NeuroImage: Clinical

Brain development follows a precisely regulated biological timetable, with defined periods of vulnerability increasingly recognized in congenital disorders affecting early brain development. This biological timing can be captured by the emerging concept of brain age, a measure of brain maturation, enabling the detection of deviation from normative developmental trajectories. Clinical conditions affect the degree of brain development during this critical period, including preterm birth and congenital heart disease (CHD). We developed a deep learning-based brain age estimation framework across the fetal-neonatal period (21-44 gestational weeks) to quantify neurodevelopment from structural MRI. Using 1056 scans from six datasets acquired at three centers, Zurich, Shanghai, and the Developing Human Connectome Project, we trained models on normative fetal and neonatal MRI data. Both structural MRI-based and segmentation-derived cortical morphology-based models were implemented to assess representation effects and cross-center generalisability. The framework was applied to two clinically relevant conditions, preterm birth and CHD, to estimate the brain age gap (BAG), defined as the difference between predicted brain age and chronological age. In preterm neonates scanned at term-equivalent age (n=90, 37-44 weeks), BAG was progressively more negative with lower gestational age at birth. Neonates born before 28 weeks showed delays of -0.7 to -0.8 weeks relative to term-born controls. In CHD (n=50, 22-34 weeks), fetal brain age did not differ from center-matched controls and no association with cardiac defect severity was observed. After birth, neonates with CHD (n=110, 37-44 weeks) showed significant (p<0.05) negative BAGs before surgery (-1.3 to -1.8 weeks) and BAGs increased significantly (p<0.05) after surgery (up to -3 weeks in center-specific analyses), indicating a delay in brain maturation from postnatal stage, but not in prenatal stage in CHD patients. These patterns were found across both structural MRI-based models and cortical morphology-based models, despite the need for cross-center calibration to minimize systematic bias. Voxel-based morphometry showed that a larger BAG was associated with regional contraction in deep frontal and peri-Rolandic white matter in preterm neonates, and perioperative spatial shifts in neonates with CHD. Saliency maps converged on deep white matter and periventricular regions, highlighting a potential link between BAG and delayed maturation of rapidly developing projection pathways. These findings may indicate neurodevelopmental delays in preterm birth and a postnatally emerging maturational gap in CHD that increases following cardiac intervention. Despite limited generalisability of our methods, these results support a continuous fetal-neonatal brain age metric as a sensitive marker of global neurological maturational timing.

Post-stroke cognitive recovery is difficult to predict using focal lesion characteristics alone. The brains capacity to maintain cognitive function depends also on structural integrity of the whole brain. One way to measure brain health is through the severity of cerebral small vessel disease (CSVD) markers, which reflect aging-related pathologies that erode structural integrity. Here, we propose a composite measure of CSVD (cCSVD) integrating three independently validated biomarkers automatically quantified using T1-weighted MRIs: white matter hyperintensity volume (WMH; representing vascular injury), perivascular space count (PVS; putative glymphatic clearance), and brain-predicted age difference (brain-PAD; structural atrophy). We hypothesize that cCSVD, which captures the shared variance across these CSVD biomarkers, will be a robust indicator of whole-brain structural integrity and predict cognitive changes 3 months after stroke. We analyzed 65 early subacute stroke survivors with assessments within 21 days (baseline) and at 90 days (follow-up) post-stroke. WMH volume, PVS count, and brain-PAD were quantified from baseline T1-weighted MRIs, and then residualized for age, sex, days since stroke, and intracranial volume. Principal component analysis (PCA) of the residualized biomarkers was used to derive cCSVD. Beta regression with stability selection using LASSO was used to model three outcomes: baseline Montreal Cognitive Assessment (MoCA) scores, follow-up MoCA scores, and longitudinal change (follow-up score adjusted for baseline score). Logistic regression was used to test if baseline cCSVD predicted improvement in those with baseline cognitive impairment (MoCA < 26). The PCA revealed that the first principal component (PC1) explained 43.1% of the total variance among WMH volume, PVS count, and brain-PAD. The three biomarkers contributed nearly equally to PC1, which was subsequently used as the baseline cCSVD score. Lower baseline cCSVD was significantly associated with better MoCA scores at follow-up ({beta} = -0.19, p = 0.009), even after adjusting for baseline MoCA ({beta} = -0.12, p = 0.042), and, importantly, outperformed all individual biomarkers. Furthermore, lower cCSVD at baseline significantly increased the likelihood of improving to cognitively unimpaired status at three months (OR = 0.34, p = 0.036), independent of age and education. The composite CSVD captures the additive impact of vascular injury, glymphatic dysfunction, and structural atrophy on recovery in a way that individual measures do not. cCSVD accounts for shared variance across these domains, reflecting a patients latent capacity for cognitive recovery, where relative integrity in one CSVD domain may mitigate effects of another. This automated, T1-based framework offers a scalable tool for predicting post-stroke recovery.

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.

BackgroundSubcortical stroke triggers heterogeneous cortical reorganization. We use neuroanatomical normative modeling to characterize individual differences of post-stroke cortical plasticity and resolve the ambiguity between dynamic reorganization and static traits. MethodsThis retrospective study included patients with acute subcortical stroke who underwent five longitudinal MRI scans and Fugl-Meyer (FM) motor assessments over 6 months. Individualized centile deviation scores for cortical thickness were computed against a normative model. Patients were stratified using spectral clustering based on baseline (<7 days) neuroanatomical profiles. Longitudinal changes in cortical thickness and their association with motor recovery were analyzed with linear mixed-effects models. We also stratified patients using raw thickness to evaluate the discriminative utility of normative model. ResultsA total of 65 patients (mean age, 52.7 {+/-} 10.4 [SD]; 47 men) and 26 matched healthy controls (mean age, 52.7 {+/-} 8.1 [SD]; 15 men) were evaluated. At baseline, the patient cohort exhibited widespread cortical thinning. Clustering revealed two distinct subgroups with similar baseline demographics and FM: Group L (n=50), with lower-than-normal thickness, and Group H (n=15), with static higher-than-normal thickness. Group L demonstrated a larger dynamic increase in contralesional cortical thickness than Group H ({beta}=0.033, 95% CI 0.0029-0.063, p=0.03), which paralleled a faster rate of FM recovery ({beta}=0.66, 95% CI 0.12-1.20, p=0.02). Furthermore, higher FM scores were associated with rising cortical thickness in Group L ({beta}=0.21, 95% CI 0.0029-0.41, p=0.03), whereas FM scores tended to decrease with higher thickness in Group H ({beta}=-0.10, 95% CI -0.097-0.16, p=0.47). Conversely, the two subgroups identified using raw thickness demonstrated no evidence of difference in the rate of recovery ({beta}=0.20, 95% CI -0.63-0.23, p=0.37). ConclusionsActive structural thickening, rather than static cortical reserve, is the important driver of motor recovery. Normative modeling distinguishes heterogeneity of stroke, providing a framework for predicting recovery potential.

An inflammatory subtype of major depressive disorder (MDD) is associated with treatment resistance pointing to an unmet need for adjunctive treatments. To evaluate treatment-related changes in brain inflammation, diffusion basis spectrum imaging (DBSI) is a promising non-radiation-based technique for longitudinal designs which has been verified with histopathology. However, its use as an endpoint in clinical trials is dependent on its individual-level reliability to robustly track changes. Here, we evaluated two DBSI runs acquired in 94 participants (including 43 participants with MDD) on the same day about 1.5 h apart to assess short-term test-retest reliability. Fiber fraction (reflecting axonal/dendrite density) and hindered fraction (reflecting edema) showed moderate to high test-retest reliability in both gray and white matter regions, whereas restricted fraction (reflecting cellularity) showed lower values in gray and white matter. Group-level reliability was similar in participants with MDD, except for lower reliability of hindered fraction in gray matter. Re-identification rates of individual brain maps were higher using voxel-level white matter signatures compared to gray matter regions of interest (ROIs) (p<.001). Crucially, participants with MDD showed reduced fiber fraction (tmax=4.68, k=38) and elevated hindered fraction (tmax=4.74, k=32) in the cingulate bundle, consistent with increased white matter inflammation, while gray matter ROI-based classification failed to identify cases. We conclude that DBSI is a promising technique to track inflammatory signatures in MDD, particularly in white matter tracts. Since several frontal and subcortical gray matter ROIs showed insufficient reliability, their assessment would require multiple DBSI runs to provide robust estimates.

ObjectiveTo compare intrathalamic morphometry in infants born preterm, with congenital heart disease (CHD) and typical controls and investigate associations with neurodevelopmental outcomes. Methods592 infants underwent T2-weighted brain MRI: 107 CHD [gestational age at birth (GA) [&ge;]37.00 weeks], 126 preterm (GA 23.00-36.86), 359 controls (GA [&ge;]37.00). We used data-driven structural covariance analysis to derive 8 components of coordinated expansion and contraction within the thalamus. Permutation testing was used to test associations between intrathalamic morphometry and group (control, CHD, preterm birth <32 weeks GA), GA in infants born preterm and controls, cerebral oxygen delivery (CDO2) in infants with CHD, and neurodevelopmental outcomes at 18-24 months. ResultsPreterm infants born <32 weeks GA differed from infants with CHD and controls in 6 components encompassing most of the thalamus. Infants with CHD differed from controls in 2 components containing medial, ventricle-bordering and some anterior and ventrolateral thalamic areas. GA was associated with 7 components covering most of the thalamus, excepting the left posterior thalamus. CDO2 was not associated with intrathalamic morphometry. Right posterior thalamus morphometry was associated with motor scores in preterm infants born <32 weeks, but not in controls or infants with CHD. InterpretationPreterm infants born <32.00 weeks showed widespread morphometric changes across the thalamus, with alterations in the right posterior thalamus associated with motor outcomes at 18 months. Thalamic alterations in CHD were less widespread, confined to medial, ventrolateral, and ventricle-bordering tissues, which were not related to CDO2. Together, these findings suggest distinct thalamic phenotypes in prematurity and CHD.

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.

BackgroundDeep brain stimulation (DBS) of the anterior limb of the internal capsule (ALIC) is an effective treatment for severe obsessive-compulsive disorder (OCD). Identifying brain readouts of positive response may guide further DBS optimization. MethodsWe measured local field potential (LFP) changes from bilateral DBS leads in 10 OCD patients implanted at a uniform tractographic network target derived from prior DBS responders. We consistently stimulated dorsal lead contacts in the ALIC white matter, while recording LFP from the ventral lead contacts in grey matter of the anterior globus pallidus externus (GPe), a key node in the basal ganglia non-motor indirect pathway. ResultsAfter six months of DBS, OCD symptoms decreased on average by 40% across subjects, along with a significant decrease in alpha activity across both hemispheres. Only one patient did not have an improvement of symptoms, and this was also the only patient to never exhibit an alpha decrease in either hemisphere. ConclusionsOur findings suggest that therapeutic ALIC DBS coincides with a stable decrease in limbic-cognitive GPe alpha power, which should be further investigated as a potential biomarker of sustained response.

BackgroundStudies applying machine learning to obsessive-compulsive disorder (OCD) typically report accuracy in homogeneous samples but rarely assess model reliability, generalizability, and interpretability needed for clinical use. MethodsWe applied a transformer-based deep learning model, the Multi-Band Brain Net, to the ENIGMA-OCD cohort - the largest available resting-state functional magnetic resonance imaging (rs-fMRI) dataset in OCD with 1,706 participants (869 cases with OCD, 837 controls) across 23 sites worldwide. We evaluated model reliability by calculating calibration - the models ability to "know what it doesnt know". We assessed generalizability using leave-one-site-out validation to test performance on unseen sites with different scanners, acquisition protocols, and patient populations. Finally, we examined interpretability by analyzing model attention weights to identify the neural connectivity patterns that influence model predictions. ResultsThe model achieved modest but competitive classification performance (AUROC = .653 {+/-} .039). Crucially, while large-scale pretraining on the UK Biobank (N = 40,783) did not boost accuracy, it significantly enhanced model calibration by reducing overconfident predictions. Leave-one-site-out validation showed a generalization gap across sites (AUROC = .427-.819). Pretraining did not close this gap but removed scanner manufacturer bias. Finally, attention-based mapping identified biologically plausible patterns of widespread hypoconnectivity in OCD relative to healthy controls, particularly in low-frequency bands involving the default mode, salience, and somatomotor networks. These findings aligned with known OCD neurobiology. ConclusionsThis study provides a framework for developing more reliable and trustworthy clinical artificial intelligence for OCD.

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.

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

BackgroundDiffusion MRI (dMRI) is widely used to assess microstructural abnormalities in multiple sclerosis (MS), yet conventional diffusion tensor imaging (DTI) is limited by single b-shell acquisitions and reduced pathological specificity. Higher-order diffusion models enabled by multi-b-shell data may provide complementary information, but their relative performance across tissue classes remains unclear. PurposeTo evaluate lesion-resolved microstructural alterations across MS tissue classes using multiple diffusion models and to assess the impact of diffusion acquisition strategy on discriminative performance. MethodsMulti-shell dMRI was acquired in 57 treatment-naive patients with early MS and 17 healthy controls. Five diffusion models were evaluated (DTI, DKI, NODDI, SMT, and SMI). 3602 manually delineated ROIs, including chronic black holes, T2 lesions, lesion-matched normal-appearing white matter (NAWM), and normal white matter (NWM), were analyzed. Microstructural differences were assessed using linear mixed-effects models, and discriminative performance was evaluated using ROC analysis across single-shell, multi-shell, and joint modeling strategies. Feature selection was performed using LASSO regression. ResultsAcross all models, lesions exhibited coherent microstructural abnormalities relative to normal white matter, while NAWM showed concordant but more subtle alterations. Lesion-normal tissue contrasts demonstrated strong discriminative performance, whereas classification of NAWM versus NWM and lesion subtypes remained limited, reflecting substantial biological overlap. Two b-shell and joint modeling approaches consistently outperformed single-shell analyses, yielding the highest AUCs. LASSO identified a small set of biologically meaningful diffusion features driving tissue discrimination. ConclusionMulti-b-shell diffusion MRI enables more robust and informative characterization of MS-related white matter pathology than single-shell acquisitions alone, supporting multi-model, multi-b-shell strategies for lesion-resolved assessment in MS.

Stroke is the third leading cause of death and disability combined worldwide and often results in hemiparesis. Functional magnetic resonance imaging (fMRI) is a non-invasive technique used to investigate changes in brain activations during tasks aimed at restoring the lost motor function. Participants with chronic stroke and residual hemiparesis in the upper extremity were recruited for a clinical intervention that included neurofeedback training and fMRI sessions with motor-execution and motor-imagery tasks. The present study provides a baseline characterization of brain activations prior to neurofeedback training. Since lesion site and volume varied across participants, two fMRI preprocessing pipelines were applied. The first one was used for twelve participants with lesions restricted to a single hemisphere and for one participant with small secondary lesions in the contralesional hemisphere, whereas the second one was used for two participants with large bilateral lesions. These were followed by quality control measures and statistical analysis. First-level (i.e., single-participant) analysis returned the strongest and most extensive activation across participants during motor-execution tasks, with clusters identified in the ipsilesional parietal lobe, bilateral occipital lobes, and cerebellum after Family-Wise Error correction. Second-level (i.e., group-level) analysis involving participants who underwent the first fMRI preprocessing pipeline revealed a significant cluster in the cerebellum after False Discovery Rate correction. These results are consistent with previous studies involving participants with chronic stroke performing motor-tasks. Cerebellar recruitment observed consistently across participants could reflect compensatory mechanisms supporting motor control after stroke.

Background: Major depressive disorder (MDD) is associated with altered brain structure and evidence of accelerated brain aging. However, previous studies have been limited by clinical samples with mixed medication status and multiple mood states, modest sample sizes, small percentage of MDD individuals older than 65 years of age, and/or reliance on summary-level data. Methods: Harmonized T1-weighted MRI from MDD (n = 645), all medication-free and in a current depressive episode, and matched healthy controls (n = 645), segmented into 145 regional volumes, from 11 sites in COORDINATE-MDD consortium. Brain age gap (BAG) was estimated using gradient boosting regression with nested cross-validation. Group differences in BAG (and age-corrected BAG [cBAG]) were examined across age strata. Regional contributions were evaluated using Shapley Additive exPlanations. Results: MDD was associated with significantly elevated cBAG compared with healthy controls (mean difference + 2.01 years). Age-stratified analyses showed no differences before mid-30s, with progressively larger gaps thereafter, reaching +6.85 years in MDD aged 55 and older. cBAG differed across neuroanatomical phenotypes associated with differential antidepressant response, cognitive impairment, increased adverse life events, increased self-harm and suicide attempts, and a pro-atherogenic metabolic profile. Key contributing regions included lateral and medial prefrontal regions, middle temporal gyrus, putamen, supplementary motor cortex, central operculum, and cerebellum. Conclusions: Accelerated structural brain aging in MDD is age-dependent and is most pronounced in a neuroanatomical phenotype associated with worse key clinical outcomes. The findings support neuroprogression models of MDD while demonstrating that cBAG is not a uniform feature of MDD and seem to be more strongly expressed in a specifically clinically vulnerable disease phenotype.

Stroke is a leading cause of mortality and morbidity worldwide. MRI-visible perivascular spaces (PVS) are an emerging marker of cerebral small vessel disease and may have prognostic value in stroke. We investigated longitudinal changes in PVS volume and cluster count following ischaemic stroke. PVS volumes and cluster counts were compared between stroke survivors (n=124; 39 women; median [Q1, Q3] age=70 [62, 76] years) and healthy controls (n=39; 15 women; median age=69 [66, 72.5] years). MRI scans were acquired at 3 months, 12 months, and 36 months post-stroke. PVS were automatically segmented from T1-weighted MRI using a validated deep learning algorithm (nnU-Net). Generalised linear mixed-effects models were used to assess group differences and longitudinal changes in PVS, adjusting for baseline age, sex, total intracranial volume, and BMI. At the 12-month timepoint, no significant differences in PVS metrics were observed between stroke and control groups. However, at the 36-month timepoint we observed a significant brain-wide reduction in PVS volume (exp({beta})=0.93, 95%CI [0.87, 1], p=0.035) and cluster count (exp({beta})=0.92, 95%CI [0.85, 0.99], p=0.003) in the stroke group compared to control. Regionally, by 36 months, stroke patients demonstrated significant PVS reductions relative to controls in the frontal (PVS volume: exp({beta})=0.93, 95%CI [0.82, 0.99], p=0.032; PVS cluster counts: exp({beta})=0.91, 95%CI [0.83, 1], p=0.037) and parietal lobes (PVS volume: exp({beta})=0.93, 95%CI [0.85, 1.01], p=0.10; PVS cluster counts: exp({beta})=0.84, 95%CI [0.68, 1.08], p<0.001). These findings suggest that ischaemic stroke is associated with dynamic and regional changes in PVS volume and counts.

Objective: While ADHD symptoms often decline from childhood into adulthood, the underlying neurobiological mechanisms, such as altered brain maturation or neural reorganization, remain incompletely understood. This study investigated how grey matter development relates to ADHD symptom trajectories into adulthood. Method: We analyzed data of individuals with ADHD and controls from the longitudinal Dutch NeuroIMAGE cohort, utilizing dimensional ADHD symptom scores (Conners Parent Rating Scale) from three waves and T1-weighted structural MRI scans from the final two waves. Using General Linear Models with permutation-based inference, we examined: 1) cross-sectional associations between ADHD symptoms and vertex-wise cortical thickness and surface area, and subcortical volumes at Wave 1 (n = 765, mean age = 16.95 years); and 2) longitudinal associations between symptom progression and brain morphometric changes (Wave 0 to 1: n = 644, mean age = 11.55-17.24 years; Wave 1 to 2: n = 149, mean age = 16.45-20.11 years). Results: Cross-sectionally, at Wave 1, more ADHD symptoms were related to widespread reductions in surface area, most prominently in the frontal cortex, and smaller volumes of the cerebellum, amygdala, and hippocampus. Longitudinally, symptom improvement from Wave 1 to Wave 2 was associated with stronger reductions in surface area, particularly in prefrontal and occipital regions, and with more pronounced cortical thinning across multiple brain regions. Conclusion: These findings suggest an association between symptom trajectories and structural brain changes, indicating that clinical improvement in ADHD behaviors might coincide with ongoing neural refinement during the transition to adulthood.