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Multimodal MRI of myalgic encephalomyelitis chronic fatigue syndrome A cross-sectional neuroimaging study toward its neuropathophysiology and diagnosis1.15 MB
Published VersionCC BY V4.0, Open Access
Journal article
Multimodal MRI of myalgic encephalomyelitis/chronic fatigue syndrome: A cross-sectional neuroimaging study toward its neuropathophysiology and diagnosis
Frontiers in Neurology, Vol.13, pp.1-11
2022
PMCID: PMC9523103
PMID: 36188362
Appears in Thompson Institute Research Collection
Abstract
Introduction: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), is a debilitating illness affecting up to 24 million people worldwide but concerningly there is no known mechanism for ME/CFS and no objective test for diagnosis. A series of our neuroimaging findings in ME/CFS, including functional MRI (fMRI) signal characteristics and structural changes in brain regions particularly sensitive to hypoxia, has informed the hypothesis that abnormal neurovascular coupling (NVC) may be the neurobiological origin of ME/CFS. NVC is a critical process for normal brain function, in which glutamate from an active neuron stimulates Ca2+ influx in adjacent neurons and astrocytes. In turn, increased Ca2+ concentrations in both astrocytes and neurons trigger the synthesis of vascular dilator factors to increase local blood flow assuring activated neurons are supplied with their energy needs.This study investigates NVC using multimodal MRIs: (1) hemodynamic response function (HRF) that represents regional brain blood flow changes in response to neural activities and will be modeled from a cognitive task fMRI; (2) respiration response function (RRF) represents autoregulation of regional blood flow due to carbon dioxide and will be modeled from breath-holding fMRI; (3) neural activity associated glutamate changes will be modeled from a cognitive task functional magnetic resonance spectroscopy. We also aim to develop a neuromarker for ME/CFS diagnosis by integrating the multimodal MRIs with a deep machine learning framework. Methods and analysis: This cross sectional study will recruit 288 participants (91 ME/CFS, 61 individuals with chronic fatigue, 91 healthy controls with sedentary lifestyles, 45 fibromyalgia). The ME/CFS will be diagnosed by consensus diagnosis made by two clinicians using the Canadian Consensus Criteria 2003. Symptoms, vital signs, and activity measures will be collected alongside multimodal MRI.The HRF, RRF, and glutamate changes will be compared among four groups using one-way analysis of covariance (ANCOVA). Equivalent non-parametric methods will be used for measures that do not exhibit a normal distribution. The activity measure, body mass index, sex, age, depression, and anxiety will be included as covariates for all statistical analyses with the false discovery rate used to correct for multiple comparisons.The data will be randomly divided into a training (N = 188) and a validation (N = 100) group. Each MRI measure will be entered as input for a least absolute shrinkage and selection operator regularized principal components regression to generate a brain pattern of distributed clusters that predict disease severity. The identified brain pattern will be integrated using multimodal deep Boltzmann machines as a neuromarker for predicting ME/CFS fatigue conditions. The receiver operating characteristic curve of the identified neuromarker will be determined using data from the validation group. Ethics and study registry: This study was reviewed and approved by University of the Sunshine Coast University Ethics committee (A191288) and has been registered with The Australian New Zealand Clinical Trials Registry (ACTRN12622001095752). Dissemination of results: The results will be disseminated through peer reviewed scientific manuscripts and conferences and to patients through social media and active engagement with ME/CFS associations.
Details
- Title
- Multimodal MRI of myalgic encephalomyelitis/chronic fatigue syndrome: A cross-sectional neuroimaging study toward its neuropathophysiology and diagnosis
- Authors
- Zack Y. Shan (Corresponding Author) - University of the Sunshine Coast, Queensland, Thompson InstituteAbdalla Z. Mohamed (Author) - University of the Sunshine Coast, Queensland, Thompson InstituteThu Andersen (Author) - University of the Sunshine Coast, Queensland, Thompson InstituteShae Rendall (Author) - University of the Sunshine Coast, Queensland, Thompson InstituteRichard A. Kwiatek (Author) - University of the Sunshine Coast, Queensland, Thompson InstitutePeter Del Fante (Author) - Thompson Institute, University of the Sunshine Coast, Birtinya, QLD, AustraliaVince D. Calhoun (Author) - Georgia State UniversitySandeep Bhuta (Author) - Gold Coast HospitalJim Lagopoulos (Author) - University of the Sunshine Coast, Queensland, Thompson Institute
- Publication details
- Frontiers in Neurology, Vol.13, pp.1-11
- Publisher
- Frontiers Research Foundation
- Date published
- 2022
- DOI
- 10.3389/fneur.2022.954142
- ISSN
- 1664-2295
- PMID
- 36188362; PMC9523103
- Copyright note
- © 2022 Shan, Mohamed, Andersen, Rendall, Kwiatek, Fante, Calhoun, Bhuta and Lagopoulos. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
- Organisation Unit
- Thompson Institute
- Language
- English
- Record Identifier
- 99678892202621
- Output Type
- Journal article
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- Clinical Neurology
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