Citations

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.

Abstract Genetic studies have significantly advanced our understanding of tauopathies, yet the genetic aetiology of Pick’s disease, a rare 3-Repeat tauopathy, remains unclear. The MAPT p.A152T variant has been identified as a risk factor for Alzheimer’s disease and progressive supranuclear palsy, but its role in Pick’s disease is unknown. In this study, we examined the prevalence of MAPT p.A152T in the largest series of neuropathologically confirmed Pick’s disease cases to date (n = 401). Through genotyping, we identified a single mutation carrier in the Pick’s disease cohort (minor allele frequency = 0.12%). We previously reported MAPT p.A152T at a 0.20% frequency in healthy controls (n = 2456), suggesting that it does not associate with 3-Repeat tauopathy risk. To further investigate the effect of the variant on MAPT transcript expression, we used bulk RNA sequencing in Alzheimer’s disease and progressive supranuclear palsy A152T mutation carriers. We did not detect significant differences in 4-Repeat tau levels, though preliminary trends may indicate more nuanced effects that need to be examined with long-read sequencing in a larger series. Overall, our study suggests that MAPT p.A152T does not increase Pick’s disease risk and may instead be linked to 4-Repeat or mixed tau pathologies, warranting further functional investigation.

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.

Abstract Background. Pain drives disability and medication use in autoimmune rheumatic diseases (ARDs), even when inflammation is controlled. The extent of pain-related polypharmacy across ARDs and chronic overlapping pain conditions (COPCs) remains poorly characterized. We aimed to quantify first-year pain medication burden following ARD diagnosis and examine variations by disease, COPC status, and time. Methods. Using the Merative MarketScan Commercial Claims database, we identified adults newly diagnosed with rheumatoid arthritis, systemic lupus erythematosus, ankylosing spondylitis, psoriatic arthritis, Sjögren’s disease, or systemic sclerosis (2008–2021). We analyzed pain-related medications across ten pharmacologic categories during the first year post-diagnosis. Polypharmacy measures included distinct medication counts, medication categories, ≥ 5 and ≥ 10 medication thresholds, and the Medication Quantification Scale. Findings. Among 149,742 adults with newly diagnosed ARDs, 57.6% had at least one COPC. During the first year after diagnosis, patients filled a mean of 9.0 distinct pain medications and 47.9% met criteria for ≥ 5 medications; 30.5% met criteria for ≥ 10 medications. Ankylosing spondylitis demonstrated the highest medication burden (mean 11.6 medications; 57.5% with ≥ 5 medications; 37.9% with ≥ 10 medications). Patients with fibromyalgia had consistently higher medication burden across ARDs and were the only COPC group in whom prescribing increased after 2015. Overall medication burden rose from 2008 to 2014–2015 and declined thereafter; however, absolute levels remained high. Interpretation. Pain-related polypharmacy is common early after ARD diagnosis and varies substantially by disease and pain phenotype. Despite modest declines after 2015, medication burden remains high, underscoring the need for phenotype-informed, nonpharmacologic, and deprescribing strategies in rheumatology. Funding. NIAMS (K01AR079039).

Abstract Background Colony stimulating factor 1 receptor–related disorder (CSF1R‐RD) is a rare, rapidly progressive neurodegenerative disease with significant clinical heterogeneity. Apolipoprotein E (ApoE) polymorphism, known to modulate microglial function and influence neurodegeneration, may act as a genetic modifier in CSF1R‐RD. Objectives The objectives were to evaluate the distribution of ApoE alleles in CSF1R‐RD and assess their association with clinical and neuropathological features. Methods ApoE genotyping was performed in 55 individuals with CSF1R‐RD. Clinical data from 25 deceased patients were analyzed based on ApoE genotype. Neuropathological evaluation was conducted on brain tissue from 14 patients, with semiquantitative scoring of white matter pathology and microglial burden. Statistical comparisons were made between carriers and noncarriers of the ApoE4 allele. Results ApoE allele frequencies in CSF1R‐RD mirrored those of the general population (ε2: 7.3%, ε3: 79.1%, ε4: 13.6%). However, ApoE4 carriers exhibited significantly earlier symptom onset (median: 37.9 vs. 50.3 years, p = 0.0123) and death (median: 41.1 vs. 55.2 years, p = 0.0072). Neuropathological analysis revealed more severe white matter involvement and reduced microglial preservation in ApoE4 carriers ( p = 0.034). Conclusions Although ApoE allele distribution does not differ from the general population, the presence of the ApoE4 allele may influence the clinical trajectory and white matter pathology in CSF1R‐RD. These findings suggest that ApoE polymorphism is a potential modifier of disease course and should be considered in therapeutic planning and future research. © 2026 International Parkinson and Movement Disorder Society.

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.