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.

A cardinal feature of the encephalopathy of prematurity is dysmaturation of developing white matter and subsequent hypomyelination. Magnetisation transfer imaging (MTI) offers surrogate markers for myelination including magnetisation transfer ratio (MTR) and magnetisation transfer saturation (MTsat). Using data from 105 neonates, we characterise MTR and MTsat in the developing brain and investigate how these markers are affected by gestational age at scan and preterm birth. We explore correlations of the two measures with fractional anisotropy (FA), radial diffusivity (RD) and T1w/T2w ratio which are commonly used markers of white matter integrity in early life. We used two complementary analysis methods: voxel-wise analysis across the white matter skeleton, and tract-of-interest analysis across 16 major white matter tracts. We found that MTR and MTsat positively correlate with gestational age at scan. Preterm infants at term-equivalent age had lower values of MTsat in the genu and splenium of the corpus callosum, while MTR was higher in central white matter regions, the corticospinal tract and the uncinate fasciculus. Correlations of MTI metrics with other MRI parameters revealed that there were moderate positive correlations between T1w/T2w and MTsat and MTR at voxel-level, but at tract-level FA had stronger positive correlations with these metrics. RD had the strongest correlations with MTI metrics, particularly with MTsat in major white matter tracts. The observed changes in MTI metrics are consistent with an increase in myelin density during early postnatal life, and lower myelination and cellular/axonal density in preterm infants at term-equivalent age compared to term controls. Furthermore, correlations between MTI-derived features and conventional measures from dMRI provide new understanding about the contribution of myelination to non-specific imaging metrics that are often used to characterise early brain development.

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.

Auditory processing disorder (APD) is a listening impairment that some school-aged children may experience as difficulty understanding speech in background noise despite having normal peripheral hearing. Recent resting-state functional magnetic resonance imaging (MRI) has revealed an alteration in regional, but not global, functional brain topology in children with APD. However, little is known about the brain structural organization in APD. We used diffusion MRI data to investigate the structural white matter connectome of 58 children from 8 to 14 years old diagnosed with APD (n=29) and children without hearing complaints (healthy controls, HC; n=29). We investigated the rich-club organization and structural connection differences between APD and HC groups using the network science approach. The APD group showed neither edge-based connectivity differences nor any differences in rich-club organization and connectivity strength (i.e., rich, feeder, local connections) compared to HCs. However, at the regional network level, we observed increased average path length (APL) and betweenness centrality in the right inferior parietal lobule and inferior precentral gyrus, respectively, in children with APD. HCs demonstrated a positive association between APL in the left orbital gyrus and the listening-in-spatialized-noise-sentences task, a measure of auditory processing ability. This correlation was not observed in the APD group. In line with previous functional connectome findings, the current results provide evidence for altered structural networks at a regional level in children with APD, and an association with listening performance, suggesting the involvement of multimodal deficits and a role for structure-function alteration in listening difficulties of children with APD.

Functional magnetic resonance imaging (fMRI) is widely used to investigate functional coupling (FC) disturbances in a range of clinical disorders. Most analyses performed to date have used group-based parcellations for defining regions of interest (ROIs), in which a single parcellation is applied to each brain. This approach neglects individual differences in brain functional organization and may inaccurately delineate the true borders of functional regions. These inaccuracies could inflate or under-estimate group differences in case-control analyses. We investigated how individual differences in brain organization influence group comparisons of FC using psychosis as a case-study, drawing on fMRI data in 121 early psychosis patients and 57 controls. We defined FC networks using either a group-based parcellation or an individually-tailored variant of the same parcellation. Individualized parcellations yielded more functionally homogeneous ROIs than group-based parcellations. At individual connections level, case-control FC differences were widespread, but the group-based parcellation identified approximately 9% more connections as dysfunctional than the individualized parcellation. When considering differences at the level of functional networks, the results from both parcellations converged. Our results suggest that a substantial fraction of dysconnectivity previously observed in psychosis can be attributed to erroneous ROI delineation, rather than a pathophysiological process related to psychosis.

Stroke leads to neurological impairment through local and widespread brain damage. Whether distal and local diffusivity changes follow similar reorganization and behavior correlation trajectories is unclear. We examined acute and chronic brain diffusivity changes in stroke patients using both connectivity and microstructural parameters, hypothesizing similar trajectories linked with behavioral scores. This perspective study assessed first-time stroke patients at two weeks and three months using behavioral tests and diffusion MRI. We applied latent factorial analysis to behavioral and microstructural data from diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) outcomes. Structural connectivity gradients were computed from whole brain tractography. Statistical analyses included cross-sectional analyses, longitudinal linear mixed models, and the assessment of the relationship between microstructure and contralateral dysconnectivity. Finally, we explored the linear relationships between diffusivity parameters and behavior. Seventy-nine patients (60{+/-}12 years) were enrolled, with 32 completing follow-up (3 months). A healthy group of n=33 (58{+/-}11 years) was included. Factorial analysis identified five latent factors explaining about 50% of behavioural variances. We described three main DTI-NODDI microstructural maps and three main structural gradients, capturing around 50% of variance each. The analysis revealed acute alterations of microstructural and connectivity patterns. Longitudinally, significant degeneration in structural connectivity was observed, echoed in microstructural parameters. Behaviourally, global structural connectivity alterations were associated with acute cognitive deficits, though this link weakened at chronic follow-up. On the contrary, local disconnectivity patterns were linked mainly with motor deficits. Finally, we reported a significant association between global connectivity alterations and microstructural changes in tracts locally disconnected. Stroke significantly alters diffusivity patterns beyond the lesion, with no recovery over time. While acute-stage effects relate to behavior, long-term connectivity changes seem mostly uncoupled, suggesting they do not correlate with stroke recovery.

Paediatric autoimmune encephalitis (e.g., acute disseminated encephalomyelitis, N-methyl-D-aspartate receptor antibody encephalitis) is an inflammatory brain disease that causes cognitive deficits, psychiatric symptoms, seizures, MRI, and EEG abnormalities. Patients can continue to experience residual cognitive difficulties months to years after the acute illness. Magnetoencephalography (MEG) can examine neural changes in the absence of frank structural abnormalities and may help identify factors predicting children at risk of long-term cognitive deficits. We predicted that theta and delta brain functional connectivity networks would be associated with processing speed and working memory in children with autoimmune encephalitis. Participants were children diagnosed with autoimmune encephalitis at least 18 months before testing and typically developing children. All completed MEG recording (Elekta Neuromag Triux) at rest, eyes open with a fixation cross during six minutes; T1 MRI scans; and cognitive evaluation using the primary subtests of the Weschler Intelligence Scale for Children, fifth edition. Brain connectivity, specifically in delta and theta brain activity, was estimated with amplitude envelope correlation, and network efficiency was measured using graph measures (global efficiency, local efficiency, modularity). The measures were compared across the two groups with permutation correction for multiple thresholds. Finally, statistical associations with processing speed and working memory scores were tested in the autoimmune encephalitis group. Age and sex-matched cohorts of 12 children with AE (11.2{+/-}3.5y, IQR 9y; 5M:7F) and 12 typically developing controls (10.6{+/-}3.2y, IQR 7y; 8M:4F) participated in this study. On average, children with autoimmune encephalitis did not differ from controls in working memory (t(21)= 1.449; p = .162; d = 0.605) but had a significantly lower processing speed (t(21) = 2.463; p = .023; Cohens d = 1.028). The groups did not differ in theta network topology measures but the autoimmune encephalitis group had a significantly lower delta local efficiency across all thresholds tested (d = -1.60 at network threshold 14%). Theta modularity was associated with lower working memory ({beta} = -.781; t(8) = -2.588, p = .032) but this effect did not survive correction for multiple comparisons (p(corr) = .224). No other graph measure was significantly associated with psychometric scores in the autoimmune encephalitis group. MEG was able to capture network alterations in paediatric autoimmune encephalitis patients, specifically in the topological organisation of delta brain activity. This preliminary study demonstrates that MEG is an appropriate tool for assessing children with autoimmune encephalitis; future studies should focus on confirming which functional networks can predict cognitive performance.

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

BackgroundPredicting development of cerebral palsy following neonatal stroke remains challenging. This study aimed to identify novel acute brain functional connectome-based correlates of cerebral palsy following neonatal stroke. MethodsStroke lesions were segmented from routine clinical diffusion images of a cohort of term-born neonates with symptomatic arterial ischemic stroke, recruited to Swiss and Australian pediatric stroke registries. Lesions, and 3T resting state functional MRIs of term-born newborns from the developing Human Connectome Project, were co-registered to a template. A neonatal stroke functional connectome was created by computing voxel-wise correlations between lesions and gray matter regions. Linear regressions compared functional connections to lesions between participants who did and did not develop cerebral palsy. ResultsEighty-five newborns with stroke were included (64% male; median age at MRI of 4 days), of which 33% developed cerebral palsy at a median age of 2.1 years. Multiple gray matter regions were more highly functionally correlated to lesions in participants who developed cerebral palsy (1721 voxels; t: 5.4-7.4; all p<0.05, family-wise error rate corrected). These regions included the basal ganglia, thalamus, cerebellum, frontal regions (inferior and orbital frontal and superior frontal), temporal regions (pole, superior, and mesial temporal including hippocampus and amygdala) and the insula. ConclusionsThis study identified functional networks related to the development of cerebral palsy following neonatal stroke. Building on prior individual lesion-based studies, this work suggests that development of cerebral palsy after neonatal stroke is related to disruptions of broader functional networks involving motor and extramotor regions, as opposed to only lesions in motor regions.

Upper and lower limb impairments are common in people with multiple sclerosis (pwMS), yet difficult to clinically identify in early stages of disease progression. Tasks involving complex motor control can potentially reveal more subtle deficits in early stages, and can be performed during functional MRI acquisition, to investigate underlying neural mechanisms, providing markers for early motor progression. We investigated brain activation during visually-guided force-matching of hand or foot in 28 minimally disabled pwMS and 17 healthy controls (HC) using ultra-high field 7-Tesla fMRI, allowing us to visualise sensorimotor network activity in high detail. Task activations and performance (tracking lag and error) were compared between groups, and correlations were performed. PwMS showed delayed (+124 s, p=0.002) and more erroneous (+0.15 N, p=0.001) lower limb tracking, together with higher primary motor and premotor cortex activation, and lower cerebellar activation compared to HC. No differences were seen in upper limb performance or activation. Functional activation levels of cerebellar, visual and motor areas correlated with task performance. These results demonstrate that ultra-high field fMRI during complex hand and foot tracking can identify subtle impairments in movement and brain activity, and differentiates upper and lower limb impairments in minimally disabled pwMS.

BackgroundThis study empirically tests a theoretical model of ADHD that identifies aberrant frontal-to-subcortical projections--particularly those involving the ventral tegmental area and, consequently, the nucleus accumbens (NAc)--as a core neurobiological vulnerability. The model predicts reduced frontal influence on subcortical structures and group-by-direction interaction effects, especially at the NAc. MethodsStructural and resting-state fMRI data from sixty individuals with ADHD and sixty healthy controls were selected from the ADHD-200 dataset. The cortex was parcellated using MOSI (Modular Analysis and Similarity Measurements). The mean amplitude of low-frequency fluctuations (ALFF) and the mean time series were extracted for each frontal node. Subcortical nuclei--including the caudate, putamen, globus pallidus, NAc, and thalamus--were included in the analyses, with each voxel treated as a neural node. Correlation analyses were performed to examine the relationship between nodal power (ALFF) and nodal degree. Directional metrics were defined as frontal-to-subcortical (F2S) and subcortical-to-frontal (S2F) correlations between nodal strength and nodal power. Group differences and interactions were tested using t-tests and linear mixed-effects (LME) models. ResultsCorrelation and LME analyses revealed a consistent bimodal pattern, with positive F2S and negative S2F associations across all subcortical nuclei, suggesting a directional excitation-inhibition balance. Reduced F2S slopes in ADHD and a significant group-by-direction interaction in the NAc (p = 0.009) supported model predictions. ConclusionsThe proposed developmental model of ADHD was empirically supported.

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.

The prenatal period is critical for a healthy development, and exposure to adversity during it may provoke alterations of several biological tissues and systems, resulting in health outcomes that may take place into childhood and adulthood. The orbito-frontal cortex (OFC), central in cognitive processes, is sensitive to negative environmental effects in the intrauterine environment. We investigated the association between OFC function and decision-making behavior in response to a poor-quality prenatal environment. We evaluated a subsample of the MAVAN longitudinal Canadian birth cohort gathering data on anthropometric measurements at birth, and resting-state functional MRI (rsfMRI) and decision-making (using the Information Sampling Task from the CANTAB battery) measured later in life. We performed a mediation analysis to investigate the direct and indirect effect of being born small for gestational age (SGA) on the Information Sampling Task performance, through OFC-related functional connectivity. Being born SGA is associated with decreased functional connectivity between the left hemisphere OFC and the middle frontal gyrus (OFC-MFG). Additionally, increased OFC-MFG connectivity is linked to better IST performance. Thus, SGA individuals have an altered OFC- MFG functional connectivity, which impacts on their performance on a decision-making task. Lower OFC-MFG functional connectivity and impulsive decision-making were associated to the SGA condition, reflecting a poor-quality prenatal environment. These findings highlight the importance of the prenatal period for a healthy development and suggest that neuroimaging focusing on the affected areas may identify individuals at higher risk of developing psychopathologies, and direct for proper interventions.

Recovery from aphasia after stroke has been hypothesized to depend on neuroplasticity in surviving brain regions. Many studies have investigated this process, but progress has been impeded by methodological limitations relating to task performance confounds, contrast validity, and sample sizes. Furthermore, few studies have accounted for the complex relationships that exist between patterns of structural damage, distributed networks of functional activity, and behavioral outcomes. The present cross-sectional study aimed to overcome these critical methodological limitations and to disentangle the relationships between structure, function, and behavior. We recruited 70 individuals with post-stroke aphasia and 45 neurologically normal comparison participants. We used a valid and reliable language mapping fMRI paradigm that adapted dynamically to each participants task performance, and carried out whole-brain permutation analyses along with hypothesis-driven analyses of individually defined functional regions of interest (ROIs). Multivariable models were constructed that incorporated lesion load estimates derived from machine learning and language activations across multiple brain regions. We found strong evidence that left posterior temporal cortex is the most critical region for language processing in post-stroke aphasia: functional activity in this region was reduced in aphasia, predictive of aphasia outcomes in a whole-brain analysis above and beyond the contribution of lesion load, and remained predictive even above and beyond other functional predictors, with a medium effect size (f2 = 0.15). We also found that right posterior temporal cortex made an independent contribution to aphasia outcomes: functional activity was attenuated in aphasia, suggesting diaschisis, yet was predictive of aphasia outcomes above and beyond left hemisphere lesion load and functional predictors, with a small effect size (f2 = 0.08). We corroborated the importance of left frontal cortex: functional activity was attenuated in aphasia and predictive of aphasia outcomes over and beyond the contribution of lesion load; however, unlike in the bilateral temporal regions, functional activity in the left frontal lobe did not remain predictive once other functional predictors were included in the model. There was no support for other potential compensatory mechanisms such as recruitment of the right frontal lobe, the bilateral multiple demand network, or perilesional regions. Taken together, our findings demonstrate that functional imaging can provide critical insights into language processing in aphasia that cannot be obtained from structural imaging alone, with the left and right posterior temporal cortices making independent contributions to aphasia outcomes after stroke.

Tourette syndrome (TS) is a neurodevelopmental disorder characterized by motor and vocal tics. TS is complex, with symptoms that involve sensory, motor, and top-down control processes and that fluctuate over the course of development. While many have studied atypical brain structure and function associated with TS, the neural substrates supporting the complex range and time-course of symptoms is largely understudied. Here, we used functional connectivity MRI to examine functional networks across the whole-brain in children and adults with TS. To investigate the functional neuroanatomy of childhood and adulthood TS, we separately considered the sets of connections within each functional network and those between each pair of functional networks. We tested whether developmental stage (child, adult), diagnosis (TS, control), or an interaction between these factors was present among these connections. We found that developmental changes for most functional networks in TS were unaltered (i.e., developmental differences in TS were similar to those in typically developing children and adults). However, there were several within-network and cross-network connections that exhibited either "divergent" or "attenuated" development in TS. Connections involving the somatomotor, cingulo-opercular, auditory, dorsal attention, and default mode networks diverged from typical development in TS, demonstrating enhanced functional connectivity in adulthood TS. In contrast, connections involving the basal ganglia, thalamus, cerebellum, auditory, visual, reward, and ventral attention networks showed attenuated developmental differences in TS. These results suggest that adulthood TS is characterized by increased functional connectivity among functional networks that support cognitive control and attention, which may be implicated in suppressing, producing, and attending to tics. In contrast, subcortical systems that have been implicated in the initiation and production of tics may be immature in adulthood TS. Jointly, our results reveal how several cortical and subcortical functional networks interact and differ across development in TS.

Stroke frequently results in long-term upper limb (UL) motor impairments, limiting independence and quality of life. Accurate prediction of recovery trajectories is essential for personalizing rehabilitation strategies. While structural brain metrics such as corticospinal tract (CST) integrity have been widely studied, they incompletely explain motor outcome variability. Functional brain activity, quantified by sensorimotor activity in the beta ({beta}) frequency range has emerged as a promising biomarker of motor system integrity and plasticity potential. This study assessed in 30 acute stroke survivors and 26 healthy controls how combining functional and structural metrics of brain function relates to initial motor severity and subsequent recovery, using clinical MRI/CT and electroencephalography during passive finger movement and rest. Structurally, grey and white matter damage were associated with initial severity. No associations with recovery were found for structural metrics alone. Functionally, {beta}-activity in response to passive movement, and resting state activity were related to recovery, independent of initial impairment. Multivariate regression revealed that combining initial severity, structural information (CST damage) and brain function (sensorimotor {beta} activity) provided the most accurate prediction of both global and UL-specific recovery (R{superscript 2} = 80.1% and 74.3%, respectively). These findings underscore the importance of integrating functional and structural neural markers for improved stroke outcome prediction.

Attention deficit hyperactivity disorder (ADHD) has been characterized by impairments among distributed functional brain networks, e.g., the frontoparietal network (FPN), default mode network (DMN), and reward and motivation-related circuits (RMN). In the current study, we evaluated the complexity and functional connectivity (FC) of resting state fMRI (rsfMRI) in pre-adolescents with ADHD for pathology-relevant networks. We leveraged data from the Adolescent Brain and Cognitive Development (ABCD) Study. The final study sample included 63 children with ADHD and 92 healthy control children matched on age, sex, and pubertal development status. For selected regions in relevant networks, ANCOVA compared multiscale entropy (MSE) and FC between the groups. Finally, differences in the association between MSE and FC were evaluated. We found significantly reduced MSE along with increased FC within the FPN of pre-adolescents with ADHD compared to matched healthy controls. Significant partial correlations between MSE and FC emerged in fewer regions in the participants with ADHD than in the controls. The observation of reduced entropy is consistent with existing literature using rsfMRI and other neuroimaging modalities. The current findings of complexity and FC in ADHD support hypotheses of altered function of inhibitory control networks in ADHD.

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.

Visual shape completion is a canonical perceptual organization process that integrates spatially distributed edge information into unified representations of objects. People with schizophrenia show difficulty in discriminating completed shapes but the brain networks and functional connections underlying this perceptual difference remain poorly understood. Also unclear is whether similar neural differences arise in bipolar disorder or vary across the schizo-bipolar spectrum. To address these topics, we scanned (fMRI) people with schizophrenia, bipolar disorder, or no psychiatric illness during rest and during a task in which they discriminated configurations that formed or failed to form completed shapes (illusory and fragmented condition, respectively). Multivariate pattern differences were identified on the cortical surface using 360 predefined parcels and 12 functional networks composed of such parcels. Brain activity flow mapping was used to evaluate the likely involvement of resting-state connections for shape completion. Illusory/fragmented task activation differences ("modulations") in the dorsal attention network (DAN) could distinguish people with schizophrenia (AUCs>.85) and could trans-diagnostically predict cognitive disorganization severity. Activity flow over functional connections from the DAN could predict secondary visual network modulations in each group, except among those with schizophrenia. The secondary visual network was strongly and similarly modulated in each subject group. Task modulations were dispersed over a larger number of networks in patients compared to controls. In summary, abnormal DAN activity emerges during perceptual organization in schizophrenia and may be related to improper attention-related feedback into secondary visual areas. Patients with either disorder may compensate for abnormal perception by relying upon non-visual networks.