Annals of Neurology最新文献

Background: Amyotrophic lateral sclerosis (ALS) shows sex differences in incidence and age of onset, yet the underlying biological mechanisms remain poorly understood.

Methods: We investigated sex-specific genetic architecture in an Italian ALS cohort with whole-genome sequencing (1,333 ALS cases, 755 controls). We performed a sex-stratified burden analysis of rare variants in ALS-associated genes and compared the proportions of male and female ALS patients carrying pathogenic or rare damaging variants. Key findings were replicated in the AnswerALS cohort (n = 723). Gene-specific sex ratios and familial history for C9ORF72, SOD1, and TARDBP were examined in an expanded dataset of 2,301 Italian ALS patients.

Results: Sex-stratified burden testing revealed that rare variants in ALS genes were enriched in female cases versus controls (odds ratio [OR] 5.47, 95% confidence interval [CI] 1.60-34.29) but not in male cases. Female ALS patients more frequently carried rare damaging variants compared to males (23.2% vs 18.3%; OR 1.38, 95% CI 1.05-1.81), a finding that was replicated in the AnswerALS cohort (18.9% vs 12.4%; OR 1.58, 95% CI 1.10-2.26). Gene-level analyses of TARDBP carriers revealed a male predominance (2.1:1), yet a higher rate of familial history among females (40.4% vs 24.5%; OR 2.13, 95% CI 1.03-4.39).

Interpretation: Females with ALS exhibited a higher overall burden of rare damaging variants, suggesting sex-related differences in genetic liability. Gene-level analyses indicate that the influence of sex varies across ALS genes, particularly TARDBP. These findings help explain epidemiological patterns and have implications for the identification of sex-linked protective mechanisms. ANN NEUROL 2026.

Objective: Genomic sequencing leaves >50% of dystonia-affected individuals without a diagnosis. Where DNA-oriented approaches remain insufficient, integrating multiomics is essential to advance genome interpretation. Herein, we incorporated RNA sequencing (RNA-seq) data from 167 patients with dystonia across a range of ages and presentations.

Methods: We leveraged an RNA-seq analysis pipeline, focused on the identification of expression and splicing aberrations, on RNA-seq from skin biopsies. The recruited patients had early-onset dystonia in 85.0%, non-focal dystonia in 92.2%, and coexisting features in 76.0%. Thirty-six patient samples with pre-identified variants (36/167, 21.6%) and 131 samples with no previously prioritized diagnostic candidates from genomic sequencing (131/167, 78.4%) were evaluated.

Results: We found that >80% of dystonia-associated genes were detected by fibroblast RNA-seq. Expression and splicing aberration analyses produced a manageable number of significant RNA defects affecting dystonia-associated genes. The approach was especially successful in validating pathogenic effects of loss-of-function variants, with disease-relevant RNA-underexpression detected for 66.7% (10/15). Studying aberrant expression and splicing in the context of other pre-identified variant types yielded relevant results in 28.6% (6/21 samples). We obtained a 6.9% (9/131) diagnostic uplift for patients without prior candidates, all of whom exhibited combined dystonia with autosomal recessive inheritance. The new diagnoses from RNA-seq and genomic reanalysis were based on previously neglected splice-region (3/9) and deep(er) intronic (6/9) variants. For the observed events, integration of new machine-learning scores predicted corresponding aberrant gene expression in the brain.

Interpretation: Fibroblast-based RNA-seq in our selected cohort improved variant interpretation and offered a modest yield in patients without prior candidate variants. ANN NEUROL 2026.

Objective: Age of symptom onset is highly variable in familial frontotemporal lobar degeneration (f-FTLD). Accurate prediction of onset would inform clinical management and trial enrollment. Prior studies indicate that individualized maps of brain atrophy can predict conversion to dementia in f-FTLD. We used a Bayesian linear mixed-effect (BLME) prediction method for identifying accelerated brain volume loss to predict conversion to dementia.

Methods: Participants included 234 asymptomatic or prodromal carriers of C9orf72, GRN, or MAPT mutations (including 21 dementia converters) with ≥3 longitudinal magnetic resonance imaging (MRI) T1-weighted scans. The BLME models established individual voxel-wise gray matter trajectories using the first 2 scans. Person-specific clusters of accelerated volume loss were estimated in subsequent scans and tested as predictors of dementia conversion compared with other approaches in time-varying Cox proportional hazard models covarying for age. Receiver-operating characteristic (ROC) curves estimated utility of cluster volume in discriminating which participants converted to dementia within 24 months.

Results: The BLME cluster volume predicted conversion to dementia in f-FTLD mutation carriers overall and separately in C9orf72, GRN, and MAPT, with comparable hazard ratios observed for atrophy W-maps and regional volumes. Within a 24-month timeframe, BLME cluster volume discriminated dementia converters from non-converters with larger areas under the curve (AUCs) than other approaches.

Interpretation: Bayesian-modeled individualized atrophy scores predict dementia progression among asymptomatic f-FTLD mutation carriers and may have increased utility compared with other structural imaging methods when studying individuals over shorter timeframes that align with clinical trial design. ANN NEUROL 2026.

Objective: The optimal treatment for distal medium vessel occlusion (DMVO) stroke remains uncertain, and evidence comparing endovascular therapy (EVT) with medical management (MM) is limited. We aimed to develop and validate a predictive modeling tool to assess individual treatment benefit in DMVO stroke using explainable counterfactual treatment estimation.

Methods: Adults with isolated DMVO stroke (M3-M4, A2-A3, or P1-P2) were retrospectively identified from 7 stroke centers. To estimate individualized probabilities of favorable outcome (modified Rankin Scale [mRS] = 0-2 at 90 days), we developed a Penalized Logistic Regression (Elastic Net) model. This framework was selected a priori over other explored machine learning algorithms (Decision Tree, Support Vector Classifier, and XGBoost) for its superior interpretability and ability to handle multicollinearity among interaction terms. Inverse Probability of Treatment Weighting (IPTW) was implemented to address confounding by indication in the observational data. Internal validation used repeated K-fold cross-validation and bootstrapping; external validation was performed on an independent cohort (n = 86).

Results: Of 321 eligible patients, 179 received EVT (55.8%) and 142 received MM (44.2%). Adjusted models showed no significant overall group differences in favorable outcome (adjusted OR [aOR] = 1.32, 95% confidence interval [CI] = 0.97-1.80), mortality (aOR = 1.20, 95% CI = 0.78-1.85), or symptomatic hemorrhage (aOR = 0.57, 95% CI = 0.21-1.58). However, the model identified significant treatment effect heterogeneity; EVT benefit was amplified in patients with higher National Institutes of Health Stroke Scale (NIHSS) and attenuated with increasing treatment delay. Internal validation demonstrated strong performance (area under the receiver operating characteristic curve [AUC] = 0.77, 95% CI = 0.71-0.82). External validation confirmed generalizability (AUC = 0.74, 95% CI = 0.63-0.84). Individualized treatment estimates showed high concordance with a benchmark causal T-Learner model (Pearson r = 0.97 internal and r = 0.98 external).

Interpretation: Although aggregate outcomes did not differ significantly, the validated Distal and Medium Vessel Occlusion Stroke (DUSK) Tool enables individualized estimation of EVT benefit in DMVO stroke. This explainable counterfactual treatment estimation framework supports precision decision making by identifying specific patient subgroups most likely to benefit from EVT over MM. ANN NEUROL 2026.

Objective: Sudden unexpected death in epilepsy (SUDEP) is a devastating consequence of some generalized convulsive seizures (GCS). Recent work has focused on seizure related apnea as a biomarker of SUDEP risk, frequently without characterizing the adequacy of non-apneic ventilation or identifying other dysfunctional breathing patterns. We hypothesized that GCS frequently induce immediate, severe, non-apneic respiratory dysfunction that can induce critical hypoxia and bradycardia and sought to characterize breathing patterns after GCS.

Methods: Adult patients admitted to an epilepsy monitoring unit were studied. The effects of GCS on breathing and heart rate were analyzed using nasal pressure transducers, chest and abdominal respiratory inductance plethysmography, capillary oxygen saturation, transcutaneous CO₂, electrocardiogram, electroencephalogram, and expert audiovisual analysis. Correlation analyses, the Mann-Whitney test, and an unpaired t test were used to analyze relationships between dysfunctional breathing patterns and both the severity of postictal hypoxemia and the heart rate.

Results: Thirty-two GCS from 22 patients were analyzed and 31 exhibited 1 or more of the following breathing patterns: disordered rhythmicity (n = 28/32, 87.5%), shallow breathing (n = 12/32, 37.5%), thoracoabdominal asynchrony (n = 24/30, 80.0%), and upper airway obstruction (n = 30/32, 93.8%). Oxygen desaturation was more severe when postictal breathing was shallow or irregular in amplitude. The latter was associated with absolute or relative bradycardia.

Interpretation: Nonfatal GCS frequently induce immediate, severe, non-apneic respiratory dysfunction temporally associated with severe hypoxia and bradycardia. Our study suggests that postictal respiratory and cardiac function are tightly coupled and highlights the importance of including all the relevant pathologic variables in studies of SUDEP pathogenesis. ANN NEUROL 2026.

Objective: Quaking-induced conversion (QuIC) tests, which detect prion-seeding activity in cerebrospinal fluid (CSF), have markedly advanced the antemortem diagnosis of prion diseases such as Creutzfeldt-Jakob disease (CJD). These tests provide high diagnostic accuracy and enable timely differentiation from other rapidly progressive neurodegenerative disorders. However, a key limitation of current QuIC tests are the reduced sensitivity in detecting inherited prion diseases and rare sporadic subtypes, including variably protease-sensitive prionopathy (VPSPr). To address this gap, we evaluated a simplified QuIC test, end-point QuIC (EP-QuIC), incorporating a novel recombinant prion protein substrate derived from the North American deer mouse (Peromyscus maniculatus).

Methods: The diagnostic performance of the modified QuIC test was evaluated using CSF samples from 61 sporadic CJD, 50 inherited prion disease, and 5 VPSPr cases.

Results: EP-QuIC with the deer mouse substrate achieved 96.6% sensitivity (111/116) and 100% specificity (35/35), outperforming both standard EP-QuIC (87.1%) and next-generation (IQ-CSF) real-time-QuIC (72.4%) across the same cohort. Notably, this enhanced assay detected inherited mutations, such as D178N, that were previously undetectable with existing diagnostic tests.

Interpretation: These findings demonstrate that adapting EP-QuIC with an optimized substrate, termed enhanced sensitivity QuIC (ES-QuIC), significantly improves diagnostic performance for inherited and atypical prion diseases. By expanding the diagnostic reach of QuIC tests, this study strengthens antemortem surveillance, reduces reliance on postmortem confirmation, and improves opportunities for early intervention and clinical trial enrollment, particularly for genetic cases most likely to benefit from emerging therapeutic strategies. ANN NEUROL 2026.