Brain Communications

Lesion anatomy has been widely used to study post stroke cognitive outcomes, but it is unclear whether lesion-based measures provide clinically meaningful prognostic information beyond established predictors. Stroke survivors (n = 408) completed the Oxford Cognitive Screen (OCS) during acute hospitalisation and at chronic (6-month) follow-up. Lesion characteristics and structural disconnection profiles associated with chronic OCS scores were identified using ROI-level, voxel-level and structural network disconnection lesion mapping approaches. The incremental predictive value of these measures, relative to acute behaviour and pre-morbid brain health, was evaluated using regression analyses, receiver operating curve (ROC) and support vector regression (SVR) models predicting continuous chronic scores. Significant lesion and disconnection correlates of chronic cognitive impairment were identified for 9/10 OCS subtests. The extent of damage to these correlates was significantly associated with chronic cognitive scores, but their diagnostic utility for identifying persistent impairment was low under conventional thresholds (AUC mean = 0.59, range= 0.46-0.66). Acute cognitive task performance was the single best predictor of chronic cognition (AUC mean = 0.66, range = 0.4-0.95). In multivariate analyses, SVR models trained on acute cognitive performance and regional atrophy severity scores both outperformed models trained on lesion anatomy or structural disconnection across most cognitive domains. SVR models combining anatomical, disconnection and behavioural predictors did not improve predictions accuracy relative to behaviour or atrophy-only models. Together, these findings demonstrate that statistically significant lesion-outcome relationships do not necessarily translate into clinically useful prognostic indicators. In a large, clinically representative stroke cohort, detailed lesion-based measures provided limited incremental prognostic value beyond acute cognitive assessment and coarse brain health markers. These results highlight the importance of explicitly evaluating predictive utility when developing prognostic models for post-stroke cognitive outcomes.

BACKGROUND: Dementia with Lewy bodies shares clinical and pathological features with both Parkinson's disease and Alzheimer's disease, but the local biological factors that render specific cortical regions vulnerable to atrophy remain poorly defined. In particular, it is unclear whether cortical thinning in dementia with Lewy bodies reflects generic neurodegenerative mechanisms, processes shared with Parkinson's disease and Alzheimer's disease, or dementia with Lewy bodies-specific molecular and network susceptibilities. METHODS: A total of 89 patients with dementia with Lewy bodies and 89 matched controls underwent T1-weighted brain MRI. Scans were processed to generate surface-based cortical thickness maps. Regional cortical thickness estimates, after slice-by-slice manual correction, were mapped to gene expression data from healthy postmortem human brains to identify transcriptomic signatures associated with decreased thickness in dementia with Lewy bodies. We assessed whether genes whose expression was increased with regional thinning converged onto established Parkinson's disease- and Alzheimer's disease-related pathways and isolated genes uniquely implicated in dementia with Lewy bodies. Spatial annotation mapping was then used to test whether patterns of cortical thinning overlapped with in vivo neurotransmitter system distributions and whether the observed thickness pattern was constrained by large-scale structural connectivity, consistent with a network-based propagation process. RESULTS: Cortical thinning predominated in regions that, in the healthy brain, show higher expression of genes involved in mitochondrial function and synaptic transmission. The transcriptomic profile associated with thinning significantly overlapped with genes belonging to Parkinson's disease and Alzheimer's disease pathways, supporting shared pathogenic mechanisms across Lewy body and Alzheimer-type neurodegeneration. However, 90 genes associated with cortical thinning did not overlap with Parkinson's disease or Alzheimer's disease pathways and were enriched for GABAergic signalling. Spatial mapping analyses showed that regions with greatest thickness reductions colocalized with GABAA, serotoninergic 5-HT1A, 5-HT1B, 5-HT4, and dopaminergic D2 receptor distributions, and that the thickness pattern followed structural connectivity. CONCLUSIONS: MRI-derived cortical thickness changes in dementia with Lewy bodies reflect selective molecular and network vulnerabilities rather than a non-specific degenerative process. Mitochondrial and synaptic genes, together with a distinct GABAergic association and connectivity constraints, delineate mechanisms explaining why some cortical territories are more affected in dementia with Lewy bodies.

Background and Objectives: Converging evidence hints at neurodevelopmental effects in genetic frontotemporal degeneration (FTD). In cross-sectional studies, for some genes, young adult FTD variant carriers show differences in brain volumes and cognition compared to familial non-carriers. However, longitudinal trajectories may more sensitively capture FTD-related neurodevelopmental vs. neurodegenerative changes than cross-sectional approaches. This study examined longitudinal trajectories of brain volumes, executive function, and plasma biomarkers in young adult carriers compared to familial non-carriers, as measures of neurodevelopmental and neurodegenerative outcomes of FTD-causing variants. Methods: This longitudinal cohort study comprised participants, aged 18-30 years, from the FTD Prevention Initiative across Europe, Canada, and the USA. Genetic groups included C9orf72 (47%), MAPT (30%), and GRN (23%). Linear mixed-effects models were computed to assess longitudinal outcomes across age between groups, controlling for sex, scanner (for brain volumes), and education (for executive function); random effects accounted for between-subject variability nested within family membership. Results: Variant carriers (n=147) and familial non-carriers (n=113) did not differ in age (mean{+/-}SD, 25.9{+/-}3.2 years), sex (53% female), or number of visits (2.1{+/-}1.7). Young adult C9orf72 repeat expansion carriers exhibited smaller thalamic volumes than non-carriers at the reference age of 26 years (b=-982.8mm3, SE=317.0, p=0.0046, f2=0.32), with relatively stable trajectories across ages 18-30 (i.e., no change over time). Trajectories of rostral anterior cingulate volumes differed in C9orf72 carriers and non-carriers across age, where carriers showed relatively stable trajectories and non-carriers showed age-appropriate declines (b=64.4mm3, SE=29.9, p=0.035, f2=0.07). For MAPT and GRN, there were little to no differences in total brain, cortical, or subcortical volumes between groups and over time. No longitudinal differences were observed between carriers and non-carriers in executive function, or plasma NfL or GFAP for any genetic group. Discussion: C9orf72 repeat expansions were linked to smaller average thalamic volumes and stable trajectories between ages 18 to 30, supporting potential neurodevelopmental origins. The modest evidence supporting an absence of difference in neurodegenerative biomarkers and executive function suggests minimal early neurodegeneration and functional preservation in young adulthood.

Background: Autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) is a genetic disease characterized by spasticity and ataxia which reflects involvement of the corticospinal tracts (CST) and cerebellum. The primary involvement of the middle cerebellar peduncles (MCP) and transverse pontine fibers (TPF) at the crossing with the CST, and their role in the pathophysiology of the disease, is currently debated. Objectives: Advanced MRI techniques capable of isolating sub-voxel microstructural parameters can test the hypothesis that the MCP and TPF are abnormally large, compressing the CST at their crossing, and potentially impairing CST development. Methods: Tract macro- and micro-structural properties, including axon and tract caliber, axon density and geometry, and myelin content were estimated from diffusion-relaxometry and magnetization transfer imaging. These features were analyzed along segments of the CST, MCP, and TPF of 9 patients and 9 age-matched controls. Results: While the CST showed significant decreases in tract size, axon caliber, and myelination throughout its length compared to controls (p<0.01), the MCP and TPF were relatively unaffected. In our group, neither the MCP nor the pons were enlarged. The proximal MCP showed an increase in axon caliber. Conclusions: The increase in fractional anisotropy and axon density towards the center of the TPF could be driven by geometric confounds related to differences in the relative sizes of the CST and TPF compared to controls. This highlights the importance of investigating tract-specific microstructural profiles, particularly in regions of geometric complexity. The findings confirm the involvement of the CST, with a relatively limited involvement of the MCP and TPF.

Rationale: Reliable electroencephalography (EEG) biomarkers of cortical excitability could improve diagnosis and longitudinal monitoring in epilepsy, yet it remains unclear which metrics best balance sensitivity across individuals with intra-individual stability over time. Methods: We analyzed scalp EEG recordings from the open-access Temple University Hospital EEG Epilepsy Corpus, comprising 1,404 recordings from 96 individuals with neurologist-confirmed epilepsy and 85 healthy controls across multiple sessions. Eight global measures were computed: aperiodic exponent and offset, sample entropy, detrended fluctuation analysis exponent and derived index, spatial gamma-band phase consistency, and absolute and relative alpha power. Group differences were assessed by permutation tests with false discovery rate correction at recording, session, and subject levels. Associations with antiseizure medication burden, temporal stability, and cross-metric correlation structure were evaluated as secondary analyses. Results: Aperiodic parameters showed the most robust case-control separation, remaining significant after subject-level averaging (exponent: median difference = 0.20, q = 0.010; offset: median difference = 0.25, q = 0.011). Entropy and alpha power distinguished groups at the recording and session levels, while gamma-band phase consistency was significant at the session level only; none of these survived subject-level averaging, suggesting greater state-dependency. Higher medication burden was associated with reductions in alpha power and detrended fluctuation analysis, and adjusting for it substantially attenuated group differences, though residual effects in the aperiodic exponent persisted. Cross-metric correlation structure was preserved between groups but modestly reorganized by medication burden. Conclusions: Aperiodic spectral parameters are the most robust EEG markers of epilepsy, reflecting stable trait-like network properties. Complexity and synchrony measures capture complementary, state-sensitive dimensions. Medication burden substantially influences multiple metrics, underscoring the need to account for pharmacological effects when interpreting EEG biomarkers in epilepsy.

High placebo response is an obstacle in developing drugs to treat agitation in Alzheimer's disease (AAD), a prevalent and burdensome symptom. However, it has proved challenging to develop actionable models of placebo response that 1) can be applied prospectively, requiring only information available at screening or baseline, 2) yield strategies for reducing placebo response without equally depressing drug response, and 3) show generalizability across trials. Here, we first investigated placebo response in AAD at the trial level using meta-regression applied to 23 clinical trials. Meta-regression identified several factors associated with increased placebo response, but most of these factors were non-specific such that they predicted improvements in drug response as well. We therefore turned to individual level clinical trial datasets and applied causal modeling to predict which participants would have high placebo response relative to predicted drug response. We successfully built and validated the causal model across two independent clinical trials of risperidone and haloperidol at the level of individual patients (ability to predict subsequent improvement on drug or placebo). Crucially, we also found efficacy improvements in the overall trial through in silico exclusion/screen failing of high placebo-predicted subjects. We further characterized features most associated with placebo response to improve explainability and, lastly, validated the effect of these features at the trial level in clinical trials of galantamine, an acetylcholinesterase inhibitor (hence in a different class of drugs than those in the other two trials used). Taken together, we have developed and applied a causal modeling framework for reducing placebo response and increasing trial-level efficacy in neuropsychiatry clinical trials using historical trial datasets.

Background: Anomia is common in frontotemporal dementia (FTD), although its clinical prominence varies by subtype, with the most marked impairment typically observed in primary progressive aphasia (PPA). It remains unclear whether naming impairment reflects language-specific impairment or broader cognitive severity, and how it relates to other cognitive domains across FTD syndromes. Methods: Fifteen healthy controls and twenty-two individuals across the FTD spectrum, including variant-specified and unclassifiable (NOS) presentations, completed two confrontation naming tasks (Boston Naming Test and Multilingual Naming Test) and a global cognitive screening measure (Montreal Cognitive Assessment, MoCA). Patient participants additionally completed a standardized language battery (Western Aphasia Battery Revised) and a comprehensive neuropsychological assessment (Uniform Data Set). Naming performance was compared between groups and associations with language severity, global cognition, and domain-specific cognitive functions were examined using regression analyses. Results: Naming was impaired in patients relative to healthy controls but did not differ between patient groups. Naming was strongly associated with language severity, but not global cognition. A significant group-by-MoCA interaction indicated that MoCA was positively associated with naming only in the unclassifiable group. In addition, naming was associated with episodic memory across both verbal and non-verbal domains. Conclusions: Naming in FTD primarily reflects language severity rather than global cognitive impairment. A robust association between naming and episodic memory suggests potential contributions from semantic cognition, shared frontally mediated retrieval processes, or parallel cognitive decline. These findings support the use of naming as a marker of language dysfunction while highlighting its relevance to broader cognitive systems in FTD.

The temporal coupling between cortical blood-oxygen-level-dependent (BOLD) activity and CSF inflow has recently been proposed as a non-invasive marker of glymphatic function, a brain-wide clearance system closely linked to sleep, neuromodulatory regulation and neurodegeneration. Reduced BOLD-CSF coupling has been previously reported in Parkinsons disease but its characterization in dementia with Lewy bodies, regional specificity and relevance to shared neuropsychiatric symptoms remain unclear. Using resting-state functional MRI, we quantified global and regional BOLD-CSF coupling in 39 participants, including 17 with Parkinsons disease (mean age 61.4 years), 10 with dementia with Lewy bodies (mean age 72.8 years) and 12 healthy controls (mean age 66.2 years), and examined the relationship with clinical and cognitive measures, as well as volumetric measures of the subcortical ascending arousal network. Parkinsons disease and dementia with Lewy bodies patients both demonstrated weaker global BOLD-CSF coupling compared to controls, with no detectable difference between patient groups. Coupling reductions were most pronounced within the unimodal and attentional networks, encompassing regions that are particularly vulnerable in Lewy body disease. Weaker coupling was associated with the severity of hallucinations and cognitive fluctuations, poorer nocturnal sleep quality and impaired attentional working memory, but not overall motor symptom burden. Associations between BOLD-CSF coupling and basal forebrain and brainstem volumes were observed, though partially age-dependent, suggesting a complex interaction between neuromodulatory system degeneration, ageing and brain-fluid dynamics. Our results provide preliminary evidence that disrupted temporal coordination between cerebrovascular activity and CSF inflow may contribute to the fluctuating neuropsychiatric features of Lewy body disease and highlight the utility of BOLD-CSF coupling as a dynamic in vivo proxy of glymphatic function. Replication in larger cohorts incorporating multimodal imaging and biomarkers of pathology will be essential to validate these findings and determine whether brain-fluid dysregulation represents a potentially modifiable therapeutic target.

Objective: To determine whether absolute ictal energy on intracranial EEG identifies brain regions whose epileptogenic involvement is attenuated under existing baseline-normalized, dynamic-systems, and event-based frameworks. Approach: Intracranial EEG from 56 patients (five centers; 21 SEEG, 35 ECoG) was analyzed using the Teager-Kaiser Energy Operator computed as z-scored and raw envelopes; energy-dominant network regions (EDNRs) were defined as electrodes whose raw-energy rank exceeded their z-score rank by at least 2 positions. Hilbert decomposition characterized instantaneous amplitude and frequency. Main results: EDNRs were identified in 51 of 56 patients (91%; mean 3.4). Hilbert decomposition revealed elevated baseline amplitude in EDNRs relative to both non-involved regions (p < 0.001) and potential seizure onset zones (PSOZs, the top-ranked electrodes under both metrics; p = 0.029), with EDNRs participating in seizure-frequency dynamics comparable to PSOZs (mean ictal frequency shift +3.7 versus +4.1 Hz). EDNR detectability correlated directly with electrode count (Spearman r = 0.899, p < 0.001) without plateau. Significance: Absolute ictal energy identifies an epileptogenic network component with elevated baseline amplitude attenuated under baseline-normalized metrics. The dual-metric framework defines a complementary energy-based axis and establishes the second layer of a two-layer approach with seizure onset and propagation mapping as the first layer. EDNR detectability scales with electrode count, directly relevant to SEEG implantation strategy and to network-level inferences from heterogeneously covered cohorts.

Young onset Parkinson's disease may be caused by biallelic mutations in PRKN or other autosomal recessive Parkinson's disease genes, but the majority of patients do not carry known monogenic variants. Previous studies have found an increased cumulative burden of common genetic risk variants for Parkinson's disease in young onset patients, but the specific genetic architecture of non-monogenic young onset Parkinson's disease is not well characterized. We conducted a genome-wide association study of 1,528 Parkinson's disease patients with symptom onset between 18 and 40 years and 20,408 controls of European ancestry using data from The Global Parkinson's Genetic Program, the International Parkinson's Disease Genomics Consortium, and the NeuroGenetics Research Consortium. We performed meta-analyses of additive and recessive regression models and investigated associations between age at onset groups and different polygenic risk scores. An additive model meta-analysis identified six independent loci passing a genome-wide significance threshold, including three loci identified in previous genome-wide association studies (near SNCA, GBA1, and HIP1R) and two loci not previously associated with Parkinson's disease (rs74950462, P = 1.24e-8 and rs72848817, P = 4.89e-8). Furthermore, we identified a significant signal at the PRKN locus, prompting a follow-up analysis employing a recessive model. The recessive genome-wide association meta-analysis identified nine loci passing a genome-wide significance threshold, including SNCA, PRKN, and seven novel variants. Patients with onset between 18 and 40 years had significantly higher polygenic risk scores than later onset patients when the score was modelled specifically on genome-wide association statistics from independent young onset Parkinson's disease participants versus healthy controls. This increased polygenic burden was driven in part by loci harbouring mitochondrial pathway genes. Our results indicate that previously unidentified common and low-frequency variants contribute specifically to the young onset subgroup of Parkinson's disease. Association signals detected uniquely with a recessive model suggest that genetic susceptibility to young onset Parkinson's disease may be partially driven by homozygous variation, in line with previous reports of increased runs of homozygosity in this particular group of patients and may be consistent with a loss of function mechanism. The findings support the notion of young onset Parkinson's disease as a partly distinct subphenotype and highlight the mitochondrial pathway. These results may have implications for future precision medicine but should be interpreted with caution pending independent replication.

There is substantial interest in understanding neurological impact and recovery over time, but there is a dearth of longitudinal assessment extending from minutes to months surrounding neural system impact. We compared rare intraoperative recordings in three patients, obtained immediately before and after anterior temporal lobe (ATL) resection during a semantic prediction task, with longitudinal source-localized electroencephalography (EEG) obtained 2-6 weeks before and 2 and 6-14 months after surgery. Relative to controls (n = 20), task performance showed sustained impairment in the two left-hemisphere patients and delayed impact in the right-hemisphere patient. Consistent with theory on ipsilateral and contralateral hemisphere compensation, all three patients exhibited bilateral EEG alterations in speech responses and effective connectivity that did not recover to pre-operative levels. Direct comparison of the datasets for intrinsic neurophysiological biomarkers associated with timescales of processing ({tau}INT) and excitatory-inhibitory balance (aperiodic slope, {chi}SPEC) showed a striking months-long reduction in rapid timescale processing and gradually increasing aperiodic slope (e.g., putatively increased cortical inhibition) in the ipsilateral hemisphere of all three patients. Amidst these neurophysiological alterations, task performance did not return to pre-operative levels. These rare longitudinal patient data advance a framework to broadly evaluate neurological impact over multiple timeframes.

Spastic dysarthria diagnosis through subjective neurologist auditory-perceptual assessment remains standard practice despite known inaccuracy. To address this gap, we developed an objective framework grounded in phonetic evidence that spastic dysarthria preferentially impairs initial consonant articulation, using automatic speech recognition (ASR) to quantify dysarthria and localize corticobulbar lesions. We created four reading sentences targeting groups of initial consonants: labial (facial), lingual-alveolar (tongue), and velopharyngeal (pharyngeal/soft-palate) sentence, along with a mixed-consonant sentence for comparative evaluation. Thirty-seven patients with neuroimaging-confirmed corticobulbar lesions and 37 controls read each sentence. ASR transcribed dysarthric speech into text, and we computed a "syllable-error score" by counting incorrectly transcribed syllables. This yields a clinically meaningful feature that makes syllable-level phonetic errors explicit. Logistic regression models were trained for each sentence, and performance was summarized by the area under the receiver operating characteristic curve (AUC) across 10,000 resampled train-test splits. Consonant-specific sentences significantly outperformed the mixed sentence: the lingual-alveolar sentence performed best with (median AUC 0.88), followed by the labial (0.80), then the velopharyngeal sentence (0.72), while the mixed-consonant sentence was lowest (0.67). These results suggest that the interpretable ASR-derived syllable error feature, combined with a relevant machine learning classifier could inform clinical insight into consonant-specific vulnerability in spastic dysarthria, with lingual-alveolar consonants appearing particularly informative. Overall, this novel ASR-based framework, together with phonetics-informed feature design provides objective, accurate, and clinically meaningful digital quantification for spastic dysarthria detection and corticobulbar lesion localization.

The Disconnection Symptom Discoverer (DSD) model proposes to predict long-term performance on neuropsychological tests from stroke lesion disconnection profiles. The model requires external validation to determine reproducibility and generalizability to new and different patients. Here, we investigated whether the DSD supports accurate multi-domain cognitive outcome predictions at three different timepoints post stroke, in a clinically representative independent cohort. In this study, the DSD was used to predict visuospatial attention, verbal memory, and language scores in an independent cohort of 74 stroke survivors (mean age = 69.2, 39% female) with 3 repeated cognitive assessments. DSD-predicted scores were compared to observed neuropsychological scores collected at <2 weeks, six months, and > 2 years post-stroke. DSD-predicted language outcomes were significantly correlated with observed behaviour at the <2 weeks timepoint, but no other significant correlations between DSD-predicted scores were identified. Importantly, DSD-predicted verbal memory and visuospatial domain scores were not significantly correlated with observed behaviour at any of the considered timepoints (minimum p-value = 0.33). Across all tests and timepoints, DSD-predicted scores had an average Mean Absolute Error (MAE) of 0.21 (SD = 0.13, range = 0.04-0.43), with the highest errors occurring between predicted and observed memory scores. Larger stroke lesions were associated with higher MAE, indicating that the DSD performance was modulated by stroke severity. Overall, these results indicate that the DSD did not yield informative predictions of long-term cognitive outcomes in this external dataset. This finding provides an important illustration of potential overfitting issues within cognitive outcome prediction models, highlighting the need for caution when aiming to predict long-term post-stroke cognitive outcomes and further external validation of proposed models.

Background and Purpose: Drug resistant epilepsy (DRE) affects approximately 15 million people worldwide, and surgery remains the only curative option. A key challenge in predicting outcomes is the lack of standardized, quantitative tools to help distinguish seizure driver regions from responder regions during stereoelectroencephalography (sEEG) recordings. We validated the CN Suite, a computational platform that uses causal network mapping and machine learning to assign criticality scores to sEEG contacts, testing whether higher scores correspond to surgically treated tissue in patients with favorable outcomes. Methods: We analyzed deidentified clinical data from 60 patients (aged 2 years and older) with focal or multifocal DRE who underwent sEEG monitoring and proceed to surgery at four U.S. Level 4 epilepsy centers. The algorithm was trained on an independent cohort (N=37) and locked prior to validation. The primary outcome was the standardized effect size (Cohens d) of the patient level surgical zone enrichment ratio between more favorable (Engel I or II) and less favorable (Engel III or IV) outcome groups. Contact level sensitivity, specificity, PPV, and NPV were evaluated at a prelocked threshold. Results: The findings support our hypothesis: the algorithm results showed significantly higher criticality values for surgically treated tissue in favorable outcome patients (d=0.74, 95% CI: 0.39 to 1.06, p=0.003). Three potentially clinically actionable findings emerged. First, high-criticality contacts formed spatially compact clusters (~9 mm nearest-neighbor distance vs. 17mm expected by chance), consistent with focal targets amenable to minimally invasive ablation. Second, sensitivity was highest in small focal procedures (80% at 10 or fewer treated contacts) and decreased with resection size. Third, in patients whose surgery failed, high-critical tissue remained outside the resection boundary, suggesting incomplete treatment coverage of the epileptogenic zone rather than mislocalization. Prediction specificity was 84% at the contact level. For adult and pediatric cases (n=28), 88% of contacts that were identified as seizure free were in fact seizure free. Conclusions: Causal network mapping of sEEG identifies compact epileptogenic targets that correspond to surgically treated tissue in patients with more favorable outcomes. CN-Suite performed best in focal procedures and may be best suited for LITT and other minimally invasive approaches. In addition, low-criticality regions were infrequently associated with seizure-generating tissue, particularly in the pediatric cohort although our sample size was small. When surgery failed, residual high-critical tissue outside the resection boundary offered both a mechanistic explanation for less favorable surgical outcome as well as a potential target for reoperation.

Predicting post-stroke aphasia severity remains challenging, in part because language outcomes reflect not only focal cortical damage but also widespread disruption of structural and functional networks. Computational models of large-scale cortical dynamics offer a principled way to infer these network-level consequences from patient-specific lesions. Here, we present and evaluate REWIRED, a lesion-informed cortical dynamics framework designed to simulate individualized alterations in large-scale brain network organization after stroke. We first evaluated whether simulation-derived functional connectivity captured patient-specific variation in empirical functional connectivity beyond lesion burden and structural disconnection alone. We then developed a multiscale feature set combining lesion volume, lesion distribution patterns, probabilistic disconnectome metrics, and simulation-derived measures of functional connectivity and effective information flow (EIF). Finally, using a nested support vector regression (SVR) framework in a separate dataset, we tested whether simulation-derived features improve prediction of chronic aphasia severity, measured by the Western Aphasia Battery - Revised Aphasia Quotient (WAB-AQ), beyond lesion-distribution and structural-connectivity predictors. Simulation-derived functional connectivity significantly predicted empirical functional connectivity beyond local lesion burden and structural disconnection alone. With respect to WAB-AQ prediction, lesion-based (Set 1) and disconnectome-based (Set 2a) features alone yielded modest accuracy. Adding simulation-derived features (Set 2b) produced substantial gains, and the full feature set (Set 3) achieved the best performance (RMSE = 14.5; r = 0.83), reaching accuracy that is competitive with recent multimodal neuroimaging approaches, despite relying solely on lesion-distribution inputs. EIF measures were consistently selected as top predictors, indicating that disruptions in interregional communication patterns carry behaviorally relevant information not captured by structural features alone. These results support REWIRED as a framework for linking structural injury to distributed network dysfunction and behavioral outcomes. By integrating lesion information with large-scale cortical dynamics modeling, REWIRED provides a foundation for future individualized modeling of recovery and rehabilitation.

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.

Alzheimer's disease (AD) is characterized by complex alterations in synaptic, glial, neuronal and inflammatory markers. Given its emerging role at the interface of synaptic dysfunction and inflammation, the astrocytic marker GFAP may represent a cross-domain hub linking synaptic, neuronal and inflammatory alterations. Using multivariate and network-based analyses we examined the relationships among cerebrospinal fluid (CSF) biomarkers of astrocytic activation and synaptic failure, inflammation, and neurodegeneration in biologically confirmed AD patients and healthy controls (HC). We studied 60 AD patients and 40 HC. CSF concentrations of Neurogranin, SNAP-25, CAP2, NfL, GFAP, IL-1 , IL-1{beta}, IL-8, MCP-1, TNF were measured. Associations were assessed using Spearman correlations, LASSO regression, and network analysis to characterize multivariate dependency structures. Compared with controls, AD patients showed significantly higher CSF levels of Neurogranin, SNAP-25, CAP2, NfL, GFAP, IL-1{beta}, TNF- .. In AD, synaptic biomarkers were strongly intercorrelated and associated with astroglial activation, inflammatory markers, and tau-related pathology. Network analysis identified GFAP as a cross-domain hub linking synaptic, inflammatory, and neurodegenerative domains in AD. In controls, GFAP was mainly associated with neuronal injury markers. Network-based modelling revealed a disease-related reorganization of biomarker connectivity in AD, with GFAP occupying a central cross-domain position, supporting a systems-level view of AD pathophysiology.

Objective. Pathological beta oscillations are a hallmark of Parkinson's Disease (PD) and are linked with symptom severity and therapeutic efficacy of deep brain stimulation (DBS). Although some studies suggest that beta oscillations may propagate from the frontal cortex to the subthalamic nucleus (STN), direct evidence based on cortical and subcortical neural recordings remains limited. This study investigates synchrony and directionality of beta-band interactions between the frontal cortex and STN in PD. Approach. Simultaneous electrocorticography and STN local field potential recordings were obtained from three PD patients undergoing awake DBS lead placement surgery. Cortical-STN beta phase synchrony was quantified using phase locking value, and directed functional connectivity was analyzed using time-resolved bivariate Granger causality. Main results. Phase locking value mapping revealed a spatially non-uniform distribution of beta phase synchrony, with the strongest coupling localized most prominently within the precentral and superior frontal gyri. Granger causality analysis demonstrated a predominance of cortical-to-subthalamic beta-band interactions across all subjects with intermittent bidirectional coupling. Significance. These findings provide evidence that pathological beta oscillations in Parkinson's may preferentially propagate from the frontal cortex to the basal ganglia, consistent with known motor pathways. These findings are consistent with a cortical contribution to pathological beta oscillations and highlight potential methods for obtaining cortical targets for phase-dependent neuromodulation.

Introduction. The cerebellum is increasingly recognized as a key contributor to cognitive reserve and network adaptation in Parkinsons disease (PD). However, how cerebellocortical and cerebellobasal ganglia connectivity reorganizes across disease duration and cognitive status remains incompletely understood. Methods. Resting state fMRI data from the Parkinsons Progression Markers Initiative were analyzed in 172 individuals with PD. We investigated cerebellobasal ganglia and cerebellocortical connectivity using ROI to ROI and seed to voxel pipelines respectively, providing novel insights into both subcortical and cortical effects. Effects of age, disease duration, cognitive status, motor symptom severity, and dopaminergic medication were assessed. Results. Across all participants, cerebellar lobule VI and vermis VI showed robust positive connectivity with the pallidum, along with high intracerebellar coupling. When controlling for dopaminergic medication, lobule V connectivity with the primary motor cortex was reduced. Age was associated with lower cerebellobasal ganglia connectivity widespread across nodes, evident across medication states. Disease duration showed region specific effects: in cognitively normal PD, longer duration corresponded to stronger lobule V and temporal cortex connectivity as well as higher Crus I and precentral gyrus connectivity than PD with cognitive dysfunction. Motor symptom severity was not related to connectivity. Conclusions. Cerebellar connectivity patterns in PD are linked to disease duration and cognitive preservation. Enhanced cerebellocortical coupling in cognitively normal PD may reflect compensatory network recruitment that diminishes with cognitive decline.

Importance: Leukodystrophies are a heterogeneous group of genetic disorders affecting the white matter of the brain, often presenting with overlapping clinical features but differing in neuroanatomical involvement. There is a critical need for quantitative tools to characterize disease burden and support diagnosis, severity stratification, and clinical trial readiness. Objective: To characterize shared and distinct neuroanatomical patterns across six genetically confirmed leukodystrophies using anatomical MRI-derived phenotypes benchmarked against brain growth charts, and to assess the utility of this methodological approach for identifying imaging biomarkers of disease severity. Design, Setting, and Participants: Cross-sectional neuroimaging study using retrospective clinical MRI data. Setting: Multicenter study incorporating data from the Global Leukodystrophy Initiative Clinical Trials Network (GLIA-CTN) and control data from the Childrens Hospital of Philadelphia. Participants: The study included 434 MRI scan sessions from 274 patients with genetically confirmed leukodystrophies (Pelizaeus-Merzbacher disease, Metachromatic leukodystrophy, Alexander disease, Aicardi-Goutieres syndrome, TUBB4A-related leukodystrophies, and POLR3-related leukodystrophy). Control MRI data (7628 scans from 7205 subjects) were drawn from the Scans with Limited Imaging Pathology cohort at the Children's Hospital of Philadelphia. Exposures: All MRI scans underwent automated segmentation using deep learning segmentation tools to derive global and regional brain volumes. Normative models of brain development ("brain growth charts") were generated for the control cohort using generalized additive models for location, scale, and shape. Centile scores were then calculated for leukodystrophy subjects to quantify deviations from typical development. Main Outcomes and Measures: Centile scores for global and regional brain volumes were compared across leukodystrophy subtypes to identify disease-specific neuroanatomical patterns and to evaluate their potential utility for severity stratification. Results: Distinct patterns of neuroanatomical deviation were observed across leukodystrophy subtypes. Certain leukodystrophies showed preferential involvement of specific cortical or subcortical regions, while others displayed more diffuse volume loss. Centile scores demonstrated potential for differentiating disease subtypes and stratifying individuals by severity. Preliminary longitudinal data suggest centile scores may also track progression over time. Conclusions and Relevance:This study demonstrates the feasibility and utility of MRI profiling of individuals with leukodystrophy using anatomical MRI-derived phenotypes benchmarked against brain growth charts. The approach enables data-driven, quantitative characterization of structural brain abnormalities, offering a scalable method for phenotyping, diagnosis, and future use in clinical trials.