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

State-Dependent Modulation of a Theta-Responsive Subnetwork by High-Frequency Stimulation of the Ventral Striatum.

From thalamic pathology to suppression stimulation of the subthalamic nucleus in Parkinson's disease: effects of variation in stimulation parameters

A Stochastic Neural Mass Model for Cortical Beta Bursts in Parkinson’s Disease

Beta-band (13–30 Hz) oscillations are increasingly understood to occur as transient “bursts” rather than sustained rhythms, with altered burst dynamics, specifically increased duration and power alongside reduced burst rates, in patients with Parkinson’s disease (PD). In this study, we utilise resting state magnetoencephalography (MEG) data from healthy adults to quantify the temporal fluctuations in the beta-band, and examine the distributions of burst statistics. We then fit a stochastic next-generation neural mass model to these empirical statistics using a Genetic Algorithm. Systematic parameter sweeps reveal that reducing background drive to excitatory and inhibitory neuronal populations reproduces the altered burst statistics observed in PD. Crucially, we show that strengthening synaptic coupling can counteract these deficits and restore healthy bursting dynamics. Together, this work establishes a computational framework linking cellular-level mechanisms to macroscale burst statistics, and highlights potential targets for therapeutic neuromodulation in movement disorders. Author summary Brain activity is comprised of rhythmic electrical patterns called “brain waves.” Traditionally, these waves were viewed as smooth and continuous, but recent evidence reveals that they actually occur in brief, intense bursts. In conditions such as Parkinson’s disease, these bursts become altered—lasting longer, growing stronger, and occurring less frequently. In this study, we developed a mathematical model of brain tissue to understand what drives these burst patterns. Using real brain scans from healthy human volunteers, we tuned our model with an optimisation algorithm until its simulated bursts closely matched real human brain activity. We then systematically varied the model’s settings to investigate how abnormal bursting arises in disease. We discovered that reducing the background signals to the brain cells reproduces the burst alterations seen in Parkinson’s disease. Importantly, our simulations showed that strengthening the connections between brain cells can counteract this deficit, restoring healthy burst patterns. By connecting microscopic cell properties to whole-brain rhythms, our work offers new insights into how movement disorders disrupt brain networks and highlights potential cellular targets to guide future brain stimulation therapies or medications.

Network state transitions under deep brain stimulation: A Wilson-Cowan model of Parkinson's Disease.

Adaptive reweighting of synaptic and intrinsic excitability by deep brain stimulation in a dystonia model.

PURPOSE Deep brain stimulation (DBS) is a standard treatment for movement disorders like dystonia or Parkinson's Disease. Although its clinical effectiveness is established, the mechanisms by which DBS influences neural motor networks are not fully understood. This study explores the development of adaptive network mechanisms. METHODS We compared functional impacts of short-term and long-term DBS on a. excitability of medium spiny neurons (MSNs) and b. synaptic transmission in the striatum in the dtsz hamster model, an in vivo model exhibiting dystonic episodes, and used mathematical modelling to gauge the functional impact of these changes. RESULTS In this electrophysiological and modelling study, we found contrasting changes in neuronal excitability and synaptic dynamics following short-term versus long-term DBS. Short-term DBS enhanced neuronal firing responses, while long-term DBS diminished them. Both short- and long-term DBS prolonged miniature excitatory postsynaptic currents (mEPSC) intervals, but only short-term DBS reduced mean frequency. Acetylcholine application reversed this effect, restoring mEPSC frequency more efficiently in tissue subjected to short-term DBS compared to long-term DBS. SIGNIFICANCE These observations indicate that DBS benefits in dystonia involve immediate and adaptive mechanisms, which have implications for improving stimulation parameters and treatment protocols. The findings reveal the temporal specificity of DBS effects and highlight the importance of understanding synaptic mechanisms to enhance therapeutic outcomes for dystonia patients.

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.