Brain Connectivity

BackgroundThe thalamus has emerged as a key region involved in cognitive dysfunction in multiple sclerosis (MS). While previous studies have identified associations between thalamic structural damage, altered functional connectivity, and cognitive performance, the specific contributions of individual thalamic nuclei and the added value of integrating structural and functional metrics remain poorly understood. MethodsT1-weighted MRI, diffusion MRI, resting-state fMRI, and neuropsychological data were collected from 102 individuals with MS and 27 healthy controls. Thalamic grey matter volume, white matter microstructural integrity, and functional controllability were calculated for each nucleus and compared between individuals with MS and healthy controls, as well as between MS cognitive subgroups. Partial Spearman correlations were used to examine the relationship between imaging metrics across the three modalities, and also between imaging metrics and cognitive performance in MS. Sparse canonical correlation analysis models were used to examine the covariance between thalamic imaging metrics and cognitive performance in MS. ResultsWidespread atrophy and microstructural damage were observed across all thalamic nuclei in individuals with MS, regardless of cognitive status. In contrast, alterations in functional controllability were more spatially specific, primarily affecting the medial dorsal anterior nuclei, and were most pronounced in cognitively impaired individuals. These functional controllability metrics were independent of grey matter volume, white matter integrity, and lesion load. Combining thalamic functional controllability with structural metrics yielded a stronger association with cognitive performance in MS than either modality alone. ConclusionThis study provides novel evidence that functional controllability in the thalamus, particularly within the medial dorsal anterior nuclei, plays a critical role in cognitive impairment in MS. By applying a network control framework, our findings offer a dynamic systems perspective that extends beyond traditional connectivity analyses, capturing the thalamuss role in supporting flexible cognitive transitions. The integration of structural and functional controllability metrics enhances the ability to characterise individual differences in cognitive performance and may inform future efforts to identify biomarkers of cognitive dysfunction in MS.

ABSTRACT INTRODUCTION: Microscopic fractional anisotropy ({micro}FA), an emerging diffusion MRI metric, may be more sensitive than conventional metrics to gray matter microstructural changes in neurodegeneration. This pilot study compared {micro}FA, mean diffusivity (MD), and volume between genetic frontotemporal dementia (FTD) variant carriers and non-carriers in the insula, frontal pole, and medial orbitofrontal cortex (mOFC). METHODS: Carriers and familial non-carriers of FTD variants in C9orf72, GRN, or MAPT were scanned between October 2024-December 2025. Non-parametric aligned rank transform ANCOVAs were computed to analyze between-group differences in {micro}FA, MD, and volume while controlling for age. RESULTS: Carriers (n=12) exhibited lower insula {micro}FA than non-carriers (n=8): F(1,19)=5.89, 95% CI [-10.7,-0.75], p=0.027, 2p=0.26. No group-differences were observed in other metrics, including MD and volume. DISCUSSION: Reduced {micro}FA in the insula, a region vulnerable to early atrophy in FTD, may be more sensitive to early microstructural changes in genetic FTD than traditional diffusivity measures.

IntroductionWhile global topological properties of brain networks reach relative maturity early in development, functional reconfigurations at the regional level continue throughout adolescence to support cognitive maturation. However, regional age and sex-specific developmental patterns of functional reconfiguration remain incompletely understood. MethodsWe analyzed resting-state fMRI data from 528 participants aged 5-21 years from the Human Connectome Project in Development. Three regional graph-theory metrics (betweenness centrality, hub score, and local efficiency) were computed for each individuals functional network. Cognition was measured using NIH toolbox. Parallel factor analysis was employed to decompose an individual x region x metric array into factors representing distinct developmental properties in the full sample and separately for males and females. Brain-cognition associations were examined in developmental subgroups (<13, 13-18, >18 years). ResultsThree factors emerged, characterizing visual, multimodal integration, and higher-order factors. Across development, metrics capturing network integration (betweenness centrality and hubness) showed general stability, while metrics capturing segregation (local efficiency) presented distinct peaks, particularly in the visual factor. Females showed earlier peaks and declines in higher-order factor, while males exhibited greater variability and protracted maturation in multimodal and higher-order factors. Brain-cognition associations were modest with early childhood and crystallized cognition composites showed small negative correlations with hub score in entire sample (r=-0.212) and local efficiency in males aged <13 years (r=-0.215). ConclusionFindings highlight nonlinear, sex-specific functional reconfiguration at region-level during childhood and adolescence, underscoring the importance of sex-stratified analyses in developmental and providing a crucial foundation for future investigations of developmental disorders.

Resting-state functional MRI has been widely used to study brain connectivity, yet the test-retest reliability of commonly used metrics remains a concern. To improve reliability, extended scan lengths and larger subject cohorts are often recommended. However, these solutions can be impractical and pose challenges, particularly in studies of clinical populations. Here, we systematically assess the reliability of two main types of functional connectivity measures: node-based connectome metrics (edge-level intraclass correlation coefficient [ICC], connectome-level ICC, functional connectivity fingerprinting, and discriminability); and voxel-based resting-state networks (RSNs) (spatial similarity of independent component analysis [ICA]-derived RSN maps quantified using the Dice coefficient). Using data from the Human Connectome Project, we evaluated the effects of scan length (3.6, 7.2, 10.8, and 14.4 minutes) and number of participants (n = 10, 20, 50, and 100), on both within-session and between-session reliability. We found that multivariate connectome metrics demonstrated greater reliability than edge-level measures, and that scan length had stronger influence on test-retest reliability than the number of participants. For connectome metrics, 14 minutes of scanning and a cohort of approximately 20 participants were sufficient to achieve reliable estimates. In contrast, RSN measures benefited from larger cohort sizes. Our findings provide practical guidelines for designing resting-state fMRI studies in terms of scan length and number of participants, balancing reliability and feasibility. Ultimately, protocol choices should be guided by the specific study objectives and the functional connectivity metric of interest.

Cognitive decline is a major non-motor feature of Parkinsons disease (PD), but reliable and accessible biomarkers remain limited. Resting-state electroencephalography (EEG) is a promising candidate because it is low-cost, portable, and well suited to repeated assessment. Recent work has increasingly focused on source-space functional connectivity (FC) for the prediction of cognition. However, the influence of source-modelling based on an individualized MRI-based head model relative to that based on standard template model is unknown. To compare these two source-space EEG FC methods, we analysed EEG data from the New Zealand Parkinsons Progression Programme, including 136 people with PD and 51 age-similar controls. Source reconstructed resting-state EEG was parcellated with the HCP-MMP1 atlas, and used to derive amplitude envelope correlation (AEC) and debiased weighted phase lag index (dwPLI) across six canonical frequency bands. The twenty-four FC modalities were evaluated using six machine-learning regression algorithms within a nested cross-validation framework. Theta-, alpha-, and beta-band FC showed the most consistent prediction of global cognition, with the strongest performance observed for theta- and alpha-band AEC and dwPLI features (maximum R{superscript 2} = 0.170, r = 0.439). Standard and individualized head models showed comparable predictive performance across nearly all modalities. Feature-importance patterns for Cole-Anticevic networks were also highly similar between the two head-model options. These findings show that source-space resting-state EEG FC can predict cognitive performance in PD. The comparability of the two head models suggests that the more user-friendly and less resource intense standard head model template is satisfactory. This supports feasible, scalable, and clinically accessible EEG-based biomarkers of cognition in PD.

BackgroundIn multiple sclerosis (MS), demyelination and degeneration of transcallosal pathways impair interhemispheric communication. While white matter damage is well documented, the impact of cortical lesions on transcallosal conduction remains unclear. ObjectiveTo determine whether cortical lesions in the sensorimotor hand area (SM1{square}HAND) contribute to impaired transcallosal motor interaction using ultra{square}high{square}field MRI and transcranial magnetic stimulation (TMS). MethodsTwenty healthy controls (HCs) and 38 MS patients underwent 7T structural and diffusion{square}weighted MRI. Structural scans were used to identify cortical lesions in SM1{square}HAND, while diffusion tensor imaging (DTI) quantified microstructural properties in the transcallosal tract connecting left and right SM1{square}HAND. Single{square}pulse TMS was delivered to each SM1{square}HAND during tonic first dorsal interosseous contraction to measure the ipsilateral silent period (iSP). Corticospinal conduction was measured with contralateral motor{square}evoked potentials (MEPs), while the iSP was used to compute transcallosal conduction time (TCT). ResultsAmong MS patients, 41 of 76 hemispheres contained an SM1{square}HAND lesion. TCT was significantly prolonged in MS relative to HCs (P<0.001). In patients, cortical lesions delayed transcallosal conduction from the non{square}lesion{square}bearing to the lesion{square}bearing hemisphere (P=0.026). This direction-specific delay was associated with an intracortical lesion type (P<0.001), but not with DTI{square}derived microstructural measures (P>0.05). ConclusionsThe presence of cortical lesions in the sensorimotor cortex affects transcallosal inhibition between homologous sensorimotor regions in MS, slowing the build-up of inhibitory influence on the corticospinal output in the lesioned cortex. This delayed inhibitory buildlup appears to be associated with an intracortical lesion type. HighlightsO_LIIpsilateral silent period reveals delayed transcallosal motor interaction in multiple sclerosis C_LIO_LICortical lesions in sensorimotor cortex delay the onset of transcallosal motor inhibition C_LIO_LIDelayed transcallosal inhibition is only present toward the lesioned cortex C_LIO_LIIntracortical lesions, not callosal microstructure, is linked to this directionlspecific delay C_LI

BackgroundMultiple sclerosis (MS) is characterized by focal white matter (WM) lesions, but subtle damage also occurs in normal-appearing white matter (NAWM). We developed a method to generate quantitative T1 maps from MPRAGE (Magnetization Prepared Rapid Gradient Echo) images and evaluated its ability to detect NAWM abnormalities across different MS phenotypes. MethodsT1 maps were derived from MPRAGE using a theoretical signal model and compared with MP2RAGE (Magnetization Prepared 2 Rapid Gradient Echoes) T1 values in four healthy volunteers. The method was then applied to 87 MS patients, divided into clinically isolated syndrome (CIS), relapsing-remitting MS (RRMS), and primary progressive MS (PPMS), with age- and sex-matched healthy controls. T1 was measured in NAWM and lesions. Histogram analysis provided mean T1, full width at half maximum (FWHM), and skewness. ResultsIn healthy volunteers, T1 values matched MP2RAGE. In controls matched to the MS cohort, T1 increased with age (r = 0.35, p < 0.05). CIS patients showed no significant differences in any metric. RRMS and PPMS patients showed unchanged mean NAWM T1 but significantly different distributions, with higher FWHM (p<0.05) and skewness (p<0.001). An increase in T1 values was observed in MS lesions compared to NAWM in all groups. ConclusionThis study confirms the feasibility of deriving quantitative T1 maps from standard MPRAGE, offering reliable information to facilitate MS monitoring without additional acquisitions.

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

Tractometry enables detailed mapping of white matter microstructure along individual tracts and is widely used to study disease effects such as those seen in Alzheimers disease (AD). However, how different tractography algorithms influence tractometry outcomes remains unclear. Here, we compared whole-brain deterministic and probabilistic tractography using the BUndle ANalytics (BUAN) framework in the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset, including 118 AD and 728 cognitively normal (CN) participants. Both approaches revealed the expected pattern of lower fractional anisotropy (FA) and higher mean, radial, and axial diffusivity (MD, RD, AxD) in AD, consistent with white matter degeneration. Despite broadly similar global trends, substantial bundle-level differences emerged between the two tractography methods. Probabilistic tracking produced stronger and more spatially extended effects in the fornix, a small and highly curved limbic pathway vulnerable to AD-related degeneration, whereas deterministic tracking showed greater sensitivity in the posterior segments of the right superior longitudinal fasciculus (SLF R). These discrepancies highlight that the choice of tractography algorithm can alter detecting disease effects, emphasizing the need for cross-method validation to ensure the robustness and interpretability of along-tract measures.

Temporal dynamics in functional connectomes provide a physiologically grounded signature of 'hidden' pathologies during preclinical stages of Alzheimer's disease (AD). We evaluated the effect of beta-amyloid (A{beta}) on dynamic functional connectomes in transgenic mice and human subjects. Functional magnetic resonance images (fMRI) were collected in two strains of A{beta} mice. fMRI-derived connectomes were segmented into discrete states using a hidden Markov model, and network strength, efficiency, and transitivity were analyzed per state. Human fMRI-derived connectome measures were analyzed across 3 states. Static network measures were significantly different between A{beta} mice and controls, the former having high values for strength, efficiency and clustering coefficient in anterior cingulate, hippocampus, and retrosplenium. Dynamic network measures were stable within-states in A{beta} mice. Similarly, human subjects with high A{beta} had high node strength in precuneus and temporoparietal areas compared to low A{beta}. Conversely, high A{beta} was associated with high switch rates, high fractional occupancy, and state dwell times. Also, global strength, efficiency, and transitivity were less stable within states in the high A{beta} group. Our results indicate that static, but not dynamic, connectome strength, efficiency, and network integration are increased in A{beta} mice, while dynamic network states appear less stable in human functional connectomes. This data supports a dissociable, species-specific impact of A{beta}, with dynamic network alterations present in humans but not in A{beta} mouse models, suggesting additional non-A{beta}-driven influences on dynamic functional connectivity in preclinical AD.

BackgroundTo understand the neurobiology underlying psychopathology, we need valid measurements of brain function. Group atlases for brain functional connectivity (FC) allow for efficient comparisons, but they fail to account for inter-individual variability in network topography, a problem that personalized methods address. We assess the validity and predictive utility of group and personalized approaches of quantifying FC by 1) comparing effect sizes of associations with clinical metrics; and 2) accounting for spatial features of brain networks when examining the association between FC and clinical metrics. Methods324 teens ages 13-16 participated. Personalized networks were estimated using a hierarchical Bayesian model. Effect size comparisons were done by comparing the correlations between FC and clinical metrics (depression, ruminative coping style, and sensitivity to punishment/reward) with Steiglers Z-test. We also conducted regressions, with clinical metrics as the dependent variables. Those models included FC and spatial features, together and alone. ResultsThe effect size comparisons did not survive FDR correction. However, exploratory permutation tests show that 1) the magnitude of the correlations with depression are larger on average for the intersection estimates of FC than the group estimates; and 2) the magnitude of the correlations with a ruminative coping style are larger on average for the intersection estimates of FC than the personalized estimate. The other comparisons conducted using permutation tests are not significant. Multiple regression analyses demonstrated that only spatial features of networks, not FC, are associated with sensitivity to reward. DiscussionThese results imply that the intersection estimates are more valid than the group estimates, and that the intersection estimates have greater predictive utility than personalized estimates. Further, spatial features of functions networks may be useful in and of themselves in certain contexts. Therefore, researchers in psychiatry should take into consideration functional network topography in order to gain a better understanding of the neurobiology underlying psychopathology.

ObjectiveFinding indicators of early response to antidepressant treatment in EEG signals recorded from patients suffering from major depressive disorder. MethodsFunctional brain connectivity networks based on weighted imaginary coherence and weighted imaginary mean phase coherence were computed for 176 patients for 6 different EEG frequency bands. Cross-hemispheric connectivity (CH) and lateral asymmetry (LA) were estimated from these networks based on EEG signals recorded before the beginning of treatment (V is1) and one week after the start of the treatment (V is2). Repeated measures ANOVA was used to check for statistically significant changes in connectivity based on these measures at V is2 w.r.t. V is1. Post-hoc analysis was performed with multiple pairwise comparison tests to determine which group means were significantly different. ResultsIt was found that CHV is2 was significantly reduced w.r.t. CHV is1 in the {beta}1 [12.5 - 17.5 Hz] frequency band for the responders to treatment. Also, LAV is2 was significantly increased w.r.t. LAV is1 in the {beta}1 frequency band for the responders. No such significant changes were observed for the non-responders. Brain networks constructed using both weighted imaginary coherence and weighted imaginary mean phase coherence were found to exhibit these results. For the CH connectivity changes, binarized networks and for the LA connectivity changes, weighted networks were found to be more reliable. ConclusionsResponders were found to show a reduction in cross-hemispheric connectivity and an increase in lateral asymmetry, both in the {beta}1 band while no such change was observed for the non-responders. SignificanceDecrease in cross-hemispheric connectivity and increase in lateral asymmetry in the {beta}1 band may represent candidate neurophysiological indicators of early treatment response, but they require independent replication before any clinical application can be considered.

Background and purpose: People after mild traumatic brain injury (mTBI) show persistent deficits in reactive balance. Cortical processes engaged during preparation and execution of balance reactions are reflected in distinct cortical activity signatures that can be measured with electroencephalography (EEG). The purpose of this study was to 1) compare preparatory cortical beta activity and evoked cortical N1 responses during balance recovery in people with mTBI and controls, and 2) explore relationships between preparatory and evoked cortical activity. Methods: Participants (age 21-35 years) with symptomatic mTBI (n=5, 27 +/- 13 days post-injury) and controls (n=5) completed the instrumented and modified push & release tests of reactive balance. Cortical activity was recorded using encephalography (EEG). Main outcome measures were 1) preparatory sensorimotor cortical beta-bust power and duration prior to balance perturbation onset (-1s-0s), and 2) cortical N1 response amplitude and latency during the post-perturbation balance recovery (50-250ms). Results: People with mTBI exhibited lower preparatory beta-burst power compared to controls (p=0.044, g=1.18). During balance recovery, cortical N1 responses occurred earlier in people with mTBI compared to controls (p=0.045, g=3.28). Relationships between preparatory and evoked cortical activity were altered after mTBI compared to controls; people after mTBI with greater beta-burst power and longer duration elicited shorter N1 latencies (r's>0.77, p's<0.010). Discussion and conclusion: The results serve as preliminary, hypothesis-generating observations to guide future research directions investigating neural signatures of reactive balance deficits in people after mTBI. The preparatory brain state before reactive balance recovery should be explored as a potential target for post-mTBI balance rehabilitation.

The aim of this study was to evaluate the contributions of age, sex, and MRI vendor to variance in Diffusion Tensor Imaging (DTI) metrics, with a focus on understanding the impact of these factors in large-scale healthy brain datasets. A dataset of 2,700 DTI scans from healthy controls across multiple sites and MRI vendors was analyzed. The DTI scalar metrics fractional anisotropy (FA) and mean diffusivity (MD) were processed and the influence of age, sex, vendor, and brain atlas selection were determined. A statistical analysis was conducted and revealed significant (p<0.05) age-related differences in DTI metrics, with older participants showing reduced FA and increased MD, in line with known microstructural changes. Sex differences were observed, with females exhibiting slightly higher FA and lower MD in certain brain regions. Vendor variability was also noted, with all three MRI vendors showing significant differences in FA with Siemens machines typically exhibiting higher FA values and GE machines lower FA values (i.e. FASiemens > FAPhilips > FAGE). Atlas selection also highlighted some specific ROI behaviour (e.g. tapetum of the corpus callosum) as one of the most significant regions of interest (ROIs) in the JHU-Tracts atlas that demonstrated a large amount of deterioration with age, particularly in females. These findings emphasize the need to account for biological factors such as age and sex, as well as technical factors like ROI selection and MRI vendor, when interpreting DTI data. The results demonstrate the potential of large-scale, multi-vendor datasets to uncover meaningful biological trends, while also addressing the challenges of scanner-specific variability. Although previous work has shown sex and age differences, this is the first large scale DTI analysis that has included age, sex, and MRI vendor as sources of variance in one model.

Most evidence on age-related network topology derives from resting-state paradigms, leaving unclear how aging alters brain organization during naturalistic processing and whether graph-theoretical metrics relate to emotional and cognitive functioning in ecologically valid contexts. We analyzed movie-fMRI and behavioral data from 72 younger and 68 older adults, examining global (small-worldness, clustering coefficient, characteristic path length), network (participation coefficient), and nodal (degree centrality, betweenness centrality, nodal efficiency) properties. Regression models were used to test associations between nodal measures and both the Emotional Resilience Index (ERI) and the Cognitive Function Index (CFI), while mediation analyses were conducted to test whether nodal measures mediate the relationship between age and ERI. Older adults exhibited increased characteristic path length and clustering coefficient, indicating reduced global integration and greater local segregation. Although small-world organization was preserved in two groups, there was less pronounced small-world architecture in older adults compared to younger adults, suggesting a shift toward more regularized, locally clustered networks and reduced long-range connections during dynamic stimuli. Participation coefficient values were higher in the somatomotor, frontoparietal, and default mode networks, and lower in the subcortical network, among older adults reflecting greater between-network integration in cortical networks but diminished subcortical coordination in aging. Five key nodes, two thalamic regions, hippocampus, and two insular regions, showed reduced centrality and efficiency in older adults during the negative movie, indicating weakened dominance of subcortical hubs under emotional salience condition. Right thalamic nodal properties were negatively associated with ERI and CFI and served as mediators in the relationship between age and emotional resilience. These findings suggest that reduced thalamic hub centrality may reflect adaptive recalibration of salience emotional processing, linking network reorganization to improved emotional resilience in aging. Key pointsO_LIOlder adults showed higher path length and clustering, suggesting reduced integration. C_LIO_LIReduced small-worldness reflects weaker balance of segregation and integration with age. C_LIO_LIOlder adults showed higher cortical but lower subcortical participation coefficients. C_LIO_LIKey nodes showed reduced centrality during negative stimuli, indicating weaker hubs. C_LIO_LIRight thalamus changes linked to resilience, mediating age-emotion relationships. C_LI

BackgroundMultiple sclerosis (MS) is a chronic neurodegenerative disease charac-terised by progressive neurological disability and heterogeneous symptom trajectories. Cur-rent clinical monitoring methods, including magnetic resonance imaging (MRI) and episodic neurological assessments, provide limited insight into subtle disease progression and real-world functional changes. Digital health technologies integrating multimodal biosignals and behavioural assessments may enable continuous monitoring and personalised rehabilitation for patients with MS. ObjectiveThis study aims to evaluate the clinical utility of the BodyMirror Clinical MS platform, a multimodal software-as-a-medical-device (SaMD) that combines wearable biosensors, neuroscience-based games, and machine learning algorithms to remotely monitor disease progression and deliver personalised neurorehabilitation for individuals with multiple sclerosis. MethodsThis study is a prospective, randomised, double-blind, controlled, multisite clinical trial enrolling 400 participants, including 300 individuals with multiple sclerosis and 100 healthy controls. MS participants will be randomly assigned (1:1) to either an adaptive neurorehabilitation intervention group or a control group receiving non-therapeutic digital activities matched for engagement and exposure. Participants will perform three 30-minute sessions per week over a 24-month period using the BodyMirror platform. The system integrates multiple biosignals, including electroencephalography (EEG), electromyography (EMG), inertial measurement unit (IMU) motion data, speech analysis, and behavioural performance metrics, to generate digital biomarkers of neurological function. The primary endpoint is change in Expanded Disability Status Scale (EDSS) score from baseline to 24 months. Secondary outcomes include changes in Multiple Sclerosis Functional Composite (MSFC), MRI brain volume, cognitive performance, patient-reported outcomes, adherence to digital rehabilitation, and health-economic outcomes. ConclusionsThis trial will provide the first large-scale clinical evaluation of a mul-timodal digital neurotechnology platform combining wearable biosensors and game-based neurorehabilitation for remote management of multiple sclerosis. If successful, BodyMirror Clinical MS may enable scalable remote monitoring, earlier detection of disease progres-sion, and personalised digital rehabilitation for individuals living with MS.

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.

Language impairments in aphasia are characterized by various representational disruptions that may be reflected in discourse production. This research examines the capacity of transformer-based language models, particularly GPT-2, to serve as a computational framework for analyzing variations in aphasic narrative speech. A longitudinal dataset of narrative speech samples collected at six time points from individuals with aphasia (N = 47) was utilized as part of an intervention study. All transcripts were processed via the GPT-2 language model to obtain activation values from each of the 12 transformer layers. Statistically significant differences in activation magnitude across aphasia subtypes were found at every layer (all p < .001), with the most pronounced effects in the deeper layers. Pairwise Tukey HSD tests revealed consistent distinctions between Brocas aphasia and both Anomic and Wernickes aphasia, suggesting a shared activation profile between the latter two. Longitudinal tests revealed significant changes over time, especially in the final three layers (10-12). These findings suggest that transformer-based activation patterns reflect meaningful variation in aphasic discourse and could complement current diagnostic tools. Overall, GPT-2 provides a scalable tool to model representational dynamics in aphasia and enhance the clinical interpretability of deep language models.

BackgroundAccelerated long-term forgetting (ALF), defined as an increased rate of memory loss over extended intervals, has so far been detected in a pilot study of patients with mild multiple sclerosis (MS). This study aimed to (I) confirm the presence of ALF in a larger, heterogeneous MS sample, (II) explore associations with patient-reported outcomes, and (III) assess the diagnostic performance of ALF tests for subjective memory impairment. MethodsThis study compared 62 MS patients and 65 age-, sex-, and education-matched healthy controls using standardized memory tests (RAVLT, WMS-IV Logical Memory subtest). Recall was assessed immediately, after 30 minutes, and after 7 days. Seven-day/30-minute recall ratios (QRAVLT, QWMS) served as primary outcomes. Self-report measures included memory complaints, fatigue, depression, and sleep disturbances. Linear regression and Receiver operating characteristic (ROC) analyses assessed predictors and diagnostic accuracy. ResultsALF was observed in multiple sclerosis since QRAVLT was lower in patients than in controls (0.64 [95% CI 0.59-0.69] vs. 0.78 [0.73-0.82], p < 0.001), as was QWMS (0.79 [95% CI 0.74-0.84] vs. 0.95 [0.90-1.00], p < 0.001), despite comparable initial learning. Greater fatigue, higher memory complaints, longer disease duration, older age, and greater disability were associated with lower ALF scores. The combined ALF score moderately discriminated subjective memory impairment (AUC 0.74; sensitivity 0.73; specificity 0.73). ConclusionMS patients showed ALF despite normal initial learning, indicating a specific memory deficit undetected by standard tests. Long-delay recall using RAVLT and WMS-IV Logical Memory subtest may improve cognitive impairment detection in MS.

Two decades of research have provided evidence for gray matter volume (GMV) differences in developmental dyslexia (or reading disability, RD) in the left perisylvian cortex. However, there are concerns about result inconsistencies, likely attributable to small sample sizes, lenient statistical thresholds, and insufficient accounting for demographic variables and global GMV (Ramus et al., 2018). To address these concerns, we conducted a Discovery and Replication Study (N=262) using data from the Adolescent Brain Cognitive Development Study. We found GMV differences between the RD and Control Groups did not replicate across the Discovery and Replication Studies using voxel-based morphometry (VBM) in Statistical Parametric Mapping (SPM), and that a more conservative threshold yielded far fewer results. We then conducted Reproducibility Studies and first found that when using surface-based morphometry in FreeSurfer instead of VBM, the Discovery and the Replication Study results again failed to converge. Second, we combined all groups in a factorial VBM/SPM analysis and the interaction analysis provided quantitative confirmation for diverging between-group difference results across the two studies. Third, we tested for the role of covariates of no interest and found that when total GMV is not controlled for, this divergence dissipates and group differences in RD (main effect of Reading Ability) are amplified. In conclusion, replication of GMV differences in RD is low, even when using large, well-matched groups, and analyses approaches play a modulating role. As such, results from prior studies using lenient statistical thresholds and not accounting for total GMV should therefore be viewed with caution.