Dataset Information

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.

SUBMITTER: Provenzano D

PROVIDER: S-EPMC7407325 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Provenzano Destie D Washington Stuart D SD Rao Yuan J YJ Loew Murray M Baraniuk James J

Brain sciences 20200717 7

<h4>Background</h4>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.<h4>Methods</h4>We ass ...[more]

PMID: 32708912

Similar Datasets

Project description:Gulf War Illness (GWI) is a debilitating condition characterized by dysfunction of cognition, pain, fatigue, sleep, and diverse somatic symptoms with no known underlying pathology. As such, uncovering objective biomarkers such as differential regions of activity within a Functional Magnetic Resonance Imaging (fMRI) scan is important to enhance validity of the criteria for diagnosis. Symptoms are exacerbated by mild activity, and exertional exhaustion is a key complaint amongst sufferers. We modeled this exertional exhaustion by having GWI (n = 80) and sedentary control (n = 31) subjects perform submaximal exercise stress tests on two consecutive days. Cognitive differences were assessed by comparing fMRI scans performed during 2-Back working memory tasks before and after the exercise. Machine learning algorithms were used to identify differences in brain activation patterns between the two groups on Day 1 (before exercise) and Day 2 (after exercise). The numbers of voxels with t > 3.17 (corresponding to p < 0.001 uncorrected) were determined for brain regions defined by the Automated Anatomical Labeling (AAL) atlas. Data were divided 70:30 into training and test sets. Recursive feature selection identified twenty-nine regions of interest (ROIs) that significantly distinguished GWI from control on Day 1 and 28 ROIs on Day 2. Ten regions were present in both models between the two days, including right anterior insula, orbital frontal cortex, thalamus, bilateral temporal poles, and left supramarginal gyrus and cerebellar Crus 1. The models had 70% accuracy before exercise on Day 1 and 85% accuracy after exercise on Day 2, indicating the logistic regression model significantly differentiated subjects with GWI from the sedentary control group. Exercise caused changes in these patterns that may indicate the cognitive differences caused by exertional exhaustion. A second set of predictive models was able to classify previously identified GWI exercise subgroups START, STOPP, and POTS for both Days 1 and Days 2 with 67% and 69% accuracy respectively. This study was the first of its kind to differentiate GWI and the three sub-phenotypes START, STOPP, and POTS from a sedentary control using a logistic regression estimation method.

Project description:One quarter of veterans returning from the 1990-1991 Persian Gulf War have developed Gulf War Illness (GWI) with chronic pain, fatigue, cognitive and gastrointestinal dysfunction. Exertion leads to characteristic, delayed onset exacerbations that are not relieved by sleep. We have modeled exertional exhaustion by comparing magnetic resonance images from before and after submaximal exercise. One third of the 27 GWI participants had brain stem atrophy and developed postural tachycardia after exercise (START: Stress Test Activated Reversible Tachycardia). The remainder activated basal ganglia and anterior insulae during a cognitive task (STOPP: Stress Test Originated Phantom Perception). Here, the role of attention in cognitive dysfunction was assessed by seed region correlations during a simple 0-back stimulus matching task ("see a letter, push a button") performed before exercise. Analysis was analogous to resting state, but different from psychophysiological interactions (PPI). The patterns of correlations between nodes in task and default networks were significantly different for START (n = 9), STOPP (n = 18) and control (n = 8) subjects. Edges shared by the 3 groups may represent co-activation caused by the 0-back task. Controls had a task network of right dorsolateral and left ventrolateral prefrontal cortex, dorsal anterior cingulate cortex, posterior insulae and frontal eye fields (dorsal attention network). START had a large task module centered on the dorsal anterior cingulate cortex with direct links to basal ganglia, anterior insulae, and right dorsolateral prefrontal cortex nodes, and through dorsal attention network (intraparietal sulci and frontal eye fields) nodes to a default module. STOPP had 2 task submodules of basal ganglia-anterior insulae, and dorsolateral prefrontal executive control regions. Dorsal attention and posterior insulae nodes were embedded in the default module and were distant from the task networks. These three unique connectivity patterns during an attention task support the concept of Gulf War Disease with recognizable, objective patterns of cognitive dysfunction.

Project description:Gulf War Illness (GWI) is a complex condition that involves multiple organ systems and is characterized by an abrupt or delayed onset of persistent/relapsing symptomatology such as memory and other neurological problems, muscle and joint pain, gastrointestinal issues, hormonal imbalance, immune dysfunction and debilitating fatigue. Currently, treatment of the condition relies solely on management of symptomatology and improvement in quality of life due to a lack of knowledgeof the underlying mechanisms of disease onset and progression. An improved understanding of the key mechanisms of GWI and dysfunction within regulatory systems will lead to more objective diagnosis methods and adequate management of patients by providing targeted approaches to treatment. Disruption of DNA methylation patterns has been tied to various immune, neurological and endocrine disease states; however, the status of this epigenetic mark in GWI remains uncertain. This study aimed at identifying biomarkers of GWI by gaining knowledge on DNA methylation patterns of GWI in an effort to determine therapeutic targets of disease activity and provide insight into disease onset and progression. Methods: Genomic DNA from 10 GWI patients and 10 healthy controls [HC] (“experimental cohort”) prepared from PBMCs was analyzed with ELISA-based DNA methylation assays and Illumina MethylationEPIC microarrays. These arrays allow measuring the methylation status at 850,000 CpG sites. Methylation changes in promoter regions were separately analyzed and used as input data in ingenuity pathway analysis software to identify altered functional pathways. Relevant differentially methylated CpG sites (DMS) were validated by pyrosequencing. Results: Global DNA methylation levels using the ELISA-based assay on the DNA isolated from PBMCs of GWI patients were similar to those of HC. However, deeper evaluation using microarray-based genome-wide technology allowed detection of DMS in GWI patients with respect to HC. As an overall 10,767 CpG sites presented with differential DNA methylation levels across promoters, gene regulatory elements and within coding regions of genes. Majority of these DMS were hypermethylated in GWI patients as compared to the HC, and more than half of these sites localized to promoters and gene regulatory elements, relative to distribution of DMSin the network as a whole, indicating gene regulatory potential. Functional pathway analysis of the 776 genes with differentially methylated promoters (DMPs) fulfilling FDR≤0.2 criteriafollowing a genomic region-based approachlinks GWI to defects in at least 15 different pathways mostly related to cell signaling with a strong immune component. Conclusions: This is the first study that has explored genome-wide epigenetic changes associated with GWI using the new MethylationEPIC microarrays covering about 850,000 CpG sites; which builds on previous methylation preliminary reports. The data obtained are consistent with previous evidence for dysregulation of the immune system in GWI and suggests a role of this epigenetic modification on the DNA pathobiology of GWI. They are also consistent with previous reports showing a Th1- to Th2-mediated immune response shift in GWI in addition to altered immune system function. Future validation studies in larger cohorts of GWI patients and evaluation of the effects of these methylation patterns on gene expression regulation are granted.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets