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.

Background: Most trials of Parkinson's disease (PD) measure progression over a short to medium time-period using continuous rating scales that may be hard to interpret and less meaningful for patients. There is a lack of evidence connecting changes in these scales to changes in outcomes important to patients. Objectives: We present causal modelling to translate the causal, short-term disease-modifying treatment effects on functional rating scales to the 10-year risk of serious clinical progression milestones. Methods: We selected four important clinical milestones of disease progression from the Oxford Parkinson's Disease Centre "Discovery" cohort: dementia, any falls, frequent falls, and mortality. We proposed a causal framework for our research objectives so we could model the potential impact of a 30% reduction in disease progression slopes ("treatment effect") using the summation of parts I and II of the Movement Disorders Society Unified Parkinson's Disease Rating Scale (UPDRS12). This outcome was regressed on time to milestone using flexible parametric survival models. Marginal predictions of survival and survival difference at year 10 were then calculated for the Discovery cohort, and a counterfactual cohort applying the treatment effect to estimate the relative and absolute reductions for the four clinical milestones. Results: The model increase in risk for each unit change in the UPDRS12 were as follows: dementia hazard ratio (HR)=1.52 (95% Confidence Interval (CI) 1.36-1.70), any falls HR=1.37 (95% CI 1.29-1.46), frequent falls HR=1.68 (95% CI 1.49-1.89), mortality=1.29 (95% CI 1.17-1.42). These models led to marginal predictions of absolute reductions, when the progression was reduced by 30%, between 4.0% (mortality) and 7.5% (frequent falls) at 10 years follow up. Conclusions: We have demonstrated how a treatment effect in a trial specified in terms of a progression change of a rating scale can be contextualised into a long-term reduction in the probability of clinically relevant milestones. Whilst we have used PD as our exemplar, we believe this methodological approach is generalisable to other chronic progressive diseases where trials are often limited to a relatively short follow-up period and use some scalar measure of progression, but significant clinical milestones usually take longer to be observed. Keywords: Clinical trials; disease modifying therapies; causal estimation; prediction models

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.

Importance: Glucose metabolic dysregulation in brain is a common feature of late-onset age-associated neurodegenerative disease (A2ND). Prior meta-analyses have identified disease-specific effects compared to healthy, unimpaired individuals. Yet, a unifying A2ND glucose dysregulation spatial signature remains undescribed. Objective: To determine the common signature of dysregulated glucose metabolism on FDG-PET using activation likelihood estimation (ALE) meta-analyses across A2ND. Data Sources: Searches were conducted using MEDLINE, Embase, PsycINFO, Scopus, and Cochrane from inception through July 2025. The search terms included controlled vocabulary and keywords for four neurodegenerative diseases Parkinson Disease, Amyotrophic Lateral Sclerosis, Alzheimer Disease, and Multiple Sclerosis, Fluorodeoxyglucose F18, glucose, and positron-emission tomography (PET). Study Selection: Studies comparing adults with late-onset neurodegenerative diseases to non-diseased controls using FDG-PET to quantify brain glucose uptake and reporting whole-brain coordinate findings in either Talairach or Montreal Neurological Institute space were included. Data Extraction and Synthesis: Three researchers, assisted by an AI screening tool, screened 7275 potential titles and abstracts for inclusion. Full texts were then retrieved for potentially relevant articles and were evaluated by three researchers using prespecified inclusion/exclusion criteria. Main Outcomes and Measures: Cluster peak and subpeak coordinates, cluster-wise t- or Z- values, and annotations indicating the disease of interest, whether the outcome was for hyper- (disease group > control) or hypometabolism (disease group < control), were extracted from included texts and analyzed using ALE. Results: A total of 130 FDG-PET studies were included in the meta-analysis, with a combined sample of 5412 individuals with A2ND and 3549 controls. Meta-analyses revealed dysregulated glucose metabolism as a unifying feature across A2ND which included both hypo- and hypermetabolic patterns. Neuroanatomical metabolic pattern was unique and disease specific. Each A2ND metabolic phenotype was associated with unique and complex patterns of neurological functionalities. Conclusions and Relevance: These data demonstrate dysregulated glucose metabolism as a common A2ND feature, suggesting responsive remodeling of neural bioenergetics. While hypometabolism is a common research focus, due to functional relevance, hypermetabolism may reflect a compensatory, maladaptive, or neuroinflammatory signal, that requires focused investigation. A2ND prevention and treatment efficacy may depend on addressing bidirectional metabolic dysregulation in addition to disease-specific drivers of pathology.

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.

Patients with functional neurological disorders (FNDs) show impaired control of voluntary actions in the absence of organic neurological damage. The inconsistency between objective neurological clinical signs and actual performance of the same movements in slightly different contexts points to an abnormal self-focused attentional role towards movement execution. Yet, it remains unexplained what triggers a higher level of self-focused attention in FNDs and how this interferes with voluntary movements. Given the known threat sensitivity manifested by patients with FNDs, I hypothesized that under negative affective conditions self-focused attention might be heightened in FNDs in an automatic way so as to impede the execution of a voluntary action. Specifically, I used fMRI to investigate effective brain connectivity in "self-referential" and "limbic" circuits to delineate the causal functional architecture accounting for the FND specific activity when preparing a movement under aversive conditions with different levels of emotion awareness. Seventeen FND participants and seventeen healthy volunteers performed a motor task (key press and release) after having been exposed to an aversive or neutral picture prime using a sandwich mask paradigm. Behaviorally, the FND group had showed a slower reaction time across all task conditions and a high rate of missing key-press responses following associated to aversive primes. Dynamic Causal Modeling (DCM) analyses showed that the FND group emotional information did not engage a limbic network as observed in the healthy control group, but rather a different self-referential associated network. In this functional architecture, the aversive masked condition exerted a direct inhibitory effect on forward connections between the left IFG and left precentral motor cortex. These findings show how affective processing can impact on voluntary motor control in FND, helping to reduce the explanatory gap between emotionality and readiness to act as a potential process of functional motor symptom production.

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.

Background: Neurite orientation dispersion and density imaging (NODDI) shows promise in providing specific insights into the neurite morphology underlying white matter (WM) damage in neurodegenerative diseases. This study aimed to advance the currently limited knowledge by characterizing NODDI-derived microstructural WM alterations in Wilson disease (WD) and examining their relationships with clinical symptoms. Methods: 30 WD patients, including 19 with predominant neurological involvement (neuro-WD) and 11 with hepatic manifestation (hep-WD), and 30 matched healthy controls underwent multi-shell diffusion-weighted magnetic resonance imaging. NODDI metrics, including neurite density index (NDI), orientation dispersion index (ODI), and isotropic volume fraction (ISOVF), and diffusion tensor imaging-based fractional anisotropy (FA) were estimated. Group differences in diffusion parameters across the WM skeleton were determined using tract-based spatial statistics. Additionally, voxel-wise correlations with neurological and cognitive scores were investigated. Results: We observed widespread NDI and ODI reductions in neuro-WD patients and ISOVF increases in hep-WD patients compared with healthy controls, particularly involving the corpus callosum, corona radiata, superior longitudinal fasciculus, external and internal capsule, and superior fronto-occipital fasciculus. A comparable yet more subtle pattern was found when comparing phenotypes. Distinct NDI and ODI constellations were identified as the microstructural determinants of FA alterations. Decreased NDI in the aforementioned fibers were correlated with neurological impairment, processing speed, and visual attention. Conclusions: Phenotype-specific microstructural WM alterations were identified, characterized by globally reduced axonal density and fiber organization in neuro-WD and excess free water in hep-WD. NODDI could be useful as an imaging biomarker for forecasting conversion to neurological WD manifestations and monitoring of disease progression.

BackgroundSubthalamic deep brain stimulation (STN-DBS) represents an established therapeutic intervention for advanced Parkinsons disease (PD), alleviating motor and non-motor symptoms. However, impulse control disorders (ICDs) present a complex challenge, with some patients experiencing postoperative improvements while others develop treatment induced impulsive-compulsive behaviours (ICB). The mechanisms determining these variable outcomes remain poorly understood, highlighting the need to predict postoperative ICB outcomes. MethodsThis prospective open-label study aimed to identify microstructural markers associated with postoperative changes in impulsive-compulsive behaviour following STN-DBS. Thirty-five patients underwent diffusion MRI and clinical evaluations preoperatively and six months postoperatively. A whole-brain voxel-wise analysis utilising diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) was conducted to explore associations between microstructural metrics and changes in the Questionnaire for Impulsive-Compulsive Disorders in Parkinsons Disease-Rating Scale (QUIP-RS). ResultsIntact microstructure in frontolimbic WM tracts, including the cingulum, insular cortex connections, and major association fibres, was associated with greater postoperative reductions in impulsive-compulsive symptoms. Conversely, intact microstructure in specific grey matter areas including paracingulate gyrus, insular cortex, and precentral gyrus were associated with lower reductions or increases in postoperative ICB. ConclusionThese findings demonstrate that preoperative microstructural integrity within frontolimbic circuits and executive control networks associates with susceptibility to treatment-emergent impulsive-compulsive behaviours following STN-DBS. The convergent evidence from multiple diffusion metrics suggests that diffusion MRI may serve as a valuable tool for identifying patients at risk for developing ICB, potentially enhancing preoperative counselling and enabling targeted behavioural monitoring strategies.

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.

Alzheimer's disease is associated with both mitochondrial dysfunction and altered neurophysiological signalling. Peripheral measures of mitochondrial respiration have been established as effective predictors of mitochondrial function in the healthy brain, and more recently, of altered brain signalling in clinical groups. Here, we sought to assess whether peripheral mitochondrial energetics are associated with altered neural signalling in Alzheimer's disease. We collected task-free magnetoencephalography (MEG) from individuals on the Alzheimer's disease continuum (69.21 [6.91] years; n = 38) and cognitively normal older adults (72.20 [4.73] years; n = 20). Each participant also provided a blood sample for analysis of mitochondrial respiration using the Seahorse XF96 Analyzer. We used region-wise linear models to test the relationship between ATP-linked mitochondrial respiration and Alzheimer's disease associated neurophysiological changes. We found that mitochondrial respiration linked to ATP production is associated with altered alpha and theta band cortical rhythms in Alzheimer's disease (: pFDR < 0.05, r = -0.7; {theta}: pFDR < 0.05, r = -0.6). We then tested colocalization of mitochondria-neurophysiological relationships with a human brain atlas of respiratory capacity and found that brain regions with lower mitochondrial respiratory capacity exhibit a stronger relationship between aperiodic signalling and peripheral ATP-linked respiration (pFDR = 0.003, r = 0.35). Our findings suggest that peripheral blood measures of mitochondrial function can offer insight into the neurophysiological alterations associated with energetic changes in Alzheimer's disease and warrant further investigation into the translational potential of joint neuronal mitochondrial markers of neurological diseases of aging.

PurposePrevious research has demonstrated effects of head impact exposure on cortical neurophysiology, which may help with understanding variability in clinical sequelae. In separate lines of research, neurochemical and gene transcription markers of vulnerability to traumatic brain injury (TBI) have been established. The purpose of this study was to examine whether these cortical neurochemical and gene transcription gradients are spatially aligned with neurophysiological effects. Methods and MaterialsMagnetoencephalography (MEG) data was collected at a total of 278 pre- and post-season timepoints from 91 high school football players across up to four seasons of play. Of the 91 football players, 10 experienced a concussion, and of the remaining 81 non-concussed players, 71 met the criteria for complete imaging and kinematic data, with post-season evaluations less than six weeks after the end of the season. Head impacts were tracked over the course of the season with helmet-mounted sensors. MEG data underwent source-imaging, frequency-transformation, spectral parameterization, and linear modeling to examine the effects of concussive and non-concussive head impact exposure on pre-to-post-season changes in rhythmic and arrhythmic neurophysiological activity. To determine clinical effects, parent reported Post-Concussive Symptom Inventory scores related to cognitive symptoms were correlated with cortical neurophysiological changes. Multi-atlas data of neurochemical system densities from neuromaps and gene expression from the Allen Human Brain Atlas were examined for alignment with head impact-related alterations in neurophysiology via nonparametric spin-tests with autocorrelation-preserving null models (5,000 Hungarian spins; pFDR <.05). ResultsConcussion-related reductions in cortical excitability (i.e., aperiodic exponent slowing) were aligned with atlas-based norepinephrine transporter (NET) and alpha-4 beta-2 nicotinic receptor (4{beta}2) densities, and with apolipoprotein E (APOE) and brain-derived neurotrophic factor (BDNF) expression levels. More severe cognitive symptoms associated with concussion-related slowing of aperiodic neurophysiology were also aligned with atlas-based NET and 4{beta}2 receptor densities. Similar changes in cortical excitability related to non-concussive head impact exposure were colocalized with serotonin receptor (5-HT1A) density maps and APOE and BDNF expression. Rhythmic alpha activity was reduced by concussion and colocalized with histamine (H3) and mu-opioid (MOR) receptors, among others, as well as with gene transcription atlases of APOE and C-C chemokine receptor 5 (CCR5). ConclusionsThese findings extend our previous work to show that the effects of head impact exposure on neurophysiology are strongest in cortical areas with specific neurochemical and genetic profiles that are known to signal vulnerability to traumatic brain injury, and that these spatial alignments are also associated with self-reported symptom severity. Clinical Relevance / ApplicationChange in cortical excitability, as measured here by MEG, has potential value as a clinical tool for concussion diagnosis and prognosis. We provide genetic and neurochemical contextualization for these changes that may extend their clinical applications, for example to concussion risk assessment and pharmacotherapies.

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.

Background and Aims: Plasma phosphorylated tau 217 (p-tau217), including %p-tau217, has emerged as a robust biomarker of Alzheimer's disease (AD) pathology, with increasing interest in its longitudinal behavior. In "Predicting onset of symptomatic Alzheimer's disease with plasma p-tau217 clocks," Petersen et al. applied disease clock models, Sampled Iterative Local Approximation (SILA) and Temporal Integration of Rate Accumulation (TIRA), to estimate age at plasma %p-tau217 positivity and reported that this measure predicts age at onset of symptomatic AD. We aimed to determine whether this apparent predictive performance reflects biomarker information or arises from structural artifacts in the analysis. Methods: We analyzed digitized data from published figures and decomposed the clock-derived predictor into baseline age and estimated time from %p-tau217 positivity. We quantified shared and unique explained variance between baseline age and the clock-derived predictor using commonality analysis. To further disentangle structural and biomarker contributions, we evaluated a null scenario in which the biomarker-derived timing component was replaced with randomly generated values drawn over the observed range, preserving the predictor distribution while removing biomarker information. Results: The reported predictive performance was largely driven by structural artifacts arising from bounded follow up and constraints among the variables. Restriction to individuals who progressed during limited follow up, together with constraints on the allowable timing of events, induced a strong association between baseline age and age at symptom onset. In ADNI, baseline age alone explained substantially more variance in age at onset than the clock-derived predictors (R2=0.78 vs. 0.337 and 0.470 for TIRA and SILA). The estimated time from %p-tau217 positivity contributed minimal additional information, and randomized predictors yielded comparable performance to baseline age alone (R2=0.79). Conclusion: The apparent predictive ability of plasma %p-tau217 disease clocks is driven largely by structural age relationships rather than independent biomarker signal. The plasma %p-tau217 timing component provided minimal predictive value, and its combination with age obscured these structural dependencies. These findings underscore the need for careful evaluation of constructed predictors and outcomes in longitudinal analyses of disease progression.

Dysarthric speech severity assessment typically requires either trained clinicians or supervised machine learning models built from labelled pathological speech data, limiting scalability across languages and clinical settings. We present a training-free method (no supervised severity model is trained; feature directions are estimated from healthy control speech using a pretrained forced aligner) that quantifies dysarthria severity by measuring the degradation of phonological feature subspaces within frozen HuBERT representations. For each speaker, we extract phone-level embeddings via Montreal Forced Aligner, compute d scores along phonological contrast directions (nasality, voicing, stridency, sonorance, manner, and four vowel features) derived exclusively from healthy control speech, and construct a 12-dimensional phonological profile. Evaluating 890 speakers across10corpora, 5 languages for the full MFA pipeline (English, Spanish, Dutch, Mandarin, French) and 3 primary aetiologies (Parkinsons disease, cerebral palsy, amyotrophic lateral sclerosis), we find that all five consonant d features correlate significantly with clinical severity (random-effects meta-analysis rho = -0.50 to -0.56, p < 2 x 10^-4; pooled Spearman rho = -0.47 to -0.55 with bootstrap 95% CIs not crossing zero), with the effect replicating within individual corpora, surviving FDR correction, and remaining robust to leave-one-corpus-out removal and alignment quality controls. Nasality d decreases monotonically from control to severe in 6 of 7 severity-graded corpora. Mann-Whitney U tests confirm that all 12 features distinguish controls from severely dysarthric speakers (p < 0.001).The method requires no dysarthric training data and applies to any language with an existing MFA acoustic model (currently 29 languages) or a model trained from healthy speech alone. It produces clinically interpretable per-feature profiles. We release the full pipeline and phone feature configurations for six languages to support replication and clinical adoption. Author SummaryOne of the authors has lived with ALS for sixteen years. Bernard Muller, who built this entire analytical pipeline using only eye-tracking technology, has experienced the progression of the disease firsthand, including the dysarthric speech that comes with advancing ALS and the tracheostomy that followed. The problem this paper addresses is not abstract to him, and that shapes how the method was designed. We developed a method to measure how well a person with dysarthria can produce distinct speech sounds, without needing any recordings of disordered speech for training. Our approach works by analysing how a widely available AI speech model organises different sound categories -- such as nasal versus oral consonants, or voiced versus voiceless sounds -- and measuring whether those categories become harder to tell apart. We tested this on 890 speakers across 10 datasets in five languages, covering Parkinsons disease, cerebral palsy, and ALS. Because the method only needs healthy speech recordings to set up, it applies to any language with an existing acoustic model, currently covering 29 languages. The resulting profiles show clinicians which specific aspects of speech production are degrading, rather than providing a single opaque severity score. This could support remote monitoring of speech decline in neurodegenerative disease and enable screening in languages and settings where specialist assessment is unavailable.

Deciding whether and how to act depends on a trade-off between the effort required to execute a given action and the potential reward for completing it. Impairments in this effort-reward trade-off have been proposed to underlie reduced movement vigor, or bradykinesia, in Parkinsons disease (PD). However, several mechanisms could alter the effort-reward trade-off in PD, each with unique implications for understanding and treating bradykinesia. Therefore, we individually examined whether people with PD (both on and off dopamine medication) demonstrated reduced sensitivity to reward value, increased perception of effort, or a biased mapping between effort and reward, compared to age- and sex-matched neurotypical controls. We found that people with PD exhibited increased effort perception and, surprisingly, no reduced sensitivity to reward value or a biased mapping between effort and reward. These findings suggest that effort perception could be an important factor driving bradykinesia in PD.

BackgroundAccumulating results suggest that reticulospinal tract (RST) excitability increases after stroke. While animal studies suggest this hyperexcitability may compensate for corticospinal tract (CST) damage, its role in motor function in people with stroke (PwS) remains debated. This study aimed to: (1) replicate findings of RST hyperexcitability in PwS using the StartReact paradigm, measuring acceleration of motor response to a startling auditory stimulus; (2) examine the relationship between RST hyperexcitability and motor impairments after stroke; and (3) explore whether RST hyperexcitability provides functional benefits in severely impaired PwS. MethodsForty-six PwS completed the StartReact paradigm and motor assessments (Fugl-Meyer, ARAT, grip strength, Modified Ashworth Scale). PwS were categorized into high StartReact effect and typical StartReact effect subgroups based on comparisons with a healthy control group (n=37). Severe impairment was defined as ARAT [&le;]10. ResultsPwS exhibited significantly greater StartReact effects than controls. The high StartReact effect subgroup showed worse motor function, weaker grip strength, and higher spasticity. Among severely impaired PwS, high StartReact effect was not associated with improved grip strength. ConclusionsThese findings confirm the existence of RST hyperexcitability after stroke and suggest it is associated with poorer motor outcomes, likely due to reduced cortical input to the brainstem. The absence of functional benefit in severely impaired individuals supports the interpretation that RST hyperexcitability is a maladaptive rather than a compensatory reaction to brain damage. These findings provide insight into the neurophysiological mechanisms underlying motor impairments after stroke and do no imply direct clinical or therapeutic applications.

Abnormal beta-band activity (13-30 Hz) within the cortico-basal ganglia network is a hallmark of Parkinsons disease (PD) and is closely linked to motor impairment. Pathological beta activity in the subthalamic nucleus (STN) occurs predominantly as brief, high-amplitude bursts rather than continuous oscillations. Although beta-band coherence between the STN and cortex increases during bursts, it remains unclear whether cortico-STN beta coupling persists outside these bursts. Using intraoperative STN local field potentials and simultaneous cortical electrocorticography from seven patients undergoing deep brain stimulation implantation surgery, cortico-STN beta coupling during burst and non-burst epochs was compared. Coupling was assessed using magnitude-squared coherence and the debiased weighted phase lag index (dwPLI) and compared against surrogate distributions generated by circular time-shifting. Both coupling metrics were significantly elevated during burst epochs relative to non-burst periods. During non-burst epochs, coupling collapsed to surrogate levels, indicating no evidence of sustained synchronization. Time-resolved analyses further demonstrated that elevated coupling was confined to burst epochs. Although a subset of motor cortical contacts exhibited elevated baseline coherence, coupling was less evident using dwPLI. These findings suggest that pathological cortico-STN beta coupling in PD is preferentially expressed during beta bursts rather than sustained across non-burst epochs, with implications for adaptive neuromodulation strategies.

Normal pressure hydrocephalus (NPH) is a potentially reversible neurological disorder characterized by urinary incontinence, gait impairment, and cognitive decline. However, postoperative improvement after shunt placement is variable, and reliable preoperative predictors are lacking, leaving patients exposed to uncertain surgical benefit and procedural risk. We therefore asked whether preoperative cerebrospinal fluid (CSF) metabolic profiles capture biological states associated with recovery potential. We analyzed ventricular CSF from patients undergoing shunt placement and identified metabolic patterns that differed between patients who improved postoperatively and those who did not. These signatures were detectable prior to intervention and were consistent across analytical approaches and patient cohorts. Multivariate models based on metabolite features were associated with postoperative improvement, with strongest performance observed for cognitive outcomes. Pathway-level analyses indicated coordinated alterations in processes related to redox balance, immune-metabolic signaling, and energy substrate utilization. These findings indicate that preoperative CSF metabolite profiles reflect biological states associated with recovery potential in NPH. The results further suggest that metabolic and immune-metabolic processes contribute to variability in surgical responsiveness and support the development of predictive biomarkers for patient stratification.