Biology subjects

Scheinost, D.

Publications and source records attributed to Scheinost, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Multimodal Connectome Markers of Disruptive Behavior Problems in Youth: Distinct Patterns of Concurrent Severity and Prospective Symptom Change

Disruptive behaviors problems, such as irritability/anger and maladaptive aggression, are associated with altered connectivity across networks supporting emotion regulation. However, multimodal connectome markers of concurrent severity and prospective symptom change, including sex-specific patterns, remain unclear. Using a large, sex-balanced sample from the Adolescent Brain Cognitive Development (ABCD) Study, we applied multimodal connectome-based predictive modelling (CPM) to test whether resting-state functional (n = 7,556; 49.3% female) and structural (N = 8,978; 48.4%) connectivity predicted baseline disruptive behavior severity (at ages 9-10) and 2-year symptom change (from ages 9-10 to 11-12). Models were adjusted for covariates, permutation-tested, and FDR corrected. Secondary analyses tested sex-stratified models separately in males and females. In models emerging as significant, follow-up analyses tested for distinct vs shared predictive edges. Functional and structural connectomes predicted baseline severity ({rho} = 0.087 and {rho} = 0.071, respectively). Functional connectivity also predicted prospective symptom change ({rho} = 0.038), whereas prospective structural prediction was predominately with negative edges ({rho} = 0.026). Functional connectivity models were significant in both sexes, while prospective structural connectivity models were significant in males, but not females. Frontoparietal, medial frontal, sensorimotor, salience, cerebellar, and visual networks contributed across models. Cross-sectional models showed a more distributed pattern while prospective models showed a more focal pattern. These findings identify complementary, largely distinct functional and structural connectome patterns associated with current disruptive behavior risk and subsequent symptom change, supporting the importance of developmental timing, sex, and imaging modality in developing brain-based biomarkers of disruptive behavior risk in youth.

neuroscience↗

bioRxiv2026

Brain age prediction from structural and functional connectivity across the lifespan using connectome-based predictive modeling

Brain maturation and aging are characterized by widespread changes in structural and functional brain organization. The extent to which these modalities capture shared versus distinct signatures of lifespan development and aging remains unclear. In this study, we applied connectome-based predictive modeling (CPM) to connectomes derived from diffusion MRI and resting-state functional MRI in Human Connectome Project Development, Young Adult, Aging, and combined Lifespan cohorts to predict chronological age. Both structural and functional connectomes significantly predicted age across cohorts, demonstrating that age-related information is distributed throughout whole-brain connectivity patterns. Prediction performance varied across lifespan stages, with generally stronger performance in the Development, Aging, and Lifespan cohorts than in Young Adulthood. Structural and functional models produced significantly correlated predicted ages in several cohorts, indicating shared age-related information across modalities. Structural-functional convergence analyses revealed limited and variable correspondence between modality-specific predictive features, suggesting that structural and functional connectomes capture both shared and complementary aspects of brain maturation and aging. Network-level analyses demonstrated that age-predictive information was distributed across canonical brain networks, including cerebellar, frontoparietal, salience, somatomotor, and subcortical networks, with patterns varying across cohorts and modalities. Multimodal CPM improved prediction relative to unimodal models in several cohorts, supporting the complementary contribution of structural and functional connectivity to age prediction. Cross-cohort, cross-sex, and cross-modality analyses further characterized the generalizability and shared information of predictive models across lifespan stages, sexes, and imaging modalities. This work demonstrates the utility of CPM for characterizing distributed structural and functional connectivity patterns associated with chronological age across the lifespan and provides a foundation for future studies of individual variability in brain maturation and aging.

neuroscience↗

bioRxiv2026

Mapping the genetic architecture of human cortical expansion and its links to neuropsychiatric disorders

Cortical expansion is a defining feature of human evolution and neurodevelopment, involving the tangential growth and gyrification of the cerebral cortex. Although disruptions to the expansion of the cortex are implicated in diverse brain-based disorders, the genetic architecture underlying this process remains undercharacterized. Here, we integrated GWAS data for five neuroanatomical phenotypes measured in up to 73,800 individuals to model a latent genomic factor capturing the shared genetic basis of cortical surface area, folding, curvature, gyrification, and intracranial volume. Using a multivariate framework, we mapped this pleiotropic architecture across biological scales, identifying novel effector genes, neural cell types, and developmental pathways involved in cortical expansion. Functional genomic evidence indicated that a diverse cellular ensemble contributes to the tangential growth and gyrification of the cortex during prenatal neurodevelopment, with progenitor cell lineages prominently involved. Regional analyses further revealed that genetic influences are spatially heterogeneous and organized along canonical anatomical, functional, and developmental gradients. Finally, cortical expansion exhibited substantial genetic overlap with neurodevelopmental, psychiatric, and neurological disorders, with shared genetic influences often concentrated in prefrontal cortical regions. Together, these findings map the genetic architecture of human cortical expansion and implicate it as a common etiological axis linking evolution, neurodevelopment, and health and disease.

genomics↗

bioRxiv2026

Development of the cognitive energy landscape from infancy to adolescence

Brain networks mature in a hierarchical sequence that parallels the emergence of cognitive functions. However, precisely how brain structural maturation supports the ordered development of cognitive functions remains largely unknown. Here, leveraging network control theory and 3712 developmental diffusion magnetic resonance imaging scans, we investigate how the brains structural effort to activate cognitive states -- quantified as control energy -- changes from infancy through adolescence. A total of 100 cognitive states were defined as meta-analytic activation maps from NeuroSynth, prioritized by their frequency in major neurodevelopmental behavioral assessments. We show that the control energy to drive most cognitive tasks decreases during development (for 96 out of 100 cognitive states). Ages to achieve optimal energy efficiency for each state concentrate around school age and late adolescence, whereas social and perceptual functions reach efficiency earlier (mean optimal age = 100.2 months) than higher-order cognitive functions (mean optimal age = 205.5 months). Further, we estimated the influence of molecular-level neurodevelopmental events on control energy by coupling control inputs to each events gene expression profile. We find that such influences vary in both temporal breadth and cognitive scope. Prenatal events (neuron differentiation and migration) exert effects mostly in infancy, while the prolonged process of myelination shapes the energy landscape across all developmental periods and the widest range of cognitive domains. Moreover, the transition energy architecture remains stable across development but becomes progressively modularized, such that transitions within the same category of cognitive states become increasingly favored. Together, these findings provide a comprehensive growth chart of how brain structural maturation supports the hierarchical emergence of cognitive abilities across early life, and establish a normative framework that enables systematic approaches to activate targeted brain circuits and facilitate selective cognitive transitions.

Developmental Biology↗

bioRxiv2026

The Hidden Landscape of Missed Effects in Human Functional Neuroimaging

Functional neuroimaging aims to uncover brain processes underlying behavior and disease, yet studies are often underpowered to detect these effects. How this literature has shaped our understanding of brain function remains unknown, and little guidance exists for planning better powered studies. An underappreciated barrier is that commonly reported effect sizes across the brain are inflated, biasing study planning. Here, we introduce a correction for this inflation bias and show how more accurate studies can be planned using corrected effect size benchmarks from a mega-analysis of 63 typical studies across seven large datasets (52,979 participants). We find that common methods of planning studies based on uncorrected effects lead to roughly half the expected detections at typical sample sizes, with limited spatial overlap with original findings. These missed effects collectively explain meaningful additional variance in the desired outcome. We show how to recover missed effects by planning not only for power but also for a target number of detections via corrected benchmarks, or by taking a whole-brain approach with multivariate effects that individual research groups can detect (n < 50 compared to n > 1,000 for a typical univariate effect). These findings lay the groundwork for more informed study planning and a richer understanding of the widespread nature of brain effects, with implications for shared challenges (and solutions) across biomedicine.

neuroscience↗

bioRxiv2026

PRISME: A MATLAB Toolbox For Large Data-Driven Multimodal Power Benchmarking

Low statistical power in neuroimaging often undermines research in the field, leading to missed effects, wasted resources, and reduced reproducibility. Performing power analyses during the study design phase is extremely important, but often prohibitively difficult due to a lack of analytical solutions and high computational costs. We present PRISME (Power Resampling Infrastructure for Statistical Method Evaluation), a MATLAB toolbox for neuroimaging power benchmarking. PRISME provides a computational framework for empirical power analysis independent of inference methods, enabling large scale power benchmarking and method comparison. The toolbox supports diverse neuroimaging data types, including both voxel-based activation and functional connectivity analyses, with a non-parametric, flexible algorithm and unified data representations. Furthermore, unlike previous empirical power approaches, PRISME supports multiple test types, such as association and difference tests with behavioral and clinical measures. Finally, PRISMEs 25x speedup from algorithmic optimizations enables larger-scale power benchmarking, including the first power analysis for the ABCD dataset. Overall, PRISME is the first method- and data-type-agnostic power benchmarking tool for neuroimaging, providing a single solution for power analysis across diverse study designs.

neuroscience↗

bioRxiv2025

Flexible brain state engagement predicts cognitive control transdiagnostically

Cognitive control supports adaptive responses in an ever-changing world. While alterations in cognitive control have been consistently observed in a range of psychiatric disorders, the neural mechanisms giving rise to this behavioral variation remain elusive. Here, we tested whether the ability to flexibly recruit recurring brain activation patterns (i.e., brain states) may serve as an intermediate phenotype supporting cognitive control in individuals with a spectrum of clinical symptoms. We leveraged machine learning and external validation to explore this question in three independent, transdiagnostic datasets (N>600), including participants with anxiety disorders, schizophrenia, mood disorders, substance use disorders, post-traumatic stress disorder, obsessive-compulsive disorder, and neurodevelopmental disorders. To capture cognitive controls multifaceted nature, we assessed two of its components--inhibition and shift-- using both task-based and questionnaire data. Flexible brain state engagement predicted all cognitive control metrics in previously unseen individuals transdiagnostically, regardless of which dataset was used for model training. Connectome-based predictive modeling also revealed that shared brain networks underpinned flexible brain state engagement in a transdiagnostic manner. Leveraging brain network dynamics, we further observed that moments of more flexible brain state engagement aligned with moments of network connectivity related to better cognitive control within the same individual. This temporal alignment was replicated in all three datasets with heterogeneous samples. Altogether, this study suggests flexible engagement of brain states may support both inter- and intra-individual differences in cognitive control across individuals with diverse mental health profiles. Funding sourcesThis work was supported by F31AA032179 and R01MH121095. Data were provided in part by the Consortium for Neuropsychiatric Phenomics (NIH Roadmap for Medical Research grants UL1-DE019580, RL1MH083268, RL1MH083269, RL1DA024853, RL1MH083270, RL1LM009833, PL1MH083271, and PL1NS062410) and the Transdiagnostic Connectome Project (TCP) dataset. Datasets were accessed through OpenNeuro (https://openneuro.org/datasets/ds005237 and https://openneuro.org/datasets/ds000030/versions/00016).

neuroscience↗

bioRxiv2025

Transdiagnostic connectome-based predictive modeling of many behavioral phenotypes reveals brain network mediators of clinical-cognitive relationships

A key assumption of the NIMHs RDoC framework is that disordered circuits in the brain should manifest in observable behaviors, including psychiatric symptomatology and cognitive deficits. However, how disordered circuitry impacts multiple behaviors remains poorly understood. Connectome-based predictive modeling (CPM) applied to functional MRI connectivity data can identify networks associated with specific behavioral measures across individuals. Prediction strength reflects how closely a measure relates to network connectivity, while derived networks provide evidence of where an individuals disordered circuits are located. Using CPM, we predicted a broad range of self-reported clinical and objective cognitive measures in a large, transdiagnostic sample with extensive fMRI data (n = 317). Prediction performance varied substantially across instruments, with objective cognitive tests yielding stronger models than self-reported clinical measures (p < 0.001). To test whether circuits underlying cognitive deficits related to symptomatology reside in regions where networks overlap, we examined the prediction strength of these sparsely shared circuits. Their connectivity strongly predicted cognitive performance and were primarily localized within the frontoparietal network and between the frontoparietal and default mode networks. These findings reveal how much various behavioral measures reflect brain networks and how circuits within the shared network space contribute to cognitive deficits associated with symptomatology.

neuroscience↗

bioRxiv2025

Fetal network controllability co-develops with synaptic density and synchronizes with maternal network controllability during pregnancy

White matter undergoes rapid changes during the fetal period that are foundational for future cognitive functions. However, how these changes contribute to the brains capacity to support its dynamic activities--its controllability--remains largely unknown. Here, we apply network control theory (NCT) to investigate the developmental trajectory of controllability from the second trimester through the first postnatal month. We analyzed structural connectivity data from fetuses and infants as part of the developing Human Connectome Project. We identified a robust, nonlinear U-shaped developmental curve of whole-brain controllability across the perinatal period, with a minimum at approximately 35 weeks of gestation. Preterm birth disrupted these trajectories, leading to greater controllability and earlier minimums compared to age-matched fetuses. Using gene expression microarray data from 18 fetal post-mortem brains, we identified genes implicated in synaptic functions that co-develop with changes in controllability during the fetal period. We then used positron emission tomography in seven pregnant rhesus macaques to quantify changes in fetal synaptic density. Increased synaptic density in non-human primates (NHPs) co-occurred with periods of reduced controllability in humans. Finally, using longitudinal scans of a pregnant woman, we mapped the trajectory of changes in maternal controllability during pregnancy. This trajectory exhibited a U-shaped pattern that inversely correlated with the fetal trajectory, reaching a maximum around 36 weeks. Together, fetal controllability follows a nonlinear trajectory that co-develops with synaptic functions and synchronizes with maternal changes in controllability during pregnancy.

neuroscience↗

bioRxiv2025

Growth charts of infant visual neurodevelopment generalize across global contexts

Normative brain growth charts in early life hold great promise for furthering basic and clinical science. We leverage the rapid, substantial development of visual cortex function that is indexed by visual-evoked potentials (VEP) in electroencephalography to create longitudinal normative growth curves of task-related brain function with 1374 observations contributed by 802 infants (57 to 579 days old) from South Africa, Brazil, and the United States. Site-specific models were cross-validated and showed excellent fits to other sites samples, demonstrating functional growth curves generalize across contexts robustly. Deviations from the normative growth models associated with early environmental and behavioral measures such as prenatal exposures and postnatal cognition. These findings demonstrate the utility of using functional growth charts to understand and potentially act on individual neurodevelopmental trajectories. VEP brain function growth charts represent a new direction for EEG research to support healthy brain development globally.

neuroscience↗

bioRxiv2025

Overlooked features lead to divergent neurobiological interpretations of brain-based machine learning biomarkers

A central objective in human neuroimaging is to understand the neurobiology underlying cognition and mental health. Machine learning models trained on brain connectivity data are increasingly used as tools for predicting behavioral phenotypes 1,2, enhancing precision medicine 3,4, and improving generalizability compared to traditional MRI studies 5. However, the high dimensionality of brain connectivity data makes model interpretation challenging 6. Prevailing practices within the field rely on sparsely selected brain connectivity features, implicitly interpreting identified feature networks as uniquely representative of a given phenotype while overlooking others. Here, we show that commonly overlooked brain connectivity features can achieve similar prediction accuracies while yielding markedly different neurobiological interpretations. Using four large-scale neuroimaging datasets spanning over 12,000 participants and 13 outcomes, we demonstrate that this phenomenon is widespread across cognitive, developmental, and psychiatric phenotypes. It extends to both functional connectivity (fMRI) and structural (DTI) connectomes and remains evident even in external validation. These findings suggest that common practices may lead to feature overinterpretation and a misrepresentation of the neurobiological bases of brain-behavior associations. Such interpretations present only the "tip of the iceberg" when certain disregarded features may be just as meaningful, potentially contributing to ongoing issues surrounding reproducibility within the field. More broadly, our results point to the possibility that multiple neurobiologically distinct models may exist for the same phenotype, with implications for identifying meaningful subtypes within clinical and research populations.

neuroscience↗

bioRxiv2025

Functional connectivity in the social perception pathway at birth is linked with attention to faces at 4 months

BackgroundProcessing faces and speech is supported by the right-lateralized social visual perception pathway (social pathway) involving medial temporal/visual 5 area (MT/V5) and superior temporal sulcus (STS). Little is known about development of the social pathway and its links with later social outcomes. We examined intrinsic functional connectivity (iFC) in the right social pathway in neurotypical neonates, compared it with iFC in the left social and bilateral dorsal attention pathways, and interrogated prospective links between iFC and social attention in neurodiverse neonates. MethodsiFC in the social and dorsal pathways was measured in 517 full-term neonates from the developing Human Connectome Project (dHCP) and 73 full-term Yale neonates [Mage=41.5 weeks (SD=1.9)]. Social attention was assessed in 36 Yale neonates at Mage=4.2 months (SD=0.4) months. ResultsIn the dHCP sample, the iFC indices were positive in all the pathways (all p-values < 0.001) and did not vary by sex [all p>0.275], but the iFC in the right-lateralized social pathway was higher than in the remaining pathways [all p<0.001] and was positively associated with age at scan [r(517)=0.251, p<0.011]. In the prospective neurodiverse Yale sample, the iFC in the right social pathway was positively associated with social attention at 4 months [p=0.007], and greater social attention at 4 months predicted better social functioning in the second year [p=0.010]. ConclusionsThe early development of intrinsic functional connectivity in the right social perception pathway represents an area of interest for identifying neural mechanisms underlying emergence of atypical social attention associated with autism.

neuroscience↗

bioRxiv2025

White-matter controllability at birth predicts social engagement and language outcomes in toddlerhood

Social engagement and language are connected through early development. Alterations in their development can have a prolonged impact on childrens lives. However, the role of white matter at birth in this ongoing connection is less well-known. Here, we investigate how white matter at birth jointly supports social engagement and language outcomes in 642 infants. We use edge-centric network control theory to quantify edge controllability, or the ability of white-matter connections to drive transitions between diverse brain states, at 1 month. Next, we used connectome-based predictive modeling (CPM) to predict the Quantitative Checklist for Autism in Toddlers (Q-CHAT) for social engagement risks and the Bayley Scales of Infant and Toddler Development (BSID-III) for language skills at 18 months from edge controllability. We created the social engagement network (SEN) to predict Q-CHAT scores and the language network (LAN) to predict BSID-III scores. The SEN and LAN were complex, spanning the whole brain. They also significantly overlapped in anatomy and generalized across measures. Controllability in the SEN at 1 month partially mediated associations between Q-CHAT and BSID-III language scores at 18 months. Further, controllability in the SEN significantly differed between term and preterm infants and predicted Q-CHAT scores in an external sample of preterm infants. Together, our results suggest that the intertwined nature of social engagement and language development is rooted in an infants white-matter controllability. Significance StatementDuring infancy and toddlerhood, social engagement and language emerge together. Delays are often observed in both simultaneously. These interactions persist into later childhood, potentially affecting life quality. We reveal that the interplay between social engagement and language milestones in toddlerhood is rooted in the infants structural connectivity, which may assist in early risk identification of developmental delays. Insights into the early brain foundations for emerging social engagement and language skills may open opportunities for individualized interventions to improve developmental outcomes.

neuroscience↗

bioRxiv2024

Testing the Tests: Using Connectome-Based Predictive Models to Reveal the Systems Standardized Tests and Clinical Symptoms Are Reflecting

BackgroundSubstantial strides have been made in developing connectome-based predictive models that establish connections between external measures of cognition and/or symptoms obtained through testing performed in a clinical setting, and the human functional connectome. Often referred to as brain-behavior modeling such models offer insights into the functional brain organization supporting the test scores of these external measures1-7. Here, we depart from the conventional feed-forward approach and introduce a feed-back approach that provides new insight into the systems the external measures are reflecting and provides a framework for developing new test instruments that better target specific brain systems. MethodsIn fMRI data from 227 demographically and clinically diverse subjects (healthy participants and patients), we a priori define connectivity networks for the six cognitive constructs and employ kernel ridge regression in a predictive modeling framework to quantify each networks contribution to performance across a spectrum of standardized tests. ResultsThis approach provides a ranking of test scores according to the predictive power of each cognitive network, allowing one to choose the best test to probe a specific brain network. It yields a brain-driven process for forming new tests through selection of combinations of measures that probe the same brain systems. These new composite tests yield better external measures, as reflected by higher predictive power in brain-behavior modeling. We also evaluate the inclusion of specific subtests within a composite score, revealing instances where composite scores are reinforced or weakened by subtest inclusion regarding the specificity of the brain network they interrogate. ConclusionsThe brain-behavior modeling problem can be reconfigured to provide a biologically driven approach to the selection of external measures directed at specific brain systems. It opens new avenues of research by providing a framework for the development of measures, both cognitive and clinical, guided by quantitative brain metrics.

neuroscience↗

bioRxiv2024

Disruption of the Social Visual Pathway in Autism Spectrum Disorder

The social visual pathway, which diverges from the dorsal pathway at the visual motion area (MT/V5) and runs from posterior down to anterior portions of the superior temporal sulcus (STS), specializes in processing dynamic social information. This study examined resting-state functional connectivity within this pathway in children with autism spectrum disorder (ASD) and typically developing (TD) children. Using data from the ABIDE (Autism Brain Imaging Data Exchange) repository, we found significant underconnectivity between the posterior and middle STS (pSTS-mSTS) in the right hemisphere in children with ASD compared to TD children. Weaker connectivity in this region of the pathway correlated with more severe social impairment symptoms in ASD and reduced social function across both ASD and TD groups. These findings suggest a specific disruption in the right hemisphere social visual pathway in ASD, potentially contributing to social deficits observed in the disorder.

neuroscience↗

bioRxiv2024

Variation in moment-to-moment brain state engagement changes across development and contributes to individual differences in executive function

Neural variability, or variation in brain signals, facilitates dynamic brain responses to ongoing demands. This flexibility is important during development from childhood to young adulthood, a period characterized by rapid changes in experience. However, little is known about how variability in the engagement of recurring brain states changes during development. Such investigations would require the continuous assessment of multiple brain states concurrently. Here, we leverage a new computational framework to study state engagement variability (SEV) during development. A consistent pattern of SEV changing with age was identified across cross-sectional and longitudinal datasets (N>3000). SEV developmental trajectories stabilize around mid-adolescence, with timing varying by sex and brain state. SEV successfully predicts executive function (EF) in youths from an independent dataset. Worse EF is further linked to alterations in SEV development. These converging findings suggest SEV changes over development, allowing individuals to flexibly recruit various brain states to meet evolving needs.

neuroscience↗

bioRxiv2024

Neural Mechanisms of Feedback Processing and Behavioral Adaptation during Neurofeedback Training

The acquisition of new skills is facilitated by providing individuals with feedback that reflects their performance. This process creates a closed loop that involves feedback processing and regulation recalibration to promote effective training. Functional magnetic resonance imaging (fMRI)-based neurofeedback is unique in applying this principle by delivering direct feedback on the self-regulation of brain activity. Understanding how feedback-driven learning occurs requires examining how feedback is evaluated and how regulation adjusts in response to feedback signals. In this pre-registered mega-analysis, we re-analyzed data from eight intermittent fMRI neurofeedback studies (N = 153 individuals) to investigate brain regions where activity and connectivity are linked to feedback processing and regulation recalibration (i.e., regulation after feedback) during training. We harmonized feedback scores presented during training in these studies and computed their linear associations with brain activity and connectivity using parametric general linear model analyses. We observed that, during feedback processing, feedback scores were positively associated with (1) activity in the reward system, dorsal attention network, default mode network, and cerebellum; and with (2) reward system-related connectivity within the salience network. During regulation recalibration, no significant associations were observed between feedback scores and either activity or associative learning-related connectivity. Our results suggest that neurofeedback is processed in the reward system, supporting the theory that reinforcement learning shapes this form of brain training. In addition, the involvement of large-scale networks in feedback processing, continuously transitioning between evaluating external feedback and internally assessing the adopted cognitive state, suggests that higher-level processing is integral to this type of learning. Our findings highlight the pivotal role of performance-related feedback as a driving force in learning, potentially extending beyond neurofeedback training to other feedback-based processes. Key PointsWe conducted a pre-registered mega-analysis integrating data from eight fMRI neurofeedback studies to examine feedback processing and regulation recalibration during neurofeedback training. During feedback processing, feedback was associated with activity in the reward system, dorsal attention network, default mode network, and cerebellum; as well as with reward system-related connectivity within the salience network. We found no positive results during regulation blocks; however, additional analyses suggest that recalibration may have already occurred during feedback presentation.

neuroscience↗

bioRxiv2024

Brain age prediction and deviations from normative trajectories in the neonatal connectome

Structural and functional connectomes undergo rapid changes during the third trimester and the first month of postnatal life. Despite progress, our understanding of the developmental trajectories of the connectome in the perinatal period remains incomplete. Brain age prediction uses machine learning to estimate the brains maturity relative to normative data. The difference between the individuals predicted and chronological age--or brain age gap (BAG)--represents the deviation from these normative trajectories. Here, we assess brain age prediction and BAGs using structural and functional connectomes for infants in the first month of life. We used resting-state fMRI and DTI data from 611 infants (174 preterm; 437 term) from the Developing Human Connectome Project (dHCP) and connectome-based predictive modeling to predict postmenstrual age (PMA). Structural and functional connectomes accurately predicted PMA for term and preterm infants. Predicted ages from each modality were correlated. At the network level, nearly all canonical brain networks--even putatively later developing ones--generated accurate PMA prediction. Additionally, BAGs were associated with perinatal exposures and toddler behavioral outcomes. Overall, our results underscore the importance of normative modeling and deviations from these models during the perinatal period.

neuroscience↗