A Unified Computational Framework for Deep Brain Stimulation at the Cellular and Network Levels

Selective gating of neural modulation through frequency- and behavior-dependent modes during cortical electrical stimulation

Electrical stimulation is widely used to modulate neuronal activity, yet its effects on neuronal circuits in vivo remain poorly understood. This, in turn, has hindered the principled design of stimulation protocols and raised questions about reproducibility that constrain the field’s translational impact. Here we combine cortical sinusoidal electrical stimulation (sES) with Neuropixels recordings to characterize stimulation-driven responses in more than 2,700 well-isolated neurons across 53 brain areas in 14 behaving, head-fixed mice. We uncover two distinct, concurrent modes of neural modulation. First is a sustained, brain-wide spike-phase entrainment effect that depends on stimulation frequency: entrainment to slow stimulation is supported by non-synaptic electric field propagation while anatomical connectivity dominates entrainment to higher stimulation frequencies. Second, we find a transient, spatially localized spike-rate modulation mainly mediated through anatomical connectivity that only emerges at high stimulation frequencies by selectively recruiting inhibitory neurons. We show that the two distinct modes are differentially shaped by behavior. By identifying how stimulation frequency governs the mechanism of neural engagement and how behavioral state selectively gates brain-wide entrainment but not local inhibitory recruitment, our results provide a mechanistic foundation for designing targeted, reproducible neuromodulation strategies.

Network Dynamics and State-Dependent Effects of Electrical Stimulation in Recurrent Excitatory-Inhibitory Populations

Deep Brain Stimulation (DBS) is an established clinical treatment for a variety of neurological disorders, including Parkinson’s Disease where it has been shown to reduce motor symptoms as well as disrupt pathological beta oscillations in the basal ganglia. The mechanisms of action of DBS on the collective activity of neuronal circuits is not fully understood. We use a recurrently-connected excitatory-inhbitory network based on the Brunel network architecture that can produce activity in a variety of states. Using a model of DBS that can reproduce observed effects such as antidromic activation, local somatic suppression, and axonal activation, we characterize the effect of stimulation across the entire parameter space of the network. We show that the effects of stimulation are dependent on the baseline state of the network, with the level of beta suppression dependent on the level of inhibition and the external drive. Specifically, networks with higher inhibition and lower drive show greater disruption of beta oscillations. We further show that networks in different states are preferentially sensitive to different frequencies of stimulation, suggesting that alternative protocols to the clinically standard high-frequency stimulation may have therapeutic efficacy.

Stimulation modulates gene-linked cell assemblies in the human brain.

Temporal interference stimulation: A new paradigm for non-invasive deep brain stimulation-mechanistic insights, clinical advances, and future directions.

Neurological and psychiatric disorders frequently arise from dysfunctional deep-brain circuits, yet targeting these subcortical structures with conventional non-invasive neuromodulation remains a significant challenge due to the lack of focal precision at depth. Temporal Interference (TI) stimulation has emerged as a transformative paradigm, leveraging the intersection of multiple high-frequency electric fields to generate a low-frequency amplitude-modulated envelope within deep-seated targets. This biophysical strategy enables the modulation of subcortical dynamics while minimizing the activation of overlying cortical tissues. Emerging preclinical evidence demonstrates that TI can robustly orchestrate neurotransmitter release, facilitate synaptic plasticity, and ameliorate deficits in both motor and cognitive domains. Preliminary clinical translations further underscore its potential in enhancing memory precision, accelerating motor skill acquisition, and suppressing epileptic biomarkers. The mechanistic understanding of TI has evolved from passive low-pass filtering to include nonlinear ion-channel rectification and, more recently, network-mediated inhibition-particularly the recruitment of parvalbumin-positive interneurons in superficial layers-as a critical determinant of spatial selectivity. Human intracranial studies have further refined this framework, revealing that TI operates in a subthreshold regime, produces carrier-independent deep modulation, and elicits a sustained carry-over effect absent from unmodulated kilohertz stimulation. The efficacy of TI is fundamentally governed by a complex interplay of controllable parameters-including carrier frequency offset (Δf), current intensity, electrode geometry, and timing-alongside uncontrollable factors such as individual anatomical heterogeneity and endogenous brain states. Furthermore, advanced computational modeling, particularly finite element simulations incorporating personalized head models, has become indispensable for characterizing electric field distributions and achieving individualized, high-precision targeting. This review provides a comprehensive synthesis of TI's mechanistic foundations, safety profiles, and therapeutic trajectory, while critically discussing the integration of closed-loop systems, multi-target paradigms, and patient-specific optimization as the next frontiers in non-invasive deep brain stimulation.

Neuromodulation in Alzheimer's disease: a review with illustrative pilot data using focused ultrasound.

Alzheimer's disease (AD) is increasingly characterized as a disorder of large-scale brain networks driven by synaptic dysfunction, excitation-inhibition imbalance, and progressive breakdown of hippocampal-cortical communication. While recently developed disease-modifying therapies target molecular pathology, their clinical benefits remain modest, underscoring the need for interventions that target dysfunctional circuits. Neuromodulatory techniques such as transcranial magnetic stimulation, transcranial electrical stimulation, and deep brain stimulation demonstrate safety and act at network/system levels to engage target sites yet are limited by their ability to modulate deep anatomic structures and/or their invasiveness. Focused ultrasound (FUS) offers the distinct capability of modulating cortical and deep subcortical networks noninvasively and with anatomic precision. Preclinical studies demonstrate that FUS neuromodulation can influence mechanosensitive ion channels, synaptic plasticity, neurotrophic signaling, and oscillatory dynamics, with downstream effects on distributed memory networks. Early human investigations similarly suggest FUS neuromodulation can alter functional connectivity within default mode, frontoparietal, and limbic networks. Together, these findings support a framework in which FUS may help reshape pathological network states that emerge prior to irreversible neurodegeneration. Here, we present a narrative review of evidence across neuromodulation approaches to define principles of circuit engagement in AD and discuss a network-based rationale for ultrasound interventions. As an illustration of this framework, we also report preliminary findings from a Phase I pilot study of FUS neuromodulation in amyloid-positive mild cognitive impairment demonstrating safety, tolerability, and measurable modulation of hippocampal connectivity. We conclude mechanistic biomarkers of network response may accelerate translational development and guide future controlled trials.

A Whole-Brain Dynamical Framework Linking Resting-State Activity to TMS-Evoked Responses

A major challenge in systems neuroscience is understanding how external perturbations interact with ongoing brain activity. Transcranial magnetic stimulation (TMS), increasingly used in both basic and clinical neuroscience and often combined with electroencephalography (EEG), provides a unique opportunity to probe this interaction. However, how intrinsic dynamics constrain the propagation of TMS-evoked activity remains poorly understood. In particular, effective connectivity (EC)—capturing directed, state-dependent interactions between brain regions—is thought to critically shape perturbational spread, yet remains difficult to estimate at the whole-brain EEG level. Here we introduce an analytically tractable, generative whole-brain model that links spontaneous EEG activity to cortical responses under perturbation. By deriving a closed-form expression for the model’s cross-spectral density, we directly fit empirical resting-state EEG spectra and infer biophysically interpretable local dynamical parameters without time-domain simulations. We then estimate stimulation-site-specific EC using only a small fraction of the TMS–EEG trials. The resulting model accurately predicts the spatiotemporal structure of TMS-evoked potentials (TEPs) in unseen trials. Moreover, even without subject-specific refitting, group-level EC templates capture canonical site-specific propagation motifs underlying single-subject early TMS responses. Together, our results establish an analytical framework for individualized whole-brain modeling of TMS-EEG with potential applicability to model-based neuromodulation.