Annals of Clinical and Translational Neurology

BACKGROUNDSignificant diagnostic delays are common in primary ciliary dyskinesia (PCD), a rare disease that is significantly underdiagnosed. Scalable screening methods could improve early identification and health outcomes. RESEARCH QUESTIONCan machine learning (ML) be used to screen for PCD in pediatric patients? STUDY DESIGN AND METHODSWe evaluated the feasibility of a random forest model to screen for PCD using data from the PCD Foundation Registry and a national claims database. We identified a cohort of pediatric patients with diagnostic codes indicative of conditions potentially associated with PCD, and studied diagnostic, procedural, and pharmaceutical codes associated with PCD to develop ML features. Models were trained on composite claims data from confirmed patients with PCD, patients with Q34.8 (Specific Congenital Malformation of the Respiratory System) diagnosed within six months of an Electron Microscopy procedure (Q34.8+EM), and a randomly-selected, matched control group. Model performance was tested through 5-fold cross-validation. RESULTSUsing 82 confirmed PCD cases and 4,161 matched controls, the model demonstrated variable performance (positive predictive value 0.45-0.73, sensitivity 0.75-0.94). Synthetic data augmentation did not improve results (positive predictive value 0.45-0.67, sensitivity 0.71-1.00). Expanding the dataset to include 319 Q34.8+EM patients and 8,214 controls improved performance (positive predictive value 0.51-0.54, sensitivity 0.82-0.90), suitable for screening. In a cohort of 1.32 million pediatric patients, 7,705 were classified as positive, consistent with the estimated prevalence of PCD (1:7,554). INTERPRETATIONThis study demonstrates the feasibility of using ML to screen for PCD using claims data, even in the absence of a specific International Classification of Disease (ICD) code. Such screening approaches may aid in the identification of individuals who may benefit from timely diagnostic testing and targeted interventions.

BackgroundDifferentiating demyelinating from axonal polyneuropathies is essential for accurate diagnosis and treatment. We hypothesized that multiparametric quantitative MRI (qMRI) of peripheral nerves can differentiate demyelination from axonal loss. This retrospective study leveraged genetically defined demyelinating and axonal polyneuropathies to test this concept. MethodsMultiparametric qMRI data of proximal (sciatic) and distal (tibial) nerves were acquired on 3T MRI, including magnetization transfer ratio (MTR), MT saturation index (MTsat), T *, T, proton density (PD), fractional anisotropy (FA), mean/axial/radial diffusivities (MD, AD, RD), and fascicular volume (fVol). Data were analyzed from patients with Charcot-Marie-Tooth type 1 (CMT1, de-/dys-myelinating, n=19), CMT2 (axonal, n=12), hereditary neuropathy with liability to pressure palsies (HNPP, a cohort who often has intermediate changes between the two classifications, n=25), and health controls (HC, n=25). A composite qMRI score, as CMT Imaging Score (CMTIS), was developed to predict disease severity using the CMT Neuropathy Score version-2 (CMTNSv2) as a clinical reference. Receiver operating characteristic (ROC) analyses assessed diagnostic performance. ResultsCMT1 showed significantly increased fVol versus HCs, while CMT2 demonstrated reduced T2*. Both CMT1 and CMT2 exhibited reduced FA, MTsat, and AD, along with elevated T1 and RD, with larger abnormalities in CMT1. ROC analyses demonstrated strong discrimination of CMT1 and CMT2 (AUCs: 0.95 and 0.85 for sciatic; 0.89 and 0.73 for tibial nerves). CMTIS correlated strongly with CMTNSv2 (r=0.67 sciatic; r=0.72 tibial; r=0.79 combined). ConclusionsMultiparametric qMRI identifies distinct imaging signatures of demyelinating versus axonal hereditary polyneuropathies. The CMTIS shows strong potential as a biomarker for disease monitoring. DATA AVAILABILITYAnonymized data used in this study is available from the corresponding author upon request and subject to institutional approvals. KEY MESSAGESO_ST_ABSWhat is already known on this topicC_ST_ABSCurrent electrophysiological tools are limited in their ability to differentiate demyelinating from axonal polyneuropathies when pathology occurs in proximal nerves. Quantitative MRI (qMRI) can assess proximal demyelination and axonal loss; however, individual qMRI metrics lack sufficient sensitivity for reliable differentiation. What this study addsThis proof-of-concept study demonstrates the feasibility of using multiparametric qMRI for patient stratification and for distinguishing demyelinating from axonal inherited polyneuropathies. The proposed composite qMRI score shows a strong correlation with clinical disease severity. How this study might affect research, practice, or policyThis study suggests that multiparametric qMRI of peripheral nerves can serve as a non-invasive adjunct to distal nerve conduction studies for improving diagnosis and treatment management of polyneuropathies. The composite qMRI score also shows potential as a monitoring biomarker for tracking disease progression.

ObjectiveCognitive motor dissociation (CMD) is associated with long-term recovery in acute brain injury, but CMD testing is only available in few centers. Our objective was to identify surface EEG patterns with high sensitivity or positive predictive value (PPV) for CMD in patients with acute disorders of consciousness to refine allocation of this resource-intensive test. MethodsIn this observational cohort study, we enrolled clinically unresponsive, acutely brain injured patients who underwent continuous surface EEG and CMD assessments. CMD was detected by applying a machine learning algorithm to EEG acquired during a motor command paradigm presentation. Electroencephalographers blinded to CMD test results applied standardized ACNS criteria to the EEGs acquired during CMD assessments. We calculated accuracy measures of surface EEG findings for CMD test results using generalized estimating equations, with an exchangeable matrix and accounting for repeated measures per patient. ResultsWe included 185 patients (mean age: 62 {+/-} 17; 85 [46%] female) and 282 CMD assessments. CMD testing was positive in 39 (14%) assessments. Sensitivity and PPV of normal background voltage, symmetry and continuity were respectively 77% (95%-CI: 60-88%) and 19% (95%-CI: 13-26%), 74% (95%-CI: 58-86%) and 14% (95%-CI: 10-20%), and 74% (95%-CI: 58-86%) and 14% (95%-CI: 9-19%). All EEGs with burst suppression, suppression, sporadic epileptiform discharges, lateralized periodic discharges, bilateral independent periodic discharges, electrographic seizures and brief potentially ictal rhythmic discharges had negative CMD tests. InterpretationSurface EEG findings are not reliable to screen for CMD or to identify patterns conferring higher CMD pretest probability.

AimTo determine an objective and clinically-feasible method to predict dystonia in cerebral palsy (CP) using magnetic resonance imaging (MRI) following neonatal hypoxic-ischemic encephalopathy (HIE). MethodsIn this retrospective case-control study, we examined brain MRIs in neonates at age 4-5 days who underwent therapeutic hypothermia for HIE at a single tertiary care center. The lower average apparent diffusion coefficient (ADC) values between the left and right striatum and thalamus were determined using clinically-integrated software (IBM iConnect Access). Neonatal neurology, movement disorder, and cerebral palsy specialist notes were screened through age 5 years for motor abnormality documentation. ResultsIn 50 subjects, ADC values significantly predicted dystonia in CP with receiver operator characteristic areas under the curve of 0.862 (p = 0.0004) in the striatum and 0.838 (p = 0.001) in the thalamus. Striatal ADC values less than 1.014x10-3 mm2/s provided 100% specificity and 70% sensitivity for dystonia. Thalamic ADC values less than 0.973x10-3 mm2/s provided 100% specificity and 80% sensitivity for dystonia. InterpretationIn this small retrospective study, analysis of clinically-acquired MRIs predicted dystonia with high specificity following neonatal HIE. This could be a useful prognostication adjunct guiding when to establish appropriate vigilance for dystonia in CP.

STXBP1-Related Disorders (STXBP1-RD) are a spectrum of neurodevelopmental conditions that often present with prominent motor impairments which affect fine and gross motor development and are in part attributed to abnormalities of neuromotor control, such as involuntary movement, dystonia, tremor, and ataxia. There is a lack of precise, scalable measurement tools to characterize motor control abnormalities, particularly in children with physical and intellectual disability who cannot complete traditional testing protocols, which has contributed to an incomplete understanding of movement disorders within the broader disease trajectory of STXBP1-RD. Emerging wearable sensor technology has the potential to meet this need. Here, we quantified the prevalence and clinical features of tremor and other motor control impairments in 31 individuals with STXBP1-RD, 64.5% with tremor, compared to 19 typically developing controls during a simple reaching task using inertial sensors on the upper arms and forearms. We then evaluated the clinical relevance of the sensor-derived metrics of motor control in the context of traditional developmental clinical assessments and additional data sources, including previously published retrospective studies, aggregated EMR data, and a prospective natural history study. Individuals with STXBP1-RD demonstrated slower reaching time, decreased arm acceleration intensity, less smooth reaching, and tremor frequency characteristics consistent with an unstable or irregular rhythmic movement pattern. Combining features from all four upper limb sensors predicted tremor with the highest accuracy (AUC = 0.87) over combinations with fewer sensors, and multiple motor control metrics were significantly related to clinical assessment scores, including those from the Bayley Scales of Infant and Toddler Development, Peabody Developmental Motor Scales and the Gross Motor Function Measure-66. We also found that more than 65% of individuals with STXBP1-RD present with tremor in the natural history study cohort. This prevalence exceeds by fivefold what has been reported in other genetic neurodevelopmental disorders, suggesting that tremor may be heavily underreported in historical, retrospective datasets. Our study provides a novel understanding of the motor control features in one of the most common genetic neurodevelopmental disorders and establishes a robust paradigm to quantify neuromotor control in individuals with developmental differences. Our observations demonstrate strong relations between motor control measures and developmental function in this population. With a range of personalized therapies currently in development for STXBP1-related disorders, assessment of neuromotor control and tremor in this cohort could serve as biomarkers for future clinical trials.

Background and ObjectivesSporadic inclusion body myositis (IBM) is the most common acquired myopathy in individuals over age 50. The disorder is slowly progressive and while many therapies have been investigated, response has generally been poor. Clinical heterogeneity may influence treatment responsiveness; however, data regarding heterogeneity in IBM is limited and often conflicting. We aim to identify clinically distinct subgroups within a large IBM cohort, as well as prognostic factors for disease progression. MethodsClinical, histologic, radiologic, and electrophysiologic data were analyzed for all patients with IBM and other forms of myositis enrolled in a longitudinal cohort from The Johns Hopkins Myositis Center from 2003-2018. Univariate, multivariate, and graphical analyses were used to identify prognostic factors in IBM patients. ResultsAmong the 335 IBM patients meeting inclusion criteria, 64% were male with an average age of disease onset of 58.7 years and a delay to diagnosis of 5.2 years. Initial misdiagnosis (52%) and immunosuppressant treatment (42%) were common. Less than half (43%) of muscle biopsies demonstrated all three pathologic hallmarks: endomysial inflammation, mononuclear cell invasion, and rimmed vacuoles. Black patients had significantly weaker arm abductors, hip flexors, and knee flexors compared to non-Black patients but were less likely to develop dysphagia. Female patients had stronger finger flexors and knee extensors compared to their male counterparts but were more likely to develop dysphagia. A significant number (20%) of patients had an age of onset less than 50 years. This group of younger patients was weaker at their first visit; however, this may be accounted for by a longer disease duration at first visit. DiscussionAlthough IBM has long been considered a disorder predominately of older, White men, female, and non-White patients comprise a significant proportion of the IBM population. Our study demonstrates that female and Black patients have distinct clinical phenotypes within the overarching IBM clinical phenotype.

ObjectivesLeg dystonia in cerebral palsy (CP) is debilitating but remains underdiagnosed. Routine clinical evaluation has only 12% accuracy for leg dystonia diagnosis compared to gold-standard expert consensus assessment. We determined whether expert-cited leg dystonia features could be quantified to train machine learning (ML) models to detect leg dystonia in videos of children with CP. MethodsEight pediatric movement disorders physicians assessed 298 videos of children with CP performing a seated task at two CP centers. We extracted leg dystonia features cited by these experts during consensus-building discussions, quantified these features in videos, used these quantifications to train 4664 ML models on 163 videos from one center, and tested the best performing models on a separate set of 135 videos from both centers. ResultsWe identified 69 quantifiable features corresponding to 12 expert-cited leg dystonia features. ML models trained using these quantifications achieved 88% sensitivity, 74% specificity, 82% positive predictive value, 84% negative predictive value, and 82% accuracy for identifying leg dystonia across both centers. Of the 25 features contributing to the best performing ML models, 17 (68%) quantified leg movement variability. We used these ML models to develop DxTonia, open-source software that identifies leg dystonia in videos of children with CP. InterpretationDxTonia primarily leverages detection of leg movement variability to achieve 82% accuracy in identifying leg dystonia in children with CP, a significant improvement over routine clinical diagnostic accuracy of 12%. Observing or quantifying leg movement variability during a seated task can facilitate leg dystonia detection in CP.

Background DYT1 dystonia is a neurological movement disorder characterized by painful sustained muscle contractions resulting in abnormal twisting and postures. In a subset of patients, it is caused by a loss-of-function mutation (ΔE302/303; or ΔE) in the luminal ATPases associated with various cellular activities (AAA+) protein torsinA encoded by the TOR1A gene. The low penetrance of the ΔE mutation (∼30-40%) suggests the existence of unknown genetic modifiers of DYT1 dystonia.Methods To identify these modifiers, we performed whole exome sequencing of blood leukocyte DNA isolated from two DYT1 dystonia patients, three asymptomatic carriers of the ΔE mutation, and an unaffected adult relative.Results A total of 264 DYT1 dystonia-associated variants (DYT1 variants) were identified in 195 genes. Consistent with the emerging view of torsinA as an important regulator of the cytoskeleton, endoplasmic reticulum homeostasis, and lipid metabolism, we found DYT1 variants in genes that encode proteins implicated in these processes. Moreover, 40 DYT1 variants were detected in 32 genes associated with neuromuscular and neuropsychiatric disorders.Conclusion The DYT1 variants described in this work represent exciting new targets for future studies designed to increase our understanding of the pathophysiology and pathogenesis of DYT1 dystonia.Competing Interest StatementThe authors have declared no competing interest.View Full Text

BackgroundPreterm birth is associated with an increased risk for neurodevelopmental impairments, requiring brain monitoring using amplitude-integrated electroencephalography (aEEG). While established tools detect severe dysfunction (e.g., Hellstrom-Westas classification), methods for assessing mild to moderate impairments--such as Burdjalov scoring or expert-based Sleep-Wake Cycle identification--are subjective and require specialized training. Automated neonatal sleep-staging models usually rely on polysomnography from term infants, a resource-intensive method rarely feasible in NICUs, where simplified single-channel aEEG is standard. MethodsaEEG recordings from 40 neurologically healthy neonates (32-42 weeks PMA) were collected and annotated for quiet (QS) and non-quiet sleep (NQS) by an expert clinician. Signals were bandpass filtered, segmented into 30 s epochs, and cleaned using impedance thresholds. 69 temporal, spectral, wavelet, EMG-inspired, and aEEG-envelope features were extracted. The 5 most relevant features were selected for QS/NQS classification using several machine-learning models validated with leave-one-subject-out cross-validation. A partial least squares model was then trained on QS-derived features to predict postmenstrual age and assess correlations with brain maturation. ResultsThe k-Nearest Neighbors (KNN) classifier showed the best QS/NQS discrimination, with mean Cohens{kappa} = 0.69 {+/-} 0.14 for preterm (32-37 weeks PMA) and 0.48 {+/-} 0.21 for term infants. QS-derived features correlated strongly with postmenstrual age (PMA). The PLS model predicted PMA with an average error of 0.88 weeks (MSE = 1.33 weeks, r = 0.91), while the fully automated version using predicted QS segments yielded an error of 1.08 weeks (r = 0.86). ConclusionAutomated QS/NQS detection from single-channel aEEG is feasible in preterm neonates. Despite reduced accuracy in term infants, QS-derived features closely track brain maturation, supporting the potential of aEEG-based models for objective, early detection of neuromaturation delays in preterm infants

Background and ObjectivesIdentifying MS in children early and distinguishing it from other neuroinflammatory conditions of childhood is critical, as early therapeutic intervention can improve outcomes. The anterior visual pathway has been demonstrated to be of central importance in diagnostic considerations for MS and has recently been identified as a fifth topography in the McDonald Diagnostic Criteria for MS. Optical coherence tomography (OCT) provides high-resolution retinal imaging and reflects the structural integrity of the retinal nerve fiber and ganglion cell inner plexiform layers. Whether multimodal deep learning models can use OCT alone to diagnose pediatric MS (POMS) is unknown. MethodsWe analyzed 3D OCT scans collected prospectively through the Neuroinflammatory Registry of the Hospital for Sick Children (REB#1000005356). Raw macular and optic nerve head images, and 52 automatically segmented features were included. We evaluated three classification approaches: (1) deep learning models (e.g. ResNet, DenseNet) for representation learning followed by classical ML classifiers, (2) ML models trained on OCT-derived features, and (3) multimodal models combining both via early and late fusion. ResultsScans from individuals with POMS (onset 16.0 {+/-} 3.1 years, 51.0%F; 211 scans) and 29 children with non-inflammatory neurological conditions (13.1 {+/-} 4.0 years, 69.0%F, 52 scans) were included. The early fusion model achieved the highest performance (AUC: 0.87, F1: 0.87, Accuracy: 90%), outperforming both unimodal and late fusion models. The best unimodal feature-based model (SVC) yielded an AUC of 0.84, F1 of 0.85 and an accuracy of 85%, while the best image-based model (ResNet101 with Random Forest) achieved an AUC of 0.87, F1 of 0.79, and accuracy of 84%. Late fusion underperformed, reaching 82% accuracy but failing in the minority class. DiscussionMultimodal learning with early fusion significantly enhances diagnostic performance by combining spatial retinal information with clinically relevant structural features. This approach captures complementary patterns associated with MS pathology and shows promise as an AI-driven tool to support pediatric neuroinflammatory diagnosis.

ObjectiveReliable, circulating biomarkers for Duchenne, Becker and facioscapulohumeral muscular dystrophies (DBMD and FSHD) remain unvalidated. Here, we investigated the plasma extracellular vesicle (EV) proteome to identify disease-specific biomarkers that could accelerate therapy approvals. MethodsWe extracted EVs from the plasma of DBMD and FSHD patients and healthy controls using size-exclusion chromatography, conducted mass spectrometry on the extracted EV proteins, and performed comparative analysis to identify disease-specific biomarkers. We correlated the levels of these biomarkers with clinical outcome measures and confounding factors. ResultsThe muscle-associated proteins PYGM, MYOM3, FLNC, MYH2 and TTN were exclusively present in DBMD EVs. PYGM, MYOM3, and TTN negatively correlated with age. PYGM and MYOM3 levels were elevated in patients without cardiomyopathy, and PYGM levels were specifically elevated in ambulatory DMD patients. On the other hand, female FSHD patients displayed significantly higher MBL2 and lower GPLD1 levels. However, male FSHD patients exhibited higher C9 and lower C4BPB levels. Additionally, desmosome proteins JUP and DSP were uniquely found in FSHD males. MBL2 positively correlated with age and C4BPB negatively correlated with FSHD severity in male patients. InterpretationOur findings underscore the sensitivity of analyzing circulating EV content to identify disease-specific protein biomarkers for DBMD and FSHD. Our results also emphasize the potential of EV-based biomarker discovery as a promising approach to monitor disease progression as well as effectiveness of therapies in muscular dystrophy, potentially contributing to their approval. Further research with larger cohorts is needed to validate these biomarkers and explore their clinical implications.

BackgroundNeurogenetic disorders caused by pathogenic variants in four genes encoding non-erythrocytic spectrins (SPTAN1, SPTBN1, SPTBN2, SPTBN4) range from peripheral and central nervous system involvement to complex syndromic presentations. Heterozygous pathogenic variants in SPTAN1 are exemplary for this diversity with phenotypes spanning almost the entire spectrum. MethodsThrough international collaboration we identified 14 families with genetically unsolved distal weakness and unreported heterozygous SPTAN1 loss-of-function variants including frameshift, nonsense and splice-acceptor variants. Clinical data, electrophysiology, muscle CT or MRI and muscle biopsy findings were collected and standardized. SPTAN1 protein, mRNA expression analysis and cDNA sequencing was performed on muscle tissue from two patients. ResultsAll 20 patients presented with early childhood onset distal weakness. The severity varied both within families and between different families. Foot abnormalities ranged from hammer toes and pes cavus to distal arthrogryposis. Electrophysiology showed mixed myogenic and neurogenic features. Muscle MRI or CT in 10 patients showed fatty infiltration of the distal lower limb anterior compartment and/or selective involvement of the extensor hallucis longus muscle. Muscle biopsy revealed myopathic changes with mild dystrophic and chronic neurogenic changes in 7 patients. Finally, we provide proof for nonsense mediated decay in tissues derived from two patients. ConclusionsWe provide evidence for the association of SPTAN1 loss-of-function variants with childhood onset distal myopathy in 14 families. This finding extends the phenotypic spectrum of SPTAN1 loss-of-function variants ranging from intellectual disability to distal weakness with a predominant myogenic cause. KEY MESSAGES{diamond} SPTAN1 loss-of-function variants, including frameshift, nonsense and splice site variants cause a novel childhood onset distal weakness syndrome with primarily skeletal muscle involvement. {diamond}Hereditary motor neuropathies and distal myopathic disorders present a well-known diagnostic challenge as they demonstrate substantial clinical and genetic overlap. The emergence of SPTAN1 loss-of-function variants serves as a noteworthy example, highlighting a growing convergence in the spectrum of genotypes linked to both hereditary motor neuropathies and distal myopathies.

BackgroundObstructive sleep apnea (OSA) is associated with a wide range of comorbidities, but large-scale phenome-wide analyses in clinical biobanks remain under-reported. In this study, we identified common comorbidities enriched in patients with OSA, tested the temporality of these associations, and analyzed relevant associations with summary sleep recording data. Methods48,251 participants with OSA in the Mass General Brigham healthcare system were identified using a natural language processing phenotyping algorithm and/or evidence of an elevated apnea-hypopnea index (AHI). Controls were matched (2:1) on demographics, body mass index (BMI), and healthcare utilization. Associations with 358 incident and 563 cross-sectional diseases were tested using Modified Poisson regression, adjusting for covariates. Sensitivity analyses examined timing by binning data in years relative to the first OSA diagnosis. Selected laboratory results were obtained based on associated diseases. Associated diseases were tested with sleep recording statistics (n [≤]18,348). Findings179 incident and 421 cross-sectional diseases were associated with OSA at Bonferroni significance. 37 diseases had Bonferroni-significant sex interactions. Several associations were significant years before the first recorded OSA diagnosis. Four red blood cell laboratory measures were significant ten years prior to the first diagnosis. One incident and 47 cross-sectional diseases were associated with the AHI and/or chronic hypoxemia. InterpretationObstructive sleep apnea is associated with enrichment of hundreds of diseases, several of which are supported by orthogonal polysomnographic evidence. Leveraging early signs of OSA in clinical data may help to identify at-risk patients. FundingNational Institutes of Health and the American Academy of Sleep Medicine Foundation.

BackgroundTremor hallmark feature is rhythmicity, which can be quantified using power spectral density analysis. However, tremor exhibits considerable variability, ranging from highly regular to more irregular patterns. Similarly, rhythmicity in myoclonus also varies, but it typically manifesting as arrhythmic jerks. ObjectivesTo develop power spectral density-based measures of movement regularity for the classification tremor and myoclonus. MethodsElectromyography data from 153 patients were analysed retrospectively, including orthostatic tremor (n=36), essential tremor (n=40), dystonic tremor (n=42), and limb cortical myoclonus (n=35). Five power spectral density analysis-derived variables were assessed: peak prominence, peak-to-broadband power ratio, peak frequency, peak width, and harmonics. Discriminant analysis evaluated classification accuracy across groups. ResultsPeak prominence was highest in orthostatic tremor and higher in essential tremor than dystonic tremor or myoclonus. Peak-to-broadband power ratio showed similar trends. Peak frequency differed across groups, with myoclonus highest and orthostatic tremor exceeding essential tremor and dystonic tremor. Peak width was larger in myoclonus and, to a less extent, in dystonic tremor compared to essential tremor. Harmonics were greater in orthostatic tremor and essential tremor compared to dystonic tremor and myoclonus. Discriminant analysis correctly classified 86.3% of cases, with overlap between essential tremor and dystonic tremor. Receiver operating characteristic curve analysis for peak prominence and width demonstrated high classification accuracy between essential tremor and dystonic tremor. ConclusionsOur findings represent a promising initial step toward establishing objective, power spectral density-based measures for the classification of tremor and myoclonus. These tools could enhance diagnostic accuracy and deepen insights into these disorders.

BackgroundWeakness of facial, ocular, and axial muscles is a common clinical presentation in congenital myopathies caused by pathogenic variants in genes encoding triad proteins. Abnormalities in triad structure and function resulting in disturbed excitation-contraction coupling and Ca2+ homeostasis can contribute to disease pathology. MethodsWe analysed exome and genome sequencing data from three unrelated individuals with congenital myopathy characterised by striking facial, ocular, and bulbar involvement. We collected deep phenotypic data from the affected individuals. We analysed the RNA-seq data of one proband and performed gene expression outlier analysis in 129 samples. ResultsThe three probands had remarkably similar clinical presentation with prominent facial, ocular, and bulbar features. Disease onset was in the neonatal period with hypotonia, poor feeding, cleft palate and talipes. Muscle weakness was generalised but most prominent in the lower limbs with facial weakness also present. All patients had myopathic facies, bilateral ptosis, ophthalmoplegia and fatiguability. While muscle biopsy on light microscopy did not show any obvious morphological abnormalities, ultrastructural analysis showed slightly reduced triads, and structurally abnormal sarcoplasmic reticulum. DNA sequencing identified three unique homozygous loss of function variants in JPH1, encoding junctophilin-1 in the three families; a stop-gain (c.354C>A; p.Tyr118*) and two frameshift (c.373del p.Asp125Thrfs*30 and c.1738del; p.Leu580Trpfs*16) variants. Muscle RNA-seq showed strong downregulation of JPH1 in the F3 proband. ConclusionsJunctophilin-1 is critical to the formation of skeletal muscle triad junctions by connecting the sarcoplasmic reticulum and T-tubules. Our findings suggest that loss of JPH1 results in a congenital myopathy with prominent facial, bulbar and ocular involvement. Key messageThis study identified novel homozygous loss-of-function variants in the JPH1 gene, linking them to a unique form of congenital myopathy characterised by severe facial and ocular symptoms. Our research sheds light on the critical impact on junctophilin-1 function in skeletal muscle triad junction formation and the consequences of its disruption resulting in a myopathic phenotype. What is already known on this topicPrevious studies have shown that pathogenic variants in genes encoding triad proteins lead to various myopathic phenotypes, with clinical presentations often involving muscle weakness and myopathic facies. The triad structure is essential for excitation-contraction (EC) coupling and calcium homeostasis and is a key element in muscle physiology. What this study adds and how this study might affect research, practice or policyThis study establishes that homozygous loss-of-function mutations in JPH1 cause a congenital myopathy predominantly affecting facial and ocular muscles. This study also provides clinical insights that may aid the clinicians in diagnosing similar genetically unresolved cases.

ObjectiveBiallelic intronic AAGGG repeat expansions in RFC1 cause Cerebellar Ataxia with Neuropathy and Vestibular Areflexia Syndrome and may also contribute to isolated sensory neuropathy. The clinical significance of both heterozygous and biallelic RFC1 expansions in more diverse patient populations remains unclear--partly due to the absence of accurate, user-friendly computational pipelines specifically tailored for tandem repeat analysis. MethodsTo discern the relationship between RFC1 expansions and idiopathic peripheral neuropathy (iPN), we performed whole-genome sequencing (WGS) followed by PCR-based confirmation in a large, well-characterized U.S. cohort consisting of 788 iPN patients (369 pure small fiber neuropathy (SFN), 266 sensorimotor, 144 pure sensory, and 9 pure motor). We developed an integrative pipeline combining ExpansionHunter Denovo and ExpansionHunter coupled with unsupervised clustering to reliably detect and genotype RFC1 expansions from short-read WGS data, achieving 98.2% concordance with repeat-primed PCR based validation. ResultsBiallelic RFC1 expansions were present in only one out of 854 controls but present in 2.3% of iPN patients (Fishers exact p = 2x10-5), including 6.9% of pure sensory, 1.1% of SFN, and 1.5% of sensorimotor neuropathy, indicating that motor nerve involvement should not exclude patients from RFC1 repeat screening. We also observed an increased frequency of monoallelic expansions in iPN compared to controls (9.4% versus 6.3%; Fishers exact p = 0.02), without evidence of secondary mutations or expansions on the other allele. InterpretationOur approach provides a robust, cost-effective method for detecting RFC1 expansions from WGS data. Our findings indicate that both heterozygous and homozygous AAGGG repeat expansions in RFC1 contribute to development of iPN.

ObjectiveVariants in SCN8A are associated with a spectrum of epilepsies and neurodevelopmental disorders. Ataxia as a predominant symptom of SCN8A variation has not been well studied. We set out to investigate disease mechanisms and genotype-phenotype correlations of SCN8A-related ataxia. MethodsWe collected genetic and electro-clinical data of ten individuals from nine unrelated families carrying novel SCN8A variants associated with chronic progressive or episodic ataxia. Electrophysiological characterizations of these variants were performed in ND7/23 cells and cultured neurons. ResultsVariants associated with chronic progressive ataxia either significantly decreased Na+ current densities and shifted activation curves towards more depolarized potentials (p.Asn995Asp, p.Lys1498Glu and p.Trp1266Cys) or resulted in a premature stop codon (p.Trp937Ter), i.e. strong loss-of-function (LOF) effects. Three variants (p.Arg847Gln and biallelic p.Arg191Trp/p.Asp1525Tyr) were associated with episodic ataxia causing LOF by decreasing Na+ current densities or a hyperpolarizing shift of the inactivation curve. Two additional episodic ataxia-associated variants caused mixed gain-and loss-of function effects in ND7/23 cells and were further examined in primary murine hippocampal neuronal cultures. Neuronal firing in excitatory neurons was increased by p.Arg1629His, but decreased by p.Glu1201Lys. Neuronal firing in inhibitory neurons was decreased for both variants. No functional effect was observed for p.Arg1913Trp. In four individuals, treatment with sodium channel blockers exacerbated symptoms. InterpretationWe identified episodic or chronic ataxia as new phenotypes caused by variants in SCN8A. Genotype-phenotype correlations revealed a more pronounced LOF effect for variants causing chronic ataxia. Sodium channel blockers should be avoided under these conditions. Summary for Social Media Twitter handles@cmbosselmann, @FiladelfiaGene1, @Katrine92658231, @Elegardella What is the current knowledge on the topic?Variants in SCN8A, a gene encoding the voltage-gated sodium channel NaV1.6, are associated with neurodevelopmental disorders, including epilepsy, intellectual disability, and autism spectrum disorder. What question did this study address?This study investigated whether SCN8A variants can cause predominant episodic or chronic ataxia, as well as the cellular and molecular mechanisms underlying these variants. What does this study add to our knowledge?Episodic or chronic ataxia as a sole or predominant symptom caused by NaV1.6 channel loss-of-function comprise new phenotypes in the broad spectrum associated with SCN8A dysfunction. Genotype-phenotype correlations help to differentiate between chronic and episodic ataxia. How might this potentially impact on the practice of neurology?Loss-of-function SCN8A variants may represent an underdiagnosed etiology in hereditary ataxia. Treatment with sodium channel blockers, commonly prescribed in other types of episodic ataxia, may harm these individuals, and should be avoided.

BackgroundSarcoendoplasmic reticulum Ca2+-ATPase isoform 2 (SERCA2), encoded by ATP2A2, is a key protein involved in intracellular Ca2+ homeostasis. The transcript SERCA2a is predominantly expressed in cardiac muscle and in type I myofibers, while SERCA2b is ubiquitously expressed including in skin cells. To date, variants in this gene were reported to be the cause of Darier disease, an autosomal dominant dermatologic disorder, but have never been linked to primary skeletal muscle disease. We describe four patients suffering from a novel hereditary myopathy caused by a homozygous missense variant in ATP2A2. MethodsWe studied a family with four affected individuals suffering from an adult-onset progressive skeletal myopathy. We performed a comprehensive evaluation of the clinical phenotype, serum CK levels, muscle MRI, and muscle biopsy, with genetic workup by means of gene panel sequencing followed by whole genome sequencing and segregation analysis. Immunohistochemistry and western blot (WB) to evaluate SERCA2 and SERCA1 expression in skeletal muscle was performed. We evaluated kinetics of Ca2+handling following caffeine exposure or voltage-induced sarcolemma depolarization in patient myoblasts and myotubes, compared to healthy controls. ResultsFour siblings in their fifties developed in early adulthood symmetric proximal weakness in lower limbs, which was slowly progressive over time. They had no skin or cardiac involvement. Biopsy findings in two affected individuals showed small vacuoles restricted to type I myofibers. Ultrastructural analysis showed dilation and proliferation of T-tubules, swelling of sarcoplasmic reticulum and autophagic vacuoles. Genome sequencing revealed a homozygous variant in ATP2A2 (c.1117G>A, p.(Glu373Lys)) which segregated with the disease. Immunohistochemistry suggested SERCA2 mislocalization in patient myofibers compared to controls. WB did not show changes in the amount or molecular weight of the protein. In vitro functional studies revealed delayed sarcoendoplasmic reticulum Ca2+reuptake in patient myotubes, consistent with an altered pumping capacity of SERCA2 after cell stimulation with caffeine or depolarization. ConclusionsWe report a novel adult-onset vacuolar myopathy caused by a homozygous variant in ATP2A2, resulting in a pure skeletal muscle phenotype with a limb-girdle distribution. Biopsy findings and functional studies demonstrating an impaired function of SERCA2 and consequent Ca2+ dysregulation in slow-twitch skeletal myofibers highly support the pathogenicity of the variant.

Individuals affected by inherited neuromuscular diseases often present with a specific pattern of muscle weakness, which can guide clinicians in genetic investigations and variant interpretation. Nonetheless, more than 50% of cases do not receive a genetic diagnosis. Oculopharyngodistal myopathy (OPDM) is an inherited myopathy manifesting with a particular combination of ptosis, dysphagia and distal weakness. Pathologically it is characterised by rimmed vacuoles and intranuclear inclusions on muscle biopsy. In recent years GCC * CCG repeat expansion in four different genes have been identified in individuals affected by OPDM in Asian populations. None of these have been identified in affected individuals of non-Asian ancestry. In this study we describe the identification of CCG expansions in ABCD3 in affected individuals across eight unrelated OPDM families of European ancestry. In two large Australian OPDM families, using a combination of linkage studies, short-read WGS and targeted ONT sequencing, we identified CCG expansions in the 5UTR of ABCD3. Independently, the ABCD3 CCG expansion was identified through the 100,000 Genomics England Genome Project in three individuals from two unrelated UK families diagnosed with OPDM. Targeted ONT sequencing confirmed the presence of mono-allelic CCG repeat expansions ranging from 118 to 694 repeats in all tested cases (n=19). The expansions were on average 1.9 times longer in affected females than affected males, and children of affected males were [~]2.3 times more likely to have the disease than those of affected females, suggesting inheritance of an expanded allele from an affected mother may have reduced penetrance. ABCD3 transcripts appeared upregulated in skeletal muscle and cells derived from affected OPDM individuals, suggesting a potential role of over-expression of CCG repeat containing ABCD3 transcript in progressive skeletal muscle degeneration. The study provides further evidence of the role of non-coding repeat expansions in unsolved neuromuscular diseases and strengthens the association between the GCC * CCG repeat motif and a specific pattern of muscle weakness with prominent cranial involvement across different populations.

BackgroundTrinucleotide repeat expansions are an emerging class of genetic variants associated with several movement disorders. Unbiased genome-wide analyses can reveal novel genotype-phenotype associations and provide a diagnosis for patients and families. ObjectivesTo identify the genetic cause of a severe progressive movement disorder phenotype in two affected brothers. MethodsA family of two affected brothers and unaffected parents had extensive phenotyping and natural history followed since birth. Whole-genome and long-read sequencing methods were used to characterize genetic variants and methylation status. Results: We describe a CGG repeat expansion in the 5-untranslated region of DIP2B in two affected male siblings presenting with a novel DIP2B phenotype including neurodevelopmental disability, dysmorphic traits, and a severe progressive movement disorder (prominent chorea, dystonia, and ataxia). ConclusionsThis is the first report of a severe progressive movement disorder phenotype attributed to a CGG repeat expansion in the DIP2B 5-UTR.