Background & Aims
Resting-state functional connectivity (FC) has been reported to be disrupted in a range of chronic pain conditions, including migraine, neuropathic pain, inflammatory bowel disease and chronic low back pain (CLBP) [1,7,9,10,13,14,17].
An important contributing confound to FC is age, which has been shown to be associated with decreased FC of various brain networks as part of healthy aging [5]. A growing body of literature indicates accelerated brain aging in chronic pain patients compared to healthy controls (HC) w.r.t. grey matter volume [4,8,11,12]. In contrast, it is currently unclear whether the effect of age on FC differs between patients with chronic pain and HC. Therefore, the aim of the present study was to investigate differences in age-related FC alterations between patients with CLBP and HC by examining the age by cohort interaction effect on FC.
Methods
Forty-five CLBP and 34 age- and sex-matched HC underwent an MRI session including a 12min resting-state (EPI, TR/TE: 846ms/30ms, voxel size: 2.2mm isotropic, FoV: 210mm), a structural (gradient echo (GRE), TR/TE: 2300ms/2.25ms, voxel size: 1mm isotropic, FoV: 240mm) and a field map (GRE, TR/TE: 4.92ms/7.38ms, voxel size: 2.2mm isotropic, FoV: 210mm) scan on a 3T Siemens Prisma scanner. MRI data was analyzed using FSL [6]. Preprocessing included motion and distortion correction, resampling to MNI152 space, smoothing with a 4mm full width half maximum Gaussian kernel and independent component analysis (ICA) based denoising using a pretrained version of the classifier FIX [15]. The age by cohort interaction effect on FC was assessed using group level ICA (30 dimensions), dual regression and permutation tests (5000 permutations, threshold-free cluster enhancement corrected) [3].
Results
Out of 30 group level components extracted with group-ICA, 22 corresponded to common resting state networks [2,16] including the default mode, salience, visual, auditory, mid-temporal, frontal-executive, frontoparietal-attention and sensorimotor networks. Permutation tests showed a significant age by cohort interaction effect on FC in several components including visuo-lateral, visuo-occipital, auditory, mid-temporal, frontoparietal-attention and sensorimotor network.
Assessment of parameter estimates indicated a larger age-dependent decrease of FC in CLBP compared to HC across all significant clusters.
Conclusions
The present study demonstrated a significantly larger age-related decrease of FC in patients with CLBP compared to HC. This extends previous findings on age-related grey matter volume decreases in chronic pain patients [4,8,11,12] and suggests that accelerated brain aging observed in chronic pain also impacts FC. Notably, differential age effects between CLBP patients and HC were detected in both sensorimotor and non-somatosensory related networks such as the visuo-lateral and auditory networks. The variety of affected networks suggests an impact of chronic pain on overall brain function with increasing age.
Additionally, this study emphasizes that for FC analyses in patients with chronic pain, age should be corrected for within group rather than across groups.
Further analysis will be performed to assess if the observed age by cohort interaction effect is related to pain characteristics and/or psychophysical pain measures.
References
[1]Baliki MN, Mansour AR, Baria AT, Apkarian AV. Functional reorganization of the default mode network across chronic pain conditions. PloS one 2014;9(9):e106133.
[2]Beckmann CF, DeLuca M, Devlin JT, Smith SM. Investigations into resting-state connectivity using independent component analysis. Philosophical transactions of the Royal Society of London. Series B, Biological sciences 2005;360(1457):1001–13.
[3]Calhoun VD, Adali T, Pearlson GD, Pekar JJ. A method for making group inferences from functional MRI data using independent component analysis. Human brain mapping 2001;14(3):140–51.
[4]Ceko M, Bushnell MC, Fitzcharles M-A, Schweinhardt P. Fibromyalgia interacts with age to change the brain. NeuroImage. Clinical 2013;3:249–60.
[5]Ferreira LK, Busatto GF. Resting-state functional connectivity in normal brain aging. Neuroscience and biobehavioral reviews 2013;37(3):384–400.
[6]Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM. FSL. NeuroImage 2012;62(2):782–90.
[7]Kregel J, Meeus M, Malfliet A, Dolphens M, Danneels L, Nijs J, Cagnie B. Structural and functional brain abnormalities in chronic low back pain: A systematic review. Seminars in arthritis and rheumatism 2015;45(2):229–37.
[8]Kuchinad A, Schweinhardt P, Seminowicz DA, Wood PB, Chizh BA, Bushnell MC. Accelerated brain gray matter loss in fibromyalgia patients: premature aging of the brain? The Journal of neuroscience the official journal of the Society for Neuroscience 2007;27(15):4004–7.
[9]Lamichhane B, Jayasekera D, Jakes R, Ray WZ, Leuthardt EC, Hawasli AH. Functional Disruptions of the Brain in Low Back Pain: A Potential Imaging Biomarker of Functional Disability. Frontiers in neurology 2021;12:669076.
[10]Lee MJ, Park B-Y, Cho S, Kim ST, Park H, Chung C-S. Increased connectivity of pain matrix in chronic migraine: a resting-state functional MRI study. The journal of headache and pain 2019;20(1):29.
[11]Liao X, Mao C, Wang Y, Zhang Q, Cao D, Seminowicz DA, Zhang M, Yang X. Brain gray matter alterations in Chinese patients with chronic knee osteoarthritis pain based on voxel-based morphometry. Medicine 2018;97(12):e0145.
[12]Moayedi M, Weissman-Fogel I, Salomons TV, Crawley AP, Goldberg MB, Freeman BV, Tenenbaum HC, Davis KD. Abnormal gray matter aging in chronic pain patients. Brain research 2012;1456:82–93.
[13]Ng SK, Urquhart DM, Fitzgerald PB, Cicuttini FM, Hussain SM, Fitzgibbon BM. The Relationship Between Structural and Functional Brain Changes and Altered Emotion and Cognition in Chronic Low Back Pain Brain Changes: A Systematic Review of MRI and fMRI Studies. The Clinical Journal of Pain 2018;34(3):237–61.
[14]Prüß MS, Bayer A, Bayer K-E, Schumann M, Atreya R, Mekle R, Fiebach JB, Siegmund B, Neeb L. Functional Brain Changes Due to Chronic Abdominal Pain in Inflammatory Bowel Disease: A Case-Control Magnetic Resonance Imaging Study. Clinical and translational gastroenterology 2022;13(2):e00453.
[15]Salimi-Khorshidi G, Douaud G, Beckmann CF, Glasser MF, Griffanti L, Smith SM. Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. NeuroImage 2014;90:449–68.
[16]Smith SM, Fox PT, Miller KL, Glahn DC, Fox PM, Mackay CE, Filippini N, Watkins KE, Toro R, Laird AR, Beckmann CF. Correspondence of the brain’s functional architecture during activation and rest. Proceedings of the National Academy of Sciences of the United States of America 2009;106(31):13040–5.
[17]Xu H, Seminowicz DA, Krimmel SR, Zhang M, Gao L, Wang Y. Altered Structural and Functional Connectivity of Salience Network in Patients with Classic Trigeminal Neuralgia. The Journal of Pain 2022;23(8):1389–99.
Presenting Author
Madeleine Hau
Poster Authors
Madeleine Hau
MSc
Balgrist University Hospital, University of Zurich
Lead Author
Laura Sirucek
Balgrist University Hospital, University of Zurich
Lead Author
Christian Beckmann
PhD
Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center
Lead Author
Petra Schweinhardt
University of Zurich
Lead Author
Topics
- Pain Imaging