Provenzano, Destie, Washington, Stuart D, Baraniuk, James N · Frontiers in computational neuroscience · 2020 · DOI
Researchers used a computer algorithm to analyze brain scans from ME/CFS patients and healthy controls while they performed a memory task, both before and after exercise. The algorithm was able to correctly identify which scans came from ME/CFS patients 80% of the time on the first day and 76% on the second day, suggesting that ME/CFS may create a distinctive pattern of brain activity that could eventually be used as an objective diagnostic test.
This study provides objective neuroimaging evidence that ME/CFS is associated with measurable differences in brain activation patterns, which helps validate ME/CFS as a biological condition rather than a psychological one. The identification of specific brain regions involved could guide future research into disease mechanisms and potentially support development of a diagnostic biomarker for ME/CFS.
This study does not prove that the identified brain activation patterns cause ME/CFS symptoms, only that they are associated with the condition. The 80% accuracy also means 20% of cases were misclassified, so this pattern alone is not yet reliable enough for clinical diagnosis. Results from a single study require independent replication before clinical application.
About the PEM badge: “PEM required” means post-exertional malaise was an explicit required diagnostic criterion for participant inclusion in this study — not that PEM was studied, observed, or discussed. Studies using criteria that do not require PEM (e.g. Fukuda, Oxford) are tagged “PEM not required”. How the atlas works →
The first block is for the primary paper and is the citation you should use in research work. The atlas-snapshot line only applies if you are specifically referring to this atlas’s reading of the paper on the date shown.
Primary citation
Provenzano, Destie, Washington, Stuart D, & Baraniuk, James N (2020). A Machine Learning Approach to the Differentiation of Functional Magnetic Resonance Imaging Data of Chronic Fatigue Syndrome (CFS) From a Sedentary Control.. Frontiers in computational neuroscience. https://doi.org/10.3389/fncom.2020.00002
BibTeX
@article{mecfsatlas-provenzano-2020-machine-learning,
author = {Provenzano, Destie and Washington, Stuart D and Baraniuk, James N},
title = {A Machine Learning Approach to the Differentiation of Functional Magnetic Resonance Imaging Data of Chronic Fatigue Syndrome (CFS) From a Sedentary Control.},
journal = {Frontiers in computational neuroscience},
year = {2020},
doi = {10.3389/fncom.2020.00002},
note = {PubMed: 32063839},
url = {https://www.mecfsatlas.com/evidence/provenzano-2020-machine-learning},
}Atlas snapshot reference
ME/CFS Atlas. Generator v1 / Scanner v1.4 / policy v0.1. Accessed 2026-05-26. https://www.mecfsatlas.com/evidence/provenzano-2020-machine-learning
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