Time-Series Signals of Affect and Neural Dynamics with Technology to Identify Depression Risk

Abstract

Despite extensive research documenting the impact of depression on basic human developmental parameters (employment, health, education, social roles, and overall quality of life), multiple individual and systemic barriers limit accessibility to clinical assistance among vulnerable populations. Research-backed digital interventions, such as smartphone applications, may serve as convenient and reliable tools for detecting and monitoring depressive symptoms and attenuate the increasing pressure on conventional mental health resources. This review evaluates the significance of key time-series signals of affect dynamics (average levels, granularity, variability, instability, inertia) and electroencephalographic (EEG) patterns (power spectrum of frequency bands, alpha asymmetry) in predicting critical transitions in depressive symptom severity. An evidence-based prototype for a smartphone application that can reliably integrate multivariate time-series signals of affect dynamics and neural oscillations is proposed, to prospectively anticipate and detect affective abnormalities with greater accuracy in individuals susceptible to depression.