Multimodal MRI of myalgic encephalomyelitis/chronic fatigue syndrome: A cross-sectional neuroimaging study toward its neuropathophysiology and diagnosis - Shan, Zack Y.; Mohamed, Abdalla Z.; Andersen, Thu; Rendall, Shae; Kwiatek, Richard A.; Fante, Peter Del; Calhoun, Vince D.; Bhuta, Sandeep; Lagopoulos, Jim (Frontiers in Neurology, 2022)
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. We hypotheses 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) represents regional brain blood flow changes in response to neural activitiesI; 2) respiration response function (RRF) represents autoregulation of regional blood flow due to carbon dioxide; 3) neural activity associated glutamate changes will be modelled 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. 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. The HRF, RRF, and glutamate changes will be compared among four groups using one-way analysis of covariance. The false discovery rate will be 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 regularised principal components regression to generate a brain pattern 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. This study was approved by University of the Sunshine Coast University Ethics committee (A191288) and registered with The Australian New Zealand Clinical Trials Registry (ACTRN12622001095752).