Neurology Genetics

Background: PD GENEration (NCT04057794, NCT04994015), sponsored by the Parkinson's Foundation in partnership with Aligning Science Across Parkinson's (ASAP) through the Global Parkinson's Genetics Program (GP2), is an international, observational, clinical research study that offers genetic testing and counseling to people living with Parkinson's disease (PwP) at no cost. PD GENEration has aimed to empower PwP and their clinicians with knowledge of their genetic status, to accelerate recruitment into precision medicine trials, and to advance research through data sharing. Since its launch in 2019, the study has expanded to enroll over 32,000 PwP (as of March 31, 2026), from 10 countries across North, Central, and South America, the Caribbean, and Israel. Methods: Over the course of 6 years, PD GENEration has evolved to accommodate the growing scientific and research needs of the Parkinson's community while also increasing the ability to return genetic test results to PwP at a greater scale. Participants with a diagnosis of Parkinson's disease (PD) may enroll in-person or virtually where informed consent and blood sample collection can occur. Samples are analyzed at a College of American Pathologists/Clinical Laboratory Improvement Amendments (CAP/CLIA)-certified laboratory using whole genome sequencing, with variants curated for a primary panel of seven PD-associated genes. Results are disclosed during a genetic counseling visit, where further testing is offered for two optional additional gene panels. Those who consent undergo analysis of additional genes, and results are returned during a genetic counseling visit for those that test positive for a variant. In addition to returning genetic results to PwP, a central pillar of the study design has been the open sharing of genomic data to advance discovery in PD research in partnership with ASAP and GP2. Discussion: PD GENEration applies a flexible framework, allowing for country specific considerations and the integration of multiple site models, evolving based on participant needs and the prioritization of equity and accessibility. We summarize PD GENEration's implementation and scaling, highlight key accomplishments and lessons learned, and provide guidance for those interested in implementing large-scale clinical genetic testing studies across other diseases and therapeutic domains.

BackgroundHuntington disease (HD) is a neurological disorder caused by an aberrant CAG expansion in the HTT gene, producing a mutant protein (mHTT). Although HD is classically characterized by adult-onset cortical and striatal degeneration, accumulating evidence suggests that altered cortical development may also contribute to disease pathogenesis. ObjectiveWe sought to investigate the impact of mHTT on neocortical patterning, which is a largely unexplored aspect of HD. MethodsUsing the HdhQ140 HD knock-in mouse model, we performed immunofluorescence and in situ hybridization to analyze the patterning of the cortex from embryonic day 10 to postnatal day 7. ResultsDuring embryogenesis, HTT expression exhibited a high medial-to-low lateral gradient in the neocortex, like that observed for key transcription factors involved in cortical patterning. Notably, HTT expression was absent from the cortical hem, a critical patterning center. In HD, the protein gradient remained unchanged whereas the expression in medial pallium seemed increased. During the early development of the cerebral hemispheres, the expression of morphogens and signaling pathways, including Shh, Fgf8, and Wnt/BMP genes, were disrupted in organizing centers, leading to altered expression of major neocortical transcription factors. At postnatal stages, the motor and somatosensory cortical areas were misplaced. These developmental alterations were associated with postnatal sensorimotor deficits relevant to HD. ConclusionsOur findings demonstrate that HD-related neurodevelopmental alterations arise as early as embryonic day 10 in mice. This supports previous work suggesting that defects in brain development contribute to HD pathogenesis prior to clinical onset.

CLN3 disease is an inherited neurodegenerative disease, typically with childhood onset, and characterized by vision loss, seizures, cognitive decline, and difficulties. The CLN3 Staging System (CLN3SS) characterizes disease progression. Our aim was to assess differences in cognitive test scores in relation to CLN3SS among individuals with CLN3 disease. We evaluated the relationship between cognitive test performance and the CLN3SS in individuals with genetically confirmed CLN3 disease. Participants completed tasks of verbal reasoning, vocabulary knowledge, attention, fund of information, and ability to recite the alphabet. One-way ANOVA testing assessed differences in mean cognitive test score among CLN3SS score groups, and Chi-square testing was used to compare the proportion in each CLN3SS group that could recite the alphabet. Data were evaluated from a sample of 85 individuals with a total 245 CLN3SS assessments conducted within 6 months of their cognitive testing, A significant decrease in test scores was found between CLN3SS Stages 1 (vision loss present) and 2 (vision loss and seizures present) for each of the cognitive tests. The proportion of participants able to recite the alphabet also decreased from Stage 1 to Stage 2 (X2=12.1, p<.01). Cognitive ability declines with advanced disease severity in CLN3 disease, though motor disability in Stage 3 likely contributes to difficulty participating in cognitive assessment at this later disease stage. Understanding the relationship between cognition and CLN3 disease stage may help guide decision making, i.e., determining who could or should undergo cognitive assessment for clinical care or for group stratification in disease modifying clinical trials.

BackgroundParkinsons disease (PD) genetics research has predominantly focused on populations of European ancestry, limiting understanding of disease mechanisms across diverse populations. African-Caribbean communities harbor complex genetic admixture and exhibit distinct clinical features, yet remain underrepresented in PD genetic studies. We investigated ancestry-specific molecular signatures underlying PD using rare variant burden analysis in geographically and genetically distinct cohorts. MethodsUsing a discovery cohort design and gene-based rare-variant aggregation testing, we performed RNA-sequencing on peripheral blood mononuclear cells from 33 participants: Montreal, Canada (n=16; 8 PD, 8 controls) and Guadeloupe, French West Indies (n=17; 9 PD, 8 controls). We conducted gene-based rare variant burden testing, protein-protein interaction network analysis, pathway enrichment, and linkage disequilibrium (LD) profiling. Clinical assessments included MDS-UPDRS, Hoehn & Yahr staging, and evaluation of prodromal and autonomic features. ResultsPrincipal component analysis revealed distinct population structure, with Montreal participants forming a homogeneous cluster and Guadeloupe participants displaying greater variance consistent with African-European admixture. Ancestry-stratified burden analysis identified divergent immune pathway enrichments: IL-17 signaling predominated in PD patients from Montreal (FDR=0.04), while MHC class II antigen presentation and interferon-{gamma} pathways characterized PD patients from Guadeloupe (FDR=1.59x10-). A NOD2 frameshift variant (rs2066847) was identified in 3/8 Montreal patients, providing a mechanistic link to IL-17 pathway dysregulation. LD analysis revealed ancestry-specific haplotype structures, with 14 African-admixed American-specific LD pairs exclusive to Guadeloupe participants and 11 European-specific pairs present in both populations, demonstrating distinct haplotype architectures shaped by ancestry. Clinical differences aligned with molecular findings: Montreal patients showed higher prevalence of REM sleep behavior disorder (71.4% vs 37.5%) and hyposmia (54.3% vs 22.5%), while Guadeloupe patients showed more autonomic symptoms. Control-only comparison showed no pathway enrichments, validating that PD findings reflect disease-associated mechanisms rather than population stratification. ConclusionsTogether, these findings provide evidence for ancestry-specific immune signatures in PD, challenging a one-size-fits-all paradigm in neurodegenerative disease genetics. The identification of distinct molecular pathways underlying clinically overlapping phenotypes suggests PD may encompass multiple molecular entities converging on shared symptoms. These findings emphasize the necessity of ancestry-inclusive research for advancing mechanistic understanding and achieving equity in precision medicine for neurodegenerative disorders.

Abstract Background Gene dosage disorders impact cognition and psychopathology, but outcomes vary widely amongst carriers of the same variant. Recent work has sought to better predict proband outcomes using measures of corresponding traits in family members. However, family-based models have not yet been prospectively quantified across several traits in different genetic disorders, nor evaluated for the precision they afford: both crucial issues for clinical implementation. Methods In a first test case for these questions, we apply regression analyses to quantify and compare family-based prediction of 12 traits (including IQ, autism- and ADHD-related traits) in 433 individuals from families including a proband with XXY or XYY syndrome (N=93 and 58, respectively). Results The 12 traits vary substantially in their proband-family associations (0.001<|r|<0.55) - with differences emerging between XXY and XYY syndrome. Only two traits also show significant and similar proband-family associations in both aneuploidies, with the greatest concordance found for IQ. A family-based model for IQ prediction in male sex chromosome trisomies significantly reduces error vs. a group mean IQ model (F = 7.4, p = 0.006), but only in 65% of probands, and with mean error reduction of ~2 IQ points. Conclusions Family-based prediction of neuropsychiatric traits in genetic syndromes likely requires trait- and syndrome- specific models. Family models can significantly improve outcome prediction for IQ, but to variable degrees across individuals and with a small mean improvement. By mapping and quantifying these limits, our work helps draft a roadmap for refinement of family-based prediction of proband outcomes in gene dosage disorders.

Background: The Alzheimer's Disease Sequencing Project Gene Verification Committee developed a systematic framework to adjudicate genetic evidence for AD and related dementias, addressing wide variation in association quality. Methods: Phase 1 established tiered criteria by evaluating 23 nominated loci across study designs. Phase 2 applied this framework to 29 large-scale genome-wide studies published since 2015, tiering 163 unique loci. Results: Phase 1 yielded 17 high-confidence loci (12 linked to specific genes), and Phase 2 identified 111 high-confidence loci/genes with replicated associations across ancestries and convergent single-variant/variant-set evidence. Prioritized loci highlight APP processing, microglial immunity, and lipid metabolism pathways, including genes not captured by existing resources like Agora or Open Targets. Summarized results can be viewed at https://topgenes.niagads.org/. Conclusion: This rigorously adjudicated catalog represents the most comprehensive AD/ADRD genetics resource to date, providing a foundation for functional validation and therapeutic discovery with broad applicability to complex diseases.

Mitochondrial diseases (MDs) are genetically and phenotypically diverse and can be difficult to diagnose. Prevalence estimates derive largely from diagnosed cases and may underestimate population MD risk. Population-based studies are limited in scope and number but indicate MD variants are common. As genomic sequencing advances have made comprehensive population-based evaluation feasible, we sought to evaluate nuclear MD variation in a population cohort to understand variant prevalence and differences in MD risk estimates We identified disease-associated nuclear gene variants in 270 nuclear MD genes across 2,845 healthy older individuals in the Medical Genome Reference Bank. From Pathogenic or Likely Pathogenic Variants (PLPVs) we estimated autosomal recessive (AR) and autosomal dominant (AD) MD risk for individual genes and all nuclear variant-associated MDs. We identified 554 PLPV alleles representing 357 unique variants in 145 genes. Combined AR MD risk was estimated at 25.8 per 100,000 (95% CI 18.7 to 32.9), or 1 in 3,880 individuals. SPG7 (12.65 per 100,000; 95% CI 7.52-20.6) and POLG (4.23 per 100,000; 95% CI 2.10-8.01) contributed the greatest single gene AR MD risks and OPA1 variants posed the greatest AD MD risk. We observed a high rate of MD-associated nuclear gene variation in this healthy older cohort. The estimated lifetime AR MD risk was higher than commonly quoted prevalence estimates for all MDs, and the presence of common AD variants suggests variant penetrance may be lower than previously understood. These data help contextualise population MD risk and may inform clinical counselling and care.

Background: Low-dose levetiracetam is under investigation as a potential treatment for slowing Alzheimer's Disease progression. This study tests whether levetiracetam enhances executive function in mid-age adults, and whether drug effects differ by Apolipoprotein e4 (APOE4+) genetic risk status. Methods: Fifty-eight adults (aged 45-65 years; 27 APOE33; 31 APOE4+) participated in a double-blind, placebo-controlled study of low-dose levetiracetam (125mg bidaily for two-weeks). At the end of each treatment phase, participants completed a switch-inhibition task. Results: Mid-age APOE4+ carriers were significantly slower and showed a greater cost of increasing executive demand than APOE33 individuals. Response times were quicker under levetiracetam, with increased benefits reported in APOE33 individuals, at younger ages, and in individuals with reduced levels of plasma-based biomarkers. Levetiracetam selectively benefitted accuracy in APOE33 individuals. Conclusion: Low-dose levetiracetam enhances executive function in midlife, particularly in individuals at lower risk of Alzheimer's Disease based on age, APOE4 genotype, and proxies of neuropathology.

Background: Reliable biomarkers for Parkinson's disease (PD) pathology detection are essential for research. The alpha-synuclein (aSyn) seed amplification assay (SAA) is a validated biomarker for misfolded aSyn. Objectives: To assess the association between aSyn SAA and LRRK2-related PD (LRRK2-PD) and its link to mitochondrial genetic burden. Methods: We included N=76 LRRK2 p.Gly2019Ser variant carriers (N=22 affected, N=54 unaffected), N=714 patients with idiopathic PD (iPD), and N=411 controls from Norway. We analyzed cerebrospinal fluid (CSF)-based aSyn SAA in N=10 PD patients and N=30 unaffected LRRK2 p.Gly2019Ser carriers, alongside N=6 controls and N=56 iPD patients. A mitochondrial polygenic score (MGS) was derived from genotyping data, using PPMI as an additional cohort (iPD: N=355, LRRK2-PD: N=118). Results: Seeding was observed in 80% of patients with LRRK2-PD, and in one unaffected variant carrier (AUC=0.97, CI 0.92-1.00). In a meta-analysis across two PD cohorts, higher MGS was associated with increased aSyn seeding (pooled beta=0.38, p=0.028). Conclusions: CSF-based aSyn SAA can discriminate between LRRK2-PD and unaffected carriers. Our findings support an association with mitochondrial burden and aSyn seeding.

Objective: Cognitive impairment is common among older adults with epilepsy, although efficient screening tools suitable for routine use are lacking. Here we examine, for the first time, the utility of the Alzheimers Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) as a screening tool to identify cognitive impairment in older adults with epilepsy. Methods: Participants included 83 adults (ages over 55) with epilepsy from the Brain, Aging, and Cognition in Epilepsy (BrACE) study and 83 age-, sex-, and education-matched cognitively healthy controls from the Alzheimers Disease Neuroimaging Initiative (ADNI-3). All completed the ADAS-Cog and a comprehensive neuropsychological battery to identify cognitive phenotypes (intact vs impaired). Performance on individual ADAS-Cog items and the total score was assessed, and diagnostic efficiency statistics were determined. Results: Epilepsy participants (mean age=66.4 years) performed significantly worse across the ADAS-Cog total score and 8 of the 13 individual test items compared to controls. The largest effect sizes were observed on verbal learning and memory tasks, particularly word recall (d=0.87) and delayed word recall (d=1.06). An ADAS-Cog total score of at or exceeding 15 yielded optimal diagnostic efficiency (67.5% accuracy, 68.8% sensitivity, 66.7% specificity) for identifying cognitive impairment. Significance: The ADAS-Cog is sensitive to detecting cognitive impairment in older adults with epilepsy and may represent a scalable screening option in this population. Additional comparative studies in older epilepsy populations are needed to determine the sensitivity of this measure to longitudinal change, cross-cultural applicability, and availability across languages. Plain language summary: Cognitive decline is common among older adults with epilepsy, although sufficient evidence supporting the use of screening tools to identify cognitive impairment in this population is lacking. The ADAS-Cog may be a useful screening option in epilepsy research and clinical care, although additional studies are needed to compare it with other cognitive screening tests and to confirm its applicability for clinical care and across cultures and healthcare settings.

Background: The clinical applicability of large language models (LLMs) in Parkinson's disease (PD) management remains insufficiently characterized, particularly in generative responses to clinical vignette scenarios. Objective: To evaluate the quality of clinical assessments and management plans generated by a general-purpose LLM (Gemini 1.5 Pro) and a medically specialized LLM (OpenEvidence), and to compare their performance. Methods: Models generated free-text responses to 45 open clinical queries, focused on assessment of the situation, and recommended management plan. Two movement disorders fellows rated outputs using 5-point Likert scales, dichotomized into clinically appropriate ([&ge;]4) versus inappropriate ([&le;]3). Discrepancies were adjudicated by a senior movement disorders specialist. Paired comparisons used McNemar's test; qualitative analysis examined severe errors. Results: Gemini 1.5 Pro and OpenEvidence showed high rates of clinically appropriate assessments (80.0% vs. 86.7%) but lower performance in management plans (48.9% vs. 57.8%). Cases in which both assessment and plan were clinically appropriate occurred in 46.7% and 55.6% of cases, respectively. None of these differences reached statistical significance. Severe errors were uncommon in assessments (6.7% vs. 8.9%) but more frequent in plans (26.7% in both), predominantly reflecting treatment strategy errors. Conclusions: In generative clinical reasoning tasks involving Parkinson's disease management vignettes, LLMs demonstrated reasonable performance in assessment, but consistent limitations in plan generation. The medically specialized LLM demonstrated several qualitative advantages but no statistically significant performance benefit over the general-purpose model. Therefore, these tools should be used with appropriate caution in Parkinson's disease management, particularly regarding treatment recommendations.

ObjectiveAccurate classification of ion channel variants of uncertain significance (VUS) remains a persistent challenge in clinical genomics, limiting diagnostic resolution in neurological disorders. MethodsWe developed a calibrated electrophysiological framework to generate functional evidence for clinical interpretation of CACNA1G variants encoding the low-voltage-activated calcium channel Cav3.1. Functional metrics derived from automated patchclamp recordings were calibrated against benign/likely benign (B/LB) and pathogenic/likely pathogenic (P/LP) reference variants to enable conservative application of ACMG/AMP functional criteria within clinical variant interpretation workflows. ResultsCalibration using 25 B/LB and 16 P/LP CACNA1G variants showed that more than 80% of P/LP variants exhibited reduced current density (CD). Deactivation kinetics ({tau}Deact) provided complementary discriminatory information by identifying gating abnormalities in variants with preserved CD. Application of this dual-metric framework to five VUS identified in Australian patients revealed two variants (Cav3.1-R186Q and R1394Q) with abnormal functional profiles consistent with voltage-sensor perturbation, supporting reassessment as likely pathogenic under ACMG/AMP guidelines. The remaining VUS displayed functional properties overlapping the benign reference distribution. ConclusionThese findings establish a calibrated functional framework for generating electrophysiological evidence that supports clinical interpretation of CACNA1G missense variants under ACMG/AMP guidelines. When applied as external functional evidence, this approach improves resolution of CACNA1G-associated VUS while maintaining conservative standards for variant classification.

Background: Anomia is common in frontotemporal dementia (FTD), although its clinical prominence varies by subtype, with the most marked impairment typically observed in primary progressive aphasia (PPA). It remains unclear whether naming impairment reflects language-specific impairment or broader cognitive severity, and how it relates to other cognitive domains across FTD syndromes. Methods: Fifteen healthy controls and twenty-two individuals across the FTD spectrum, including variant-specified and unclassifiable (NOS) presentations, completed two confrontation naming tasks (Boston Naming Test and Multilingual Naming Test) and a global cognitive screening measure (Montreal Cognitive Assessment, MoCA). Patient participants additionally completed a standardized language battery (Western Aphasia Battery Revised) and a comprehensive neuropsychological assessment (Uniform Data Set). Naming performance was compared between groups and associations with language severity, global cognition, and domain-specific cognitive functions were examined using regression analyses. Results: Naming was impaired in patients relative to healthy controls but did not differ between patient groups. Naming was strongly associated with language severity, but not global cognition. A significant group-by-MoCA interaction indicated that MoCA was positively associated with naming only in the unclassifiable group. In addition, naming was associated with episodic memory across both verbal and non-verbal domains. Conclusions: Naming in FTD primarily reflects language severity rather than global cognitive impairment. A robust association between naming and episodic memory suggests potential contributions from semantic cognition, shared frontally mediated retrieval processes, or parallel cognitive decline. These findings support the use of naming as a marker of language dysfunction while highlighting its relevance to broader cognitive systems in FTD.

PurposeCarrier screening for hereditary conditions is challenged by genes with complex genomic architecture, where short-read sequencing can fail to detect clinically relevant variants. This study evaluated a unified, amplification-based nanopore sequencing workflow across multiple laboratories for comprehensive analysis of such loci. MethodsA modular long-read sequencing assay was evaluated across five laboratories using targeted PCR enrichment, Oxford Nanopore sequencing, and automated variant analysis. The workflow interrogated genes associated with spinal muscular atrophy, thalassemia, cystic fibrosis, fragile X syndrome, congenital adrenal hyperplasia, Gaucher disease, and hemophilia A. Performance was assessed against orthogonal methods for single nucleotide variants (SNVs), indels, copy-number variants, repeat expansions, and structural rearrangements. ResultsAcross 882 unique samples (1,266 tests), overall agreement with comparator methods exceeded 96% for variant-level detection and 97% for genotype status classification. Long-read sequencing enabled phasing of paralogous loci, integrated sizing and interruption analysis for FMR1 repeats, and simultaneous detection of SNVs and structural variants in globin loci and CYP21A2-TNXB region, reducing reliance on multiple workflows. ConclusionThis multisite evaluation suggests that targeted long-read sequencing can consolidate complex variant detection into a single workflow, improving analytical completeness and operational efficiency for carrier screening.

Assessing reproducibility across different molecular profiling studies is a persistent methodological challenge (Zhang et al., 2009; Sweeney et al., 2017; Ioannidis, 2005). Differences in platform technology, cohort composition, analytical pipelines, and feature definitions often make it difficult to interpret cross-study comparisons based solely on gene-identity overlap. In this study, we conducted a retrospective computational analysis of seven publicly available analytical datasets (including alternative analytical pipelines applied to the same cohort) derived from five biologically independent peripheral blood transcriptomic and DNA methylation cohorts, comprising 3,487 samples (1,824 Parkinsons disease cases and 1,663 controls). Reproducibility was evaluated using gene-identity overlap, enrichment-based comparisons, and a permutation-based framework to assess directional consistency of effect estimates across datasets. We also tested the robustness of results by varying false discovery rate thresholds and applying alternative probe-to-gene collapsing strategies. All analyses were performed using reproducible workflows implemented in R and Python with fixed random seeds. Across independent cohorts, gene-identity overlap was generally limited, with enrichment ratios close to one, especially when datasets were generated using different platforms. In several datasets, limited numbers of statistically significant features further constrained overlap-based comparisons. In contrast, directional consistency showed greater stability. High levels of directional consistency were observed across independent cohort comparisons when restricted to overlapping statistically significant features and remained stable across statistical thresholds (90.0% at FDR < 0.05 and 82.8% at FDR < 0.10). When evaluated across the full shared gene universe without conditioning on statistical significance, directional consistency was substantially lower ([~]30 to 32%) but remained significantly above permutation-based null expectations. Permutation testing confirmed that the observed directional consistency exceeded what would be expected by chance. A combined analysis including methodological replicates (n [&ge;] 3 datasets) showed 98.3% directional consistency; however, this estimate includes non-independent analytical pipelines applied to the same cohort and reflects analytical stability rather than independent biological replication. Rather than introducing a new statistical method, this study examines how commonly used reproducibility metrics behave under crossstudy heterogeneity and identifies their practical limitations and appropriate use boundaries.

Importance: Leukodystrophies are a heterogeneous group of genetic disorders affecting the white matter of the brain, often presenting with overlapping clinical features but differing in neuroanatomical involvement. There is a critical need for quantitative tools to characterize disease burden and support diagnosis, severity stratification, and clinical trial readiness. Objective: To characterize shared and distinct neuroanatomical patterns across six genetically confirmed leukodystrophies using anatomical MRI-derived phenotypes benchmarked against brain growth charts, and to assess the utility of this methodological approach for identifying imaging biomarkers of disease severity. Design, Setting, and Participants: Cross-sectional neuroimaging study using retrospective clinical MRI data. Setting: Multicenter study incorporating data from the Global Leukodystrophy Initiative Clinical Trials Network (GLIA-CTN) and control data from the Childrens Hospital of Philadelphia. Participants: The study included 434 MRI scan sessions from 274 patients with genetically confirmed leukodystrophies (Pelizaeus-Merzbacher disease, Metachromatic leukodystrophy, Alexander disease, Aicardi-Goutieres syndrome, TUBB4A-related leukodystrophies, and POLR3-related leukodystrophy). Control MRI data (7628 scans from 7205 subjects) were drawn from the Scans with Limited Imaging Pathology cohort at the Children's Hospital of Philadelphia. Exposures: All MRI scans underwent automated segmentation using deep learning segmentation tools to derive global and regional brain volumes. Normative models of brain development ("brain growth charts") were generated for the control cohort using generalized additive models for location, scale, and shape. Centile scores were then calculated for leukodystrophy subjects to quantify deviations from typical development. Main Outcomes and Measures: Centile scores for global and regional brain volumes were compared across leukodystrophy subtypes to identify disease-specific neuroanatomical patterns and to evaluate their potential utility for severity stratification. Results: Distinct patterns of neuroanatomical deviation were observed across leukodystrophy subtypes. Certain leukodystrophies showed preferential involvement of specific cortical or subcortical regions, while others displayed more diffuse volume loss. Centile scores demonstrated potential for differentiating disease subtypes and stratifying individuals by severity. Preliminary longitudinal data suggest centile scores may also track progression over time. Conclusions and Relevance:This study demonstrates the feasibility and utility of MRI profiling of individuals with leukodystrophy using anatomical MRI-derived phenotypes benchmarked against brain growth charts. The approach enables data-driven, quantitative characterization of structural brain abnormalities, offering a scalable method for phenotyping, diagnosis, and future use in clinical trials.

AIM: STXBP1-related disorder (STXBP1-RD) is a severe developmental and epileptic encephalopathy characterized by early-onset seizures and persistent cognitive and motor impairments. With disease-modifying trials emerging, a disorder-specific severity scale is needed. To address this, we adapted a validated clinician-reported measure from CDKL5 Deficiency Disorder to develop the STXBP1 Clinical Severity Assessment (S-CSA) and evaluated its psychometric properties. METHOD: The S-CSA was adapted from the CDKL5 Clinical Severity Assessment through expert consensus sessions with STXBP1 clinicians. Revisions addressed gaps in motor and vision domains, adding tremor and vision items. The measure was administered to 123 individuals with STXBP1-RD. Psychometric evaluation included confirmatory factor analysis, internal consistency, composite reliability, average variance extracted, and distinctiveness, compared with recommended thresholds. RESULTS: Analyses supported a three-domain structure (motor, communication, vision) with factor loadings >0.5 and strong internal consistency (Cronbachs alpha >0.7; composite reliability >0.88). Model fit and variance metrics met recommended standards, and domains demonstrated distinctiveness. No ceiling or floor effects were observed. Minimal skew was seen in motor (0.34) and communication (0.16) domains; positive skew in vision (2.2) was seen, identifying patients with and without cortical visual impairment. INTERPRETATION: The S-CSA demonstrates strong validity and reliability in STXBP1-RD and may show utility in clinical trials for STXBP1-RD and potentially other severe DEEs. Key Words: STXBP1-Related Disorder, Developmental and Epileptic Encephalopathies, Clinical Outcome Assessments

Huntingtons disease (HD) is a neurodegenerative disorder caused by CAG repeat expansion in the huntingtin (HTT) gene, with longer repeats linked to earlier onset. Somatic CAG expansion, particularly in the striatum, contributes to disease progression and is influenced by HTT biology and genetic modifiers. Modulating somatic expansion is emerging as a promising approach to slow or prevent HD, and mouse models have been crucial for preclinical testing of different therapeutic strategies. The BAC-CAG model, developed on the FVB strain, has been used to study somatic expansion of human expanded HTT. However, comparisons with other key HD mouse models have been limited by differences in genetic background, as many other models are on the C57BL/6 strain. The BAC-CAG model has now been developed on a C57BL/6 background. To determine whether the C57BL/6 BAC-CAG model can be used to study and modulate somatic expansion, we compared CAG expansion in mice on C57BL/6 or FVB backgrounds, with and without intraventricular divalent small interfering RNAs (siRNA) targeting HD modifiers MutS homolog 3 (MSH3) and HTT. Both strains exhibited robust, comparable somatic expansion over two months, which was blocked by MSH3-, but not HTT-, targeted siRNA. RNA sequencing identified gene expression differences primarily in pseudogenes, with no differences in endogenous Htt, human HTT, or mismatch repair genes. These results demonstrate that BAC-CAG mice on a C57BL/6 background exhibit somatic CAG expansion comparable to the validated FVB strain, providing a model to study and preclinically test therapies targeting somatic expansion in HD.

BackgroundCLN3 disease, also known as juvenile neuronal ceroid lipofuscinosis, is a rare and neurodegenerative disorder characterized by the accumulation of lipopigments in the cells, progressive cognitive decline, seizures, and vision loss. Biomarker discovery in CLN3 disease is essential for enabling early and accurate diagnosis, which is critical given its neurodegenerative course. Biomarkers provide objective measures to track disease progression, stratify patients, and serve as surrogate endpoints in clinical trials, thereby accelerating therapeutic development. They also offer valuable insights into underlying disease mechanisms and treatment response, ultimately advancing individualized medicine and improving clinical outcomes. MethodsWe developed various machine learning models to predict potential protein biomarkers in CLN3 disease using proteomics data and laboratory tests collected from participants in a prospective, observational cohort. To prioritize and evaluate these candidates, we conducted protein-protein interaction (PPI) network analysis and pathway enrichment, ranking proteins based on their topological importance. The top 20 proteins were selected as candidate biomarkers and corroborated using a publicly available CLN3 transcriptomic dataset. Receiver operating characteristic (ROC) curve analysis was performed to assess the discriminative power of each candidate, with AUROC values calculated to quantify their classification performance. ResultsOur computational approach identified six promising biomarker candidates: OSM, IL6R, LMNB1, HIF1A, NPM1, and CSF1. Among them, OSM and HIF1A showed marked differential expression in CLN3 patients, particularly those with slow disease progression. LMNB1 expression was elevated in patients with faster disease progression, suggesting its utility as a prognostic biomarker. These findings highlight the robustness of our biomarker selection, indicating that these six genes may serve as effective diagnostic markers for CLN3 disease. ConclusionsOur findings demonstrate the utility of data-driven approaches for biomarker discovery in CLN3 and offer new insights into the molecular mechanisms of the disease, with broader implications for improving diagnosis and prognosis in other rare diseases.

Multiple plasma phosphorylated tau assays are now commercially available for detection of Alzheimers disease (AD) pathology, yet clinical laboratories lack a comprehensive comparative evaluation to guide implementation decisions. Diagnostic accuracy and analytical performance were assessed in a cohort of 273 participants with paired EDTA plasma and CSF specimens. CSF AD core biomarkers were used as the reference standard, and index tests included three plasma pTau217 assays by Roche, Fujirebio, and Meso Scale Discovery [MSD], and a pTau181 assay by Roche. Participants had a median age of 70 [IQR: 64-76] years, 42% were female and 60% were AD-positive. Diagnostic performance was statistically similar across all pTau217 assays (range: 0.88-0.89 area under the receiver operating characteristic curve [AUC]) with the pTau181 assay having lower accuracy (AUC = 0.85). All assays were resistant to hemolysis, icterus, and lipemia. Automated assays (Roche, Fujirebio) showed superior analytical precision and freeze/thaw stability ([&ge;]6 cycles) compared to the manual MSD assay (2 cycles). Given that plasma pTau217 assays demonstrated high and comparable accuracy in this head-to-head comparison, their differences in analytical performance characteristics and general clinical laboratory suitability became the differentiating factors for clinical implementation.