AI Insight
Researchers developed a computational model showing that slow fluctuations in arousal states can explain the complex patterns observed in human brain connectivity over time. By incorporating arousal-driven changes in neural excitability and noise into a whole-brain network model, they reproduced the distinctive statistical features of functional connectivity dynamics, including the alternation between stable periods and rapid reconfigurations. The model suggests that neuromodulatory changes associated with arousal allow the brain to explore different dynamic states near critical transitions, generating the rich temporal variability seen in resting brain activity.
Why it matters
This work provides a mechanistic explanation for how the brain generates its complex spontaneous activity patterns, linking arousal fluctuations to functional network dynamics. Understanding these mechanisms could inform treatments for disorders involving dysregulated arousal or abnormal brain dynamics, and may help explain why arousal states affect cognitive performance.
Understand the Science
by Anagh Pathak, Demian Battaglia
Ongoing brain activity displays rich temporal variability associated with efficient cognition, with functional connectivity (FC) continually reconfiguring over time. The resulting functional connectivity dynamics (FCD) specifically show complex, fat-tailed statistics that alternate between persistent epochs and faster reconfiguration transients. While nonlinear whole-brain models tuned nearby a critical point have reproduced some aspects of FCD, they fall short of capturing its full temporal complexity. We propose that slow fluctuations in arousal offer a biologically plausible mechanism for exploring critical regimes in large-scale brain dynamics and thus enrich FCD. Using a connectome-based model of coupled cortical populations, we identified phase boundaries where system dynamics transition between regimes of faster or slower FCD. We then phenomenologically incorporated arousal changes, modeling them as stochastic fluctuations in key parameters such as cortical excitability, input gain, and noise amplitude. This explicitly time-dependent formulation enables the system to roam dynamically across regime boundaries, flexibly tuning its distance from critical transition lines and producing intermittent transitions that mirror the stochastic evolution observed in empirical FCD. Fitting these models to human resting-state fMRI and performing model comparison, we find that arousal-driven models more accurately reproduce the distinctive quantitative features of FCD, with the greatest improvements coming from the previously poorly accounted fat-tailed portions of the distributions. Together, these results suggest that arousal fluctuations—likely mediated by changes in neuromodulatory tone—shape the brain’s attractor landscape over time, expanding the repertoire of accessible functional network states and providing a mechanistic basis for the complexity of spontaneous functional dynamics.
Source: Arousal-driven critical roaming reproduces human functional connectivity dynamics