Irregular optogenetic stimulation waveforms can induce naturalistic patterns of hippocampal spectral activity

Abstract

Introduction: Brain stimulation is a fundamental and effective therapy for neurological diseases including Parkinsons disease, essential tremor, and epilepsy. One key challenge in delivering effective brain stimulation is identifying the stimulation parameters, such as the amplitude, frequency, contact configuration, and pulse width, that induce an optimal change in symptoms, behavior, or neural activity. Most clinical and translational studies use constant frequency pulses of stimulation, but stimulation with irregular pulse patterns or non-pulsatile waveforms might induce unique changes in neural activity that could enable better therapeutic responses. Here, we comprehensively evaluate several optogenetic stimulation waveforms, report their differing effects on hippocampal spectral activity, and compare these induced effects to activity recorded during natural behavior. Methods: Sprague Dawley rats were prepared for panneuronal excitatory optogenetic stimulation of the medial septum (hSyn ChR2) and 16-channel microelectrode recording in CA1 and CA3 layers of the hippocampus. We performed grid and random sampling of the parameters comprising several stimulation waveforms, including standard pulse, nested pulse, sinusoid, double sinusoid, and Poisson pulse waveforms. Results: We comprehensively report the effects of changing stimulation parameters in these parameter spaces on two key biomarkers of hippocampal function, theta and gamma power. Similarly, robust excitation of hippocampal gamma power was observed across all waveforms, whereas no set of stimulation parameters was sufficient to consistently increase power in the theta band beyond baseline levels of activity (despite the prominent role of the medial septum in pacing hippocampal theta oscillations). Using a manifold learning algorithm to compare high dimensional neural activity, we show that irregular stimulation patterns produce differing effects with respect to multi-band patterns of activity and can induce activity patterns that more closely resemble activity recorded during natural behavior than conventional parameters. Conclusion: Our counter intuitive findings, that stimulation of the medial septum ubiquitously does not increase hippocampal theta power, and that different waveforms have similar effects on single power bands, contradict recent trends in brain stimulation research, necessitating greater caution and fewer mechanistic assumptions as to how a given stimulation target or waveform will modulate a neurophysiological biomarker of disease. We also reveal that irregular stimulation patterns can have biomimetic utility, promoting their exploration in medical applications where inducing a particular activity pattern can have therapeutic benefit. Last, we demonstrate a scalable data driven analysis strategy that can make the discovery of such physiologically informed temporal stimulation patterns more empirically tractable in translational settings.

SAFE-OPT: A Bayesian optimization algorithm for learning optimal deep brain stimulation parameters with safety constraints

“…30, 32 Additionally, it has been deployed for modulating hippocampal oscillations through the adjustment of optogenetic stimulation parameters. 33, 34…”

Seizure Event Detection Using Intravital Two-Photon Calcium Imaging Data

“…Despite great advancements for management of epilepsy over the past century, nearly one-third of patients’ seizures are still poorly controlled with medication 34, 35 and surgical treatment options 36-39 are only effective for a subset of these patients. Pre-clinical optical imaging with cellular resolution might be used to evaluate novel brain stimulation targets for epilepsy with the potential for superior cell-specific modulation, 40-43 or to provide unbiased evaluation of different stimulation patterns and parameters 44, 45 for seizure control at scale.…”

“…Despite great advancements for management of epilepsy over the past century, nearly one-third of patients' seizures are still poorly controlled with medication 34,35 and surgical treatment options [36][37][38][39] are only effective for a subset of these patients. Pre-clinical optical imaging with cellular resolution might be used to evaluate novel brain stimulation targets for epilepsy with the potential for superior cell-specific modulation, [40][41][42][43] or to provide unbiased evaluation of different stimulation patterns and parameters 44,45 for seizure control at scale. Efficient seizure detection methods might also be combined with mean-field imaging to provide feedback for real-time optimization and control paradigms, as brain stimulation for seizure control may benefit by responding to the dynamics of different cell populations in real-time.…”

Adaptive Bayesian Optimization for State-Dependent Brain Stimulation

“…Multiple different types of non-stationarities and statedependent properties observed in different types of neural systems could require different machine learning strategies for adaptation to achieve best performance. Common sources of non-stationarities may include physical movement or damage to recording electrodes, buildup of scar tissue around implanted or stimulated sites, behavioral or task-related changes in brain state, and dynamic neural changes induced by a stimulation pulse train such as synaptic exhaustion [4,[18][19][20]. Many such applications have been addressed with Kalman-inspired tracking methods and are characterized by slow and sustained rather than rapid or frequently changing dynamics, suggesting that methods assuming conserved change could be suitable for a wide array of applications in state-dependent brain stimulation [18].…”