A week in the life of the human brain reveals stable states punctuated by chaotic-like transitions.

The brain's neural activity during everyday, unconstrained behavior transitions between stable states through bursts of chaotic, exploratory activity that then settle into new patterns. Using multi-electrode brain recordings from twenty people over a week, combined with deep learning analysis, researchers identified neural dynamics linked to circadian rhythms, heart rate, and behaviors such as socializing, screen watching, and sleep depth. Despite this chaos, large-scale dynamics remain anchored to a stabilizing center involving the default mode network. Sleep deprivation made these transitions more chaotic and suppressed the stabilizing center, suggesting diminished neural control. The findings indicate the brain balances dynamic exploration with stable equilibria during real-world behavior.

Study at a glance

Abstract

Critical real-world neurocognitive processes, such as settling into a conversation and neurophysiological fluctuations, vary over minutes-to-days in real-world environments. We harnessed simultaneous multi-electrode intracranial and video recordings in twenty people during a week of unconstrained, spontaneous behavior. Using dynamical deep learning algorithms, we found neurodynamics linked to circadian rhythm, heart rate, and multiple aspects of behavior (socializing, watching a screen, sleep depth, etc.). Transitioning between behaviors was associated with bursts of rapid, chaotic neural exploration that stabilized into new states. Despite this chaos, large-scale dynamics anchored to a stabilizing center manifold associated with neurophysiological and conscious states, with a central attractor involving default mode network activation. When perturbed by sleep deprivation, neural transitions were more chaotic and dynamics around the central attractor were suppressed, suggesting diminished neurodynamic control due to lack of sleep. These findings highlight how the brain chaotically transitions around a stabilizing equilibrium to balance dynamic exploration and stable equilibria during real-world behavior.