NeuroImage: Clinical

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.

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.

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.

Catatonia is a severe psychomotor syndrome that occurs across psychiatric diagnoses and is increasingly conceptualized as reflecting neurodevelopmental vulnerability. The anterior cingulate cortex (ACC) plays a central role in motor initiation and cognitive-affective integration and displays substantial interindividual variability in its sulcal morphology, which is established prenatally and remains stable across life. In this MRI study, we examined whether ACC sulcal patterns represent a structural trait marker of catatonia. We analyzed high-resolution T1-weighted images from a hospital-based cohort comprising patients with catatonia (N = 109), psychiatric patients without catatonia (N = 323), and healthy controls (N = 91). The presence of the paracingulate sulcus (PCS) in each hemisphere was determined through blinded visual inspection, and regression analyses tested associations with diagnostic group, adjusting for age, sex, scanner type, intracranial volume, and benzodiazepine and antipsychotic exposure. Patients with catatonia exhibited a significantly reduced prevalence of the left PCS and diminished hemispheric asymmetry compared with both non-catatonic patients and healthy controls. These effects were independent of whether catatonia occurred within psychotic or mood disorders. PCS size did not differ across groups, and sulcal pattern did not correlate with catatonia severity among affected individuals. The findings demonstrate that ACC sulcal deviations are specifically associated with catatonia across diagnostic categories, supporting a neurodevelopmental etiology and reinforcing ACC involvement in its pathophysiology. Early-determined sulcal morphology may represent a trait-level marker contributing to vulnerability for catatonia, with implications for early identification, risk stratification, and targeted intervention strategies.

Regional volumetric assessment of perinatal brain development is currently limited by the lack of consistent high quality multi-regional segmentation methods applicable to both fetal and neonatal MRI. We present Multi-BOUNTI, a deep learning pipeline for automated multi-lobe segmentation of fetal and neonatal T2w brain MRI. The method is based on a dedicated 43-label parcellation protocol and a 3D Attention U-Net trained on brain MRI datasets of subjects spanning 21-44 weeks gestational/postmenstrual age. The pipeline integrates preprocessing, segmentation and volumetric analysis, and was evaluated on independent datasets, demonstrating fast (< 10 min/case) and accurate performance with high agreement to manually refined labels. We demonstrate the application of the framework with 267 fetal and 593 neonatal MRI datasets from the developing Human Connectome Project without reported clinically significant brain anomalies to derive normative volumetric growth models across 21-44 weeks GA/PMA. These models were used to characterise developmental trajectories, assess differences between fetal and preterm neonatal cohorts, and analyse longitudinal changes. The resulting normative models were integrated into an automated reporting framework enabling subject-specific volumetric assessment via centiles and z-scores. Multi-BOUNTI provides a unified and scalable approach for perinatal brain segmentation and volumetry, supporting large-scale studies and facilitating future clinical translation. The full pipeline is publicly available at https://github.com/SVRTK/perinatal-brain-mri-analysis.

Schizophrenia frequently follows a chronic relapsing-remitting course, comprising alternating episodes with and without psychotic symptoms (hereafter: psychosis and psychotic remission). One potential neurobiological correlate of this course is aberrant dopamine synthesis and storage (DSS) in the striatum, which can be estimated by 18F-DOPA positron emission tomography (PET). We hypothesised that striatal DSS in patients with schizophrenia decreases from psychosis to psychotic remission, with lower striatal DSS in patients during psychotic remission compared to healthy subjects. Additionally, we explored whether striatal DSS is associated with psychotic relapse after remission. 18F-DOPA PET scans and clinical assessments were conducted in 28 patients with schizophrenia at two timepoints, first during psychosis and second during early psychotic remission 6 weeks to 12 months after the first timepoint, as well as in 21 healthy controls, assessed twice in a comparable time interval. The averaged influx constant kicer as proxy for DSS was calculated for striatal subregions (i.e., nucleus accumbens, caudate, and putamen) using voxel-wise Patlak modelling with a cerebellar reference region. Mixed-effects models and post hoc analyses were used to test for longitudinal changes in kicer and cross-sectional group differences. An exploratory clinical follow-up 12 months after the second scan was conducted to assess psychotic relapse, and post hoc ANCOVAs were used to test for differences in kicer at each session between relapsing and non-relapsing patients. Kicer in both caudate and nucleus accumbens significantly changed from psychosis to psychotic remission compared to healthy controls, with a significant longitudinal decrease of caudate kicer in patients. Furthermore, kicer in both caudate and accumbens was significantly lower in patients during early psychotic remission compared to controls. At the exploratory clinical follow-up, 32% of patients had experienced a psychotic relapse; they showed higher caudate kicer compared to non-relapsing patients during psychosis, with no difference during psychotic remission. These findings provide evidence for the link between striatal, particularly caudate, DSS and the relapsing-remitting course of psychotic symptoms in schizophrenia, with lower caudate DSS during early psychotic remission. Data suggest altered striatal dopamine synthesis together with impaired DSS dynamics along the course of psychotic symptoms in schizophrenia.

BackgroundPsilocybin is a classic psychedelic that acutely alters brain functional connectivity. These changes are linked to therapeutic doses and subjective effects, with some evidence that changes persist beyond acute drug administration. However, the effects of lower doses on sustained connectivity changes remain unclear. MethodsTen healthy volunteers received three psilocybin doses (between 5 and 20 mg) in a randomized and blinded order, with at least one week between doses. Resting-state functional magnetic resonance imaging was completed at baseline and one week after a single dose. Functional connectivity changes were analyzed in relation to dose and altered conscious states at both the level of individual brain region connections (edges) and resting-state networks. ResultsDose-dependent changes in 77 edges (76 increases, 1 decrease, of 1275 possible) were observed, but none survived multiple-comparison correction. At the network level, we observed one dose-dependent between-network increase (of 21 possible), and one dose-dependent within-network increase (of seven possible); the latter surviving correction. Alterations in conscious state were positively associated with widespread connectivity changes (dose-adjusted), with many network-level associations surviving correction. These directional patterns showed that lower doses and smaller conscious state alterations were linked to decreased connectivity, whereas higher doses and greater conscious state alterations were linked to increased connectivity. ConclusionsDose level and acute subjective effects were positively associated with multiple functional connectivity changes one week after a low-to-moderate psilocybin dose. Further research is warranted to characterize these sustained effects and their therapeutic relevance to inform studies adopting similar dosing regimens in clinical cohorts. Trial RegistrationAustralian New Zealand Clinical Trials Registry: ACTRN12621000560897 Date registered: 12 May 2021 URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=381526&isReview=true

Multimodal machine learning leverages complementary information from diverse data sources and has shown strong promise in medical imaging, where multimodal data is critical for clinical decision making. In glioma grading, integrating MRI modalities with clinical data can improve diagnostic accuracy, yet systematic comparisons of fusion strategies remain limited. This study evaluates early, intermediate, and late fusion approaches, addressing the question: How does the inclusion of clinical data alongside MRI modalities influence grading performance? To assess modality contributions, we design adaptable fusion layers and employ interpretability techniques, including attention-based analysis. Our results show that incorporating clinical data consistently outperforms unimodal and MRI-only baselines, with intermediate fusion yielding the most reliable gains. Beyond accuracy, the framework reveals how MRI and clinical features jointly shape predictions, underscoring the importance of both fusion design and interpretability for clinical adoption.

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.

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.

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.

BackgroundThe severity of positive psychotic symptoms largely defines emerging psychosis syndromes. However, depressive and negative symptoms are strongly psychologically and biologically interlinked. A transdiagnostic exploration of symptom severity across early illness syndromes could enhance the understanding of shared common factors and future trajectories of mental illness. We aimed to identify subgroups based on the severity of positive, negative, and depressive symptoms and assess relationships with: 1) premorbid functioning, 2) longitudinal illness course, 3) genetic risk, and 4) brain volume differences. MethodsWe analysed 749 participants from a multisite, naturalistic, longitudinal (18 months) cohort study of: clinical high risk for psychosis (n=147), recent onset psychosis (n=161), and healthy controls (n=286), and recent onset depression (n=155). Participants were stratified into subgroups based on severity of baseline positive, negative, and depression symptoms. Baseline and longitudinal differences between groups for clinical, functioning, and polygenic risk scores (schizophrenia, depression, cross-disorder) were assessed with ANOVAs and linear mixed models. Voxel-based morphometry was used to examine whole-brain grey matter volume differences. Discovery findings were replicated in a held-out sample (n=610). ResultsParticipants were stratified into no (n=241), mild (n=50), moderate (n=182), and severe symptom (n=254) subgroups. The mean (SD) age was 25.3 (6.0) and 344 (47.3%) were male. Symptom severity was associated with poorer premorbid functioning and illness trajectory, greater genetic risk, and lower brain volume. Findings were not confounded by the original study groups or symptoms and were largely replicated. Conclusions and relevanceTransdiagnostic symptom severity is linked to shared aetiologies, prognoses, and biological markers across diagnoses and illness stages. Such commonalities could guide therapeutic selection and future research aiming to detect unique contributions to specific psychopathologies.

ObjectiveSocial difficulties are transdiagnostic in childhood, but their heterogeneity is poorly characterised and rarely treated as a primary neurodevelopmental phenotype. This matters because childhood and adolescence are sensitive periods for peer relationships and brain development. We used data-driven modelling and non-linear mapping to derive social profiles and test their clinical, cognitive, and neural correlates. MethodsParticipants were 992 children aged 5-18 years from CALM (Mage = 9.6). Social items from the SDQ, CCC-2, and Conners-3 were modelled using a regularised partial correlation network to derive core social dimensions. A self-organising map captured graded social profiles. Simulated archetypes, SVM-based island identification, and permutation testing defined profile regions and centroid-distance scores. Profiles were related to referral, diagnosis, cognition, BRIEF indices, and T1-derived MIND network structure in an MRI subsample (n = 431). ResultsWe identified four profiles: social engagement, friendship difficulties, social withdrawal, and peer victimisation. Profile expression tracked variation in referral and diagnostic pathways. Social withdrawal showed the clearest disadvantage across cognitive domains, whereas social engagement was associated with fewer executive function difficulties across BRIEF indices. MIND strength components covaried with profile expression (a significant PLS latent variable, p = 0.02), with covariance strongest for social withdrawal and peer victimisation. ConclusionsChildhood social functioning organises graded signatures that relate to clinically relevant pathways, cognitive and executive outcomes, and brain structure. Profiling social signatures provides a scalable framework for identifying social need beyond diagnostic categories, motivating studies to test directionality and improve developmental outcomes.

Harmonisation is widely used to mitigate site- and scanner-related batch variability in multisite neuroimaging studies and is particularly critical in longitudinal clinical trials, where detection of subtle biological or treatment-related changes depends on reliable measurement across scanners and timepoints. However, the effectiveness of harmonisation in small, heterogeneous clinical datasets remains insufficiently understood, particularly in relation to subject-level variability and consistency across acquisition settings, and its impact on both removal of technical variability and preservation of biological variation in pooled multisite analyses. We systematically evaluated a range of image-based and statistical harmonisation methods using a clinically realistic multisite, multiscanner structural T1-weighted (T1w) MRI test-retest dataset comprising three controlled acquisition scenarios: repeatability, intra-scanner reproducibility and inter-scanner reproducibility. Methods were applied under different batch specifications (site, scanner, or both) and performance was assessed within each scenario and in pooled data using a multi-metric framework capturing both technical and biological variability in volumetric imaging-derived phenotypes (IDPs) relevant to aging and dementia research. Across IDPs, before harmonisation variability was lowest in the repeatability scenario (median variability=0.6 to 2.7%, rank consistency {rho} [&ge;]0.9), with modest increases under intra-scanner reproducibility (0.5 to 3.2%, {rho}=0.5 to 1.0) and substantially greater variability under inter-scanner reproducibility conditions (1.7 to 19.2%, {rho} =-0.1 to 0.9). These results offer important information to consider for multisite study design, including sample size calculation in clinical trials. Harmonisation performance was strongly context dependent, with clearer benefits emerged in inter-scanner scenarios where both variability reduction and improvements in subject-level consistency were observed. In pooled data, approaches that explicitly modelled site as batch and accounted for repeated-measure structure showed greater consistency across IDPs in batch effect mitigation and more accurately reflected underlying biological variation. Our evaluation metrics enabled disentangling the removal of global batch effect while highlighting residual variability at the phenotype-specific or multivariate levels. These findings demonstrate that harmonisation cannot be treated as a one-size-fits-all solution and must be interpreted relative to the acquisition context, dataset structure, and downstream analytic goals. Multi-metric evaluation under realistic clinical constraints is essential to support reliable and translatable neuroimaging inference by ensuring appropriate correction of batch effects while preserving longitudinal biological signals and sensitivity to clinically meaningful change in multisite studies.

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.

Biological aging is accelerated in people with multiple sclerosis, but whether such acceleration occurs during the pre-symptomatic phase or varies by organ system is understudied. We analyzed two independent proteomics datasets profiled using distinct platforms: the Johns Hopkins cohort profiled using the SomaScan platform (348 multiple sclerosis/49 age-matched controls) and the Department of Defense cohort profiled using the Olink platform (134 multiple sclerosis/79 age-matched controls), including 117 pre-symptomatic samples from people with multiple sclerosis (median lead time: 4.0 years), to estimate systemic and organ-specific proteomic age gaps using established clocks in pre-symptomatic and symptomatic phases, and assess their associations with severity. In the Johns Hopkins cohort, people with multiple sclerosis demonstrated acceleration of systemic ({beta}=2.2, 95% CI 1.2-3.2, P<0.001, FDR<0.001), brain ({beta}=1.7, 95% CI 0.6-2.7, P=0.003, FDR=0.01), muscle ({beta}=2.5, 95% CI 1.3-3.7, P<0.001, FDR<0.001), and immune age ({beta}=1.8, 95% CI 0.6-2.9, P=0.003, FDR=0.01), with findings reproduced in the Department of Defense cohort for systemic ({beta}=0.7, 95% CI 0.0-1.4, P=0.04, FDR=0.34) and brain age (3.2 years, 95% CI 2.1-4.3, P<0.001, FDR<0.001). Proteomic age acceleration was evident prior to symptom onset [systemic: ({beta}=1.0, 95% CI 0.4-1.7, P=0.002, FDR=0.02); brain: ({beta}=2.4, 95% CI 1.2-3.7, P<0.001, FDR=0.002)], whereas no immune age acceleration was detected before or after onset. Higher systemic age gap was associated with greater global Age-Related Multiple Sclerosis Severity Score ({beta}=0.14, 95% CI 0.05-0.24, P=0.005, FDR=0.03) and slower walking speed ({beta}=0.02, 95% CI 0.01-0.03, P=0.006, FDR=0.04), while higher muscle age gap was associated with greater global Age-Related Multiple Sclerosis Severity Score ({beta}=0.17, 95% CI 0.10-0.24, P<0.001, FDR<0.001), poorer manual dexterity ({beta}=0.28, 95% CI 0.04-0.52, P=0.03, FDR=0.30), slower walking speed ({beta}=0.02, 95% CI 0.01-0.03, P=0.002, FDR=0.02), lower peripapillary retinal nerve fiber layer ({beta}= -0.26, 95% CI -0.41 to -0.10, P=0.001, FDR=0.02) and ganglion cell-inner plexiform layer thicknesses ({beta}= -0.35; 95% CI -0.65 to -0.05; P=0.02, FDR=0.30). Higher brain age gap was associated with several imaging measures, including lower whole-brain ({beta}= -0.002, 95% CI -0.003 to -0.001, P=0.002, FDR=0.02), and lower peripapillary retinal nerve fiber layer thickness ({beta}= -0.21, 95% CI -0.39 to -0.03, P=0.02, FDR=0.10). Proteomic age acceleration in multiple sclerosis is detectable years before symptom onset and distinct organ-specific aging signatures are associated with disease severity. Proteomic aging may provide a biologically informative marker of early disease processes and a clinically relevant readout of disease heterogeneity.

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.

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.

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.