NeuroImage: Clinical

Apathy is characterized by reduced motivation for goal-directed behaviour and may emerge following brain injury. Currently, little is known about apathy in children and adolescents with neurodevelopmental disorders (NDDs) exposed to repetitive head impacts. This exploratory study investigated associations between apathy, repetitive head-banging behaviour, and substantia nigra neuromelanin-sensitive MRI (NM-MRI) signal in youth with NDDs. Forty-seven participants (14 typically developing; 33 ADHD/ASD) completed Behaviour Assessment System for Children (BASC-3) measures, from which apathy-related items were harmonized across developmental forms and subjected to principal component analysis. A one-component solution explained 47.3% of variance and was used to derive apathy scores. Although head-banging severity and NM-MRI signal were not independently associated with apathy, a significant interaction emerged, whereby greater head-banging severity strengthened the relationship between apathy and substantia nigra NM-MRI signal. These preliminary findings suggest repetitive self-injurious head impacts may influence dopaminergic systems linked to motivational dysfunction in youth with NDDs.

Resting state networks RSNs measured through functional connectivity FC emerge in utero and are detectable within hours of birth. Although neonatal growth metrics predict later neurodevelopmental outcomes and structural brain maturation their relationship to early functional network organization remains poorly understood. We examined associations between anthropometric growth metrics and resting state FC in a cohort of healthy near term and term born neonates using functional near infrared spectroscopy fNIRS acquired during the first few days of life. Task free fNIRS data were recorded in 121 neonates 67 males 55 percent mean postnatal age equals 25.6 hours mean gestational age equals 38.63 weeks. Based on birthweight percentiles 12 9 percent newborns were small for gestational age SGA and 13 11 percent were large for gestational age LGA. Growth metrics included birth weight for gestational age z score BGZ head circumference for gestational age z score HGZ birth weight for length z score BLZ and z scored Ponderal Index PIz. Whole brain FC was calculated as the mean Fisher Z transformed correlation across valid channel pairs. Channel wise associations were examined using general linear and linear mixed effects models controlling for gestational age postnatal age and sex. Linear and quadratic terms were tested and multiple comparisons were controlled using the false discovery rate. None of the anthropometric measures were associated with global FC however significant nonlinear quadratic relationships emerged at the channel pair level. BGZ B range equals negative 0.102 to negative 0.074 FDR corrected p less than 0.005 and PIz B range equals negative 0.088 to negative 0.074 FDR corrected p less than 0.001 demonstrated negative quadratic associations with inter and intra hemispheric connectivity such that newborns with both lower SGA and higher LGA growth values showed reduced FC relative to those with average growth. In contrast HGZ demonstrated positive quadratic associations B range equals 0.051 to 0.074 FDR corrected p less than 0.001 with infants at the lower and higher ends of the head size distribution exhibiting increased FC relative to infants near the mean. BLZ showed no significant associations after correction. Results indicate that early somatic growth is reflected in the organization of neonatal functional brain networks and that deviations from average growth whether smaller or larger are associated with altered regional connectivity. Findings suggest that neonatal growth metrics may provide an accessible marker of early brain health reflected in regionally specific functional connectivity patterns.

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.

Introduction Deep brain stimulation (DBS) of the ventral capsule/ventral striatum (VC/VS) can benefit patients with treatment-refractory obsessive-compulsive disorder (OCD). However, time to respond post-operatively ranges from weeks to over a year. We examined neuroanatomical determinants of this variability. Methods We studied 16 treatment-refractory OCD patients who responded to VC/VS DBS, classifying them as rapid (less than or equal to 3 months) or slow (greater than 3 months) responders. We compared contact locations along anterior-posterior, dorsal-ventral, and medial-lateral axes. In 11 patients with diffusion-weighted magnetic resonance imaging (dMRI), we utilized volumes of tissue activated (VTAs) for both initial and most recent effective DBS settings to filter tractograms of the anterior limb of the internal capsule to 11 predefined prefrontal cortical regions. We analyzed streamline counts as a proxy for connectivity strength with mixed-effects models. Results Rapid (n=8) and slow (n=8) responders exhibited a clear bimodal distribution of time-to-response, supported by a Bayesian Information Criterion difference of 9.14. Rapid responders right-hemisphere contacts were positioned more superiorly, and there was a trend toward their left-hemisphere contacts being positioned more posteriorly. Connectivity fingerprints and mixed-effects modeling showed greater dorsolateral prefrontal cortex engagement in rapid responders than in slow responders, whereas slow responders showed enhanced central orbitofrontal cortex connectivity over time. Discussion Variability in VC/VS contact placement corresponds to distinct prefrontal cortical connectivity patterns and response timelines. Patient-specific targeting and connectivity-informed programming may accelerate response to treatment.

The choroid plexus (ChP) plays a central role in cerebrospinal fluid production, immune signaling, and metabolic clearance, and has emerged as a potential imaging biomarker of neurodegeneration. However, accurate and scalable quantification of ChP volume remains challenging due to its complex morphology and low contrast on conventional MRI. The Automatic Segmentation of Choroid Plexus (ASCHOPLEX), a deep learning framework originally trained on healthy controls and multiple sclerosis cohorts, has not been systematically evaluated in neurodegenerative populations. Using T1-weighted MRI from the multi-center COMPASS-ND study, we assessed standard ASCHOPLEX performance in cognitively unimpaired (CU), Alzheimer's disease (AD), and Parkinson's disease (PD) participants (N = 30), followed by fine-tuning using expert manual segmentations (N = 60). Segmentation accuracy was evaluated using Dice, Jaccard, precision, and recall. The fine-tuned model was then applied to a larger cohort (N = 277) to derive normalized ChP volumes, which were compared across diagnostic groups using linear regression models. Fine-tuning significantly improved segmentation accuracy across all metrics (Dice: 0.45 to 0.84; Jaccard: 0.32 to 0.73; all p < 0.0001), enabling robust ChP delineation across sites and conditions. In the full cohort, normalized ChP volume was significantly higher in AD compared with CU and PD (p < 0.0001), while PD did not differ from CU (p = 0.31). These findings demonstrate that dataset-specific adaptation is essential for deploying deep learning segmentation models in heterogeneous neuroimaging cohorts. The refined ASCHOPLEX framework enables scalable ChP quantification and supports its use as a structural imaging marker in neurodegenerative disease.

Although language acquisition delays are frequently observed in children with autism spectrum disorder (autism), our current understanding of the neurobiological mechanisms underlying language development in autism is sparse. Previous studies have found resting-state electroencephalography (EEG) power to be associated with language abilities in autistic children. However, longitudinal studies examining resting-state EEG phase coherence in relation to language development in preschool-aged children with autism are limited. This study aimed to characterize age- and group-related changes in whole-brain coherence in neurotypical children and in autistic children with and without language delay. Resting-state EEG and language data were collected at 2, 3, and 4 years of age. Peak phase coherence within the alpha band (6-11 Hz) was calculated at each timepoint and differences in the developmental trajectory of peak alpha coherence (PAC) were analyzed. In neurotypical children, PAC increased between 2 and 4 years of age. In contrast, PAC did not significantly change with age in children with autism. However, when examining autistic children based on language delay status, PAC increased with age in autistic children without language delay, but not in children with language delay. Exploratory analysis revealed evidence for an interaction between PAC and age, suggesting that the direction of the association between PAC and VDQ varied across age. Overall, these results support previous findings of altered oscillatory connectivity in autism and suggest that differences become apparent early in development. Importantly, phase coherence may not only differentiate diagnostic groups but also capture meaningful variability within the autism group. Future research should further investigate the use of EEG coherence as a biomarker of language development in autism.

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.

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.

Generalizable neuroimaging biomarkers that detect cerebral cortical changes after traumatic brain injury (TBI) and predict patient outcomes are needed to improve care and to develop targeted therapies. We used morphometric inverse divergence (MIND) analysis of structural MRI to investigate cortical gray matter morphological networks cross-sectionally and longitudinally after TBI and correlate these with symptoms, disability and cognition six months after injury. Our findings support the Triple Network Model from functional MRI of post-traumatic alterations in the relationship between task-positive, default mode and salience networks. However, the strongest associations between early cortical similarity metrics and long-term patient outcomes involved the dorsal attention network and the limbic network as well as similarity metrics across Mesulam's hierarchy of laminar differentiation. Since MIND mapping of cortical gray matter networks only requires data that is a routine part of standard clinical MRI protocols and does not need image harmonization across different scanners, this work reports a promising new tool that is immediately available for advancing research and clinical care in TBI.

Aim: To determine whether the neural phenotype (whole-brain resting-state functional connectivity pattern) of attention deficit hyperactivity disorder associated with prenatal alcohol exposure (ADHD+PAE) differs from that in unexposed children with ADHD of probable familial origin (ADHD-PAE). Method: Resting-state functional MRI was acquired from 26 children with ADHD+PAE, 25 with ADHD-PAE, and 25 typically developing (TD) children, all aged 8-13 years. Mean connectivity matrices based on the Cole-Anticevic Brainwide Network Parcellation of the brain were compared between the groups. Results: Within the frontoparietal network (FPN), children with ADHD+PAE showed widespread lower group-mean connectivity than children with ADHD-PAE; effects were concentrated primarily in cerebellar-cerebral cortical and cerebral cortical-cerebral cortical connections. Children with ADHD-PAE showed widespread hyperconnectivity relative to TD children. Children with ADHD+PAE showed mixed hyper- and hypoconnectivity relative to TD. Interpretation: These results are consistent with other MRI findings indicating that ADHD+PAE is neurally distinct from ADHD-PAE; PAE may be associated with broadly reduced connectivity, especially across cerebellar-cerebral cortical systems.

Objective. Continuous, unsupervised monitoring of cognitive brain responses has long been constrained by the demands of laboratory EEG. Whether the P300 event-related potential, an established marker of attention and cognitive processing, can be elicited as an incidental byproduct of genuine gameplay, recorded with a minimal wearable EEG system under unsupervised home conditions, has not been established. Approach. Ten healthy adults played a gamified visual oddball task in which infrequent target stimuli (green gates) were embedded among frequent non-targets (red gates) within a continuous third-person running game. EEG was recorded with a four-channel dry-electrode headband (EEG channels: O1, O2, T3, T4; forehead reference; 250Hz) with self-mounted electrodes in a home setting, without experimenter supervision. Group-level effects were assessed with cluster-based permutation tests and peak-amplitude tests. Single-trial classification used linear discriminant analysis (LDA) with four features per channel (16 total). Additional analyses included a within-subject comparison with a classical visual oddball paradigm using identical hardware, pilot data from a patient with relapsing-remitting multiple sclerosis, within-subject stability across 48 sessions, and pilot recordings with a headphone form factor. Main results. A robust P300-like difference wave emerged on all four channels at the group level (cluster-based permutation tests, p < 0.05), with individual-level detection in 8 of 10 participants (exact binomial p < 0.001). Single-trial LDA yielded a median cross-validated AUC of 0.730 (95% CI 0.672-0.820), with 9 of 10 participants exceeding chance. In a within-subject comparison, waveform morphology was closely preserved relative to a classical laboratory oddball, and classification performance was markedly higher in the game condition (AUC 0.820 versus 0.555). A patient with relapsing-remitting multiple sclerosis produced a clear P300 (AUC 0.853) with latencies within the healthy range. Within-session split-half reliability was high (r > 0.70 on three of four channels), though between-session reliability was near zero across 48 sessions in one participant, with a declining classification trend over time. Pilot recordings with a headphone form factor also yielded a P300-like deflection. Significance. These results demonstrate that the P300 can be elicited as a gameplay-integrated neural readout during genuine gameplay with a wearable, dry-electrode EEG system under unsupervised conditions. Gamification does not compromise P300 elicitation; in the within-subject comparison, it enhanced single-trial discriminability. The findings indicate that gamified, home-based P300 monitoring is achievable with minimal hardware and provide preliminary evidence for applicability in clinical populations, most notably multiple sclerosis, where P300 has established biomarker value but where the logistical burden of laboratory assessment currently precludes longitudinal use.

Glioblastoma progression is spatially heterogeneous, but conventional imaging provides limited information about where subsequent tumor progression is likely to occur. We developed a directional magnetic resonance elastography framework to test whether local post-treatment tumor-brain interface mechanics are associated with later spatial tumor progression. In a secondary analysis of a prospectively acquired glioblastoma cohort, wedge-level viscoelastic instability features were extracted from the first post-treatment MRE scan and related to novel tumor burden on the second post-treatment scan after excluding tumor already present on pretreatment or first post-treatment imaging. Nine patients had longitudinal imaging suitable for spatial comparison; six lesions showed net interval growth and were included in the primary wedge-level directional analysis, while three non-growing lesions were retained for descriptive comparison. In growing lesions, several directional mechanical features were descriptively associated with later novel tumor burden. In cluster-aware models accounting for within-patient dependence among wedges, mean {Delta}tan{delta} ; showed the most consistent association with later wedge-level novel tumor fraction across mixed-effects and generalized estimating equation analyses. Associations were directionally stable across wedge-width sensitivity analyses. These findings provide proof of principle that post-treatment glioblastoma interface mechanics contain spatially resolved information related to where later tumor emergence occurs, supporting further validation of directional MRE as a framework for longitudinal mapping of progression geometry.

Abstract: BACKGROUND: Perivascular spaces (PVS), visible on brain MRI, contribute to the brain clearance system and are associated with age and neurodegenerative disorders. While lower volumes of PVS in the forebrains white matter and basal ganglia have been also demonstrated in preterm-born neonates, the long-term trajectory of PVS after premature birth remains unclear. This study tests for altered PVS volumes in very preterm/very low birthweight-born (VP/VLBW) adults compared to full-term controls and explores potential associations with cognitive performance. METHODS: PVS were assessed on T2-weighted MRI from 97 VP/VLBW and 89 full-term (FT) subjects at 26 years from the prospective, population-based Bavarian Longitudinal Study. PVS volume and count was based on automated nnU-Net-based segmentation. Regional PVS volumes were normalized by corresponding regional parenchyma volumes. Cognitive performance was assessed by the Wechsler Adult Intelligence Scale. MANCOVA was used for PVS group comparisons, Spearman rank correlations for testing PVS relationships with birth variables and cognitive scores. RESULTS: VP/VLBW-born adults showed significantly higher normalized PVS volumes in bilateral basal ganglia (p < 0.001, partial eta-squared = 0.096) and insula-related white matter (p = 0.001, partial eta-squared = 0.057). In the basal ganglia, higher PVS volumes were negatively correlated with gestational age (rho = -0.223, p = 0.030) and positively correlated with the Intensity of Neonatal Treatment Index (rho = 0.222, p = 0.030) in the VP/VLBW group. PVS volume was not associated with IQ scores. CONCLUSION: We demonstrate region-specific alterations of perivascular spaces in VP/VLBW-born adults. Data suggest that prematurity has lasting impact on the PVS.

BackgroundParkinsons disease (PD) is the second most common neurodegenerative disorder. PD currently lacks effective disease-modifying treatments, likely due to its diverse clinical features and underlying neuropathology. The vascular role in PD is emerging, with vascular mechanisms increasingly implicated, yet the literature remains conflicted, motivating large-data analyses with greater statistical power. White matter lesions (WML) are an accepted imaging marker of small vessel disease. Accurate automated WML segmentation techniques are crucial for large-scale studies in PD due to the impracticality of manual segmentation for extensive datasets and to ensure consistency. Evaluation of the optimum approach in PD for large-scale analysis is lacking. This study aimed to evaluate various automated WML segmentation algorithms to determine the most accurate and reliable method, among those selected, for assessing WML for multi-site large data analysis in PD. MethodsWe assessed whole-brain volumetric T1-weighted and FLAIR images from 201 PD patients (mean age, 66.6 {+/-} 7.86 years) and 64 healthy controls (HC; mean age, 66.3 {+/-} 8.67) across three datasets: the Parkinsons Progression Markers Initiative (PPMI), the University of Pennsylvania (UPenn) and the Montreal Neurological Institute Biobank: Clinical Biological Imaging and Genetic Repository (C-BIG). The sample included different scanners, imaging parameters and lesion loads, as would be expected for multi-site data. WML were manually segmented to provide the gold standard, and four freely available automated algorithms were evaluated: FSLs BIANCA, FreeSurfer, SPMs LST-LPA and U-Net-pgs using the performance metrics: Dice score, Hausdorff distance, recall, precision, F1 score, log absolute volume difference (LOGAVD) and intraclass correlation coefficient (ICC). Subgroup analyses were performed based on lesion load and lobar regions. The associations of data from these automated approaches with age, and with Fazekas and Wahlund visual rating scales, were assessed through partial correlation analysis. ResultsU-Net-pgs performed best overall, with the highest Dice score (PD: 0.46 {+/-} 0.21; HC: 0.39 {+/-} 0.21), recall (PD: 0.76 {+/-} 0.25; HC: 0.62 {+/-} 0.31), precision (PD: 0.49 {+/-} 0.25; HC: 0.63 {+/-} 0.27), F1 score (PD: 0.54 {+/-} 0.22; HC: 0.56 {+/-} 0.22) and ICC (PD: 0.965; HC: 0.967) and lowest Hausdorff distance (PD: 8.89 {+/-} 3.96; HC: 6.33 {+/-} 2.91). U-Net-pgs achieved the lowest LOGAVD in the PD group (0.31 {+/-} 0.31) whereas BIANCA-LOO with a threshold of 0.9 was lowest in HC (0.27 {+/-} 0.30). U-Net also showed superior performances in all lesion loads for PD and overall across various brain regions in both PD and HC. ConclusionOverall, U-Net-pgs emerged as the best performing automated method, of those we evaluated, for WML segmentation in PD and HC within a dataset collected with various scanner and image acquisition parameters. U-Net-pgs consistently outperformed other automated approaches across lesion loads and brain regions, for most metrics. The accuracy and reliability of U-Net-pgs make it a promising tool for large-scale analyses, facilitating future research investigating WML in PD.

Background: Individuals with body dysmorphic disorder misperceive defects of their physical appearance. Current evidence suggests that visual processing abnormalities may underlie this core symptom. Separate pre-clinical studies testing perceptual and attentional interventions and non-invasive neuromodulation suggest that these visual processing abnormalities may be modifiable, but their combined effects on neural connectivity and perceptual processing remain unclear. Methods: Thirty-nine unmedicated men and women with body dysmorphic disorder or subclinical body dysmorphic disorder received intermittent theta burst stimulation and continuous theta burst stimulation targeting the lateral parietal cortex combined with a visual attention modification paradigm during functional magnetic resonance imaging, in a crossover design. Dynamic effective connectivity within dorsal and ventral visual stream pathways was calculated, and global visual processing biases were assessed using the face inversion effect before and after stimulation plus attention modification. Results: Intermittent theta burst stimulation resulted in increased connectivity in higher-level dorsal visual stream pathways during naturalistic viewing following attention modification, whereas continuous theta burst stimulation was associated with reduced connectivity in lower-level dorsal pathways and increased connectivity in ventral stream pathways. These changes were accompanied by differential effects on global visual processing, with stimulation type modulating the magnitude of the face inversion effect. Conclusions: Combined neuromodulation and visual attention modification modulate visual system connectivity and perceptual processing in individuals with body dysmorphic disorder symptoms. These findings support a mechanistic link between dorsal-ventral stream dynamics and perceptual biases. Integrating neuromodulation with perceptual retraining may represent a viable approach for targeting core symptoms of distorted appearance perception.

Objective: To systematically evaluate pathway-informed polygenic risk score (PRS) strategies and determine which approaches most effectively leverage biological annotations for risk prediction, using brain amyloid-beta positivity as a case study. Methods: We systematically benchmarked approaches for integrating pathway information into PRS construction to predict brain A{beta} positivity. Using two cohorts, the Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 969) and Australian Imaging, Biomarkers and Lifestyle (AIBL, n = 251), we compared Apolipoprotein E (APOE) genetic risk score (GRS), clumping and thresholding (C+T) PRS, pathway-guided single nucleotide polymorphism (SNP) selection PRS, and pathway-specific PRSs ensembled via machine learning. Pathways were derived from manually curated literature or from pathway databases via Functional Mapping and Annotation (FUMA). Results: In cross-validation on the ADNI cohort, pathway-informed PRS using a narrow-set of pathways to guide SNP selection (PathPRS-SNPLit without APOE locus) significantly outperformed the standard PRS model (median AUC = 0.742, p = 0.006) and the APOE locus model (median AUC = 0.736, p = 5.1 x 10-5) based on the Mann-Whitney U test, achieving a median AUC of 0.763. This model showed enhanced ability to identify subgroups within the 10% lowest- and highest-risk groups compared to the current standard of APOE locus alone (odds ratio = 0.67, 95% CI: 0.56-0.81; and OR = 13.23, 95% CI: 10.23-17.11), highlighting its clinical potential. Using a focused set of literature-curated pathways outperformed using a broader set of database-derived pathways across configurations. When contrasting strategies for aggregating information across pathways, we observed that using pathways to guide selection of SNPs and then building a single PRS performed comparably to building PRS for each pathway and using machine learning (ML) to aggregate these, though the latter enabled pathway-level interpretability. Similar trends were observed in the external AIBL validation dataset. Interpretation: Pathway-informed PRS can meaningfully improve genetic risk enrichment for A{beta} positivity beyond APOE and standard C+T approaches, provided pathway definitions are carefully curated. The choice of pathway source has the strongest impact on predictive performance, with aggregation strategies or ML model choice having far less impact. Our findings highlight the utility of literature-curated, pathway-informed PRSs for A{beta} prediction and offer practical guidance for pathway-informed PRS construction in other polygenic traits.

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.

BACKGROUNDDamage to the early visual cortex after an occipital stroke typically results in the loss of conscious vision in the contralateral hemifield. Nonetheless, extensive perceptual training can restore visual motion discrimination in the blind-field. Here, we assessed, in a cohort study, whether improvements transferred to an untrained Gabor detection task and whether awareness within the blind field increased. We then explored the neural underpinnings of these changes. METHODSEighteen participants (6 female; aged 24-74 years; >6 months post-stroke) completed at least six months of visual rehabilitation in their blind field. Rehabilitation consisted of participants practicing a two-alternative, forced-choice, motion discrimination task using random dot stimuli, five days/week, at home, at one or two non-overlapping, locations in their blind-field. Each participant also completed two in-lab visits: one pre- and one post-training. A subset returned to the lab for a follow-up visit three months later to assess persistence of recovery. In addition to the trained task, an untrained, drifting-Gabor detection task was used to measure transfer of learning and changes in visual awareness at the trained locations. To investigate neural mechanisms underlying generalisation of improvements, participants completed MRI scanning at each lab visit. Magnetic resonance spectroscopy (MRS) was used to quantify GABA and glutamate concentrations in the ipsilesional motion sensitive area, hMT+, and a control voxel in the sensorimotor cortex. Functional MRI was conducted to assess BOLD signal changes in hMT+ and across the rest of the brain during passive viewing of high contrast Gabor stimuli in the blind field. RESULTSParticipants showed significant improvements in motion direction discrimination (trained task) between pre- and post-training in-lab visits, which generalised to improvements in Gabor detection and awareness (untrained task). Reduced GABA and glutamate in ipsilesional hMT+ was linked to improved Gabor detection, but not awareness. Increased BOLD signals in hMT+ and dorsolateral prefrontal cortex also correlated with improved Gabor detection, while awareness changes were linked to higher-level areas associated with visual attention in the contralesional prefrontal cortex (area 46) and inferior parietal lobule. CONCLUSIONSLong-term visual rehabilitation using a global motion discrimination task generalised to enhance both detection and awareness of moving Gabors within the blind field of occipital stroke survivors. Improvements were supported by selective changes in brain regions known to be involved in motion perception and attention respectively, suggesting that a broad network supports recovery, which could be targeted to enhance outcomes.

Background: Prior neuroimaging suggests brain differences between children with attention deficit hyperactivity disorder due to prenatal alcohol exposure (ADHD+PAE) and non-exposed children with ADHD due to other, e.g., familial, causes (ADHD-PAE). There has been interest in regional brain levels of ;gamma-aminobutyric acid (GABA) and glutamate (Glu) measured in vivo with magnetic resonance spectroscopy (MRS) as possible indicators of local inhibitory, respectively, excitatory activity in ADHD. For the first time, we report here a comparison of GABA and Glu in ADHD+PAE vs. ADHD-PAE. Methods: At 3 T, we used J-difference-edited single-voxel MRS to assay GABA and Glu in 28 children with ADHD+PAE, 20 with ADHD-PAE, and 28 typically developing (TD) controls, all aged 8-14 years. MRS was sampled from midline anterior middle cingulate cortex (aMCC), the cognitive cingulate considered functionally relevant to ADHD. Spectra were fit with custom software, including a unique technique for isolating the GABA signal from the confounding macromolecular baseline (MMBL). Results: aMCC GABA was higher in ADHD+PAE and ADHD-PAE than in TD. GABA increased with age in TD, but not in ADHD+PAE or ADHD-PAE. Similar effects were observed for the ratios GABA/Glu and GABA/Glx. For GABA+MMBL (GABA+) these effects were not seen, rather GABA+ and MMBL increased with age for the ADHD+PAE group only. No significant effects were found for Glu or Glx. Conclusions: GABA in the aMCC does not distinguish the two etiologies of ADHD, rather elevated GABA that follows an abnormal developmental appears to be common to both. High GABA may reflect increased inhibition of the aMCC impairing its cognitive functions. GABA+ results in ADHD may not tract reliably with underlying GABA values. Negative results for Glu and Glx should be reexamined at shorter echo-times.

ObjectiveLate adolescence is a critical developmental period that typically precedes psychosis onset, yet the neural correlates of subclinical hallucinatory experiences that may impact psychosis risk are poorly understood. Given evidence from adult psychosis models implicating abnormal "triple network" connectivity among the frontoparietal (FPN), default mode (DMN) and salience/cingulo-opercular (CON) networks, as well as dopaminergic abnormalities, we examined whether hallucinatory experiences in adolescents are associated with altered triple network organization and dopamine-related measures in the midbrain. MethodsWe performed a cross-sectional analysis of 171 community adolescents aged 14-17 who underwent resting-state functional magnetic resonance imaging and neuromelanin-sensitive MRI. Hallucinatory experience severity was measured using the Specific Psychotic Experiences Questionnaire. Resting-state functional connectivity was calculated among a priori DMN, FPN, and CON cortical regions; we examined associations between connectivity, hallucinatory experience severity, within-network connectivity, system segregation, and neuromelanin signal in the ventral tegmental area (VTA). ResultsGreater hallucinatory experience severity was associated with stronger connectivity in a subnetwork composed of CON-DMN and CON-FPN edges. Greater hallucinatory experience severity was also associated with lower global network segregation. VTA neuromelanin signal was not directly associated with hallucinatory experience severity, but greater VTA signal predicted lower connectivity in the hallucination-related subnetwork. Greater VTA neuromelanin signal was also associated with a distinct pattern of stronger connectivity within DMN midline regions. ConclusionsThese findings implicate altered triple network organization in hallucinatory experiences during late adolescence and suggest that dopamine-related midbrain signal may reflect broader developmental variation in cortical network organization rather than symptom severity directly. Plain Language SummaryHallucinatory experiences during adolescence may signal increased risk for later psychotic disorders, but their brain basis is unclear. We studied 171 adolescents aged 14-17 using resting-state fMRI to measure brain network activity and neuromelanin-sensitive MRI to estimate dopamine-related midbrain signal. More severe hallucinatory experiences were linked to abnormal communication among three brain networks often implicated in psychosis. Dopamine-related signal was not directly related to hallucination severity but was associated with developmentally relevant network organization. Overall, this work serves to improve our understanding of the risk factors that may contribute to psychosis conversion in adulthood.