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.
The relation between psychological distress and the pattern of the electroencephalogram (EEG) recorded from distressed subjects has been studied since the discovery of the EEG (Lemere, 1936). In major depressive disorder (MDD), for example, many attempts were made to identify visual features, spectral characteristics, or other linear properties of the signal that would allow identification of risk, confirm diagnosis, permit monitoring of the effect of treatment, and/or predict treatment response (Olbrich and Arns, 2013). Changes in absolute or relative alpha power were probably the most frequently identified variables associated with MDD, but not with sufficient consistency to warrant clinical application (Knott and Lapierre, 1987, Pozzi et al., 1995, Grin-Yatsenko et al., 2009 and Jaworska et al., 2012).
Various methods based on analysis of the nonlinear dynamical complexity in the EEG were proposed for studying mental disorders (Bystritsky et al., 2012). Within the limitations of this perspective (Rapp, 1994), various approaches were developed to distinguish between the presence and absence of MDD (Olbrich and Arns, 2013) and to predict treatment efficacy (Arns et al., 2014). Similar observations were reported for other mental disorders including schizophrenia (Paulus and Braff, 2003) and autism (Bosl et al., 2011).
Analysis of brain recurrence (ABR) is a computational method designed to detect and quantify deterministic temporal patterns in the EEG (non-random brain activity) not detectable by conventional EEG methods such as pattern-recognition or spectral analysis (Carrubba et al., 2012a). ABR was used to study a range of problems in basic and clinical neuroscience (Frilot et al., 2014). Patients with multiple sclerosis were identified using ABR (Carrubba et al., 2010 and Carrubba et al., 2012b), and it was used to create a novel paradigm in which the concepts of sleep depth and variability could be quantified (Carrubba et al., 2012a and Wang et al., 2013). Employing markers based on these variables, patients with mild or moderate obstructive sleep apnea were distinguished using the sleep-staged EEG from a single derivation (Wang et al., 2013), illustrating the concept that a complex physiologic disorder leaves an objectively discernible and specific footprint on brain electrical activity.
We became interested in whether the sleep-acquired EEG could similarly be used to classify subjects with psychological distress. Our ultimate goal was to develop objective analytical methods to help in the diagnosis and classification of subjects with neurocognitive disorders. In the present study we tested the hypothesis that sleep depth and fragmentation markers extracted from the staged, sleep-acquired EEG could be employed to accurately assign subjects into classes with higher or lower levels of distress, using scores from the Mental Health Inventory questionnaire (MHI-5) as ground truth. If the subjects could be correctly classified, we planned to interpret the result as an indication that psychological distress was objectively associated with a specific type of algorithmically-determinable change in the sleep EEG
Based on algorithmic analyses of single-derivation staged, sleep-acquired EEGs, subjects in a population-based sample of adults could be correctly classified at a respectable accuracy level regarding the presence or absence of negative neurobehavioral symptoms, using the MHI-5 score as ground truth.
