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).
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) (Fig. 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).
Correlation analysis revealed a significant relationship between SN-DMN sFC and pain interference (Rho = 0.43, P = 0.03) (Fig. 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) (Fig. 5B).
This study has provided novel insight into the NP features in patients with MS through the lens of the relationship between MS pain and cross-network functional reorganization within the dynamic pain connectome. Our key findings were that: (1) approximately 50% of patients with MS experienced pain and sensory features of mixed NP, (2) patients with mixed NP had abnormal cross-network connectivity between the salience and DMNs, (3) all patients exhibited a disruption in brain signal variability between the SN and the ascending nociceptive pathway, but only patients with mixed NP have disrupted brain signal variability between the SN and descending pain-modulation pathway, (4) patients exhibited amplified regional neural variability in the DMN, and (5) abnormalities in sFC and brain signal variability were related to the degree of interference of pain on activities of daily living. Therefore, our study indicates that SN cross-network communication is dysfunctional in patients with MS pain, and the degree of abnormality reflects both features of NP and how much pain interferes in daily functioning.
We examined cross-network sFC to explore pain-related alterations in network organization and found that MS patients with mixed NP have abnormally high SN-DMN connectivity. The right temporoparietal junction, a key node of the SN, detects relevant stimuli from the environment and coordinates other brain networks, such as the DMN, to generate a behavioral response.15,44,56 By contrast, the DMN is active during internally directed, self-referential thought and suppressed when attention is externally engaged.19,38 In healthy individuals, the activity between the SN and the DMN is anticorrelated. Disruptions in this cross-network anticorrelation can been identified in several chronic pain conditions and the degree of sFC abnormality is related to pain symptoms.2,24,46 In the current study, we found that a disruption in SN-DMN anticorrection was present only in the mixed NP group, and across all patients with MS, the degree of abnormality was related to pain interference scores. In mixed NP pain, the relationship between the SN and DMN may be altered due to the experience of salient fluctuating pain and greater pain interference. Acute pain experienced by healthy individuals is salient and able to allocate attention away from internal thought, thereby suppressing the DMN. By contrast, in patients with chronic pain, ongoing mixed NP pain may actually cause thoughts to be more directed towards pain. This is consistent with evidence from patients with temporomandibular disorder in which sFC within the DMN corresponded to rumination about pain.37 Furthermore, salience network-DMN cross-network sFC abnormalities are also evident in patients with posttraumatic stress disorder52,53 and are related to inattention symptoms in attention deficit hyperactivity disorder (ADHD).36 This suggests that SN-DMN sFC abnormalities are not specific to pain, but rather that over time, the continuous experience of salient symptoms become ingrained in internal thought and alters attention. Thus, SN-DMN abnormalities in MS patients with mixed NP likely reflect an alteration in the neural salience–pain relationship.
The organization of brain networks and the relationship between networks have typically been described using static measures of FC, which assume that these measures remain stable in the resting-state brain over time.29,57 However, it is now known that the relationship between brain regions is dynamic and can fluctuate over time.29 Therefore, here we also examine the salience–pain relationship using measures of dFC to understand whether SN-ascending nociceptive or SN-descending modulation pathways are dynamically altered in MS pain. We found that although SN-ascending nociceptive pathway dFC was increased in patients, patients with mixed NP had a decreased SN-descending dFC. It is important to note that our sFC and dFC findings were distinct and that only dFC measures were sensitive to differences in patients vs controls or between mixed NP and non-NP subgroups in these brain regions. Findings of dFC but not sFC differences are consistent with previous studies that have demonstrated that the most dynamic connections are intermodular and those with low sFC.57 Therefore, using measures of dFC provides additional insight into MS pain-related functional reorganization of the salience–pain relationship. Abnormalities in the dynamic relationship between SN-ascending and SN-descending modulation pathway may reflect alterations in “the balance between efficient information processing and metabolic expenditure.”57 In HCs, higher dFC between the salience network and executive control networks has been shown to reflect a greater ability to prioritize a cognitive task over a pain stimulus.12 Thus, in patients with higher pain severity and interference, the SN may “overcoordinate” or “overengage” with the ascending nociceptive pathway as a result of or, alternatively, as a driver of the barrage of painful and salient input experienced by these patients. Conversely, MS patients with mixed NP had a decrease in dFC between the SN-descending modulation pathway. This may reflect an inability of the SN to dynamically engage with the descending modulation system to respond to ongoing pain. Therefore, the relationship between dFC and pain is likely network-specific and context-specific. Previous studies have demonstrated that dFC measures are sensitive to variability in behaviour.12,28 Because pain fluctuates over time and mixed NP is highly dynamic, measures of brain dynamics within the dynamic pain connectome may capture dynamics in pain perception. However, future studies that acquire continuous measures of ongoing pain or more thoroughly measure the temporal dimension of pain are required to resolve the precision with which measures of dFC track pain fluctuations.
Measures of sFC and dFC inform how regions and networks of the brain are communicating with one another; an additional consideration is the baseline neural variability at a regional level. Using measures of BOLD variability, we found that patients with MS had greater BOLD variability (sw3) in the DMN. This is inconsistent with our hypothesis and deviates from findings in HCs in whom greater BOLD variability was associated with lower pain sensitivity and better pain-coping.49 However, it is consistent with previous findings that describe increased power in regions of the DMN in patients with chronic lower back pain.1 BOLD signal variability could reflect regional cortical excitability.1,3,21 Enhanced variability in the DMN could therefore reflect increased engagement in internal thoughts and rumination about pain. In chronic pain, this increase in DMN baseline activity could in part account for the inability to suppress this network when engaging in an external task.2 Default mode network BOLD variability (sw3) was also predictive of pain interference scores; we suggest that the inability to disengage from thoughts on pain likely consumes attentional resources and disrupts the ability to fully engage in day-to-day activities.
There are several limitations to this study. We found that patients had higher depression scores than controls, and patients with mixed NP had slightly higher (although largely overlapping) depression scores compared with non-NP patients. Depression is highly comorbid in chronic pain conditions and the neurobiological underpinnings are overlapping.6 Therefore, as it is difficult to separate the effects of depression vs pain on the brain, the results presented here may also, in part, be influenced by the depression. Age and disease severity are also known to alter brain function. However, our supplementary analyses (available online as supplemental digital content at http://links.lww.com/PAIN/A617) suggested that our results did not change when controlling for age and that disease severity was not related to our findings (Supplementary Table 1, available online as supplemental digital content at http://links.lww.com/PAIN/A617). Another potential confound is the level of drowsiness or vigilance in our resting state scan because these factors are known to influence measures of dFC.40 We did not assess this and therefore are unable to control for or remove the effects of vigilance in our study. However, we also do not have any evidence that there would be systematic group difference in vigilance that could account for our results. Therefore, any effect of vigilance likely adds noise to our data that obscures our results, rather than inflate them. Of note, the variance of the dynamic conditional correlation, which we use as our measure of dFC, has been shown to be scan–rescan reliable,13 which alleviates some of the concerns regarding noise confounds on this measure. Finally, patients were not required to stop taking medications; the potential physiological effects of these medications on the BOLD fMRI signal are unknown in this study.
Altogether, our findings reveal that the relationship between salience and pain is disrupted in patients with MS pain. This is reflected in abnormalities in brain communication of the salience network—specifically SN-DMN sFC, SN-ascending, and SN-descending dFC, and also BOLD variability in the DMN that relate to pain interference and mixed NP pain features. Disruption in the salience–pain relationship is evident in a number of different chronic pain conditions including diabetic neuropathy9 and lower back pain.24 Therefore, these phenomena are a hallmark across chronic pain conditions, suggesting that the neural underpinnings of MS pain can also be described by a functional reorganization in key regions of the dynamic pain connectome.
The authors have no conflict of interest to declare.
The authors thank Dr Aaron Kucyi for his help in developing the analysis pipeline and Eugen Hlasny and Keith Ta for expert technical assistance in MRI acquisition.
Supplemental digital content
Supplemental digital content associated with this article can be found online at http://links.lww.com/PAIN/A617.
. Baliki MN, Baria AT, Apkarian AV. The cortical rhythms of chronic back pain. J Neurosci 2011;31:13981–90.
. Baliki MN, Geha PY, Apkarian AV, Chialvo DR. Beyond feeling: chronic pain
hurts the brain, disrupting the default-mode network dynamics. J Neurosci 2008;28:1398–403.
. Baliki MN, Mansour AR, Baria AT, Apkarian AV. Functional reorganization of the default mode network
across chronic pain
conditions. PLoS One 2014;9:e106133.
. Behzadi Y, Restom K, Liau J, Liu TT. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI
. Neuroimage 2007;37:90–101.
. Bjelland I, Dahl AA, Haug TT, Neckelmann D. The validity of the Hospital Anxiety and Depression Scale. An updated literature review. J Psychosom Res 2002;52:69–77.
. Boakye PA, Olechowski C, Rashiq S, Verrier MJ, Kerr B, Witmans M, Baker G, Joyce A, Dick BD. A critical review of neurobiological factors involved in the interactions between chronic pain
, depression, and sleep disruption. Clin J Pain 2016;32:327–36.
. Borsook D, Edwards R, Elman I, Becerra L, Levine J. Pain and analgesia: the value of salience circuits. Prog Neurobiol 2013;104:93–105.
. Buzsaki G, Draguhn A. Neuronal oscillations in cortical networks. Science 2004;304:1926–9.
. Cauda F, D'Agata F, Sacco K, Duca S, Geminiani G, Vercelli A. Functional connectivity of the insula in the resting brain. Neuroimage 2011;55:8–23.
. Chai XJ, Castanon AN, Ongur D, Whitfield-Gabrieli S. Anticorrelations in resting state networks without global signal regression. Neuroimage 2012;59:1420–8.
. Chang C, Glover GH. Time-frequency dynamics of resting-state brain connectivity measured with fMRI
. Neuroimage 2010;50:81–98.
. Cheng JC, Bosma RL, Hemington KS, Kucyi A, Lindquist MA, Davis KD. Slow-5 dynamic functional connectivity
reflects the capacity to sustain cognitive performance during pain. Neuroimage 2017;157:61–8.
. Choe AS, Nebel MB, Barber AD, Cohen JR, Xu Y, Pekar JJ, Caffo B, Lindquist MA. Comparing test-retest reliability of dynamic functional connectivity
methods. Neuroimage 2017;158:155–75.
. Di Martino A, Ghaffari M, Curchack J, Reiss P, Hyde C, Vannucci M, Petkova E, Klein DF, Castellanos FX. Decomposing intra-subject variability in children with attention-deficit/hyperactivity disorder. Biol Psychiatry 2008;64:607–14.
. Downar J, Mikulis DJ, Davis KD. Neural correlates of the prolonged salience of painful stimulation. Neuroimage 2003;20:1540–51.
. Dworkin RH, Turk DC, Farrar JT, Haythornthwaite JA, Jensen MP, Katz NP, Kerns RD, Stucki G, Allen RR, Bellamy N, Carr DB, Chandler J, Cowan P, Dionne R, Galer BS, Hertz S, Jadad AR, Kramer LD, Manning DC, Martin S, McCormick CG, McDermott MP, McGrath P, Quessy S, Rappaport BA, Robbins W, Robinson JP, Rothman M, Royal MA, Simon L, Stauffer JW, Stein W, Tollett J, Wernicke J, Witter J. Core outcome measures for chronic pain
clinical trials: IMMPACT recommendations. PAIN 2005;113:9–19.
. Eippert F, Bingel U, Schoell ED, Yacubian J, Klinger R, Lorenz J, Buchel C. Activation of the opioidergic descending pain control system underlies placebo analgesia. Neuron 2009;63:533–43.
. Foley PL, Vesterinen HM, Laird BJ, Sena ES, Colvin LA, Chandran S, MacLeod MR, Fallon MT. Prevalence and natural history of pain in adults with multiple sclerosis
: systematic review and meta-analysis. PAIN 2013;154:632–42.
. Fox MD, Raichle ME. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 2007;8:700–11.
. Freynhagen R, Baron R, Gockel U, Tolle TR. painDETECT: a new screening questionnaire to identify neuropathic components in patients with back pain. Curr Med Res Opin 2006;22:1911–20.
. Garrett DD, Kovacevic N, McIntosh AR, Grady CL. The importance of being variable. J Neurosci 2011;31:4496–503.
. Green R, Cutter G, Friendly M, Kister I. Which symptoms contribute the most to patients' perception of health in multiple sclerosis
? Mult Scler J Exp Transl Clin 2017;3:2055217317728301.
. Heesen C, Bohm J, Reich C, Kasper J, Goebel M, Gold SM. Patient perception of bodily functions in multiple sclerosis
: gait and visual function are the most valuable. Mult Scler 2008;14:988–91.
. Hemington KS, Wu Q, Kucyi A, Inman RD, Davis KD. Abnormal cross-network functional connectivity in chronic pain
and its association with clinical symptoms. Brain Struct Funct 2016;221:4203–19.
. Hodkinson DJ, Wilcox SL, Veggeberg R, Noseda R, Burstein R, Borsook D, Becerra L. Increased amplitude of thalamocortical low-frequency oscillations in patients with migraine. J Neurosci 2016;36:8026–36.
. Hong JY, Kilpatrick LA, Labus JS, Gupta A, Katibian D, Ashe-McNalley C, Stains J, Heendeniya N, Smith SR, Tillisch K, Naliboff B, Mayer EA. Sex and disease-related alterations of anterior insula functional connectivity in chronic abdominal pain. J Neurosci 2014;34:14252–9.
. Hubbard CS, Khan SA, Keaser ML, Mathur VA, Goyal M, Seminowicz DA. Altered brain structure and function correlate with disease severity and pain catastrophizing in migraine patients. eNeuro 2014;1:e20. 14.
. Hutchison RM, Morton JB. It's a matter of time: reframing the development of cognitive control as a modification of the brain's temporal dynamics. Dev Cogn Neurosci 2016;18:70–7.
. Hutchison RM, Womelsdorf T, Allen EA, Bandettini PA, Calhoun VD, Corbetta M, Della Penna S, Duyn JH, Glover GH, Gonzalez-Castillo J, Handwerker DA, Keilholz S, Kiviniemi V, Leopold DA, de Pasquale F, Sporns O, Walter M, Chang C. Dynamic functional connectivity
: promise, issues, and interpretations. Neuroimage 2013;80:360–78.
. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. FSL. Neuroimage 2012;62:782–90.
. Kalia LV, O'Connor PW. Severity of chronic pain
and its relationship to quality of life in multiple sclerosis
. Mult Scler 2005;11:322–7.
. Krall SC, Rottschy C, Oberwelland E, Bzdok D, Fox PT, Eickhoff SB, Fink GR, Konrad K. The role of the right temporoparietal junction in attention and social interaction as revealed by ALE meta-analysis. Brain Struct Funct 2015;220:587–604.
. Kucyi A, Davis KD. The dynamic pain connectome. Trends Neurosci 2015;38:86–95.
. Kucyi A, Davis KD. The neural code for pain: from single-cell electrophysiology to the dynamic pain connectome. Neuroscientist 2017;23:397–414.
. Kucyi A, Hodaie M, Davis KD. Lateralization in intrinsic functional connectivity of the temporoparietal junction with salience- and attention-related brain networks. J Neurophysiol 2012;108:3382–92.
. Kucyi A, Hove MJ, Biederman J, Van Dijk KR, Valera EM. Disrupted functional connectivity of cerebellar default network areas in attention-deficit/hyperactivity disorder. Hum Brain Mapp 2015;36:3373–86.
. Kucyi A, Moayedi M, Weissman-Fogel I, Goldberg MB, Freeman BV, Tenenbaum HC, Davis KD. Enhanced medial prefrontal-default mode network
functional connectivity in chronic pain
and its association with pain rumination. J Neurosci 2014;34:3969–75.
. Kucyi A, Salomons TV, Davis KD. Mind wandering away from pain dynamically engages antinociceptive and default mode brain networks. Proc Natl Acad Sci U S A 2013;110:18692–7.
. Kurtzke JF. Rating neurologic impairment in multiple sclerosis
: an expanded disability status scale (EDSS). Neurology 1983;33:1444–52.
. Laumann TO, Snyder AZ, Mitra A, Gordon EM, Gratton C, Adeyemo B, Gilmore AW, Nelson SM, Berg JJ, Greene DJ, McCarthy JE, Tagliazucchi E, Laufs H, Schlaggar BL, Dosenbach NUF, Petersen SE. On the stability of BOLD fMRI
correlations. Cereb Cortex 2017;27:4719–32.
. Lindquist MA, Xu Y, Nebel MB, Caffo BS. Evaluating dynamic bivariate correlations in resting-state fMRI
: a comparison study and a new approach. Neuroimage 2014;101:531–46.
. Lutkenhoff ES, Rosenberg M, Chiang J, Zhang K, Pickard JD, Owen AM, Monti MM. Optimized brain extraction for pathological brains (optiBET). PLoS One 2014;9:e115551.
. Martino M, Magioncalda P, Huang Z, Conio B, Piaggio N, Duncan NW, Rocchi G, Escelsior A, Marozzi V, Wolff A, Inglese M, Amore M, Northoff G. Contrasting variability patterns in the default mode and sensorimotor networks balance in bipolar depression and mania. Proc Natl Acad Sci U S A 2016;113:4824–9.
. Menon V, Uddin LQ. Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct 2010;214:655–67.
. Meyer-Moock S, Feng YS, Maeurer M, Dippel FW, Kohlmann T. Systematic literature review and validity evaluation of the Expanded Disability Status Scale (EDSS) and the Multiple Sclerosis
Functional Composite (MSFC) in patients with multiple sclerosis
. BMC Neurol 2014;14:58.
. Napadow V, LaCount L, Park K, As-Sanie S, Clauw DJ, Harris RE. Intrinsic brain connectivity in fibromyalgia is associated with chronic pain
intensity. Arthritis Rheum 2010;62:2545–55.
. O'Connor AB, Schwid SR, Herrmann DN, Markman JD, Dworkin RH. Pain associated with multiple sclerosis
: systematic review and proposed classification. PAIN 2008;137:96–111.
. Osborne TL, Raichle KA, Jensen MP, Ehde DM, Kraft G. The reliability and validity of pain interference measures in persons with multiple sclerosis
. J Pain Symptom Manage 2006;32:217–29.
. Rogachov A, Cheng JC, Erpelding N, Hemington KS, Crawley AP, Davis KD. Regional brain signal variability: a novel indicator of pain sensitivity and coping. PAIN 2016;157:2483–92.
. Seixas D, Foley P, Palace J, Lima D, Ramos I, Tracey I. Pain in multiple sclerosis
: a systematic review of neuroimaging studies. Neuroimage Clin 2014;5:322–31.
. Seixas D, Palace J, Tracey I. Chronic pain
disrupts the reward circuitry in multiple sclerosis
. Eur J Neurosci 2016;44:1928–34.
. Sharp TJ, Harvey AG. Chronic pain
and posttraumatic stress disorder: mutual maintenance? Clin Psychol Rev 2001;21:857–77.
. Sripada RK, King AP, Welsh RC, Garfinkel SN, Wang X, Sripada CS, Liberzon I. Neural dysregulation in posttraumatic stress disorder: evidence for disrupted equilibrium between salience and default mode brain networks. Psychosom Med 2012;74:904–11.
. Tampin B, Bohne T, Callan M, Kvia M, Melsom Myhre A, Neoh EC, Bharat C, Slater H. Reliability of the English version of the painDETECT questionnaire. Curr Med Res Opin 2017;33:741–8.
. Turk DC, Dworkin RH, Allen RR, Bellamy N, Brandenburg N, Carr DB, Cleeland C, Dionne R, Farrar JT, Galer BS, Hewitt DJ, Jadad AR, Katz NP, Kramer LD, Manning DC, McCormick CG, McDermott MP, McGrath P, Quessy S, Rappaport BA, Robinson JP, Royal MA, Simon L, Stauffer JW, Stein W, Tollett J, Witter J. Core outcome domains for chronic pain
clinical trials: IMMPACT recommendations. PAIN 2003;106:337–45.
. Uddin LQ. Salience processing and insular cortical function and dysfunction. Nat Rev Neurosci 2015;16:55–61.
. Zalesky A, Fornito A, Cocchi L, Gollo LL, Breakspear M. Time-resolved resting-state brain networks. Proc Natl Acad Sci U S A 2014;111:10341–6.
. Zigmond AS, Snaith RP. The hospital anxiety and depression scale. Acta Psychiatr Scand 1983;67:361–70.
. Zuo XN, Di Martino A, Kelly C, Shehzad ZE, Gee DG, Klein DF, Castellanos FX, Biswal BB, Milham MP. The oscillating brain: complex and reliable. Neuroimage 2010;49:1432–45.