Document Detail

Automatic sleep staging using fMRI functional connectivity data.
MedLine Citation:
PMID:  22743197     Owner:  NLM     Status:  MEDLINE    
Recent EEG-fMRI studies have shown that different stages of sleep are associated with changes in both brain activity and functional connectivity. These results raise the concern that lack of vigilance measures in resting state experiments may introduce confounds and contamination due to subjects falling asleep inside the scanner. In this study we present a method to perform automatic sleep staging using only fMRI functional connectivity data, thus providing vigilance information while circumventing the technical demands of simultaneous recording of EEG, the gold standard for sleep scoring. The features to classify are the linear correlation values between 20 cortical regions identified using independent component analysis and two regions in the bilateral thalamus. The method is based on the construction of binary support vector machine classifiers discriminating between all pairs of sleep stages and the subsequent combination of them into multiclass classifiers. Different multiclass schemes and kernels are explored. After parameter optimization through 5-fold cross validation we achieve accuracies over 0.8 in the binary problem with functional connectivities obtained for epochs as short as 60s. The multiclass classifier generalizes well to two independent datasets (accuracies over 0.8 in both sets) and can be efficiently applied to any dataset using a sliding window procedure. Modeling vigilance states in resting state analysis will avoid confounded inferences and facilitate the study of vigilance states themselves. We thus consider the method introduced in this study a novel and practical contribution for monitoring vigilance levels inside an MRI scanner without the need of extra recordings other than fMRI BOLD signals.
Enzo Tagliazucchi; Frederic von Wegner; Astrid Morzelewski; Sergey Borisov; Kolja Jahnke; Helmut Laufs
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't     Date:  2012-06-26
Journal Detail:
Title:  NeuroImage     Volume:  63     ISSN:  1095-9572     ISO Abbreviation:  Neuroimage     Publication Date:  2012 Oct 
Date Detail:
Created Date:  2012-08-28     Completed Date:  2013-01-29     Revised Date:  2013-09-23    
Medline Journal Info:
Nlm Unique ID:  9215515     Medline TA:  Neuroimage     Country:  United States    
Other Details:
Languages:  eng     Pagination:  63-72     Citation Subset:  IM    
Copyright Information:
Copyright © 2012 Elsevier Inc. All rights reserved.
Department of Neurology and Brain Imaging Center, Goethe University Frankfurt am Main, Germ.
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MeSH Terms
Artificial Intelligence
Brain / physiology*
Connectome / methods*
Electroencephalography / methods
Image Enhancement / methods
Image Interpretation, Computer-Assisted / methods
Information Storage and Retrieval / methods*
Magnetic Resonance Imaging / methods*
Nerve Net / physiology*
Pattern Recognition, Automated / methods*
Reproducibility of Results
Sensitivity and Specificity
Sleep Stages / physiology*
Young Adult
Erratum In:
Neuroimage. 2013 Nov 1;81:506

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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