White matter tracts are bundles of myelinated nerve fibers that connect different regions of the brain, facilitating communication between them. These tracts play an important role in the cognitive and behavioral functioning of the brain. Understanding the structure and connectivity of white matter tracts is crucial for studying brain function and diagnosing neurological disorders. In this study, we propose a new method to map fiber tract information onto the cortical regions via fiber to cortex minimal distances. Diffusion properties of the fiber tracts weighted by these distances can then be incorporated as adjacency weights in a graph convolutional neural network. Our approach provides a multi-modality framework that integrates structural and diffusion MRI, providing a comprehensive view of the brain’s architecture. We evaluate this framework in two longitudinal studies, predicting later cognitive outcomes.

Abstract Adolescent cognitive ability develops within a complex landscape of environmental exposures, yet the patterns of brain maturation through which these exposures relate to cognitive development remain poorly understood. Resolving this requires an integrated longitudinal view of the multidimensional exposome, multimodal brain change, and cognitive growth. Here, we examined whether brain maturation mediates the relationship between the exposome and cognitive development in a large longitudinal cohort of 1,112 adolescents, using 4,882 multimodal brain features and 112 exposome variables spanning six domains. Brain maturation patterns associated with cognitive development showed moderate yet consistent spatial correspondence with those associated with the exposome, with the family environment domain showing the strongest correspondence. We identified four latent brain patterns associated with distinct components of the exposome-cognition relationship. Collectively, these results indicate that distinct exposome domains relate to cognitive development through distinct brain maturation pathways, which also exhibit different molecular annotation profiles.

A bstract Objective In temporal lobe epilepsy (TLE), the thalamus acts as a nexus in a pathophysiological network that implicates mesiotemporal, subcortical, and neocortical regions. Studying a large multimodal and multicentre dataset, we profiled thalamic, hippocampal, and neocortical functional connectivity (FC), assessed structural mediators, and examined clinical associations. Methods We studied resting-state FC alongside structural and diffusion MRI data in 250 unilateral TLE patients and 259 healthy controls, with measures aggregated across four independent datasets. Data were processed using open-access neuroinformatics workflows and analyzed at a subregional level to maximize anatomical precision. Statistical analysis and mediation models assessed between-group FC changes, structural contributors, and clinical correlations. Results Compared to controls, TLE patients presented with reduced thalamo-cortical FC, which was most marked in mesiotemporal, fronto-central, and occipital regions. Thalamo-hippocampal FC was also reduced, with effects seen in all CA subfields. In the thalamus, FC reductions peaked in the ventral posterior nucleus when considering neocortical target regions and in the mediodorsal nucleus when considering hippocampal target regions. While ipsilateral hippocampal volume and diffusion changes mediated thalamo-hippocampal FC, thalamo-cortical FC appeared decoupled from structural alterations. Findings were consistent in left and right TLE patients, in patients with short and long disease duration, and across imaging sites, suggesting that thalamo-cortical FC imbalances are a consistent signature of TLE. Conversely, thalamo-hippocampal FC was elevated in patients with focal-to-bilateral-tonic-clonic seizures and FC alterations were more marked in the subgroup of operated patients that became seizure-free after surgery. Conclusion Our multi-site findings demonstrate marked thalamic circuit fragmentation in TLE. Ipsilateral findings robustly showed subdivision-specific effects, which point to both mesiotemporal co-lateralization as well as broader system-level involvement. Mediation analyses furthermore confirmed a key role of hippocampal pathology in disrupted thalamo-hippocampal connectivity in TLE, while broader thalamo-cortical fragmentation becomes increasingly independent of mesiotemporal compromise. Critically, thalamic FC represents a network substrate for seizure generalization and can serve as a prognostic indicator for surgical outcome. These results underscore the contribution of the thalamus as a hub in macroscale dysfunction in TLE.

Adolescence is a sensitive period for the emergence of psychopathology. During this time, physiological changes and environmental exposures jointly shape brain development and influence cognitive and personality maturation, collectively heightening vulnerability to mental disorders. However, the complexity of interactions between these factors has hindered a systems-level understanding of mental health and the causal roles of cognition and personality in psychopathology. In this study, we proposed a multifactorial causal framework integrating brain, pubertal, environmental, and behavioral factors to characterize heterogeneity in adolescent mental health trajectories at the individual level. We then investigated latent causal pathways linking cognition and personality to mental health outcomes and identified potential personalized intervention targets. Leveraging the Adolescent Brain Cognitive Development (ABCD) dataset (N = 4,501), we analyzed 165 behavioral pairs connecting cognition and personality traits to mental health symptoms. Using cross-sectional multivariate mediation and longitudinal interaction-inclusive analyses, we identified 68 behavioral pairs showing significant causal relationships, with brain and environmental exposures contributing to most pathways, while pubertal factors exhibited limited involvement. Individualized interpretive analyses further revealed 23 pairs suggesting potential interventions with response rates exceeding 50%. Among these, behavioral inhibition, negative urgency, and processing speed emerged as the most common intervention targets, whereas psychosis symptoms and attention problems were the most likely issues to improve. Overall, our study advances a comprehensive framework capturing the multifactorial and heterogeneous nature of adolescent mental health, delineates specific causal pathways from cognitive and personality traits to psychopathology, and provides a principled basis for potential individualized intervention strategies.

The MNI CIVET pipeline for automated extraction of cortical surfaces and evaluation of cortical thickness from in vivo human magnetic resonance imaging has recently been extended to process macaque brains and, following this work, has since been broadened to process chimpanzee (Pan troglodytes) brains. The pipeline uses the NCBR (National Chimpanzee Brain Resource) 3 T dataset, the largest high-resolution chimpanzee magnetic resonance imaging collection, and processing is performed in a derived standardized MNI-like NCBR reference space. An average population surface, constructed from the cortical surfaces of the individual NCBR subjects, serves as the basis for surface-based registration for group comparisons and ROI-based analyses, with integrated Davi130 and Brainnetome parcellations. A surface-based transformation is provided for mapping common human average surfaces (MNI152 and fsavg) to the MNI-NCBR chimpanzee average surface to facilitate inter-species studies. The modifications needed to adapt CIVET from the macaque to the chimpanzee brain are detailed and cortical thickness maps obtained for the NCBR subjects are compared to other published maps. The open usage of CIVET-Macaque and CIVET-Chimp is expected to promote collaborative efforts in data collection and processing, automated analyses, and data sharing, thereby contributing to advances in the field of non-human primate brain imaging.

The claustrum is a thin, bilateral structure embedded deep within the human brain. Its widespread cortical connectivity has motivated perhaps the broadest range of functional hypotheses of any subcortical structure. Yet its complex, sheet-like morphology has hindered investigation in living humans, leaving a small in vivo MRI literature marked by large and often implausible discrepancies. Here, we construct a three-dimensional histological “gold standard” model of the human claustrum and systematically evaluate three ultra-high field 7-Tesla MRI datasets against this reference and its downsampled derivatives. We show that apparent discrepancies in MRI-based claustrum morphology arise primarily from resolution-dependent effects rather than contrast limitations, which transform the claustrum’s intricate sheet into an artifactually thickened ribbon. Despite this, submillimeter MRI reliably captures a dorsal “core” containing most claustral volume and cell density and encompassing major corticoclaustral connectivity, and at the highest acquired resolution (0.5 mm isotropic), the ventral claustrum’s extension into the temporal lobe is partially recovered, with uncertainty reflecting boundary imprecision rather than anatomical absence. Together, these findings overturn the view that the human claustrum is inaccessible to MRI and establish a foundation for future functional and clinical investigation in the living human brain.

Significant changes occur in brain structure and cognition during adolescence. Investigating their association can provide insight into brain-based cognitive development, yet previous studies are limited by narrow measures, small samples, and lacking focus on age-dependence. Using a large cohort (n = 8534, age 9-15) with structural MRI and diffusion imaging, we derive 16 regional metrics and integrate them via morphometric similarity networks to characterize 16,563 brain features. We apply large-scale models to investigate their associations with seven cognitive subtests and general intelligence (g), as well as age-dependence. Brain areas most strongly associated with cognition also show the greatest age-dependence of the associations, primarily in the frontal, temporal, and occipital lobes. Stronger and more age-dependent associations with cognition are observed for structural MRI measures and global hub measures, compared with diffusion-derived metrics and local measures, respectively. Overall, our study provides a comprehensive and reliable characterization of adolescent brain structure-cognition associations.

Visual processing undergoes rapid development in the first year of life, supporting the emergence of higher-order cognitive, language, and motor functions. Visual evoked potentials (VEPs) provide a noninvasive measure of visual system maturation that may shed light on heterogeneous developmental trajectories among infants at high familial likelihood for autism. Infants with an older sibling with autism spectrum disorder (N = 177 at 6 months; N = 132 at 12 months) participated in the Infant Brain Imaging Study-Early Prediction (IBIS-EP) study. Pattern-reversal VEPs were recorded at 6 and 12 months, and developmental skills were assessed at 24 months using the Bayley Scales of Infant and Toddler Development (Bayley-4). VEP components (P1 and N1) were characterized by their amplitude and latency, as well as trial-to-trial variability in these measures. Associations with 24-month cognitive, language, and motor scores were examined using general linear models controlling for age, site, sex, and trial count. Robust VEPs were observed at both time points, with age-appropriate morphology and expected developmental changes, including decreases in P1 latency and amplitude from 6 to 12 months. Greater trial-to-trial variability in P1 latency at both time points was associated with higher cognitive and language scores at 24 months. In contrast, conventional measures of mean P1 latency and amplitude were not associated with developmental outcomes. These findings suggest that temporal variability in early visual responses may index adaptive sensory-circuit flexibility during a period of rapid experience-dependent development. VEP response-timing variability may therefore provide an early mechanistic marker of sensory-circuit organization relevant to later developmental trajectories.

The mammalian cortex is organized along hierarchical gradients that extend from primary sensory regions to transmodal association networks. Converging neuroanatomical theory and data-driven analyses place the hippocampus at the apex of this hierarchy, where its subregional organization mirrors large-scale cortical networks and their evolutionary expansion. Building on these observations, we propose that the hippocampus functions as a predictive learning engine, generating latent training signals that support cortical learning. This view aligns with self-supervised machine learning frameworks, in which predictive processes occupy the top of hierarchical models. We suggest that hippocampal predictive learning constitutes a foundational mechanism of mammalian intelligence, linking cortical organization with principles underlying modern artificial systems.

The human Bed nucleus of the Stria terminalis is a basal forebrain structure and a key player in stress response and anxiety perception. It is a heterogenous structure and consists of distinct subdivisions. However, subdivisions are not visible in routine MR imaging due to low contrast and small size, which makes the exact assignment and identification in images of the living brain difficult or even impossible. Here, we mapped the Bed nucleus in serial, cell body-stained sections of 10 human brains in its full extent, resulting in 1130 annotations. Four subdivisions, a central, dorsal, medial, and a posterior part were identified. Texture analysis was applied to further characterize the subdivisions. Two sets of maps were generated: (1) Probabilistic cytoarchitectonic maps of the Bed nucleus in MNI space, which consider interindividual variability among the brains; (2) Ultra-high-resolution maps of the four subdivisions in the BigBrain 3D histology dataset, to capture complex shape and topology at microscopical level. The maps are openly available, to serve as anatomical reference to neuroimaging studies in healthy subjects and patients and inform modeling and simulation.

The default mode network (DMN) is implicated in many aspects of complex thought and behavior. Here, we leverage postmortem histology and in vivo neuroimaging to characterize the anatomy of the DMN to better understand its role in information processing and cortical communication. Our results show that the DMN is cytoarchitecturally heterogenous, containing cytoarchitectural types that are variably specialized for unimodal, heteromodal and memory-related processing. Studying diffusion-based structural connectivity in combination with cytoarchitecture, we found the DMN contains regions receptive to input from sensory cortex and a core that is relatively insulated from environmental input. Finally, analysis of signal flow with effective connectivity models showed that the DMN is unique amongst cortical networks in balancing its output across the levels of sensory hierarchies. Together, our study establishes an anatomical foundation from which accounts of the broad role the DMN plays in human brain function and cognition can be developed.

This commentary reflects three decades of interaction between the Cuban neuroinformatics tradition and the statistical parametric mapping (SPM) framework. From the early development of neurometrics in Cuba to global initiatives like the Global Brain Consortium, our trajectory has paralleled and intersected with that of SPM. We highlight shared commitments to generative modeling, Bayesian inference, and population-level brain mapping, as shaped through collaborations, workshops, and joint theoretical work with Karl Friston and his group. This convergence continues to guide our efforts toward global, open, and explicable neuroscience.

BACKGROUND:Discovery of predictive biomarkers is essential for understanding the neurobiological underpinnings of autism spectrum diagnosis (ASD) and improving identification. Resting-state functional connectivity analyses of individuals with ASD have established sensitivity of brain connectivity at the group level. However, the extensive heterogeneity in ASD limits the translation of these findings into reliable individual-level biomarkers. We analyzed the Autism Brain Imaging Data Exchange 1 and 2 datasets, calculating Pearson's correlation-based functional connectivity across 18 brain networks. Using transductive conformal prediction, a machine learning approach that assigns confidence scores to predictions based on conformality to known classes, we classified individuals with ASD and neurotypical controls. RESULTS:By combining predictors into an ensemble using hierarchical agglomerative clustering, we identified a signature that confers a more than 7-fold increase in individual risk of ASD, yet is still identified in an estimated 1 in 200 individuals in the general population. The individual risk conferred by the model is increased 4-fold over that of previously published imaging models and outperforms the current state of the art in precision for ASD classification. The high-risk signature was characterized by underconnectivity of transmodal brain networks, including the frontoparietal and basal ganglia network, and subcomponents of the limbic and default mode networks. CONCLUSIONS:A highly targeted prediction model can identify a subset of functional connectivity alterations that confer high risk for ASD at the individual level, which may be masked by traditional machine learning models due to ASD heterogeneity. Results could help disentangle the multitude of etiological pathways and behavioral symptoms that challenge our understanding of ASD by focusing on highly penetrant connectivity signatures.

Human induced pluripotent stem cell (iPSC) derived cortical organoids (hCOs) model neurogenesis on an individual's genetic background. The degree to which hCO phenotypes recapitulate the brain growth of the participants from which they were derived is not well established. We generated up to 3 iPSC clones from each of 18 participants in the Infant Brain Imaging Study, who have undergone longitudinal brain imaging during infancy. We identified consistent hCO morphology and cortical cell types across clones from the same participant. hCO cross-sectional area and production of cortical hem cells were associated with in vivo cortical growth rates. Cell cycle associated genes expression in early progenitors at the crux of fate decision trajectories were correlated with cortical growth rate from 6-12 months of age, and were enriched in microcephaly and neurodevelopmental disorder genes. Our data suggest the hCOs capture inter-individual variation in cortical cell types influencing infant cortical surface area expansion.

Current electroencephalogram (EEG) decoding models are typically trained on small numbers of subjects performing a single task. Here, we introduce a large-scale, code-submission-based competition comprising two challenges. First, the Transfer Challenge asks participants to build and test a model that can zero-shot decode new tasks and new subjects from their EEG data. Second, the Psychopathology factor prediction Challenge asks participants to infer subject measures of mental health from EEG data. For this, we use an unprecedented, multi-terabyte dataset of high-density EEG signals (128 channels) recorded from over 3,000 child to young adult subjects engaged in multiple active and passive tasks. We provide several tunable neural network baselines for each of these two challenges, including a simple network and demographic-based regression models. Developing models that generalise across tasks and individuals will pave the way for ML network architectures capable of adapting to EEG data collected from diverse tasks and individuals. Similarly, predicting mental health-relevant personality trait values from EEG might identify objective biomarkers useful for clinical diagnosis and design of personalised treatment for psychological conditions. Ultimately, the advances spurred by this challenge could contribute to the development of computational psychiatry and useful neurotechnology, and contribute to breakthroughs in both fundamental neuroscience and applied clinical research.

The superficial white matter (SWM), immediately beneath the cortical mantle, is thought to play a major role in cortico-cortical connectivity as well as large-scale brain function. Yet, this compartment remains rarely studied due to its complex organization. Our objectives were to develop and disseminate a robust computational framework to study SWM organization based on 3D histology and high-field 7T MRI. Using data from the BigBrain and Ahead 3D histology initiatives, we first interrogated variations in cell staining intensities across different cortical regions and different SWM depths. These findings were then translated to in-vivo 7T quantitative myelin-sensitive MRI, including T1 relaxometry (T1 map) and magnetization transfer saturation (MTsat). As indicated by the statistical moments of the SWM intensity profiles, the first 2 mm below the cortico-subcortical boundary were characterized by high structural complexity. We quantified SWM microstructural variation using a non-linear dimensionality reduction method and examined the relationship of the resulting microstructural gradients with indices of cortical geometry, as well as structural and functional connectivity. Our results showed correlations between SWM microstructural gradients, as well as curvature and cortico-cortical functional connectivity. Our study provides novel insights into the organization of SWM in the human brain and underscores the potential of SWM mapping to advance fundamental and applied neuroscience research. Highlights ### Competing Interest Statement The authors have declared no competing interest. * SWM : Superficial white matter MRI : Magnetic resonance imaging T1 map : T1 relaxometry MTsat : Magnetization transfer saturation GM : Gray matter WM : White matter

Down syndrome (DS), resulting from Trisomy 21, is the most prevalent chromosomal disorder and a leading cause of intellectual disability. Despite the significant impact of Trisomy 21 on brain development, research on white matter (WM) microstructure in infants with DS remains limited. While widespread reductions in WM integrity have been identified in children and young adults with DS, no study has examined WM microstructure in infancy. This study investigates early WM microstructure in infants with DS using diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI). Forty-nine infants with DS (28 [57.14%] female) and 36 control (18 [48.65%] female) infants were scanned at 6 months of age. Infants with DS showed significant reductions in fractional anisotropy and neurite density index across multiple association tracts, particularly in the inferior fronto-occipital fasciculus and superior longitudinal fasciculus II, consistent with reduced structural integrity and neurite density. Increased radial diffusivity was observed in these tracts, a feature associated with disrupted myelination. In the inferior fronto-occipital fasciculus, superior longitudinal fasciculus II, and uncinate fasciculus, an elevated orientation dispersion index suggested increased neurite dispersion and fanning in infants with DS. These findings reveal widespread WM developmental alterations in DS, providing new insights into the early neurodevelopment of DS, which may inform timing of early therapeutic interventions.

The PResymptomatic EValuation of Experimental or Novel Treatments for Alzheimer's Disease (PREVENT-AD) is an investigator-driven study that was created in 2011 and enrolled cognitively normal older adults with a family history of sporadic AD. Participants are deeply phenotyped and have now been followed annually for more than 12 years (median follow-up 8.0 years, SD 3.1). Multimodal magnetic resonance imaging (MRI), genetic, neurosensory, clinical, cerebrospinal fluid, and cognitive data collected until 2017 on 348 participants who agreed to open sharing with the neuroscience community were already available. We now share a new release including 6 years of additional follow-up cognitive data, and additional MRI follow-ups, clinical progression, new longitudinal behavioral and lifestyle measures (questionnaires, actigraphy), longitudinal AD plasma biomarkers, amyloid-beta and tau positron emission tomography (PET), magnetoencephalography, as well as neuroimaging analytic measures from all MRI modalities. We describe the PREVENT-AD study, the data shared with the global research community, as well as the model we created to sustain longitudinal follow-ups while also allowing new innovative data collection.

fcMRI correlates of autism spectrum disorder (ASD) diagnosis and familial liability were studied in 24-month-olds at high (older affected sibling) and low familial likelihood for ASD. fcMRI comparisons of high-familial-likelihood (HL) ASD-positive (HLP, N = 23) and ASD-negative (HLN, N = 91), and low-likelihood ASD-negative (LLN, N = 27) 24-month-olds from the Infant Brain Imaging Study (IBIS) Network were conducted, employing object oriented data analysis (OODA), support vector machine (SVM) classification, and network-level fcMRI enrichment analyses. OODA (alpha = 0.0167, 3 comparisons) revealed differences in HLP and LLN fcMRI matrices (p = 0.012), but none for HLP versus HLN (p = 0.047) nor HLN versus LLN (p = 0.225). SVM distinguished HLP from HLN (accuracy = 99

Mental processing delineates the functions of human mind encompassing a wide range of motor, sensory, emotional, and cognitive processes, each of which is underlain by the neuroanatomical substrates. Identifying accurate representation of functional neuroanatomy substrates of mental processing could inform understanding of its neural mechanism. The challenge is that it is unclear whether a specific mental process possesses a 'functional neuroanatomy fingerprint', i.e., a unique and reliable pattern of functional neuroanatomy that underlies the mental process. To address this question, we utilized a multi-task deep learning model to disentangle the functional neuroanatomy fingerprint of seven different and representative mental processes including Emotion, Gambling, Language, Motor, Relational, Social, and Working Memory. Results based on the functional magnetic resonance imaging data of two independent cohorts of 1235 subjects from the US and China consistently show that each of the seven mental processes possessed a functional neuroanatomy fingerprint, which is represented by a unique set of functional activity weights of whole-brain regions characterizing the degree of each region involved in the mental process. The functional neuroanatomy fingerprint of a specific mental process exhibits high discrimination ability (93% classification accuracy and AUC of 0.99) with those of the other mental processes, and is robust across different datasets and using different brain atlases. This study provides a solid functional neuroanatomy foundation for investigating the neural mechanism of mental processing.