Timescale-normalized fMRI reveals disrupted dynamic signal-energy balance in schizophrenia.

Neural systems operate across diverse and nested timescales: fast unimodal fluctuations are gradually integrated within higher-order transmodal networks, complicating direct comparison of regional activity. We propose a systems principle of timescale normalization for coordinated signal-energy balance, in which healthy brains reconcile variable internal clocks so that inter-regional activity remains energetically proportionate over time. To operationalize this principle, we time-align regional fMRI signals via a time-resolved dynamic time-warping method that removes temporal distortions and isolates moment-to-moment amplitude differences. The residual amplitudes define a dynamic signal-energy disparity metric, quantifying coordination across intrinsic networks. Validation in the Human Connectome Project cohort (n = 1,200) demonstrated robust adherence to this system principle. Applied to a multi-site schizophrenia cohort (controls=160, patients=151), the approach revealed more heterogeneous signal-energy disparities in patients, particularly during faster fluctuations. State-switching analyses further showed that patients occupied and re-entered less balanced configurations more frequently, characterized by reduced spectral gap and longer mixing time, indicating slower re-stabilization of balanced energy states, greater heterogeneity and sluggish re-stabilization were associated with higher symptom severity and poorer working memory and reasoning. In summary, schizophrenia reflects a breakdown of timescale normalization, producing heterogeneous disparities in signal energy and impaired recovery of balanced network states. These findings establish dynamic signal-energy balance as a core systems mechanism directly linked to symptoms, cognition, and potential intervention monitoring.