The Endogenous Analgesia Signature in the Resting Brain of Healthy Adults and Migraineurs

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The Endogenous Analgesia Signature in The Resting Brain of Healthy Adults and Migraineurs Yuval Argaman , Lee B. Kisler , Yelena Granovsky , Robert C. Coghill , Elliot Sprecher , David Manor , Irit Weissman-Fogel PhD PII: DOI: Reference:

S1526-5900(19)30883-1 https://doi.org/10.1016/j.jpain.2019.12.006 YJPAI 3831

To appear in:

Journal of Pain

Received date: Accepted date:

15 October 2019 19 December 2019

Please cite this article as: Yuval Argaman , Lee B. Kisler , Yelena Granovsky , Robert C. Coghill , Elliot Sprecher , David Manor , Irit Weissman-Fogel PhD , The Endogenous Analgesia Signature in The Resting Brain of Healthy Adults and Migraineurs, Journal of Pain (2020), doi: https://doi.org/10.1016/j.jpain.2019.12.006

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Highlights 

Episodic migraine patients can inhibit pain as efficiently as healthy individuals.



The resting-state connectivity of pain inhibitory areas is altered in migraineurs.



This difference is related to the individual pain inhibitory efficiency.

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Title The endogenous analgesia signature in the resting brain of healthy adults and migraineurs Running title Pain Inhibition Efficiency Reflected in the Resting Brain Authors Yuval Argaman1#, Lee B. Kisler1#, Yelena Granovsky1,2, Robert C. Coghill4, Elliot Sprecher2, David Manor5,6, Irit Weissman-Fogel3* 1

Laboratory of Clinical Neurophysiology, Technion Faculty of Medicine, Haifa, Israel;

2

Department of Neurology, Rambam Health Care Campus, Haifa, Israel; 3Faculty of Social

Welfare and Health Sciences, University of Haifa, Haifa, Israel; 4Department of Anesthesiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA; 5MRI Unit, Rambam Health Care Campus, Haifa, Israel; 6Faculty of Natural Sciences, University of Haifa, Haifa, Israel. # equal contribution Funding This study was financially supported by the United States–Israel Binational Science Foundation, grant number 2009097. All authors declare no conflict of interest. Corresponding author Irit Weissman-Fogel, PhD; Dept. of Physical Therapy, Faculty of Social Welfare & Health Sciences, University of Haifa, Haifa, Israel; Phone: +972-4-8288398; Fax: +972-4-8288140, Email: [email protected]

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Abstract Altered pain modulation and resting state functional connectivity (rsFC) were found to be related to migraine pathology and clinical manifestation. We examined how pain modulation psychophysical measures are related to resting-state networks (RSNs) and rsFC between bottomup and top-down pain modulation areas. Thirty-two episodic migraineurs and 23 age-matched healthy individuals underwent temporal summation of pain (TSOP) and conditioned pain modulation (CPM) tests, followed by a resting-state imaging scan. No differences in TSOP and CPM were found between groups. However, in healthy individuals, more efficient CPM was correlated with: 1) stronger rsFCs of the posterior cingulate cortex (PCC), with the ventromedial prefrontal cortex (vmPFC) and with the pregenual anterior cingulate cortex (pgACC); 2) weaker rsFC of the anterior insula with the angular gyrus. However, in migraineurs, the association between CPM and rsFC was altered. Our results suggest that the functional connectivity within the default mode network (DMN) components and the functional coupling between the DMN and pain inhibitory brain areas is linked with pain inhibition efficiency. In migraineurs, this interplay is changed, yet enables normal pain inhibition. Our findings shed light on potential functional adaptation of the DMN and its role in pain inhibition in health and migraine.

Perspective This article establishes evidence for the relationship between the resting-state brain and individual responses in psychophysical pain modulation tests, in both migraine and healthy individuals. The results emphasize the significant role of the default mode network in maintaining pain inhibition efficiency in heath and in the presence of chronic pain.

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Key words Conditioned pain modulation, resting-state, imaging, networks, migraine

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Introduction Imaging studies link migraine pathophysiology to functional abnormalities in pain modulatory brain areas

57,75

. When interictal migraineurs are exposed to evoked pain, the pain-modulatory

areas become hyperactive, and the level of hyperactivity is associated with attack frequency and pain intensity

56,63,81,96

. Migraineurs also exhibit an association between migraine clinical

characteristics, and altered resting-state functional connectivity (rsFC) of mesencephalic, cingulate, insular and prefrontal brain areas involved in affective processing and descending inhibition of pain. 17,55. rsFC can also manifest as resting-state networks (RSNs) - specific patterns of synchronized activity in spatially-distinct brain regions across studies in chronic pain diseases

25,31,88

26,59,60

. Three of these networks appear consistently

, including migraine: 1) the default-mode network

(DMN), composed of mainly the medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), and angular gyri

32,102

; 2) the salience network (SN), consisting of the anterior insula

(aINS) and the dorsal anterior cingulate cortex (ACC) (dACC)

101

; and 3) the executive control

network (ECN), composed of the posterior parietal and lateral prefrontal cortices 21. Migraineurs have exhibited lower intrinsic DMN and ECN rsFC, and lower ECN-SN rsFC

94,95

. Also,

stronger rsFC of the right aINS with the DMN and ECN positively correlated with migraine duration 110, suggesting clinically-relevant functional alterations in RSNs in migraine. Moreover, these RSNs are linked to pain processing; in particular, stronger rsFCs of the DMN with the periaqueductal gray (PAG), as well as higher rsFC within the SN, were associated with enhanced individual pain inhibition ability in healthy individuals 51,50. At the psychophysical level, studies employing the conditioned pain modulation (CPM) paradigm

111

, a laboratory test evaluating top-down pain inhibitory system’s efficiency, reveal 5

inconsistent results when comparing migraineurs with healthy individuals. While some studies show deficient CPM in migraineurs health

23,48,76,92

40,83,97,98

, others demonstrate CPM levels comparable to

. Neuroimaging of CPM-related brain activity in healthy individuals includes:

decreased activity in bottom-up pain processing areas, including the posterior insula, the somatosensory cortex, and the thalamus

68,89

in top-down pain processing e.g. the PFC

68

; and decreased deactivation in regions participating

. Additionally, greater CPM efficiency is related to

increased functional connectivity of the PAG with the subgenual and pregenual ACC (pgACC) during CPM

89

. Alternatively, bottom-up pain modulation can be measured by temporal

summation of pain (TSOP), which reflects the wind-up response to repeated painful stimuli. Enhanced TSOP was found in interictal migraineurs, compared to healthy individuals

98,76,107,80

.

In healthy individuals, TSOP is related to activations in the thalamus, primary and secondary somatosensory cortices, posterior INS 90, aINS, cingulate and prefrontal affective-cognitive brain regions

11,90

. In addition, stronger rsFC between the thalamus and the primary somatosensory

cortex (S1), and lower rsFC between the subgenual ACC and the rostroventral medial medulla (RVM), were associated with higher TSOP magnitude in healthy individuals 18. Overall, the above suggest that psychophysical pain correlates can be driven by the balance of resting or active synchronization between bottom-up and/or top-down pain processing regions. However, evidence for migraine-specific associations between rsFC and psychophysical pain measures is absent. We chose to use both exploratory and hypothesis-driven analysis approaches to investigate possible associations of rsFC with CPM or TSOP. We hypothesized that in healthy individuals :1) lower CPM efficiency will be associated with decreased rsFC between top-down pain modulation regions, such as the dorsolateral and ventromedial PFC (dlPFC and vmPFC, respectively), aINS, and pgACC ; 2) greater TSOP will be related to increased rsFC between

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bottom-up pain modulation areas, such as the thalamus, S1, and the posterior insula (pINS); and 3) lower rsFC between pain modulation areas and the DMN and the SN will correlate with lower CPM magnitudes. We expect that in episodic migraineurs, the associations of RSN and the rsFC of pain modulation areas with the magnitudes of CPM and TSOP will be disrupted. We did not have any hypothesis about the directionality of the disruption.

Methods Participants Sixty-four right-handed migraineurs and 35 healthy participants were enrolled in the trial. All migraine patients had fulfilled the inclusion criteria set by the International Classification of Headache Disorders for the diagnosis of migraine (Beta version, 2013) and were screened by a neurologist who confirmed the migraine diagnosis. Additionally, patients were included in the study only if they suffered an average of 4-15 migraine attacks/days per month and did not use migraine prophylactic therapy during the 3 months preceding the study. Exclusion criteria included psychiatric, cognitive and/or neurological deficit; acute and/or chronic pain disorders (except migraine in the patient group); use of medication on a regular basis (except for oral contraceptives and analgesics for migraine acute relief); inability to undergo an MRI scan (e.g. claustrophobia); inability to provide informed consent, communicate and understand the purpose and instructions of the study; inability to endure the painful stimuli administered in the psychophysical tests; and test alone (Ts-alone) pain ratings lower than 4/10111 during the familiarization set of the first meeting. Participants were asked to avoid alcohol and painprophylactic consumption 24 hours before the experiment, and to refrain from caffeine 3 hours before the experiment. Migraine patients were examined at least 24 hours after their last attack.

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The institutional review board of the Rambam Healthcare Campus approved the study protocol in accordance with the Helsinki Declaration. The experiment was conducted in the Rambam Health Care Campus, Haifa, Israel, during the period of May 2013-Nov 2016. Written informed consent was obtained from each subject before the beginning of the experiment. The final cohort included 23 healthy participants (3 males; median age 26.0 years, range 20.0-39.0 years), and 32 migraineurs (5 males; median age 26.0 years, range 23.0-44.0 years; 16 migraineurs with aura). Twelve healthy controls and 32 migraineurs were excluded on the following grounds: 8 healthy individuals and 5 migraineurs had Ts-alone pain ratings of less than 4 when familiarized with the experimental pain tests; 3 migraineurs could not endure the test stimulus for its whole duration; 2 healthy individuals and 4 migraineurs were claustrophobic inside the scanner; 2 healthy controls and 6 migraineurs exhibited excessive motion inside the scanner; 1 migraineur had a lesion in the temporal lobe; data from 1 migraineur could not be preprocessed; 5 migraineurs could not schedule an MRI scan; 6 migraineurs had an attack during the scan; and 1 migraineur had trouble rating her pain. This paper presents the results of pain modulation brain-related functional coupling in migraineurs and healthy individuals. It is part of a larger longitudinal study (ClinicalTrials.gov identifier: CT01470339) that examined the predictive value of psychophysical measures of endogenous analgesia (EA) and structural and functional MRI for migraine prevention therapy. Experimental Design Psychophysical tests took place outside the scanner in a separate session, on a different day within a week before the MRI scan. All participants completed the state-trait anxiety inventory (STAI) and pain catastrophizing scale (PCS) questionnaires in a randomized order. Following

8

this, they were familiarized with the heat and cold stimuli and the pain rating process and were screened for the experimental stimuli in the CPM paradigm. At this point, participants with average rating of ≤4 for the test-alone (Ts-alone) were excluded and did not complete the study. After a 10 min break, the TSOP for the forehead and forearm were performed, followed by the CPM assessment, with an interval of 10 min between the TSOP and CPM tests. We also performed other psychophysical tests, that are reported elsewhere 48. Clinical outcome measures Migraineurs included in the study were interviewed regarding their clinical characteristics, including number of years they had suffered from migraine, aura, migraine triggers, and regular medication regimen. Also, in relation to the month preceding study inclusion, migraineurs were asked about: the number of attacks; attack duration; and mean and maximum pain during an attack while under medication, rated on a 0-10 numerical pain scale (NPS). Psychological outcome measures Anxiety was tested using the validated Hebrew version of Spielberger’s State-Trait Anxiety Inventory

93

. This questionnaire is composed of 2 twenty-item parts relating to one’s current

(state) and tendency towards (trait) anxiety. Participants were requested to rate each statement on a 5-point Likert-like scale (0 to 4). This questionnaire is considered reliable (Cronbach’s  > 0.89) 6. Pain catastrophizing was assessed by administration of the validated Hebrew version of the Pain Catastrophizing Scale (PCS) questionnaire

37

. The PCS questionnaire includes 13 items

corresponding to rumination (e.g. “I can’t seem to keep it out of my mind”); magnification (e.g.

9

“I wonder whether something serious may happen”); and helplessness (e.g. “There is nothing I can do to reduce the intensity of pain”). Participants were asked to rate how well each item relates to their experience of pain, on a 5-point Likert-like scale (0-4). This questionnaire is also considered reliable (Cronbach’s  0.86) 37. CPM paradigm The CPM test was conducted outside of the MRI scanner and included 2 series of stimuli. Each series included 3 stimuli of either a test stimulus applied alone (Ts-alone) or simultaneous to a conditioning stimulus (Ts+Cs). The order of these series was counterbalanced across participants within each group. The Ts was applied to the left volar forearm using a 3x3 cm2 thermode (PATHWAY, Medoc, Israel). It consisted of a 47°C contact heat stimulus for 30 secs, with increase and decrease rates of 3 and 6oC/sec, respectively. Of note, a fixed temperature of 47°C was chosen based on a pilot study; using this stimulus we found that pain ratings had remained relatively stable over its course. This temperature was less likely to cause habituation compared to lower temperatures that are often used in personally-calibrated stimuli, which also exhibit wider inter-individual variability

106

. The baseline temperature between stimuli was 350C.

Immersion of the right foot in a cold-water bath (90C) for 76 secs, served as the Cs. The temperature of the water was kept steady using ice. In case participants had a difficulty holding their foot in the water for the entire length (76 sec) of leg immersion, the temperature was a bit increased (max 120C). During the Ts-alone series, the Ts was repeated 3 times with a 120-sec inter-stimulus interval (ISI). In the Ts+Cs series, 30 secs after commencement of foot immersion (i.e. Cs), the Ts was applied. This was repeated 3 times, with an ISI of 78-sec between each Cs. Following each Ts, either in the Ts-alone or the Ts+Cs, the thermode was moved to an adjacent

10

area of the skin to avoid sensitization or habituation. Four and 8-min inter-series intervals were kept following the Ts-alone and Ts+Cs series, respectively 66. Participants reported their Ts pain level continuously but reported their Cs pain only once, upon its termination. Ratings were collected using a computerized visual analog scale (VAS) with anchors of no pain and most intense pain imaginable. The VAS was presented on a computer screen and subjects used a computer mouse to indicate their pain level on the VAS. CPMmagnitude was calculated by subtracting the averaged pain ratings for the Ts during the Ts-alone series from the Ts' ratings during the Ts+Cs series (i.e. Ts+Cs minus Ts-alone). Therefore, efficient CPM is denoted as a negative CPM-magnitude. TSOP paradigm Mechanical TSOP was tested in 2 body locations: the left volar forearm and the left side of the forehead. Migraineurs with predominantly right-side pain were tested on the right side of the forehead. The two locations were randomized in their order of testing, with a 10 min break between locations. In each location, a single stimulus was applied using a 180gr von-Frey filament (Bioseb, France), followed by 10 consecutive stimuli with an ISI of 1-2 sec within an area of 1 cm2. Participants were asked to rate their pinprick pain intensity using a 0-100 NPS, after the first and last stimulus of the series. The difference between the 2 NPS scores was considered as the TSOP measure. Statistical analysis of psychological and psychophysical outcome measures Statistical analysis was performed on the RStudio platform, version 1.0.136 (Boston, MA), running R version 3.3.2 (R Core Team (2016). R: A language and environment for statistical

11

computing; R Foundation for Statistical Computing, Vienna, Austria). The significance threshold for all tests was chosen as p<0.05. For the included participants, Analysis of between-group sex differences were carried out using Fisher’s Exact Test. The parameters of age, STAI and PCS scores, Ts-alone, Ts+Cs, CPMmagnitudes (i.e. Ts+Cs minus Ts-alone), and TSOP (i.e. the difference in pain ratings between the first and last stimuli on the forehead and volar forearm) were first tested for normality using quantile-quantile plots and Shapiro-Wilk test. Subsequently, proper hypothesis tests (student’s t, Wilcoxon’s, or median tests) were applied to examine between-group differences in these outcome measure. Also, within-group effects in Ts-alone vs. Ts+Cs for CPM, and first vs. last for TSOP within each location, were examined by paired Student’s t-test, or paired Wilcoxon’s signed rank test, or paired median test. Descriptive statistics appear as mean±SD, or median with range, respectively. Due to the association between experimental pain responses and age 27, as well as psychological measures such as PCS 16,108, we aimed to control for such relative contribution to between-group differences in CPM and TSOP. PCS, but not age or STAI, differed between our study groups. Therefore, a 1-way analysis-of-covariance (ANCOVA) was conducted to test between-group differences in psychophysical outcome measures by using group as an independent variable while controlling for the effect of PCS. Finally, several participants were excluded for different reasons e.g. excessive motion during the MRI. Therefore, to check for potential selection bias, we performed additional analyses to test for group differences in age, psychological and psychophysical outcome measures in the entire cohort, that also included participants who were excluded (see supplementary information).

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Imaging MRI scans were performed at the imaging unit of Rambam Healthcare Campus (Haifa, Israel), on a 3T General Electric scanner (Signa MR750, GE Medical Systems) with an 8-channel phased-array head coil. Participants were placed inside the scanner in a supine position, with ear plugs to reduce scanner noise and foam pads to minimize head motion. First, a high-resolution T1 structural image was collected, using a spoiled-gradient recall (SPGR) sequence with a voxel size of 1x1x1 mm, flip angle = 12; matrix size = 256×256, slices = 172, field of view (FOV) = 25.6 cm. Then, a 10-min resting-state fMRI (rsfMRI) scan was acquired with a whole-brain gradient echo-planar imaging (EPI) sequence with a voxel size of 3.4x3.4x3.4 mm, TR = 2000 ms, TE = 30 ms, flip angle = 75°, matrix size = 64x64 mm, slices = 43, FOV=22 cm. During the rsfMRI scan, participants were instructed to remain awake with eyes closed, and to clean their head of thoughts. A total of 300 volumes were collected for rsfMRI. Image pre-processing Pre-processing was carried out using Statistical Parametric Mapping (SPM8, Wellcome Department, London, UK) and CONN functional connectivity toolbox, version 17.0f

109

. First, a

rigid-body transformation in 6 directions (3 translational, 3 rotational) of functional and anatomical images was performed to align them to the anterior commissure. Next, slice-timing correction of the functional scans was performed to compensate for differences in slice-time acquisition, discarding the first 6 functional volumes. Thereafter, functional images were realigned and motion-corrected using the middle slice as a reference, yielding individual motion parameters. Participants who exceeded the motion constraints of 1.5mm translational and 1.5° rotational dislocations were excluded. Following realignment, anatomical and mean functional

13

images were co-registered to one another. Then, functional images were spatially normalized to a standard template (Montreal Neurological Institute, MNI152), with a resampled voxel size of 3×3×3 mm. Finally, images were smoothed with a 6 mm Gaussian kernel. To yield a more accurate BOLD map, anatomical images were segmented in CONN into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) 4, which were normalized into standard space, and were then used to create implicit GM masks for each subject. Signal timecourses of WM, CSF, and motion nuances were then regressed out of the BOLD signal. Additionally, the BOLD signal for each participant was de-spiked, corrected for temporal drift using linear de-trending, and band-pass filtered (0.01-0.1Hz). These corrections yielded voxel residual BOLD time-course maps for each participant. Resting state functional connectivity (rsFC) analyses First-level Group independent component analysis Group independent-component analysis (gICA) was performed as previously described 1. Briefly, voxel-wise data from all participants was concatenated to create group maps. Then, principle component analysis was used to reduce data dimensionality, yielding 64 principle components. Then, group data reduction was performed using the Fast-ICA algorithm

44

, in

which 25 independent components (IC) were retained. Finally, in the back-reconstruction step, time-courses from each IC were regressed onto the individual participants’ spatial maps, resulting in individual beta maps for each IC and each participant. The DMN, SN and ECN were identified by trained individuals (I.W.F and Y.A), and by calculating the spatial match between our ICs and a priori RSN templates using Dice’s Coefficient 12.

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Seed-to-whole-brain correlation analysis (SCA) SCA was performed by placing spherical regions-of-interest (ROIs) in MNI space in brain regions involved in either TSOP (i.e. ascending, bottom-up, or magnitude-location encoding) or CPM (descending, top-down, or pain-inhibiting). This selection was based on previous fMRI studies that examined their pain-related activation and/or connectivity in relation to these tests 7– 9,18,84,85,89

(Figure legends

Fig. 1). Brain areas involved in processing of ascending sensory information corresponding to the hand were represented in ROIs located at the left and right primary somatosensory cortex (S1; x=-30 y=-7 z=68 and x=30 y=-7 z=68, respectively); while ascending pathways corresponding to the face were located at the left and right S1 (x=-58 y=-14 z=41 and x=58 y=14

z=41, respectively). The left and right thalamus were demarcated using Automated

Anatomical Labeling masks, rather than pre-defined spheres (x=-12 y=-17 z=9 and x=12 y=17 z=9, respectively) 100. In addition, the left and right posterior insula (pINS; x=-38 y-=11 z=7 and x=38 y=-11 z=7, respectively), which are involved in pain magnitude estimation 91 were included as part of the ascending pain pathways. Fig. 1 goes here ROIs representing top-down pain inhibitory areas included the: left and right amygdala (x=-22 y=2 z=-20, and x=22 y=2 z=-20, respectively) 15; left and right pgACC (x=-7 y=39 z=2 and x=7 y=39 z=2, respectively)

99,105

; left and right aINS (x=-35 y=16 z=3 and x=35 y=16 z=3,

respectively) 91; left and right ventromedial prefrontal cortex (vmPFC) (x=-9 y=56 z=-12 and x=9 y=56 z=-12, respectively) 41; and lateral orbitofrontal cortex (x=-43 y=30 z=-11 and x=40 y=31 z=-14, respectively) 45. Cortical ROIs were defined as 6mm diameter spheres, while subcortical

15

areas and the OFCs were defined as 3mm diameter spheres. Then, for each participant, ROI-towhole-brain connectivity maps were constructed using a simple bivariate correlation model. Second-level analysis First, simple between-group differences in connectivity maps of ROIs in SCA, and RSNs in gICA, were investigated without including any psychophysical outcome measures as covariatesof-interest. Next, we were interested in whether the relationship between quantitative sensory testing (QST) and rsFC in either neuroimaging analysis differed between groups. For this, in CONN, we used a general linear model (GLM): RSN or seed rsFC values were the dependent variable, and group-by-TSOP or group-by-CPM interactions were the independent variables. In this analysis, as previously suggested

33

, PCS was added as a covariate in this model, as it

differed between groups. Importantly, the gICA of the DMN, SN and ECN was conducted only for CPM, given the role of their constituting brain regions in pain modulation. Conversely, in SCA, 2 analyses were performed: pain bottom-up sensory ROIs with TSOP, and pain top-down inhibitory ROIs with CPM. In all analyses, statistical threshold was set as p<0.001 at the voxellevel, and as p<0.05 at the false-discovery-rate (FDR)-corrected cluster-level. Also, in SCA, a further Bonferroni correction for the number of ROIs (n=8 in bottom-up and n=10 in top-down pathways) was applied. Cluster-level significance in each analysis method was tested using a non-parametric permutation testing, with 5000 permutations

73

. To visualize the correlations

between rsFC of each seed-region and CPM-efficiency, individual CPM-magnitudes were plotted against their respective Fisher-transformed connectivity values that were extracted from clusters that showed significant interactions. Finally, for migraineurs only, we also tested the correlation between these Fisher-transformed rsFC values and clinical variables (i.e. disease duration, mean and max clinical pain with drug), using Spearman’s rank-order test. 16

Results

Demographic, clinical and psychological parameters Age and state and trait anxiety were not normally distributed in either group. However, the distribution of age in migraineurs and healthy individuals was similar, thus a median test was used to test for group differences in age. The distribution of STAI in migraineurs differed from that in healthy individuals, so a Wilcoxon’s test was used to test for group differences in anxiety. No differences were found for age (healthy individuals: median 26.0, range 20.0-39.0; migraineurs: median 26.0, range 23.0-44.0; median test p=0.756), sex (Fisher’s Exact Test: p=1.000), state anxiety (healthy participants: median 28.0, range 20.0-46.0; migraineurs: median 28.0, range 20.0-57.0; Wilcoxon: p=0.771) and trait anxiety (healthy participants: median 33.0, range 23.0-54.0; migraineurs: median 28.0, range 22.0-53.0, Wilcoxon: p=0.976). PCS scores distributed normally; migraineurs had significantly higher PCS scores (mean±SD) than healthy participants: 31.60±10.30 versus 19.20±8.50, respectively; t-test p<0.001. Thus, the effects of the individual PCS scores were controlled for in the subsequent psychophysical and imaging analyses. In migraineurs, disease duration and max pain during an attack were not normally distributed. The median disease duration in years was 10.0, range 3.0-30.0; monthly attack frequency was 5.0±2.2 (mean±SD); mean pain during an attack was 48.0±20.4 (mean±SD); and the median of max pain during an attack was 72.5, ranging from 0.0-100.0 VAS. When pooling in the included and excluded participants i.e. the entire study cohort, the difference in PCS between the healthy individuals and migraineurs has persisted, while no differences were found in age, state/trait anxiety, TSOP or CPM, so that there seems to be no

17

selection bias. Additional details regarding all outcome measures in the entire study cohort (included and excluded participants) can be found in the supplementary material. Psychophysical outcome measures In both groups, participants rated the Ts-alone pain as significantly higher than the Ts+Cs pain (healthy individuals: Ts-alone=5.90±1.90, Ts+Cs=5.30±2.10, paired t-test p=0.013; migraineurs: Ts-alone=6.29±2.16, Ts+Cs=5.64±2.38, paired t-test p =0.009). Thus, CPM magnitudes were 0.67±1.18 for healthy individuals, and -0.73±1.12 for migraineurs. Simple between-group analysis revealed no significant differences in Ts-alone (median test p=0.427), Cs (Student’s t p=0.292), Ts+Cs (Median test p=0.875) or CPM (Student’s t p=0.874). A subsequent 1-way ANCOVA controlling for individual PCS scores revealed similar results: Ts-alone (F=0.325, p=0.571), Ts+Cs (F=0.293, p=0.591), and CPM-magnitudes (F=0.043, p=0.836). Both groups exhibited significant increase in last vs first stimulus in the forehead TSOP (paired Wilcoxon test: healthy individuals: 95% CI = 6.5-32.5, p=0.001; migrainuers: 95% CI = 7.520.0, p<0.001). Also, migraineurs had a marginal increase in last vs first stimulus in forearm TSOP, which had ranged from null to positive summation (95% CI = 0.0-1.15, p=0.049). In contrast, healthy individuals exhibited only a trend towards significant forearm pain increase during TSOP, which ranged from habituation to facilitation (95% CI = -2.7-1.5, p=0.064). When examining simple group differences in TSOP measures, we found no differences in forehead (median test p=0.120) or arm (median test p=0.919). Controlling for PCS, we found that forehead TSOP model residuals had a strong left skewness in both groups (data not shown). Therefore, in order to minimize bias, all forehead TSOP values were cubic-root-transformed to accommodate the 1-way ANCOVA model controlling for PCS. No such skewness of deviation

18

from ANCOVA assumptions was observed in forearm TSOP, so no transformation was required. Overall, controlling for PCS, we did not find significant between-group differences (Fforehead=0.677, pforehead=0.415, Fforearm=0.800, pforearm=0.375). Resting-state functional imaging Group differences between healthy participants and migraineurs The main component that constituted the SN, DMN, and ECN networks resulting from gICA are presented in Fig. 2. We did not find group differences in either gICA or SCA when not considering CPM or TSOP magnitude. Fig. 2 goes here Group differences between healthy participants and migraineurs considering CPM or TSOP magnitudes magnitude In all analyses, we added PCS scores as a covariate of no interest because they differed significantly between migraineurs and healthy individuals. gICA analysis: No significant clusters were found when testing for group-by-CPM interactions in DMN, SN and ECN, regardless of controlling for PCS scores. SCA analysis: No significant interactions were found between group and TSOP regardless of controlling for PCS scores. However, significant interactions between group and CPMmagnitude were found in the strength of rsFC between: 1) the right pgACC seed and the PCC/precuneus

(p=0.0000078);

2)

between

the

right

vmPFC

seed

and

the

PCC/precuneus(p=0.00089), and 3) left aINS seed and the right AG (p=0.0059). However, the latter became a trend towards significance after correcting for multiple ROIs (see Error! Reference source not found.- p-values after additional correction for multiple ROIs). 19

Specifically, in healthy individuals, stronger rsFC of the right pgACC and vmPFC with the PCC/Precuneus (i.e. pgACC-PCC/precuneus, vmPFC-PCC/precuneus) was significantly related to more efficient CPM. However, in migraineurs, the correlation between CPM efficiency and vmPFC-PCC/precuneus rsFC was weakened and lost for right pgACC-PCC/precuneus rsFC. Furthermore, in healthy individuals, stronger rsFC of the left aINS with the right AG was significantly associated with less efficient CPM. However, the opposite was observed in migraineurs where stronger rsFC between these regions was significantly related to more efficient CPM (Fig. 3). Fig. 3 goes here Relationship between clinical outcome measures and CPM-related rsFC in migraineurs No associations were found between rsFC and disease duration, monthly attack frequency, or mean/max migraine-pain in migraineurs, in neither SCA nor gICA. Also, no correlations were found between these clinical characteristics and the CPM-related rsFC, as described above.

Discussion

This is the first study to investigate the association between 2 psychophysical pain modulation measures (TSOP and CPM) and patterns of intrinsic resting-state brain connectivity in migraine. We found no group differences in either TSOP or CPM and no group differences in rsFC. Nevertheless, CPM was associated with different rsFC patterns in migraineurs compared to controls. While in healthy individuals, more efficient CPM was related to stronger rsFCs within the DMN (i.e. vmPFC-Precuneus/PCC) and between key DMN nodes (i.e. Precuneus/PCC) and the pgACC (i.e. DMN-pgACC); these associations were altered in migraineurs. However, group differences in the coupling between rsFC and TSOP were not found. Overall, our results 20

strengthen previous evidence of potentially normal CPM in attack-free migraineurs. Also, our results imply that: 1) the distinction between healthy individuals and migraineurs may rely on inter-individual variability in the association between dynamic psychophysical tests quantifying the pain inhibition efficiency and rsFC; and 2) this distinction can exist even when significant between-group differences in pain ratings are absent. Psychophysical findings relating to pain modulation in migraine Deficient CPM is a common characteristic in several chronic pain conditions, such as: chronic and episodic tension-type headaches, temporomandibular disorder, knee osteoarthritis (OA), rheumatoid arthritis, fibromyalgia syndrome (FMS), chronic lower back pain (CLBP) 52, acute 24 and chronic whiplash-associated disorder

72

, and provoked vestibulodynia or painful bladder

syndromes 39. Moreover, a recent study showed that CPM-efficiency varies among patients with identical diagnosis, depending, for example, on pain characteristics e.g. spatial spread, i.e. CLBP patients with localized pain exhibited efficient CPM, while those with widespread pain did not 35. A finding which emphasizes the importance of addressing interpersonal differences and/or patient population sub-groups. CPM in attack-free migraineurs is disputed. While some studies report deficient CPM compared to healthy individuals

40,66,68,83,97,98

, others show normal CPM

23,48,67,92

. This controversy might

result from differences in CPM methodology and disease characteristics; while CPM is generally considered as a reliable measure, its reliability varies across test modalities and methodologies 46. Nonetheless, our results support previous evidence for normal CPM efficiency in migraineurs; this is strengthened by the relative stability and reliability of tonic heat pain as a test stimulus, and cold water as the conditioning stimulus 34,46 . In line, our research group previously reported

21

similar psychophysical and neurophysiological CPM responses in migraineurs and healthy individuals at the group level, in a comparable participant cohort 48. Finally, we did not observe between-group differences in TSOP magnitude, and both groups only showed a small TSOP magnitude. Our results correspond to previous reports about TSOP magnitudes that were comparable in episodic migraineurs and healthy individuals. Nevertheless, other studies showed that inter-ictal migraineurs experience enhanced TSOP

98,76,107,80

. Of note,

in healthy populations, individual responses to TSOP protocols were found to vary widely among individuals

2,30

and depend on the type of stimulation modality

71

. Additionally,

specifically in migraineurs, altered responses to static and dynamic QST may become evident only when certain body areas are tested, or when a certain stimulation modality is used

69

.

Therefore, our results strengthen previous evidence that show normal bottom-up pain processing in attack-free migraineurs. How is migraine reflected in the RSNs? Various evidence attributes clinical relevance to connectivity alterations within and between the DMN, SN, and ECN, in chronic pain conditions, compared to healthy individuals. Largely, evidence point to associations between inter-RSN connectivity and clinical pain intensity 5; disease impact on daily function 42,70, and PCS scores 47 While RSN functional connectivity is associated with clinical pain in persistent chronic pain disorders, reports of rsFC alterations in attack-free migraineurs are conflicting. Compared to healthy individuals, higher rsFC was found within the DMN 54 and between the DMN and frontal and temporal areas 95. Furthermore, longer disease duration was associated with stronger rsFC of the aINS with the DMN, ECN

110

, and the caudate nucleus

22

113

. Nonetheless, although restricted

to migraine with aura, Hougaard et al. 43 reported no differences in rsFC strength in migraineurs compared to healthy subjects. Similarly, we found neither group differences in the rsFC strength of the DMN, ECN or SN, nor any relationships between rsFC and migraine clinical characteristics. We 48 and others 20 suggest therefore that the diverging evidence might arise from heterogeneity among the patient population, and might be partially associated with clinical attributes. It is also possible that migraineurs show rsFC changes that are too subtle to be detected in conventional rsFC analyses (gICA and SCA). This might be attributed to the method by which functional neuroimaging analysis takes place. It is important to clear sources of signal noise, which are not related to neural activity: physiological effects, such as respiration and heart-rate variability 65,74; subject motion artifacts 13

36,114

; thermal noise and magnetic field inhomogeneities

. These can be removed using band-pass filters. Typically, noise can be removed by modelling

and regressing it out using general linear models (GLM)

13

(our approach), and by using data-

driven approaches 38,82. However, using GLM might sometimes lead to modelling important data as noise, thus removing meaningful sources of true-positive correlations in the data 13,14. Finally, factors such as age of-interest itself

64

22,53

, the selection of network analysis method

19,61,86

and even the network-

can affect the variability in rsFC, and affect our ability to detect group

differences, should they exist. Relationship between the psychophysical CPM and rsFC – bridging the gap between health and disease During CPM induction, fMRI studies show changes in brain activity. Specifically, studies in healthy individuals tested under similar heat-pain-based CPM paradigms, report that activity in

23

pain areas such as the thalamus, pINS, and aMCC, is attenuated in the presence of a conditioning stimulus, along with decreased deactivation in the various areas including the PCC and mPFC 68,77,112

. These studies suggest that the mPFC and PCC, key DMN nodes, might have a role in the

pain inhibition process and its efficiency. This is the first study to investigate the relationship between the resting brain and EA efficiency as reflected by the CPM magnitude, as it appears in health and disease. We discovered that when considering CPM magnitudes, in addition to simple group distinction, higher connectivity strength within the DMN, and between the DMN and the pgACC were associated with more efficient CPM in healthy participants. This further emphasizes the role of the DMN’s brain areas in pain modulation, adding to their previously reported role during the CPM paradigm 68. This is also supported by previous findings: during placebo analgesia, functional connectivity between the pgACC and the PAG, a key brainstem pain modulatory area, increases 8; and the vmPFC was also found to be active during uncertain expectation of pain, while the pgACC showed greater activity when pain was known and imminent

79

. Of note, a study that explored cognitive pain

modulation in the context of pain expectation, found that stronger rsFC between the pgACCmPFC complex and brain areas pertaining to the ECN was related to greater pain inhibition

49

;

also, higher TSOP in healthy individuals was linked to stronger rsFC between the thalamus and the S1, and weaker rsFC between the subgenual ACC and the RVM

18

. Although these studies

were conducted in a single cohort, they underscore the relevance of inter-individual variability in studying human pain-related behavior. This may uncover interesting details regarding the underlying pathophysiology of pain syndromes. We therefore propose that increased synchronization with pain-modulatory brain area such as the pgACC during rest is associated with greater pain inhibition in healthy individuals. Interestingly,

24

we found that in migraine, these relationships were lost. As migraineurs face unexpected bouts with pain and constantly worry about the next attack

58,62

, we speculate that the pgACC and

mPFC might be more occupied in maintaining normal pain inhibition due to this hypervigilance, and this might alter their coupling with the DMN. On the other hand, weaker connectivity strength between components of DMN-SN (i.e. left aINS with the right AG) was associated with greater CPM efficiency in health. We propose that this result reflects the anti-correlated neural activity normally observed between the DMN and SN

25

. The finding of a trend toward

significance of a reversal in the association between aINS-AG rsFC and the CPM-magnitude in migraineurs might reflect the stronger DMN-to-aINS coupling previously reported in migraine 110

and in other pain disorders such as FMS 70. Finally, as recurrent exposure to a painful stressor

may lead to greater resource allocation towards pain inhibition, our results might point to possible changes in rsFC-pain inhibition relationship in migraineurs, which are necessary to maintain normal levels of pain inhibition. However, if this is indeed the case, further study is needed to elucidate whether such neuroplasticity reflects an adaptive systematic response to an increasing allostatic stress load manifesting as chronic pain

10,28,103

, or rather a predisposition to

the development of chronic pain. It is important to note that we found no relationship between the CPM-related rsFC and the clinical characteristics of migraine in our study cohort. By itself, lower CPM efficiency was found to be associated with clinical pain intensity in knee OA 3,87, post-herpetic neuralgia 78, and chronic musculoskeletal pain

104

. This might imply that chronic pain is maintained by defective

EA. However, similar findings were not replicated in migraine. Moreover, the association between CPM efficiency and clinical pain properties does not necessarily appear in different

25

chronic pain disorder, and at times does not even replicate across studies of the same chronic pain disorder 29.

Study limitations

We excluded some participants, and this may potentially bias our results. However, similar to the sample that was eventually included in the analysis, we observed no differences between healthy individuals and migraineurs in our main outcome measures i.e. age, PCS, STAI, CPM and TSOP, when also including the excluded migraineurs and healthy individuals. Another possible limitation is the CPM paradigm, which is known to show large variability 46. Thus, it is possible that our findings are limited to the specific CPM paradigm used in this study.

Conclusion

To summarize, we found that inter-individual differences in CPM in healthy individuals is related to the DMN- and SN-rsFC. However, in migraineurs, this association is altered. Certainly, speculations can be made regarding how these alterations are involved in the maintenance of EA in migraineurs. A further investigation of the relationship between neurophysiological and psychophysical measures, on top of simple group differences, can allow a deeper understanding of the EA functioning in chronic pain conditions.

Acknowledgments This study was financially supported by the United States–Israel Binational Science Foundation, grant number 2009097.

26

All authors declare no conflict of interest.

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Figure legends Fig. 1 – ROIs used for the SCA analysis. Coordinates are denoted in MNI152 standard space. aINS, anterior insula; Amyg; amygdala; lOFC, lateral orbitofrontal cortex; pgACC, pregenual anterior cingulate cortex; pINS, posterior insula; S1, primary somatosensory cortex; Thal, thalamus.

Fig. 2 - RSN maps of healthy participants (top panels, red) and migraineurs (bottom panels, blue). All maps are FDR corrected at a cluster-level of p<0.05. DMN, default-mode network; ECN, executive control network; SN, salience network.

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Fig. 3 – Inter-individual variability in CPM magnitude drives differences in rsFC between healthy individuals and migraineurs. r, Pearson correlation between rsFC and CPM-magnitude in healthy individuals and migraineurs. *p<0.05, ** p<0.01, ***p<0.001. aINS, anterior insula; pgACC, pregenual anterior cingulate cortex; vmPFC, ventromedial prefrontal cortex; Precuneus/PCC, precuneus/posterior cingulate cortex.

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Table 1 Seed

Result

MNI peak t values voxel coordinate (x, y z)

Cluster size Cluster-size (voxels) p-FDR*

Right pgACC

Left PCC/PCu

-2, -56, 36

-4.56

453

0.001

Right vmPFC

Right PCC/PCu

6, -52, 36

-4.77

583

0.009

Left aINS

Right AG

58, -58, 24

4.92

313

0.059

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rsFC results of the interactions between group and CPM magnitude. AG, angular gyrus; aINS, anterior insula; PCC, posterior cingulate cortex; pgACC, pregenual ACC; PCu, precuneus; vmPFC, ventromedial prefrontal cortex. * Cluster-size p-FDR values after further correcting for the number of seeds using a Bonferroni correction.

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