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.
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