Abstract
Purpose
The aim of this study was to investigate alterations in the topological organization of whole-brain functional networks in patients with chronic low back pain (CLBP) and characterize the relationship of these alterations with pain characteristics.
Methods
Thirty-three CLBP patients and 34 matched healthy controls (HCs) underwent fMRI scans. A graph-theoretical approach was applied to identify brain network changes in patients suffering from chronic low back pain given its nonspecific etiology and complexity. Graph theory-based analysis was used to construct functional connectivity matrices and extract the features of small-world networks of the brain in both groups. Then, the whole-brain functional connectivity differences were characterized by network-based statistics (NBS) analysis, and the relationship between the altered brain features and clinical measures was explored.
Results
At the global level, patients with CLBP showed significantly decreased gamma, sigma, global efficiency, and local efficiency and increased lambda and shortest path length compared with HCs. At the regional level, there were deficits in nodal efficiency within the default mode network and salience network. NBS analysis demonstrated that decreased functional connectivity was present in the CLBP patients, mainly in the frontolimbic circuit and temporal regions. Furthermore, aspects of topological dysfunctions in CLBP were correlated with pain severity.
Conclusion
This study highlighted the aberrant topological organization of functional brain networks in CLBP, which may shed light on the pathophysiology of CLBP and support the development of pain management approaches.
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References
Buchbinder R, Hartvigsen J, Cherkin D, Foster NE, Maher CG, Underwood M et al (2018) What low back pain is and why we need to pay attention. Lancet 391:2356–2367. https://doi.org/10.1016/S0140-6736(18)30480-X
Deyo RA, Von Korff M, Duhrkoop D (2015) Opioids for low back pain. BMJ 350:g6380. https://doi.org/10.1136/bmj.g6380
Davis KD, Moayedi M (2013) Central mechanisms of pain revealed through functional and structural MRI. J Neuroimmune Pharmacol 8:518–534. https://doi.org/10.1007/s11481-012-9386-8
Farmer MA, Baliki MN, Apkarian AV (2012) A dynamic network perspective of chronic pain. Neurosci Lett 520:197–203. https://doi.org/10.1016/j.neulet.2012.05.001
Kucyi A, Davis KD (2015) The dynamic pain connectome. Trends Neurosci 38:86–95. https://doi.org/10.1016/j.tins.2014.11.006
Hohenfeld C, Werner CJ, Reetz K (2018) Resting-state connectivity in neurodegenerative disorders: is there potential for an imaging biomarker? NeuroImage: Clin 18:849–870. https://doi.org/10.1016/j.nicl.2018.03.013
Letzen JE, Boissoneault J, Sevel LS, Robinson ME (2020) Altered mesocorticolimbic functional connectivity in chronic low back pain patients at rest and following sad mood induction. Brain Imaging Behav 14:1118–1129. https://doi.org/10.1007/s11682-019-00076-w
Tu Y, Jung M, Gollub RL, Napadow V, Gerber J, Ortiz A et al (2019) Abnormal medial prefrontal cortex functional connectivity and its association with clinical symptoms in chronic low back pain. Pain 160:1308–1318. https://doi.org/10.1097/j.pain.0000000000001507
Yu S, Li W, Shen W, Edwards RR, Gollub RL, Wilson G et al (2020) Impaired mesocorticolimbic connectivity underlies increased pain sensitivity in chronic low back pain. NeuroImage 218:116969. https://doi.org/10.1016/j.neuroimage.2020.116969
Li H, Song Q, Zhang R, Zhou Y, Kong Y (2021) Enhanced temporal coupling between thalamus and dorsolateral prefrontal cortex mediates chronic low back pain and depression. Neural Plast 2021:7498714. https://doi.org/10.1155/2021/7498714
Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10:186–198. https://doi.org/10.1038/nrn2575
Van Den Heuvel MP, Hulshoff Pol HE (2010) Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol 20:519–534. https://doi.org/10.1016/j.euroneuro.2010.03.008
Hashmi JA, Kong J, Spaeth R, Gollub RL, Khan S, Kaptchuk TJ (2014) Functional network architecture predicts psychologically mediated analgesia related to treatment in chronic knee pain patients. J Neurosci 34:3924–3936. https://doi.org/10.1523/JNEUROSCI.3155-13.2014
Kaplan CM, Schrepf A, Ichesco E, Kochlefl L, Harte SE, Clauw DJ et al (2019) Functional and neurochemical disruptions of brain hub topology in chronic pain. Pain 160:973–983. https://doi.org/10.1097/j.pain.0000000000001480
Zhang F, Li F, Jia Z, Gong Q, Yang H, Jin Y et al (2022) Altered brain topological property associated with anxiety in experimental orthodontic pain. Front Neurosci 16:907216. https://doi.org/10.3389/fnins.2022.907216
De Pauw R, Meeus M, Coppieters I, Caeyenberghs K, Cagnie B, Aerts H et al (2020) Hub disruption in patients with chronic neck pain: a graph analytical approach. Pain 161:729–741. https://doi.org/10.1097/j.pain.0000000000001762
Lamichhane B, Jayasekera D, Jakes R, Glasser MF, Zhang J, Yang C et al (2021) Multi-modal biomarkers of low back pain: a machine learning approach. NeuroImage: Clinical 29:102530. https://doi.org/10.1016/j.nicl.2020.102530
Yuan J, Purepong N, Kerr DP, McDonough S, Park J, Bradbury I (2008) Effectiveness of acupuncture for low back pain: a systematic review. Spine 33:E887–E900. https://doi.org/10.1097/BRS.0b013e318186b276
Jinhui W, Xindi W, Mingrui X, Xuhong L, Alan E, Yong H (2015) GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Front Hum Neurosci 9:00386. https://doi.org/10.3389/fnhum.2015.00386
Tzourio-Mazoyer N, Papathanassiou D, Crivello F, Etard O, Delcroix N, Joliot M et al (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15:273–289. https://doi.org/10.1006/nimg.2001.0978
Danon L, Diaz-Guilera A, Arenas A (2006) The effect of size heterogeneity on community identification in complex networks. J Stat Mech Theory Exp:P11010. https://doi.org/10.1088/1742-5468/2006/11/P11010
Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440. https://doi.org/10.1038/30918
Buckner RL, Sepulcre J, Krienen FM, Hedden T, Andrews-Hanna JR, Talukdar T et al (2009) Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. J Neurosci 29:1860–1873. https://doi.org/10.1523/JNEUROSCI.5062-08.2009
Liang X, Zou Q, He Y, Yang Y (2013) Coupling of functional connectivity and regional cerebral blood flow reveals a physiological basis for network hubs of the human brain. Proc Natl Acad Sci U S A 110:1929–1934. https://doi.org/10.1073/pnas.1214900110
Liao X-H, Xia M-R, Xu T, Dai Z-J, Cao X-Y, Niu H-J et al (2013) Functional brain hubs and their test–retest reliability: a multiband resting-state functional MRI study. NeuroImage 83:969–982. https://doi.org/10.1016/j.neuroimage.2013.07.058
Liao X, Cao M, Xia M, He Y (2017) Individual differences and time-varying features of modular brain architecture. NeuroImage 152:94–107. https://doi.org/10.1016/j.neuroimage.2017.02.066
Achard S, Bullmore E (2007) Efficiency and cost of economical brain functional networks. PLoS Comput Biol 3:0174–0183. https://doi.org/10.1371/journal.pcbi.0030017
Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: Uses and interpretations. NeuroImage 52:1059–1069. https://doi.org/10.1016/j.neuroimage.2009.10.003
Mingrui X, Jinhui W, Yong H (2013) BrainNet Viewer: a network visualization tool for human brain connectomics. PloS One 8:e68910. https://doi.org/10.1371/journal.pone.0068910
Keown CL, Datko MC, Chen CP, Maximo JO, Jahedi A, Müller RA (2017) Network organization is globally atypical in autism: a graph theory study of intrinsic functional connectivity. Biol Psychiatry Cogn Neurosci Neuroimaging 2:66–75. https://doi.org/10.1016/j.bpsc.2016.07.008
Zhang J, Wang J, Wu Q, Kuang W, Huang X, He Y et al (2011) Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biol Psychiatry 70:334–342. https://doi.org/10.1016/j.biopsych.2011.05.018
Zalesky A, Fornito A, Bullmore ET (2010) Network-based statistic: identifying differences in brain networks. NeuroImage 53:1197–1207. https://doi.org/10.1016/j.neuroimage.2010.06.041
Finn ES, Shen X, Holahan JM, Scheinost D, Lacadie C, Papademetris X et al (2014) Disruption of functional networks in dyslexia: a whole-brain, data-driven analysis of connectivity. Biol Psychiatry 76:397–404. https://doi.org/10.1016/j.biopsych.2013.08.031
Tanya W, Shulan H (2016) Network-based analysis reveals functional connectivity related to internet addiction tendency. Front Hum Neurosci 10:6. https://doi.org/10.3389/fnhum.2016.00006
Fan X, Wu Y, Cai L, Ma J, Pan N, Xu X et al (2021) The differences in the whole-brain functional network between cantonese-mandarin bilinguals and mandarin monolinguals. Brain Sci 11:1–18. https://doi.org/10.3390/brainsci11030310
Joel D, Berman Z, Tavor I, Wexler N, Gaber O, Stein Y et al (2015) Sex beyond the genitalia : the human brain mosaic. Proc Natl Acad Sci U S A 112:15468–15473. https://doi.org/10.1073/pnas.1509654112
Hou Y, Feng F, Zhang L, Ou R, Lin J, Gong Q et al (2022) Disrupted topological organization of resting-state functional brain networks in Parkinson’s disease patients with glucocerebrosidase gene mutations. Neuroradiology: A Journal Dedicated to Neuroimaging and Interventional. Neuroradiology 65:361–370. https://doi.org/10.1007/s00234-022-03067-9
Jin M, Wang L, Wang H, Han X, Diao Z, Guo W et al (2021) Altered resting-state functional networks in patients with hemodialysis: a graph-theoretical based study. Brain Imaging Behav 15:833–845. https://doi.org/10.1007/s11682-020-00293-8
Li X, Yang C, Xie P, Han Y, Su R, Li Z et al (2021) The diagnosis of amnestic mild cognitive impairment by combining the characteristics of brain functional network and support vector machine classifier. J Neurosci Methods 363:109334. https://doi.org/10.1016/j.jneumeth.2021.109334
Wang W, Mei M, Gao Y, Huang B, Qiu Y, Zhang Y et al (2020) Changes of brain structural network connection in Parkinson’s disease patients with mild cognitive dysfunction: a study based on diffusion tensor imaging. J Neurol 267:933–943. https://doi.org/10.1007/s00415-019-09645-x
Liu J, Zhang F, Liu X, Zhuo Z, Wei J, Du M et al (2018) Altered small-world, functional brain networks in patients with lower back pain. Sci China Life Sci 61:1420–1424. https://doi.org/10.1007/s11427-017-9108-6
Baliki MN, Mansour AR, Baria AT, Apkarian AV (2014) Functional reorganization of the default mode network across chronic pain conditions. PloS One 9:1–13. https://doi.org/10.1371/journal.pone.0106133
Li J, Zhang J-H, Yi T, Tang W-J, Wang S-W, Dong J-C (2014) Acupuncture treatment of chronic low back pain reverses an abnormal brain default mode network in correlation with clinical pain relief. Acupunct Med 32:102–108. https://doi.org/10.1136/acupmed-2013-010423
Baliki MN, Geha PY, Apkarian AV, Chialvo DR (2008) Beyond feeling: chronic pain hurts the brain, disrupting the default-mode network dynamics. J Neurosci 28:1398–1403. https://doi.org/10.1523/JNEUROSCI.4123-07.2008
Yu R, Gollub RL, Spaeth R, Napadow V, Wasan A, Kong J (2014) Disrupted functional connectivity of the periaqueductal gray in chronic low back pain. NeuroImage: Clinical 6:100–108. https://doi.org/10.1016/j.nicl.2014.08.019
Baliki MN, Baria AT, Vania Apkarian A (2011) The cortical rhythms of chronic back pain. J Neurosci 31:13981–13990. https://doi.org/10.1523/JNEUROSCI.1984-11.2011
Baliki MN, Petre B, Torbey S, Herrmann KM, Huang L, Apkarian AV et al (2012) Corticostriatal functional connectivity predicts transition to chronic back pain. Nat Neurosci 15:1117–1119. https://doi.org/10.1038/nn.3153
Zhang B, Jung M, Tu Y, Gollub R, Lang C, Ortiz A et al (2019) Identifying brain regions associated with the neuropathology of chronic low back pain: a resting-state amplitude of low-frequency fluctuation study. Br J Anaesth 123:e303–e311. https://doi.org/10.1016/j.bja.2019.02.021
Baumbach P, Meißner W, Reichenbach JR, Gussew A (2022) Functional connectivity and neurotransmitter impairments of the salience brain network in chronic low back pain patients: a combined resting-state functional magnetic resonance imaging and 1H-MRS study. Pain 163:2337–2347. https://doi.org/10.1097/j.pain.0000000000002626
Kolesar TA, Bilevicius E, Kornelsen J (2017) Salience, central executive, and sensorimotor network functional connectivity alterations in failed back surgery syndrome. Scand J Pain 16:10–14. https://doi.org/10.1016/j.sjpain.2017.01.008
Kobayashi Y, Sekiguchi M, Konno SI, Kurata J, Kokubun M, Akaishizawa T et al (2009) Augmented cerebral activation by lumbar mechanical stimulus in chronic low back pain patients: An fMRI study. Spine 34:2431–2436. https://doi.org/10.1097/BRS.0b013e3181b1fb76
Pablo B, Ariel C, Daniel F, Ignacio C, Carolina S, Pedro M et al (2010) Modular organization of brain resting state networks in chronic back pain patients. Front Neuroinform 4:00116. https://doi.org/10.3389/fninf.2010.00116
Masoumbeigi M, Alam NR, Kordi R, Rostami M, Afzali M, Yadollahi M et al (2022) rTMS pain reduction effectiveness in non-specific chronic low back pain patients using rs-fMRI functional connectivity. J Med Biol Eng 42:647–657. https://doi.org/10.1007/s40846-022-00721-8
Keltner JR, Furst A, Fan C, Redfern R, Inglis B, Fields HL (2006) Isolating the modulatory effect of expectation on pain transmission: a functional magnetic resonance imaging study. J Neurosci 26:4437–4443. https://doi.org/10.1523/JNEUROSCI.4463-05.2006
Grahn JA, Parkinson JA, Owen AM (2008) The cognitive functions of the caudate nucleus. Prog Neurobiol 86:141–155. https://doi.org/10.1016/j.pneurobio.2008.09.004
Absinta M, Rocca MA, Colombo B, Falini A, Comi G, Filippi M (2012) Selective decreased grey matter volume of the pain-matrix network in cluster headache. Cephalalgia 32:109–115. https://doi.org/10.1177/0333102411431334
Cui Ping M, Zhi Lan B, Xiao Na Z, Qiu Juan Z, Lei Z (2016) Abnormal subcortical brain morphology in patients with knee osteoarthritis: a cross-sectional study. Front Aging Neurosci 8:00003. https://doi.org/10.3389/fnagi.2016.00003
Wartolowska K, Hough MG, Jenkinson M, Andersson J, Tracey I, Wordsworth BP (2012) Structural changes of the brain in rheumatoid arthritis. Arthritis Rheum 64:371–379. https://doi.org/10.1002/art.33326
Koechlin H, Kossowsky J, Coakley R, Schechter N, Werner C (2018) The role of emotion regulation in chronic pain: a systematic literature review. J Psychosom Res 107:38–45. https://doi.org/10.1016/j.jpsychores.2018.02.002
Sven V, Jae-Jin S, Dirk De R (2018) Thalamocortical dysrhythmia detected by machine learning. Nat Commun 9:1–13. https://doi.org/10.1038/s41467-018-02820-0
Li H, Li X, Feng Y, Gao F, Kong Y, Hu L (2020) Deficits in ascending and descending pain modulation pathways in patients with postherpetic neuralgia. NeuroImage 221:117186. https://doi.org/10.1016/j.neuroimage.2020.117186
Bräscher AK, Becker S, Hoeppli ME, Schweinhardt P (2016) Different brain circuitries mediating controllable and uncontrollable pain. J Neurosci 36:5013–5025. https://doi.org/10.1523/JNEUROSCI.1954-15.2016
Mao CP, Wilson G, Cao J, Meshberg N, Huang Y, Kong J (2022) Abnormal anatomical and functional connectivity of the thalamo-sensorimotor circuit in chronic low back pain: resting-state functional magnetic resonance imaging and diffusion tensor imaging study. Neuroscience 487:143–154. https://doi.org/10.1016/j.neuroscience.2022.02.001
Seifert F, Maihöfner C (2007) Representation of cold allodynia in the human brain—a functional MRI study. NeuroImage 35:1168–1180. https://doi.org/10.1016/j.neuroimage.2007.01.021
Apkarian VA, Baliki MN, Geha PY (2009) Towards a theory of chronic pain. Prog Neurobiol 87:81–97. https://doi.org/10.1016/j.pneurobio.2008.09.018
Baker KS, Georgiou-Karistianis N, Gibson SJ, Giummarra MJ (2017) Optimizing cognitive function in persons with chronic pain. Clin J Pain 33:462–472. https://doi.org/10.1097/AJP.0000000000000423
Neugebauer V, Galhardo V, Maione S, Mackey SC (2009) Forebrain pain mechanisms. Brain Res Rev 60:226–242. https://doi.org/10.1016/j.brainresrev.2008.12.014
Freeman S, Yu R, Egorova N, Chen X, Kirsch I, Claggett B et al (2015) Distinct neural representations of placebo and nocebo effects. NeuroImage 112:197–207. https://doi.org/10.1016/j.neuroimage.2015.03.015
Vachon-Presseau E, Tétreault P, Petre B, Huang L, Berger SE, Baria AT et al (2016) Corticolimbic anatomical characteristics predetermine risk for chronic pain. Brain 139:1958–1970. https://doi.org/10.1093/brain/aww100
Koechlin H, Coakley R, Schechter N, Werner C, Kossowsky J (2018) The role of emotion regulation in chronic pain: a systematic literature review. J Psychosom Res 107:38–45. https://doi.org/10.1016/j.jpsychores.2018.02.002
Chudler Eric H, Upadhyay J, Borsook D, Becerra L (2010) A key role of the basal ganglia in pain and analgesia - insights gained through human functional imaging. Mol Pain 6:27. https://doi.org/10.1186/1744-8069-6-27
Kim MJ, Hamilton JP, Gotlib IH (2008) Reduced caudate gray matter volume in women with major depressive disorder. Psychiatry Res: Neuroimaging 164:114–122. https://doi.org/10.1016/j.pscychresns.2007.12.020
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The authors thank all the participants for their participation in the study.
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This work was supported by grants from Natural Science Foundation of Shaanxi Province (2022SF-347, 2021SF-147, 2018SF-135), National Natural Science Foundation of China (81501455), and China Scholarship Council (201806285075).
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Yang, H.J., Wu, H.M., Li, X.H. et al. Functional disruptions of the brain network in low back pain: a graph-theoretical study. Neuroradiology 65, 1483–1495 (2023). https://doi.org/10.1007/s00234-023-03209-7
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DOI: https://doi.org/10.1007/s00234-023-03209-7