Validation of non-REM sleep stage decoding from resting state fMRI using linear support vector machines

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Validation of non-REM sleep stage decoding from resting state fMRI using linear support vector machines

Altmann, A; Schröter, MS; Spoormaker, VI; Kiem, SA; Jordan, D; Ilg, R; Bullmore, ET; ... Sämann, PG; + view all (2016) Validation of non-REM sleep stage decoding from resting state fMRI using linear support vector machines. Neuroimage , 125 pp. 544-555. 10.1016/j.neuroimage.2015.09.072. Green open access

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Abstract

A growing body of literature suggests that changes in consciousness are reflected in specific connectivity patterns of the brain as obtained from resting state fMRI (rs-fMRI). As simultaneous electroencephalography (EEG) is often unavailable, decoding of potentially confounding sleep patterns from rs-fMRI itself might be useful and improve data interpretation. Linear support vector machine classifiers were trained on combined rs-fMRI/EEG recordings from 25 subjects to separate wakefulness (S0) from non-rapid eye movement (NREM) sleep stages 1 (S1), 2 (S2), slow wave sleep (SW) and all three sleep stages combined (SX). Classifier performance was quantified by a leave-one-subject-out cross-validation (LOSO-CV) and on an independent validation dataset comprising 19 subjects. Results demonstrated excellent performance with areas under the receiver operating characteristics curve (AUCs) close to 1.0 for the discrimination of sleep from wakefulness (S0|SX), S0|S1, S0|S2 and S0|SW, and good to excellent performance for the classification between sleep stages (S1|S2:~0.9; S1|SW:~1.0; S2|SW:~0.8). Application windows of fMRI data from about 70 s were found as minimum to provide reliable classifications. Discrimination patterns pointed to subcortical-cortical connectivity and within-occipital lobe reorganization of connectivity as strongest carriers of discriminative information. In conclusion, we report that functional connectivity analysis allows valid classification of NREM sleep stages.

Type:	Article
Title:	Validation of non-REM sleep stage decoding from resting state fMRI using linear support vector machines
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1016/j.neuroimage.2015.09.072
Publisher version:	http://dx.doi.org/10.1016/j.neuroimage.2015.09.072
Language:	English
Additional information:	© 2015 Elsevier Inc. All rights reserved. This article is published under a Creative Commons Attribution Non-commercial Non-derivative 4.0 International license (CC BY-NC-ND 4.0). This license allows you to share, copy, distribute and transmit the work for personal and non-commercial use providing author and publisher attribution is clearly stated. Further details about CC BY licenses are available at http://creativecommons.org/ licenses/by/4.0. Access may be initially restricted by the publisher.
Keywords:	Classification, EEG, EEG-fMRI, Resting state fMRI, Sleep
UCL classification:	UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI:	https://discovery.ucl.ac.uk/id/eprint/1472393