An official website of the United States government
Deep brain stimulation (DBS) is a commonly used treatment for medication resistant Parkinson’s disease and is an emerging treatment for other neurological disorders. More recently, phase-specific adaptive DBS (aDBS), whereby the application of stimulation is locked to a particular phase of tremor, has been proposed as a strategy to improve therapeutic efficacy and decrease side effects. In this work, in the context of these phase-specific aDBS strategies, we investigate the dynamical behavior of large populations of coupled neurons in response to near-periodic stimulation, namely, stimulation that is periodic except for a slowly changing amplitude and phase offset that can be used to coordinate the timing of applied input with a specified phase of model oscillations. Using an adaptive phase-amplitude reduction strategy, we illustrate that for a large population of oscillatory neurons, the temporal evolution of the associated phase distribution in response to near-periodic forcing can be captured using a reduced order model with four state variables. Subsequently, we devise and validate a closed-loop control strategy to disrupt synchronization caused by coupling. Additionally, we identify strategies for implementing the proposed control strategy in situations where underlying model equations are unavailable by estimating the necessary terms of the reduced order equations in real-time from observables.
more »
« less
Phase model-based neuron stabilization into arbitrary clusters
Deep brain stimulation (DBS) is a common method of combating pathological conditions associated with Parkinson’s disease, Tourette syndrome, essential tremor, and other disorders, but whose mechanisms are not fully understood. One hypothesis, supported experimentally, is that some symptoms of these disorders are associated with pathological synchronization of neurons in the basal ganglia and thalamus. For this reason, there has been interest in recent years in finding efficient ways to desynchronize neurons that are both fast-acting and low-power. Recent results on coordinated reset and periodically forced oscillators suggest that forming distinct clusters of neurons may prove to be more effective than achieving complete desynchronization, in particular by promoting plasticity effects that might persist after stimulation is turned off. Current proposed methods for achieving clustering frequently require either multiple input sources or precomputing the control signal. We propose here a control strategy for clustering, based on an analysis of the reduced phase model for a set of identical neurons, that allows for real-time, single-input control of a population of neurons with low-amplitude, low total energy signals. After demonstrating its effectiveness on phase models, we apply it to full state models to demonstrate its validity. We also discuss the effects of coupling on the efficacy of the strategy proposed and demonstrate that the clustering can still be accomplished in the presence of weak to moderate electrotonic coupling.
more »
« less
- Award ID(s):
- 1635542
- PAR ID:
- 10075850
- Date Published:
- Journal Name:
- Journal of computational neuroscience
- Volume:
- 44
- ISSN:
- 0929-5313
- Page Range / eLocation ID:
- 363-378
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Three-dimensional neuronal organoids, spheroids, and tissue mimics are increasingly used to model cognitive processes in vitro. These 3D constructs are also used to model the effects of neurological and psychiatric disorders and to perform computational tasks. The brain’s complex network of neurons is activated via feedforward sensory pathways. Therefore, an interface to 3D constructs that models sensory pathway-like inputs is desirable. In this work, an optical interface for 3D neuronal constructs was developed. Dendrites and axons extended by cortical neurons within the 3D constructs were guided into microchannel-confined bundles. These neurite bundles were then optogenetically stimulated, and evoked responses were evaluated by calcium imaging. Optical stimulation was designed to deliver distinct input patterns to the network in the 3D construct, mimicking sensory pathway inputs to cortical areas in the intact brain. Responses of the network to the stimulation possessed features of neuronal population code, including separability by input pattern and mixed selectivity of individual neurons. This work represents the first demonstration of a pathway-like activation of networks in 3D constructs. Another innovation of this work is the development of an all-optical interface to 3D neuronal constructs, which does not require the use of expensive microelectrode arrays. This interface may enable the use of 3D neuronal constructs for investigations into cortical information processing. It may also enable studies into the effects of neurodegenerative or psychiatric disorders on cortical computation.more » « less
-
Abstract Failure to direct axon regeneration to appropriate targets is a major barrier to restoring function after nerve injury. Development of strategies that can direct targeted regeneration of neurons such as retinal ganglion cells (RGCs) are needed to delay or reverse blindness in diseases like glaucoma. Here, we demonstrate that a new class of asymmetric, charge balanced (ACB) waveforms are effective at directing RGC axon growth, in vitro, without compromising cell viability. Unlike previously proposed direct current (DC) stimulation approaches, charge neutrality of ACB waveforms ensures the safety of stimulation while asymmetry ensures its efficacy. Furthermore, we demonstrate the relative influence of pulse amplitude and pulse width on the overall effectiveness of stimulation. This work can serve as a practical guideline for the potential deployment of electrical stimulation as a treatment strategy for nerve injury.more » « less
-
ABSTRACT Achieving targeted perturbations of neural activity is essential for dissecting the causal architecture of brain circuits. A crucial challenge in targeted manipulation experiments is the identification ofhigh efficacyperturbation sites whose stimulation exerts desired effects, currently done with costly trial-and-error procedures. Can one predict stimulation effects solely based on observations of the circuit activity, in the absence of perturbation? We answer this question in dissociated neuronal cultures on High-Density Microelectrode Arrays (HD-MEAs), which, compared toin vivopreparations, offer a controllablein vitroplatform that enables precise stimulation and full access to network dynamics. We first reconstruct theperturbome- the full map of network responses to focal electrical stimulation - by sequentially activating individual single sites and quantifying their network-wide effects. The measured perturbome patterns cluster into functional modules, with limited spread across clusters. We then demonstrate that the perturbome can be predicted from spontaneous activity alone. Using short baseline recordings in the absence of perturbations, we estimate Effective Connectivity (EC) and show that it predicts the spatial organization of the perturbome, including spatial clusters and local connectivity. Our results demonstrate that spontaneous dynamics encode the latent causal structure of neural circuits and that EC metrics can serve as effective, model-free proxies for stimulation outcomes. This framework enables data-driven targeting and causal inferencein vitro, with potential applications to more complex preparations such as human iPSC-derived neurons and brain organoids, with implications for both basic research and therapeutic strategies targeting neurological disorders. Significance StatementNeuronal cultures are increasingly used as controllable platforms to study neuronal network dynamics, neuromodulation, and brain-inspired therapies. To fully exploit their potential, we need robust methods to probe and interpret causal interactions. Here, we develop a framework to reconstruct the perturbome—the network-wide map of responses to localized electrical stimulation—and show that it can be predicted from spontaneous activity alone. Using simple, model-free metrics of Effective Connectivity, we reveal that ongoing activity encodes causal structure and provides reliable proxies for stimulation outcomes. This validates EC as a practical measure of causal influence in vitro. Our methodology refines the use of neuronal cultures for brain-on-a-chip approaches, and paves the way for data-driven neuromodulation strategies in human stem cell–derived neurons and brain organoids.more » « less
-
In this article, we devise two related control algorithms to change the degree of synchrony of a population of noise-free, identical, uncoupled neural oscillators using a single control input. The algorithms are based on phase reduction, and use a population-level partial differential equation formulation to change the phase distribution of the neurons as desired. Motivated by the pathological neural synchronization hypothesized to be present in patients suffering from essential and parkinsonian tremor, we take our control objective to be the desynchronization of an initially synchronized neural population. Through numerical simulations, we are able to show that our algorithms work for both Type I and Type II neural populations. To demonstrate the versatility of our control algorithms, we also show that they can be applied to synchronize an initially desynchronized neural population as well. For the systems considered in this paper, the control algorithms can be applied to achieve any desired traveling-wave neural phase distribution, as long as the combination of initial and desired phase distributions is non-degenerate.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- The DOI is not currently available.
