Brain Communications

Temporal measures, such as time from diagnosis or symptom onset are often used to track disease severity in neurodegenerative diseases. Due to variations in symptom awareness, clinical presentation timing, diagnostic delays, and disease progression rates, these temporal proxies introduce substantial variance and bias, making it very difficult to map progression clearly and accurately, and to severity-match across contrastive patient groups. To address this challenge, we explored a data-driven approach to derive a transdiagnostic severity metric that is independent of time and, instead, treats temporal metrics as observed, dependent data. We analysed data from the Genetic Frontotemporal Dementia Initiative (GENFI 1 and 2). We entered neuropsychological scores for symptomatic individuals including any visits prior to conversion from at-risk to symptomatic (n = 265, 522 visits) in an unrotated principal component analysis to derive a transdiagnostic phenotype-severity model. A single component emerged (Kaiser-Meyer-Olkin = 0.92), explaining 65% of the variance, with all neuropsychological assessments loading highly. This global severity component fitted the data equally well across genetically or clinically defined groups, as well as severity levels. The severity measures validity was supported by a clear relationship with the Clinical Dementia Rating scale, and its stability was confirmed when a much broader range of neuropsychological and behavioural measures were included. Additionally, the severity score accounted for a high portion of the total variance in neuropsychological test scores, substantially more than the low proportion accounted for by standard temporal measures. To derive a time-efficient sub-battery, we demonstrated that three neuropsychological assessments (Digit Symbol, Verbal fluency (letters) and Trail Making Test- Part B were able to explain the majority of unique variance in cognitive severity. Finally, by treating time as an observed dependent variable, we showed that the baseline velocity (change in severity measure over time) varied by genetic group, with progranulin mutation carriers being the fastest. This data-driven approach provides an objective, precise measure of disease severity and progression, and it may shed new light on when clinical heterogeneity reflects distinct subtypes rather than differences in disease stage.

Lesion network mapping (LNM) and related techniques have been used in over 200 studies, primarily to test whether anatomically distributed lesions that cause the same symptom fall within a common brain network. A recent article1 challenges the specificity and validity of this technique, suggesting that lesion network maps primarily reflect intrinsic properties of the normative connectome rather than lesion-symptom relationships. However, the data and procedures in van den Heuvel et al. do not reflect those used in most LNM studies. Further, the main conclusions were based on similarity between maps, but similarity does not imply the absence of meaningful differences. In contrast, LNM provides evidence for meaningful differences using specificity testing. Exemplary analyses of 1090 lesion locations from 34 prior LNM studies do not support van den Heuvels concerns and confirm the lesion-deficit specificity of LNM. While we encourage further methodological investigation, the analyses of van den Heuvel et al. do not invalidate prior LNM findings or future applications.

Repurposed noradrenergic drugs have been proposed to treat neuropsychiatric symptoms in Parkinsons disease and related conditions. While there is evidence that these drugs can be beneficial for cognition in selected patients, questions remain about their cardiovascular effects. Here we tested whether heart rate variability (HRV) is altered in people with Parkinsons disease, following a single-dose challenge with the noradrenaline reuptake inhibitor atomoxetine (40 mg, oral). Consistent with previous work, our cohort of people with idiopathic Parkinsons disease (n=15) had lower HRV than healthy controls (n=22). Decreased HRV in people with Parkinsons disease was associated with reduced integrity of the caudal locus coeruleus, measured using neuromelanin-sensitive ultra-high field 7T magnetic resonance imaging. Following a randomised double-blind placebo-controlled crossover challenge in the Parkinsons disease group, short-term resting HRV was not significantly altered following atomoxetine. Using Bayesian statistical inference, we demonstrated confidence in the preservation of HRV across measures in the time, frequency, and non-linear domains. Our findings are in favour of a safe cardiovascular profile for atomoxetine in Parkinsons disease, further supporting noradrenergic modulation as a viable treatment strategy for neuropsychiatric symptoms in Parkinsons disease and related disorders.

The mechanisms of aphasia recovery following left-hemisphere stroke remain debated. Two broad hypotheses have been proposed for how recovery occurs when specialized systems, such as the language system, are affected by brain damage: i) recovery depends on the remaining components of the language system; and ii) recovery depends on functional remapping in brain areas outside of the language system. A key candidate for such takeover of language function is the Multiple Demand (MD) system--an extensive bilateral network that supports executive functions and is associated with the ability to flexibly adapt to task goals. The theoretical premise is that this system is capable of a wide range of cognitive tasks and can potentially be repurposed for language when specialized resources are no longer sufficient. We used precision functional MRI to evaluate these two hypotheses about aphasia recovery in 37 individuals (mean age = 58.3, SD = 8.4) with chronic aphasia due to a single left-hemisphere stroke, along with 38 age-matched controls (mean age = 61.6, SD = 9.2). Participants performed extensively validated functional localizers to identify the language network and the MD network within individuals. Participants with aphasia additionally completed extensive behavioral assessments that evaluated linguistic and executive skills. We first examined responses during language processing--audio-visual speech comprehension and reading--in each of the two networks, and then we related activity and functional connectivity measures from the two networks to linguistic ability. Our results do not support the hypothesis of drastic reorganization of the language system in the form of co-opting parts of the MD system in chronic aphasia. First, the language network and the MD network remain robustly dissociated: the language network responds strongly and selectively to language across modalities (left-hemisphere language regions: pFDR < 0.003), and no MD region shows increased activation during language comprehension relative to controls (pFDR > 0.24). Second, functional connectivity analyses reveal no evidence for increased integration between the two networks during language processing. Third, linguistic ability, as measured by an extensive behavioral battery of tests, is associated with the strength of activity and functional connectivity within the language network, but not within the MD network. Although we cannot rule out a role for the MD network in aphasia recovery during the acute and subacute phases or in more severely impaired patients, it appears that during the chronic phase, language comprehension relies on the same specialized network as prior to the injury.

Following observations from a pilot study that, contrary to expectations, indicated that critical white matter (WM) connections were not more vulnerable to either SVD or AD pathologies than non-critical connections, we set out to systematically evaluate the relation between these pathologies and both connections types. For patients with CADASIL (n=59), Mixed pathology (n=57) and autosomal dominant AD (ADAD; n=50) we reconstructed WM networks based on diffusion tensor imaging and subsequently defined critical and non-critical connections. Associations between AD markers (CSF A{beta}42, p-tau levels, estimated years of onset (EYO)) and SVD markers (WM hyperintensity (WMH) volume) and both connection types were tested with linear regression analyses. WMH volume showed equally strong associations to the strength of both critical and non-critical connections. A{beta}-positivity, A{beta}42 levels, p-tau levels and EYO, while less strongly related to the strength of the WM connections, did consistently show similar effect sizes for both connection types. Sensitivity analyses using different definitions of connectivity yielded similar results. SVD burden influenced WM integrity more than AD, but we found no support for critical connections being more vulnerable to these disease effects than non-critical connections.

Cerebellar ataxias are a rare group of disorders manifesting with motor incoordination and cognitive-affective deficits of variable severity. Although neurogenetic has revealed multiple mutations, the study of ataxias still relies on clinical evaluation, while the underlying neural network changes remain unclear. It has been argued that the less severe symptoms in congenital (like Joubert syndrome, JS) than in slowly progressive (SP) ataxias reflect a different interplay of alteration and compensation but direct evidence is still lacking. Moreover, it is unclear why, in front of a wide heterogeneity of molecular alterations, SPs show common clinical symptoms. To address these questions, we created brain digital twins for each participant by combining volumetry, graph theory analysis of structural and functional connectivity, and dynamical simulations using the virtual brain. We studied 8 JS (3 females, 21{+/-}6years), 8 SP (3 females, 20{+/-}5years) and 11 healthy controls (HC; 5 females, 21{+/-}2years).Volumetry quantified atrophy, graph metrics (centrality, segregation and integration) characterized topology, and neural dynamical simulations estimated excitation/inhibition balance, providing anatomo-physiological parameters within the somatomotor (SMN) and ventral attention (VAN) networks. Anatomo-physiological parameters were correlated with clinical/neuropsychological scores, and unsupervised clustering was applied to assess whether network features can discriminate between JS and SP beyond clinical classification. MRI morphometry confirmed selective vermis reduction in JS and a widespread cerebellar atrophy in SP compared to HC. In both ataxia groups, SMN and VAN showed reduced volume and structural connectivity but with different patterns of topological and dynamical alterations. In the SMN of SP, reduced centrality and excitation/inhibition balance depressed information transfer through the network. In the VAN of JS, reduced centrality, segregation, and integration, were detrimental but coexisted with a higher number of functional core nodes and an increased large-scale excitatory coupling, supporting compensatory reorganisation in extracerebellar nodes. Clustering confirmed that SMN better differentiates SP, whereas VAN better clusters JS. Importantly, anatomo-physiological parameters of network volume, topology, and dynamics correlated with patients motor and cognitive performance. In conclusion, primary cerebellar damage secondarily impacts large-scale brain networks, altered in both ataxia groups but compensated only in JS. Similar clinical symptoms in SP reflects the similarity of network changes, while differential involvement of SMN and VAN in JS and SP reflects the connectivity pattern of the lesioned areas inside these large-scale brain circuits. Importantly, anatomo-physiological parameters are sufficient to explain individual motor and cognitive performance, offering a basis for improved patient profiling and personalized therapies.

ObjectivePostoperative pain outcomes following spinal fusion surgery remain difficult to predict, as structural and surgical indicators alone offer limited insight into who will experience meaningful relief. A substantial proportion of patients continue to report persistent pain after surgery, underscoring the need for objective markers that can help identify those at risk of poor recovery. Peak alpha frequency (PAF) has emerged as a promising trait-like neural signature of pain sensitivity in experimental models, where individuals with slower PAF tend to exhibit heightened pain sensitivity. Yet despite this link, its ability to forecast longer-term postoperative pain trajectories remains unclear. MethodsSeventeen adults undergoing cervical or lumbar fusion surgery were included. Resting-state, eyes-closed EEG was recorded preoperatively and at multiple visits after surgery. PAF was extracted from central electrodes using the centre-of-mass method. Pain intensity was assessed longitudinally on standardised self-report pain scales. Associations between PAF measures and postoperative pain change were examined using correlation analyses, and receiver operating characteristic (ROC) analyses evaluated discrimination of pain responders ([&ge;]50% improvement). ResultsPreoperative peak alpha frequency (PAF) was positively associated with longer-term pain reduction at the 3-month follow-up, but showed no consistent relationship with early postoperative pain. Across pain measures, a consistent pattern emerged across the Brief Pain Inventory (BPI), visual analogue scale (VAS), and numerical rating scale (NRS), but not the verbal rating scale (VRS) or Short-Form McGill (SF-MPQ). At the 3-month follow-up, associations reached statistical significance for BPI-Worst ({rho} = 0.67, p = 0.017), and BPI-Average Pain ({rho} = 0.62, p = 0.033). VAS and NRS showed moderate-to-strong effects that approached significance in non-parametric analyses and were significant for VAS when treated as an approximately interval measure (Pearson r = 0.63, p = 0.022). ROC analyses using BPI-Worst pain improvement demonstrated good discriminative ability of preoperative PAF for identifying treatment responders at 3 months (AUC = 0.84; 95% CI: 0.61-1.00), with high specificity and moderate sensitivity at the Youden-optimal threshold of 10.11 Hz. By contrast, changes in PAF over time were not reliably related to changes in pain scores, suggesting that PAF functions more as a stable, trait-like predictor than a dynamic biomarker in this context. ConclusionThis study demonstrates the feasibility and potential clinical value of preoperative EEG for characterising individual differences in postoperative pain recovery following spinal fusion. The results identify faster preoperative PAF as a stable neural signal that captures meaningful variability in longer-term pain reduction, with convergent support across multiple patient-reported measures. While replication in a larger cohort is required, these findings establish a clear foundation for evaluating PAF as a candidate neurophysiological marker to inform preoperative risk profiling and potentially personalised perioperative pain-management strategies in spinal fusion patients.

INTRODUCTIONCore cognitive deficits in iNPH include slowed information processing, psychomotor slowing and executive dysfunction. However, the cognitive benefits of iNPH treatment with shunt surgery are not well understood. This review synthesised evidence on cognitive assessment methods and outcomes following shunt surgery in iNPH. METHODSPubMed, Scopus, PsycINFO and Web of Science were searched for peer-reviewed studies including adults with iNPH who underwent shunt surgery and had within-subject cognitive evaluations pre- and post-operatively. Key data were extracted and study quality was assessed. Random-effects meta-analyses were performed on pooled baseline and post-shunt difference scores for frequently reported cognitive tests with comparable data. RESULTSOf 1,876 records, 195 met the inclusion criteria, comprising 11,445 patients. Cognitive evaluation methods ranged from subjective reports and NPH grading scales to brief screening tools and comprehensive test batteries. Over 193 distinct tests were reported and 54.4% of studies did not formally assess any core iNPH cognitive deficits. Post-shunt improvement rates, follow-up times and criteria for defining improvement varied widely. Eighty-five studies contributed data to meta-analyses of ten outcomes. Pooled estimates indicated post-shunt cognitive improvement, with Trail Making Test-A, Grooved Pegboard-Dominant and Trail Making Test-B showing changes exceeding thresholds for clinically significant improvement. CONCLUSIONSCognitive assessment in iNPH is highly heterogeneous and frequently omits core domains, limiting detection of treatment effects. When domain-relevant cognitive measures are used, shunt surgery is associated with statistically and clinically significant cognitive improvement. These findings highlight the need for standardised iNPH-specific cognitive evaluation tools with validated criteria for detecting clinically meaningful change and have direct implications for clinical assessment, interpretation of shunt response and the selection of cognitive endpoints in future interventional studies. Summary BoxO_ST_ABSWhat is already known on this topicC_ST_ABSCognitive outcomes after shunt surgery for idiopathic normal pressure hydrocephalus (iNPH) have been inconsistently reported, with cognitive improvement reported less reliably than gait outcomes, in the context of highly variable assessment practices across centres. What this study addsThis systematic review of 195 studies (11,445 patients) shows substantial heterogeneity in iNPH cognitive assessment and demonstrates that when tests sensitive to frontal-subcortical dysfunction are used, shunt surgery is associated with statistically and clinically meaningful cognitive improvement. Widely used dementia screening tools, including the MMSE and MoCA, show changes largely within expected practice-effect ranges and do not adequately capture core iNPH cognitive deficits. How this study might affect research, practice or policyThese findings demonstrate the need to standardise cognitive assessment in iNPH using appropriate iNPH-specific tools with validated metrics for determining clinically meaningful improvement. This will enable robust trial endpoints and accurate evaluation of cognitive benefits of shunting in routine clinical practice.

BackgroundFunctional motor disorder (FMD) is a common and disabling condition with incompletely understood pathophysiology. Eye-tracking offers a method to objectively examine cognitive and motor control processes and their underlying neural pathways. We aimed to quantify saccade, blink and pupil responses in FMD and healthy controls performing an interleaved pro-/anti-saccade task, and to investigate the relationships between oculomotor measures and motor and non-motor symptom severity. MethodsWe conducted video-based eye-tracking in 104 patients with clinically definite FMD and 115 age- and sex-matched healthy controls performing the saccade task. Patients completed questionnaires on depressive, pain-related, dissociative, non-motor somatic symptoms. Clinician-rated motor severity and centrally acting medication was recorded in FMD patients. ResultsCompared to controls, FMD patients showed increased anti-saccade error rates (p < 0.001), anticipatory saccades (p [&le;] 0.003), altered blink distribution (p < 0.001), and reduced pupil dilation velocity (p < 0.001). However, reduced pupil dilation velocity was not significant in subsample of unmedicated patients. Higher anti-saccade error rates were significantly associated with depressive symptoms, pain severity, dissociative symptoms, non-motor somatic symptom burden, and motor severity (all p < 0.05). ConclusionsWe hypothesize that the altered saccade and blink responses result from altered processing in the frontal cortex and basal ganglia which provide critical input to brainstem oculomotor control areas in FMD. These results support neurobiological models proposing altered predictive and attentional processing underlying FMD. Association between oculomotor measures and symptom severity suggests that specific cognitive abnormalities may play a role in the pathophysiology of these symptoms in FMD. WHAT IS ALREADY KNOWN ON THIS TOPICFMD is increasingly interpreted through predictive coding models suggesting abnormalities in predictions about motor and sensory states driven by abnormally focused attention. Yet the underlying neurobiology remains poorly defined. Empirical studies directly probing basic predictive processes in FMD are scarce, and implicit cognitive-motor interactions, particularly those involving motor learning and adaptation, have been insufficiently explored. WHAT THIS STUDY ADDSOnly two previous studies have used eye-tracking in FMD, focusing mainly on diagnostic saccadic markers. Using time-series analyses of saccadic, blink, and pupillary data, we show abnormalities in inhibitory control, predictive processing, and implicit learning. Due to strong homology between human and primate neurophysiology and neuroimaging findings in oculomotor control, the findings can be linked to dysfunction within cortico-basal ganglia circuits. HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICYOculomotor abnormalities correlated with motor and non-motor symptom severity, indicating mechanistic relevance. The findings provide empirical support for predictive coding accounts and point to involvement of subcortical structures including projections from the frontal cortex to the basal ganglia. This highlights the value of studying cortico-basal ganglia circuits with implications for treatment and of developing oculomotor measures as potential biomarkers in FMD.

BackgroundAphasia commonly occurs after left-hemispheric stroke, yet substantial inter-individual variability in language outcomes remains insufficiently explained by established clinical systems neuroscience concepts. Emerging evidence suggests that the integrity of specific neurotransmitter systems may influence functional outcomes after stroke. This study examined whether the damage to neurotransmitter-related structural networks is associated with post-stroke language impairment. MethodsData of 270 patients with left-hemispheric stroke from two openly available cohorts were analyzed: the acute Washington Stroke Cohort and the chronic Aphasia Recovery Cohort. Neurotransmitter-related network damage was quantified by embedding individual stroke lesion masks into normative connectomes weighted by PET-derived density maps of 16 neurotransmitter receptors and transporters. Partial least squares (PLS) regression identified informative predictors of language functioning, followed by linear regression analyses adjusted for age, sex, lesion volume, and time post-stroke. ResultsAcross both cohorts, PLS analyses converged on a neurochemical profile in which damage to networks related to serotonergic (5-HT1a, 5-HT2a) and dopaminergic (D1) receptor distributions showed the strongest associations with poorer language performance. Damage to the 5-HT1a and D1-related networks remained significant in fully adjusted models, leading to substantially improved model fit. ConclusionThe disruption of large-scale serotonergic (5-HT1a) and dopaminergic (D1) brain networks is associated with language impairment in acute and chronic stroke. Neurotransmitter-related network damage explained additional variability in language performance beyond clinical variables and lesion burden. This work adds a neurochemically informed network perspective to aphasia research and may pave the way for future biological patient stratification to support targeted rehabilitation strategies, such as pharmacological interventions.

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.

Migraine is a common and disabling neurological disorder linked to alterations in neuronal activity and waste clearance in the brain. MRI-visible perivascular spaces (PVS) are key components of the glymphatic system which may serve as imaging biomarker of such disorder. We hypothesised that higher frequency of migraine episodes would be associated with increased PVS burden, reflecting greater levels of impaired glymphatic clearance. In this retrospective case-control study of 90 participants (20 episodic migraineurs, 18 chronic migraineurs, and 52 age- and sex-matched healthy controls; 58 females, median [Q1, Q3] age=28.6 [25.1, 39.4] years) we investigated PVS alterations in episodic migraineurs (n=20) and 18 chronic migraineurs (n=18). PVS volumes and cluster counts were quantified in the white matter (WM), basal ganglia (BG), midbrain, and hippocampus. We stratified PVS metrics by white matter lobes and arterial vascular territories. After adjusting for age, sex, and total brain volume, episodic migraineurs exhibited significantly lower BG-PVS volumes (exp({beta})=0.76, 95%CI [0.61, 0.94], p=0.01) compared to controls. Chronic migraineurs exhibited significantly lower PVS cluster counts in the parietal (exp({beta})=0.8, 95%CI [0.68, 0.94], p=0.01) and temporal lobes (exp({beta})=0.72, 95%CI [0.53, 0.96], p=0.03) and middle cerebral artery territory (exp({beta})=0.82, 95%CI [0.68, 0.97], p=0.03) compared to healthy controls. Within migraineurs, those with aura (n=20) exhibited significantly lower PVS burden in all brain regions, vascular territories, and across the frontal, parietal, and temporal lobes (all pFDR<0.05). Our findings suggest that the aura symptom, rather than the migraine disorder itself, may primarily drive changes in perivascular spaces, with effects varying across brain regions.

Distinguishing atypical parkinsonian disorders (APS) from Parkinsons disease (PD) remains challenging due to overlapping clinical features, yet accurate differentiation is critical for prognosis and treatment. Here, we employed multi-model diffusion MRI (dMRI) analysis to characterize microstructural alterations across corticobasal syndrome (CBS), progressive supranuclear palsy-Richardson syndrome (PSP-RS) and PD, with the aim of identifying which dMRI model provides optimum differentiation. We analyzed 25 CBS, 42 PSP-RS, and 21 PD participants compared to 35 age and sex-matched controls. Using a clinically feasible 3-shell high angular resolution diffusion imaging (HARDI) protocol, we applied 11 metrics from five complementary dMRI models--diffusion tensor imaging (DTI), free-water-eliminated model of DTI (FWE), neurite orientation dispersion and density imaging (NODDI), tissue-weighted NODDI, and Fixel Density (FD) in fixel-based analysis (FBA) --to comprehensively assess regional white and gray matter integrity. Group differentiation was assessed using Cohens d effect sizes and spearman correlations were assessed between dMRI metrics and clinical scales. Distinct microstructural signatures were observed across disorders and the sensitivity of the dMRI models differed. In group contrasts, DTI and NODDI-derived metrics consistently captured the strongest effects in midbrain and peduncular pathways for PSP-RS, whereas precentral and corticospinal alterations in CBS were most prominent using NODDI and FBA measures. Free-water-corrected metrics showed attenuated group differences. Across clinical-diffusion analyses, NODDI metrics exhibited the most robust associations with disease severity, while DTI and FWE measures detected more limited, regionally constrained effects. Together, these findings highlight complementary yet distinct sensitivities of tensor, free-water, multi-compartment, and fixel-based models to APS-related neurodegeneration.

BackgroundFunctional neurological disorder (FND) is one of the most common, but least researched, conditions in neurology. Debate exists as to whether the clinical entity referred to as FND is truly a single disorder or is in fact multiple entities which have been erroneously amalgamated into the same condition. We sought to provide empirical evidence on this question by treating it as a problem of model comparison. MethodsWe formulated statistical models equivalent to: (1) FND being a single entity with variation in phenotype, represented by latent trait (binary factor/item response theory) models, and (2) FND being multiple discrete entities, represented by latent class analysis (LCA) models. We fitted these models to data on the symptoms experienced by 697 people with FND from the FND Research Connect database (fnd-research.org) and used Bayesian model comparison methods to compare them. ResultsAll but one of the latent trait models, representing FND as a single entity with heterogeneous phenotype, fit the data better than all the LCA models. Secondary analysis of the LCA models showed results compatible with the models capturing discretisation of continuous variation rather than true discrete categories. DiscussionOur results suggest that the symptom structure of FND is the result of a single pathophysiological process, either as a single entity, or a common pathway preceded by multiple causative processes where the common pathway is solely responsible for the phenotype of the condition.

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

BackgroundHereditary cerebellar ataxias (HCAs) are rare neurodegenerative disorders characterised by progressive motor impairment and overlapping clinical phenotypes. Although genetic testing provides etiological diagnosis, diagnostic delays frequently arise before targeted testing, owing to non-specific presentation and limited clinician familiarity. Imaging-derived biomarkers that capture phenotypic expression and network-level consequences of disease could support earlier recognition of hereditary ataxia, guide appropriate genetic testing, and provide sensitive measures of disease evolution. Building on evidence that cortical geometry shapes functional organisation, we hypothesised that geometric signatures derived from structural magnetic resonance imaging (sMRI) could discriminate HCA subtypes and yield progression-sensitive biomarkers, while enabling scalable prediction of function. MethodsWe decomposed sMRI and task-evoked functional MRI data from three independent cohorts using cortical geometric eigenmodes, intrinsic spatial patterns defined by cortical surface geometry, to obtain structural and functional geometric signatures. Structural signatures were used to train neural networks for disease classification and to derive biomarkers sensitive to annual progression. We further modelled structure-to-function mappings to predict functional geometric signatures from sMRI and evaluated their diagnostic and longitudinal utility. FindingsOur framework achieved high diagnostic performance, distinguishing healthy controls from Friedreich ataxia (FRDA) with a maximum AUC of 0.93 and separating FRDA from spinocerebellar ataxia type 1 (SCA1) and SCA3, with AUCs up to 0.81, showing cross-cohort generalisability. Structure-to-function-signature prediction achieved coefficient of determination up to 0.62 and correlation reaching 0.86 across health and disease, while predicted functional signatures improved classification beyond structural signatures alone and enabled partial reconstruction of individual task-activation map. Geometric brain signatures showed greater progression sensitivity than conventional volumetric MRI measures. InterpretationThis geometry-driven framework offers novel, objective, multiscale biomarkers for diagnostic-decision-support and monitoring HCAs and provides proof-of-concept for the feasibility of predicting fMRI-equivalent biomarkers in disease from routine sMRI, which is far more practical in movement-disorder populations. FundingFriedreich Ataxia Research Alliance USA. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed, Scopus, IEEE Xplore, and Google Scholar, for peer-reviewed studies published up to 2025 using combinations of terms related to hereditary cerebellar ataxia (HCAs), Friedreich ataxia (FRDA), spinocerebellar ataxia (SCA), diagnosis, progression, MRI biomarkers, structural MRI (sMRI), functional MRI (fMRI), machine learning (ML), deep learning (DL), task activation maps, prediction, and geometric eigenmodes. We found that while sMRI studies in HCAs consistently showed patterns of cerebellar, brainstem, and supratentorial atrophy, highlighting their potential diagnostic value as non-invasive biomarkers, existing studies on imaging-based diagnostic tools for HCAs typically used small sample size, single-site data and focused on narrow classification tasks rather than generalisable frameworks for differential diagnosis. Clinically meaningful objective biomarkers sensitive to disease progression are also limited, with most outcome measures relying on subjective clinical rating scales or conventional MRI metrics with restricted sensitivity and reproducibility. In addition, fMRI reveals important network-level abnormalities in disease, however, motion artefacts, task-performance difficulties and long acquisition times limit its applicability in movement-disorder populations. Recent work in healthy populations showed that structural data could predict task-fMRI activation using DL, yet disease-specific and clinically deployable investigations remain unexplored. In parallel, advances in brain geometric eigenmode research underscore that cortical geometry provides a principled structural basis that shapes multi-scale functional organisation. However, no study has investigated cortical geometric signatures as tools to address three major challenges in hereditary ataxia research and clinical care: diagnostic delay, lack of progression-sensitive objective biomarkers, and practical limitations of functional imaging acquisition. Added value of this studyUsing a combined geometric and ML framework, we showed that cortical geometric signatures captured multiscale brain organisation that constitute novel, generalisable biomarkers for differential diagnosis across HCA subtypes. Our models reliably distinguished healthy controls from individuals with FRDA, demonstrated consistent performance across independent cohorts, and further separated FRDA from multiple SCA subtypes. Importantly, we provided proof-of-concept for structure-to-function prediction and showed that fMRI-equivalent functional signatures could be inferred from sMRI in both health and disease, enabling reliable approximation of individual task-activation maps without requiring fMRI acquisition. Incorporating these predicted functional signatures improved diagnostic accuracy beyond structural measures alone. Both structural and predicted functional biomarkers demonstrated greater sensitivity to annual disease progression than conventional volumetric metrics, with comparable performance to clinical scales. Implications of all the available evidenceCortical geometric signatures pave the way for a clinically deployable neuroimaging diagnostic decision-support tool that could guide clinicians toward targeted genetic testing and potentially reduce diagnostic delay in HCAs. These biomarkers provide objective, rater-independent measures of disease evolution that are more scalable and reproducible than clinical ratings alone, and more sensitive than conventional imaging measures, with important implications for improving clinical trial design and monitoring therapeutic efficacy. The ability to infer functional signatures from sMRI allows clinicians to probe aspects of functional organisation and network disruption using routine sMRI, which is substantially more practical in movement-disorder populations, thereby introducing a novel fMRI-equivalent biomarker that may further improve diagnosis and progression monitoring.

Permutation entropy (PE) is increasingly studied as an EEG biomarker for Alzheimers disease and related dementias. However, PE requires specifying two free parameters (embedding order and delay) whose interaction with sampling rate determines the timescale being measured. No study in the PE-dementia literature has tested whether results are robust across parameterizations. Here we demonstrate they are not. On 1,177 clinical EEGs from the CAUEEG dataset (457 Normal, 414 MCI, 306 Dementia), we computed PE on alpha-band (8-12 Hz) signals using four parameterizations spanning different timescales and state-space sizes, on eyes-closed segments with artifact exclusion and per-segment entropy computation to avoid boundary artifacts. The results diverged dramatically: effect sizes for dementia vs. normal ranged from d = -0.700 (decreased PE, p < 0.0001) to d = +0.709 (increased PE, p < 0.0001) depending solely on parameter choice, with two parameterizations yielding complete nulls (d {approx} 0). The commonly used sub-cycle parameterization (order = 3, delay = 1) produced a large effect by measuring local waveform curvature rather than ordinal pattern complexity. With theoretically appropriate alpha-timescale parameters (order = 5, delay = 5; 100 ms embedding window spanning one full alpha cycle), PE was a complete null (d = -0.025, p = 0.73). To contextualize these findings against conventional spectral analysis, we computed the relative alpha/theta power ratio as a spectral baseline (d = -0.727, AUC = 0.739). We show that PEs null at proper parameters reflects a structural limitation: PEs rank-order design discards all distance information between values, rendering it blind to the regularity structure that distinguishes healthy alpha oscillations from the fragmented activity in dementia. By contrast, alpha-band sample entropy (SE), whose Chebyshev distance metric preserves absolute differences between time points, showed the strongest entropy effect (d = 0.519, age-corrected d = 0.373, AUC = 0.720) and was essentially independent of spectral power (r = -0.043). Combining SE with the spectral power ratio in a bivariate model yielded AUC = 0.786 for dementia detection, demonstrating that these orthogonal features are complementary. The alpha/theta Lempel-Ziv Complexity ratio (d = 0.471, age-corrected d = 0.364) shared 52% of its variance with the power ratio, indicating it largely indexes spectral content. We additionally report a pre-specified validation of the LZC ratio from a discovery dataset (N = 88). These findings indicate that PE results are not comparable across studies unless parameters and sampling rates match exactly, that PE is structurally unsuited to detecting the regularity disruption characteristic of oscillatory pathology, and that distance-based entropy measures like SE deserve priority over ordinal measures in EEG biomarker research for dementia.

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.

Lysosomal dysfunction is central to Parkinsons disease pathogenesis, with GBA1 as the strongest established genetic risk factor. Numerous other genes involved in lysosomal sphingolipid, glycosphingolipid and ceramide metabolism have been proposed as contributors to Parkinsons disease, underscoring the need for comprehensive genetic analyses across these pathways. We analysed rare variants (minor allele frequency < 0.01) across 36 lysosomal genes (excluding GBA1) in 8,267 individuals with Parkinsons disease and 68,208 controls, including a subset of 793 early-onset Parkinsons disease ([&le;]50 years) cases. Targeted sequencing was performed in four cohorts at McGill University (3,456 Parkinsons disease patients and 2,664 controls) and results were combined with whole-genome sequencing data from the UK Biobank (2,848 cases, 62,451 controls), and from the Accelerating Medicines Partnership - Parkinsons Disease (1,963 cases, 3,093 controls). We analysed the association of rare variants in these genes with Parkinsons disease using Sequence Kernel Association Test-Optimal (SKAT-O) across variant classes (all rare variants, nonsynonymous, loss-of-function and predicted damaging variants with a Combined Annotation Dependent Depletion (CADD) score >20), with meta-analysis across cohorts. We additionally performed per-domain analyses for variants in gene segments encoding functional domains. False discovery rate correction was applied. Meta-analysis identified a significant association between rare variants in ST3GAL3 and Parkinsons disease (Pfdr=0.04). Several additional lysosomal genes showed nominal associations (P<0.05), including HGSNAT, ASAH1, CTSD, HEXA, ST3GAL4 and SGPP1. Domain-based analyses identified a strong enrichment of nonsynonymous variants within the beta-acetyl-hexosaminidase-like domain of HEXA (P = 8.0 x 10), although this signal did not survive correction for multiple testing (Pfdr=0.154). In early-onset Parkinsons disease, domain-based analyses revealed significant associations in NAGLU (Pfdr=7.3x10) and ST3GAL5 (Pfdr=0.03). Together, these results provide genetic evidence that rare variants across multiple lysosomal pathways, particularly those related to sialylation, ganglioside metabolism, ceramide biology, and lysosomal proteolysis, may contribute to Parkinsons disease susceptibility beyond GBA1, highlighting biologically coherent pathways for future replication and functional investigation.