Machine Learning Detects Pattern of Differences in Functional Magnetic Resonance Imaging (fMRI) Data between Chronic Fatigue Syndrome (CFS) and Gulf War Illness (GWI)

Abstract

Background: Gulf War Illness (GWI) and Chronic Fatigue Syndrome (CFS) are two debilitating disorders that share similar symptoms of chronic pain, fatigue, and exertional exhaustion after exercise. Many physicians continue to believe that both are psychosomatic disorders and to date no underlying etiology has been discovered. As such, uncovering objective biomarkers is important to lend credibility to criteria for diagnosis and to help differentiate the two disorders. Methods: We assessed cognitive differences in 80 subjects with GWI and 38 with CFS by comparing corresponding fMRI scans during 2-back working memory tasks before and after exercise to model brain activation during normal activity and after exertional exhaustion, respectively. Voxels were grouped by the count of total activity into the Automated Anatomical Labeling (AAL) atlas and used in an “ensemble” series of machine learning algorithms to assess if a multi-regional pattern of differences in the fMRI scans could be detected. Results: A K-Nearest Neighbor (70%/81%), Linear Support Vector Machine (SVM) (70%/77%), Decision Tree (82%/82%), Random Forest (77%/78%), AdaBoost (69%/81%), Naïve Bayes (74%/78%), Quadratic Discriminant Analysis (QDA) (73%/75%), Logistic Regression model (82%/82%), and Neural Net (76%/77%) were able to differentiate CFS from GWI before and after exercise with an average of 75% accuracy in predictions across all models before exercise and 79% after exercise. An iterative feature selection and removal process based on Recursive Feature Elimination (RFE) and Random Forest importance selected 30 regions before exercise and 33 regions after exercise that differentiated CFS from GWI across all models, and produced the ultimate best accuracies of 82% before exercise and 82% after exercise by Logistic Regression or Decision Tree by a single model, and 100% before and after exercise when selected by any six or more models. Differential activation on both days included the right anterior insula, left putamen, and bilateral orbital frontal, ventrolateral prefrontal cortex, superior, inferior, and precuneus (medial) parietal, and lateral temporal regions. Day 2 had the cerebellum, left supplementary motor area and bilateral pre- and post-central gyri. Changes between days included the right Rolandic operculum switching to the left on Day 2, and the bilateral midcingulum switching to the left anterior cingulum. Conclusion: We concluded that CFS and GWI are significantly differentiable using a pattern of fMRI activity based on an ensemble machine learning model.

Exercise modifies glutamate and other metabolic biomarkers in cerebrospinal fluid from Gulf War Illness and Myalgic encephalomyelitis / Chronic Fatigue Syndrome

“…The STOPP – START dichotomy in GWI was associated with differences in postexercise cerebrospinal fluid glutamate ( Fig 1 ), miRNA [ 26 ] and differential activation of cerebellar and other brain regions by fMRI [ 31 – 33 ]. This dichotomy does not appear to be as influential in ME/CFS or control subsets.…”

“…First, “nonexercise” subjects rested overnight before history and physical, phlebotomy and lumbar puncture (nonexercise groups abbreviated as sc0 , cfs0 and gwi0 ) [ 26 ]. Second, a model of exertional exhaustion was developed by having subjects perform submaximal bicycle exercise on 2 consecutive days with functional magnetic resonance imaging (fMRI) studies performed at baseline and after the stress tests [ 27 – 33 ]. Lumbar puncture was performed after the post-exercise MRI in sedentary control ( SC ) and GWI subjects.…”

“…The START phenomenon was also induced by exercise in ME/CFS and control groups [ 34 , 35 ]. However, START versus STOPP status was not associated with postexercise changes in fMRI in control or ME/CFS [ 31 – 33 ].…”

Neuroimaging Markers for Studying Gulf-War Illness: Single-Subject Level Analytical Method Based on Machine Learning

“…ML algorithms have been applied to study a wide range of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and traumatic brain injury [ 17 , 18 ]. These studies have reported promising results for identifying diagnostic biomarkers [ 19 , 20 ]. The ML approach have strengths on exploiting features from different domains (i.e., neuropsychological, genetic and neuroimaging) and providing further insights on the potential interactions between different markers for classifying illness [ 21 ].…”

Latest Advances in Human Brain Dynamics

“…Brain activity and connectivity encapsulate important and complementary features related to human brain dynamics that can further feed a machine learning scheme. A novel study by Provenzano et al [ 7 ] attempts to identify the critical brain areas in which their brain pattern can differentiate subjects with Gulf War Illness (GWI) and chronic fatigue syndrome (CFS). Both disorders share similar symptomatology that involves fatigue, chronic pain, and exertional exhaustion after exercise.…”