Brain Communications

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.

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.

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.

Synapse loss is an early feature of neurodegeneration and may provide sensitive biomarkers for experimental medicine. Positron emission tomography (PET) with the synaptic vesicle glycoprotein 2A radioligand [11C]UCB-J shows widespread signal reduction across dementias. However, it remains unclear which aspects of synaptic integrity [11C]UCB-J PET measures. We developed a histological-imaging pipeline to quantify structurally intact synapses in post-mortem brain tissue. We applied it to six donors with the tauopathy progressive supranuclear palsy (PSP) who had ante-mortem [11C]UCB-J-PET, alongside six controls across 11 brain regions. Synapse loss in PSP was widespread but region-specific across cortical, subcortical, and brainstem regions. Greater synapse loss was associated with higher tau burden and pathology, and cortical synaptic density correlated with ante-mortem cognition. Post-mortem synaptic density correlated with in vivo [11C]UCB-J-PET signal. This study provides validation of SV2A PET as a biomarker of synaptic density and supports integration of imaging with histopathology in neurodegenerative disease research.

Background. Tethered cord syndrome (TCS) is classically associated with a low-lying conus medullaris, yet many surgically treated children have a normally positioned conus (occult TCS). Large-scale normative data on conus position in children, and the diagnostic value of quantitative conus assessment, are limited. Purpose. To establish a large-cohort reference distribution for conus medullaris termination level in children, to quantify conus position in children surgically treated for presumed (occult) TCS, and to test whether automated conus segmentation and radiomics can distinguish TCS from normal. Materials and Methods. In this retrospective single-center study, conus termination level was extracted from structured radiology reports of consecutive pediatric lumbosacral MRI examinations and encoded numerically (L1 = 1, L2 = 2, etc.). Children surgically treated for tethered cord were identified by linkage to an operative registry (name and date of birth) and restricted to preoperative examinations. A deep-learning model (nnU-Net) was trained for conus segmentation on axial T2-weighted images. IBSI-compliant radiomic features were extracted; reproducibility was assessed by intra- and inter-observer intraclass correlation (ICC). A case-control radiomics analysis used batch-only ComBat harmonization and cross-validated L1-penalized logistic regression; discrimination was compared with conus level by paired bootstrap. Results. Among 9,808 examinations with a parseable conus level (98.5% of reports; parser validated against dual blinded annotation, 99.4% agreement, weighted kappa 0.946), the conus terminated in the L1 region in 85.7% and the L2 region in 14.3% of the reference cohort (postoperative examinations excluded, n = 9,655); a low-lying conus (>=L3) occurred in only 0.05% (5/9,655), and remained rare (0.14%, 14/9,808) including operated examinations (median L1; mean 1.13 +/- 0.33). A slightly more cephalad position was seen with increasing age (negligible correlation). Among 475 preoperative children surgically treated for tethered cord, 99.6% had a normally positioned conus (<=L2) and only 0.4% were low-lying. Automated conus segmentation achieved a held-out Dice of 0.85. Conus radiomics likewise did not distinguish TCS from controls (equivalence-tested null; full segmentation/radiomics pipeline reported in the companion methodological paper). Conclusion. In children, the conus medullaris terminates at L1-L2 in more than 99% of cases and is normally positioned in virtually all children surgically treated for TCS. Within the conus, neither position nor texture (radiomics) identifies tethered cord; whether the filum terminale carries a diagnostic signal was not tested here.

Background: Cognitive impairment in Parkinson's disease (PD) is linked to degeneration of the cholinergic basal forebrain, particularly cholinergic nucleus 4 (Ch4) in the nucleus basalis of Meynert. Structural and diffusion MRI separately detect this degeneration, but few studies have combined these modalities across the PD cognitive spectrum. Methods: We analyzed 92 participants: 14 healthy controls (HC), 35 PD with normal cognition (PD-NC), 33 with mild cognitive impairment (PD-MCI), and 10 with dementia (PDD). For Ch4 and cholinergic nuclei 1, 2, and 3 (Ch1-3) in the medial septal/diagonal band complex, we determined TIV-normalized gray matter density (GMD) and free-water (FW) fraction. We evaluated group differences, cognitive correlations, adjusted multivariable regression, and exploratory ROC discrimination. Results: Ch4 GMD was significantly lower in PDD compared to PD-MCI (p=0.007), PD-NC (p<0.001), and HC (p<0.001). Ch4 GMD was also lower in PD-MCI versus HC (p=0.028); the PD-MCI versus PD-NC difference was not significant after correction (p=0.074). Ch1-3 GMD was lower in PDD versus PD-NC (p=0.008) and HC (p=0.009). Ch4 and Ch1-3 FW were elevated in PDD versus all other groups (all p<0.01). Among PD patients (n=78), MoCA was positively correlated with Ch4 GMD ({rho}=0.49) and Ch1-3 GMD ({rho}=0.42) and negatively correlated with Ch4 FW ({rho}=-0.51) and Ch1-3 FW ({rho}=-0.40; all p<0.001). In the full four-metric model, Ch4 GMD and Ch4 FW were the only independent basal forebrain predictors (Ch4 GMD {beta}=+2.04, p<0.001; Ch4 FW {beta}=-1.46, p=0.005) of MoCA score. The combined Ch4 GMD + Ch4 FW model showed high discrimination for PDD versus non-demented PD (AUC=0.934; optimism-corrected AUC=0.925). Conclusions: Structural and free-water diffusion MRI provide complementary information about Ch4 degeneration in PD. The combined Ch4 model showed promising exploratory discrimination of PDD; validation in larger independent samples is needed.

Traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) frequently co-occur in Veterans, producing overlapping symptoms and shared autonomic dysregulation. Heart rate variability (HRV) offers a noninvasive measure of autonomic function. Univariate HRV analyses often fail to capture complex, multivariate patterns associated with comorbidity. This study applied machine learning to HRV features extracted from MEG-derived electrocardiogram (M-ECG) signals to differentiate Veterans with TBI alone (TBI-alone; n = 42) from those with comorbid PTSD (TBI+PTSD; n = 40). Time-domain, frequency-domain, geometric, and nonlinear HRV metrics were analyzed using nested cross-validated Random Forest and XGBoost classifiers, with Boruta-based feature selection and SHapley Additive exPlanations for model interpretability. Both classifiers achieved above-chance discrimination (Random Forest AUC = 0.663; XGBoost AUC = 0.635). Multivariate models identified distributed autonomic signatures in TBI+PTSD, including altered sympathovagal balance, increased low-frequency proportion, and greater heart rate complexity. In contrast, univariate HRV differences were subtle and did not survive correction for multiple comparisons. These findings demonstrate how using multivariate machine learning HRV analysis could help with detecting comorbidity-specific autonomic patterns, suggesting that HRV-derived signatures may serve as exploratory biomarkers for risk assessment and targeted interventions in Veterans with TBI and PTSD.

Normal emotional experience depends on dynamic modulation of neural excitability across limbic and prefrontal circuits, yet the spectral markers that reflect these shifts in humans remain incompletely understood. In this study, we combined a validated video-based emotion induction paradigm with stereotactic electroencephalography (SEEG) in 31 patients with drug-resistant epilepsy to investigate how positive and negative affective states modulate oscillatory and aperiodic (asynchronous) neural activity. Using spectral parameterization to dissociate oscillatory power from the aperiodic 1/f component, we found that emotional valence robustly altered the aperiodic slope in a regionally specific manner: negative valence flattened the slope in thalamus, posterior insula, and posterior cingulate cortex, whereas positive valence produced flattening in dorsolateral prefrontal cortex. Simultaneous oscillatory changes included increased high-frequency activity and decreased alpha/beta power during negative affect, and reduced alpha power during positive affect, which were elucidated after adjusting for broadband aperiodic spectral shifts. These effects persisted after controlling for audiovisual stimulus or physiological features and were not evident in simultaneously recorded scalp EEG, underscoring their localization to intracranial sites. Together, these results provide the first direct evidence that active induction of emotional states modulates the aperiodic slope of human intracranial field potentials, reflecting valence-dependent shifts in local circuit excitability. The findings highlight the 1/f slope as a sensitive neural marker of affective brain states and for mood dysregulation.

Adapting ongoing gait patterns to environmental challenges is essential for safe navigation through the environment. Impairment of gait adaptation is common in many neurodegenerative disorders, such as Parkinson's disease (PD), where it hampers mobility and limits quality of life. The neural control of gait adaptation remains largely unclear, thereby limiting the development of targeted treatments, such as deep brain stimulation of the subthalamic nucleus (STN-DBS). We integrated clinical, kinematic, brain metabolic imaging, and electrophysiological data, obtained during a fully immersive virtual reality overground walking task, to characterize the neural underpinnings of gait adaptation performance during dynamic obstacle avoidance and its improvement with STN-DBS. Movement kinematics, brain oscillatory activity, and metabolic activation were simultaneously acquired in 12 patients with PD during rest and gait adaptation, under active or paused STN-DBS, using inertial measurement units, electroencephalography, and three separate [18F]fluorodeoxyglucose positron emission tomography scans. Eight age-matched healthy subjects completed the same task for comparative kinematic analyses. All patients showed significant clinical improvement with STN-DBS. During the gait adaptation task with paused stimulation, patients exhibited increased metabolic activity in the cerebellum and sensorimotor cortex. Active STN-DBS selectively enhanced thalamic and superior frontal gyrus (SFG) metabolism, while concomitantly reducing cerebellar uptake. Right-lateralized SFG metabolism correlated with gait adaptation performance, with DBS-driven shifts toward greater right SFG activity predicting the magnitude of gait adaptation improvement. This correlation was independent of baseline asymmetry in clinical impairment, electrode placement, or structural connectivity to the SFG. Of note, STN-DBS amplitude asymmetry emerged as an independent predictor of right-lateralization of SFG metabolism. EEG recordings confirmed this lateralized network modulation, with theta-band asymmetry paralleling PET findings. Our findings identify a lateralized thalamo-cortical network supporting gait adaptation in PD and highlight a distinctive role for the SFG. We further show that effective STN-DBS acts as a lateralized regulator, dynamically rebalancing cortico-thalamic circuits to support context-appropriate gait control. The observed right-hemispheric lateralization may foster novel image-guided programming strategies to enhance the consistency and effectiveness of gait control in PD.

Using data from a randomized clinical trial, we examined whether daily biofeedback training that modulates heart rate oscillations is associated with changes in microstructural brain texture in Alzheimer's disease signature cortical (ADSC) and hippocampal regions. Younger and older adults were randomly assigned to one of two daily biofeedback practices for five weeks: slow-paced breathing designed to increase heart rate oscillations (Osc+) or self-selected strategies aimed at decreasing oscillations (Osc-). Intervention effects were observed in both ADSC and hippocampus regions and were confined to a composite texture factor dominated by uniformity and entropy. Across regions, effects were expressed primarily as Time x Condition interactions, indicating differential texture trajectories between Osc+ and Osc-. In the hippocampus, this pattern was further qualified by a Time x Condition x Age Group interaction, reflecting more pronounced effects in older adults, whereas younger adults showed no reliable texture modulation. Partial least squares correlation analyses further demonstrated that training-related texture changes in the left hippocampus, right fusiform gyrus, and right entorhinal cortex covaried with concurrent changes in plasma AD-related biomarkers, with tau- and p-tau related measures contributing most strongly to the multivariate association. Together, these findings suggest that HRV biofeedback may selectively influence specific dimensions of brain microstructural texture and that such changes are meaningfully coupled with plasma AD-related biomarker profiles.

Background: Vascular cognitive impairment (VCI) and small vessel disease (SVD) involve many interconnected factors influencing multiple outcomes, also beyond cognitive decline. Bayesian networks (BNs) can help unravel these complex interrelations, which we demonstrate in this proof-of-concept study in the Heart-Brain Connection cohort, including memory-clinic patients with SVD, patients with heart failure, carotid occlusive disease, and reference participants. Methods: We trained BNs and jointly modelled cognitive decline (Clinical Dementia Rating (CDR) increase) and major adverse cardiovascular events (MACE) over five years as outcomes in relation to multiple demographic and disease factors and emerging imaging and plasma biomarkers, also considering possible non-random dropout. Results: Of 566 individuals (median age 68, 64% men), 134 had MACE and 112 experienced CDR increase. Diagnostic group and baseline cognition were key determinants of both outcomes. The BN identified baseline clinical severity as a non-random dropout source. Plasma biomarkers formed an interconnected subnetwork, linked to demographic and vascular factors, but without direct dependencies with outcomes. The trained BN also provides individualized inference under partial evidence, informing on outcome probabilities. Conclusion: This proof-of-concept study demonstrates how BNs quantify and visualize the dependency structure underlying prognostic heterogeneity in VCI and SVD, including non-random dropout and positioning of emerging biomarkers.

Background and Objectives: Older adults with epilepsy are at increased risk for Alzheimer's disease (AD), yet the mechanisms underlying this association remain poorly understood. We applied a validated AD neuroimaging signature to older adults with epilepsy to examine 1) whether older adults with epilepsy mirror AD-related changes, 2) associations with clinical, cognitive, and plasma biomarker outcomes, and 3) utility for identifying subgroups at heightened risk for cognitive decline. Our multicenter, prospectively enrolled cohort allowed for direct examination of differences in AD signatures between those with early-onset and late-onset unexplained epilepsy. Methods: Participants included 449 older adults: 87 with focal epilepsy from the multicenter Brain Aging and Cognition in Epilepsy (BrACE) cohort (age=66.10 [SD=6.86], including early-onset (<55 years at seizure onset) and late-onset ([&ge;]55 years at seizure onset) epilepsy); 362 from the Alzheimer's Disease Neuroimaging Initiative (ADNI), including cognitively unimpaired (CU) healthy controls and individuals with mild cognitive impairment (MCI) or AD dementia. An AD signature was derived from regional cortical thickness and hippocampal volume weighted by their sensitivity to AD-related neurodegeneration in prior work. Associations between the AD signature, epilepsy characteristics, plasma biomarkers ({beta}-amyloid 42/40, phosphorylated tau [pTau217, pTau181], neurofilament light chain [NfL]), and cognition were evaluated in BrACE. Results: Participants with epilepsy demonstrated more AD-like signatures compared to ADNI CU controls ({beta}= -0.43, p<0.001), reflecting reduced thickness/volume in AD-vulnerable regions. This effect was stronger among early-onset ({beta}= -0.57) versus late-onset ({beta}= -0.26) epilepsy. In BrACE, the AD signature correlated with NfL ({beta}= -0.30, p=0.050), memory performance ({beta}= 0.30, p=0.006), and predicted greater odds of cognitive impairment specifically among those with early-onset, but not late-onset, epilepsy (interaction p=0.043). Further, among those with early-onset epilepsy, the AD signature significantly improved identification of cognitive impairment over and beyond the effects of plasma AD biomarkers (p=0.041). Findings were similar when examining the effects of epilepsy duration rather than epilepsy onset age. Discussion: AD neuroimaging signatures may help identify clinically meaningful subgroups among older adults with epilepsy, particularly when integrated with AD biomarkers. Findings support a multimodal framework for assessing AD-related risk in epilepsy and highlight interactive effects of epilepsy chronicity and AD-related processes that can influence cognitive outcomes.

PURPOSE: Alzheimer disease (AD) is associated with cognitive impairment, brain atrophy, and elevated amyloid-beta and tau. The study aimed to characterize regional atrophy associated with elevated amyloid-beta and tau, as measured by [18F]florbetapir (FBP) and [18F]flortaucipir (FTP) positron emission tomography (PET), respectively, and determine whether combining PET and atrophy data improves the prediction of cognitive impairment. METHODS: Alzheimer Disease Neuroimaging Initiative data (n = 381) were retrospectively analyzed. PET results were correlated with cortical thickness, gray matter (GM) volumes, Mini-Mental State Examination, and Montreal Cognitive Assessment. Linear/logistic regression and area under the curve (AUC) were used to evaluate for significant correlations and compare performances in distinguishing cognitive impairment, respectively. RESULTS: Incremental loss of cortical thickness and GM volume was observed from FBP-/FTP- (n = 205) to single PET-positive (FBP+/FTP-, n = 133; FBP-/FTP+, n = 5) and FBP+/FTP+ (n = 38) groups, particularly in the temporal and parietal lobes. FBP+/FTP+ showed the most severe cortical thickness loss in the entorhinal cortex, temporal lobe GM atrophy, and cognitive impairment. Adding brain atrophy as the third variable resulted in higher odds ratios and improved AUCs for cognitive impairment, with FBP+/FTP+/temporal GM or entorhinal cortical atrophy+ demonstrating the strongest associations with cognitive impairment. CONCLUSION: A multimodal approach combining PET and MRI may help improve the assessment of cognitive impairment in AD.

Amyotrophic lateral sclerosis (ALS) is increasingly recognized as a multisystem neurodegenerative disorder in which motor-neuron degeneration is accompanied by widespread alterations in cortical dynamics. Among its most reproducible neurophysiological signatures is cortical hyperexcitability, yet how this local excitability imbalance shapes distributed whole-brain activity remains poorly understood. Here, we combined source-reconstructed resting-state MEG data, tractography-informed whole-brain modeling, and simulation-based inference to investigate whether ALS-related alterations in large-scale brain dynamics can be mechanistically explained by changes in cortical excitability. First, we characterized empirical brain dynamics using complementary features spanning regional activity amplitude and variability, functional connectivity, and avalanche-based metrics. These analyses revealed significant alterations in ALS patients relative to healthy controls, as well as associations with clinical impairment and disease staging. To mechanistically interpret these changes, we employed a reduced Wong-Wang whole-brain model in which local recurrent excitation modulates emergent large-scale neural dynamics. Simulations showed that increasing excitability systematically reproduced the empirical dynamical signatures observed in ALS. We then applied a simulation-based inference framework to estimate latent excitability parameters directly from empirical observations. Whole-brain model inversion revealed increased excitability in ALS patients compared with controls. The recovered excitability parameter was associated with disease staging, supporting its clinical relevance as a model-derived descriptor of ALS progression. Finally, by extending the model to estimate frontal and non-frontal excitability separately, we found that ALS-related alterations were predominantly associated with increased frontal excitability, whereas non-frontal regions appeared comparatively less affected. The recovered parameters related to disease staging. Together, these findings provide a mechanistic framework linking altered large-scale brain dynamics in ALS to selective cortical hyperexcitability, explaining how local excitability changes can give rise to global network reorganization. More broadly, they show how computational model inversion can recover latent multiscale pathophysiological processes from empirical neural recordings, offering a non-perturbative alternative to complex experimental paradigms typically required to causally probe local-to-global mechanisms.

Age-related hearing loss is a leading modifiable risk factor for dementia and is increasingly recognized as a non-motor feature of Parkinson's disease (PD). The apolipoprotein E (APOE) E4 allele is the strongest genetic risk factor for Alzheimer's disease and is associated with cognitive decline in PD, yet its relationship to hearing loss remains unclear. Therefore, we examined the independent and interactive effects of PD status and APOE E4 carrier status on age-related hearing loss using a validated web-based speech-in-noise (SIN) assessment in 239,620 23andMe Research Institute participants without PD and 4,361 PD cases. Generalized additive models for location, scale, and shape (GAMLSS) showed that both PD and APOE E4 independently exacerbated age-related hearing decline, with speech reception thresholds (SRTs) worsening non-linearly with advancing age, but without evidence of synergistic interaction. However, longitudinal analyses in a subcohort completing at least two assessments (1,434 PD cases; 36,242 controls) using GAMLSS mixed models showed a significant three-way interaction between PD status, APOE E4, and age2, such that SIN hearing loss accelerated more steeply with age in APOE E4 carriers with PD. Males and individuals with lower educational attainment also exhibited worse SIN hearing loss. These results identify APOE E4 carriers with PD as a priority population for hearing screening and intervention, and support the integration of SIN assessments into routine PD care to detect hearing decline that may compound cognitive and communicative burden in aging.

Introduction: Variants in the polymerase gamma (POLG) gene are associated with a wide range of mitochondrial disorders. Emerging evidence suggests a potential link between POLG variants and Parkinson's disease (PD); yet, results remain inconclusive. Objectives: To investigate the genetic spectrum and prevalence of POLG variants in PD across diverse ancestries. Methods: We leveraged multi-ancestry genetic data from the Global Parkinson's Genetics Program (GP2), including genotyping data from 98,589 and short-read sequencing data from 36,022 individuals. We performed a POLG rare variant screen, case-control association, and gene-level burden analyses. Results: Five PD cases carried potentially biallelic rare pathogenic/likely pathogenic POLG variants. Additionally, 228 individuals (<1%; 161 PD cases, 28 individuals with other neurological disorders, and 39 controls) carried 34 distinct rare pathogenic/likely pathogenic heterozygous variants, with no significant frequency differences between cases and controls, except for the p.Ala467Thr variant in the European population. The co-inherited pathogenic variants p.Thr251Ile and p.Pro587Leu were present in <1% of both cases and controls, with no significant group differences. Burden and variant-level association analyses showed no association between rare POLG variant burden or common POLG variant enrichment and PD. Conclusions: POLG variants are overall rare in PD. The identification of rare pathogenic variants among PD cases suggests that POLG-related mitochondrial dysfunction may contribute to PD in isolated instances, particularly under recessive inheritance. Our findings support a role for POLG variants in select cases and underscore the need for larger-scale sequencing and functional studies.

Background: Mono-allelic Dehydrodolichyl Diphosphate Synthase (DHDDS) variants are associated with juvenile Parkinsonism, developmental delay and seizures. Symptoms are progressive, and various mechanisms, such as defective glycosylation, lysosomal dysfunction and cholesterol accumulation have been hypothesized to underlie disease symptoms. There is no treatment for DHDDS-related disease. Methods: Patient-derived cortical forebrain organoids were created to elucidate disease mechanisms and evaluate potential treatments. In these neuronal models, glycosylation, lipidomics, proteomics, cholesterol/ganglioside accumulation, mitochondrial function and electrophysiological activity were assessed. Finally, we investigated the effects of nicotinamide mononucleotide (NMN), identified through a yeast-based drug screen, in neuronal cell models and in six patients in an off-label, N-of-1, observational series. Results: DHDDS-patient derived organoids showed visual signs of degeneration after four months of culturing. This was accompanied by significant cholesterol accumulation in astrocytes, decreased mitochondrial respiration and loss of deep-layer neurons. In addition, we identified glycosylation abnormalities, showing for the first time that glycosylation in human tissue is affected by monoallelic DHDDS variants. Proteomic analysis revealed altered protein expression of proteins involved in lipid metabolism, cytoskeletal organization and neuronal development. We found that oral Nicotinamide Mononucleotide supplementation led to significant improvement in mitochondrial respiration and electrophysiological parameters in organoids, concurring with clinical improvements in all of the treated patients, particularly regarding their ataxia and tremor. Conclusion: Our findings reveal a progressive phenotype in DHDDS-patient-derived brain organoids, with mitochondrial dysfunction and astrocyte-specific metabolic alterations contributing to disease pathology. Notably, NMN treatment led to clinical improvements in patients with heterozygous DHDDS variants, highlighting its potential as a therapeutic strategy.

Mild malformation of cortical development with oligodendroglial hyperplasia and epilepsy (MOGHE) is a recently recognized cause of drug-resistant focal epilepsy. It is often MRI-negative or shows imaging features mimicking focal cortical dysplasias, which makes recognition difficult and limits presurgical counseling. We aimed to identify an intracranial EEG (iEEG) biomarker that distinguishes MOGHE from other developmental brain lesions encountered in epilepsy surgery. In a retrospective multicenter test cohort of 38 patients (18 MOGHE, 20 non-MOGHE), we analyzed long-term stereo-EEG and subdural recordings. Only MOGHE patients showed highly stereotyped clusters of very brief low-voltage fast activity (LVFA) events, organized into status-like 3 to 12-minute episodes that often lacked clear clinical symptoms. LVFA clusters were present in 16/18 MOGHE and 0/22 non-MOGHE patients. We then tested diagnostic performance in an independent, blinded single-center validation cohort of 22 patients (11 MOGHE, 11 non-MOGHE), in which visual identification of LVFA clusters correctly classified 10/11 MOGHE and 10/11 non-MOGHE cases (Cohens kappa=0.82). Penalized logistic regression further confirmed MOGHE histology as the strongest predictor of LVFA clusters, independent of age and lobe localization. Because LVFA clusters can be recognized visually on routine intracranial EEG recordings without specialized software, this biomarker is readily applicable in clinical practice and may improve presurgical identification of MOGHE. Future prospective studies should determine whether its recognition influences surgical planning, improves outcome prediction, or facilitates selection of patients for mechanism-based therapies.

The objective assessment of patients with disorders of consciousness (DOC) remains a significant clinical challenge. Behavioral scales like the Coma Recovery Scale-Revised (CRS-R) are susceptible to rater subjectivity and have difficulty in detecting patients with cognitive-motor dissociation (CMD), while existing electrophysiological paradigms typically evaluate isolated processing levels, especially in visual functions. To address these limitations, we developed a novel, hierarchical visual EEG framework that evaluates three progressive tiers of visual processing--sensory input, selective attention, and object discrimination--within a single, unified paradigm. This framework uses steady-state and event-related potentials, analyzed with statistical testing and machine learning, to provide objective detection. In a cohort of 85 participants, the framework demonstrated a robust alignment with behavioral CRS-R levels and successfully identified CMD patients missed by bedside behavioral examinations. Notably, model predictions derived from this framework showed a significant correlation with 3-month clinical outcomes. This prognostic utility generalized effectively and remained consistent across distinct EEG acquisition systems in an independent validation cohort of 17 patients. In summary, this work offers electrophysiological validation for the hierarchical design of the CRS-R and provides a practical tool for bedside objective assessment of DOC.