Nonlinear dynamics and multiscale mechanisms of deep brain stimulation

Deep brain stimulation (DBS) is an established treatment for movement disorders and an expanding therapy for several neuropsychiatric conditions, yet its mechanisms of action remain incompletely understood. Early interpretations largely relied on linear and focal models, framing DBS as local excitation, inhibition, or a reversible lesion. Accumulating evidence, however, indicates that DBS reorganizes neural activity across multiple spatial and temporal scales, engaging distributed circuits and network-level dynamics. Here, we synthesize experimental, computational, and clinical findings supporting a nonlinear dynamical perspective on DBS. Within this framework, pathological brain states, such as excessive β synchrony in Parkinson's disease or hypersynchronous epileptic activity, can be conceptualized as maladaptive network regimes. DBS perturbs these regimes in a state-dependent manner, disrupting pathological synchrony, modulating intrinsic oscillations, inducing threshold-like state transitions, and, in some contexts, altering temporal complexity. This perspective helps explain why DBS effects depend on ongoing brain state and why modest changes in stimulation timing or pattern can produce disproportionate clinical effects. Rather than prescribing specific technologies, nonlinear dynamics provides an integrative framework for interpreting diverse DBS phenomena and for understanding the principles underlying adaptive, temporally patterned, and individualized neuromodulation strategies. Together, these insights position DBS as a state-dependent, network-level intervention operating within a nonlinear brain, complementing classical mechanisms and offering a unified lens through which to interpret its diverse therapeutic effects.