Annals of Clinical and Translational Neurology

Background and Objectives: Deep sequencing of brain tissue in the research setting has established that mosaic variants are a major cause of malformations of cortical development (MCDs) and epilepsy. However, genetic testing in the clinical setting primarily detects germline variants using clinically accessible samples. We aimed to determine the diagnostic yield and clinical utility of deep sequencing in the clinical setting to identify pathogenic mosaic variants for this population. Methods: We performed a retrospective cohort analysis of individuals at Boston Children's Hospital with MCDs with or without epilepsy who received clinical deep sequencing between September 2017 and February 2026. Demographic, clinical, and genetic testing data were abstracted from the medical record. For individuals without systemic features, we classified brain tissue as an affected tissue sample. For individuals with systemic features, we classified brain or relevant non-brain tissue as affected. The primary outcome was the diagnostic yield of clinical deep sequencing performed using affected vs unaffected tissue samples. The secondary outcome was the clinical utility of genetic diagnoses. Results: Our cohort included 37 individuals (19/37 (51%) female, 18/37 (49%) male) with MCDs, of whom 35/37 (95%) had epilepsy (25 with brain tissue samples available from epilepsy surgery) and 8/37 (22%) had systemic features. Most (35/37 (95%)) had dysplasia phenotypes on MRI and 12/27 (44%) with pathology available had Focal Cortical Dysplasia Type I or II. The diagnostic yield was 53% (17/32; 16 mosaic and 1 germline variant) when clinical deep sequencing was performed using an affected tissue sample vs 0% (0/6) using an unaffected tissue sample (p=0.016). Of the diagnosed cases, 13/17 (76%) had testing performed on brain tissue (1 with systemic features) and 4/17 (24%) on non-brain tissue (3 buccal and 1 duodenal tissue, all with systemic features). All but one diagnosis involved the mTOR pathway. All diagnoses had clinical utility. Discussion: Clinical deep sequencing, when performed using an affected tissue sample, has high diagnostic yield and clinical utility for individuals with MCDs, especially dysplasia phenotypes, and epilepsy. Our findings support implementation of clinical deep sequencing for this population, especially as the genetic diagnoses have implications for emerging precision therapies.

Objective: In KCNQ2-related disorders (KCNQ2-RD), neurodevelopmental outcome remains variable despite established genotype-phenotype correlations. Our aim is to improve counselling, by developing and internally validating models predicting neurodevelopmental outcomes based on early clinical and genetic features, universally available to clinicians. Methods: We conducted a multicentric retrospective cohort study including 277 individuals carrying a (likely) pathogenic variant in the KCNQ2 gene, with a minimum follow-up age of three years. Mosaic variants were excluded. The cohort was randomly split into training (70%) and validation (30%) sets. Ten expert selected parameters with minimal missing data were used to train random forest models to predict (i) dichotomous outcomes and (ii) three-category outcomes for cognition, language, and gross motor milestones. Results: Models incorporated seven clinical (neonatal hypotonia, EEG characteristics, age at seizure onset, seizure type, and seizure frequency at onset, prematurity, and sex) and three genetic variables (de novo status, exon localisation, and position within known KCNQ2-developmental and epileptic encephalopathy (DEE) hotspot regions). Dichotomous models showed the highest predictive performance, with accuracies of 0.83 for normal vs. mild-profound intellectual disability (ID), 0.83 for achievement of first words, and 0.86 for achievement of independent walking. Three category models remained clinically informative: accuracies were 0.79 for normal vs. mild vs. moderate-profound ID, 0.70 for first words [≤]16 months vs. >16 months vs. never, and 0.71 for independent walking [≤]18 months vs. >18 months vs. never. The strongest predictors for adverse neurodevelopmental outcomes were presence of hypotonia at birth, seizure onset within the first day of life, multiple seizures per day at onset, tonic seizures at onset, a burst-suppression pattern on EEG at onset, the presence of a de novo variant, and variant location within exons 6-7. Significance: These prediction models demonstrate the feasibility of early prognostication in KCNQ2-RD and support future prospective external validation. They enable more accurate individualised counselling by integrating clinical and genetic information readily available at time of genetic diagnosis and provide an objective foundation for early intervention planning and future precision medicine trial stratification.

Background. Early diagnosis and intervention are crucial in multiple sclerosis (MS), yet diagnostic delays are common. Large language models (LLMs) such as generative pre-trained transformers (GPTs) may help streamline diagnostic workflows by extracting MS diagnostic signals from clinical notes. Objective. To derive MS diagnosis status from the first neurology note using a computable algorithm based on the 2017 McDonald criteria and applying GPT-4 for node-level reasoning within a structured decision framework. Methods. We analyzed first neurology notes from 125 randomly selected patients (including those with MS, related disorders, and controls) enrolled in a clinic cohort between 2017 and 2023. We included the clinical history and diagnostic testing sections but redacted the assessment and plan. We converted the 2017 McDonald criteria into a decision tree and provided expert-curated clinical knowledge to guide GPT-4 reasoning at each decision node. GPT-4 generated binary decisions at each node to traverse the tree and classified MS diagnoses at terminal nodes. We evaluated performance against neurologist-assessed diagnoses and characterized hallucinations (non-factual, incongruent, irrelevant, over-reliant, and logical reasoning errors). Results. In this study cohort (mean age 40{+/-}13 years; 81% women) representative of the clinic population, GPT-4 performed well in predicting MS diagnosis (84% accuracy, 79% precision, 74% recall, 91% specificity) using first neurology notes. Hallucinations occurred in 32 cases (26%), most commonly incoherence (75%) and overreliance (47%). Conclusion. A structured, LLM-guided decision framework can flag MS diagnoses from early clinical documentation. Large-scale studies are needed to mitigate hallucinations, validate this approach, and test implementation in clinical settings.

Background: Serum neurofilament light chain (NfL) indexes axonal injury and glial fibrillary acidic protein (GFAP) astrocytic pathology in multiple sclerosis (MS). GFAP rises disproportionately as relapsing-remitting MS (RRMS) shifts to progressive forms on research-grade SIMOA. The commercial Roche Elecsys ECLIA platform reads six-fold lower and is undescribed across subtypes. Objective: To describe both markers by MS subtype on ECLIA. Methods: Retrospective single-center analysis of 603 MS patients (2022-2026). NfL and GFAP were measured by LabCorp Roche Elecsys ECLIA; subtype came from ICD-10 codes and notes. We examined both markers by subtype, their correlation, and NfL against gadolinium-enhancing (Gd+) MRI lesions. Results: Median NfL was 1.32 pg/mL (IQR 1.01-1.91). Both rose with stage, steeper for GFAP: NfL 1.18 (RRMS), 1.54 (SPMS, p<0.001), 1.78 (PPMS, p=0.001); GFAP 41.90, 63.80 (p<0.0001), 75.75 (p=0.08, n=6). SPMS and PPMS GFAP did not differ (p=0.83). The markers correlated moderately (r=0.569). Of 34 Gd+ encounters with NfL within 30 days, 3 (9%) were elevated. Conclusion: On ECLIA, both markers rose with MS stage, GFAP more steeply, and both progressive subtypes exceeded RRMS. NfL rarely flagged a recent Gd+ lesion, consistent with its delayed kinetics. The two index distinct processes and reproduce on an orderable assay a profile once confined to research-grade SIMOA.

INTRODUCTION Myasthenia gravis (MG) and Lambert-Eaton myasthenic syndrome (LEMS) are autoimmune diseases of the neuromuscular junction resulting in fatigable muscle weakness. Rituximab (RTX) is used to treat patients refractory to standard immunosuppression, but evidence for its efficacy remains inconsistent. Here, we analyzed real-world data on the clinical course and side effects of RTX in MG and LEMS patients. METHODS This was a single-center study of all patients diagnosed with MG (n=64) or LEMS (n=5) treated with RTX from 2011 until 2021. Outcomes of RTX treatment were recorded retrospectively with Myasthenia Gravis Foundation of America Post-Intervention Status (MGFA-PIS), number of rescue therapies, myasthenic crises, and steroid dose at 1-year and 2-year follow-ups. RESULTS MGFA-PIS improved at both 1-year (y) and 2-y follow-up compared with baseline. Incidence rates of rescue therapies per 100 person-months (95% CI) decreased from 15.0 (11.8-18.8) at baseline to 7.5 (4.7-12.3) at 1-y and 4.3 (2.5-7.8) at 2y-follow-up. The number of patients without myasthenic crises within one year increased from baseline (49, 86.0%) to 1y-follow-up (55, 96.5%). Median (IQR) daily steroid dose decreased from 10 (5-22.5) mg/d at baseline to 4 (0-10) mg/d at 1y-follow-up, and to 2.5 (0-10) mg/d at 2y-follow-up. CONCLUSION This study indicates that RTX was associated with a stabilized clinical course and decreased steroid use in patients with autoimmune myasthenic syndromes, including those with thymoma-associated MG. Our data suggest that therapeutic benefit is apparent within the first year of treatment and is maintained through two years.

Background: Serial glial fibrillary acidic protein (GFAP) trajectories have become an important framework for contextualizing evolving secondary-injury pathophysiology after moderate-to-severe traumatic brain injury (msTBI). However, total GFAP pools release and clearance signals that may be less useful for longitudinal bedside decisions than a proteoform-resolved assay. We compared total GFAP with neoGFAP, defined here as calpain-generated GFAP proteoforms intended to index active astroglial proteolysis during the subacute phase. Methods: We analyzed 651 serial serum samples from 95 msTBI patients from a previously described single-site cohort. Total GFAP and neoGFAP were measured on the same MSD platform from 6 to 240 hours after injury. Early (6 to 72 h) and late (96 to 240 h) windows, data-derived tertiles, and serial trajectory summaries were calculated directly from serial samples. Models were benchmarked against age plus admission post-resuscitation Glasgow Coma Scale (GCS) and the admission IMPACT extended risk score using five-fold stratified cross-validation. Outcomes were unfavorable outcome (GOSE 1 to 4), less-than-good recovery (GOSE 1 to 6), Disability Rating Scale (DRS) [&ge;]15, mortality, and neuroimaging worsening at 6 months. Results: The cohort contributed 95 serial biomarker profiles, with 90 participants evaluable for 6-month GOSE and 89 for DRS. Unfavorable outcome occurred in 57/90 (63.3%), and less-than-good recovery in 79/90 (87.8%). For unfavorable outcome, IMPACT plus early neoGFAP reached AUROC 0.85 versus 0.84 for IMPACT plus early total GFAP and 0.81 for IMPACT alone. For less-than-good recovery, IMPACT plus late neoGFAP achieved AUROC 0.90 versus 0.84 for late total GFAP and 0.82 for IMPACT alone. Secondary analyses for DRS, mortality, and neuroimaging worsening showed smaller differences. Conclusions: In this retrospective analysis, neoGFAP provided clearer incremental value than total GFAP for recovery-oriented monitoring, especially when late-window reassessment of patients who remained at risk for less-than-good recovery was required. Results support prospective testing of neoGFAP as a pathophysiology-informed adjunct to serial bedside decision making, repeat-assessment thresholds, and recovery stratification.

Spinocerebellar ataxia type 7 (SCA7) is a rare neurodegenerative disorder characterized by progressive cerebellar ataxia and visual impairment. We investigated longitudinal changes in resting-state functional connectivity and their clinical associations. Resting-state functional MRI was acquired from 16 individuals with SCA7 and 16 age- and sex-matched healthy controls across three visits over 24 months. Network-to-network functional connectivity was quantified, and machine-learning models were trained using functional connectivity features. SCA7 showed lower MoCA (p = 0.045) and MMSE (p = 0.025) scores and progressive worsening of ataxia (SARA, p < 0.001). Significant Group x Visit interactions were observed for Visual-Default Mode (p = 0.012) and Somatomotor-Cerebellar Dorsal Attention connectivity (p = 0.038). Functional connectivity abnormalities involved cortical, cortico-cerebellar, and cerebellar networks that became more widespread at the final follow-up assessment, with the visual network emerging as the most consistently affected system across analyses. Functional connectivity abnormalities were associated with cognitive performance (MMSE: r = -0.62, p = 0.01) and disease severity (SARA: r = 0.589, p = 0.016). Functional connectivity features accurately classified SCA7 and healthy controls (accuracy = 96.4%, F1 = 0.969). These findings support resting-state functional connectivity as a candidate biomarker warranting further validation in larger, independent cohorts.

Myasthenia gravis (MG) is an autoimmune disease caused by pathogenic autoantibodies against proteins at the neuromuscular junction (NMJ). The diagnosis and clinical management of MG patients largely relies on the detection of antigen-specific autoantibodies targeting acetylcholine receptor (AChR) or muscle-specific kinase (MuSK). Yet a subset of patients remains seronegative for known MG autoantibodies, highlighting a critical need for alternative approaches to identify pathogenic NMJ antibodies. We established a new human in vitro model of the NMJ based on primary human muscle cells that recapitulates key features of the NMJ: differentiation to myotubes, expression of key NMJ proteins and formation of postsynaptic AChR clusters in response to agrin stimulation. The model allows new insights into myogenesis and genetic muscle diseases, and the new muscle cell-based assay (CBA) detected autoantibodies in sera from patients with AChR- and MuSK-positive MG with 96.43% sensitivity and 100% specificity, while healthy control sera showed no reactivity. Incubation with patient sera significantly reduced AChR clustering compared to controls, demonstrating functional pathogenic effects. Thus, we established a physiologically relevant human NMJ model that enables detection and functional characterization of neuromuscular autoantibodies. This novel approach addresses a key limitation of current antigen-specific diagnostics and provides a method for improved detection and characterization of MG antibodies, independent of antigen specificity. One Sentence SummaryWe established a postsynaptic human in vitro neuromuscular junction model to assess binding and pathogenicity of MG autoantibodies. Key messagesO_ST_ABSWhat is already known on this topic?C_ST_ABSCurrent diagnosis of myasthenia gravis (MG) relies largely on the detection of antigen-specific autoantibodies against AChR and MuSK, leaving a clinically relevant subset of patients seronegative. What are the new findings?We established a physiologically relevant human in vitro neuromuscular junction model based on primary human muscle cells and developed a novel muscle cell-based assay (CBA) for the detection of neuromuscular autoantibodies. How might this impact on clinical practice or future developments?The CBA detected autoantibodies in patients with AChR- or MuSK-positive MG with high sensitivity and specificity and demonstrated their functional pathogenic effects on AChR clustering. This antigen-independent approach may improve the detection and functional characterization of MG autoantibodies, particularly in patients who are seronegative in current diagnostic assays. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/743478v1_ufig1.gif" ALT="Figure 1000"> View larger version (38K): org.highwire.dtl.DTLVardef@18ed154org.highwire.dtl.DTLVardef@151036corg.highwire.dtl.DTLVardef@1b7ab34org.highwire.dtl.DTLVardef@1490fe9_HPS_FORMAT_FIGEXP M_FIG C_FIG

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. Three disease-modifying therapies (DMTs) for spinal muscular atrophy (SMA) have been approved since 2016, yet many adults remain untreated. Identifying them depends on ICD-10 codes that capture SMA but do not reliably distinguish it from other related conditions. We examined, in one U.S. health system, both patients' engagement with therapy and the accuracy of the codes used to find them. Methods. We conducted a retrospective chart review of adults in an academic health system identified by SMA-associated ICD-10 codes, with manual adjudication of diagnosis and DMT status. Confirmed SMA-positive, DMT-naive patients were invited to a structured telephone interview on treatment awareness and barriers. Results. Of 60 charts, 22 (36.7%; 95% CI 25.6-49.3%) were appropriately coded for SMA or a related disorder; only 16 (26.7%) had molecularly confirmed SMA. The other 38 (63.3%) were miscoded, spanning spinal and bulbar muscular atrophy, asymptomatic carriers, prenatal screening, and conditions unrelated to SMA. Ten of the 16 confirmed patients (62.5%) were DMT-naive; one was interviewed, one declined, and eight could not be reached. The non-response is itself a finding: the patients least visible to administrative data are the hardest to reach. Conclusions. ICD-10 ambiguity is a barrier to treatment access in adult SMA, as is loss to follow-up. We make two recommendations: continuous documentation-coding alignment that uses natural language processing to verify the genetic precondition, and type-specific SMA codes (subcodes for Types 0-4) anchored on molecular SMN1 confirmation. Together these would support cohort identification, outreach, and evidence generation without adding to clinician burden.

ObjectivePlasma metabolomics offers insight into multiple sclerosis (MS) pathophysiology, but existing studies are limited by small sample sizes and incomplete clinical data. MethodsWe conducted plasma metabolomic profiling of 411 deeply phenotyped patients with MS and 46,443 controls, analyzing 162 metabolites in 30 biologically related metabolite groups. We characterized associations with MS diagnosis, disability, disease subtype, and inflammatory disease activity using regression and differential network enrichment analysis. We additionally examined 25 pre-diagnosis individuals whose samples were collected before their first demyelinating event. ResultsFourteen of 30 metabolite groups were associated with MS after false discovery rate correction, with the strongest positive associations observed for atherogenic lipoproteins, glycine, cholines, and saturated fatty acids, and the strongest negative associations for aromatic amino acids, branched-chain amino acids, alanine, and citrate. Differential network enrichment analysis identified two dysregulated subnetworks encompassing amino acid and energy metabolism and lipid and lipoprotein metabolism. Five metabolite groups were negatively associated with disability: small high-density lipoprotein particles, histidine, branched-chain amino acids, albumin, and aromatic amino acids. The omega-6/omega-3 fatty acid ratio was significantly associated with recent relapse (OR = 1.92, FDR-p = 0.030) and nominally associated with future MRI activity, especially in patients on moderate or high-efficacy disease-modifying therapy. The MS metabolic signature was not detectable in pre-diagnosis samples. InterpretationThese findings highlight coordinated dysregulation of amino acid and lipoprotein metabolism as hallmarks of established MS and identify a novel association of the omega-6/omega-3 ratio with inflammatory disease activity.

Parkinsons disease (PD) arises through disruption of multiple interconnected cellular processes, but the genetic contributions to these processes may differ across ancestries. We investigated functional convergence among genes harboring pathogenic or likely pathogenic (P/LP) variants and variants of uncertain significance (VUS) in a multicenter Indian cohort recruited through the Genetics of Parkinsons Disease in India Young Onset Parkinsons Disease project (GOPI YOPD). The cohort included 668 participants (463 males 69.3%) with a mean age at motor onset of 39.4+/-8.8 years. P/LP variants and VUS identified through previously reported whole-exome or whole genome sequencing were retained as separate evidential categories. The P/LP-associated gene set comprised 11 unique genes and the VUS associated set comprised 40 unique genes. Separate STRING functional-enrichment analyses evaluated Gene Ontology Biological Process, Molecular Function and Cellular Component terms, KEGG pathways, WikiPathways and STRING local network clusters. Terms meeting a Benjamini Hochberg false discovery rate threshold of <0.05 were organized into eight non-mutually-exclusive ontology/pathway categories. Gene to pathway mappings were subsequently projected to individual participants to estimate pathway representation and examine clinical associations. At least one reportable P/LP variant or VUS was identified in 336/668 participants (50.3%): 35 had a P/LP variant alone, 282 had VUS alone and 19 had a P/LP variant together with VUS in one or more additional genes. The most frequently represented categories were mitochondrial organization (247/336, 73.5%), autophagy related processes (228/336, 67.9%) and regulation of synaptic vesicle transport (201/336, 59.8%). PRKN was the most frequent P/LP-associated gene, occurring in 29/54 P/LP carriers, followed by PLA2G6 and PINK1. Lysosomal transport was represented exclusively by VUS-associated genes, particularly GBA1, VPS13C and LRRK2. Among P/LP carriers, additional VUS in distinct genes were not associated with age at onset (P = 0.81) or family history (52.6% versus 31.4%; P = 0.15). No pathway phenotype association remained significant after correction for multiple testing. Genetic findings in this Indian cohort converged across an interconnected mitochondrial autophagic lysosomal vesicular network, with different contributions from P/LP-associated and VUS associated gene sets. This study provides the first pathway resolved South Asian genetic profile and a framework for comparative studies across populations.

Background: Amyotrophic lateral sclerosis (ALS) is a rare neurodegenerative disease with no known cure. Disease progression in people living with ALS is heterogeneous, hindering personalized treatment development. The current gold standard for measuring disease progression in ALS, the ALS Functional Rating Scale - Revised (ALSFRS-R), is widely used but based on subjective measurements. Blood-based neurofilament light (NfL) has been studied as a diagnostic and prognostic biomarker but less information exists on its utility as a disease progression biomarker. Methods: We present results from blood draws of 300 participants in the FDA-funded Clinic-Based Multi-Site ALS Natural History and Biofluid study of the ALS Natural History Consortium (NHC). Plasma NfL levels were measured and analyzed against different disease progression metrics based on the ALSFRS-R. Results: NfL levels were found to be correlated with the ALSFRS-R average rate of change (r=-0.53, 95% CI -0.62 to -0.42). This association differed at a cutoff value of 61 pg/mL, with stronger correlations below this cutoff (r=-0.51 vs r=-0.18). Survival differed stratifying by this cutoff value, with participants under the cutoff having higher survival probabilities. The predictive value of NfL when predicting time to death was higher compared with the first ALSFRS-R across different event horizons. A model including both was better when predicting events up to 2 years after diagnosis. Conclusions: These results highlight the utility of NfL as a disease progression biomarker in ALS alongside ALSFRS-R based disease progression metrics. The cutoff value can aid in clinical trial stratification, pragmatic trial planning, and clinical care.

Background: Neurological, neuropsychiatric, and neurodevelopmental disorders cluster in ALS families, sharing a common genetic architecture with ALS. Pathogenic variants in genes associated with other neurological, neurodevelopmental, or neuropsychiatric disorders may also co-occur in ALS and modify phenotype. We have sought to determine the prevalence and clinical pattern of likely-pathogenic/pathogenic (LP/P) non-ALS neurological, neurodevelopmental, and neuropsychiatric variants, alone and in combination with ALS-gene variants, in two large ALS cohorts. Methods: Whole-genome sequencing (WGS) of 469 Irish and 774 Answer ALS people with ALS (pwALS) was analysed for ClinVar LP/P variants associated with other neurological (n = 15541), neurodevelopmental (n = 9761), and neuropsychiatric (n = 321) phenotypes. Inheritance patterns for associated genes (autosomal recessive/autosomal dominant) along with the associated phenotype were validated using OMIM. Standardised clinical data included family history, site and age of onset, El Escorial category, survival, motor decline, and cognitive and behavioural assessments. Known ALS-gene variants and C9orf72 repeat expansion status were included for each cohort. Results: Non-ALS neurological variants were identified in 47/469 (10.0%) Irish and 69/774 (8.9%) Answer ALS participants, most frequently in hereditary spastic paraplegia-associated genes (3.2% Irish; 2.8% Answer ALS). Irish neurological variant carriers showed higher frequency of respiratory onset (10.6% vs 1.2%, Fisher's exact p = 0.002, {Phi} = 0.20) and fewer premorbid behavioural symptoms (0.92 +/- 0.56 vs 3.08 +/- 0.97, Cohen's d = -0.40). Neurodevelopmental variants occurred in 12/469 (2.6%) Irish and 20/774 (2.6%) Answer ALS participants. In the Irish cohort, neurodevelopmental variant carriers had significantly shorter survival in Cox proportional hazards model (log-rank p = 0.005), corresponding to a more than two-fold increased hazard of death (HR = 2.25, 95% CI 1.26-4.00), and had significantly increased familial burden of neuropsychiatric disorders among first- and second-degree relatives (negative binomial IRR for carriers = 2.41, 95% CI: 1.12-5.18, p = 0.025). Across combined cohorts, 18 individuals (Irish n = 8; Answer ALS n = 10) carried [&ge;]2 LP/P variants spanning ALS and non-ALS genes. Conclusion: Rare LP/P variants in genes associated with other neurological and neurodevelopmental disorders occur in up to 12% of pwALS across two independent cohorts. Carriers show distinct phenotypes, shorter survival, and characteristic family history patterns. These findings suggest that extended pleiotropic and oligogenic architectures may contribute to ALS heterogeneity.

A major obstacle for clinical trials is the lack of objective, sensitive, and reliable measures that can detect modest changes in disease progression. Here, we determine whether acoustic and linguistic digital speech measures automatically obtained during a functionally relevant passage-reading task capture multiple dimensions of disease in ataxia, including functional communication impairment, subclinical cerebellar dysfunction and disease progression. A total of 157 individuals with ataxia and 84 controls contributed cross-sectional data, and 54 individuals with ataxia and 43 controls contributed longitudinal data within the ongoing Neurobooth natural history study. Participants completed standardized speech recordings, patient-reported outcome measures (PROMs) and neurologist-rated clinical evaluations. A novel speech processing pipeline was developed to automatically transcribe audio recordings, identify word boundaries and extract a predefined set of linguistic and within-word acoustic features. Individuals with ataxia exhibited marked disruption of speech timing, coordination and articulatory control, including slowed speech (d=1.23), prolonged inter-word pauses (d=-0.91), higher/more variable vocal intensity (|d|=0.43-0.51) and altered spectral content (|d|=0.43-0.79) compared to healthy controls. Linguistic features (e.g. speaking rate and within-word pause duration) showed strong associations with clinician-rated severity and PROMs (|r|=0.23-68), indicating alignment with functional communication impairment and patient-perceived disease burden. In contrast, acoustic features derived from cepstral measures captured subtle abnormalities in speech motor control, differentiating not only individuals with ataxia (d=0.65) but also pre-ataxic individuals (d=0.56), and those without clinically evident dysarthria (d=0.45), from controls. These findings indicate that acoustic features reflect subclinical cerebellar motor dysfunction involving impaired temporal coordination and vocal control before overt clinical speech impairment emerges. Longitudinally, several acoustic measures were sensitive to disease progression (MSDR=0.19-0.68), even in cases where clinical scales showed no detectable change. Speech-derived changes correlated with changes in clinical scales and PROMs. Both acoustic and linguistic features exhibited strong intra-session reliability. During passage reading, acoustic and linguistic measures provide complementary but different clinical information in ataxias. Linguistic measures primarily reflect downstream functional consequences of ataxic dysarthria, whereas acoustic measures provide sensitive indicators of subclinical cerebellar motor dysfunction and progression. These findings demonstrate that natural speech analysis can produce digital measures for detecting subclinical disease, quantifying functional impairment, monitoring progression in ataxia, with strong potential for application in clinical trials and remote monitoring.

Introduction/Aims To evaluate the usefulness of the Goal Attainment Scale (GAS) light for assessing response to risdiplam in patients with SMA aged [&ge;]15 years. Methods In this population-based, longitudinal, ambispective study, patients were evaluated before and at 12 and 24 months after risdiplam initiation using motor scales (SMA Functional Composite Score Revised [SMA-FCR]), pinch strength (MyoPinch), functional scales (EK2, ALSFRS-R), patient and clinician global impression of change (PGIC and CGIC), and GAS light. Longitudinal changes were assessed using linear mixed-effects models. The minimal detectable change (MDC) and minimal clinically important change (MCIC) of GAS light were calculated. Results Forty-four patients (median age 32 years; 56.8% female) were included: 31.8% non-sitters, 56.8% sitters, and 11.4% walkers. GAS light priorities differed across functional subgroups, with patients prioritising moderately affected domains. After 24 months of risdiplam treatment, motor outcomes showed non-significant improvements in walkers, whereas functional scales improved significantly only in non-sitters. In contrast, GAS light detected significant, increasing improvements across all functional subgroups. The MCIC and MDC for GAS light were 6.5 and 10.65 points, respectively. According to the CGIC, 58% of patients improved slightly, 29% remained stable, and 13% worsened slightly at 24 months. Using the MCIC threshold, 64.5% achieved clinically meaningful goal improvement. Discussion GAS light is a feasible, sensitive, patient-centred tool that may complement standardised outcome measures when evaluating treatment response in adults with SMA. These findings further support risdiplam as a valuable therapeutic option in this population.

Background: Motor neuron disease (MND) is a fatal neurodegenerative condition with significant clinical heterogeneity that is incompletely captured by existing phenotype classifications based on onset site. Electronic health records (EHRs) contain detailed symptom documentation in clinical narratives that may enable data-driven discovery of clinically meaningful patient subgroups. Methods: We developed a natural language processing (NLP) pipeline using MedCAT to extract symptoms from clinical notes of 2,361 people with a confirmed diagnosis of MND at a tertiary neurology center. MND cohort confirmation used three complementary methods: clinic attendance records, text-based diagnosis detection, and NLP extraction with negation detection. Extracted symptoms were filtered to Unified Medical Language System semantic type T184 (Sign or Symptom) with removal of negated concepts. Patients were clustered using latent class analysis on binary symptom profiles. Survival differences were assessed using Kaplan-Meier analysis, log-rank tests, and Cox proportional hazards regression. Results: From the first clinical notes, we identified four clusters of symptoms among 872 patients and 76 symptoms: Motor-Bulbar (n=373), Motor-Tremor (n=154), Sensory-Pain (n=222), and Motor-Respiratory (n=123). When extended to all clinical notes (n=2,065; 184 symptoms), these reorganized into three clusters: Autonomic-Respiratory (n=472), Nocturnal-Respiratory (n=338), and Classic Motor (n=1,255). Survival differences were significant across all clusters in both the first notes and all notes analyses (log-rank p < 0.001). Conclusions: NLP-based symptom extraction from EHRs identifies clinically meaningful MND subgroups that extend beyond traditional onset-site classifications. Autonomic-respiratory symptom burden is associated with poorer survival while a newly identified Sensory-Pain subtype with a better prognosis. These data-driven phenotypes may improve prognostication and inform targeted supportive care.

Background: Interim stage outcomes in multi-arm multi-stage trials need not be the same as the final primary outcome, and should be selected with the goal of providing the best chance of continuing with an effective treatment in the study during the early stage where there is also potential to drop an ineffective arm. Jointly considering multiple outcomes can enhance that ability to detect an emerging signal. Methods: The Optimal Clinical Trials Platform for Progressive Multiple Sclerosis (OCTOPUS) study is a randomised, placebo-controlled, double-blind, phase 3, MAMS trial testing treatments for people with progressive multiple sclerosis. The interim analysis was to be based solely on an MRI outcome; reduction in whole brain atrophy rate. Accumulating data from external trials led to concern that this outcome may result in prematurely rejecting an effective treatment. As a solution we propose adding 3 clinical outcomes to the MRI outcome and propose a multivariate mixed model to accommodate them jointly, despite significant differences in scale and even measurement type. We show how use of the model-derived covariances allows us to linearly combine the treatment effects of disparate outcomes into a single treatment effect. We also describe how to analytically calculate power for this combination and compared it to the individual components' performance. Results: Based on variance data from a previous study, we found power was increased moderately by 7%, from 83% to our target 90% given the assumed respective treatment effect sizes for OCTOPUS. When considering observed effect sizes from other trials these power improvements were maintained, despite there being great variability between the outcome effects. Conclusions: Whilst not greatly boosting power, we argue that this strategy improves the interim outcome measure by also making it more resilient to the uncertainty surrounding effect size, and mitigating against unexpected negative results by spreading the liability across related but distinct outcomes.

Diabetic neuropathy is associated with substantial systemic disease burden, but short-term mortality risk among affected intensive care unit (ICU) patients remains difficult to characterize. We evaluated whether temporal information from the first 24 hours of ICU care improves post-landmark mortality prediction beyond severity scores and static clinical summaries. Patients aged > 18 years with diabetic neuropathy were identified in MIMIC-IV v3.1. A 24-hour landmark was used: only patients alive and still hospitalized at 24 hours were included, and the outcome was subsequent in-hospital death. The final cohort included 1,347 patients, including 83 deaths (6.16%). Data were divided into an 80% development set and a locked 20% test set. Feature selection, hyperparameter tuning, calibration, and threshold selection were restricted to development data. Logistic regression, random forest, and XGBoost were evaluated. Random forest had the highest development cross-validated PR-AUC and was selected for interpretation. On the locked test set, random forest achieved an AUROC of 0.851 (95% CI 0.765-0.924), PR-AUC of 0.339, and Brier score of 0.051; XGBoost and logistic regression achieved AUROCs of 0.847 and 0.806. In a post hoc strictly nested analysis, adding temporal predictors increased discrimination across all three algorithms; random-forest AUROC increased from 0.815 with severity and static predictors to 0.870 with the full temporal representation. First-day temporal information therefore showed additional prognostic value, but external validation is required before clinical use.

Introduction[18F]fluorodeoxyglucose positron emission tomography (FDG-PET) frequently reveals hypometabolism extending beyond the epileptogenic zone in focal cortical dysplasia (FCD). However, it is unclear whether these peripheral hypometabolic areas harbour pathological cells potentially contributing to seizure generation. This study characterized histopathology in the lesion epicentre vs borders of the FDG-PET hypometabolism-informed resections in pediatric patients undergoing epilepsy surgery. MethodsFourteen children with intractable, extra-temporal focal epilepsy (mean age 9.0{+/-}5.0 years; 9 female) were retrospectively reviewed. FDG-PET contributed significantly to surgical planning in all cases, with the resection encompassing the visually-apparent MRI signal abnormalities as well as areas of surrounding hypometabolism when safely feasible. Multiple pathological specimens were obtained from the epicentre and surrounding hypometabolic areas. Overall, 136 specimens were analyzed: 64 epicentre (mean 4.6{+/-}3.2/patient) and 72 border (mean 5.1{+/-}3.5/patient). ResultsPathology was identified in 75% of epicentre specimens (59% with frank FCD (fFCD) IIa/b, 16% with dysmorphic neurons only (DNO)). Border specimens showed pathology in 62% (31% fFCD IIa/b, 31% DNO). We fitted a Bayesian logistic mixed model with pathology as outcome variable, location as predictor, and subject as a random effect. Compared to negative pathology, the log-odds of fFCD in the epicentre was 1.00 (confidence interval (CI) 0.32, 1.77) and -1.25 in the border (CI -2.17, -0.40). The log-odds of DNO vs negative pathology was non-significant in both locations. All patients achieved Engel Ia status at one-year follow-up with no long-term neurological deficits. ConclusionThese findings suggest a gradient of histopathology, with fFCD concentrated in the epicentre and DNO present in both the epicentre and hypometabolic borders. Thus, FDG-PET may be used to better detect the histopathological borders of FCD type II, and the high seizure-freedom rate presented here supports the inclusion of these surrounding hypometabolic regions in the surgical resection (when safe to do so), potentially improving the likelihood of removing epileptogenic cells. Key PointsO_LIPathological cells are present not only in the MRI signal abnormality in FCD but also in the periphery of the FDG-PET hypometabolism. C_LIO_LIWe observe a gradient of histopathology, with frank FCD concentrated in the epicentre and dysmorphic neurons spread throughout the area of hypometabolism. C_LIO_LIMaximal safe resection of the area of hypometabolism may increase likelihood of removing epileptogenic cells, thus improving surgical outcome. C_LI