Abstract
Analysis of brain recurrence (ABR) is a novel computational method that uses two variables for sleep depth and two for sleep fragmentation to quantify temporal changes in non-random brain electrical activity. We postulated that ABR of the sleep-staged EEG could identify an EEG signature specific for the presence of mental health symptoms. Using the Mental Health Inventory Questionnaire (MHI-5) as ground truth, psychological distress was assessed in a study cohort obtained from the Sleep Heart Health Study. Subjects with MHI-5 <50 (N=34) were matched for sex, BMI, age, and race with 34 subjects who had MHI-5 scores > 50. Sixteen ABR markers derived from the EEG were analyzed using linear discriminant analysis to identify marker combinations that reliably classified individual subjects. A biomarker function computed from 12 of the markers accurately classified the subjects based on their MHI-5 scores (AUROC=82%). Use of additional markers did not improve classification accuracy. Subgroup analysis (20 highest and 20 lowest MHI-5 scores) improved classification accuracy (AUROC=89%). Biomarker values for individual subjects were significantly correlated with MHI-5 score (r=0.36, 0.54 for N=68, 40, respectively). ABR of EEGs obtained during sleep successfully classified subjects with regard to the severity of mental health symptoms, indicating that mood systems were reflected in brain electrical activity.
Original language | English (US) |
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Pages (from-to) | 335-340 |
Number of pages | 6 |
Journal | Psychiatry Research - Neuroimaging |
Volume | 224 |
Issue number | 3 |
DOIs | |
State | Published - Mar 21 2014 |
Externally published | Yes |
Keywords
- AUROC
- Biomarker
- Brain recurrence
- Electroencephalography
- MHI-5
- Mental disorders
ASJC Scopus subject areas
- Neuroscience (miscellaneous)
- Radiology Nuclear Medicine and imaging
- Psychiatry and Mental health