Classification and characterisation of brain network changes in chronic back pain: a multicenter study

Matsubara, Hiroaki; Kotecha, Gopal; Leibnitz, Kenji; Matsubara, Takashi; Nakae, Aya; Shenker, Nicholas; Shibata, Masahiko; Voon, Valerie; Yoshida, Wako; Lee, Michael; Yanagida, Toshio; Kawato, Mitsuo; Rosa, Maria J.; Seymour, Ben

doi:10.1101/223446

Cited by 7 publications

(17 citation statements)

References 49 publications

(71 reference statements)

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“…Closely following methods recently described (Mano et al, 2018), we estimated a modularity consensus or AM for each group (OA, HC), and computed the difference between group matrices, generating the agreement difference matrix (Figure 5a). Here, each entry takes a value from 1 (red), to −1 (blue).…”

Section: Resultsmentioning

confidence: 99%

“…We studied brain networks at an intermediate scale of organization—community structure or modularity—following methods described by Mano et al (2018)); this approach is based on community detection in multislice networks (Mucha, Richardson, Macon, Porter, & Onnela, 2010) and focuses on detecting differences in brain network modularity between groups (OA, HC) by calculating a measure of consensus modularity pattern – modularity agreement matrix (AM). Analytical steps are depicted in Figure 1d.…”

Section: Methodsmentioning

confidence: 99%

“…First, for each group separately (OA discovery, HC), we use a categorical multislice modularity algorithm (Jeub, Bazzi, Jutla, & Mucha, 2011), where the same node is coupled among all subjects (slices), with 10% link density matrices. Coupling strength (ω = 0.1) and modularity resolution (γ = 1.5) are free parameters that were a priori defined based on previous research (Mano et al, 2018; Mucha et al, 2010). This allows us to create a single symmetric AM representing each group, where each entry has a value within [0,1], representing the agreement between pairs of nodes.…”

Section: Methodsmentioning

confidence: 99%

“…Finally, following Mano et al (2018), in order to evaluate the statistical significance, we performed a permutational analysis, were we randomly resampled pain and control subjects into two groups and repeated the full analysis—also 1,000 times. Based on the proportion of times the resampled nodal modular reorganization exceeded the correct value we calculated one‐sided p ‐values and used a threshold of p < .01 to consider significant nodal modular reorganization values.…”

Section: Methodsmentioning

confidence: 99%

“…Analysis was performed using MATLAB 2019.b (MATLAB and Brain Connectivity Toolbox release 2019a, The MathWorks, Inc., Natick, MA) and tools from the brain connectivity toolbox (Rubinov & Sporns, 2010). Code for modularity reorganization analysis was adapted from Mano et al (http://doi.org/10.5281/zenodo.1183399) (Mano et al, 2018). Brain figures of ROI and functional connectivity networks were visualized on a surface rendering of a human brain atlas with BrainNet Viewer (http://www.nitrc.org/projects/bnv/).…”

Section: Methodsmentioning

confidence: 99%

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Reorganization of functional brain network architecture in chronic osteoarthritis pain

Barroso

Wakaizumi

Reis³

et al. 2020

Human Brain Mapping

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Osteoarthritis (OA) manifests with chronic pain, motor impairment, and proprioceptive changes. However, the role of the brain in the disease is largely unknown. Here, we studied brain networks using the mathematical properties of graphs in a large sample of knee and hip OA (KOA, n = 91; HOA, n = 23) patients. We used a robust validation strategy by subdividing the KOA data into discovery and testing groups and tested the generalizability of our findings in HOA. Despite brain global topological properties being conserved in OA, we show there is a network wide pattern of reorganization that can be captured at the subject‐level by a single measure, the hub disruption index. We localized reorganization patterns and uncovered a shift in the hierarchy of network hubs in OA: primary sensory and motor regions and parahippocampal gyrus behave as hubs and insular cortex loses its central placement. At an intermediate level of network structure, frontoparietal and cingulo‐opercular modules showed preferential reorganization. We examined the association between network properties and clinical correlates: global disruption indices and isolated degree properties did not reflect clinical parameters; however, by modeling whole brain nodal degree properties, we identified a distributed set of regions that reliably predicted pain intensity in KOA and generalized to hip OA. Together, our findings reveal that while conserving global topological properties, brain network architecture reorganizes in OA, at both global and local scale. Network connectivity related to OA pain intensity is dissociated from the major hub disruptions, challenging the extent of dependence of OA pain on nociceptive signaling.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Reorganization of functional brain network architecture in chronic osteoarthritis pain

Barroso

Wakaizumi

Reis³

et al. 2020

Human Brain Mapping

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Neural oscillations and connectivity characterizing the state of tonic experimental pain in humans

Nickel

Dinh

et al. 2019

Human Brain Mapping

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Pain is a complex phenomenon that is served by neural oscillations and connectivity involving different brain areas and frequencies. Here, we aimed to systematically and comprehensively assess the pattern of neural oscillations and connectivity characterizing the state of tonic experimental pain in humans. To this end, we applied 10‐min heat pain stimuli consecutively to the right and left hand of 39 healthy participants and recorded electroencephalography. We systematically analyzed global and local measures of oscillatory brain activity, connectivity, and graph theory‐based network measures during tonic pain and compared them to a nonpainful control condition. Local measures showed suppressions of oscillatory activity at alpha frequencies together with stronger connectivity at alpha and beta frequencies in sensorimotor areas during tonic pain. Furthermore, sensorimotor areas contralateral to stimulation showed significantly increased connectivity to a common area in the medial prefrontal cortex at alpha frequencies. Together, these observations indicate that the state of tonic experimental pain is associated with a sensorimotor‐prefrontal network connected at alpha frequencies. These findings represent a step further toward understanding the brain mechanisms underlying long‐lasting pain states in health and disease.

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The medial temporal lobe in nociception: a meta-analytic and functional connectivity study

Ayoub

Barnett

Leboucher

et al. 2019

Pain

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processing. Of importance, there has yet to be a formal investigation of the spatial consistency of MTL activations in healthy participants during experimental pain.In addition to studies in healthy participants, extant evidence indicates aberrant MTL activity in various chronic pain conditions [56,104,108]. Recent evidence in patients with back pain suggests that the transition from subacute to chronic pain may be mediated by MTL structures [134]. Specifically, reduced MTL resting state functional connectivity (rsFC) to other cortical regions [107] and smaller MTL volume [135] predict this transition. However, as with experimental pain, it is not clear which regions within the MTL are implicated in chronic pain and what their role may be.The overall aim of this study was to determine regions of the MTL consistently involved in nociceptive processing and pain modulation in health and disease. First, we sought to determine which regions of the MTL show consistent spatial activation in response to (1) experimental pain in healthy participants compared to baseline control conditions, and (2) chronic pain patients compared to healthy participants. To that end, we performed two coordinate-based meta-analyses of functional MRI (fMRI) studies of pain that indicated MTL engagement. We expected our first meta-analysis to show consistent MTL activation in the HC and PHG across studies reporting MTL activity. We expected our second metaanalysis to show consistent MTL activation in the HC across chronic pain neuroimaging studies. These analyses are the first formalized investigation of MTL functioning in nociceptive processing.To generate hypotheses about the mechanistic role of the HC in nociceptive processing from existing data, it is important to explore its network interactions. Thus, we aimed to determine the connectivity of those regions elucidated by our meta-analyses using data from four distinct cohorts of chronic low back pain patients (CBP). Given previous observations of abnormal MTL engagement in CBP, we predicted that CBP would show abnormal rsFC to regions involved in processing the affective dimension of pain, i.e., the anterior insula, midcingulate cortex, amygdala and medial prefrontal cortex [25,31,33,111,134]. METHODSOur ALE meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2015 guidelines and checklist [103]. Article selection criteriaOur article selection followed the PRISMA flow diagrams [102] as shown in Fig. 1. Studies were excluded based on any of the following nine criteria: (1) animal studies; (2) did not use standardized stereotactic (Montreal Neurological Institute (MNI) or Talairach) brain coordinates; (3) case reports; (4) diagnostic or surgical MRI; (5) structural imaging; (6) studies of acute medical pain conditions; (7) functional magnetic resonance imaging (fMRI) studies without a baseline control (for experimental pain studies) or control group (for chronic pain studies); (8) studies not written in the English language; and (9) studies ...

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Classification and characterisation of brain network changes in chronic back pain: a multicenter study

Cited by 7 publications

References 49 publications

Reorganization of functional brain network architecture in chronic osteoarthritis pain

Reorganization of functional brain network architecture in chronic osteoarthritis pain

Neural oscillations and connectivity characterizing the state of tonic experimental pain in humans

The medial temporal lobe in nociception: a meta-analytic and functional connectivity study

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