Evidence supporting deep brain stimulation of the medial septum in the treatment of temporal lobe epilepsy.

Electrical brain stimulation has become an essential treatment option for more than one third of epilepsy patients who are resistant to pharmacological therapy and are not candidates for surgical resection. However, currently approved stimulation paradigms achieve only moderate success, on average providing approximately 75% reduction in seizure frequency and extended periods of seizure freedom in nearly 20% of patients. Outcomes from electrical stimulation may be improved through the identification of novel anatomical targets, particularly those with significant anatomical and functional connectivity to the epileptogenic zone. Multiple studies have investigated the medial septal nucleus (i.e., medial septum) as such a target for the treatment of mesial temporal lobe epilepsy. The medial septum is a small midline nucleus that provides a critical functional role in modulating the hippocampal theta rhythm, a 4-7-Hz electrophysiological oscillation mechanistically associated with memory and higher order cognition in both rodents and humans. Elevated theta oscillations are thought to represent a seizure-resistant network activity state, suggesting that electrical neuromodulation of the medial septum and restoration of theta-rhythmic physiology may not only reduce seizure frequency, but also restore cognitive comorbidities associated with mesial temporal lobe epilepsy. Here, we review the anatomical and physiological function of the septohippocampal network, evidence for seizure-resistant effects of the theta rhythm, and the results of stimulation experiments across both rodent and human studies, to argue that deep brain stimulation of the medial septum holds potential to provide an effective neuromodulation treatment for mesial temporal lobe epilepsy. We conclude by discussing the considerations necessary for further evaluating this treatment paradigm with a clinical trial.

Seizure event detection using intravital two-photon calcium imaging data.

Significance: Intravital cellular calcium imaging has emerged as a powerful tool to investigate how different types of neurons interact at the microcircuit level to produce seizure activity, with newfound potential to understand epilepsy. Although many methods exist to measure seizure-related activity in traditional electrophysiology, few yet exist for calcium imaging. Aim: To demonstrate an automated algorithmic framework to detect seizure-related events using calcium imaging-including the detection of pre-ictal spike events, propagation of the seizure wavefront, and terminal spreading waves for both population-level activity and that of individual cells. Approach: We developed an algorithm for precise recruitment detection of population and individual cells during seizure-associated events, which broadly leverages averaged population activity and high-magnitude slope features to detect single-cell pre-ictal spike and seizure recruitment. We applied this method to data recorded using awake in vivo two-photon calcium imaging during pentylenetetrazol-induced seizures in mice. Results: We demonstrate that our detected recruitment times are concordant with visually identified labels provided by an expert reviewer and are sufficiently accurate to model the spatiotemporal progression of seizure-associated traveling waves. Conclusions: Our algorithm enables accurate cell recruitment detection and will serve as a useful tool for researchers investigating seizure dynamics using calcium imaging.

Organellular imaging in vivo reveals a depletion of endoplasmic reticular calcium during post-ictal cortical spreading depolarization.

During cortical spreading depolarization (CSD), neurons exhibit a dramatic increase in cytosolic calcium, which may be integral to CSD-mediated seizure termination. This calcium increase greatly exceeds that during seizures, suggesting the calcium source may not be solely extracellular. Thus, we sought to determine if the endoplasmic reticulum (ER), the largest intracellular calcium store, is involved. We developed a two-photon calcium imaging paradigm to simultaneously record the cytosol and ER during seizures in awake mice. Paired with direct current recording, we reveal that CSD can manifest as a slow post-ictal cytosolic calcium wave with a concomitant depletion of ER calcium that is spatiotemporally consistent with a calcium-induced calcium release. Importantly, we observed both naturally occurring and electrically induced CSD suppressed post-ictal epileptiform activity. Collectively, this work links ER dynamics to CSD, which serves as an innate process for seizure suppression and a potential mechanism underlying therapeutic electrical stimulation for epilepsy.

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

Objective. Therapeutic brain stimulation is conventionally delivered using constant-frequency stimulation pulses. Several recent clinical studies have explored how unconventional and irregular temporal stimulation patterns could enable better therapy. However, it is challenging to understand which irregular patterns are most effective for different therapeutic applications given the massively high-dimensional parameter space.Approach. Here we applied many irregular stimulation patterns in a single neural circuit to demonstrate how they can enable new dimensions of neural control compared to conventional stimulation, to guide future exploration of novel stimulation patterns in translational settings. We optogenetically excited the septohippocampal circuit with constant-frequency, nested pulse, sinusoidal, and randomized stimulation waveforms, systematically varying their amplitude and frequency parameters.Main results.We first found equal entrainment of hippocampal oscillations: all waveforms provided similar gamma-power increase, whereas no parameters increased theta-band power above baseline (despite the mechanistic role of the medial septum in driving hippocampal theta oscillations). We then compared each of the effects of each waveform on high-dimensional multi-band activity states using dimensionality reduction methods. Strikingly, we found that conventional stimulation drove predominantly 'artificial' (different from behavioral activity) effects, whereas all irregular waveforms induced activity patterns that more closely resembled behavioral activity.Significance. Our findings suggest that irregular stimulation patterns are not useful when the desired mechanism is to suppress or enhance a single frequency band. However, novel stimulation patterns may provide the greatest benefit for neural control applications where entraining a particular mixture of bands (e.g. if they are associated with different symptoms) or behaviorally-relevant activity is desired.

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

Objective.To treat neurological and psychiatric diseases with deep brain stimulation (DBS), a trained clinician must select parameters for each patient by monitoring their symptoms and side-effects in a months-long trial-and-error process, delaying optimal clinical outcomes. Bayesian optimization has been proposed as an efficient method to quickly and automatically search for optimal parameters. However, conventional Bayesian optimization does not account for patient safety and could trigger unwanted or dangerous side-effects.Approach.In this study we develop SAFE-OPT, a Bayesian optimization algorithm designed to learn subject-specific safety constraints to avoid potentially harmful stimulation settings during optimization. We prototype and validate SAFE-OPT using a rodent multielectrode stimulation paradigm which causes subject-specific performance deficits in a spatial memory task. We first use data from an initial cohort of subjects to build a simulation where we design the best SAFE-OPT configuration for safe and accurate searchingin silico. Main results.We then deploy both SAFE-OPT and conventional Bayesian optimization without safety constraints in new subjectsin vivo, showing that SAFE-OPT can find an optimally high stimulation amplitude that does not harm task performance with comparable sample efficiency to Bayesian optimization and without selecting amplitude values that exceed the subject's safety threshold.Significance.The incorporation of safety constraints will provide a key step for adopting Bayesian optimization in real-world applications of DBS.

Refining Brain Stimulation Therapies: An Active Learning Approach to Personalization.

Brain stimulation holds promise for treating brain disorders, but personalizing therapy remains challenging. Effective treatment requires establishing a functional link between stimulation parameters and brain response, yet traditional methods like random sampling (RS) are inefficient and costly. To overcome this, we developed an active learning (AL) framework that identifies optimal relationships between stimulation parameters and brain response with fewer experiments. We validated this framework through three experiments: (1) in silico modeling with synthetic data from a Parkinson's disease model, (2) in silico modeling with real data from a non-human primate, and (3) in vivo modeling with a real-time rat optogenetic stimulation experiment. In each experiment, we compared AL models to RS models, using various query strategies and stimulation parameters (amplitude, frequency, pulse width). AL models consistently outperformed RS models, achieving lower error on unseen test data in silico ( p < 0.0056 , N = 1000 ) and in vivo ( p = 0.0036 , N = 20 ) . This approach represents a significant advancement in brain stimulation, potentially improving both research and clinical applications by making them more efficient and effective. Our findings suggest that AL can substantially reduce the cost and time required for developing personalized brain stimulation therapies, paving the way for more effective and accessible treatments for brain disorders.

Refining Brain Stimulation Therapies: An Active Learning Approach to Personalization.

Brain stimulation holds promise for treating brain disorders, but personalizing therapy remains challenging. Effective treatment requires establishing a functional link between stimulation parameters and brain response, yet traditional methods like random sampling (RS) are inefficient and costly. To overcome this, we developed an active learning (AL) framework that identifies optimal relationships between stimulation parameters and brain response with fewer experiments. We validated this framework through three experiments: (1) in silico modeling with synthetic data from a Parkinson's disease model, (2) in silico modeling with real data from a non-human primate, and (3) in vivo modeling with a real-time rat optogenetic stimulation experiment. In each experiment, we compared AL models to RS models, using various query strategies and stimulation parameters (amplitude, frequency, pulse width). AL models consistently outperformed RS models, achieving lower error on unseen test data in silico (p<0.0056, N=1,000) and in vivo (p=0.0036, N=20). This approach represents a significant advancement in brain stimulation, potentially improving both research and clinical applications by making them more efficient and effective. Our findings suggest that AL can substantially reduce the cost and time required for developing personalized brain stimulation therapies, paving the way for more effective and accessible treatments for brain disorders.

Multisensory flicker modulates widespread brain networks and reduces interictal epileptiform discharges.

Modulating brain oscillations has strong therapeutic potential. Interventions that both non-invasively modulate deep brain structures and are practical for chronic daily home use are desirable for a variety of therapeutic applications. Repetitive audio-visual stimulation, or sensory flicker, is an accessible approach that modulates hippocampus in mice, but its effects in humans are poorly defined. We therefore quantified the neurophysiological effects of flicker with high spatiotemporal resolution in patients with focal epilepsy who underwent intracranial seizure monitoring. In this interventional trial (NCT04188834) with a cross-over design, subjects underwent different frequencies of flicker stimulation in the same recording session with the effect of sensory flicker exposure on local field potential (LFP) power and interictal epileptiform discharges (IEDs) as primary and secondary outcomes, respectively. Flicker focally modulated local field potentials in expected canonical sensory cortices but also in the medial temporal lobe and prefrontal cortex, likely via resonance of stimulated long-range circuits. Moreover, flicker decreased interictal epileptiform discharges, a pathological biomarker of epilepsy and degenerative diseases, most strongly in regions where potentials were flicker-modulated, especially the visual cortex and medial temporal lobe. This trial met the scientific goal and is now closed. Our findings reveal how multi-sensory stimulation may modulate cortical structures to mitigate pathological activity in humans.

Seizure Event Detection Using Intravital Two-Photon Calcium Imaging Data.

Significance: Genetic cellular calcium imaging has emerged as a powerful tool to investigate how different types of neurons interact at the microcircuit level to produce seizure activity, with newfound potential to understand epilepsy. Although many methods exist to measure seizure-related activity in traditional electrophysiology, few yet exist for calcium imaging. Aim: To demonstrate an automated algorithmic framework to detect seizure-related events using calcium imaging - including the detection of pre-ictal spike events, propagation of the seizure wavefront, and terminal spreading waves for both population-level activity and that of individual cells. Approach: We developed an algorithm for precise recruitment detection of population and individual cells during seizure-associated events, which broadly leverages averaged population activity and high-magnitude slope features to detect single-cell pre-ictal spike and seizure recruitment. We applied this method to data recorded using awake in vivo two-photon calcium imaging during pentylenetetrazol induced seizures in mice. Results: We demonstrate that our detected recruitment times are concordant with visually identified labels provided by an expert reviewer and are sufficiently accurate to model the spatiotemporal progression of seizure-associated traveling waves. Conclusions: Our algorithm enables accurate cell recruitment detection and will serve as a useful tool for researchers investigating seizure dynamics using calcium imaging.

Multi-objective data-driven optimization for improving deep brain stimulation in Parkinson's disease.

Objective.Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease (PD) but its success depends on a time-consuming process of trial-and-error to identify the optimal stimulation settings for each individual patient. Data-driven optimization algorithms have been proposed to efficiently find the stimulation setting that maximizes a quantitative biomarker of symptom relief. However, these algorithms cannot efficiently take into account stimulation settings that may control symptoms but also cause side effects. Here we demonstrate how multi-objective data-driven optimization can be used to find the optimal trade-off between maximizing symptom relief and minimizing side effects.Approach.Cortical and motor evoked potential data collected from PD patients during intraoperative stimulation of the subthalamic nucleus were used to construct a framework for designing and prototyping data-driven multi-objective optimization algorithms. Using this framework, we explored how these techniques can be applied clinically, and characterized the design features critical for solving this optimization problem. Our two optimization objectives were to maximize cortical evoked potentials, a putative biomarker of therapeutic benefit, and to minimize motor potentials, a biomarker of motor side effects.Main Results.Using thisin silicodesign framework, we demonstrated how the optimal trade-off between two objectives can substantially reduce the stimulation parameter space by 61 ± 19%. The best algorithm for identifying the optimal trade-off between the two objectives was a Bayesian optimization approach with an area under the receiver operating characteristic curve of up to 0.94 ± 0.02, which was possible with the use of a surrogate model and a well-tuned acquisition function to efficiently select which stimulation settings to sample.Significance.These findings show that multi-objective optimization is a promising approach for identifying the optimal trade-off between symptom relief and side effects in DBS. Moreover, these approaches can be readily extended to newly discovered biomarkers, adapted to DBS for disorders beyond PD, and can scale with the development of more complex DBS devices.