Journal of Neurology, Neurosurgery & Psychiatry

Background. CNM-Au8, a catalytic nanocrystalline gold suspension, is proposed to support neuronal bioenergetics, but its effect on chronic lesion evolution in multiple sclerosis (MS) has not been reported. Objectives. To evaluate the effect of CNM-Au8 on chronic lesion tissue expansion (CLTE). Results. In this exploratory post hoc analysis of the VISIONARY randomised placebo-controlled trial, CLTE was quantified from serial FLAIR MRI. Fifty-five participants were included (CNM-Au8, n=40; placebo, n=15). Compared with placebo, CNM-Au8 was associated with lower lesion expansion rate (p=0.001; Cliffs =-0.58) and volume (p<0.001; Cliffs =-0.65). Conclusions. CNM-Au8 treatment was associated with substantially reduced chronic lesion tissue expansion.

Familial recurrence of amyotrophic lateral sclerosis (ALS) or frontotemporal dementia (FTD) is the clinical cornerstone for assuming Mendelian, monogenic inheritance which informs genetic testing, counselling and gene-discovery strategies in standard practice. Polygenic inheritance can lead to familial clustering of ALS, while pathogenic variants with reduced penetrance may present sporadically in small, modern-day pedigrees. As both rare genetic variants and polygenic disease contribute to the occurrence of ALS, the accuracy of a positive family history to predict the underlying genetic architecture remains unknown. Using growing knowledge of the genetic architecture of ALS, we designed the APECS simulator: a large-scale pedigree simulator using historical demographic parameters, unifying frameworks for monogenic and polygenic disease models. We simulated 1,000,000 three-generation pedigrees and modelled ALS, FTD, and other dementias under both inheritance models. For index patients with ALS, we examined the performance of proposed familial ALS (fALS) definitions to predict the underlying genetic architecture. Simulation results were benchmarked against real-world population estimates, over a wide range of demographic and genetic parameters. We assessed the phenocopy rate for monogenic index patients, defined as relatives with polygenic ALS or a pathogenic allele from a different ancestor. Finally, we illustrated the value of distant relatedness among apparently sporadic, but monogenic ALS patients. Simulations generated 55.3 million individuals, yielding 5,431 ALS-affected index patients (16.4% monogenic). The proposed criterion for 'definite fALS' (2 or more relatives with ALS among relatives within two degrees) had a 31.1% sensitivity, while falsely classifying 10.0% of 'fALS' as monogenic. The sensitivity of 'definite fALS' was improved to 43.3% when also considering relatives with FTD. Considering any type of dementia raised the false positive rate to 35.9%. In total, 5.3% of monogenic index patients had a phenocopy with ALS in relatives up to the third degree. Of seemingly sporadic monogenic index patients, 83.1% had distant relatives with ALS/FTD up to the fifth degree. A family history provides limited accuracy for inferring genetic architecture in ALS. Considering unspecified dementias in the family history reduces this accuracy, while adding definite FTD improves accuracy. Relying on traditional fALS criteria risks both missing cases for genetic testing and treatments, and falsely alarming families with polygenic ALS. Given the limited value of a family history and risk of phenocopies, routine genetic testing regardless of family history and systematic approaches for detecting distant relatedness will help to find known and novel genetic causes in ALS.

Background: To determine the crude and age-adjusted point prevalence of motor neuron disease (MND) and its phenotypes (classical amyotrophic lateral sclerosis [ALS], primary lateral sclerosis [PLS] and progressive muscular atrophy [PMA]) using a comprehensive multi-source case ascertainment strategy. Methods: Cross-sectional population-based study with a prevalence date of 1 January 2025. Cases were identified through five complementary regional data sources and confirmed by clinical review against the revised El Escorial and Gold Coast criteria. Cases were classified by phenotype as ALS, PLS or PMA. The reference population was the population aged 15 years or older in Valencian Community (n=4,433,999). Crude prevalence was calculated per 100,000 with 95% Poisson confidence intervals; age-adjusted rates were obtained by direct standardisation to the 2013 European Standard Population. Results: A total of 374 confirmed MND cases were identified. The overall crude prevalence of MND was 8.43 per 100,000 (95% CI: 7.60-9.29) and the age-adjusted prevalence was 7.85 per 100,000 (95% CI: 7.05-8.65). By phenotype, ALS accounted for 325 cases (86.9%; crude prevalence 7.33 per 100,000), PLS for 26 (7.0%; 0.59 per 100,000) and PMA for 23 (6.1%; 0.52 per 100,000). Prevalence was consistent across provinces. Mean age was 65.9 years, the male-to-female ratio was 1.37:1, and 83.4% were receiving riluzole. Conclusions: This is the first comprehensive, multi-source population-based estimate of MND prevalence and its subtypes in the Valencian Community. The estimate is higher than the previous single-unit figure and consistent with recent European data, underscoring the value of multi-source ascertainment.

Progressive multiple sclerosis (PMS) lacks blood-based biomarkers of the neurodegenerative processes driving disability. We measured synaptopodin, a postsynaptic protein, in neuronal-enriched extracellular vesicles (NEVs) across two independent cohorts: the SPRINT-MS trial (n=184) and a real-world validation cohort (n=268). A one SD increase in baseline synaptopodin was associated with ~32% lower risk of confirmed disability progression in PMS (p=0.011), with the strongest effect in secondary PMS (SPMS) (~57% lower odds, p=0.019). This SPMS association was replicated in the validation cohort (~60% lower odds, p=0.009). Higher baseline synaptopodin was associated with future slower whole-brain atrophy and preserved microstructural integrity across MRI metrics (p=0.007-0.013). Longitudinal changes in NEV synaptopodin were associated with disability worsening (p=0.007) and paralleled MRI microstructural changes. NEV synaptopodin, but not NfL, was associated with confirmed disability progression in PMS participants with both biomarkers measured (HR 0.71 vs 1.12, p=0.007 vs 0.22 for NEV synaptopodin and NfL, respectively) and SPMS (HR 0.58 vs 1.10, p=0.003 vs 0.50). Adding synaptopodin, but not NfL, to a base model improved discrimination for disability progression in SPMS in both cohorts (e.g. validation AUC 0.755 to 0.838, p=0.005). These findings identify NEV synaptopodin as a prognostic and longitudinal, blood biomarker of synaptic integrity in PMS, particularly SPMS, complementing NfL.

Background and Objectives Huntington's disease is a rare neurodegenerative disease in which optimizing clinical trial design is essential. Although baseline characteristics are known to predict disease progression, the statistical efficiency gains achievable by formally integrating prognostic predictions into trial analyses have not been systematically quantified. We aimed to benchmark prognostic modeling approaches, quantify the trial efficiency gains from prognostic covariate adjustment, and deliver a ready-to-deploy framework for immediate implementation. Methods: We developed a two-step modeling pipeline using longitudinal Enroll-HD natural history data (NCT01574053) to train nine machine learning algorithms to predict disease progression across five UHDRS endpoints. For this, we used 16 predictive baseline covariates representing stage of disease (baseline score levels), disease specific (CAP, CAG length), demographics and co-medication use that could be quantified in one clinical visit. For Total Function Capacity (TFC), frequently used as a primary endpoint in clinical trials, we also devised a minimal prognostic model based on 6 covariates. Model performance was tested using the placebo arm of the GENERATION HD1 phase 3 trial (NCT03761849). Results: Models were trained on 16,398 Enroll-HD participants and tested on 260 participants from the GENERATION HD1 placebo arm. Models that take into account nonlinear relationships between the covariates showed no performance advantage over linear-based models on the test set. For TFC, a minimal six-covariate linear prognostic model performed on par with the full 16-feature model. Adjusting for the prognostic score increased the signal-to-noise ratio across different trial visits by 8-18%, raising statistical power from 80% to up to 90% at a fixed sample size, or, equivalently, reducing required sample size by 15-28% compared to unadjusted analyses (no covariate adjustment). Compared to routinely used covariate adjustments (including baseline score, age, CAG repeat length, and CAP score), adjusting for a prognostic score consistently delivered meaningful additional efficiency gains. Discussion: Prognostic covariate adjustment using a simple six-covariate linear score derived from a single baseline visit yields meaningful gains in statistical power for HD clinical trials, while remaining fully embedded within standard, regulator-accepted analysis frameworks. This approach offers a practical and immediately deployable tool to improve trial design.

Transcriptional risk scores, an integrated score of genomic susceptibility variants with expression Quantitative Trait Loci (eQTL) data, may more accurately distinguish clinical phenotypes compared to polygenic risk scores. This approach has not been applied to multiple sclerosis. We integrated data from the largest GWAS of MS, involving 14,802 MS patients, with eQTL data from 7,466 blood donors and 5,494 brain donors, using summary-data-based Mendelian randomization (SMR) followed by the heterogeneity in dependent instruments (HEIDI) test. MS SNVs with a significant eQTL were integrated with single-cell RNA sequencing data from 1,27 million peripheral blood mononuclear cells from 927 donors and 750,614 single nucleus RNA sequencing central nervous system cells from 192 individuals using Bayesian colocalization. Derived polygenic and cell-line specific transcriptional risk scores were compared with age-related MS severity score (ARMSS) in a cohort of 1,077 MS patients from the South Wales MS Registry and with MRI-derived age-adjusted total brain volumes of 272 MS patients within the UK Biobank. Cell-line specific transcriptional risk scores were associated with MS outcomes in two independent cohorts. Our results identified MS-associated eGenes enriched across several biological pathways, providing insights into pathophysiological mechanisms in MS that could facilitate precision medicine and identify novel druggable pathways.

Objective To examine whether prospectively recorded maternal BMI in early pregnancy is associated with offspring multiple sclerosis (MS) risk, including childhood-onset and adult-onset MS. Design Nationwide population-based cohort study. Setting Swedish national health and demographic registers. Participants 1,524,863 singleton children born in Sweden from 1992 to 2007 and followed through 2025. Exposures Maternal BMI recorded at the first antenatal visit, categorised as underweight (<18.5), normal weight (18.5-24.9), overweight (25.0-29.9), and obesity ([&ge;]30.0), and also modelled continuously. Main outcome measures MS in offspring, identified as at least three ICD-10 G35 codes recorded on separate dates. Cox models estimated adjusted hazard ratios (HR) and 95% confidence intervals (CI) adjusting for confounders. Results During follow-up, 1539 offspring (0.1%) were diagnosed with MS at a median age of 24 years (interquartile range, 20-27 years). MS incidence rates by maternal BMI category were 0.37 per 10,000 person-years for both underweight, and normal weight, 0.42 for overweight, and 0.45 for obesity. Compared with normal weight women, maternal overweight and obesity were associated with higher hazards of offspring MS, with adjusted HRs of 1.20 (95% CI, 1.05 to 1.37) and 1.35 (95% CI, 1.11 to 1.64), respectively. Onset-specific analyses showed similar findings for adult-onset MS and less precise but directionally consistent estimates for paediatric-onset MS. Each 1-unit increase in maternal BMI was associated with a 3%, 4%, and 3% higher hazard of overall, paediatric-onset, and adult-onset offspring MS, respectively. Conclusions In this population-based cohort study, higher maternal BMI were associated with an increased rate of MS in offspring. The findings provide large-scale prospective evidence that maternal adiposity may be related to offspring MS risk and support further investigation of the prenatal metabolic environment in the early-life origins of MS.

Background: Relapsing-remitting multiple sclerosis (RRMS) often progresses to secondary-progressive MS (SPMS), but diagnosis is typically retrospective, delaying potential interventions. Existing rule-based and machine learning approaches often lack prospective validation and provide no uncertainty estimates, limiting clinical adoption. Objective: To prospectively evaluate a conformal prediction (CP)-enabled random forest model for reproducing clinician-recorded disease course labels in the Swedish Multiple Sclerosis Registry (SMSReg) and compare its classifications with the Lorscheider criteria. Methods: The CP model was trained and calibrated on SMSReg-2022 (10,787 individuals, 85,461 hospital visits) and then validated on SMSReg-2025 (15,719 individuals, 45,077 hospital visits; not included in the SMSReg-2022 training/calibration/testing datasets). Performance was assessed at the hospital visit and individual levels and compared with the Lorscheider criteria. Results: The model maintained good calibration on the prospective dataset. At the per-hospital visit-level, the model accuracies were 92% (RRMS) and 95% (SPMS). At the individual level, sensitivity was 98%, specificity 89%, and F1-score 0.75. With clinician-recorded labels as reference, the model showed higher sensitivity than the Lorscheider criteria (98% vs. 71%), more transition assignments (90% vs. 57%), and higher F1-score (0.76 vs. 0.69). Conclusion: The CP-enabled model prospectively reproduced clinician-recorded disease course labels with calibrated uncertainty, supporting harmonization and decision support.

Tissue-based assays provide broad screening for neural autoantibodies but remain vulnerable to technical and reader-dependent variability. We evaluated an integrated workflow combining automated 3,3'-diaminobenzidine tissue-based assay processing, digital whole-slide imaging, and interpretable deep learning. Model development used 2,357 retrospective serum and cerebrospinal fluid images referred from more than 80 institutions and processed in a centralized laboratory, with reference labels established by expert-consensus tissue-based assay interpretation. A prospective validation cohort included 200 images from 200 unique patients (154 positive and 46 negative; 100 serum and 100 cerebrospinal fluid). The same images were evaluated by two expert and two novice readers in a fixed-order two-round study, with unaided interpretation followed by artificial-intelligence-assisted interpretation after a washout of at least two weeks. The standalone binary classifier achieved an area under the receiver operating characteristic curve of 0.922, accuracy of 86.5%, sensitivity of 85.7%, and specificity of 89.1% in prospective validation. Pooled reader accuracy was 90.4% in the unaided round and 97.4% in the artificial-intelligence-assisted round (McNemar P < 0.001), with specificity of 82.6% and 99.5%, respectively. Novice-reader accuracy was 83.3% unaided and 96.5% with artificial-intelligence assistance (P < 0.001), approaching unaided expert accuracy of 97.5%. Among tissue-based-assay-positive cases, reader accuracy for cytoplasmic/nuclear antigen-associated versus neural surface antigen-associated staining-pattern subclassification was 88.8% unaided and 94.0% with artificial-intelligence assistance (P < 0.001). These findings support further multicentre, cross-platform evaluation of artificial-intelligence-assisted neural autoantibody tissue interpretation.

There are known alterations in decision-making attributed to Parkinson's disease. Yet little is known about how those alterations affect decisions in social contexts. We aimed to assess social decision-making in healthy human volunteers as well as people with Parkinson's disease treated with deep brain stimulation of the subthalamic nucleus. Twenty healthy controls and twenty-two people with Parkinson's in both the stimulation ON and OFF conditions took part in a social discounting task that measured the degree of generosity toward others as a function of social-emotional distance. Using a computational approach, we modelled mechanisms of social discounting across three different models that differed in how they mapped choice valuation onto observed decisions. People with Parkinson's less often made generous choices than controls. Across all analyses, we found converging evidence that this effect was attributable to increased static and dynamic decision noise, i.e. inhibitory and attentional processes, but not changes in prosocial valuation. While stimulation sped up decisions, there was no effect of deep brain stimulation on social decision-making. Present results indicate that people with Parkinson's are more prone to random processes such as increased impulsivity and attentional lapses during choices in social situations. Such changes attributed to the disease appear to evade clinical care with dopaminergic compensation and deep brain stimulation.

The Vanishing White Matter (VWM) Registry is a single-center database containing longitudinal survey data from >470 untreated patients with DNA- and MRI-confirmed VWM. VWM is a rare genetic disorder of the brain's white matter with an estimated live birth incidence of 1 per 100,000 and prevalence of 1.3 per 1,000,000 people in the Netherlands. The Registry represents a remarkably extensive collection of systematically gathered information on this disease, enabling crucial research into the natural history of VWM and providing a valuable resource for control arms in clinical trials. Data, initially collected on paper from 2004, have been maintained electronically since 2018, and include validated scales alongside a disease-specific customized questionnaire covering clinical, functional, and psychosocial domains. Survey packages, available in eight languages, are sent one to four times a year, depending on patient age. Built-in validity checks and review by pediatric and adult neurologists ensure data quality. The dataset is continuously updated with questionnaires expanding its scope, and structured for analysis and reuse in research on VWM.

Background Sporadic Parkinson disease (PD) lacks a molecular taxonomy that stratifies patients by dominant molecular drivers, limiting enrollment for disease-modifying trials, especially anti-inflammatory agents targeting NLRP3/NF-{kappa}B. Methods We developed ORIGIN-PD, an interpretable whole-blood transcriptomic classifier comprising 34 gene-level features and a composite low-expression score, with gene-level contributions organized into eight prespecified functional modules: midbrain dopaminergic (mDA) neuron identity/development, pathway-related, oxidative/mitochondrial, proteostasis, inflammation/ immunity, microglia, ncRNA, and other. Using LASSO regression on stringently quality-controlled whole-blood RNA-seq data from the Parkinson Progression Markers Initiative (PPMI) (Training Set: 204 PD and 92 controls; Locked Test Set: 51 PD and 23 controls), we assigned each patient a module-contribution profile. Module dominance was defined as >30% of total score. Results ORIGIN-PD discriminated PD from controls with high accuracy (Locked Test Set AUC 0.944, sensitivity 0.843, specificity 0.913). Among 255 sporadic PD patients, 49.4% exhibited a dominant module-contribution pattern, most frequently mDA neuron identity/development (22.0%), followed by pathway-related (11.8%), microglia/glial (7.5%), and inflammation/immunity (3.5%). Among the 50.6% with multi-module contributions, mDA neuron identity/development ranked in the top three in 62.8% of patients versus 35.7% for inflammation/immunity, indicating broader involvement of mDA neuron identity genes than inflammatory genes. The model showed high specificity against Alzheimer disease, ALS, and inflammatory controls. Conclusions ORIGIN-PD enables rational enrichment for NLRP3/NF-{kappa}B inhibitor trials by identifying patients with inflammatory involvement. In addition, the predominant contribution of the mDA neuron identity/development module, centred on WNT1, MSX1, NPM1, and LMX1B, identifies a larger patient subgroup with altered expression of mDA neuron identity module genes, suggesting that ORIGIN-PD may also be useful for patient enrichment in future trials of Wnt/{beta}-catenin pathway agonists. This dual-purpose framework provides a transparent, interpretable basis for pathogenic subtyping and precision enrichment in sporadic PD, with the aim of guiding disease-modifying therapy.

Peripheral inflammation in Parkinson's disease (PD) remains poorly characterised among carriers of Asian-prevalent LRRK2 G2385R and R1628P variants. We investigated plasma immunoinflammatory markers (i.e., IL-6, TNF-, CCL2, CX3CL1, CCL5, and VCAM-1) via multiplex immunoassay in 240 participants: PD-G2385R (n=53), PD-R1628P (n=58), PD-G2385R+R1628P (n=5), idiopathic PD (iPD; n=62), and controls (n=62), and their clinicobiological correlates including monocyte LRRK2 kinase activity (pRab10Thr73). Compared with iPD and/or controls, TNF-, CCL2, CX3CL1, and CCL5 were lower in PD-G2385R, while IL-6, TNF-, CCL2, and CCL5 were lower in PD-R1628P. Higher LRRK2 kinase activity correlated with lower TNF-. CX3CL1 correlated with greater motor severity in iPD but less disability in both variant groups. CCL5 correlated with less motor complications in PD-G2385R, while IL-6 and VCAM-1 correlated with worse disability and/or cognition in PD-R1628P. These findings suggest that immunoinflammatory profiles and their clinical relevance in PD differ by genotype, with implications for stratification in future immunomodulatory trials.

Background: Huntington's disease (HD) is an inherited neurodegenerative disorder primarily associated with motor and psychiatric manifestations. No disease-modifying treatment currently exists, with vesicular monoamine transporter type-2 inhibitors (VMAT2i's) being the only FDA-approved therapy for motor features in HD. Previous studies evaluated the antichorea effects of VMAT2i's but did not address its effects on non-choreiform features. Objectives: This study aimed to characterize the effect of VMAT2i's on hyperkinetic, hypokinetic and total hypokinetic subscores of the UHDRS motor assessment in patients with motor-manifest HD from the Enroll-HD research platform. Methods: Data from 622 participants were analyzed. Linear mixed effects models were constructed to compare the rates of change of choreiform and non-choreiform motor scores before and after the initiation of a VMAT2i. We calculated and plotted the direction, magnitude and significance of VMAT2i administration on the rates of change of motor subscores. A similar analysis was performed investigating the effect of antipsychotic medications on motor subscales in HD. Results: VMAT2i's reversed progression of hyperkinesis but significantly increased the rates of change in the hypokinetic and total hypokinetic subscores. Antipsychotics also displayed efficacy in reversing progression of chorea but did not significantly influence progression of hypokinesis. Conclusions: The initiation of VMAT2i's was associated with an improvement in hyperkinesis but was associated with worsening of other motor features of HD. Hypokinesis increases progressively in HD and is correlated with functional impairment and global decline. Therefore, the use of VMAT2i's to treat motor features in HD should be a nuanced decision, considering its multipronged impact.

Background and objectives: Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL) is the most common monogenic cerebral small vessel disease. Though neuropsychiatric symptoms (NPS) of CADASIL profoundly impact function and quality of life, these symptoms remain understudied. This analysis characterizes the prevalence, severity, and profile of NPS among individuals with CADASIL-causing NOTCH3 variants and non-carrier biological family controls in the United States CADASIL Consortium (USCC). Methods: This cross-sectional analysis used multimodal baseline data from 12 enrollment sites across the country. Statistical analyses used nonparametric tests and regression models to compare clinical and neuropsychiatric outcomes across CADASIL risk tiers and controls, adjusting for age, sex, race, and education where appropriate. The primary outcome measure was the Neuropsychiatric Inventory Questionnaire (NPI-Q), an informant-reported measure which assesses 12 categories of NPS. NPI-Q item severity and distress were evaluated for individuals with confirmed cysteine-altering NOTCH3 variants and compared with controls. Results: Four hundred fifty-nine USCC participants were included in the analytic cohort: 343 cases [256 high-risk (HR); 63 medium-risk (MR); 24 low-risk (LR)]; and 116 controls. Among participants with available NPI-Q data, at least one neuropsychiatric symptom was reported by 72.6% (n = 228/314) of cases compared with 53.2% (n = 58/109) of controls. Reported symptoms were more common among CADASIL cases than controls, occurring in 75.0%, 71.2%, and 72.7% of participants in the LR, MR, and HR groups, respectively, compared with 53.2% of controls. Compared with controls, participants in the MR group and HR group had higher NPI-Q severity scores. HR participants also had higher NPI-Q distress scores than controls. Discussion: Our findings support the notion that NPS are an important clinical manifestation of CADASIL. Beyond previously reported symptoms, additional neuropsychiatric manifestations were identified. Given the higher burden and variability in the neuropsychiatric presentation of CADASIL, the relationships among neuropsychiatric, neurological, and patient-reported outcomes merit further investigation. The NPS profile may enhance the evaluation of the safety and efficacy of different treatments to target these symptoms for CADASIL patients. Trial Registration Information: The study is registered at ClinicalTrials.gov (NCT05677880, 12/12/2022).

Introduction: Neuroinflammation is a known pathophysiologic mechanism in multiple sclerosis and a proposed mechanism in fibromyalgia and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Positron Emission Tomography (PET) imaging of the translocator protein (TSPO) has been widely used to measure neuroinflammation, though second-generation ligands are highly sensitive to a single nucleotide polymorphism, rs6971, resulting in high, mixed, and low affinity binders. It is unknown whether this polymorphism is associated with neuroinflammatory disorders. Materials and Methods: Eighty-six total participant genotypes for TSPO binding affinity were identified through Sanger sequencing (30 fibromyalgia, 21 ME/CFS, 11 multiple sclerosis, and 24 healthy controls). Associations between patient group and genotype were assessed with Fishers Exact test. Associations between genotype and symptom severity from self-report questionnaires were assessed with Kruskal-Wallis tests. Results: In the present study individuals with fibromyalgia, (ME/CFS), or multiple sclerosis were more likely to have a mixed- or low-affinity genotypes compared to healthy controls. Presence of the low or mixed genotypes were also associated with greater self-reported fatigue severity and depressive symptoms. Discussion: These findings should be confirmed in larger samples but suggest functional consequences of rs6971 polymorphism that may be related to the pathophysiology of pain and fatigue illnesses.

Frontotemporal lobar degeneration (FTLD) encompasses clinically and biologically heterogeneous syndromes that are unlikely to be captured by a single blood biomarker. Although plasma neurofilament light (NfL) chain reflects neuroaxonal injury, multiplex profiling may identify complementary inflammatory, vascular and proteostatic processes relevant to clinical phenotype and progression. We characterised plasma CNS-related and inflammatory proteomic signatures across FTLD-related clinical syndromes and tested their associations with neuroaxonal injury, clinical severity, regional brain atrophy and survival. Plasma samples from 177 participants with behavioural variant frontotemporal dementia (bvFTD), primary progressive aphasia (PPA), progressive supranuclear palsy (PSP) or corticobasal syndrome (CBS) and 67 healthy controls were analysed using the high-sensitivity nucleic acid-linked immuno-sandwich assay (NULISA) platform. We quantified 130 CNS-related and 250 inflammation-related proteins. Differential expression and multivariate classification analyses were complemented by hierarchical clustering, penalised regression and survival analyses. Canonical correlation analysis tested multivariate associations between disease-associated proteins and regional MRI brain volumes. Sixty-one CNS-related and 61 inflammation-related proteins differed between participants with FTLD-related syndromes and controls after false-discovery-rate correction. NfL was the strongest individual discriminator between all patients and controls, with an area under the receiver operating characteristic curve of 0.91. In exploratory full-cohort models, the multivariate CNS signature achieved an area under the curve of 0.93, compared with 0.82 for the multivariate inflammation signature. However, inflammatory proteins provided stronger discrimination for selected syndrome comparisons, including PPA versus PSP. Among inflammation-related proteins, SPP1 and CD276 showed the strongest positive associations with NfL. Higher CD276 and SPP1 were associated with increased mortality, whereas higher amyloid-{beta}42 was associated with lower mortality. A multivariate protein signature weighted towards tumour necrosis factor and immune signalling was associated with lower regional brain volumes across predominantly frontotemporal and limbic regions (canonical correlation 0.43; permutation p=0.018). Plasma multiplex proteomics identified complementary dimensions of neuroaxonal injury and immune dysregulation across frontotemporal lobar degeneration-related syndromes. CNS-related proteins, particularly NfL, predominantly captured case-control neurodegeneration, whereas inflammatory signatures provided additional information about syndrome heterogeneity, structural brain changes and survival. SPP1, CD276 and broader immune-related signatures are candidates for independent biomarker validation and mechanistic investigation.

The brain differences of dyslexia are well described and its genetic risk is now measurable; the brain systems that connect the two are unknown. That link decides which measures complement one another in early identification and which systems intervention research should examine. Here we compared large-scale polygenic score maps of brain structure in dyslexia with normative maps of brain neurotransmitter systems. The structural signature of genetic liability aligned with the cortical distribution of neurotransmitter systems. The strongest and most consistent alignment appeared for the histamine H3 receptor, the vesicular acetylcholine transporter (VAChT) and the N-methyl-D-aspartate (NMDA) glutamate receptor. The alignment was most pronounced in cingulate, medial and superior frontal cortex and the paracentral lobule. The H3 alignment held after adjustment for cortical position and for metabolic and cognitive reference maps. Once the component common to all nineteen maps and position were removed together, no individual system remained significant, while the shared component itself aligned with liability. The signature therefore follows the topography these systems share. In 169 children, per-person alignment with the H3 map related to reading skill with the opposite sign to liability. To our knowledge, this is the first study to relate the structural signature of genetic liability for dyslexia to normative maps of neurotransmitter receptor and transporter density. It places that signature within the shared geography of the systems that set attention, arousal and synaptic plasticity during learning, and gives the genetics of dyslexia a testable biological axis.

Background: Secondary progressive multiple sclerosis (SPMS) is traditionally viewed as predominantly neurodegenerative, with limited inflammatory activity. However, chronic active lesions characterised by persistent rim inflammation and gradual expansion may continue to contribute to tissue injury. Objectives: To characterise chronic lesion dynamics in SPMS and determine whether chronic lesion expansion contributes to tissue damage and neurodegeneration. Thirty-one patients with SPMS underwent longitudinal MRI including T1-weighted, FLAIR and diffusion imaging. White matter lesions were manually segmented and chronic lesion tissue expansion (CLTE) was quantified as annualised percentage change. Associations between lesion expansion, mean diffusivity (MD) changes and brain atrophy were assessed using correlation, regression and mixed-effects modelling. Results: CLTE remained prominent, with a mean annual expansion rate of 7.1 +- 4.1%. Expansion substantially exceeded the contribution of newly formed lesions and occurred at rates comparable to relapsing multiple sclerosis. CLTE demonstrated moderate intra-patient clustering (ICC = 0.23). MD increased significantly within lesions during follow-up and was associated with lesion expansion. Chronic lesion expansion was also strongly associated with ventricular enlargement (partial r = 0.72, p < 0.001). Conclusions: Chronic lesion expansion persists in SPMS and represents a major source of tissue injury associated with microstructural degeneration and brain atrophy.

Amyotrophic lateral sclerosis (ALS) is a heterogeneous neurodegenerative disorder lacking established biomarkers of underlying disease biology. Here, we identify lipidomic signatures of circulating extracellular vesicles (EVs) that distinguish ALS from neurologically healthy controls and related neuromuscular diseases. Using mass spectrometry-based profiling and multivariate modelling, we separate ALS (n=77) from healthy controls (n=89) with an area under the receiver operating characteristic curve (AUROC) of 0.90 (95% CI 0.85-0.94), rising to 0.92 (0.89-0.96) when low-confidence predictions are withheld, and retaining performance in an independent cohort (n=119; AUROC 0.78, 0.70-0.86). In Tofersen-treated SOD1-ALS patients, biomarker trajectories change in parallel with clinical measures while individuals with spinal muscular atrophy (n=50) exhibit a lipid fingerprint distinct from that observed in ALS, supporting disease specificity within a shared systemic framework of abnormal systemic lipogenesis. These findings establish EV-associated lipid signatures as a systemic, blood-based biomarker platform for ALS diagnosis, stratification, and treatment monitoring, with structurally distinct lipid species indicating widespread membrane remodelling.