Dynamic Pain Connectome Functional Connectivity and Oscillations Reflect Multiple Sclerosis Pain

Rachael L. Bosma; Junseok A. Kim; Joshua C. Cheng; Anton Rogachov; Kasey S. Hemington; Natalie R. Osborne; Jiwon Oh; Karen D. Davis

Disclosures

Pain. 2018;159(11):2267-2276. 

In This Article

Results

Pain and Clinical Characteristics of Patients With Multiple Sclerosis

The demographic and clinical characteristics of the patients with MS and corresponding HC information are presented in Table 1. There was no significant differences in age (t(60) = 0.15, P = 0.88) or sex between controls and patients. However, patients with mixed NP features were significantly older than patients with non-NP (t(29) = 3.40, P = 0.002). Patients with mixed NP also had significantly higher Expanded Disability Status Scale scores (t(29) = 3.24, P = 0.003), although there was no difference in disease duration between the 2 subgroups (t(29) = 1.48, P = 0.15). Compared with controls, patients had significantly higher anxiety (t(60) = 3.79, P < 0.001) and depression scores (t(60) = 4.38, P< 0.001). Patients with mixed NP had significantly higher depression (t(29) = 2.39, P = 0.02), but not anxiety scores (t(29) = 1.76, P = 0.09), than patients with non-NP. Patients with mixed NP also had significantly higher pain severity (t(29) = 2.22, P = 0.03) and pain interference (t(29) = 2.24, P = 0.03) scores compared with patients with non-NP (Figure 1). Common examples of nociceptive pain in our sample were musculoskeletal back pain and migraines, whereas a common example of NP included ongoing extremity pain. Neuropathic pain was assessed using the painDETECT questionnaire and psychophysical testing. Information regarding the chronicity as well as examples of pain in each subgroup is described in Supplementary Table 2 (available online as supplemental digital content at http://links.lww.com/PAIN/A617).

Figure 1.

Characterization of pain in patients with multiple sclerosis (MS). PainDETECT, pain interference, and pain severity ratings from patients with MS. PainDETECT scores indicate the likelihood of NP features of pain, and patients with scores above 12 were categorized in NP group. Pain interference and pain severity scores were obtained from the Brief Pain Inventory. NP, neuropathic pain.

Abnormal SN-DMN Cross-network Static Functional Connectivity in Multiple Sclerosis

The time series between the core nodes of the salience network (rTPJ, anterior insula, and midcingulate cortex) in both HC and patients with MS had strong positive functional connectivity as expected between these regions (supplementary materials, available online as supplemental digital content at http://links.lww.com/PAIN/A617). Static FC analysis in controls confirmed the characteristic anticorrelations normally present between nodes of the SN and the DMN (Figure 2). This cross-network anticorrelation was significantly diminished for the whole MS group (t(60) = 4.08, P < 0.001), but this finding was driven mostly by the patients with mixed NP who had a profound cross-network abnormality (t(30) = 4.43, P < 0.001). The cross-network abnormality in the patients with non-NP was only a trend (P = 0.09) (Figure 2). However, mixed NP and non-NP groups did not have significantly different SN-DMN sFC (P = 0.50). At the group level, there was no significant association between SN-DMN sFC and painDETECT scores in patients with MS (P = 0.85).

Figure 2.

Static functional connectivity. Mean static functional connectivity (±SE) between the SN and the (A) DMN, (B) ascending nociceptive pathway, and (C) descending modulation pathway. Healthy controls (HC) and multiple sclerosis (MS) whole group results are shown above and NP vs non-NP subgroup comparisons are shown below. Significant differences (or trends) are indicated by horizontal bars/P-values. DMN, default mode network; NP, neuropathic pain; non-NP, non-neuropathic pain.

The sFC between the SN and ascending nociceptive pathway did not differ between patients and controls (P = 0.29), nor did it differ between pain subgroups (mixed NP vs HC, P = 0.81; non-NP vs HC, P = 0.22; mixed NP vs non-NP, P = 0.56). Similarly, salience-descending sFC did not differ between patients and controls (P = 0.34), nor did it differ between pain subgroups (mixed-NP vs HC, P = 0.65; non-NP vs HC, P= 0.38; mixed-NP vs non-NP, P = 0.55). At the group level, there were no significant associations between SN-Asc and SN-Des sFC and painDETECT scores in patients with MS (P = 0.90 and P = 0.56, respectively).

Altered SN-ascending and SN-descending Dynamic Functional Connectivity in Multiple Sclerosis

In both controls and patients, dFC values in the dynamic pain connectome ranged from approximately 0.2 to 0.65. There was no significant difference in SN-DMN dFC between controls and patients (P = 0.06) or between pain subgroups (mixed-NP vs HC, P = 0.17; non-NP vs HC, P = 0.12; mixed-NP vs non-NP, P = 0.89) (Figure 3). There was greater SN-ascending dFC for the whole patient group compared with controls (t(60) = 3.10, P = 0.03). Both mixed NP and non-NP groups had higher dFC compared with their respective control groups, although only the non-NP was significantly different (mixed-NP; t(30) = 1.17, P = 0.06, non-NP; t(28) = 2.84, P = 0.03). The SN-ascending dFC was not statistically different for the mixed-NP vs non-NP subgroups (P = 0.72). Finally, the SN-descending dFC did not differ significantly between patients and control groups (P = 0.24); however, it was significantly attenuated in the mixed NP group compared with the non-NP group (t(29) = 2.39, P = 0.02). At the group level, there was no significant association between SN-DMN sFC, SN-Asc, or SN-Des sFC and painDETECT scores in patients with MS (P = 0.45, P = 0.81, and P = 0.60, respectively).

Figure 3.

Dynamic functional connectivity. Mean dynamic functional connectivity (±SE) between the SN and the (A) DMN, (B) ascending nociceptive pathway, and (C) descending modulation pathway. Healthy controls (HC) and multiple sclerosis (MS) whole group results are shown above and NP vs non-NP subgroup comparisons are shown below. Significant differences (or trends) are indicated by horizontal bars/P-values. DMN, default mode network; NP, neuropathic pain; non-NP, non-neuropathic pain.

Increased BOLD Variability in the Default Mode Network

BOLD variability values were calculated for each voxel within the regions comprising the dynamic pain connectome described above (SN, DMN, ascending nociceptive pathway, and descending modulation pathway). In controls, the standardized BOLD variability values in the regions of the dynamic pain connectome ranged from −0.68 to 0.96, whereas in patients, these values ranged from −0.37 to 1.96. We next evaluated BOLD variability for specific frequency bands. In slow wave 3 (sw3), there was abnormally high BOLD variability for the whole group of patients in the DMN (t(60) = 2.46, P = 0.01) (Figure 4). However, when divided into mixed NP and non-NP subgroups, these findings were only marginally significant (mixed-NP vs HC, P = 0.06; non-NP vs HC, P = 0.12) and there was not a significant difference between mixed NP and non-NP subgroups (P = 0.50). In slow wave 4 (sw4), there was greater BOLD variability in DMN in the whole patient group compared with controls (t(60) = 3.42, P = 0.001). Note that the group differences in sw3 and sw4 were in the same brain region (PCC of the DMN); however, the spatial extent of these findings was more restricted in sw4. Both mixed NP (P = 0.05) and non-NP (t(28) = 2.73, P = 0.01) had higher sw4 BOLD variability in the DMN. The sw4 DMN BOLD variability was not statistically different between mixed NP and non-NP subgroups (P = 0.87). There were no other significant BOLD variability differences between patients and controls in other voxels of the dynamic pain connectome in sw3 or sw4 (P > 0.05). In slow wave 5, there were no voxels that survived the threshold of significance when comparing controls and patients.

Figure 4.

BOLD variability. Contrast between BOLD variability values in slow wave 3 (top) and slow wave 4 (bottom) in the dynamic pain connectome of healthy controls (HC) and patients with multiple sclerosis (MS). On the left is the region of the brain where BOLD variability was significantly greater in patients with MS and HC. There were no regions in which BOLD variability was greater in HC compared with MS. The bar graphs compare the mean BOLD variability (±SE) values extracted from this region (DMN) in the HC vs MS group and in the non-NP vs NP subgroups. DMN, default mode network; NP, neuropathic pain; non-NP, non-neuropathic pain.

Relationship Between Brain Functional Reorganization and Pain Interference

Correlation analysis revealed a significant relationship between SN-DMN sFC and pain interference (Rho = 0.43, P = 0.03) (Figure 5A). However, there was no significant relationship between pain interference and SN-ascending sFC (P = 0.30), or SN-descending sFC (P = 0.15). Furthermore, there were no significant relationships between pain interference and dFC in SN-DMN (P = 0.47), SN-ascending (P = 0.19), and SN-Des (P = 0.95). Finally, there was no univariate relationship between BOLD variability (in sw3 or sw4). However, results from a multivariate analysis revealed that the pattern of BOLD variability in the PCC (in sw3) was associated with pain interference (r = 0.44, P = 0.01) (Figure 5B).

Figure 5.

Brain abnormalities and pain interference. (A) Univariate correlation analysis demonstrating that the degree of abnormal SN-DMN sFC was significantly correlated with pain interference scores. (B) Multivariate analysis weight map indicating the pattern of BOLD variability in the DMN that was significantly associated with pain interference scores. The colour bar indicates the contribution of brain regions in making significant generalizable inferences about pain interference scores. The scatterplot indicates the model fit between the pain interference scores and estimates of these scores derived from the multivariate pattern of BOLD variability. DMN, default mode network.

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