Brain Communications

Background Friedreich ataxia (FRDA) is a rare neurodegenerative disorder with substantial heterogeneity in clinical presentation and progression, complicating prognosis and trial design. Neuroimaging offers objective biomarkers to track disease evolution, yet variability in progression patterns remains poorly understood. Objective To identify biologically meaningful FRDA progression subtypes using longitudinal multimodal MRI and assess their associations with demographic, genetic, and clinical factors. Methods Longitudinal structural and diffusion MRI data from 54 FRDA and 57 controls were analysed. Annualised progression rates of macrostructural (volumetric) and microstructural (diffusion) features across cerebellum, brainstem, and spinal cord regions were clustered using Gaussian Mixture Models. Cluster robustness was assessed using per-cluster Jaccard similarity and other validation metrics. Random Forest classification examined predictors of cluster membership. Results Three reproducible clusters/subtypes emerged: micro-dominant/dual progression, characterised by widespread microstructural deterioration with modest volumetric decline; macro-dominant, marked by pronounced volumetric decline with minimal microstructural change; and minimal/no progression, showing negligible change in all measures. FRDA participants predominated in the first two clusters. Random Forest prediction of cluster membership using clinical and demographic variables identified length of the trinucleotide repeat expansion in the FXN gene as key predictor. Conclusions Data-driven clustering of longitudinal MRI identified distinct FRDA subtypes with unique co-progression patterns, underscoring genetic burden as a key driver. Recognising such heterogeneity can improve patient stratification, enable personalised monitoring, and guide targeted therapeutic strategies. Future studies should validate these subtypes in larger, more diverse cohorts and integrate additional biomarkers for enhanced precision.

Objective: Spinocerebellar ataxia type 1 (SCA1) is a rare, inherited neurodegenerative disease characterised by progressive deterioration of motor and cognitive function. Here, we illustrate the pattern and evolution of brain atrophy in people with SCA1 using a large multisite dataset. Methods: Structural magnetic resonance imaging data from SCA1 (n=152) and healthy control (n=131) participants from seven sites and two consortia were analyzed using voxel-based morphometry. Cross-sectional stratification and correlations were undertaken with ataxia severity and duration to profile disease evolution. Cerebrocerebellar structural covariance analysis was used to understand the relationship between cerebral and cerebellar tissue atrophy. Results: Atrophy in SCA1 first manifests in the lower brainstem and cerebellar white matter (WM), before progressing to the pons, anterior cerebellum, and cerebellar lobule IX. The midbrain and peri-thalamic WM and the remainder of the cerebellar cortex are then affected, with preferential involvement of specific motor and cognitive areas. Finally, degeneration in the striatum and cerebral WM corresponding to the corticospinal tract become apparent. Atrophy and correlations with ataxia severity are most pronounced in the cerebellar WM and pons. Structural covariance analysis showed reduced correlations between cerebellar and cerebral WM volume in SCA1 participants. Interpretation: Cross-sectional stratification of a large SCA1 cohort by ataxia severity indicates a pattern of atrophy spread across the brainstem, cerebellum, and subcortical grey and white matter. Ongoing volume loss throughout the disease course is most evident in a core set of infra-tentorial brain regions. Atrophy of cerebellum spans both motor and cognitive functional zones. Cerebellar degeneration is not directly mirrored by downstream effects in the cerebrum.

Post-stroke cognitive recovery is difficult to predict using focal lesion characteristics alone. The brain's capacity to maintain cognitive function depends also on structural integrity of the whole brain. One way to measure brain health is through the severity of cerebral small vessel disease (CSVD) markers, which reflect aging-related pathologies that erode structural integrity. Here, we propose a composite measure of CSVD (cCSVD) integrating three independently validated biomarkers automatically quantified using T1-weighted MRIs: white matter hyperintensity volume (WMH; representing vascular injury), perivascular space count (PVS; putative glymphatic clearance), and brain-predicted age difference (brain-PAD; structural atrophy). We hypothesize that cCSVD, which captures the shared variance across these CSVD biomarkers, will be a robust indicator of whole-brain structural integrity and predict cognitive changes 3 months after stroke. We analyzed 65 early subacute stroke survivors with assessments within 21 days (baseline) and at 90 days (follow-up) post-stroke. WMH volume, PVS count, and brain-PAD were quantified from baseline T1-weighted MRIs, and then residualized for age, sex, days since stroke, and intracranial volume. Principal component analysis (PCA) of the residualized biomarkers was used to derive cCSVD. Beta regression with stability selection using LASSO was used to model three outcomes: baseline Montreal Cognitive Assessment (MoCA) scores, follow-up MoCA scores, and longitudinal change (follow-up score adjusted for baseline score). Logistic regression was used to test if baseline cCSVD predicted improvement in those with baseline cognitive impairment (MoCA < 26). The PCA revealed that the first principal component (PC1) explained 43.1% of the total variance among WMH volume, PVS count, and brain-PAD. The three biomarkers contributed nearly equally to PC1, which was subsequently used as the baseline cCSVD score. Lower baseline cCSVD was significantly associated with better MoCA scores at follow-up ({beta} = -0.19, p = 0.009), even after adjusting for baseline MoCA ({beta} = -0.12, p = 0.042), and, importantly, outperformed all individual biomarkers. Furthermore, lower cCSVD at baseline significantly increased the likelihood of improving to cognitively unimpaired status at three months (OR = 0.34, p = 0.036), independent of age and education. The composite CSVD captures the additive impact of vascular injury, glymphatic dysfunction, and structural atrophy on recovery in a way that individual measures do not. cCSVD accounts for shared variance across these domains, reflecting a patient's latent capacity for cognitive recovery, where relative integrity in one CSVD domain may mitigate effects of another. This automated, T1-based framework offers a scalable tool for predicting post-stroke recovery.

Qualitative models of Alzheimers pathology often posit that amyloid accumulation follows a sigmoid curve, indicating that the rate of deposition wanes over time. Longitudinal PET data now allow us to investigate amyloid accumulation trajectories with greater detail and over longer follow-up periods. We combine inferences from simulated amyloid trajectories, empirical PET data from the Alzheimers Disease Neuroimaging Initiative (ADNI), and the sampled iterative local approximation algorithm (SILA) to assess whether amyloid accumulation reaches a physiologic ceiling. We find that SILA reliably detects a ceiling, when present, across a range of simulated scenarios that impose a sigmoid shape. When fit to empirical data from ADNI, however, SILA does not appear to indicate the presence of a ceiling. Thus, we conclude that amyloid trajectories may not reach a physiologic ceiling during the stages of Alzheimers disease typically observed while patients remain under follow-up in cohort studies. Fits using SILA indicate that illustrative models of biomarker cascades, while useful tools for conceptualizing and interrogating pathologic processes, may not represent the shapes of amyloid trajectories accurately. Summary for General PublicAmyloid, a protein implicated in Alzheimers disease, is thought to reach a plateau in the brain, but methods that estimate how amyloid changes over time suggest it grows unabated. Gantenberg et al. use one such method and simulations to argue that amyloid does not reach a plateau during the typical course of Alzheimers.

BackgroundTheory of Mind (ToM) deficits are well-documented in right-hemisphere stroke but remain understudied in post-stroke aphasia. Prior work suggests that performance on tasks assessing ToM may be relatively preserved in aphasia and dissociable from language impairment, but these findings are based largely on small studies. This study examined performance on nonverbal false-belief tasks in post-stroke aphasia, its relationship with aphasia severity, and whether vascular brain health, operationalized using cerebral small vessel disease (CSVD) markers, contributed to variability in performance. MethodsForty-four individuals with aphasia completed two nonverbal belief-reasoning tasks assessing spontaneous perspective-taking and self-perspective inhibition. Task accuracy served as the primary outcome. Linear regression models examined associations between task performance, aphasia severity (Western Aphasia Battery-Revised Aphasia Quotient), and CSVD markers, including white matter hyperintensities, cerebral microbleeds, lacunes and enlarged perivascular spaces in the basal ganglia and centrum semiovale. ResultsPerformance was heterogeneous across tasks, with reduced performance observed in 23% of participants on the Reality-Unknown task and 36% on the Reality-Known task. Aphasia severity was not associated with task accuracy. Greater cerebral microbleed count was associated with lower accuracy on both tasks, while greater basal ganglia enlarged perivascular spaces burden showed a more selective association with lower performance. ConclusionsPerformance on nonverbal false-belief tasks in aphasia is variable and not explained by aphasia severity alone. These findings suggest that apparent ToM-related difficulties in aphasia may be shaped by broader vascular brain health, supporting a more multidimensional framework for interpreting social-cognitive task performance after stroke.

ObjectiveIn individuals with drug-resistant epilepsy, accurately identifying the brain regions where seizures originate is a critical prerequisite to guide surgical treatment and achieve seizure freedom. To accomplish this, intracranial EEG is considered the gold standard, providing the spatiotemporal high-resolution data necessary to pinpoint epileptogenic activity. However, this precision is achieved through an invasive procedure with significant patient burden, which is fundamentally limited by the electrode placement and spatial coverage. MethodsIn this study, we investigated the potential utility of preoperative resting-state fMRI to non-invasively map alterations in brain dynamics at the whole brain level. Region-wise brain dynamics were quantified with complementary measures of local autocorrelation decay rates. We assessed the capacity of these derived features to effectively identify intracranial EEG confirmed seizure onset zones in 18 individuals with drug-resistant medial temporal lobe epilepsy. Overall, the study cohort contained 3867 implanted electrodes of which 159 classified as seizure onset zones by two independent board-certified epileptologists. ResultsOverall, our findings reveal more constrained temporal dynamics for brain regions associated with seizure onsets compared to non-seizure onset zones. Individual-level prediction showed a performance better than chance in 15 of the 18 patients. The overall predictive performance across all patients yielded a median AUC of 0.81, a median true positive rate of 0.75, and a median true negative rate of 0.83. Furthermore, in a subset of 13 patients, those with negative seizure outcomes showed higher probabilities of seizure onset zone predictions outside the resection area compared to those with good outcomes. SignificanceOverall, our findings suggest that altered temporal dynamics derived from preoperative resting-state fMRI represent a promising non-invasive approach for delineating epileptogenic tissue, potentially informing intervention strategies and guiding electrode placement.

Focal cortical dysplasias (FCDs) are one of the most common structural causes of drug-resistant epilepsy in children but are frequently subtle and difficult to detect on conventional MRI. Many automated lesion detection methods have therefore been proposed to support neuroradiological assessment. In this study, we externally validated two recently developed deep-learning approaches for FCD detection, MELD Graph and 3D-nnUNet, in a pediatric cohort. In this retrospective single-center study, brain MRI scans of 71 children evaluated for epilepsy were analyzed, including 35 MRI-positive patients with suspected FCD and 36 MRI-negative cases based on the primary radiology reports. Both models were applied to standard 3D T1-weighted and 3D FLAIR images. Detected lesions were reviewed by an experienced pediatric neuroradiologist and classified as true positive, false positive, or false negative. Clinical semiology and EEG findings were additionally evaluated for cases with false-positive detections. At the lesion level, MELD Graph achieved a precision of 0.85 and recall of 0.52, while 3D-nnUNet achieved a precision of 0.91 and recall of 0.48. In the MRI-negative patients, MELD Graph produced more false-positive detections than 3D-nnUNet (0.53 vs. 0.14 false-positive lesions per patient). At the patient level, MELD Graph showed slightly higher sensitivity than 3D-nnUNet (0.63 vs. 0.54), whereas 3D-nnUNet demonstrated markedly higher specificity (0.86 vs. 0.56). Improved FLAIR image quality was associated with trends toward improved model performance. Both models demonstrated high precision but moderate sensitivity, indicating that they are valuable decision-support tools but cannot replace expert neuroradiological evaluation. Optimized MRI acquisition protocols are needed to further improve automated lesion detection in pediatric epilepsy.

INTRODUCTIONAlzheimers disease (AD) manifests a specific spatial progression pattern, but its propagation mechanisms remain unclear. METHODSWe employed nine brain connectomes spanning multiple biological levels to investigate the mechanisms underlying cortical atrophy propagation in AD. Individual gray matter atrophy maps were quantified using normative modeling and were then mapped onto the connectomes by assessing the relationship between regional atrophy and the atrophy of neighboring regions defined by each connectome. RESULTSCross-sectionally, node-neighbor relationship was weak in the preclinical stage, suggesting limited influence of connectome architecture. Longitudinally, atrophy became progressively more aligned with the neurotransmitter receptor similarity connectome in individuals with MCI converting to AD dementia and dementia patients. DISCUSSIONOur findings described a stage-dependent shift in cortical atrophy propagation, with neurotransmitter receptor similarity playing an increasing role as AD progresses.

BackgroundAlthough neurogenic orthostatic hypotension (nOH) is a common and debilitating feature of multiple system atrophy (MSA), little is known about the burden of symptoms in the real world. ObjectivesTo design and conduct a cross-sectional community-based research survey targeting patients with MSA, with and without nOH. MethodsWe recruited patients with MSA to complete an anonymous online survey covering three core themes: 1) timely diagnosis, 2) nOH pharmacotherapy and refractory symptoms, and 3) confidence in physician knowledge. Responses were grouped by pre-specified diagnostic certainty levels. Relationships between symptoms, function, and pharmacotherapy were assessed using univariate and multivariate methods. ResultsWe analyzed 259 respondents with a self-reported diagnosis of MSA (age: M=64.38, SD=8.09 years; 44% female). In total, 42% also had a diagnosis nOH; 40% had symptoms highly suspicious of nOH, but no diagnosis; and 21% reported having never had their blood pressure measured in the standing position at a clinical visit. Treatment with a pressor agent was independently associated with the presence of other symptoms of autonomic failure. Each additional nOH symptom reported increased the odds of requiring pharmacotherapy by 18%. Yet, despite anti-hypotensive medication use, 97% of patients reported limitations in their ability to bathe, cook, or arise from a chair/bed with 76% needing caregiver support for refractory nOH symptoms. ConclusionsThis cross-sectional representative sample shows nOH is underrecognized and undertreated in MSA patients, leading to substantial functional limitations. It is our hope that these findings are leveraged for planning future trials and advocating for better treatments.

Parkinson's Disease (PD) is a complex neurodegenerative disorder that manifests through systemic, large-scale physiological reorganizations. While research often focuses on region-specific neural changes, there is a growing need for multidomain approaches to capture the complexity of the disease and its clinical heterogeneity. This study proposes an analytical pipeline to evaluate Brain-Heart Interplay (BHI) as a novel systemic biomarker for neurodegeneration and healthy ageing. In this study we assessed BHI across three open-source datasets (EEG and ECG signals). We compared Healthy Young, Healthy Elderly, and PD patients in resting state to investigate the effects of ageing and cognitive performance. Additionally, we studied BHI trends in PD patients in the moment of freezing of gait (FOG). Methodologically, brain network organization was quantified using coherence-based EEG connectivity and graph theory, while heart activity was analyzed through Poincare plot-derived measures of cardiac autonomic activity. The coupling between these two systems was measured using the Maximal Information Coefficient to capture linear and non-linear dependencies between global cortical organization and cardiac autonomic outflow. The results demonstrate that BHI is a sensitive biomarker for detecting early multisystem dysfunction in both neurodegeneration and ageing. Furthermore, the identification of specific BHI trends during FOG onset suggests new opportunities for understanding the physiological mechanisms driving motor complications in PD. Our proposed pipeline provides a guiding tool for large-scale physiological assessment in clinical research.

Motor neuron (MN) loss is a hallmark of neurodegenerative disorders, yet its assessment remains variable, confounding mechanistic and therapeutic interpretation. To address this, we conducted a systematic review and meta-analysis of spinal muscular atrophy (SMA) mouse studies, revealing 60% variability in reported MN loss, largely attributable to nonspecific spinal cord sampling. Using a whole-segment approach with tissue clearing, MN tracing, and multimodal imaging, we confirmed segment-dependent differences in MN counts. Common MN markers (SMI-32, Nissl) lacked specificity, whereas choline acetyltransferase (ChAT) provided robust labeling in murine and human spinal cords. Deep learning-based whole-mount segmentation enabled unbiased MN quantification and validated manual counts. Integrating analysis with computational modeling established segment sampling as a key driver of variability and revealed degeneration patterns: widespread MN loss in amyotrophic lateral sclerosis (ALS), selective MN loss in severe SMA, and preservation in mild SMA models. These findings establish a framework for reproducible MN quantification. HighlightsO_LISpinal cord segment-specific analysis reduces variability and allows accurate MN quantification C_LIO_LIChAT is the most reliable MN marker in murine and human spinal cords C_LIO_LIDeep learning-based segmentation enables unbiased MN quantification in intact spinal cords C_LIO_LIMN degeneration is widespread in ALS but restricted to pools innervating proximal muscles in severe SMA C_LI

A prominent radiological manifestation of cerebral amyloid angiopathy (CAA) is enlargement of perivascular spaces (EPVS), which is suggested to result from fluid stagnation due to impaired perivascular clearance. Here, we report a novel observation of hypointense rims in cerebral white matter surrounding EPVS near haemorrhages on in vivo 7T Gradient Echo MRI. We hypothesised that the observed black rim pattern denotes iron accumulation that may be caused by incomplete clearance following bleeding. We investigated the occurrence and localisation of this marker on in vivo and ex vivo MRI and examined its histopathological correlates. From MRI data of the prospective longitudinal natural history study of hereditary Dutch-type CAA (D-CAA) at Leiden University Medical Centre, we selected the first 20 consecutive patients who underwent 7T imaging and assessed the presence of black rims on MRI. Post-mortem material was available from one donor with black rims on in vivo scans. Formalin-fixed coronal brain slabs were scanned at 7T MRI, including a high-resolution T2*-weighted sequence. Guided by ex vivo MRI, tissue blocks from representative areas with black rims were sampled for histopathological analysis. Serial sections were stained for iron, calcium, myelin, and general tissue morphology. On in vivo 7T MRI, 9 out of 20 participants exhibited one or several black rims, all located close to a haemorrhage. In the D-CAA donor, ex vivo MRI signal loss matched the in vivo contrast changes. Thirty-six vessels with ex vivo-observed black rims were retrieved and histopathologically examined, showing iron accumulation surrounding perivascular spaces, but the pattern and severity of iron deposition varied. Across groups, vessels displayed microvascular degeneration, including hyaline vessel wall thickening, adventitial fibrosis, and perivascular inflammation. We identified black rims on in vivo 7T MRI and confirmed their correspondence on ex vivo imaging. Iron deposition was determined as the underlying correlate of black rims, but the histopathology appears heterogeneous. The preferential deposition of iron around EPVS may indicate incomplete clearance of iron-positive blood-breakdown products after bleeding. The varied pattern of iron accumulation and microvascular alterations may reflect different pathophysiological mechanisms related to the formation and maintenance of black rims in D-CAA.

Objectives: To evaluate the diagnostic performance of the -synuclein seed amplification assay (SAA) and characterize the impact of -synuclein co-pathology on cognitive and biological profiles in routine clinical practice. Methods: We included 398 patients from the prospective multicenter ALZAN cohort recruited from memory clinics in Montpellier, Nimes, and Perpignan. All participants underwent CSF and blood sampling with measurement of CSF biomarkers (A{beta}42/40, tau, ptau181) and plasma biomarkers (A{beta}42/40, ptau181, ptau217, GFAP, NfL). Cognitive assessment was performed using the Mini-Mental State Examination (MMSE). Clinical diagnoses were independently confirmed by two senior neurologists. Syn status was determined by SAA (RT-QuIC). Results: Of 398 patients, 19 out of 20 patients with Lewy body dementia (LBD) (95.0%) and 32 out of 203 patients with AD (15.8%) were SAA+. SAA-positivity presented a sensitivity of 95% and a specificity of 93.5% for distinguishing LBD from patients without LBD or AD. In the entire cohort, SAA+ patients showed lower MMSE scores (p<0.01), lower CSF A{beta}42/40 ratio (p<0.01), and elevated plasma GFAP (p<0.05). Within the AD group, no significant differences in CSF or blood biomarkers were observed between SAA+ and SAA- patients. Within the AD subgroup, no significant differences in CSF or blood biomarkers were observed between SAA+ and SAA- patients, except for a lower CSF A{beta}42/40 ratio in SAA+ patients (p<0.01). Interpretation: SAA demonstrates good diagnostic capabilities for detecting LBD and confirms notable Syn co-pathology in AD. This study highlights the limitations of routine CSF and emerging blood biomarkers in capturing Syn pathology and the value of integrating SAA into routine neurodegenerative disease assessment.

Traumatic brain injuries (TBIs) are more than mere lesions and generate a persistent secondary pathology. This, combined with functional reorganization of circuits post-injury, may explain the increased risk for psychiatric disorders in patients with TBI. In the current studies, we demonstrate that frontal TBI changed the Pavlovian behavioral response to reinforcer-predicting cues and reduced the motivational value of cues. TBI also chronically impaired decision-making on a gambling-like task with reinforcer-paired cues. To investigate how these changes occur, we evaluated the nucleus accumbens (NAc) core. At a subacute time point (14 days), we confirmed reduced input to the NAc with optogenetics and evaluated electrophysiological and transcriptional changes. TBI increased neuronal excitability and the single nucleus RNA sequencing profile indicated a substantial stress and inflammatory response, but also high indicators of plasticity, particularly in D1- and D2-positive medium spiny neurons. To evaluate how these subacute changes transitioned to chronic NAc dysfunction, we measured immunohistochemical surrogates of activity post-mortem and recorded calcium activity from the NAc after TBI during Pavlovian conditioning. TBI reduced histological markers of activity and reduced cue-evoked calcium activity. Overall, these data indicate that substantial reorganization of the NAc occurs following frontal brain injury. A primary effect of this is to reduce the salience of environmental cues linked to outcomes. The inability to properly process outcomes could contribute to broader psychiatric symptoms after TBI, including impairments in decision-making, behavioral flexibility, and impulsivity but also presents a potential treatment target.

Chronic inflammation is a common feature of aging and is observed across various age-related neurodegenerative diseases, including Alzheimers disease (AD). It has, however, been challenging to develop measurements of brain structure directly linked to peripheral measures of neuroinflammation. This cross-sectional study examined whether plasma levels of markers related to inflammation are associated with diffusion magnetic resonance imaging (dMRI) measures of white matter microstructure: mean diffusivity (MD) and Neurite Orientation Dispersion and Density Imaging (NODDI) free water fraction (FWF) and orientation dispersion index (ODI). Participants included 457 dementia-free individuals (mean age=63.82, SD=7.63). Blood plasma markers related to inflammation included two measures of systemic inflammation, (1) high-sensitivity C-reactive protein (CRP), and (2) a composite of pro-inflammatory cytokines (IL-1, IL-1{beta}, IL-2, IL-6, IL-8, TNF-, TNF-{beta}), as well as (3) glial fibrillary acidic protein (GFAP), a measure of astrocytic activation. Higher cytokine composite levels were associated with higher values of all three measures (FWF, ODI, MD) in cerebral white matter, and with higher ODI in the cerebellar peduncles. Higher CRP levels were associated with higher ODI in cerebral and cerebellar white matter. Associations with GFAP were not significant after adjusting for multiple comparisons. Results were consistent after accounting for plasma biomarkers of AD pathology (p-tau181/A{beta}42). Thus, higher levels of peripheral pro-inflammatory markers are associated with white matter microstructure (higher FWF, ODI, and MD), supporting the view that these dMRI-based metrics are sensitive to inflammatory processes. Additionally, the sensitivity of dMRI-based measures to inflammation may differ by inflammatory marker types.

Disability worsening is the critical long-term outcome in multiple sclerosis, yet the Expanded Disability Status Scale incompletely captures neurological deterioration and has limited sensitivity in the short time windows of clinical trials. Composite endpoints incorporating functional measures have been proposed to address these limitations, but whether they reliably improve detection of treatment effects has not been established across trials. We conducted a post-hoc analysis of individual patient data from ten phase III randomised controlled trials (ASCEND, BRAVO, CONFIRM, DEFINE, EXPAND, INFORMS, OLYMPUS, OPERA I/II, and ORATORIO; n = 9,369), spanning relapsing-remitting and progressive multiple sclerosis. Confirmed disability worsening was defined using harmonised criteria with the msprog package and confirmed at 24 weeks. Treatment effects were estimated using Cox proportional hazards models and combined across trials in a one-stage individual patient data framework. Composite endpoints were constructed from the Expanded Disability Status Scale, the timed 25-foot walk test, and the nine-hole peg test using logical unions (OR-type), intersections (AND-type), and majority-vote structures. Sensitivity to treatment effect was quantified using Z-scores (the ratio of the pooled log-hazard ratio to its standard error) and compared to the Expanded Disability Status Scale reference using interaction tests. Event rates varied across components: the timed walk test generated the highest rates (up to 46.8%) while the nine-hole peg test generated the lowest (as low as 2.1%). OR-type composite endpoints showed weaker treatment effects than the Expanded Disability Status Scale alone, with the largest reductions in sensitivity observed for endpoints incorporating the timed walk test ({Delta}Z up to +2.26; interaction p = 0.004). These findings were confirmed across disease subtypes and were pronounced in relapsing-remitting trials, where no composite endpoint outperformed the Expanded Disability Status Scale. In progressive multiple sclerosis, the combination of the Expanded Disability Status Scale and the nine-hole peg test showed numerically stronger treatment effects ({Delta}Z = -1.65), though interaction tests did not reach statistical significance (p = 0.051). Composite endpoints do not systematically improve treatment effect detection in multiple sclerosis trials. Increased event capture driven by the timed walk test introduces noise that dilutes the treatment signal rather than amplifying it, highlighting that event rate and endpoint quality are not interchangeable. Upper limb function assessed by the nine-hole peg test provides complementary and specific information, particularly in progressive disease. The combination of global disability and upper limb measures represents a promising direction for future endpoint development in progressive multiple sclerosis trials, warranting validation.

Alzheimers disease (AD) neuropathological changes can be detected with blood-based biomarkers during the long preclinical phase that precedes clinical diagnosis. Tau phosphorylated at threonine 217 (p-tau217) has been found to closely correlate with brain A{beta} burden. A recent large-scale cross-sectional study showed elevated p-tau217 concentrations in older individuals (Aarsland et al., 2025). This increase was higher in those with AD dementia and mild cognitive impairment (MCI), and lower in those with intact cognition and higher educational attainment. Thus, intact cognition and higher education may be associated with lower levels of AD neuropathological changes. Here we tested this hypothesis using longitudinal data from the population-based Betula study (n=1005; 1531 samples). The results revealed increases with increasing age over 10 years in p-tau217, where individuals with accelerated episodic-memory decline had the strongest increase. There were no differences in p-tau217 trajectories between individuals with lower or higher education or with well-maintained or age-typical decline in episodic memory. The lack of association with education was further replicated in the independent BioFINDER-2 cohort. These findings underscore the value of plasma p-tau217 for detecting early pathological changes in population-based settings but provide no support that individuals with well-maintained episodic memory or high educational attainment are spared from neuropathological changes.

1.PurposeSYNGAP1-related developmental and epileptic encephalopathy (SYNGAP1-DEE) is characterized by high rates of both epilepsy and autism spectrum disorder (ASD). While the clinical spectrum is well-documented, the link between specific seizure semiologies and caregiver-reported autistic behaviors is not well understood. This study analyzed the correlation between ten distinct seizure types, their frequencies, and a caregiver-reported autistic behavior score. MethodClinical data were extracted from the PATRE (PATient-based phenotyping and evaluation of therapy for Rare Epilepsies) Registry for SYNGAP1, in the framework of the EURAS project (Grant No. 101080580, Horizon Europe). This study employed a retrospective cross-sectional analysis of caregiver-reported registry data. Analysis was restricted to an analytic cohort of N=337 participants with complete data for both the epilepsy questionnaire (including epilepsy status, seizure semiology, and peak seizure frequency items) and the behavior questionnaire (from a total N=522 registry participants). Caregiver-reported autistic behaviors were quantified using a standardized caregiver-reported scale (Likert 1-5). Statistical associations were evaluated using the Wilcoxon rank-sum test to compare caregiver-reported autistic behavior scores across different seizure semiologies and Spearmans rank correlation to assess the impact of seizure frequency (9-point scale). ResultsWithin the analytic cohort (N=337), epilepsy was reported in 259 patients. Eyelid myoclonia was the most prevalent semiology, affecting 64.9% (n=168) of the epilepsy-positive group. Atypical absences (n=77) demonstrated the most profound and statistically robust association with higher caregiver-reported autistic behavior scores (FDR-adjusted p = 0.001). Significant associations were also observed for typical absences (n=70, FDR-adjusted p = 0.018), eyelid myoclonia (FDR-adjusted p = 0.018), myoclonic-atonic seizures (n=40, FDR-adjusted p = 0.019), and atonic seizures (n=72, FDR-adjusted p = 0.025). Focal and tonic-clonic seizures showed weaker associations (FDR-adjusted p = 0.026 and p = 0.047, respectively). Crucially, quantitative analysis revealed no significant correlation between ordinal caregiver-reported peak seizure frequency ratings and caregiver-reported autistic behavior scores across all semiologies (e.g., Eyelid Myoclonia: p=0.096; Atypical Absences: p=0.744), indicating no detectable association between peak-frequency ratings and caregiver-reported autistic behavior scores. ConclusionHigher caregiver-reported autistic behavior scores in SYNGAP1-DEE were most strongly associated with the presence of atypical absences, representing a generalized, thalamocortical seizure network dysfunction. In contrast, no detectable association was observed between caregiver-reported autistic behavior scores and the ordinal caregiver-reported peak seizure frequency metric. Atypical absences and related semiologies may serve as clinical "red flags" for increased neurodevelopmental comorbidity severity, regardless of reported peak seizure frequency. Abstract SummaryThis study investigates the relationship between ten seizure semiologies, seizure frequency, and severity of caregiver-reported autistic behaviors in a large-scale international cohort of N=337 patients with SYNGAP1-DEE. We identify a robust association between elevated caregiverreported autistic behavior scores and specific thalamocortical seizure patterns, most prominently atypical absences. Notably, our analysis reveals that this association is independent of seizure frequency, demonstrating no detectable association between this ordinal, caregiver-reported seizure frequency metric and caregiver-reported autistic behaviors.

BackgroundEpidemiological studies show an inverse association between cigarette smoking and Parkinsons disease (PD), suggesting a potential protective effect of smoking on PD incidence, despite the well-established and overwhelming harms of smoking to human health. We integrated genomic and proteomic approaches to investigate the causality and molecular basis of this potential relationship. MethodsWe analyzed summary statistics from genome-wide association studies (GWAS) of smoking initiation (SmkInit), smoking intensity, and PD. Two-sample Mendelian randomization (MR) tested whether genetic liability to smoking behaviors causally influences PD risk. Shared genomic architecture was quantified using MiXeR, and conjunctional false discovery rate (conjFDR) analysis identified loci jointly associated with smoking and PD, which were then mapped to genes and tested for tissue enrichment. To identify mediating proteins, we integrated dorsolateral prefrontal cortex proteomic data with GWAS using proteome-wide association studies (PWAS), summary-based MR, heterogeneity in dependent instruments testing, and colocalization. Finally, the druggability of convergent genes was evaluated. ResultsMR analyses indicated a protective effect of genetic liability to SmkInit on PD risk (OR = 0.78, 95% CI: 0.67-0.91, P = 1.5 x 10-3), which was consistent across sensitivity analyses and not suggestive of directional pleiotropy. However, no significant effect of genetic liability to cigarettes per day (CigDay) on PD risk was found. MiXeR revealed modest polygenic overlap between SmkInit and PD (13.9%; genetic correlation rg = -0.16) and between CigDay and PD (22.9%; rg = -0.09). ConjFDR identified 95 shared loci for SmkInit-PD and 26 for CigDay-PD. SmkInit-PD loci mapped to genes involved in neurotrophic signaling, synaptic organization, microglial modulation, and mitochondrial stress responses, with expression enriched in substantia nigra, basal ganglia, and interconnected cortical regions. PWAS identified 11 proteins shared by PD and SmkInit and 5 shared with CigDay, several of which (AKT3, MAPT, RIT2, EXD2, and PPP3CC) were supported by both genomic and proteomic analyses. Druggability assessment highlighted six proteins with existing pharmacologic modulation potential, spanning neurotrophic, microglial, proteostatic, and ion-channel pathways. ConclusionsGenetic liability to smoking initiation appears to confer modest protection against PD. Integrative genomic and proteomic evidence converges on neurotrophic, synaptic, microglial, and mitochondrial pathways as shared mechanisms, identifying biologically coherent potential therapeutic targets for advancing smoke-free neuroprotective strategies in PD.

BackgroundPhenoconversion to Parkinsons disease (PD) or dementia with Lewy bodies (DLB) currently relies on established clinical diagnostic criteria. Availability of in vivo biomarkers--CSF -synuclein seed amplification assay (CSFaSynSAA) and dopamine transporter (DAT) imaging--offer the opportunity to investigate congruency between clinical phenoconversion and biologically defined disease. MethodsWe analyzed Parkinso[n]s Progression Markers Initiative participants who phenoconverted to PD, DLB, multiple system atrophy (MSA), Alzheimers disease (AD) or other dementias from prodromal and non-manifesting genetic carrier (NMC) groups and controls. Site investigators determined phenoconversion based on established diagnostic criteria. All phenoconverters with [&ge;]1 annual follow-up visit, with available biomarkers and persistent clinically defined diagnosis at last observation were included. Neuronal alpha-Synuclein Disease Integrated Staging System (NSD-ISS) staging was applied. ResultsAmong 121 phenoconverters, 103 had evaluable CSFaSynSAA and DAT data and were included in analysis: 92 PD, 7 DLB, 2 MSA, 2 AD/other dementias. Phenoconversion annual rates varied widely across groups: iRBD 7.9%, hyposmia 4.2%, GBA1 0.3%, LRRK2 1.3%, LRRK2+GBA1 0.9%, and controls 0.5%. Median time from baseline to phenoconversion ranged from 13-14 months in iRBD and hyposmia to 36-85 months in NMCs. The expected biomarker profile (CSFaSynSAA+/DAT+) for clinically-diagnosed synucleinopathy occurred in 74 (71.8%) participants. Biological alignment (CSFaSynSAA+/DAT+) was present in 87% hyposmics and 72% iRBD phenoconverters. CSFaSynSAA negativity was high among LRRK2 phenoconverters (67%), who also were more likely to have a preserved sense of smell (83%). Phenoconversion occurred later than onset of functional impairment: 15/47 (31.9%) iRBDs and 7/38 (18.4%) hyposmics were already NSD-ISS stage [&ge;]4 at time of phenoconversion. ConclusionsClinical phenoconversion did not necessarily align with biological evidence of synucleinopathy or dopaminergic loss and can be delayed compared to onset of meaningful functional impairment. Longitudinal follow up on converters without biological evidence of PD is required to confirm conversion diagnosis and evaluate for a later occurrence of biomarker positivity.