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.

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.

Novel requirements for HAP2/GCS1-mediated gamete fusion in Tetrahymena.

The ancestral gamete fusion protein, HAP2/GCS1, plays an essential role in fertilization in a broad range of taxa. To identify factors that may regulate HAP2/GCS1 activity, we screened mutants of the ciliate Tetrahymena thermophila for behaviors that mimic Δhap2/gcs1 knockout phenotypes in this species. Using this approach, we identified two new genes, GFU1 and GFU2, whose products are necessary for membrane pore formation following mating type recognition and adherence. GFU2 is predicted to be a single-pass transmembrane protein, while GFU1, though lacking obvious transmembrane domains, has the potential to interact directly with membrane phospholipids in the cytoplasm. Like Tetrahymena HAP2/GCS1, expression of GFU1 is required in both cells of a mating pair for efficient fusion to occur. To explain these bilateral requirements, we propose a model that invokes cooperativity between the fusion machinery on apposed membranes of mating cells and accounts for successful fertilization in Tetrahymena's multiple mating type system.

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.

Neural pathway activation in the subthalamic region depends on stimulation polarity.

Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease (PD); however, there is limited understanding of which subthalamic pathways are recruited in response to stimulation. Here, by focusing on the polarity of the stimulus waveform (cathodic vs. anodic), our goal was to elucidate biophysical mechanisms that underlie electrical stimulation in the human brain. In clinical studies, cathodic stimulation more easily triggers behavioral responses, but anodic DBS broadens the therapeutic window. This suggests that neural pathways involved respond preferentially depending on stimulus polarity. To experimentally compare the activation of therapeutically relevant pathways during cathodic and anodic subthalamic nucleus (STN) DBS, pathway activation was quantified by measuring evoked potentials resulting from antidromic or orthodromic activation in 15 PD patients undergoing DBS implantation. Cortical evoked potentials (cEP) were recorded using subdural electrocorticography, DBS local evoked potentials (DLEP) were recorded from non-stimulating contacts and EMG activity was recorded from arm and face muscles. We measured: 1) the amplitude of short-latency cEP, previously demonstrated to reflect activation of the cortico-STN hyperdirect pathway, 2) DLEP amplitude thought to reflect activation of STN-globus pallidus (GP) pathway, and 3) amplitudes of very short-latency cEP and motor evoked potentials (mEP) for activation of cortico-spinal/bulbar tract (CSBT). We constructed recruitment and strength-duration curves for each EP/pathway to compare the excitability for different stimulation polarities. We compared experimental data with the most advanced DBS computational models. Our results provide experimental evidence that subcortical cathodic and anodic stimulation activate the same pathways in the STN region and that cathodic stimulation is in general more efficient. However, relative efficiency varies for different pathways so that anodic stimulation is the least efficient in activating CSBT, more efficient in activating the HDP and as efficient as cathodic in activating STN-GP pathway. Our experiments confirm biophysical model predictions regarding neural activations in the central nervous system and provide evidence that stimulus polarity has differential effects on passing axons, terminal synapses, and local neurons. Comparison of experimental results with clinical DBS studies provides further evidence that the hyperdirect pathway may be involved in the therapeutic mechanisms of DBS.

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: 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.

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.

Wherever I may roam-Human activity alters movements of red deer (Cervus elaphus) and elk (Cervus canadensis) across two continents.

Human activity and associated landscape modifications alter the movements of animals with consequences for populations and ecosystems worldwide. Species performing long-distance movements are thought to be particularly sensitive to human impact. Despite the increasing anthropogenic pressure, it remains challenging to understand and predict animals' responses to human activity. Here we address this knowledge gap using 1206 Global Positioning System movement trajectories of 815 individuals from 14 red deer (Cervus elaphus) and 14 elk (Cervus canadensis) populations spanning wide environmental gradients, namely the latitudinal range from the Alps to Scandinavia in Europe, and the Greater Yellowstone Ecosystem in North America. We measured individual-level movements relative to the environmental context, or movement expression, using the standardized metric Intensity of Use, reflecting both the directionality and extent of movements. We expected movement expression to be affected by resource (Normalized Difference Vegetation Index, NDVI) predictability and topography, but those factors to be superseded by human impact. Red deer and elk movement expression varied along a continuum, from highly segmented trajectories over relatively small areas (high intensity of use), to directed transitions through restricted corridors (low intensity of use). Human activity (Human Footprint Index, HFI) was the strongest driver of movement expression, with a steep increase in Intensity of Use as HFI increased, but only until a threshold was reached. After exceeding this level of impact, the Intensity of Use remained unchanged. These results indicate the overall sensitivity of Cervus movement expression to human activity and suggest a limitation of plastic responses under high human pressure, despite the species also occurring in human-dominated landscapes. Our work represents the first comparison of metric-based movement expression across widely distributed populations of a deer genus, contributing to the understanding and prediction of animals' responses to human activity.

Behavioral responses of terrestrial mammals to COVID-19 lockdowns.

COVID-19 lockdowns in early 2020 reduced human mobility, providing an opportunity to disentangle its effects on animals from those of landscape modifications. Using GPS data, we compared movements and road avoidance of 2300 terrestrial mammals (43 species) during the lockdowns to the same period in 2019. Individual responses were variable with no change in average movements or road avoidance behavior, likely due to variable lockdown conditions. However, under strict lockdowns 10-day 95th percentile displacements increased by 73%, suggesting increased landscape permeability. Animals' 1-hour 95th percentile displacements declined by 12% and animals were 36% closer to roads in areas of high human footprint, indicating reduced avoidance during lockdowns. Overall, lockdowns rapidly altered some spatial behaviors, highlighting variable but substantial impacts of human mobility on wildlife worldwide.

The Tetrahymena bcd1 mutant implicates endosome trafficking in ciliate, cortical pattern formation.

Ciliates, such as Tetrahymena thermophila, evolved complex mechanisms to determine both the location and dimensions of cortical organelles such as the oral apparatus (OA: involved in phagocytosis), cytoproct (Cyp: for eliminating wastes), and contractile vacuole pores (CVPs: involved in water expulsion). Mutations have been recovered in Tetrahymena that affect both the localization of such organelles along anterior-posterior and circumferential body axes and their dimensions. Here we describe BCD1, a ciliate pattern gene that encodes a conserved Beige-BEACH domain-containing protein a with possible protein kinase A (PKA)-anchoring activity. Similar proteins have been implicated in endosome trafficking and are linked to human Chediak-Higashi syndrome and autism. Mutations in the BCD1 gene broaden cortical organelle domains as they assemble during predivision development. The Bcd1 protein localizes to membrane pockets at the base of every cilium that are active in endocytosis. PKA activity has been shown to promote endocytosis in other organisms, so we blocked clathrin-mediated endocytosis (using "dynasore") and inhibited PKA (using H89). In both cases, treatment produced partial phenocopies of the bcd1 pattern mutant. This study supports a model in which the dimensions of diverse cortical organelle assembly-platforms may be determined by regulated balance between constitutive exocytic delivery and PKA-regulated endocytic retrieval of organelle materials and determinants.

Large carnivores avoid humans while prioritizing prey acquisition in anthropogenic areas.

Large carnivores are recovering in many landscapes where the human footprint is simultaneously growing. When carnivores encounter humans, the way they behave often changes, which may subsequently influence how they affect their prey. However, little research investigates the behavioural mechanisms underpinning carnivore response to humans. As a result, it is not clear how predator-prey interactions and their associated ecosystem processes will play out in the human-dominated areas into which carnivore populations are increasingly expanding. We hypothesized that humans would reduce predation risk for prey by disturbing carnivores or threatening their survival. Alternatively, or additionally, we hypothesized that humans would increase predation risk by providing forage resources that congregate herbivorous prey in predictable places and times. Using grey wolves Canis lupus in Jackson Hole, Wyoming, USA as a study species, we investigated 170 kill sites across a spectrum of human influences ranging from heavily restricted human activities on protected federal lands to largely unregulated activities on private lands. Then, we used conditional logistic regression to quantify how the probability of predation changed across varied types and amounts of human influences, while controlling for environmental characteristics and prey availability. Wolves primarily made kills in environmental terrain traps and where prey availability was high, but predation risk was significantly better explained with the inclusion of human influences than by environmental characteristics alone. Different human influences had different, and even converse, effects on the risk of wolf predation. For example, where prey were readily available, wolves preferentially killed animals far from motorized roads but close to unpaved trails. However, wolves responded less strongly to humans, if at all, where prey were scarce, suggesting they prioritized acquiring prey over avoiding human interactions. Overall, our work reveals that the effects of large carnivores on prey populations can vary considerably among different types of human influences, yet carnivores may not appreciably alter predatory behaviour in response to humans if prey are difficult to obtain. These results shed new light on the drivers of large carnivore behaviour in anthropogenic areas while improving understanding of predator-prey dynamics in and around the wildland-urban interface.

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.

Ambidexterity in left-handed and right-handed individuals and implications for surgical training.

Surgical training has historically been geared toward right-handed individuals. This could cause mentors to perceive left-handed students and residents as being more difficult to train. This study examined whether differences in dexterity exist between left-handed and right-handed individuals and the implications this may have on surgical training. Recent literature suggests that surgical training may need to be updated to train left-handed students more effectively. In this prospective, crossover study, 50 right-handed and 50 left-handed individuals of varying ages and occupations were evaluated using the O'Conner Tweezer Dexterity Test. Participants were timed while they used flat-tipped forceps to pick up 1″ brass metal pins and sequentially place one pin each in 100 pinholes 1/16″ in diameter on a 21″ × 12″ board. Participants completed this exercise with their dominant hand followed by their nondominant hand. Nondominant hand dexterity was measured by dividing the nondominant hand completion time by the dominant hand completion time, with a higher percentage associated with higher ambidexterity. Using the Student's two-tailed t test, we found that left-handed individuals had an 97.2% congruence between dominant and nondominant hands while right-handed individuals had a 71.6% congruence (P < 0.001). In conclusion, our results show that left-handed individuals have a greater degree of ambidexterity than their right-handed counterparts. These results suggest that any perceived difference between left-handed and right-handed surgical residents may not be due to innate skill or dexterity, but rather a combination of external influences.

Eyes on the herd: Quantifying ungulate density from satellite, unmanned aerial systems, and GPScollar data.

Novel approaches to quantifying density and distributions could help biologists adaptively manage wildlife populations, particularly if methods are accurate, consistent, cost-effective, rapid, and sensitive to change. Such approaches may also improve research on interactions between density and processes of interest, such as disease transmission across multiple populations. We assess how satellite imagery, unmanned aerial system (UAS) imagery, and Global Positioning System (GPS) collar data vary in characterizing elk density, distribution, and count patterns across times with and without supplemental feeding at the National Elk Refuge (NER) in the US state of Wyoming. We also present the first comparison of satellite imagery data with traditional counts for ungulates in a temperate system. We further evaluate seven different aggregation metrics to identify the most consistent and sensitive metrics for comparing density and distribution across time and populations. All three data sources detected higher densities and aggregation locations of elk during supplemental feeding than non-feeding at the NER. Kernel density estimates (KDEs), KDE polygon areas, and the first quantile of interelk distances detected differences with the highest sensitivity and were most highly correlated across data sources. Both UAS and satellite imagery provide snapshots of density and distribution patterns of most animals in the area at lower cost than GPS collars. While satellite-based counts were lower than traditional counts, aggregation metrics matched those from UAS and GPS data sources when animals appeared in high contrast to the landscape, including brown elk against new snow in open areas. UAS counts of elk were similar to traditional ground-based counts on feed grounds and are the best data source for assessing changes in small spatial extents. Satellite, UAS, or GPS data can provide appropriate data for assessing density and changes in density from adaptive management actions. For the NER, where high elk densities are beneath controlled airspace, GPS collar data will be most useful for evaluating how management actions, including changes in the dates of supplemental feeding, influence elk density and aggregation across large spatial extents. Using consistent and sensitive measures of density may improve research on the drivers and effects of density within and across a wide range of species.

Anterior-posterior pattern formation in ciliates.

As single cells, ciliates build, duplicate, and even regenerate complex cortical patterns by largely unknown mechanisms that precisely position organelles along two cell-wide axes: anterior-posterior and circumferential (left-right). We review our current understanding of intracellular patterning along the anterior-posterior axis in ciliates, with emphasis on how the new pattern emerges during cell division. We focus on the recent progress at the molecular level that has been driven by the discovery of genes whose mutations cause organelle positioning defects in the model ciliate Tetrahymena thermophila. These investigations have revealed a network of highly conserved kinases that are confined to either anterior or posterior domains in the cell cortex. These pattern-regulating kinases create zones of cortical inhibition that by exclusion determine the precise placement of organelles. We discuss observations and models derived from classical microsurgical experiments in large ciliates (including Stentor) and interpret them in light of recent molecular findings in Tetrahymena. In particular, we address the involvement of intracellular gradients as vehicles for positioning organelles along the anterior-posterior axis.

Upper Lip Anatomy, Mechanics of Local Flaps, and Considerations for Reconstruction.

Reconstruction of defects involving the upper lip can be challenging. The purpose of this review was to analyze the anatomy and function of the upper lip and provide an approach for reconstruction of upper lip defects. The primary role of the upper lip is coverage of dentition and animation, whereas the lower lip is critical for oral competence, speech, and eating. The orbicularis oris (OO) and several other muscles contribute to upper lip function. There are various insertion points for animation muscles, including the upper lip dermis, OO, and modiolus. Special attention should be paid to the philtrum, Cupid's bow, and vermilion border during reconstruction. Advantages and disadvantages of the Abbe, Estlander, and Karapandzic flaps are presented. Knowledge of mechanics, indications, and properties of local flaps while considering unique characteristics of upper lip anatomy and function are crucial for optimal aesthetic and functional outcomes.

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.