NeuroImage: Clinical

Premature birth occurs during a phase of intense brain maturation, making white matter (WM) particularly vulnerable to injury. Beyond major lesions, subtle and widespread microstructural alterations also contribute to later neurodevelopmental impairments. We aimed to characterize the impact of key clinical risk factors on global and tract-specific WM microstructure at term-equivalent age (TEA), using 3T-diffusion-MRI data of 111 infants born before 33 weeks of gestation. We developed a lesion-robust tractography pipeline suitable for heterogeneous neonatal anatomy and extracted diffusion tensor imaging (DTI) metrics in sensorimotor tracts: corticospinal tract (CST), superior thalamic radiation (STR), frontal aslant tract (FAT), forceps minor (FMI) and middle cerebellar peduncle (MCP). Associations with risk factors were assessed accounting for age at MRI or global WM microstructure. Tractography succeeded in most infants despite marked anatomical variability and/or overt lesions. Being a male, small for gestational age (SGA) at birth, encountering sepsis and having severe Kidokoro radiological score for WM were associated with altered global WM metrics. At the tract level, CST and STR showed the strongest susceptibility to SGA, prolonged parenteral nutrition, and Kidokoro score. In contrast, for FAT, associations with extreme prematurity, SGA and invasive ventilation were contrary to the expected direction, after adjustment for global WM microstructure. Findings were partially replicated in infants without macroscopic abnormalities, supporting the presence of WM dysmaturation even in the absence of visible injury. DTI metrics thus provide tract-specific biomarkers of early WM microstructure in preterm infants, which are sensitive to risk factors and could inform targeted prevention and intervention.

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 brain's 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 patient's 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.

BackgroundRecovery from chronic post-stroke aphasia is highly heterogeneous and shaped by lesion characteristics, brain integrity, and systemic health. Traditional group-level models struggle to capture this multidimensional, dynamic variability. Digital twin approaches - patient-specific, continually updating models - may enable individualized prediction and counterfactual evaluation of modifiable risk factors. Therefore, the aim was to develop and validate a proof-of-concept digital twin that predicts individual naming outcomes during language treatment and quantifies the estimated impact of modifiable health factors on naming. This study represents the first application of digital twin modeling to aphasia recovery, and we hypothesize that this could constitute a critical first step toward dynamically adaptive, personalized models for aphasia rehabilitation. MethodsWe analyzed longitudinal data from 106 chronic stroke survivors with aphasia enrolled in the POLAR randomized clinical trial. For each participant we combined baseline demographic/health variables (age, sex, education, days post-stroke, hypertension, diabetes, BMI), lesion load in left-hemisphere language ROIs (JHU atlas), ROI-level white-matter microstructure (FA), and resting-state functional connectivity restricted to language regions. A continual-learning linear model (River framework; Adam optimizer) was pretrained on baseline data and updated across timepoints. Model performance was assessed by R{superscript 2} at the final timepoint. Counterfactual simulations systematically altered hypertension, diabetes, and BMI to estimate isolated and combined effects on predicted Philadelphia Naming Test (PNT) scores. ResultsThe digital twin predicted final PNT scores with R{superscript 2} = 0.5848 (explaining approximately 58% of variance). The largest contributors were prior naming performance, age, lesion load in language regions, and white-matter integrity in temporal regions (notably right MTG and STG pole). Counterfactual results estimated modest but consistent effects of health factors, with them collectively accounting for approximately 25% of the variance in treatment gains. The average change in PNT score with counterfactual changes was 7.92 (SD = 16.11). Therefore, diabetic status explained 2% of the variance in treatment gains, hypertensive status explained 4.75%, and increasing BMI explained 18.5%. ConclusionsThis study demonstrate the feasibility and clinical potential of applying a digital twin framework to chronic post-stroke aphasia, with the model successfully predicting more than half the variance in naming performance during language treatment. Through counterfactual simulation, we demonstrated that modifiable health factors exert measurable, bidirectional influences on predicted treatment outcomes, underscoring the role of systemic health in shaping language recovery. Although the individual effects of these factors were modest in magnitude, their cumulative influence on treatment gains illustrates how multiple small biological contributors can add up to shape meaningful differences in language outcomes. More broadly, these findings illustrate the potential value of digital twin models for aphasia treatment, particularly as a tool to integrate diverse biological factors and generate individualized, dynamically updated predictions. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/26345022v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@138aae1org.highwire.dtl.DTLVardef@15ab619org.highwire.dtl.DTLVardef@695958org.highwire.dtl.DTLVardef@68c278_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundStructural white matter (WM) alterations are recognized in Autism Spectrum Disorder (ASD), yet the functional connectivity (FC) of WM networks and its clinical significance remain largely under-explored. MethodsThis study aimed to investigate aberrant FC patterns within intra-WM (WM-WM) and WM-gray matter (WM-GM) networks in a large ASD cohort. Resting-state fMRI data from 272 ASD individuals and 368 typical controls (TC) from the ABIDE-II dataset were analyzed. We constructed WM-WM and WM-GM FC networks using Pearson correlations between atlas-defined regions, applied ComBat harmonization, and employed Network-Based Statistics (NBS) to identify group differences. Associations with clinical symptoms were assessed using Social Responsiveness Scale (SRS) scores, and a CatBoost algorithm was used for diagnostic classification based on connectivity features. ResultsNBS analyses revealed significantly increased connectivity in ASD for 116 WM-WM pairs and 58 WM-GM pairs (P<0.05, FWER-corrected). Critically, the strength of these aberrant WM-WM functional connections exhibited a significant negative correlation with SRS total scores (r = -0.22, P < 0.001), whereas WM-GM connectivity showed no such significant association. The hybrid CatBoost classifier, integrating both WM-WM and WM-GM features, achieved moderate diagnostic discrimination (AUC = 0.669 {+/-} 0.040). ConclusionThese results offer novel insights into the aberrant functional architecture of WM-related networks in ASD, particularly linking intra-WM dysconnectivity to symptom severity, thereby enhancing our understanding of the neural substrates underlying social-communicative deficits.

BackgroundClinical trajectories in functional neurological disorder (FND) are variable, and the mechanisms underlying this heterogeneity remain poorly understood. ObjectiveThis longitudinal study examined resting-state functional connectivity predictors and mechanisms of symptom change in FND. MethodsThirty-two adults with FND (motor and/or seizure phenotypes) completed baseline questionnaires and a functional MRI (fMRI) session, followed by naturalistic treatment for 6.8{+/-}0.8 months. All participants completed follow-up questionnaires; 28 individuals completed a follow-up fMRI. At each timepoint, three graph-theory network metrics of functional connectivity were computed: weighted-degree (centrality), integration (between-network connectivity), and segregation (within-network connectivity). Analyses adjusted for age, sex, anti-depressants, head motion, time between sessions, and baseline score-of-interest, with cluster-wise correction. Results were contextualized against 50 age-, sex-, and head motion-matched healthy controls (HCs). ResultsBased on patient-reported Clinical Global Impression of Improvement, 59.4% improved, 31.3% were unchanged, and 9.3% worsened. Psychometric scores of core FND symptoms and non-core physical symptoms showed variable trajectories, with no group-level changes. Greater improvement in core FND symptoms was associated with higher baseline between-network integrated connectivity and reduced integration longitudinally within salience, frontoparietal, and default mode network regions. Right anterior insula integration emerged as a prognostic marker and mechanistic site of reorganization, with the most improved participants showing elevated baseline integration compared to HCs. Increased baseline within-network segregated connectivity in dorsal attention network regions correlated with non-core physical symptom improvement. Findings remained significant adjusting for FND phenotype. ConclusionsThis study identified large-scale network interactions as potential prognostic and mechanistically-relevant sites of reorganization related to symptom change in FND.

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.

Background: Deep 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. Methods: We 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. Results: After 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. Conclusions: Our 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.

BackgroundWhite matter hyperintensities (WMH) are widely used to assess cerebral small vessel disease (SVD) but reflect late-stage injury. Diffusion MRI based biomarkers have been proposed to capture earlier SVD-related microstructural damage but their temporal progression relative to WMH and the risk factors associated with this progression have not been explored. MethodsWe analyzed longitudinal neuroimaging data from 2,047 participants from a population-based cohort study (aged 49-101 years, 47% female). Using multi-output nonlinear mixed-effects models, we characterized the temporal progression of WMH and four diffusion MRI based biomarkers: fractional anisotropy of the genu of the corpus callosum (Genu-FA), peak width of skeletonized mean diffusivity (PSMD), free water (FW), and Arteriolosclerosis-score (ARTS). Models incorporated participant-specific time shifts, correlations between biomarkers, and effects of risk factors (sex, education, APOE {varepsilon}4 status, and cardiometabolic conditions). ResultsARTS, Genu-FA, FW, and PSMD became abnormal in 50% of the study population 16, 12, 10, and 7 years before WMH, respectively. Global markers (ARTS, FW, PSMD, WMH) were correlated, indicating shared substrates of widespread white matter injury. Genu-FA, a vascular risk microstructural injury biomarker, was weakly coupled with WMH and had an earlier but more linear worsening across adulthood. Cardiometabolic conditions predicted earlier worsening of all biomarkers. Females showed earlier WMH, Genu-FA, and ARTS abnormalities whereas males exhibited earlier PSMD and FW abnormalities. ConclusionsDiffusion MRI based biomarkers capture microstructural injury at least a decade before appearance of WMH, revealing a prolonged phase of early SVD and highlighting their potential for SVD prevention.

In-scanner head motion is a recognized source of bias in structural magnetic resonance imaging (sMRI), yet it remains under-addressed in psychiatric neuroimaging where structural difference in patient populations are considered foundational. We examined motion-related bias in grey matter volume estimates across eight independent cohorts comprising 9,664 individuals, including 8,979 neurotypical controls (NC), 497 patients with schizophrenia (SCZ), and 188 patients with bipolar disorder (BD). Motion estimates were derived from multiple fMRI scans acquired within the same scanning session and summarized using principal component analysis. In NC, motion accounted for 1-6% of regional grey matter variance, a magnitude comparable to reported psychiatric case-control effect sizes. Adjusting for motion attenuated SCZ-NC group differences, reducing effect sizes in 85% of brain regions and yielding 5% fewer significant ROIs (pFDR<0.05). In BD, motion correction reduced effect sizes in 97% of regions, with a 24% reduction in significant ROIs. Cross-diagnostic spatial patterns were significantly correlated (r=0.63, p=3x10-{superscript 1}3), explaining a sizable portion of SCZ-BD commonalities. Critically, a falsification analysis in UK Biobank (N=5,123) showed that stratifying NC by motion alone produced grey matter differences accounting for 45-62% of SCZ case-control effect magnitude, underscoring how difficult it is to interpret SCZ-like morphometric differences as tissue properties rather than as motion-driven patterns. These findings urge caution in interpretations of sMRIdifferences in patient-control comparisons and use of systematic fMRI based motion control as standard practice in sMRI analyses.

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.

BackgroundAphasia commonly occurs after left-hemispheric stroke, yet substantial inter-individual variability in language outcomes remains insufficiently explained by established clinical systems neuroscience concepts. Emerging evidence suggests that the integrity of specific neurotransmitter systems may influence functional outcomes after stroke. This study examined whether the damage to neurotransmitter-related structural networks is associated with post-stroke language impairment. MethodsData of 270 patients with left-hemispheric stroke from two openly available cohorts were analyzed: the acute Washington Stroke Cohort and the chronic Aphasia Recovery Cohort. Neurotransmitter-related network damage was quantified by embedding individual stroke lesion masks into normative connectomes weighted by PET-derived density maps of 16 neurotransmitter receptors and transporters. Partial least squares (PLS) regression identified informative predictors of language functioning, followed by linear regression analyses adjusted for age, sex, lesion volume, and time post-stroke. ResultsAcross both cohorts, PLS analyses converged on a neurochemical profile in which damage to networks related to serotonergic (5-HT1a, 5-HT2a) and dopaminergic (D1) receptor distributions showed the strongest associations with poorer language performance. Damage to the 5-HT1a and D1-related networks remained significant in fully adjusted models, leading to substantially improved model fit. ConclusionThe disruption of large-scale serotonergic (5-HT1a) and dopaminergic (D1) brain networks is associated with language impairment in acute and chronic stroke. Neurotransmitter-related network damage explained additional variability in language performance beyond clinical variables and lesion burden. This work adds a neurochemically informed network perspective to aphasia research and may pave the way for future biological patient stratification to support targeted rehabilitation strategies, such as pharmacological interventions.

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.

Distinguishing atypical parkinsonian disorders (APS) from Parkinsons disease (PD) remains challenging due to overlapping clinical features, yet accurate differentiation is critical for prognosis and treatment. Here, we employed multi-model diffusion MRI (dMRI) analysis to characterize microstructural alterations across corticobasal syndrome (CBS), progressive supranuclear palsy-Richardson syndrome (PSP-RS) and PD, with the aim of identifying which dMRI model provides optimum differentiation. We analyzed 25 CBS, 42 PSP-RS, and 21 PD participants compared to 35 age and sex-matched controls. Using a clinically feasible 3-shell high angular resolution diffusion imaging (HARDI) protocol, we applied 11 metrics from five complementary dMRI models--diffusion tensor imaging (DTI), free-water-eliminated model of DTI (FWE), neurite orientation dispersion and density imaging (NODDI), tissue-weighted NODDI, and Fixel Density (FD) in fixel-based analysis (FBA) --to comprehensively assess regional white and gray matter integrity. Group differentiation was assessed using Cohens d effect sizes and spearman correlations were assessed between dMRI metrics and clinical scales. Distinct microstructural signatures were observed across disorders and the sensitivity of the dMRI models differed. In group contrasts, DTI and NODDI-derived metrics consistently captured the strongest effects in midbrain and peduncular pathways for PSP-RS, whereas precentral and corticospinal alterations in CBS were most prominent using NODDI and FBA measures. Free-water-corrected metrics showed attenuated group differences. Across clinical-diffusion analyses, NODDI metrics exhibited the most robust associations with disease severity, while DTI and FWE measures detected more limited, regionally constrained effects. Together, these findings highlight complementary yet distinct sensitivities of tensor, free-water, multi-compartment, and fixel-based models to APS-related neurodegeneration.

ImportancePrenatal cannabis exposure is increasing in prevalence, yet its associations with early brain development--particularly how the timing and frequency of exposure across gestation relate to neonatal brain structure--remain insufficiently understood. Clarifying these associations is essential for informing early risk identification and guiding perinatal care. ObjectiveTo examine associations between patterns of maternal prenatal cannabis exposure, including exposure presence, gestational timing, and frequency of exposure, and neonatal brain structure and microstructure during the first month of life. Design, Setting, and ParticipantsThis cohort study included 1,782 mother-infant dyads (221 with PCE) from the HEALthy Brain and Child Development Study. Mother-reported prenatal cannabis exposure was assessed using the validated Timeline Follow-back method. Infants underwent natural-sleep magnetic resonance imaging, including T2-weighted structural imaging and diffusion imaging, within the first month of life. Main Outcomes and MeasuresAssociations between prenatal cannabis exposure and regional T2-weighted volumes and diffusion white matter microstructure metrics examined (1) exposure presence, (2) gestational timing of exposure, and (3) frequency of exposure within exposed infants. ResultsAny prenatal cannabis exposure was associated with brain volume differences in cerebellar and subcortical limbic regions, including smaller amygdala, thalamic, and cerebellar vermis volumes and larger caudate, hippocampal, and cerebellar cortex volumes. Timing-specific analyses revealed divergent patterns: first trimester exposure was associated with smaller volumes in select regions, whereas exposure that continued into the third trimester was associated with larger volumes in overlapping structures, with additional subcortical volumetric differences observed. White matter microstructure alterations were observed only among infants with exposure that continued into the third trimester. Within the exposed subgroup, higher frequency of cannabis exposure was associated with larger cerebral white matter volumes and white matter microstructural differences in white matter regions. Conclusions and RelevanceIn infants with maternal prenatal cannabis exposure, we observed timing- and frequency-dependent differences in brain development within the first month of life. These findings underscore the importance of considering not only the presence of exposure, but also when and how much cannabis is used during pregnancy to support targeted prenatal counseling and early developmental monitoring for exposed infants. Key PointsO_ST_ABSQuestionC_ST_ABSIs prenatal cannabis exposure associated with brain development in the first month of life? FindingsIn a cohort[ABS] of 1,782 mother-infant dyads, prenatal cannabis exposure was associated with region-specific differences in neonatal brain volumes. Brain volume and diffusion white matter microstructure associations differed between exposure limited to the first trimester versus exposure that continued into the third trimester. Greater frequency of exposure across gestation was also associated with volumetric and microstructural differences. MeaningThe timing and frequency of prenatal cannabis exposure is associated with alterations in neonatal brain development, underscoring the importance of addressing cannabis use in pregnancy.

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.