Switching neuronal state: optimal stimuli revealed using a stochastically-seeded gradient algorithm

Experimental deep brain stimulation in rodent models of movement disorders

Deep brain stimulation (DBS) is the preferred treatment for therapy-resistant movement disorders such as dystonia and Parkinson's disease (PD), mostly in advanced disease stages. Although DBS is already in clinical use for ~30 years and has improved patients' quality of life dramatically, there is still limited understanding of the underlying mechanisms of action. Rodent models of PD and dystonia are essential tools to elucidate the mode of action of DBS on behavioral and multiscale neurobiological levels. Advances have been made in identifying DBS effects on the central motor network, neuroprotection and neuroinflammation in DBS studies of PD rodent models. The phenotypic dt^sz mutant hamster and the transgenic DYT-TOR1A (ΔETorA) rat proved as valuable models of dystonia for preclinical DBS research. In addition, continuous refinements of rodent DBS technologies are ongoing and have contributed to improvement of experimental quality. We here review the currently existing literature on experimental DBS in PD and dystonia models regarding the choice of models, experimental design, neurobiological readouts, as well as methodological implications. Moreover, we provide an overview of the technical stage of existing DBS devices for use in rodent studies.

Falling off a limit cycle using phase-agnostic stimuli: Definitions and conceptual framework

Nearly a half-century of biomedical research has revealed methods and mechanisms by which an oscillator with bistable limit cycle kinetics can be stopped using critical stimuli applied at a specific phase. Is it possible to construct a stimulus that stops oscillation regardless of the phase at which the stimulus is applied? Using a radial isochron clock model, we demonstrate the existence of such stimulus waveforms, which can take on highly complex shapes but with a surprisingly simple mechanism of rhythm suppression. The perturbation, initiated at any phase of the limit cycle, first corrals the oscillator to a narrow range of new phases, then drives the oscillator to its phase singularity. We further constructed a library of waveforms having different durations, each achieving phase-agnostic suppression of rhythm but with varying rates of phase corralling prior to amplitude suppression. The optimal stimulus energy to achieve phase-agnostic suppression of rhythm is dependent on the rate of phase corralling and the configuration of the phaseless set. We speculate that these results are generic and suggest the existence of stimulus waveforms that can stop the rhythm of more complex oscillators irrespective of the applied phase.

Optimizing stimulus waveforms for electroceuticals

There has been a growing interest in the use of electrical stimulation as a therapy across diverse medical conditions. Most electroceutical devices use simple waveforms, for example sinusoidal or rectangular biphasic pulses. Clinicians empirically tune the waveform parameters (e.g. amplitude, frequency) without altering the fundamental shape of the stimulus. In this article, we review computational strategies that have been used to optimize the shape of stimulus waveforms in order to improve clinical outcomes, and we discuss potential directions for future exploration.

Optimizing stimulus waveforms for suppressing epileptic activity reveals a counterbalancing mechanism

Electrical stimulation is used to treat drug- resistant epilepsy, and excessive stimulation can lead to adverse effects for patients. In this article, we use an extrema featured stochastic search algorithm to find energy-efficient stimulus waveforms that suppress seizure activity in two different computational models of epilepsy. We infer general principles that may provide insight into future design of energy efficient stimulus for epilepsy treatments.

Evolution of extrema features reveals optimal stimuli for biological state transitions

The ability to define the unique features of an input stimulus needed to control switch-like behavior in biological systems is an important problem in computational biology and medicine. We show in this study how highly complex and intractable optimization problems can be simplified by restricting the search to the signal's extrema as key feature points, and evolving the extrema features towards optimal solutions that closely match solutions derived from gradient-based methods. Our results suggest a model-independent approach for solving a class of optimization problems related to controlling switch-like state transitions.

Optimal stimulus waveforms for eliciting a spike: How close is the spike-triggered average?

Computing the average input stimulus preceding a spike, the spike-triggered average (STA), has been a powerful tool for discovering a neuron's 'preferred' stimulus feature that enables efficient encoding of sensory information. Recent work in the squid giant axon has shown that STA waveforms can be remarkably similar to the energetically optimal stimulus waveforms for eliciting a spike. In the present study, we show using the Hodgkin-Huxley model that the STA can deviate from the global optimal solution if there is averaging of multiple solutions across different time scales and of multiple modes of spike induction. These findings inform attempts to develop model-free stochastic algorithms for finding energy-optimal stimuli, which is relevant to the efficient delivery of exogenous therapeutic stimuli in neurological diseases.