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Published on: November 21, 2013
Beyond the At-Risk Mental State: Metastability and Hybrid States of Reality in Emerging Psychosis
Philippe Nuss1, Maharajah Ponnaiah2, Vince Calhoun3
1Sorbonne University, Psychiatry Department Hopital Saint-Antoine, AP-HP, Sorbonne University, INSERM UMRS 938 Centre de recherche Saint-Antoine (CRSA), 75012, Paris, France.
Abstract:
The ultra-high-risk (UHR) and at-risk mental state (ARMS) constructs were originally developed to identify individuals at imminent risk of transition to psychosis. However, declining transition rates, limited predictive value, and the persistence of significant non-psychotic morbidity increasingly challenge the view of ARMS as a brief, transitional stage along a linear continuum from health to illness.Here, we propose here an alternative conceptualization of ARMS/UHR as a relatively enduring clinical configuration. Clinically, this state is characterized by persistent experiences in which unusual perceptions or beliefs are layered onto ordinary reality, maintaining a sense of coherence. From this perspective, symptoms are not only markers of pathology, but can be understood as coherence-seeking responses when the self is destabilized. At the neurobiological level, we argue that this clinical configuration is paralleled by brain dynamics operating in a metastable regime. The brain is viewed as a dynamic system that flexibly reorganizes large-scale networks, balancing integration and segregation while minimizing energetic costs. Within this framework, ARMS may be understood as a self-sustaining, non-stationary network configuration that accommodates psychotic-like experiences without necessarily progressing to psychosis. Integrating phenomenological accounts of lived experience with systems-level models of brain dynamics, this perspective moves beyond risk prediction alone. It supports clinical approaches aimed at strengthening flexibility and reducing neural and cognitive effort while preserving adaptive functioning, and motivates research paradigms capable of capturing individual-level, time-varying brain dynamics in real-world contexts.
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