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

Sevel, Landrew S.; Boissoneault, Jeff; Letzen, Janelle E.; Robinson, Michael E.; Staud, Roland

doi:10.1007/s00221-018-5301-8

Research Article
Published: 30 May 2018

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

Landrew S. Sevel¹,
Jeff Boissoneault¹,
Janelle E. Letzen¹,
Michael E. Robinson¹ &
Roland Staud ORCID: orcid.org/0000-0002-1317-2368²

Experimental Brain Research volume 236, pages 2245–2253 (2018)Cite this article

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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 group classification of CFS. Our findings suggest that sMRI abnormalities are useful for discriminating CFS patients from HC, but SR ratings remain most effective in classification tasks.

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Acknowledgements

This research was supported by NIH (R01 NR014049).

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Affiliations

Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA
Landrew S. Sevel, Jeff Boissoneault, Janelle E. Letzen & Michael E. Robinson
Department of Medicine, College of Medicine, University of Florida, PO Box 100277, Gainesville, 2610-0277, FL, USA
Roland Staud

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Landrew S. Sevel
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Jeff Boissoneault
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Janelle E. Letzen
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Michael E. Robinson
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Roland Staud
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Correspondence to Roland Staud.

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Sevel, L.S., Boissoneault, J., Letzen, J.E. et al. Structural brain changes versus self-report: machine-learning classification of chronic fatigue syndrome patients. Exp Brain Res 236, 2245–2253 (2018). https://doi.org/10.1007/s00221-018-5301-8

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Received: 24 January 2018
Accepted: 24 May 2018
Published: 30 May 2018
Issue Date: August 2018
DOI: https://doi.org/10.1007/s00221-018-5301-8

Keywords

Machine learning
Gray matter
Self-report
Chronic fatigue
Classification