Investigating the functional communication network in patients with knee osteoarthritis using graph-based statistical models

authors:

avatar Fatemeh Pourmotahari ORCID , avatar Nasrin Borumandnia , avatar Seyyed Mohammad Tabatabaei , avatar Hamid Alavi Majd , *


how to cite: Pourmotahari F, Borumandnia N, Tabatabaei S M, Alavi Majd H. Investigating the functional communication network in patients with knee osteoarthritis using graph-based statistical models. koomesh. 2023;25(1):e152798. 

Abstract

Introduction: Osteoarthritis of the knee is the most prevalent type of arthritis that causes persistent pain and reduces the quality of life. However, no treatment alleviates symptoms or stops the disease from progressing. Functional magnetic resonance imaging (fMRI) studies can provide information on neural mechanisms of pain by assessing correlation patterns among the different regions of the brain. This study aimed to determine brain connectivity patterns in patients with knee osteoarthritis compared to healthy individuals using advanced statistical models. Materials and Methods: The data of this study were downloaded from https://openneuro.org/. These data included fMRI imaging of 36 knee osteoarthritis patients with a range age between 45-70 years old and 12 healthy individuals with a range age between 48-78 years old. Graph-based models were used to examine the brain functional alterations in knee osteoarthritis patients. Results: The results showed a disease-related cluster of eight regions in the brain, including the right Rolandic operculum, right amygdala, left caudate nucleus, left putamen, right putamen, left pallidum, and right pallidum. According to correlation comparisons in the cluster, the connectivity of 18 pair regions revealed a significant difference between the two groups. In comparison to the other regions, the right Rolandic and right amygdala had more communication. Conclusion: Interestingly, in patients with knee osteoarthritis, the effect of chronic pain can cause functional alterations in the brain.

References

  • 1.

    Heiden TL, Lloyd DG, Ackland TR. Knee joint kinematics, kinetics and muscle co-contraction in knee osteoarthritis patient gait. Clin Biomech 2009; 24: 833-841.

  • 2.

    Sim HS, Ang KX, How CH, Loh YJ. Management of knee osteoarthritis in primary care. Singapore Med J 2020; 61: 512-516.

  • 3.

    Taghizadeh Delkhoush C, Fatemy E, Ghorbani R. A comparative study on the effects of aerobic walking and strength training programs on balance in patients with knee osteoarthritis. Koomesh 2022 10; 24: 147-154. (Persian).

  • 4.

    Bosomworth NJ. Exercise and knee osteoarthritis: benefit or hazard? Can Fam Physician 2009; 55: 871-878.

  • 5.

    Taruc-Uy RL, Lynch SA. Diagnosis and treatment of osteoarthritis. Prim Care Clin Off Pract 2013; 40: 821-836.

  • 6.

    Richmond J, Hunter D, Irrgang J, Jones MH, Levy B, Marx R, et al. Treatment of osteoarthritis of the knee (nonarthroplasty). J Am Acad Orthop Surg 2009; 17: 591-600.

  • 7.

    Michael JW-P, Schlter-Brust KU, Eysel P. The epidemiology, etiology, diagnosis, and treatment of osteoarthritis of the knee. Dtsch Arztebl Int 2010; 107: 152-162.

  • 8.

    Valizadeh N, Khodakarim S, Tabatabaei SM, Saffar A, Akbarzadeh Baghban A. Application of modified balanced iterative reducing and clustering using hierarchies algorithm in parceling of brain performance using fMRI data. Koomesh 2020; 22: 644-649. (Persian).##https://doi.org/10.29252/koomesh.22.4.644.

  • 9.

    Tagliazucchi E, Balenzuela P, Fraiman D, Chialvo DR. Brain resting state is disrupted in chronic back pain patients. Neurosci Lett 2010; 485: 26-31.

  • 10.

    Selvarajah D, Awadh M, Gandhi R, Wilkinson ID, Tesfaye S. Alterations in somatomotor network functional connectivity in painful diabetic neuropathy-a resting state functional magnetic resonance imaging study. Am Diabetes Assoc 2018; 67: 61.##https://doi.org/10.2337/db18-61-OR.

  • 11.

    Ushio K, Nakanishi K, Mikami Y, Yoshino A, Takamura M, Hirata K, et al. Altered resting-state connectivity with pain-related expectation regions in female patients with severe knee osteoarthritis. J Pain Res 2020; 13: 3227-3234.

  • 12.

    Xia Y, Li L. Matrix graph hypothesis testing and application in brain connectivity alternation detection. Stat Sin 2019; 29: 303-328.

  • 13.

    Simpson SL, Moussa MN, Laurienti PJ. An exponential random graph modeling approach to creating group-based representative whole-brain connectivity networks. Neuroimage 2012; 60: 1117-1126.

  • 14.

    Chen S, Kang J, Xing Y, Wang G. A parsimonious statistical method to detect groupwise differentially expressed functional connectivity networks. Hum Brain Mapp 2015; 36: 5196-5206.

  • 15.

    Wu Q, Huang X, Culbreth AJ, Waltz JA, Hong LE, Chen S. Extracting brain diseaserelated connectome subgraphs by adaptive dense subgraph discovery. Biometrics 2021; 1-13##https://doi.org/10.1101/2020.10.07.330027.

  • 16.

    Fiecas M, Cribben I, Bahktiari R, Cummine J. A variance components model for statistical inference on functional connectivity networks. Neuroimage 2017; 149: 256-266.

  • 17.

    Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 2002; 15: 273-289.

  • 18.

    Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Soc (Methodological) 1995; 57: 289-300.##https://doi.org/10.1111/j.2517-6161.1995.tb02031.x.

  • 19.

    Ell SW, Helie S, Hutchinson S, Costa A, Villalba E. Contributions of the putamen to cognitive function. Horizons Neurosci Res 2011; 29-52.

  • 20.

    Iwabuchi SJ, Krishnadas R, Li C, Auer DP, Radua J, Palaniyappan L. Localized connectivity in depression: a meta-analysis of resting state functional imaging studies. Neurosci Biobehav Rev 2015; 51: 77-86.

  • 21.

    Graff-Radford J, Williams L, Jones DT, Benarroch EE. Caudate nucleus as a component of networks controlling behavior. Neurology 2017; 89: 2192-2197.

  • 22.

    Histad M, Barbas H. Sequence of information processing for emotions through pathways linking temporal and insular cortices with the amygdala. Neuroimage 2008; 40: 1016-1033.

  • 23.

    Mwansisya TE, Wang Z, Tao H, Zhang H, Hu A, Guo S, et al. The diminished interhemispheric connectivity correlates with negative symptoms and cognitive impairment in first-episode schizophrenia. Schizophr Res 2013; 150: 144-150.

  • 24.

    Friebel U, Eickhoff SB, Lotze M. Coordinate-based meta-analysis of experimentally induced and chronic persistent neuropathic pain. Neuroimage 2011; 58: 1070-1080.

  • 25.

    Tracey I. Nociceptive processing in the human brain. Curr Opin Neurobiol 2005; 15: 478-87.

  • 26.

    Baliki MN, Geha PY, Jabakhanji R, Harden N, Schnitzer TJ, Apkarian AV. A preliminary fMRI study of analgesic treatment in chronic back pain and knee osteoarthritis. Mol Pain 2008; 4: 1744-8069.

  • 27.

    De Pauw R, Aerts H, Siugzdaite R, Meeus M, Coppieters I, Caeyenberghs K, et al. Hub disruption in patients with chronic neck pain: a graph analytical approach. Pain 2020; 161: 729-741.

  • 28.

    Ruffle JK, Coen SJ, Giampietro V, Williams SCR, Aziz Q, Farmer AD. Preliminary report: parasympathetic tone links to functional brain networks during the anticipation and experience of visceral pain. Sci Rep 2018; 8: 1-12.

  • 29.

    Balenzuela P, Chernomoretz A, Fraiman D, Cifre I, Sitges C, Montoya P, et al. Modular organization of brain resting state networks in chronic back pain patients. Front Neuroinform 2010; 4: 116.

  • 30.

    Lan F, Lin G, Cao G, Li Z, Ma D, Liu F, et al. Altered Intrinsic Brain Activity and Functional Connectivity Before and After Knee Arthroplasty in the Elderly: A Resting-State fMRI Study. Front Neurol 2020; 11: 1087.

  • 31.

    Cottam WJ, Iwabuchi SJ, Drabek MM, Reckziegel D, Auer DP. Altered connectivity of the right anterior insula drives the pain connectome changes in chronic knee osteoarthritis. Pain 2018; 159: 929.