A Deep Learning-Derived Transdiagnostic Signature Indexing Hypoarousal and Impulse Control: Implications for Treatment Prediction in Psychiatric Disorders.

Abstract

Background: Psychiatric disorders are traditionally classified within diagnostic categories, but this approach has limitations. Research Domain Criteria (RDoC) constitute a research classification system for psychiatric disorders based on dimensions within domains that cut across these psychiatric diagnoses. The overall aim of RDoC is to better understand mental illness in terms of dysfunction in fundamental neurobiological and behavioral systems, leading to better diagnosis, prevention and treatment.

Methods: A unique electroencephalographic (EEG) feature, referred to as spindling excessive beta (SEB), has been studied in relation to impulse control and sleep, as part of the arousal/regulatory systems RDoC domain. Here, we study EEG frontal beta activity as a potential transdiagnostic biomarker capable of diagnosing and predicting impulse control and sleep problems.

Results: We show in the first dataset (n=3279) that the probability of having SEB, classified by a deep learning algorithm, is associated with poor sleep maintenance and low daytime impulse control. Furthermore, in two additional, independent datasets (iSPOT-A, n=336; iSPOT-D, n=1008), we revealed that conventional frontocentral beta power and/or SEB probability, referred to as Brainmarker-III, is associated with a diagnosis of attention deficit hyperactivity disorder (ADHD), with remission to methylphenidate in children with ADHD in a sex-specific manner, and with remission to antidepressant medication in adults with a major depressive disorder in a drug-specific manner.

Conclusion: Our results demonstrate the value of the RDoC approach in psychiatry research for the discovery of biomarkers with diagnostic and treatment prediction capacities.