Objective Assessment of Mental Health Condition from Physiological Sensing: Single- and Multi-System Perspectives in Network Physiology

Mental health assessment has traditionally relied on self-reported symptoms and clinician-administered interviews, which are inherently subjective, episodic, and often insensitive to short-term or subclinical changes. Recent advances in physiological sensing—enabled by wearable, mobile, and laboratory-based technologies—now allow continuous, objective measurement of signals such as heart rate variability, electrodermal activity, respiration, sleep dynamics, temperature, and movement. These developments present new opportunities to study mental health conditions through underlying physiological regulation rather than behavioural or perceptual proxies.Within the emerging field of network physiology, mental health conditions can be conceptualised as states of altered or dysregulated physiological organisation, expressed through changes in dynamics, variability, and coupling within and across physiological systems. While multimodal datasets offer a direct means to examine interactions among cardiovascular, autonomic, respiratory, and neural processes, their availability remains limited due to cost, participant burden, and practical constraints. In contrast, single-modal physiological datasets—particularly those derived from cardiovascular or autonomic measures—are widely available and increasingly used in real-world settings. Importantly, even single physiological modalities exhibit structured, network-like temporal dynamics that can reveal underlying psychophysiological regulation and adaptation.This Research Topic aims to bring together work that advances objective inference of mental health conditions using both single-modal and multimodal physiological data, grounded in concepts from network physiology, complexity science, and dynamical systems. By integrating methodological, empirical, and theoretical contributions, the collection seeks to bridge practical data constraints with system-level physiological insight.GoalThe goal of this Research Topic is to advance objective, physiology-based characterisation of mental health conditions by leveraging single-modal and multimodal physiological sensing within a network physiology framework. The collection seeks to identify robust methods, signatures, and conceptual models that capture psychophysiological regulation, dysregulation, and adaptation across time scales.Scope and Details for AuthorsThis Research Topic welcomes original research, methodological contributions, reviews, and perspective articles focused on objective assessment of mental health through physiological data. Submissions may utilise single-modal physiological signals (e.g. cardiovascular, autonomic, respiratory, or sleep-related measures) or multimodal sensing approaches. Studies applying network-based, dynamical, or complexity-oriented analyses within a single physiological system or across multiple systems are strongly encouraged.Topics of interest include physiological correlates of depression, anxiety, stress, burnout, and affective disorders; longitudinal and ambulatory monitoring; methodological advances for analysing sparse, noisy, or real-world physiological data; and theoretical frameworks linking mental health to physiological organisation and regulation. Studies relying solely on self-report data without physiological signals are outside the scope of this Research Topic. Topic editors: Ahsan Habib, Md Nazmul Karim, Chandan Karmakar Source: https://www.frontiersin.org/research-topics/81650/objective-assessment-of-mental-health-condition-from-physiological-sensing-single--and-multi-system-perspectives-in-network-physiology