Baker, Matthew

Abstract Background Deep brain stimulation (DBS) is a widely used therapy for neurologic and psychiatric disorders. Conventional DBS delivers highly regular stimulation patterns that suppress pathological activity but can induce stimulation-related side effects, limiting the therapeutic window. Introducing controlled temporal variability through stochastic pulse timing may represent an alternative programming dimension to improve tolerability while preserving clinical benefit. Methods An adult in their 60’s with bilateral Vim DBS underwent evaluation of tonic, pink-noise, and white-noise stimulation patterns delivered through his chronically implanted Boston Scientific Genus system using the Chronos research platform. We assessed tremor and stimulation-induced side effects using accelerometry, spiral drawing tasks, standardized speech recordings, and patient-reported paresthesias. Results Pink noise stimulation preserved meaningful tremor suppression while improving tolerability compared with conventional tonic 130 Hz stimulation. Under tonic stimulation, dysarthria and paresthesias were prominent at 2.0 mA, narrowing the usable therapeutic window. In contrast, pink noise maintained tremor control across the same amplitude range with reduced side-effect burden. White noise stimulation demonstrated intermediate effects, providing improved tolerability relative to tonic stimulation but less tremor suppression than pink noise. Findings were consistent across accelerometry and functional drawing tasks. Conclusion This study provides first-in-human evidence that temporally structured stochastic pulse timing can preserve therapeutic benefit while expanding the tolerable stimulation range relative to tonic DBS. These findings suggest that temporal structure represents a clinically meaningful programming dimension that may broaden the DBS therapeutic window using software based updates to existing hardware. Further evaluation in larger cohorts is warranted.

Background: Major depressive disorder remains a leading cause of disability. While subgenual cingulate cortex (sgCC) deep brain stimulation (DBS) shows promise for medically refractory depression, clinical outcomes have been heterogeneous, suggesting that individual differences in neural circuitry engagement may critically influence therapeutic efficacy. We aimed to define the electrophysiological signatures of sgCC efferent connectivity using single-pulse electrical stimulation (SPES) with intracranial stereo-EEG (sEEG) to inform rational targeting and physiological biomarkers for sgCC-DBS. Methods: In four patients undergoing clinically indicated sEEG for seizure mapping, SPES was delivered through sgCC pairs, while distributed brain stimulation-evoked potentials (BSEPs) were recorded across cortical and subcortical sites. Responses were characterized using Canonical Response Parameterization to extract reproducible waveforms and per-trial reliability. Results: sgCC stimulation elicited reproducible, spatially organized BSEPs across frontal, limbic, and paralimbic networks, aligning with known anatomical pathways. Frontal recruitment featured robust, lateralized orbitofrontal activation favoring the ipsilateral central, medial OFC and bilateral ventromedial prefrontal responses. Limbic effects demonstrated bilateral cingulate activation with stronger ipsilateral recruitment and lateralized amygdala and hippocampal responses. Paralimbic engagement included insular responses with subject-specific anterior predominance and bi-hemispheric temporal-polar slow-wave deflections. Conclusion: These findings provide direct electrophysiological evidence of distributed, lateralized sgCC divergent network connectivity in the human brain, offering physiologic confirmation of its role in affective circuitry. The observed topography and laterality have direct applications for sgCC-DBS targeting and implicate BSEP signatures as candidate biomarkers to guide patient-specific therapy.

Abstract Neurosurgical and neuromodulation therapies such as deep brain stimulation (DBS) require millimeter-level accuracy to effectively target functional brain regions. Yet, many neuroanatomical boundaries remain invisible to current imaging and electrophysiology methods, limiting precision and contributing to suboptimal patient outcomes. Here, we introduce a self-supervised artificial intelligence (AI) framework that learns to delineate functional subregions directly from the spectral content of intraoperative local field potential (LFP) recordings, without the need for predefined biomarkers or anatomical labels. The framework identifies physiologic structure across the full spectrum of the signal and, through explainable AI (XAI), reveals the specific frequency components underlying these distinctions. Validated in the subthalamic nucleus (STN), the model aligned with clinically defined borders and rediscovered known beta oscillations. Applied to the motor thalamus in tremor patients, it consistently identified functional transitions corresponding to the ventral oralis posterior (Vop) and ventral intermediate (Vim) nuclei—regions where conventional methods fail to provide reliable boundaries. To assess clinical relevance, physiologically defined clusters were functionally evaluated using monopolar review data at their first DBS clinic postoperative visit, demonstrating distinct stimulation-response profiles across clusters and linking electrophysiologic segmentation to clinically meaningful programming outcomes. These findings demonstrate that intraoperative LFP recordings can be transformed into both a real-time guidance resource and a data-driven platform for biomarker discovery, establishing a foundation for more precise, individualized neuromodulation therapies and advancing our understanding of functional brain organization.

Background: Tremor in multiple sclerosis (MS) is often refractory to medications. Deep brain stimulation (DBS) may help, but possible side effects and disease exacerbation limit its use. Objectives: Evaluate outcomes of DBS for MS-associated tremor. Methods: Retrospective review of 18 patients with MS-associated tremor who underwent DBS, analyzing Expanded Disability Status Scale (EDSS) and Fahn–Tolosa–Marin (FTM) tremor scores. Results: Median age at surgery was 49.5 years (range, 28–75), with postoperative follow-up 2.5 years (range, 0–22). No relapses (0/18) or new lesions (0/8) occurred postoperatively. All had ventral intermediate nucleus leads; other targets were not used in programming. EDSS remained stable (median 6 to 6.25; p = 0.2). FTM scores improved (median 14.5 to 10, p = 0.008). Patients with EDSS 3.5–6 had variable outcomes ( p = 0.02). Conclusions: DBS is a potential treatment for severe MS tremor and did not provoke relapse. Targeting VIM alone may optimize tremor control and maintain magnetic resonance imaging (MRI) access.

Background Falls in people with multiple sclerosis (pwMS) lead to morbidity and expense. Objective Identify clinical metrics associated with falls. Methods Eighty-six pwMS completed fall surveys, timed 25-foot walk (T25FW), and motion analysis with Clario Opal devices. Logistic regression models were created. Results Median age was 54.5 years (range 21–73), 62% (53) were female. The cohort included 58% with relapsing (50) and 42% with progressive MS (36). Those who reported falling in the last year were older (median age 58 vs 52.5, p = .03) and had a higher Patient Determined Disease Step (PDDS) score (median 3 vs 1, p < .0001). Falls were associated with worse balance metrics including sway area (median 2.3 degrees 2 vs 1.2, p = .01), jerk (median 3.3 m 2 /s 5 vs 1.6, p = .005), and slower T25FW (median 11.5 s vs 8; p < .0001). A multivariable regression model based on gait aid use and T25FW time >10.8 s (c = 0.80) was derived. Having both features portended a probability of falling of 0.97, while having neither, a probability of 0.26. Conclusions Falls in pwMS are more frequent in patients who are older, have higher PDDS, slower walking, and worse balance. Gait aid use and T25FW >10.8 s were strongly associated with falls in the past year.

Abstract Deep brain stimulation (DBS) is a targeted manipulation of brain circuitry to treat neurological and neuropsychiatric conditions. Optimal DBS lead placement is essential for treatment efficacy. Current targeting practice is based on preoperative and intraoperative brain imaging, intraoperative electrophysiology, and stimulation mapping. Electrophysiological mapping using extracellular microelectrode recordings aids in identifying functional subdomains, anatomical boundaries, and disease-correlated physiology. The shape of single-unit action potentials may differ due to different biophysical properties between cell-types and brain regions. Here, we describe a technique to parameterize the structure and duration of sorted spike units using a novel algorithmic approach based on canonical response parameterization, and illustrate how it may be used on DBS microelectrode recordings. Isolated spike shapes are parameterized then compared using a spike similarity metric and grouped by hierarchical clustering. When spike morphology is associated with anatomy, we find regional clustering in the human globus pallidus. This method is widely applicable for spike removal and single-unit characterization and could be integrated into intraoperative array-based technologies to enhance targeting and clinical outcomes in DBS lead placement. Author summary We developed a new algorithmic approach to capture the structure of single neuron electrophysiology from microelectrode recordings during deep brain stimulation surgery. This method has broad applications for spike characterization, clustering, removal from the background field potential, and comparison across recording sites. Using this technique, we illustrate regional clustering of spike morphologies in the human globus pallidus.

We studied movement-associated spectral changes in human motor thalamus and observed focal increases in broadband power with movement. This biomarker may be used as a tool for intraoperative functional mapping, DBS targeting, and closed-loop device control.

Abstract Studying mammalian brain function aids our understanding of human brain evolution. We implanted a beagle with a prototype human neuromodulation platform that measures activity from the brain surface. One year later, a set of simple sensory tasks was performed, finding visual and somatosensory representation in the canine homologs of the expected areas in humans. Surprisingly, the canine anterior ectosylvian gyrus, which is anatomically homologous to human receptive speech areas, was selectively active during independent social reinforcement tasks. This suggests that human speech understanding may have evolved from more general mammalian brain structures that are specialized for social reinforcement.

An imbalance in goal-directed and habitual behavioral control is a hallmark of decision-making–related disorders, including addiction. Although external globus pallidus (GPe) is critical for action selection, which harbors enriched astrocytes, the role of GPe astrocytes involved in action-selection strategies remained unknown. Using in vivo calcium signaling with fiber photometry, we found substantially attenuated GPe astrocytic activity during habitual learning compared to goal-directed learning. The support vector machine analysis predicted the behavioral outcomes. Chemogenetic activation of the astrocytes or inhibition of GPe pan-neuronal activities facilitates the transition from habit to goal-directed reward-seeking behavior. Next, we found increased astrocyte-specific GABA (γ-aminobutyric acid) transporter type 3 (GAT3) messenger RNA expression during habit learning. Notably, the pharmacological inhibition of GAT3 occluded astrocyte activation–induced transition from habitual to goal-directed behavior. On the other hand, attentional stimuli shifted the habit to goal-directed behaviors. Our findings suggest that the GPe astrocytes regulate the action selection strategy and behavioral flexibility.