Structural brain changes versus self-report: machine-learning classification of chronic fatigue syndrome patients

Experimental Brain Research
Landrew SevelRoland Staud

Abstract

Chronic fatigue syndrome (CFS) is a disorder associated with fatigue, pain, and structural/functional abnormalities seen during magnetic resonance brain imaging (MRI). Therefore, we evaluated the performance of structural MRI (sMRI) abnormalities in the classification of CFS patients versus healthy controls and compared it to machine learning (ML) classification based upon self-report (SR). Participants included 18 CFS patients and 15 healthy controls (HC). All subjects underwent T1-weighted sMRI and provided visual analogue-scale ratings of fatigue, pain intensity, anxiety, depression, anger, and sleep quality. sMRI data were segmented using FreeSurfer and 61 regions based on functional and structural abnormalities previously reported in patients with CFS. Classification was performed in RapidMiner using a linear support vector machine and bootstrap optimism correction. We compared ML classifiers based on (1) 61 a priori sMRI regional estimates and (2) SR ratings. The sMRI model achieved 79.58% classification accuracy. The SR (accuracy = 95.95%) outperformed both sMRI models. Estimates from multiple brain areas related to cognition, emotion, and memory contributed strongly to group classification. This is the first ML-based gr...Continue Reading

References

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Citations

Oct 17, 2019·Expert Review of Molecular Diagnostics·Michael MaesGerwyn Morris
Dec 19, 2020·NeuroImage. Clinical·Bidhan LamichhaneAmmar H Hawasli

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Methods Mentioned

BETA
pregnancy test

Software Mentioned

RapidMiner Studio
RapidMiner
FreeSurfer

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