Reduced cognitive and emotional flexibility is a core feature across multiple psychiatric conditions, yet most neurobiological models rely on static functional connectivity measures that do not directly capture the temporal dynamics underlying flexible behavior. Here, we introduce Neurodynamic Rigidity (NR) as a measurable systems-level property of large-scale brain dynamics, defined by increased persistence of neural states and reduced flexibility of state transitions. Using high-temporal-resolution functional MRI data from healthy adults (Human Connectome Project), we modeled whole-brain activity during resting-state and working memory task conditions using Hidden Markov Modeling to infer metastable brain states. Three complementary dynamic metrics—state switching rate, mean dwell time, and lag-1 temporal autocorrelation—were extracted and integrated into a composite NR index. Within-subject comparisons revealed that cognitive task engagement was associated with accelerated state switching, reduced dwell times, and decreased temporal autocorrelation, resulting in a robust reduction of Neurodynamic Rigidity relative to rest. Importantly, NR was statistically independent of static functional connectivity strength, indicating that temporal rigidity captures a distinct dimension of brain function not explained by average network coupling. These findings demonstrate that neurodynamic rigidity is a context-sensitive property of brain dynamics rather than a fixed architectural constraint, providing a falsifiable, metric-based framework for quantifying neural flexibility. This metric-first approach has direct relevance for transdiagnostic models of psychopathology characterized by impaired state-switching and reduced adaptive flexibility. Keywords: brain dynamics, neural flexibility, Hidden Markov Models, metastability, functional MRI, transdiagnostic psychiatry Status: This manuscript is a preprint. Data acquisition and analyses are ongoing, and the present version reflects a stable methodological framework and initial empirical results.
Hugo Evaristo Tapia Castañeda (Fri,) studied this question.