ArticleFrontiers in computational neuroscience2020
A Machine Learning Approach to the Differentiation of Functional Magnetic Resonance Imaging Data of Chronic Fatigue Syndrome (CFS) From a Sedentary Control.
Article in Frontiers in computational neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 3 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Recent research in myalgic encephalomyelitis/chronic fatigue syndrome: an evidence map.Health technology assessment (Winchester, England) · 2025Pooled it
- Brain connectivity changes underlying depression and fatigue in relapsing-remitting multiple sclerosis: A systematic review.PloS one · 2024Pooled it
- Biomarkers for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS): a systematic review.BMC medicine · 2023Pooled it
- Pathophysiological mechanisms of fatigue and multidisciplinary management strategies (Review).Experimental and therapeutic medicine · 2026Review
- Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS): diagnosis and management.Journal of translational medicine · 2025Review
- Factors influencing AI adoption by Chinese mathematics teachers in STEM education.Scientific reports · 2025Article
- Identification of Resting-State Network Functional Connectivity and Brain Structural Signatures in Fibromyalgia Using a Machine Learning Approach.Biomedicines · 2022Article
- What do users care about? Research on user behavior of mobile interactive video advertising.Heliyon · 2022Article
- Article
- Brainstem Abnormalities in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Scoping Review and Evaluation of Magnetic Resonance Imaging Findings.Frontiers in neurology · 2021Article
- Neuropsychobiological Fingerprints of Chronic Fatigue in Sarcoidosis.Frontiers in behavioral neuroscience · 2021Article
- Article
- Machine Learning Detects Pattern of Differences in Functional Magnetic Resonance Imaging (fMRI) Data between Chronic Fatigue Syndrome (CFS) and Gulf War Illness (GWI).Brain sciences · 2020Article
- Logistic Regression Algorithm Differentiates Gulf War Illness (GWI) Functional Magnetic Resonance Imaging (fMRI) Data from a Sedentary Control.Brain sciences · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Chronic Fatigue Syndrome (CFS) is a debilitating condition estimated to impact at least 1 million individuals in the United States, however there persists controversy about its existence. Machine learning algorithms have become a powerful methodology for evaluating multi-regional areas of fMRI activation that can classify disease phenotype from sedentary control. Uncovering objective biomarkers such as an fMRI pattern is important for lending credibility to diagnosis of CFS. fMRI scans were evaluated for 69 patients (38 CFS and 31 Control) taken before (Day 1) and after (Day 2) a submaximal exercise test while undergoing the n-back memory paradigm. A predictive model was created by grouping fMRI voxels into the Automated Anatomical Labeling (AAL) atlas, splitting the data into a training and testing dataset, and feeding these inputs into a logistic regression to evaluate differences between CFS and control. Model results were cross-validated 10 times to ensure accuracy. Model results were able to differentiate CFS from sedentary controls at a 80% accuracy on Day 1 and 76% accuracy on Day 2 (
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