English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

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

MPS-Authors
/persons/resource/persons80250

Altmann,  A.
Dept. Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Max Planck Society;

/persons/resource/persons200347

Schröter,  M. S.
Dept. Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Max Planck Society;

/persons/resource/persons80538

Spoormaker,  V. I.
Dept. Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Max Planck Society;

/persons/resource/persons80396

Kiem,  S. A.
Dept. Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Max Planck Society;

/persons/resource/persons80295

Czisch,  M.
Dept. Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Max Planck Society;

/persons/resource/persons80505

Sämann,  P. G.
Dept. Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Altmann, A., Schröter, M. S., Spoormaker, V. I., Kiem, S. A., Jordan, D., Ilg, R., et al. (2016). Validation of non-REM sleep stage decoding from resting state fMRI using linear support vector machines. NEUROIMAGE, 125, 544-555. doi:10.1016/j.neuroimage.2015.09.072.


Cite as: https://hdl.handle.net/11858/00-001M-0000-002C-0912-9
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 vertical bar SX), S0 vertical bar S1, S0 vertical bar S2 and S0|SW, and good to excellent performance for the classification between sleep stages (S1 vertical bar S2: similar to 0.9; S1 vertical bar SW:similar to 1.0; S2 vertical bar SW:similar to 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. (C) 2015 Elsevier Inc. All rights reserved.