Annals of Neurology

BackgroundMechanisms underlying the suboptimal effect of ventral intermediate nucleus deep brain stimulation in patients with essential tremor and co-morbid peripheral neuropathy remain unclear. ObjectivesWe compared disease-related (location and extension of the ventral intermediate nucleus) and surgery-related (targeting, intraoperative testing) factors in essential tremor patients with and without peripheral polyneuropathy treated with deep brain stimulation of the ventral intermediate nucleus, testing whether the overlap between volume of tissue activated and ventral intermediate nucleus (target coverage) was associated with clinical outcomes. MethodsPreoperative diffusion magnetic resonance imaging was used for thalamic segmentation, based on preferential cortical connectivity. The target coverage was estimated using a finite element model. Tremor severity was scored at rest, posture, action, and handwriting at baseline, 6, and 12 months. Tremor improvement <50% at 12 months was deemed suboptimal. Vertex-wise shape analysis and edge analysis were performed to compare the ventral intermediate nucleus location and extension. Results9.7% (18/185) of essential tremor patients treated with deep brain stimulation had co-morbid polyneuropathy. These patients showed a more medial (p=0.03) and anterior (p=0.04) location of the ventral intermediate nucleus, lower target coverage (p=0.049), and worse clinical outcomes (p=0.006) compared to those without polyneuropathy. No differences were observed in the volume of tissue activated between the two groups. Optimal clinical outcomes were associated with greater target coverage (optimal coverage >48%). ConclusionsIn essential tremor, co-morbid polyneuropathy may result in suboptimal deep brain stimulation outcomes and lower target coverage, likely related to a reorganization of the ventral thalamic nuclei.

Background and ObjectivesIsolated/idiopathic REM sleep behavior disorder (iRBD) and Lewy body dementia (LBD) are synucleinopathies that have partial genetic overlap with Parkinsons disease (PD). Previous studies have shown that neuroinflammation plays a substantial role in these disorders. In PD, specific residues of the human leukocyte antigen (HLA) were suggested to be associated with a protective effect. This study examined whether the HLA locus plays a similar role in iRBD, LBD and PD. MethodsWe performed HLA imputation on iRBD genotyping data (1,072 patients and 9,505 controls) and LBD whole-genome sequencing (2,604 patients and 4,032 controls) using the multi-ethnic HLA reference panel v2 from the Michigan Imputation Server. Using logistic regression, we tested the association of HLA alleles, amino acids and haplotypes with disease susceptibility. We included age, sex and the top 10 principal components as covariates. We also performed an omnibus test to examine which HLA residue positions explain the most variance. ResultsIn iRBD, HLA-DRB1*11:01 was the only allele passing FDR correction (OR=1.57, 95% CI=1.27-1.93, p=2.70e-05). We also discovered associations between iRBD and HLA-DRB1 70D (OR=1.26, 95%CI=1.12-1.41, p=8.76e-05), 70Q (OR=0.81, 95% CI=0.72-0.91, p=3.65e-04) and 71R (OR=1.21, 95% CI=1.08-1.35, p=1.35e-03). In HLA-DRB1, position 71 (pomnibus=0.00102) and 70 (pomnibus=0.00125) were associated with iRBD. We found no association in LBD. DiscussionThis study identified an association between HLA-DRB1 11:01 and iRBD, distinct from the previously reported association in PD. Therefore, the HLA locus may play different roles across synucleinopathies. Additional studies are required better to understand HLAs role in iRBD and LBD.

BackgroundSensitive outcome measures are critical for evaluating the efficacy of novel treatments for Parkinson disease (PD). Recently, we demonstrated that a tremor detection algorithm could reliably detect and quantify real-life PD tremor from wearable sensor data. Here, we assess the sensitivity to change of sensor-derived daily-life tremor measures over two years in an unmedicated and medicated cohort of persons with early PD. MethodsWe used two-year continuous wrist sensor data (median wear time: 22 hours/day) from the Personalized Parkinson Project (n=462 medicated; n=78 unmedicated at baseline), in combination with annual clinical evaluations of tremor severity. From the raw gyroscope data, we derived previously validated weekly measures for tremor time and power, which were smoothed over time using piecewise linear trend estimation. One- and two-year standardized response means (SRMs) were computed to compare the sensitivity to change between the sensor-derived tremor measures and clinical tremor scores. FindingsIn unmedicated participants with tremor, sensor-derived tremor measures demonstrated a high sensitivity to progression (two-year SRMs ranged from 0.67 to 1.09), which was significantly larger than clinical tremor scores (two-year SRMs ranged from 0.21 to 0.41). In medicated participants, sensor-derived tremor time decreased (two-year SRM of -0.18 in participants with tremor), which was associated with both an increase in dopaminergic medication dose and higher disease duration. In contrast, the sensor-derived tremor power measures and clinical rest tremor scores (measured in the OFF state) increased slightly (two-year SRMs ranging from 0.11 to 0.27). InterpretationPrior to initiation of symptomatic treatment, sensor-derived daily-life tremor measures are substantially more sensitive to disease progression than clinical tremor scores, making them a promising tool to evaluate the efficacy of disease-modifying treatments in early PD.

BackgroundUse of digital sensors to passively collect long-term offers a step change in our ability to screen for early signs of disease in the general population. Smartwatch data has been shown to identify Parkinsons disease (PD) several years before the clinical diagnosis, however, has not been evaluated in comparison to biological and pathological markers such as dopaminergic imaging (DaTscan) or cerebrospinal fluid (CSF) alpha-synuclein seed amplification assay (SAA) in an at-risk cohort. MethodsTo address this, we performed a cohort study using longitudinal clinical assessment data from the Parkinsons Progression Marker Initiative (PPMI) cohort collected between 2010 and 2020 with additional long-term (mean: 485 days) at-home digital monitoring data (collected 2018-2020) from the Verily Study Watch. We derived a digital risk score and evaluated it in an at-risk cohort (N = 109) consisting of people with genetic markers (LRRK2, GBA) or prodromal symptoms (hyposmia, polysomnography-proven Rapid-Eye-Movement behavioral sleep disorder) without a diagnosis of PD for whom all modalities were available (digital, DaTscan, SAA). The digital risk score was compared to the Movement Disorder Society (MDS) research criteria for prodromal PD, alpha-synuclein SAA and DaTscan. FindingsIn the at-risk cohort (N=109, mean age = 64.62{+/-}6.86, 37% male), the digital risk correlated with the MDS research criteria for prodromal PD (r = 0.36, p-value = 1.46x10-4) and was increased in individuals with subthreshold Parkinsonism (UPDRS III > 6) (p-value = 4.99x10-6) and hyposmia (p-value = 3.77x10-2). Notably, the digital risk was correlated with DaTscan putamen binding ratio (r = -0.32, p-value = 6.64x10-4) and CSF SAA (r = 0.2, p-value = 3.9x10-2). The digital risk achieved higher sensitivity in identifying people with SAA positivity (0.71 vs 0.43) or DaTscan positivity (0.43 vs 0.14) than the MDS prodromal score but performed on-par or worse than hyposmia (SAA+: 0.71 vs 0.71, DaT+: 0.48 vs 0.57). InterpretationA digital risk score from smartwatch data could be used as a sensitive screening tool for early detection of PD followed by more specific tests.

BackgroundSleep-wake and circadian disturbances are increasingly recognised in people living with amyotrophic lateral sclerosis (plwALS), but endogenous circadian phase timing and its prognostic significance in early disease remain unclear. We assessed whether salivary dim-light melatonin onset (DLMO), an objective marker of central circadian phase, is altered in early plwALS and whether it provides prognostic information. MethodsIn this prospective longitudinal observational study, plwALS within 18 months of symptom onset underwent home-based salivary melatonin sampling under dim-light conditions at six predefined time points around habitual sleep onset (HSO). Melatonin profiles were modeled using cubic smoothing splines, and DLMO was defined as the first time the fitted curve reached 3 pg/mL. Clinical, respiratory, and sleep assessments were collected at baseline (T0) and after 6 months (T6); a subgroup repeated saliva sampling at T6. Age- and sex-matched controls underwent melatonin profiling. Associations with disease progression, incident respiratory symptoms, and survival/tracheostomy were examined using regressions and survival analyses. ResultsFifty plwALS were enrolled. Compared with controls, plwALS showed an earlier DLMO (20:24{+/-}1:18 vs 20:58{+/-}0:50; p=0.028) despite similar HSO and chronotype. Within ALS cohort, a later baseline DLMO correlated with worse functional/motor status, faster progression of disease, incident dyspnea/orthopnea by T6 (adjusted OR 3.02; p=0.017), and poorer survival/tracheostomy-free outcome. In re-sampled subgroup (n=28), DLMO and other melatonin-derived metrics did not change over [~]6 months. ConclusionsCircadian phase alterations are detectable in early-ALS. Baseline DLMO may represent a non-invasive prognostic biomarker for progression, respiratory symptom emergence and survival, warranting validation in larger multicentre cohorts. Key messagesO_ST_ABSWhat is already known on this topicC_ST_ABSSleep and circadian disturbances are increasingly recognised as early, biologically relevant non-motor features of amyotrophic lateral sclerosis (ALS), and recent translational and neuroimaging studies support early involvement of sleep-regulatory and hypothalamic networks. Dim-light melatonin onset (DLMO) is an established objective marker of central circadian phase, but endogenous melatonin timing in ALS and its prognostic relevance have not been previously defined. What this study addsIn a prospective cohort of patients with early-ALS, salivary DLMO was altered relative to matched controls, and within the ALS cohort a later baseline DLMO was associated with worse functional and motor status, faster subsequent progression, incident respiratory symptoms at 6 months, and poorer survival/tracheostomy-free outcome. These findings identify circadian phase timing as a clinically informative signal in early-ALS. How this study might affect research, practice or policyIf validated, DLMO could complement established prognostic tools in early-ALS and support enrichment of phase-aware clinical trials. They also provide a rationale for phase-aware longitudinal studies integrating circadian phenotyping, respiratory, imaging, and plasmatic biomarker and for testing whether interventions targeting circadian alignment can improve symptoms or clinical trajectories in selected patients with ALS.

Deep brain stimulation is an effective treatment for Parkinsons disease, but clinical programming remains subjective and time-consuming. Neurophysiological biomarkers may offer an objective and scalable approach to guide stimulation settings. To examine whether cortical oscillations--particularly in alpha, beta, and narrowband gamma bands--reflect deep brain stimulation parameter changes and their dependency on the stimulating contact location. Thirteen Parkinsons patients with subthalamic deep brain stimulation (21 hemispheres) underwent electroencephalography during routine programming sessions. Arm-task segments across multiple stimulation amplitudes were analysed. Alpha (8-12 Hz), beta (13-30 Hz), and narrowband gamma (60-90 Hz) power and burst features were extracted from the ipsilateral motor cortex. Volumes of tissue activated were computed and overlapped with the motor subthalamic nucleus to assess stimulation targeting. Relationships between neurophysiological features, stimulation amplitude, motor subthalamic-nucleus overlap, and active contact location were evaluated. Cortical alpha burst amplitude and duration significantly decreased with stimulation amplitude--but only when active contacts were located within the motor subthalamic-nucleus. Cortical beta-band features showed no significant modulation across amplitudes or locations. Cortical narrowband gamma power and burst rate increased with stimulation amplitude, especially when stimulation overlapped with the motor subthalamic-nucleus, though effects were less spatially specific than for alpha. Cortical alpha and narrowband gamma oscillations provide sensitive and complementary physiomarkers of deep brain stimulation parameter change. Alpha dynamics reflect spatially precise stimulation within the motor subthalamic-nucleus, while narrowband gamma scales with amplitude. These features may support EEG-guided programming and future adaptive deep brain stimulation strategies.

BackgroundDeep brain stimulation (DBS) targets grey matter structures for most clinical indications, such as the thalamic ventral intermediate nucleus (VIM) to treat essential tremor (ET). Alternatively, white matter tracts like the dentatorubrothalamic tract (DRTT) in ET have been suggested to be the actual effector sites of DBS. A direct link between excitation of myelinated fibers and clinically relevant behavior, however, is missing. Here, we retrospectively analyze clinical measurements in patients assessed for VIM-DBS to test the hypothesis that tremor suppression is directly related to the fraction of DRTT-fibers recruited by DBS. MethodsTremor intensity was accelerometrically quantified at 100 different electrode contacts in 15 patients, while stimulation amplitude was systematically varied. Contact positions were located by stereotactic x-ray imaging. We determined the fraction of fibers recruited within the range of effective DBS-spread by diffusion tensor imaging (DTI) and probabilistic fiber tracking. ResultsUtilizing regression analysis, we found that the fraction of activated DRTT-fibers was linearly related to tremor suppression (F(1,592) = 451.55, p < 0.001) with a slope of 1.02 (95% confidence interval [0.93, 1.12]), i.e., relative tremor suppression matched identically the fraction of recruited DRTT-fibers. ConclusionOur results show that tremor suppression by DBS is causally related to the recruitment of DRTT-fibers and that clinically relevant behavioral effects of DBS can be already predicted from fiber densities pre-operatively. Our analysis approach would enable retrospective identification of DBS effector sites in neuropsychiatric diseases, as well as personalized prospective planning of DBS, substantially reducing intra- and post-operative clinical testing time. What is already known on this topicPrevious studies have demonstrated correlations between clinical outcome in essential tremor suppression by DBS and electrode contact distance to the DRTT. In order to prove that the DRTT is the actual effector site of DBS a direct, a quantitative link between excitation of DRTT fibers and tremor suppression is required. What this study addsOur study shows that the percent tremor suppression identically matches the fraction of DRTT-fibers recruited by DBS up to a constant offset demonstrating a causal link between tremor suppression and DRTT excitation. How this study might affect research, practice or policyOur finding solves a long standing dispute and paves the way for novel network interventions through deep brain stimulation. Our analysis approach further paves the way for novel connectomic DBS-targeting strategies. It would allow for personalized prospective planning of DBS substantially reducing intra- and post-operative clinical testing time. It could also be key for the retrospective identification of novel effector sites among candidate sites in various neuropsychiatric diseases.

Background Childhood neurodegenerative disorders are usually rare, genetic, and life-limiting. Whilst targeted approaches present huge potential, significant hurdles include disease rarity, geographical dispersion of patients, funding, clinical trial design, and execution. Crucially, the paucity of robust biomarkers and objective measures of disease progression hampers evaluation of efficacy, drug development and regulatory approval. To address this paradigm, we developed a Multimodal Multiomics Machine Learning (MMM) framework, integrating large-scale, multi-source patient datasets to generate quantitative metrics for disease stratification and longitudinal tracking. We applied MMM to PLA2G6-associated neurodegeneration (PLAN), an ultra-rare condition currently lacking validated biomarkers, where precision gene therapy approaches are at an advanced preclinical stage. Methods A large, single time-point international natural history study (n = 310) was conducted alongside development of a disease-specific rating scale (CoPLAN-DRS), prospective longitudinal neuroimaging, and multiomic biomarker discovery. Machine learning methods were applied to the integrated dataset. Results Kaplan-Meier analyses enabled estimates for survival and time to loss of ambulation. Multiple clinical, radiological, and biofluid biomarkers were identified, clearly correlating with disease progression. The CoPLAN-DRS and brain MRI Quantitative Susceptibility Mapping showed strong positive correlation with age (rho = 0.69, 0.96 respectively). Nicastrin, a critical structural component of the gamma-secretase complex in Amyloid Precursor Protein (APP) processing, was identified as a novel biomarker. Neurofilament light levels showed strong negative correlation with disease progression (rho = -0.74). The complex multi-dimensional dataset was distilled into a simplified, clinically intuitive Digital Disease Dashboard (DDD), enabling real-time visualisation of disease severity. Conclusions Our study highlights the clinical utility of MMM in integrating multi-dimensional data from rare disease cohorts, delivering an unbiased, data-driven, optimised biomarker set. Condensing this into the DDD provides a pragmatically useful tool for clinicians, facilitating longitudinal tracking of disease. The MMM and DDD have accelerated clinical-trial readiness for PLAN, and potentially applicable to a broad range of neurogenetic disorders.

Background and ObjectivesAutoimmune-associated epilepsy (AAE), a condition which responds favorably to immune therapies but not traditional anti-seizure interventions, is emerging as a significant contributor to cases of drug-resistant epilepsy. Current standards for the diagnosis of AAE rely on screening for known neuronal autoantibodies in patient serum or cerebrospinal fluid. However, this diagnostic method fails to capture a subset of drug-resistant epilepsy patients with suspected AAE who respond to immunotherapy yet remain seronegative (snAAE) for known autoantibodies. MethodsTo identify potential biomarkers for snAAE, we evaluated the most comprehensive panel of assayed cytokines and autoantibodies to date, comparing patients with snAAE, anti-seizure medication (ASM) responsive epilepsy, and patients with other neuroinflammatory diseases. ResultsWe found a unique signature of 14 cytokines significantly elevated in snAAE patients including: GM-CSF, MCP-2/CCL8, MIP-1a/CCL3, IL-1RA, IL-6, IL-8, IL-9, IL-10, IL-15, IL-20, VEGF-A, TNF-b, LIF, and TSLP. Based on prior literature, we highlight IL-6, IL-8, IL-10, IL-13, VEGF-A, and TNF-b as potentially actionable cytokine biomarkers for snAAE, which could be of diagnostic utility in clinical evaluations of snAAE patients. Autoantibody-ome screening failed to identify autoantibodies targeting neuronal channel proteins in snAAE patients. Interestingly, ASM-responsive epilepsy patients displayed elevations in the proportion of autoantibodies targeting brain plasma membrane proteins, possibly pointing to the presence of immune hyperactivity/dysfunction despite well-controlled seizure activity and suggesting ASM-responsive patients may experience disease progression independent of seizure activity (PISA). DiscussionOverall, our findings suggest that simply expanding existing autoantibody screens may not sufficiently enhance diagnostic power for snAAE. Instead, we propose that cytokine analysis may serve as a promising diagnostic avenue for identifying immune dysregulation in AAE patients and enabling opportunities for trials of immunotherapies.

ObjectiveTo characterize DNA methylation differences between sporadic Parkinsons Disease and healthy control individuals enrolled in the Parkinsons Progression Markers Initiative. MethodsWe characterized cross-sectional and longitudinal DNA methylation differences between individuals with sporadic (i.e., non-genetic) PD and healthy controls. We included 282 individuals (196 Parkinsons Disease individuals and 86 healthy control individuals). DNA methylation data was collected at the time of enrollment and longitudinally over three years. ResultsThis analysis revealed 81,604 differentially methylated positions and 5,281 differentially methylated regions between sporadic PD and healthy controls. Gene ontology analysis revealed that these differentially methylated positions and regions were associated with genes involved in diverse cellular processes, including several with specific functions in the brain (Focal adhesion", "Cholinergic synapse", "Glutamatergic synapse", "Dopaminergic synapse"). Integration of both differentially methylated sites and expressed genes showed 20 genes that were hypomethylated and overexpressed and one gene, CTSH that was hypermethylated and associated with reduced expression. Interpretation of ResultsOur study provides evidence that alterations in the methylome in Parkinsons Disease are discernible in blood, evolve over time, and reflect cellular processes linked to ongoing neurodegeneration. These findings lend support to the potential of blood DNA methylation as an epigenetic biomarker for Parkinsons Disease. To fully comprehend DNA methylation changes throughout the progression of Parkinsons Disease, additional profiling at longer intervals and during the prodromal stage will be necessary.

The biological mechanisms giving rise to the extreme symptoms exhibited by rare disease patients are complex, heterogenous, and difficult to discern. Understanding these mechanisms is critical for developing treatments that address the underlying causes of diseases rather than merely the presenting symptoms. Moreover, the same dysfunctional biological mechanisms implicated in rare recessive diseases may also lead to milder and potentially preventable symptoms in carriers in the general population. Seizures are a common, extreme phenotype that can result from diverse and often elusive biological pathways in patients with ultrarare or undiagnosed disorders. In this pilot study, we present an approach to understand the biological pathways leading to seizures in patients from the Undiagnosed Diseases Network (UDN) by analyzing aggregated genotype and phenotype data from the UK Biobank (UKB). Specifically, we look for enriched phenotypes across UKB participants who harbor rare variants in the same gene known or suspected to be causally implicated in a UDN patients recessively manifesting disorder. Analyzing these milder but related associated phenotypes in UKB participants can provide insight into the disease-causing molecular mechanisms at play in the rare disease UDN patient. We present six vignettes of undiagnosed patients experiencing seizures as part of their recessive genetic condition, and we discuss the potential mechanisms underlying the spectrum of symptoms associated with UKB participants to the severe presentations exhibited by UDN patients. We find that in our set of rare disease patients, seizures may result from diverse, multi-step pathways that involve multiple body systems. Analyses of large-scale population cohorts such as the UKB can be a critical tool to further our understanding of rare diseases in general.

BackgroundProgression independent of relapses (PIRA) is a major therapeutic challenge in multiple sclerosis (MS). Nasal anti-CD3 treats animal models of progressive MS by inducing regulatory T cells (Tregs) that suppress central nervous system (CNS) inflammation and lessen clinical disease. MethodsTen patients with non-active secondary progressive MS (naSPMS) that continued to progress on B cell therapy were treated with nasal anti-CD3 (foralumab) for a minimum of six months in an open label study. Safety monitoring included otolaryngology evaluation and neurologic assessments including Expanded Disability Status Scale (EDSS), Multiple Sclerosis Functional Composite (MSFC-4), Modified Fatigue Impact Scale (MFIS), California Verbal Learning Test (CVLT-II) and Low Contract Visual Acuity (LCVA). MRI and microglial translocator protein (TSPO)-PET imaging with [F-18]PBR06 were conducted. Serum and cerebrospinal fluid (CSF) proteomic biomarkers and single cell RNA sequencing of blood was performed to evaluate foralumab-induced immunomodulation. The endpoints of our study were safety, clinical effects, microglial signal and immune measures. ResultsAll patients stabilized on EDSS scores and three of four patients treated continuously for 12 months had improvement on EDSS. Six of 10 patients had improvement in fatigue on the MFIS scale. There were no treatment-related serious adverse events (SAEs) or severe AEs and no new T2 lesions were observed on MRI. There was a reduction in TSPO-PET signal over six months (p<0.05). Changes in peripheral blood gene expression occurred as early as three months and affected antigen presentation, interferon responses and regulatory pathways in multiple cell types including FoxP3+ Tregs, CD4+ Tcm cells, CD8+ Tem cells, CD14+ and CD16+ monocytes and B cells. TGF{beta} expression was increased across cell multiple subsets. InterpretationThese findings identify a novel, non-toxic immune based therapy for the treatment for PIRA that acts by the induction of a regulatory immune responses and dampens microglial inflammation. Double blind placebo-controlled trials are warranted to explore nasal foralumab for the treatment of naSPMS.

BackgroundGlutamatergic neuron-glioma synaptogenesis and peritumoral hyperexcitability promote glioma growth in a positive feedback loop. The objective of this study was to evaluate the feasibility and estimated effect sizes of the AMPA-R antagonist, perampanel, on intraoperative electrophysiologic hyperexcitability and clinical outcomes. MethodsAn open-label trial was performed comparing perampanel to standard of care (SOC) in patients undergoing resection of newly-diagnosed radiologic high-grade glioma. Perampanel was administered as a pre-operative loading dose followed by maintenance therapy until progressive disease or up to 12-months. SOC treatment involved levetiracetam for 7-days or as clinically indicated. The primary outcome of hyperexcitability was defined by intra-operative electrocorticography high frequency oscillation (HFO) rates. Seizure-freedom and overall survival (OS) were estimated by the Kaplan-Meier method. Tissue concentrations of perampanel, levetiracetam, and metabolites were measured by mass spectrometry. ResultsHFO rates were similar between perampanel-treated and SOC cohorts. The trial was terminated early after interim analysis for futility, and outcomes assessed in 11 patients (7 perampanel-treated, 4 SOC). Over a median 281 days of post-enrollment follow-up, 27% of patients had seizures, including 14% treated with perampanel and 50% treated with SOC. OS in perampanel-treated patients was similar to a glioblastoma reference cohort (p=0.81). Glutamate concentrations in surface biopsies were positively correlated with HFO rates in adjacent electrode contacts and were not significantly associated with treatment assignment or drug concentrations. ConclusionsA peri-operative loading regimen of perampanel was safe and well-tolerated, with similar peritumoral hyperexcitability as in levetiracetam-treated patients. Maintenance anti-glutamatergic therapy was not observed to impact survival outcomes.

ObjectiveTo evaluate how within-person changes in accelerometry-derived activity patterns translate to brain atrophy and disability worsening in people with multiple sclerosis (PwMS). MethodsWe included PwMS aged [&ge;]40 years with approximately annual brain MRI who wore GT9X Actigraph accelerometers every three months over three years. Accelerometry-derived indices included total and 2-hour specific activity, sedentary time, and circadian rhythm parameters. Confirmed disability worsening was characterized using the composite Expanded Disability Status Scale-plus (EDSS+) and whole brain segmentation used SLANT-CRUISE. We modeled within- and between-person effects using Cox (for EDSS+) and linear mixed effects (for MRI) multivariable-adjusted models adjusted for age, sex, and body mass index that included only accelerometry measures obtained prior to EDSS+. ResultsAmong 239 PwMS (mean age 54.8, 29% male), 120 experienced EDSS+-confirmed progression over a mean 2.9 years (SD: 1.1 years). Participants wore accelerometers an average of 7.4 times over 67 days. Total activity declined an average of 48,694 activity counts (~0.10 SD per year; 95% CI: -33092, -64297; p=1.21x10-9), and people with progressive MS exhibited more pronounced declines in total activity relative to RRMS (p=2.34x10-5). Within-person decreases in daytime activity (particularly 8:00-14:00) were significantly associated with higher risk of EDSS+. For example, a 1 SD decrease in within-person (individual-level) activity from 8:00-10:00, 10:00-12:00 and 12:00-14:00 was associated with a respective 1.20 (95% CI: 1.04, 1.39; p=0.01), 1.22 (95% CI: 1.04, 1.41; p=0.008), and 1.23 (95% CI: 1.07, 1.42; p=0.007) higher risk of confirmed disability progression by EDSS+. MRI also models demonstrated that within-person declines in morning activity (8:00-10:00) were associated with greater whole brain, deep gray matter and thalamic volume loss (for whole brain -0.16%; 95% CI: -0.29, -0.04; p=0.009; deep gray: -0.32%; 95% CI: -0.13, -0.51; p=0.0009; thalamic: -0.30%; 95% CI: -0.52, -0.08; p=0.007). Lower between-person mean MVPA was associated with lower brain volumes over time but was not associated with EDSS+. InterpretationWithin-person reductions in daytime activity patterns precede clinical disability worsening and brain atrophy in PwMS. Longitudinal accelerometry may offer sensitive, non-invasive biomarkers of subclinical disease progression in MS.

Neuroinflammation is a feature of many neurodegenerative diseases, and can be quantified in vivo by PET imaging with radioligands for the translocator protein (TSPO, e.g. [11C]-PK11195). TSPO radioligand binding correlates with clinical severity and predicts clinical progression. However, the cellular substrate of altered TSPO binding is controversial and requires neuropathological validation. We used progressive supranuclear palsy (PSP) as a demonstrator condition, to test the hypothesis that [11C]-PK11195 PET reflects microglial changes. We included people with PSP-Richardsons syndrome who had undergone [11C]-PK11195 PET in life. In post-mortem brain tissue from the same participants we characterised cell-type specific TSPO expression with double-immunofluorescence labelling and quantified microgliosis in eight cortical and eleven subcortical regions with CD68 immunohistochemistry. Double-immunofluorescence labelling for TSPO and cell markers showed TSPO expression in microglia, astrocytes, and endothelial cells. Microglial TSPO expression was higher in donors with PSP compared to controls, which was not the case for astrocytic TSPO expression. There was a significant positive correlation between regional [11C]-PK11195 binding potential ante-mortem and the density of post-mortem CD68+ phagocytic microglia, as well as microglial TSPO expression. We conclude that [11C]-PK11195 binding in vivo is driven by microglia and can be interpreted as a biomarker of microglia-mediated neuroinflammation in tauopathies.

Common and rare variants in the LRRK2 locus are associated with Parkinsons disease (PD) risk, but the downstream effects of these variants on protein levels remains unknown. We performed comprehensive proteogenomic analyses using the largest aptamer-based CSF proteomics study to date (7,006 aptamers (6,138 unique proteins) in 3,107 individuals). We identified eleven independent SNPs in the LRRK2 locus associated with the levels of 26 proteins as well as PD risk. Of these, only eleven proteins have been previously associated with PD risk (e.g., GRN or GPNMB). Proteome-wide association study (PWAS) analyses suggested that the levels of ten of those proteins were genetically correlated with PD risk and seven were validated in the PPMI cohort. Mendelian randomization analyses identified five proteins (GPNMB, GRN, HLA-DQA2, LCT, and CD68) causal for PD and nominate one more (ITGB2). These 26 proteins were enriched for microglia-specific proteins and trafficking pathways (both lysosome and intracellular). This study not only demonstrates that protein phenome-wide association studies (PheWAS) and trans-protein quantitative trail loci (pQTL) analyses are powerful for identifying novel protein interactions in an unbiased manner, but also that LRRK2 is linked with the regulation of PD-associated proteins that are enriched in microglial cells and specific lysosomal pathways.

ImportanceSeizures significantly impact outcomes after stroke, underscoring the need for accurate predictors of post-stroke epilepsy. ObjectiveTo evaluate whether electrographic biomarkers detected early after acute ischemic stroke enhance the prediction of post-stroke epilepsy. DesignMulticenter cohort study with data collected from 2002 to 2022 and final data analysis completed in July 2024. SettingEleven international cohorts from tertiary referral centers, six with available EEG data. Participants1,105 stroke survivors with neuroimaging-confirmed ischemic stroke (mean age 71, 54% male) who underwent EEG within the first 7 days post-stroke. ExposurePresence of electrographic biomarkers detected through EEG. Main Outcome and MeasuresOccurrence of post-stroke epilepsy. The impact of electrographic biomarkers on the risk of post-stroke epilepsy was assessed using Cox proportional hazards regression, adjusted through inverse probability weighting. ResultsAmong 1,105 participants, 119 (11%) developed post-stroke seizures. Epileptiform activity (lateralized periodic discharges, interictal epileptiform discharges, and electrographic seizures; (odds ratio [OR] 2.0, 95% confidence interval [CI]: 1.3-3.0, p=0.001)) and regional slowing (OR 1.9, 95% CI: 1.2-2.9, p=0.004) were independently associated with developing post-stroke epilepsy. The novel SeLECT-EEG prognostic model, specifically developed for stroke survivors without acute symptomatic seizures (ASyS),, outperformed the previous gold-standard model (SeLECT2.0; 0.71 [95% CI: 0.65-0.76]) with a concordance statistic of 0.75 (95% CI: 0.71-0.80; p < 0.001). Conclusions and RelevanceElectrographic findings significantly enhance the prediction of post-stroke epilepsy beyond previously known clinical risk factors and may serve as prognostic biomarkers. The integration of these biomarkers into the SeLECT-EEG model in patients without acute symptomatic seizures provides a more accurate prognostic tool for early post-stroke epilepsy prediction. Key pointsO_ST_ABSQuestionC_ST_ABSCan early detection of electrographic biomarkers after acute ischemic stroke improve the prediction of post-stroke epilepsy? FindingsAmong 1,105 stroke survivors who received early EEG ([&le;] 7 days after stroke), post-stroke seizures occurred in 119 (11%). Stroke survivors with epileptiform activity had a 42% risk (95% CI 30%-49%) of developing post-stroke epilepsy 5 years after stroke, compared to a 13% risk (95% CI 9%-16%) in those without. Additionally, the 5-year risk of post-stroke epilepsy was twice as high in those with regional slowing (24%, 95% CI 18%-29%) compared to those without it (11%, 95% CI 5%-15%). Beyond known clinical risk factors, epileptiform activity and regional slowing were independently associated with developing post-stroke epilepsy. We integrated these findings into a novel prognostic model (SeLECT-EEG; concordance statistic 0.75 [95% CI: 0.71-0.80]), which outperformed the previous gold-standard model (SeLECT2.0; concordance statistic 0.71 [95% CI: 0.65-0.76]; p < 0.001). MeaningEarly electrographic biomarkers improve the prediction of post-stroke epilepsy and may inform counseling and management strategies for stroke survivors at risk of seizures.

Background and ObjectivesPost-ischemic stroke epilepsy (PISE) reduces quality of life, and early risk prediction can guide prevention strategies and anti-epileptogenesis treatment trials. Stroke severity predicts both PISE and mortality, and ignoring mortality can overestimate epilepsy risk. We sought to enhance PISE risk stratification by modeling death as a competing outcome, integrating quantitative clinical, neuroimaging, and electroencephalography (EEG) biomarkers to distinguish shared and distinct predictors of epilepsy and mortality. MethodsWe developed a PISE prediction model using retrospective data from Yale-New Haven Hospital. The training cohort included patients from 2014-2020; the testing cohort from 2021-2022. Eligible patients were adults with acute ischemic stroke who underwent neuroimaging and EEG monitoring <7 days post-stroke and had follow-up >7 days. ResultsOf 280 patients, 53 developed PISE first, 104 died first, and the rest were censored. Quantitative PISE biomarkers included greater 72h stroke severity (HR{Delta}3 [95%CI], 1.2 [1.1-1.4]), infarct volume (HR{Delta}10mL, 1.06 [1.04-1.08]), EEG epileptiform abnormality burden (HR{Delta}10%, 1.2 [1.1-1.3]), and EEG power asymmetries (HR{Delta}10%, 2.0 [1.4-2.9]). Death predictors included older age (HR{Delta}10years, 1.7 [1.4-2.0]), worse pre-stroke functional status (HR, 1.4 [1.2-1.7]), atrial fibrillation history (HR, 2.4 [1.6-3.7]), cardioembolism etiology (HR, 1.9 [1.2-3.0]), anterior cerebral artery involvement (HR, 2.2 [1.2-3.7]), and greater EEG global theta-band powers (HR{Delta}10{micro}V, 6.2 [2.3-17]). Our model, CRIMEPISE, integrating these features, allows prediction of PISE-first and death-first risk scores with AUC of 0.72 (95%CI, 0.60-0.83) and 0.79 (0.72-0.85), respectively. Compared with the benchmark SeLECT model, CRIMEPISE better predicted PISE in patients with [&ge;]4 SeLECT points (AUC, 0.72 vs 0.58) but not those with <4 points (AUC, 0.33 vs 0.52). In the testing cohort, CRIMEPISE identified a more selective group (n=18 vs 44 per SeLECT) with a higher PISE rate (39% vs 20%) and a lower mortality rate (22% vs 45%). DiscussionCRIMEPISE enhances PISE prediction by accounting for mortality as a competing outcome and incorporating multimodal quantitative biomarkers. Because its benefits over SeLECT are most pronounced in high-risk patients, a two-stage approach--SeLECT screening followed by CRIMEPISE in SeLECT-positive cases--may better target candidates for anti-epileptogenesis trials by prioritizing patients likely to survive long-term and develop epilepsy.

Background: Peri-lead edema (PLE) occurs in up to 15% of Deep Brain Stimulation (DBS) cases, can cause morbidity, and its etiology remains unknown. We hypothesized that PLE represents a secondary brain injury modulated by hypoxemia, and that patients with obstructive sleep apnea (OSA) are at elevated risk. Methods: We conducted a retrospective case-control study of 121 Parkinson's disease (PD) patients undergoing DBS at a single center (2019-2024). PLE severity was quantified by CT volumetric segmentation and Hounsfield unit (HU) measures. Perioperative SpO2 and PaO2 were recorded. Polysomnography (PSG) was available in 26 patients; and the REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ) was administered retrospectively. Results: Symptomatic PLE occurred in 12 patients (9.9%), with onset at 3.5 (2-9) days postoperatively. PLE patients had higher body mass index (p = 0.022) and higher OSA prevalence (75% vs. 30%; p = 0.002). Perioperative SpO2 was lower in the PLE group in both the operating room and post-anesthesia care unit (PACU; p < 0.05); PaO2 was lower in the PACU (p = 0.037). In the PSG subgroup, REM Sleep Behavior Disorder (RBD) incidence was lower in PLE patients (20% vs. 60%; unadjusted p = 0.048), and PLE severity correlated significantly with sleep-related hypoxemia and respiratory indices. RBDSQ scores were positively associated with edema density (normalized HU: rho = 0.86, p = 0.024). Conclusions: OSA and perioperative hypoxemia are associated with symptomatic PLE following DBS, while RBD appears protective. Preoperative sleep evaluation and optimized perioperative airway management warrant prospective investigation as PLE prevention strategies.

ObjectiveThe effects of deep brain stimulation (DBS) manifest across multiple timescales, spanning seconds to months, and involve direct electrical effects, neuroplasticity, and network reorganization. In epilepsy, the delayed impact of DBS on seizures presents challenges for optimization. Single-pulse stimulation and resulting brain stimulation evoked potentials (BSEPs) provide a means to assess effective connectivity and network excitability. This study integrates BSEPs and short trials of DBS during stereoelectroencephalography (sEEG) to map seizure network engagement, modulate network dynamics, and monitor excitability and interictal abnormalities, for biomarker informed neuromodulation. MethodsTen individuals with drug resistant epilepsy undergoing clinical sEEG were enrolled in this retrospective cohort study of epilepsy neuromodulation biomarkers. Each patient underwent a trial of high frequency (145 Hz) thalamic DBS. BSEPs were acquired before and after DBS trials. Baseline BSEP amplitude assessed seizure network engagement, and modulation of amplitude (pre vs. post DBS) assessed change in network excitability. Interictal epileptiform discharges were tracked by an automated classifier. ResultsBaseline BSEPs delineated distinct patterns of network engagement between thalamic subfields, with maximal frontotemporal engagement achieved with stimulation of the anterior nucleus of the thalamus-ventral anterior nucleus junction. DBS delivered for >1.5 hours reduced BSEP amplitudes compared to baseline, and the degree of modulation correlated with baseline connectivity strength. Shorter DBS trials did not induce reliable BSEP amplitude suppression, but did immediately suppress interictal epileptiform discharge rates in well-connected seizure networks. InterpretationBSEPs and trials of DBS during sEEG provide novel network biomarkers to evaluate the modulation of large-scale networks across multiple timescales, advancing biomarker informed neuromodulation.