spectral den- 2.4. Magnetoencephalography data preprocessing, region of interest and sity (pwelch function in MATLAB R2015b) method was used to estimate beamforming the power spectral density for each frequency point in each of the 14 ROIs (i.e. sources). The Welch method divides the time series into 8 or We preprocessed the MEG data using the same approach we pre- less overlapping segments (50% overlap), computes the power spectral viously described (Kim et al., 2019). Briefly, to analyze the resting state density of each segment using a fast Fourier transform with a Hamming data we used the FieldTrip toolbox, run on MATLAB software (ver window and then averages the power spectral density of all segments R2015b). The first and last 10 s of the recorded data were removed, (Welch, 1967). This process reduces the variance of the estimated leaving 280 s of resting state data for each participant. Data were power spectral density and resulted in a power spectral density value bandpass-filtered between 1 and 150 Hz and a notch filter was applied for each frequency in the 1–150 Hz range, for each of the ROIs. at 60 Hz. Down sampling to 300 Hz was then performed and In- dependent component analysis (ICA) used (“runica” function) to re- 2.6. Statistical analysis move artifacts associated with cardiac artifacts, eye blinks, breathing and muscle activity. This was done by visually inspecting the different Statistical analysis was performed in GraphPad Prism (version 7.03 components and removing components that appear to be noise and for Windows, GraphPad Software, La Jolla California USA, www. periodic signals. The procedure for visual ICA removal varied for each graphpad.com). Our analyses focused on the theta (4–7 Hz), alpha subject as some subjects had more noise than others. Thus there was a (8–13 Hz), beta (14–30 Hz) and low gamma (31–59 Hz) range. For each range of components that were removed but it was not more than 15 frequency point, within each ROI, we calculated the spectral power out of 204 components in each subject. To register the resting state mean and standard deviation (SD) for HCs, the entire group of AS pa- MEG data to the participant's anatomical image, the fiducial points of tients and also the NP and NNP sub-groups. Because of a previous re- the nasion and bilateral pre-auricular were first identified on the ana- port of a shift in the PAF in chronic neuropathic pain patients tomical image and these were used to co-register the MEG data to the (Kim et al., 2019), we also calculated the mean and SD of the individual anatomical image. After each person's MEG data is registered to their PAF within each group by identifying the maximal power value within own MRIs by using fiducial points previously defined before the MEG the alpha band for each participant. scan, each individuals preprocessed data is then warped into a template brain. The anatomical image was then segmented using statistical 2.6.1. Demographic characteristics parametric mapping (SPM), resulting in a geometrical representation of Differences in age, BDI score and sex between HCs and AS patients the brain which was then used in a single-shell forward model. were tested using Student t-tests (age and BDI) and Chi-square (sex) We used a linearly constrained minimum variance beamformer tests. Because of the small number of NP patients, nonparametric (Veen et al., 1997) to extract a continuous time series for nodes of the Mann–Whitney U test was used to examine differences between NP and dynamic pain connectome. The beamformer technique assumes that NNP subgroups in age, BDI, the number of years in pain, average pain there is no long term perfect correlation in the local field potentials of and BASDAI. Sex differences were tested using Chi-square. different sources and uses spatial filtering to maximize the signal in an area of interest while suppressing it in sources of no interest (Hillebrand 2.6.2. Spectral power and Barnes, 2005; Hillebrand et al., 2005). This is accomplished by To examine group differences that were related to chronic pain, we applying a weighted vector of linear combination of sensor outputs that first compared the entire group of AS patients with the group of HCs. yield a separate time course for each source that can then be used for Thereafter, to examine abnormalities that were specific to the like- further analysis (Dymond et al., 2014; Engels et al., 2016; Hillebrand lihood of a neuropathic pain component, we compared NP and NNP and Barnes, 2005; Kim et al., 2019). Thus, 14 regions of interest (ROI) subgroups to age/sex-matched HCs in a separate analysis. As NP pa- were defined and used as “virtual sensors”. These ROIs coordinates (x, tients were significantly older than NNP patients, we compared our NP y, z) in MNI space, were defined based on previous publications patients with a subgroup of the NNP patients, who were age/sex-mat- (Hemington et al., 2016; Kim et al., 2019; Kucyi et al., 2013; ched. As the spectral power was normalized these group comparisons Rogachov et al., 2016) and included areas of the ANP: bilateral tha- were examined using parametric tests as follows: These were performed lamus (+/−12, −18, 8), left (−34, −30, 54) and right (34, −28, 54) for the power spectra of each frequency point in the range 4–60 Hz as primary somatosensory cortex (S1), left (−60, −30, 20) and right (60, well as the individual PAF. We used multiple student t-tests for group −22, 18) secondary somatosensory cortex (S2), bilateral (+/−34, comparisons and corrected for the number of ROIs with a false dis- −20, 18) posterior insula (pINS); SN: right temporoparietal junction covery rate (FDR, P < 0.05) using the Benjamini Hochberg method (TPJ) (50, −32, 28), right anterior insula (aINS) (34, 18, 4), mid-cin- (Benjamini and Hochberg, 1995). We repeated the analysis for regions gulate cortex (2, 12, 34, MCC) and the right dorsolateral prefrontal with significant results, correcting for the multiple frequencies with a cortex (34, 46, 22, DLPFC); DMN: posterior cingulate cortex (−2, −46, false discovery rate (FDR, P < 0.05) using the Benjamini Hochberg 28, PCC) and medial prefrontal cortex (−2, 50, 2, mPFC). The SN in- method. The effect size for statistically significant differences is re- cluded only regions within the right hemisphere because the SN is a ported using Cohen's d, as calculated using the effect size calculator at: right-lateralized network and these area show higher activity when https://www.uccs.edu/lbecker/. It should be noted that although we participants attend to pain (Kucyi and Davis, 2015). examined individual frequency bins, we also divided them into bands in order to interpret the results with the existing knowledge (see Results 3 L.B. Kisler, et al. NeuroImage: Clinical 26 (2020) 102241 and Discussion). We then performed Spearman's correlation analysis between AS clinical measures (BASDAI, average pain in the last 4 weeks – i.e. trait-pain) and the power spectra of areas and frequencies that significantly differed between NP and NNP subgroups. We chose the BASDAI and trait-pain as two measures that reflect different aspects of the AS disease. The BASDAI includes multiple factors other than pain (e.g., fatigue, swelling and stiffness) and the trait-pain reflect the overall experience of pain, not limited to the current moment (Davis and Cheng, 2019). As this analysis was exploratory and based on the results of the previous step, it was not corrected for multiple com- parisons and was considered statistically significant for p < 0.05. 3. Results Fig 1. PainDETECT scores in the inflammatory non-neuropathic (NNP) and mixed inflammatory and neuropathic (NP) pain subgroups – The NP subgroup 3.1. Demographics had a PainDETECT score of 13 or higher and the NNP subgroup had a PainDETECT score of 12 or less. The graph also indicates the mean and 95% Our main analysis compared the data from 38 AS patients to 38 age/ confidence interval for each subgroup. sex-matched HCs (23 M, 15F in each group). In most cases, patients were matched within 3 years to a HC. However, given the difficulty in recruiting older HCs, in some instances we accepted a difference of 4 (Mann–Whitney U: p = 0.085) nor between the NP and the restricted NNP subgroup (Mann–Whitney U: p = 0.155). years (5 patients) and 5 years (1 patient). Demographic characteristics of both HCs and chronic pain patients and the patients’ disease char- 3.2. Increased theta and decreased gamma resting state power in chronic acteristics (AS patients, NP and NNP subgroups) are provided in Table 1. Individual PainDETECT scores are presented in Fig. 1. The AS pain group included 12 inflammatory pain patients with a likelihood of NP (PainDETECT score ≥ 13) and 26 inflammatory pain patients with The whole group analysis indicated that AS chronic pain patients’ NNP. There were no significant differences in age or sex between HCs exhibit abnormal resting state activity in the theta and gamma bands and AS patients (age and sex: p > 0.917), nor between the HCs matched within several nodes of the DMN, ANP, and SN of the DPC (Fig. 2), but to each AS subgroup (age and sex: p > 0.831). Between NP and NNP there were no significant group differences in the PAF for any of the subgroups, there were no significant differences in sex (Chi-square: ROIs (p ≥ 0.878). Specifically, the AS chronic pain group exhibited p = 0.367), the number of years in pain (Mann–Whitney U: p = 0.097), significantly increased resting state activity power in the theta (4, 6 Hz; current (Mann–Whitney U: p = 0.231) and average (Mann–Whitney U: max effect size: d = 0.74) band but decreased gamma band power (max p = 0.311) pain, or BASDAI scores (Mann–Whitney U: p = 0.224). effect size: d = 0.87) within the mPFC of the DMN (FDR corrected at However, since the NP group was significantly older than the NNP p < 0.05) compared to the HC group (Fig. 2A). Furthermore, compared group (Mann–Whitney U: p = 0.002), we also examined group differ- to HCs, the AS chronic pain group had decreased resting state gamma power within various nodes of the ANP (right S1, right S2, right and left ences between NP patients and 12 age/sex-matched NNP patients (i.e. restricted group). This analysis included an additional 7 patients with pINS; max effect size: d = 0.77) (Fig. 2B) and the SN (right aINS, MCC, NNP that were not included in the main analysis between patients with right DLPFC; max effect size: d = 0.68) (Fig. 2C). For more details on AS and HCs. Of note, these 7 NNP patients were not included in the the significance and effect sizes see Supplemental Table 1. When we group comparison to HCs because we could not age/sex match them to repeated the analysis with a more conservative threshold, corrected for HCs. There were no differences between the NP and the restricted group the multiple frequency points, the NP patients still exhibited increased of NNP patients in age, current and average pain, BASDAI or years with theta activity in the DMN (mPFC) and decreased gamma activity in the pain (Mann–Whitney U: age p = 0.966; current pain p = 0.877; ANP (right S2) and the DMN (mPFC) (Fig. 2A and B). average pain p = 0.984; BASDAI p = 0.247; years with pain p = 0.139). The BDI scores were higher in the AS group (t-test: 3.3. Increased theta and alpha power and decreased gamma power specific p < 0.001) and the NP subgroup (Mann–Whitney U: p = 0.009) but not to neuropathic pain in the NNP subgroup (the 26 NNP that were compared to HCs) (Man- n–Whitney U: p = 0.082) compared to the HCs group. There was no We evaluated whether the abnormalities identified for the entire AS significant difference in BDI scores between the NP and NNP group are related to chronic pain per se or specifically to the likelihood Table 1 Demographic and disease characteristics . HC Group AS Chronic pain group All NP NNP NNP age/sex-matched to NP N 38 38 122612 a b Age* (years) 31.5 ± 9.8 31.7 ± 9.9 39.0 ± 9.1 28.4 ± 8.5 38.8 ± 10.1 Sex 23 M,15F 23 M,15F 6 M,6F 17 M,9F 6 M,6F BASDAI n/a 3.4 ± 2.3 4.3 ± 2.4 2.9 ± 2.2 3.3 ± 2.9 PainDetect n/a 9.0 ± 5.4 15.3 ± 2.5 6.1 ± 3.6 5.9 ± 3.2 Current pain n/a 2.7 ± 2.4 3.3 ± 2.4 2.4 ± 2.4 3.2 ± 2.6 Average pain (0–10 NPS) n/a 3.6 ± 2.4 4.0 ± 2.0 3.4 ± 2.6 4.3 ± 2.8 Years with pain n/a 13.7 ± 9.4 17.2 ± 8.8 12.2 ± 9.4 22.8 ± 10.1 a b b BDI* 3.7 ± 3.3 8.2 ± 7.1 11.3 ± 7.2 6.7 ± 6.7 7.4 ± 8.2 Group data are shown as mean +/- standard deviation. Attributes that have significant group differences are indicated by *, with the specific groups that are significantly different from one another indicated by a,b. The Bath AS Disease Activity Index (BASDAI) and average pain scores were missing for 3 NNP and 1 NNP patients, respectively. NPS-numerical pain scale. 4 L.B. Kisler, et al. NeuroImage: Clinical 26 (2020) 102241 Fig 2. AS chronic pain patients have increased theta power and decreased gamma power in the dynamic pain connectome. Average resting state MEG activity in the A. default mode network (left medial prefrontal cortex (mPFC)), B. the salience network (right anterior insula (aINS), mid-cingulate cortex (MCC), dorsolateral prefrontal cortex (DLPFC)), and the C. ascending nociceptive pathway (right primary somatosensory cortex (S1), secondary somatosensory cortex (S2), bilateral posterior insula (pINS)) is shown for the ankylosing spondylitis (AS) chronic pain group (red) and healthy control (HC) group (blue). Increased power in the theta (4−7 Hz) band and decreased power in the low gamma (31–60 Hz) band in the AS compared to HC group is shown. Lines indicate mean ± SE. Significant group differences (indicated by *) was set using false discovery rate (FDR, p < 0.05) corrected for the number of regions. Regions that survived correction for the multiple frequencies are marked in a red frame. Brain images are displayed using the radiological convention. d – the maximal effect size of significant differences within the region. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article). 5 L.B. Kisler, et al. NeuroImage: Clinical 26 (2020) 102241 Fig 3. NP patients have increased theta and alpha power and decreased gamma in the default mode network. Average resting state MEG activity in the left medial prefrontal cortex (mPFC) of the default mode network is shown for the mixed inflammatory and neuropathic pain (NP) subgroup (pink), inflammatory none- neuropathic pain (NNP) subgroup (green) and healthy control (HC) subgroups (blue). Increased power in the theta (4–7 Hz) and alpha (8–13 Hz) band and decreased power in the low gamma (31–60 Hz) band in the NP compared to HC group is shown. Lines indicate mean ± SE. Significant group differences (indicated by *) was set using false discovery rate (FDR, p < 0.05) corrected for the number of regions. This region also survived correction for the multiple frequencies, marked in a red frame. Brain images are displayed using the radiological convention. d – the maximal effect size of significant differences within the region. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article). of a neuropathic pain component. To do this, we compared the power differences between the NP and NNP subgroups were further examined spectra of the subgroups of NP and NNP patients to age/sex-matched to identify Spearman correlations with disease activity (BASDAI score) HCs (see Figs. 3–5). Compared to HCs, the NP but not the NNP patients and clinical trait-pain (average pain in the last 4 weeks). The BASDAI showed significantly increased theta (4 Hz; max effect size: d = 0.84) score was missing for 2 NNP patients so that the analysis for association power in the mPFC of the DMN (Fig. 3), as well as increased alpha with the BASDAI score was done for 10 NNP and 12 NP patients. For all (12–13 Hz) power in various areas of the DPC including the mPFC (max significant correlations in this restricted group of NNP patients, we also effect size: d = 0.57) of the DMN (Fig. 3), the ANP (right and left examined the association in the larger group of 33 NNP (i.e. full NNP thalamus, right S1, right and left S2, right and left pINS; max effect size: subgroup) patients to determine whether the finding was present across d = 0.92) (Fig. 4) and the SN (right aINS, right TPJ, and right DLPFC; a broader age span. As the BASDAI score was missing for 3 NNP pa- max effect size: d = 1.12) (Fig. 5). The NP but not the NNP subgroup tients, this analysis was done for 30 NNP patients. also showed decreased gamma power in the mPFC (max effect size: We found that the alpha band power spectra in nodes of the ANP d = 0.97) of the DMN (Fig. 3), in the left S1 (effect size: d = 0.71) of were negatively correlated with the BASDAI score in the restricted the ANP (Fig. 4), and in the right DLPFC (max effect size: d = 0.77) of group of NNP patients but not in the NP patients. These associations the SN (Fig. 5). Details on the significance and effect size of these were identified in the right S1 and bilaterally in the thalamus, and findings are provided in Supplemental Table 2. When we repeated the pINS. These associations remained significant in the full NNP subgroup analysis correcting for the multiple frequency points, NP patients con- in the left thalamus, S2 and pINS (see Fig. 9 and Supplemental Table 4). tinued to exhibit increased alpha activity in the ANP (right pINS) and We also identified significant positive correlations between clinical SN (right DLPFC and right TPJ) and decreased gamma activity in the pain in NP, but not in NNP, with the alpha (9–10 Hz) power in nodes of DMN (mPFC) (Fig. 3–5). There were no significant differences between the ANP, SN and DMN. Specifically, this relationship was found in the HCs and NNP in any of the regions of interested tested for any of the right thalamus, S1, and pINS of the ANP, the right aINS of the SN, and frequencies (p > 0.05) (See Figs. 3–5). There were also no differences in the mPFC of the DMN (see Fig. 9, Supplemental Table 5). between HCs and NP or NNP patients in the PAF (p ≥ 0.4). Because the patients with NP were significantly older than those 4. Discussion with NNP, we conducted a secondary analysis using selected 12 age/ sex-matched NNP. Compared to the patients with NNP, those with NP A brain signature of chronic pain that is consistently present across showed higher alpha power (9–12 Hz) within multiple nodes of the different types of pain has been elusive, partly due to the complexities DPC: in the ANP (left and right thalamus, left and right S1, left and right and heterogeneity of chronic pain conditions. Identifying these ab- S2, left and right pINS; max effect size: d = 1.68) (Fig. 6), the SN (right normalities can help guide the development of new treatments for pain TPJ, right aINS, MCC, right DLPFC; max effect size: d = 1.50) (Fig. 7); relief such as neurofeedback (Mayaud et al., 2019; Vuckovic et al., and the DMN (PCC, mPFC; max effect size: d = 1.53) (Fig. 8). For more 2019). A promising approach towards identifying chronic pain markers details on the significance and effect size see Supplemental Table 3. is to use a technique such as MEG that is capable of both identifying the When we repeated the analysis correcting for the multiple frequency location and temporal characteristics of such a brain signature. In this points, NP patients continued to exhibit increased alpha activity in the MEG study, our aim was to determine whether aberrant temporal dy- ANP (right pINS and right thalamus) (Fig. 6). There were no significant namics in the DPC and their association with disease activity and subgroup differences in the theta, beta or lower gamma bands, nor in clinical pain is a marker of chronic pain in general, or whether these the PAF (p > 0.8). abnormalities are only present when there may be a component of NP. We found that the AS patients who had a mix of inflammatory and 3.4. Associations with clinical pain and disease activity neuropathic pains did not exhibit PAF slowing or beta attenuation compared to NNP patients. However, detailed analyses of the NP and The power spectra in areas and frequencies that showed significant NNP subgroups revealed several prominent findings that indicate 6 L.B. Kisler, et al. NeuroImage: Clinical 26 (2020) 102241 Fig 4. NP patients have increased alpha power and decreased gamma power in the ascending nociceptive pathway. Average resting state MEG activity in the bilateral primary somatosensory cortex (S1), secondary somatosensory cortex (S2), and posterior insula (pINS) of the ascending nociceptive pathway is shown for the mixed inflammatory and neuropathic pain (NP) subgroup (pink), inflammatory none-neuropathic pain (NNP) subgroup (green) and healthy control (HC) subgroups (blue). Increased power in the alpha (8–13 Hz) band and decreased power in the low gamma (31–60 Hz) band in the NP compared to HC group is shown. Lines indicate mean ± SE. Significant group differences (indicated by *) was set using false discovery rate (FDR, p < 0.05) corrected for the number of regions. Regions that survived correction for the multiple frequencies are marked in a red frame. Brain images are displayed using the radiological convention. d – the maximal effect size of significant differences within the region. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article). particular abnormalities in the DPC's networks and pathways: (1) Pa- increased trait-pain in the NP group. Interestingly, alpha power was tients with AS chronic pain had increased theta (4–7 Hz) power in the negatively associated with AS disease activity in the NNP group. DMN and decreased gamma (31–60 Hz) power in the ANP and DMN EEG has identified abnormalities in chronic pain (Pinheiro et al., compared to healthy controls. (2) A subgroup analysis revealed that the 2016); usually increased theta and alpha power but there is variability theta and gamma band abnormalities along with increased alpha across studies and pain conditions. For example, increased theta power (8–13 Hz) power are specific to patients who are likely to have NP. (3) was reported in orofacial NP, chronic neurogenic pain, complex re- Increased alpha power throughout the DPC was associated with gional pain syndrome (CRPS) and central NP (Pietro et al., 2018; 7 L.B. Kisler, et al. NeuroImage: Clinical 26 (2020) 102241 Fig 5. NP patients have increased alpha power and decreased gamma power in the salience network. Average resting state MEG activity in the right temporoparietal junction (TPJ), anterior insula (aINS), and dorsolateral prefrontal cortex (DLPFC) of the salience network is shown for the mixed inflammatory and neuropathic pain (NP) subgroup (pink), inflammatory none-neuropathic pain (NNP) subgroup (green) and healthy control (HC) subgroups (blue). Increased power in the alpha (8–13 Hz) band and decreased power in the low gamma (31–60 Hz) band in the NP compared to HC group is shown. Lines indicate mean ± SE. Significant group differences (indicated by *) was set using false discovery rate (FDR, p < 0.05) corrected for the number of regions. Regions that survived correction for the multiple frequencies are marked in a red frame. Brain images are displayed using the radiological convention. d – the maximal effect size of significant differences within the region. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article). Sarnthein et al., 2005; Stern et al., 2006; Vuckovic et al., 2014; differences in PAF or power (Dinh et al., 2019). Another large cohort Walton et al., 2010). Increased alpha power was found in orofacial NP, study of NP patients found increased theta, beta and gamma and de- rheumatoid arthritis, post cancer persistent pain and central NP creased alpha power (Vanneste et al., 2018). However, most of these (Pietro et al., 2018; Meneses et al., 2016; Broeke et al., 2013; studies examined a small number of patients (Boord et al., 2008; Vuckovic et al., 2014). Decreased alpha power was also reported for Vries et al., 2013; Kim et al., 2019; Lim et al., 2016; Sarnthein et al., central NP (Sarnthein et al., 2005; Stern et al., 2006). Decreased beta 2005; Stern et al., 2006; Broeke et al., 2013; Vanneste et al., 2018; power was reported for neurogenic pain and MS-related NP (Kim et al., Walton et al., 2010) or a large group of patients from multiple pain 2019; Sarnthein et al., 2005; Stern et al., 2006), while increased beta conditions (Dinh et al., 2019; Vanneste et al., 2018). This likely led to and gamma was reported in fibromyalgia (Lim et al., 2016). PAF the variability in findings across studies and suggests that there is no slowing was reported for central NP, NP following spinal cord injury consistent brain activity marker of chronic pain per se. However, (SCI), chronic pancreatitis, fibromyalgia and MS-related NP temporal abnormalities in brain activity are reported more often in NP (Boord et al., 2008; Vries et al., 2013; Kim et al., 2019; Lim et al., 2016; than in NNP and these may also be different for peripheral versus Sarnthein et al., 2005; Vuckovic et al., 2014; Wydenkeller et al., 2009). central NP. However, no abnormalities were found for low back pain patients Different frequency bands of resting state brain activity are asso- (Schmidt et al., 2012). Given both the similarities and discrepancies ciated with multiple and distinct functions (Buzsaki, 2006). Under- across studies, the Ploner group (Dinh et al., 2019) sought to identify an standing these associations can provide insight into the temporal ab- EEG marker across multiple pain conditions but did not find striking normalities that are often reported in chronic pain. In healthy 8 L.B. Kisler, et al. NeuroImage: Clinical 26 (2020) 102241 Fig 6. Compared to NNP, NP patients have increased alpha power in the ascending noci- ceptive pathway. Average resting state MEG activity in the bilateral thalamus, primary so- matosensory cortex (S1), secondary somato- sensory cortex (S2), and posterior insula (pINS) of the ascending nociceptive pathway is shown for the mix inflammatory and neuropathic pain (NP) subgroup (pink) and inflammatory none- neuropathic pain (NNP) subgroup (green). Increased power in the alpha (8–13 Hz) band in the NP compared to NNP subgroup is shown. Lines indicate mean ± SE. Significant group differences (indicated by *) was set using false discovery rate (FDR, p < 0.05) corrected for the number of regions. Regions that survived correction for the multiple frequencies are marked in a red frame. Brain images are dis- played using the radiological convention. d – the maximal effect size of significant differ- ences within the region. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article). individuals, alpha and beta band activity is high at rest and decreases (Pietro et al., 2018; Llinas et al., 1999), increased cortical theta power when a sensory stimulus is presented, whereas gamma and theta ac- in chronic pain may be due to thalamocortical dysrhythmia, resulting tivity show the opposite pattern (Hanslmayr et al., 2011). from increased thalamic theta firing activity. As the thalamic activity Our finding of increased alpha and theta power and decreased was not elevated in our cohort, this explanation is less likely. None- gamma power in NP could be attributed to several factors related to the theless, this controversial model (Dinh et al., 2019) is supported by temporal features and location of the aberrant activity. In our study, studies (Pietro et al., 2018; Sarnthein and Jeanmonod, 2008; compared to HCs, patients who were also likely to have a NP compo- Sarnthein et al., 2003; Vanneste et al., 2018) showing a therapeutic nent showed increased theta power. As suggested by others lesion to the thalamus reduces excessive theta activity in neurogenic 9 L.B. Kisler, et al. NeuroImage: Clinical 26 (2020) 102241 Fig 7. Compared to NNP, NP patients have in- creased alpha power in the salience network. Average resting state MEG activity in the right temporoparietal junction (TPJ), anterior insula (aINS), mid-cingulate cortex (MCC) and dorso- lateral prefrontal cortex (DLPFC) of the salience network is shown for the mixed inflammatory and neuropathic pain (NP) subgroup (pink) and in- flammatory none-neuropathic pain (NNP) sub- group (green). Increased power in the alpha (8–13 Hz) band in the NP compared to NNP sub- group is shown. Lines indicate mean ± SE. Significant group differences (indicated by *) was set using false discovery rate (FDR, p < 0.05) corrected for the number of regions. Brain images are displayed using the radiological convention. d – the maximal effect size of significant differences within the region. (For interpretation of the refer- ences to color in this figure legend, the reader is referred to the web version of this article). pain (Sarnthein et al., 2005; Stern et al., 2006). This is also supported pain, gamma oscillations were observed in response to tonic by pain models in animal studies (LeBlanc et al., 2017; Leblanc et al., (Nickel et al., 2017; Schulz et al., 2015) and brief (Gross et al., 2007; 2014). Hauck et al., 2007) pain stimuli. Notably, increase in gamma oscillation We identified increased alpha power in the NP patients within was also reported in relation to ongoing chronic pain intensity, but this multiple nodes of the DPC compared to HCs, and in the ANP when effect disappeared when the effect of time was considered (May et al., compared to the NNP patients. This suggests that increased alpha ac- 2019). Together, we suggest that both increased alpha and decreased tivity is unique to patients who also show signs of NP, at least AS-re- gamma power seem to support an inward attentive state in patients that lated NP. Additionally, the NP patients showed decreased gamma are likely to have NP. As chronic pain patients show increased attention power. Alpha band activity is associated with attention (Palva and to pain-related stimuli (Schoth et al., 2012) and alpha activity is also Palva, 2011), but alpha and gamma activity may have different roles in associated to pain expectation and negative pain-related affect attention and executive function (Doesburg et al., 2008). A review (Albu and Meagher, 2016), the increased alpha power might reflect paper on the role of alpha oscillation suggested that high alpha activity pain vigilance as well as rumination and magnification. In line, alpha may point towards an ‘internal attentive state’ where the attention is power in the NP subgroup was positively associated to higher trait-pain. oriented inward rather than outward. In this case, the appearance of an This association was not observed in the inflammatory-NNP patients, in external stimulus is more likely to be missed (Hanslmayr et al., 2011; which alpha power was negatively associated with disease activity. O'Connell et al., 2009). Notably, gamma activity is elevated when we Given the abnormal alpha activity in NP, increased alpha in this po- attend to an external stimulus (Doesburg et al., 2008). Specifically, in pulation may be associated with detrimental symptoms (e.g. increased Fig 8. Compared to NNP, NP patients have in- creased alpha power in the default mode network. Average resting state MEG activity in the posterior cingulate cortex (PCC) and the medial prefrontal cortex (mPFC) of the default mode network is shown for the mixed inflammatory and neuro- pathic pain (NP) subgroup (pink) and in- flammatory none-neuropathic pain (NNP) sub- group (green). Increased power in the alpha (8–13 Hz) band in the NP compared to NNP sub- group is shown. Lines indicate mean ± SE. Significant group differences (indicated by *) was set using false discovery rate (FDR, p < 0.05) corrected for the number of regions. Brain images are displayed using the radiological convention. d – the maximal effect size of significant differences within the region. (For interpretation of the refer- ences to color in this figure legend, the reader is referred to the web version of this article). 10 L.B. Kisler, et al. NeuroImage: Clinical 26 (2020) 102241 Fig 9. Increased alpha power in the ascending nociceptive pathway is associated with lower disease activity in NNP patients and with higher clinical pain in NP patients. Spearman correlation between the alpha power and disease activity (i.e., BASDAI) is shown for the left secondary somatosensory cortex (S2) of the ascending nociceptive pathway. Spearman correlation between the alpha power and trait clinical pain intensity (average pain over the last 4 weeks) is shown for the right primary somatosensory cortex (S1) of the ascending nociceptive pathway. Associations are shown in the entire ankylosing spondylitis (AS) chronic pain group (left), in the mixed inflammatory and neuropathic pain (NP) subgroup (pink, middle), and in the matched inflammatory none-neuropathic pain (NNP, right). Significant correlation is indicated by *. The lines are only meant to visually show treadlines to demonstrate the direction of the Spearman relationship and do not indicate a linear relationship. Brain images are displayed using the radiological convention. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article). pain). However, NNP show normal alpha activity and for these patients, contrast, to the patients in the NNP subgroup which clearly had only increased alpha may be associated with protective qualities (e.g. re- inflammatory pain and was relatively homogenous. Second, we note duced disease activity). Importantly, our measure of disease activity is that none of the patients were prescribed medication specifically for based on the BASDAI score that reflects the inflammatory nature of AS NP. Third, given the small n in the NP subgroup, the abnormalities pain. Nonetheless, some patients also suffer from a mix of in- identified should be interpreted with care (Button et al., 2013) and flammatory-NP. Our results suggest that the mechanisms for pain in the future studies are needed to determine if the findings replicate across alpha band are different between these two subgroups. other pain conditions. NP patients showed temporal abnormalities in the DMN and SN. The Finally, some factors inherent to studies of chronic pain should be DMN is active during rest and deactivated during a task while the SN considered. Depression was unlikely to have contributed to the in- shows the opposite pattern and is associated with the level of attention creased alpha power in the NP patients because it did not differ be- (Kucyi and Davis, 2015). These networks show abnormalities in chronic tween the NP and NNP subgroups. We also considered age effects given pain (Baliki et al., 2008; Bosma et al., 2018; Hemington et al., 2016; that the NP group was older than the NNP group. Age effects have been Kim et al., 2019; Kucyi and Davis, 2015) that might relate to the effect reported to impact PAF and spectral power (Giaquinto and Nolfe, 1986; chronic pain has on attention and executive function (Berryman et al., Hashemi et al., 2016), and accelerated gray matter aging has been 2014; Kucyi and Davis, 2015; Schoth et al., 2012). Temporal abnorm- shown in chronic pain patients (Kuchinad et al., 2007; Moayedi et al., alities were also observed in the ANP that include pain processing areas. 2012). However, our finding that the NP patients also showed temporal Importantly, pain and disease activity were associated with the spectral abnormalities when compared to age/sex-matched NNP patients pro- power in various nodes of the DPC but this was more pronounced in the vide evidence that our findings were not solely resulting from an age ANP. This supports other studies that showed associations between pain effect. A possible limitation of the study is that we were not able to and the ANP (Pietro et al., 2018; Kim et al., 2019). completely rule out the effect of sex due to the smaller number of We do note a few considerations to interpret our findings con- women in the study (AS is twice as prevalent in men than in women servatively: First, we previously demonstrated that chronic pain in AS is (Braun and Sieper, 2007)). Nonetheless, 50% of the mixed in- typically inflammatory or a mix of inflammatory and neuropathic pain flammatory-NP patients were women likely because NP is more pre- (see our previous psychophysical study) (Wu et al., 2013). We divided valent in women (Bouhassira, 2019). Furthermore, given known sex the patients into those who are likely to have some NP (in addition to differences in pain mechanisms (Hashmi and Davis, 2014; Jausovec and their inflammatory pain) and those with solely inflammatory-NNP Jausovec, 2010; Mogil, 2012; Wang et al., 2014), the abnormalities that based on painDETECT scores. We included in the NP subgroup patients we found might be different between the sexes. Also, the results of the with scores of 13–18 because these patients could have some NP in correlation analysis should be taken with care as it was not corrected 11 L.B. Kisler, et al. NeuroImage: Clinical 26 (2020) 102241 for multiple comparisons. 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Abnormal alpha band power in the dynamic pain connectome is a marker of chronic pain with a neuropathic component

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NeuroImage: Clinical 26 (2020) 102241 Contents lists available at ScienceDirect NeuroImage: Clinical journal homepage: www.elsevier.com/locate/ynicl Abnormal alpha band power in the dynamic pain connectome is a marker of chronic pain with a neuropathic component a a,b a,b a,b a,b Lee B. Kisler , Junseok A. Kim , Kasey S. Hemington , Anton Rogachov , Joshua C. Cheng , a a,b f,g,h b,d,i Rachael L. Bosma , Natalie R. Osborne , Benjamin T. Dunkley , Robert D. Inman , a,b,c,e, Karen D. Davis Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada Institute of Medical Science, University of Toronto, Toronto, ON, Canada Department of Surgery, University of Toronto, Toronto, ON, Canada Department of Medicine, University of Toronto, Toronto, ON, Canada Surgery, University of Toronto, Toronto, ON, Canada Neurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, ON, Canada Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, Canada Department of Medical Imaging, University of Toronto, Toronto, ON, Canada Arthritis Institute, Krembil Research Institute, University Health Network, Toronto, ON, Canada ARTIC L E I NF O ABSTRAC T Keywords: We previously identified alpha frequency slowing and beta attenuation in the dynamic pain connectome related Chronic pain to pain severity and interference in patients with multiple sclerosis-related neuropathic pain (NP). Here, we Neuropathic pain determined whether these abnormalities, are markers of aberrant temporal dynamics in non-neuropathic in- Magnetoencephalography flammatory pain (non-NP) or when NP is also suspected. We measured resting-state magnetoencephalography Default mode network (MEG) spectral density in 45 people (17 females, 28 males) with chronic back pain due to ankylosing spondylitis Salience network (AS) and 38 age/sex matched healthy controls. We used painDETECT scores to divide the chronic pain group into Somatosensory cortex those with only non-NP (NNP) and those who likely also had a component of NP in addition to their in- flammatory pain. We also assessed pain severity, pain interference, and disease activity with the Brief Pain Inventory and Bath AS Disease Activity Index (BASDAI). We examined spectral power in the dynamic pain connectome, including nodes of the ascending nociceptive pathway (ANP), default mode (DMN), and salience networks (SN). Compared to the healthy controls, the AS patients exhibited increased theta power in the DMN and decreased low-gamma power in the DMN and ANP, but did not exhibit beta-band attenuation or peak-alpha slowing. The NNP patients were not different from HCs. Compared to both healthy controls and NNP, NP pa- tients had increased alpha power in the ANP. Increased alpha power within the ANP was associated with reduced BASDAI in the NNP group, and increased pain in the mixed-NP group within the DMN, SN, and ANP. Thus, high theta and low gamma activity may be markers of chronic pain but high alpha-band activity may relate to particular features of neuropathic chronic pain. 1. Introduction pain connectome (DPC) and include nodes in the ascending (ANP) and descending nociceptive pathways, the default mode (DMN) and salience People who have chronic pain exhibit functional abnormalities in (SN) networks (Kucyi and Davis, 2015, 2017). Findings of abnormal brain areas that are associated with attention, salience, and pain pro- activity in the DPC in chronic pain is mostly based on data acquired cessing and modulation (Baliki et al., 2008; Davis and Moayedi, 2013; using slow, hemodynamic-based fMRI that has a temporal resolution of Hemington et al., 2016; Kaplan et al., 2019; Kim et al., 2019; seconds and so cannot capture faster occurring brain activity (Kucyi and Porreca et al., 2002). These areas are collectively known as the dynamic Davis, 2015, 2017). However, aberrant activity within the DPC that Corresponding author at: Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, Krembil Research Institute, University Health Network, 399 Bathurst St, Room MP12-306, Toronto, ON M5T 2S8, Canada. E-mail address: karen.davis@uhnresearch.ca (K.D. Davis). https://doi.org/10.1016/j.nicl.2020.102241 Received 29 December 2019; Received in revised form 25 February 2020; Accepted 10 March 2020 Available online 13 March 2020 2213-1582/ © 2020 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/). L.B. Kisler, et al. NeuroImage: Clinical 26 (2020) 102241 occurs in the frequency range of 1–100 Hz that is associated with and alcohol for at least 1 and 8 h, respectively, before testing. chronic pain can be examined using techniques that have a temporal The inclusion criteria for participants with AS was a diagnosis of AS resolution of milliseconds, such as EEG or magnetoencephalography based on the modified New York criteria (Van Der Linden et al., 1984), (MEG). Using these methods, slower resting peak alpha frequency that includes low back pain that improves with exercise, impaired (PAF) in healthy individuals predicted greater pain sensitivity during mobility of the spine and sacroiliitis finding at radiography. This was pain (Furman et al., 2018). Beta and gamma power has also been shown verified in the Spondylitis clinic at the Toronto Western Hospital. Ex- to be related to the intensity of ongoing chronic pain (May et al., 2019). clusion criteria for all participants were: (1) the presence of acute pain Furthermore some studies have reported that patients with chronic pain or a history of chronic pain (other than AS in the patient group), (2) any exhibit PAF slowing (Boord et al., 2008; Vries et al., 2013; Lim et al., diagnosis of neurological or psychiatric disorder, (3) any diagnosis of a 2016; Sarnthein et al., 2005; Stern et al., 2006; Wydenkeller et al., major health issue (e.g. diabetes), and (4) taking medications on a 2009) and increased alpha and theta power oscillations (Pietro et al., regular basis, except for the AS-related treatments. 2018; Kim et al., 2019; Lim et al., 2016; Meneses et al., 2016; There were 83 (45 AS, 38 HCs) participants in this study that were Pinheiro et al., 2016; Sarnthein et al., 2005; Stern et al., 2006; included in different analyses: The main analysis of the AS vs HCs Broeke et al., 2013; Walton et al., 2010) that can normalize following groups comprised 38 AS and 38 age/sex-matched HCs. However, we treatment (Sarnthein et al., 2005; Stern et al., 2006). Reduced beta also performed analyses on several subgroups. We selected participants (Kim et al., 2019) and increased gamma (Lim et al., 2016) brain activity to ensure age/sex-matching between groups and for subgroup analyses have also been found in chronic pain conditions and in some cases in- (see Section 2.2) that resulted in: (1) 12 AS and 12 age/sex-matched HC creased band power was linked to pain (Pietro et al., 2018; Kim et al., (for the comparison of mixed-NP vs HCs groups), (2) 26 AS and 26 age/ 2019; Lim et al., 2016; Schmidt et al., 2012). sex-matched HCs (for the comparison of NNP vs HCs groups), and (3) Despite the evidence that there can be aberrant brain activity in 12 NP and 12 age/sex-matched NNPs (for the comparison of mixed-NP patients with chronic pain, it is unclear whether these dysfunctions are vs NNP groups). ubiquitous across pain conditions. Notably, one EEG study failed to identify any temporal abnormalities in chronic pain (Schmidt et al., 2.2. Clinical assessment and questionnaires 2012) and in another study, abnormalities were only seen in patients with neuropathic pain (NP) (Vuckovic et al., 2014). Our recent MEG Key AS symptoms were evaluated by the Bath Ankylosing study of multiple sclerosis (MS) (Kim et al., 2019) also showed that Spondylitis Disease Activity Index (BASDAI) (Garrett et al., 1994), ob- abnormalities in activity differed between the patients who had non-NP tained at the most recent visit of the patient to the AS clinic. The and those with mixed-NP. As a chronic pain group, these MS patients BASDAI index includes 6 items that assess fatigue, joint swelling and had increased alpha power in nodes of the ANP and the SN. However, pain, local tenderness and morning stiffness. The composite BASDAI the subgroup of patients with mixed-NP had slowing of the PAF in score ranges from 0 to 10, with 10 indicative of high disease activity. various DPC nodes and reduced beta-band power in nodes of the ANP Patients were also asked to verbally rate their current pain (typically and these abnormalities were associated with pain interference. Thus, localized to the lower back/buttock area) and the average general pain temporal abnormalities seem to be more evident when NP is likely in the last 4 weeks, on a scale that ranged from 0 (“none”)to10(“max”) (Boord et al., 2008; Pietro et al., 2018; Sarnthein et al., 2005; using the painDETECT questionnaire that also asses pain severity Stern et al., 2006; Walton et al., 2010; Wydenkeller et al., 2009), which (Freynhagen et al., 2006). AS can include a NP component (Wu et al., occurs in up to 20% of the general population (Bouhassira, 2019; 2013), and to assess this we used the painDETECT questionnaire Bouhassira et al., 2008; Fayaz et al., 2016; Harifi et al., 2013; (Freynhagen et al., 2006). The painDETECT scores range from 0–38 and Toth et al., 2009). These findings raise the question of how aberrant patients rate how much they suffer from different neuropathic com- neural dynamics relate to disease and pain measures and whether these ponents (e.g. burning, tingling, electric shocks). Scores of 12 and under relationships are a general marker of chronic pain or if they are specific indicate that NP is unlikely; patients with these scores were deemed to to certain attributes such as NP. have inflammatory non-neuropathic pain (NNP). Scores of 19 and Thus, the aim of this study was to determine whether abnormalities greater indicate a high likelihood of NP. A score of 13–18 indicates that in resting state MEG spectral density and their association with disease there is a likelihood of some NP and so patients with these scores were activity and clinical pain differ for those with only inflammatory pain categorized as having mixed inflammatory-NP. In this study we con- (non-NP, NNP) and those who are also likely to have a NP component. sidered patients with a score of 13 or higher as having mixed in- We hypothesized that abnormalities would be specific to the ankylosing flammatory-NP (NP) to be consistent with our previous studies spondylitis (AS) patients who have a component of NP. Towards this (Bosma et al., 2018; Cheng et al., 2018; Kim et al., 2019; Wu et al., goal, we focussed on patients with AS because this patient population 2013). All participants also completed the Beck Depression Inventory has relatively few co-morbidities and affects adults much younger than (BDI) questionnaire. The BDI evaluates the cognitive and affective most other types of chronic pain (Braun and Sieper, 2007). These pa- symptoms of depression in the past week and consists of 21 questions, tients suffer from spondyloarthritis that mostly affects the spine and each rated from 0 to 3. The total score thus ranges from 0 to 63; scores causes chronic pain that can be mixed neuropathic and inflammatory of 0–9 classified as none to minimal depression, and scores of 10–18, pains (Pathan and Inman, 2017; Wu et al., 2013), which allowed us to 19–29, and 30–63 considered as mild-moderate, moderate-severe, and examine the effect of a NP component. severe depression, respectively (Beck et al., 1988). 2. Methods 2.3. Magnetoencephalography and MRI acquisition 2.1. Participants All participants underwent a 5 min resting state MEG scan acquired with a 306 channel Elekta Neuromag TRIUX system, with a sampling Participants with AS were recruited from Toronto Western Hospital rate of 1000 Hz and a DC bandpass of 330 Hz. Prior to the MEG scan, Spondylitis clinic in collaboration with staff rheumatologists. Age/sex- the nasion and bilateral pre-auricular positions were marked for each matched healthy control (HCs) participants were recruited through participant. These locations were used as fiducial reference points that advertisements that were posted at the University Health Network and later served for motion correction and registration to the MRI anato- through word of mouth. The study was approved by the Research Ethics mical scan (Velmurugan et al., 2014). Participants were scanned sitting Board of the University Health Network and all participants signed an in an upright position with their eyes open and fixated on a cross on a informed consent form. Participants were asked to refrain from caffeine screen in front of them with the room lights off. Participants were 2 L.B. Kisler, et al. NeuroImage: Clinical 26 (2020) 102241 instructed to avoid structured thinking and to let their mind wander. 2.5. Power spectra analysis The position of the participant's head was monitored continuously through head position indicator coils affixed to the participant's head. The output time series from beamforming analysis was normalized The spatiotemporal signal space separation (tSSS) algorithm (Gonzalez- (z-score) within subject and as previously described (Kim et al., 2019). Moreno et al., 2014; Taulu and Simola, 2006), implemented in the Specifically, after obtaining each individual's resting state time-series MaxFilter program, was used for artifact and head movement correc- from beamforming (a “virtual electrode”), the power spectrum density tion. Following the MEG scan, a 3T MRI (GE) high resolution T1 ana- (PSD) was obtained using Welch's Power Density. The PSD values were tomical image was obtained with the following acquisition parameters: then normalized because each individual will have different raw mag- voxel size = 1 mm , matrix = 256 × 256, 180 axial slices, TR = 7.8 s, nitude of relative power spectra thus it must be normalized before TE = 3 ms, inversion time = 450 ms. group level inferences could be made. Thus z-scoring was used to normalize each individual's resting state timer-series before calculating the PSD used for group level statistics. The Welch power spectral den- 2.4. Magnetoencephalography data preprocessing, region of interest and sity (pwelch function in MATLAB R2015b) method was used to estimate beamforming the power spectral density for each frequency point in each of the 14 ROIs (i.e. sources). The Welch method divides the time series into 8 or We preprocessed the MEG data using the same approach we pre- less overlapping segments (50% overlap), computes the power spectral viously described (Kim et al., 2019). Briefly, to analyze the resting state density of each segment using a fast Fourier transform with a Hamming data we used the FieldTrip toolbox, run on MATLAB software (ver window and then averages the power spectral density of all segments R2015b). The first and last 10 s of the recorded data were removed, (Welch, 1967). This process reduces the variance of the estimated leaving 280 s of resting state data for each participant. Data were power spectral density and resulted in a power spectral density value bandpass-filtered between 1 and 150 Hz and a notch filter was applied for each frequency in the 1–150 Hz range, for each of the ROIs. at 60 Hz. Down sampling to 300 Hz was then performed and In- dependent component analysis (ICA) used (“runica” function) to re- 2.6. Statistical analysis move artifacts associated with cardiac artifacts, eye blinks, breathing and muscle activity. This was done by visually inspecting the different Statistical analysis was performed in GraphPad Prism (version 7.03 components and removing components that appear to be noise and for Windows, GraphPad Software, La Jolla California USA, www. periodic signals. The procedure for visual ICA removal varied for each graphpad.com). Our analyses focused on the theta (4–7 Hz), alpha subject as some subjects had more noise than others. Thus there was a (8–13 Hz), beta (14–30 Hz) and low gamma (31–59 Hz) range. For each range of components that were removed but it was not more than 15 frequency point, within each ROI, we calculated the spectral power out of 204 components in each subject. To register the resting state mean and standard deviation (SD) for HCs, the entire group of AS pa- MEG data to the participant's anatomical image, the fiducial points of tients and also the NP and NNP sub-groups. Because of a previous re- the nasion and bilateral pre-auricular were first identified on the ana- port of a shift in the PAF in chronic neuropathic pain patients tomical image and these were used to co-register the MEG data to the (Kim et al., 2019), we also calculated the mean and SD of the individual anatomical image. After each person's MEG data is registered to their PAF within each group by identifying the maximal power value within own MRIs by using fiducial points previously defined before the MEG the alpha band for each participant. scan, each individuals preprocessed data is then warped into a template brain. The anatomical image was then segmented using statistical 2.6.1. Demographic characteristics parametric mapping (SPM), resulting in a geometrical representation of Differences in age, BDI score and sex between HCs and AS patients the brain which was then used in a single-shell forward model. were tested using Student t-tests (age and BDI) and Chi-square (sex) We used a linearly constrained minimum variance beamformer tests. Because of the small number of NP patients, nonparametric (Veen et al., 1997) to extract a continuous time series for nodes of the Mann–Whitney U test was used to examine differences between NP and dynamic pain connectome. The beamformer technique assumes that NNP subgroups in age, BDI, the number of years in pain, average pain there is no long term perfect correlation in the local field potentials of and BASDAI. Sex differences were tested using Chi-square. different sources and uses spatial filtering to maximize the signal in an area of interest while suppressing it in sources of no interest (Hillebrand 2.6.2. Spectral power and Barnes, 2005; Hillebrand et al., 2005). This is accomplished by To examine group differences that were related to chronic pain, we applying a weighted vector of linear combination of sensor outputs that first compared the entire group of AS patients with the group of HCs. yield a separate time course for each source that can then be used for Thereafter, to examine abnormalities that were specific to the like- further analysis (Dymond et al., 2014; Engels et al., 2016; Hillebrand lihood of a neuropathic pain component, we compared NP and NNP and Barnes, 2005; Kim et al., 2019). Thus, 14 regions of interest (ROI) subgroups to age/sex-matched HCs in a separate analysis. As NP pa- were defined and used as “virtual sensors”. These ROIs coordinates (x, tients were significantly older than NNP patients, we compared our NP y, z) in MNI space, were defined based on previous publications patients with a subgroup of the NNP patients, who were age/sex-mat- (Hemington et al., 2016; Kim et al., 2019; Kucyi et al., 2013; ched. As the spectral power was normalized these group comparisons Rogachov et al., 2016) and included areas of the ANP: bilateral tha- were examined using parametric tests as follows: These were performed lamus (+/−12, −18, 8), left (−34, −30, 54) and right (34, −28, 54) for the power spectra of each frequency point in the range 4–60 Hz as primary somatosensory cortex (S1), left (−60, −30, 20) and right (60, well as the individual PAF. We used multiple student t-tests for group −22, 18) secondary somatosensory cortex (S2), bilateral (+/−34, comparisons and corrected for the number of ROIs with a false dis- −20, 18) posterior insula (pINS); SN: right temporoparietal junction covery rate (FDR, P < 0.05) using the Benjamini Hochberg method (TPJ) (50, −32, 28), right anterior insula (aINS) (34, 18, 4), mid-cin- (Benjamini and Hochberg, 1995). We repeated the analysis for regions gulate cortex (2, 12, 34, MCC) and the right dorsolateral prefrontal with significant results, correcting for the multiple frequencies with a cortex (34, 46, 22, DLPFC); DMN: posterior cingulate cortex (−2, −46, false discovery rate (FDR, P < 0.05) using the Benjamini Hochberg 28, PCC) and medial prefrontal cortex (−2, 50, 2, mPFC). The SN in- method. The effect size for statistically significant differences is re- cluded only regions within the right hemisphere because the SN is a ported using Cohen's d, as calculated using the effect size calculator at: right-lateralized network and these area show higher activity when https://www.uccs.edu/lbecker/. It should be noted that although we participants attend to pain (Kucyi and Davis, 2015). examined individual frequency bins, we also divided them into bands in order to interpret the results with the existing knowledge (see Results 3 L.B. Kisler, et al. NeuroImage: Clinical 26 (2020) 102241 and Discussion). We then performed Spearman's correlation analysis between AS clinical measures (BASDAI, average pain in the last 4 weeks – i.e. trait-pain) and the power spectra of areas and frequencies that significantly differed between NP and NNP subgroups. We chose the BASDAI and trait-pain as two measures that reflect different aspects of the AS disease. The BASDAI includes multiple factors other than pain (e.g., fatigue, swelling and stiffness) and the trait-pain reflect the overall experience of pain, not limited to the current moment (Davis and Cheng, 2019). As this analysis was exploratory and based on the results of the previous step, it was not corrected for multiple com- parisons and was considered statistically significant for p < 0.05. 3. Results Fig 1. PainDETECT scores in the inflammatory non-neuropathic (NNP) and mixed inflammatory and neuropathic (NP) pain subgroups – The NP subgroup 3.1. Demographics had a PainDETECT score of 13 or higher and the NNP subgroup had a PainDETECT score of 12 or less. The graph also indicates the mean and 95% Our main analysis compared the data from 38 AS patients to 38 age/ confidence interval for each subgroup. sex-matched HCs (23 M, 15F in each group). In most cases, patients were matched within 3 years to a HC. However, given the difficulty in recruiting older HCs, in some instances we accepted a difference of 4 (Mann–Whitney U: p = 0.085) nor between the NP and the restricted NNP subgroup (Mann–Whitney U: p = 0.155). years (5 patients) and 5 years (1 patient). Demographic characteristics of both HCs and chronic pain patients and the patients’ disease char- 3.2. Increased theta and decreased gamma resting state power in chronic acteristics (AS patients, NP and NNP subgroups) are provided in Table 1. Individual PainDETECT scores are presented in Fig. 1. The AS pain group included 12 inflammatory pain patients with a likelihood of NP (PainDETECT score ≥ 13) and 26 inflammatory pain patients with The whole group analysis indicated that AS chronic pain patients’ NNP. There were no significant differences in age or sex between HCs exhibit abnormal resting state activity in the theta and gamma bands and AS patients (age and sex: p > 0.917), nor between the HCs matched within several nodes of the DMN, ANP, and SN of the DPC (Fig. 2), but to each AS subgroup (age and sex: p > 0.831). Between NP and NNP there were no significant group differences in the PAF for any of the subgroups, there were no significant differences in sex (Chi-square: ROIs (p ≥ 0.878). Specifically, the AS chronic pain group exhibited p = 0.367), the number of years in pain (Mann–Whitney U: p = 0.097), significantly increased resting state activity power in the theta (4, 6 Hz; current (Mann–Whitney U: p = 0.231) and average (Mann–Whitney U: max effect size: d = 0.74) band but decreased gamma band power (max p = 0.311) pain, or BASDAI scores (Mann–Whitney U: p = 0.224). effect size: d = 0.87) within the mPFC of the DMN (FDR corrected at However, since the NP group was significantly older than the NNP p < 0.05) compared to the HC group (Fig. 2A). Furthermore, compared group (Mann–Whitney U: p = 0.002), we also examined group differ- to HCs, the AS chronic pain group had decreased resting state gamma power within various nodes of the ANP (right S1, right S2, right and left ences between NP patients and 12 age/sex-matched NNP patients (i.e. restricted group). This analysis included an additional 7 patients with pINS; max effect size: d = 0.77) (Fig. 2B) and the SN (right aINS, MCC, NNP that were not included in the main analysis between patients with right DLPFC; max effect size: d = 0.68) (Fig. 2C). For more details on AS and HCs. Of note, these 7 NNP patients were not included in the the significance and effect sizes see Supplemental Table 1. When we group comparison to HCs because we could not age/sex match them to repeated the analysis with a more conservative threshold, corrected for HCs. There were no differences between the NP and the restricted group the multiple frequency points, the NP patients still exhibited increased of NNP patients in age, current and average pain, BASDAI or years with theta activity in the DMN (mPFC) and decreased gamma activity in the pain (Mann–Whitney U: age p = 0.966; current pain p = 0.877; ANP (right S2) and the DMN (mPFC) (Fig. 2A and B). average pain p = 0.984; BASDAI p = 0.247; years with pain p = 0.139). The BDI scores were higher in the AS group (t-test: 3.3. Increased theta and alpha power and decreased gamma power specific p < 0.001) and the NP subgroup (Mann–Whitney U: p = 0.009) but not to neuropathic pain in the NNP subgroup (the 26 NNP that were compared to HCs) (Man- n–Whitney U: p = 0.082) compared to the HCs group. There was no We evaluated whether the abnormalities identified for the entire AS significant difference in BDI scores between the NP and NNP group are related to chronic pain per se or specifically to the likelihood Table 1 Demographic and disease characteristics . HC Group AS Chronic pain group All NP NNP NNP age/sex-matched to NP N 38 38 122612 a b Age* (years) 31.5 ± 9.8 31.7 ± 9.9 39.0 ± 9.1 28.4 ± 8.5 38.8 ± 10.1 Sex 23 M,15F 23 M,15F 6 M,6F 17 M,9F 6 M,6F BASDAI n/a 3.4 ± 2.3 4.3 ± 2.4 2.9 ± 2.2 3.3 ± 2.9 PainDetect n/a 9.0 ± 5.4 15.3 ± 2.5 6.1 ± 3.6 5.9 ± 3.2 Current pain n/a 2.7 ± 2.4 3.3 ± 2.4 2.4 ± 2.4 3.2 ± 2.6 Average pain (0–10 NPS) n/a 3.6 ± 2.4 4.0 ± 2.0 3.4 ± 2.6 4.3 ± 2.8 Years with pain n/a 13.7 ± 9.4 17.2 ± 8.8 12.2 ± 9.4 22.8 ± 10.1 a b b BDI* 3.7 ± 3.3 8.2 ± 7.1 11.3 ± 7.2 6.7 ± 6.7 7.4 ± 8.2 Group data are shown as mean +/- standard deviation. Attributes that have significant group differences are indicated by *, with the specific groups that are significantly different from one another indicated by a,b. The Bath AS Disease Activity Index (BASDAI) and average pain scores were missing for 3 NNP and 1 NNP patients, respectively. NPS-numerical pain scale. 4 L.B. Kisler, et al. NeuroImage: Clinical 26 (2020) 102241 Fig 2. AS chronic pain patients have increased theta power and decreased gamma power in the dynamic pain connectome. Average resting state MEG activity in the A. default mode network (left medial prefrontal cortex (mPFC)), B. the salience network (right anterior insula (aINS), mid-cingulate cortex (MCC), dorsolateral prefrontal cortex (DLPFC)), and the C. ascending nociceptive pathway (right primary somatosensory cortex (S1), secondary somatosensory cortex (S2), bilateral posterior insula (pINS)) is shown for the ankylosing spondylitis (AS) chronic pain group (red) and healthy control (HC) group (blue). Increased power in the theta (4−7 Hz) band and decreased power in the low gamma (31–60 Hz) band in the AS compared to HC group is shown. Lines indicate mean ± SE. Significant group differences (indicated by *) was set using false discovery rate (FDR, p < 0.05) corrected for the number of regions. Regions that survived correction for the multiple frequencies are marked in a red frame. Brain images are displayed using the radiological convention. d – the maximal effect size of significant differences within the region. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article). 5 L.B. Kisler, et al. NeuroImage: Clinical 26 (2020) 102241 Fig 3. NP patients have increased theta and alpha power and decreased gamma in the default mode network. Average resting state MEG activity in the left medial prefrontal cortex (mPFC) of the default mode network is shown for the mixed inflammatory and neuropathic pain (NP) subgroup (pink), inflammatory none- neuropathic pain (NNP) subgroup (green) and healthy control (HC) subgroups (blue). Increased power in the theta (4–7 Hz) and alpha (8–13 Hz) band and decreased power in the low gamma (31–60 Hz) band in the NP compared to HC group is shown. Lines indicate mean ± SE. Significant group differences (indicated by *) was set using false discovery rate (FDR, p < 0.05) corrected for the number of regions. This region also survived correction for the multiple frequencies, marked in a red frame. Brain images are displayed using the radiological convention. d – the maximal effect size of significant differences within the region. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article). of a neuropathic pain component. To do this, we compared the power differences between the NP and NNP subgroups were further examined spectra of the subgroups of NP and NNP patients to age/sex-matched to identify Spearman correlations with disease activity (BASDAI score) HCs (see Figs. 3–5). Compared to HCs, the NP but not the NNP patients and clinical trait-pain (average pain in the last 4 weeks). The BASDAI showed significantly increased theta (4 Hz; max effect size: d = 0.84) score was missing for 2 NNP patients so that the analysis for association power in the mPFC of the DMN (Fig. 3), as well as increased alpha with the BASDAI score was done for 10 NNP and 12 NP patients. For all (12–13 Hz) power in various areas of the DPC including the mPFC (max significant correlations in this restricted group of NNP patients, we also effect size: d = 0.57) of the DMN (Fig. 3), the ANP (right and left examined the association in the larger group of 33 NNP (i.e. full NNP thalamus, right S1, right and left S2, right and left pINS; max effect size: subgroup) patients to determine whether the finding was present across d = 0.92) (Fig. 4) and the SN (right aINS, right TPJ, and right DLPFC; a broader age span. As the BASDAI score was missing for 3 NNP pa- max effect size: d = 1.12) (Fig. 5). The NP but not the NNP subgroup tients, this analysis was done for 30 NNP patients. also showed decreased gamma power in the mPFC (max effect size: We found that the alpha band power spectra in nodes of the ANP d = 0.97) of the DMN (Fig. 3), in the left S1 (effect size: d = 0.71) of were negatively correlated with the BASDAI score in the restricted the ANP (Fig. 4), and in the right DLPFC (max effect size: d = 0.77) of group of NNP patients but not in the NP patients. These associations the SN (Fig. 5). Details on the significance and effect size of these were identified in the right S1 and bilaterally in the thalamus, and findings are provided in Supplemental Table 2. When we repeated the pINS. These associations remained significant in the full NNP subgroup analysis correcting for the multiple frequency points, NP patients con- in the left thalamus, S2 and pINS (see Fig. 9 and Supplemental Table 4). tinued to exhibit increased alpha activity in the ANP (right pINS) and We also identified significant positive correlations between clinical SN (right DLPFC and right TPJ) and decreased gamma activity in the pain in NP, but not in NNP, with the alpha (9–10 Hz) power in nodes of DMN (mPFC) (Fig. 3–5). There were no significant differences between the ANP, SN and DMN. Specifically, this relationship was found in the HCs and NNP in any of the regions of interested tested for any of the right thalamus, S1, and pINS of the ANP, the right aINS of the SN, and frequencies (p > 0.05) (See Figs. 3–5). There were also no differences in the mPFC of the DMN (see Fig. 9, Supplemental Table 5). between HCs and NP or NNP patients in the PAF (p ≥ 0.4). Because the patients with NP were significantly older than those 4. Discussion with NNP, we conducted a secondary analysis using selected 12 age/ sex-matched NNP. Compared to the patients with NNP, those with NP A brain signature of chronic pain that is consistently present across showed higher alpha power (9–12 Hz) within multiple nodes of the different types of pain has been elusive, partly due to the complexities DPC: in the ANP (left and right thalamus, left and right S1, left and right and heterogeneity of chronic pain conditions. Identifying these ab- S2, left and right pINS; max effect size: d = 1.68) (Fig. 6), the SN (right normalities can help guide the development of new treatments for pain TPJ, right aINS, MCC, right DLPFC; max effect size: d = 1.50) (Fig. 7); relief such as neurofeedback (Mayaud et al., 2019; Vuckovic et al., and the DMN (PCC, mPFC; max effect size: d = 1.53) (Fig. 8). For more 2019). A promising approach towards identifying chronic pain markers details on the significance and effect size see Supplemental Table 3. is to use a technique such as MEG that is capable of both identifying the When we repeated the analysis correcting for the multiple frequency location and temporal characteristics of such a brain signature. In this points, NP patients continued to exhibit increased alpha activity in the MEG study, our aim was to determine whether aberrant temporal dy- ANP (right pINS and right thalamus) (Fig. 6). There were no significant namics in the DPC and their association with disease activity and subgroup differences in the theta, beta or lower gamma bands, nor in clinical pain is a marker of chronic pain in general, or whether these the PAF (p > 0.8). abnormalities are only present when there may be a component of NP. We found that the AS patients who had a mix of inflammatory and 3.4. Associations with clinical pain and disease activity neuropathic pains did not exhibit PAF slowing or beta attenuation compared to NNP patients. However, detailed analyses of the NP and The power spectra in areas and frequencies that showed significant NNP subgroups revealed several prominent findings that indicate 6 L.B. Kisler, et al. NeuroImage: Clinical 26 (2020) 102241 Fig 4. NP patients have increased alpha power and decreased gamma power in the ascending nociceptive pathway. Average resting state MEG activity in the bilateral primary somatosensory cortex (S1), secondary somatosensory cortex (S2), and posterior insula (pINS) of the ascending nociceptive pathway is shown for the mixed inflammatory and neuropathic pain (NP) subgroup (pink), inflammatory none-neuropathic pain (NNP) subgroup (green) and healthy control (HC) subgroups (blue). Increased power in the alpha (8–13 Hz) band and decreased power in the low gamma (31–60 Hz) band in the NP compared to HC group is shown. Lines indicate mean ± SE. Significant group differences (indicated by *) was set using false discovery rate (FDR, p < 0.05) corrected for the number of regions. Regions that survived correction for the multiple frequencies are marked in a red frame. Brain images are displayed using the radiological convention. d – the maximal effect size of significant differences within the region. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article). particular abnormalities in the DPC's networks and pathways: (1) Pa- increased trait-pain in the NP group. Interestingly, alpha power was tients with AS chronic pain had increased theta (4–7 Hz) power in the negatively associated with AS disease activity in the NNP group. DMN and decreased gamma (31–60 Hz) power in the ANP and DMN EEG has identified abnormalities in chronic pain (Pinheiro et al., compared to healthy controls. (2) A subgroup analysis revealed that the 2016); usually increased theta and alpha power but there is variability theta and gamma band abnormalities along with increased alpha across studies and pain conditions. For example, increased theta power (8–13 Hz) power are specific to patients who are likely to have NP. (3) was reported in orofacial NP, chronic neurogenic pain, complex re- Increased alpha power throughout the DPC was associated with gional pain syndrome (CRPS) and central NP (Pietro et al., 2018; 7 L.B. Kisler, et al. NeuroImage: Clinical 26 (2020) 102241 Fig 5. NP patients have increased alpha power and decreased gamma power in the salience network. Average resting state MEG activity in the right temporoparietal junction (TPJ), anterior insula (aINS), and dorsolateral prefrontal cortex (DLPFC) of the salience network is shown for the mixed inflammatory and neuropathic pain (NP) subgroup (pink), inflammatory none-neuropathic pain (NNP) subgroup (green) and healthy control (HC) subgroups (blue). Increased power in the alpha (8–13 Hz) band and decreased power in the low gamma (31–60 Hz) band in the NP compared to HC group is shown. Lines indicate mean ± SE. Significant group differences (indicated by *) was set using false discovery rate (FDR, p < 0.05) corrected for the number of regions. Regions that survived correction for the multiple frequencies are marked in a red frame. Brain images are displayed using the radiological convention. d – the maximal effect size of significant differences within the region. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article). Sarnthein et al., 2005; Stern et al., 2006; Vuckovic et al., 2014; differences in PAF or power (Dinh et al., 2019). Another large cohort Walton et al., 2010). Increased alpha power was found in orofacial NP, study of NP patients found increased theta, beta and gamma and de- rheumatoid arthritis, post cancer persistent pain and central NP creased alpha power (Vanneste et al., 2018). However, most of these (Pietro et al., 2018; Meneses et al., 2016; Broeke et al., 2013; studies examined a small number of patients (Boord et al., 2008; Vuckovic et al., 2014). Decreased alpha power was also reported for Vries et al., 2013; Kim et al., 2019; Lim et al., 2016; Sarnthein et al., central NP (Sarnthein et al., 2005; Stern et al., 2006). Decreased beta 2005; Stern et al., 2006; Broeke et al., 2013; Vanneste et al., 2018; power was reported for neurogenic pain and MS-related NP (Kim et al., Walton et al., 2010) or a large group of patients from multiple pain 2019; Sarnthein et al., 2005; Stern et al., 2006), while increased beta conditions (Dinh et al., 2019; Vanneste et al., 2018). This likely led to and gamma was reported in fibromyalgia (Lim et al., 2016). PAF the variability in findings across studies and suggests that there is no slowing was reported for central NP, NP following spinal cord injury consistent brain activity marker of chronic pain per se. However, (SCI), chronic pancreatitis, fibromyalgia and MS-related NP temporal abnormalities in brain activity are reported more often in NP (Boord et al., 2008; Vries et al., 2013; Kim et al., 2019; Lim et al., 2016; than in NNP and these may also be different for peripheral versus Sarnthein et al., 2005; Vuckovic et al., 2014; Wydenkeller et al., 2009). central NP. However, no abnormalities were found for low back pain patients Different frequency bands of resting state brain activity are asso- (Schmidt et al., 2012). Given both the similarities and discrepancies ciated with multiple and distinct functions (Buzsaki, 2006). Under- across studies, the Ploner group (Dinh et al., 2019) sought to identify an standing these associations can provide insight into the temporal ab- EEG marker across multiple pain conditions but did not find striking normalities that are often reported in chronic pain. In healthy 8 L.B. Kisler, et al. NeuroImage: Clinical 26 (2020) 102241 Fig 6. Compared to NNP, NP patients have increased alpha power in the ascending noci- ceptive pathway. Average resting state MEG activity in the bilateral thalamus, primary so- matosensory cortex (S1), secondary somato- sensory cortex (S2), and posterior insula (pINS) of the ascending nociceptive pathway is shown for the mix inflammatory and neuropathic pain (NP) subgroup (pink) and inflammatory none- neuropathic pain (NNP) subgroup (green). Increased power in the alpha (8–13 Hz) band in the NP compared to NNP subgroup is shown. Lines indicate mean ± SE. Significant group differences (indicated by *) was set using false discovery rate (FDR, p < 0.05) corrected for the number of regions. Regions that survived correction for the multiple frequencies are marked in a red frame. Brain images are dis- played using the radiological convention. d – the maximal effect size of significant differ- ences within the region. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article). individuals, alpha and beta band activity is high at rest and decreases (Pietro et al., 2018; Llinas et al., 1999), increased cortical theta power when a sensory stimulus is presented, whereas gamma and theta ac- in chronic pain may be due to thalamocortical dysrhythmia, resulting tivity show the opposite pattern (Hanslmayr et al., 2011). from increased thalamic theta firing activity. As the thalamic activity Our finding of increased alpha and theta power and decreased was not elevated in our cohort, this explanation is less likely. None- gamma power in NP could be attributed to several factors related to the theless, this controversial model (Dinh et al., 2019) is supported by temporal features and location of the aberrant activity. In our study, studies (Pietro et al., 2018; Sarnthein and Jeanmonod, 2008; compared to HCs, patients who were also likely to have a NP compo- Sarnthein et al., 2003; Vanneste et al., 2018) showing a therapeutic nent showed increased theta power. As suggested by others lesion to the thalamus reduces excessive theta activity in neurogenic 9 L.B. Kisler, et al. NeuroImage: Clinical 26 (2020) 102241 Fig 7. Compared to NNP, NP patients have in- creased alpha power in the salience network. Average resting state MEG activity in the right temporoparietal junction (TPJ), anterior insula (aINS), mid-cingulate cortex (MCC) and dorso- lateral prefrontal cortex (DLPFC) of the salience network is shown for the mixed inflammatory and neuropathic pain (NP) subgroup (pink) and in- flammatory none-neuropathic pain (NNP) sub- group (green). Increased power in the alpha (8–13 Hz) band in the NP compared to NNP sub- group is shown. Lines indicate mean ± SE. Significant group differences (indicated by *) was set using false discovery rate (FDR, p < 0.05) corrected for the number of regions. Brain images are displayed using the radiological convention. d – the maximal effect size of significant differences within the region. (For interpretation of the refer- ences to color in this figure legend, the reader is referred to the web version of this article). pain (Sarnthein et al., 2005; Stern et al., 2006). This is also supported pain, gamma oscillations were observed in response to tonic by pain models in animal studies (LeBlanc et al., 2017; Leblanc et al., (Nickel et al., 2017; Schulz et al., 2015) and brief (Gross et al., 2007; 2014). Hauck et al., 2007) pain stimuli. Notably, increase in gamma oscillation We identified increased alpha power in the NP patients within was also reported in relation to ongoing chronic pain intensity, but this multiple nodes of the DPC compared to HCs, and in the ANP when effect disappeared when the effect of time was considered (May et al., compared to the NNP patients. This suggests that increased alpha ac- 2019). Together, we suggest that both increased alpha and decreased tivity is unique to patients who also show signs of NP, at least AS-re- gamma power seem to support an inward attentive state in patients that lated NP. Additionally, the NP patients showed decreased gamma are likely to have NP. As chronic pain patients show increased attention power. Alpha band activity is associated with attention (Palva and to pain-related stimuli (Schoth et al., 2012) and alpha activity is also Palva, 2011), but alpha and gamma activity may have different roles in associated to pain expectation and negative pain-related affect attention and executive function (Doesburg et al., 2008). A review (Albu and Meagher, 2016), the increased alpha power might reflect paper on the role of alpha oscillation suggested that high alpha activity pain vigilance as well as rumination and magnification. In line, alpha may point towards an ‘internal attentive state’ where the attention is power in the NP subgroup was positively associated to higher trait-pain. oriented inward rather than outward. In this case, the appearance of an This association was not observed in the inflammatory-NNP patients, in external stimulus is more likely to be missed (Hanslmayr et al., 2011; which alpha power was negatively associated with disease activity. O'Connell et al., 2009). Notably, gamma activity is elevated when we Given the abnormal alpha activity in NP, increased alpha in this po- attend to an external stimulus (Doesburg et al., 2008). Specifically, in pulation may be associated with detrimental symptoms (e.g. increased Fig 8. Compared to NNP, NP patients have in- creased alpha power in the default mode network. Average resting state MEG activity in the posterior cingulate cortex (PCC) and the medial prefrontal cortex (mPFC) of the default mode network is shown for the mixed inflammatory and neuro- pathic pain (NP) subgroup (pink) and in- flammatory none-neuropathic pain (NNP) sub- group (green). Increased power in the alpha (8–13 Hz) band in the NP compared to NNP sub- group is shown. Lines indicate mean ± SE. Significant group differences (indicated by *) was set using false discovery rate (FDR, p < 0.05) corrected for the number of regions. Brain images are displayed using the radiological convention. d – the maximal effect size of significant differences within the region. (For interpretation of the refer- ences to color in this figure legend, the reader is referred to the web version of this article). 10 L.B. Kisler, et al. NeuroImage: Clinical 26 (2020) 102241 Fig 9. Increased alpha power in the ascending nociceptive pathway is associated with lower disease activity in NNP patients and with higher clinical pain in NP patients. Spearman correlation between the alpha power and disease activity (i.e., BASDAI) is shown for the left secondary somatosensory cortex (S2) of the ascending nociceptive pathway. Spearman correlation between the alpha power and trait clinical pain intensity (average pain over the last 4 weeks) is shown for the right primary somatosensory cortex (S1) of the ascending nociceptive pathway. Associations are shown in the entire ankylosing spondylitis (AS) chronic pain group (left), in the mixed inflammatory and neuropathic pain (NP) subgroup (pink, middle), and in the matched inflammatory none-neuropathic pain (NNP, right). Significant correlation is indicated by *. The lines are only meant to visually show treadlines to demonstrate the direction of the Spearman relationship and do not indicate a linear relationship. Brain images are displayed using the radiological convention. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article). pain). However, NNP show normal alpha activity and for these patients, contrast, to the patients in the NNP subgroup which clearly had only increased alpha may be associated with protective qualities (e.g. re- inflammatory pain and was relatively homogenous. Second, we note duced disease activity). Importantly, our measure of disease activity is that none of the patients were prescribed medication specifically for based on the BASDAI score that reflects the inflammatory nature of AS NP. Third, given the small n in the NP subgroup, the abnormalities pain. Nonetheless, some patients also suffer from a mix of in- identified should be interpreted with care (Button et al., 2013) and flammatory-NP. Our results suggest that the mechanisms for pain in the future studies are needed to determine if the findings replicate across alpha band are different between these two subgroups. other pain conditions. NP patients showed temporal abnormalities in the DMN and SN. The Finally, some factors inherent to studies of chronic pain should be DMN is active during rest and deactivated during a task while the SN considered. Depression was unlikely to have contributed to the in- shows the opposite pattern and is associated with the level of attention creased alpha power in the NP patients because it did not differ be- (Kucyi and Davis, 2015). These networks show abnormalities in chronic tween the NP and NNP subgroups. We also considered age effects given pain (Baliki et al., 2008; Bosma et al., 2018; Hemington et al., 2016; that the NP group was older than the NNP group. Age effects have been Kim et al., 2019; Kucyi and Davis, 2015) that might relate to the effect reported to impact PAF and spectral power (Giaquinto and Nolfe, 1986; chronic pain has on attention and executive function (Berryman et al., Hashemi et al., 2016), and accelerated gray matter aging has been 2014; Kucyi and Davis, 2015; Schoth et al., 2012). Temporal abnorm- shown in chronic pain patients (Kuchinad et al., 2007; Moayedi et al., alities were also observed in the ANP that include pain processing areas. 2012). However, our finding that the NP patients also showed temporal Importantly, pain and disease activity were associated with the spectral abnormalities when compared to age/sex-matched NNP patients pro- power in various nodes of the DPC but this was more pronounced in the vide evidence that our findings were not solely resulting from an age ANP. This supports other studies that showed associations between pain effect. A possible limitation of the study is that we were not able to and the ANP (Pietro et al., 2018; Kim et al., 2019). completely rule out the effect of sex due to the smaller number of We do note a few considerations to interpret our findings con- women in the study (AS is twice as prevalent in men than in women servatively: First, we previously demonstrated that chronic pain in AS is (Braun and Sieper, 2007)). Nonetheless, 50% of the mixed in- typically inflammatory or a mix of inflammatory and neuropathic pain flammatory-NP patients were women likely because NP is more pre- (see our previous psychophysical study) (Wu et al., 2013). We divided valent in women (Bouhassira, 2019). Furthermore, given known sex the patients into those who are likely to have some NP (in addition to differences in pain mechanisms (Hashmi and Davis, 2014; Jausovec and their inflammatory pain) and those with solely inflammatory-NNP Jausovec, 2010; Mogil, 2012; Wang et al., 2014), the abnormalities that based on painDETECT scores. We included in the NP subgroup patients we found might be different between the sexes. Also, the results of the with scores of 13–18 because these patients could have some NP in correlation analysis should be taken with care as it was not corrected 11 L.B. Kisler, et al. NeuroImage: Clinical 26 (2020) 102241 for multiple comparisons. Finally, MEG findings from deep subcortical modeling of subcortical activity using magnetoencephalographic/electroencephalo- graphic data. Rev. Neurosci. 23 (1), 85–95. areas should be considered with care as signal extraction from deep Attal, Y., Schwartz, D., 2013. Assessment of subcortical source localization using deep sources is less reliable with MEG, although we employed a source re- brain activity imaging model with minimum norm operators: a MEG study. PLoS ONE construction method (beamforming) that increases the signal from the 8 (3), e59856. Baliki, M.N., Geha, P.Y., Apkarian, A.V., Chialvo, D.R, 2008. Beyond feeling: chronic pain ROI and supresses it from the surrounding areas (Hillebrand and hurts the brain, disrupting the default-mode network dynamics. J. Neurosci. 28 (6), Barnes, 2005). Nonetheless, the thalamus results only represent a minor 1398–1403. aspect of our findings and methods such as beamforming were shown to Bardouille, T., Ross, B., 2008. MEG imaging of sensorimotor areas using inter-trial co- herence in vibrotactile steady-state responses. Neuroimage 42 (1), 323–331. locate signals in deep structures (Attal et al., 2012; Attal and Beck, A.T., Steer, R.A., Carbin, M.G, 1988. Psychometric properties of the Beck depression Schwartz, 2013; Bardouille and Ross, 2008; Cornwell et al., 2007; inventory: twenty-five years of evaluation. Clin. Psychol. Rev. 8 (1), 77–100. Cornwell et al., 2008; Cornwell et al., 2008; Dumas et al., 2013; Benjamini, Y., Hochberg, Y., 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Stat. Methodol. 57 (1), Hamada et al., 2004; Quraan et al., 2011; Roux et al., 2013; 289–300. Tewarie et al., 2013). Berryman, C., Stanton, T.R., Bowering, K.J., Tabor, A., McFarlane, A., Moseley, G.L, 2014. In conclusion, we propose that high theta and low gamma activity in Do people with chronic pain have impaired executive function? A meta-analytical review. Clin. Psychol. Rev. 34 (7), 563–579. the dynamic pain connectome may be general markers of chronic in- Boord, P., Siddall, P., Tran, Y., Herbert, D., Middleton, J., Craig, A, 2008. flammatory pain but high alpha-band activity may only be present Electroencephalographic slowing and reduced reactivity in neuropathic pain fol- when neuropathic pain is likely. lowing spinal cord injury. Spinal Cord 46 (2), 118. Bosma, R.L., Kim, J.A., Cheng, J.C., et al., 2018. Dynamic pain connectome functional connectivity and oscillations reflect multiple sclerosis pain. Pain 159 (11), Funding 2267–2276. Bouhassira, D, 2019. Neuropathic pain: definition, assessment and epidemiology. Rev. This work was supported by the Canadian Institute of Health Neurol. (Paris) 175 (1–2), 16–25. Bouhassira, D., Lantéri-Minet, M., Attal, N., Laurent, B., Touboul, C, 2008. Prevalence of Research (PJT 162347, and SCA-145102 Strategy for Patient-Oriented chronic pain with neuropathic characteristics in the general population. Pain 136 (3), Research (SPOR) funding of the Canadian Chronic Pain Network), and 380–387. The Mayday Fund. J.A. Kim was supported by an MS Society of Canada Braun, J., Sieper, J, 2007. Ankylosing spondylitis. Lancet 369 (9570), 1379–1390. Button, K.S., Ioannidis, J.P., Mokrysz, C., et al., 2013. Power failure: why small sample endMS Doctoral Studentship Award, K.S. Hemington, A. Rogachov and size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14 (5), 365–376. J.C. Cheng were supported by CIHR Doctoral Research Award and R.L. Buzsaki, G., 2006. Rhythms of the Brain. Oxford University Press. Bosma was a recipient of a CIHR Post-doctoral Research Award. Cheng, J.C., Rogachov, A., Hemington, K.S., et al., 2018. 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NeuroImage : ClinicalPubmed Central

Published: Mar 13, 2020

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