Biology subjects

Barch, D. M.

Publications and source records attributed to Barch, D. M..

At least 19 recordsLinked to original sources

bioRxiv2026

Insula functional connectivity development follows a canonical antero-posterior axis of organization

The insula is a heterogeneous region associated with a range of functional domains including sensory, cognitive, and emotional processing. Cytoarchitectural and functional studies have described an antero-posterior axis of insula organization. However, the insula's functional development remains sparsely described. We delineated the development of insula resting-state functional connectivity across 562 individuals (6-22 years) using generalized additive models and population value decomposition. We show insulo-cortical functional connectivity development differs across the insula: anterior subregions have predominantly decreasing connectivity across the age range examined, whereas posterior subregions have variable connectivity that aligns with the sensorimotor-association axis. Vertex-level analysis revealed that age-related trajectories followed an antero-posterior axis that aligned with estimates of insula microstructure. Our results emphasize that the development of insula connectivity is spatially heterogeneous and aligns with known cytoarchitecture. The normative insula development described here provides a foundation for investigating how emerging psychopathology may be linked to variation in insula development.

neuroscience↗

bioRxiv2026

Movie Watching for Individual Functional Brain Network Estimation in Adolescents

Precision functional mapping (PFM) reveals stable individual differences in brain functional network organization and holds promise for advancing neuropsychiatric clinical care. PFM requires sufficient high-quality and densely-sampled functional MRI data at the individual level that is typically acquired during the so-called resting state while passively fixating on a crosshair. However, crosshair fixation acquisitions can be challenging especially for pediatric or patient populations, where alternatives like movie watching might be used to improve data quality and reduce participant burden. Here, we compare the use of resting state crosshair fixation and movie watching fMRI data to derive individual-specific functional brain network topography using PFM data (28-38 minutes of low-motion fMRI data per-participant per-condition) in nineteen adolescents (12-18 years) with a range of transdiagnostic psychiatric diagnoses. Network assignments were largely conserved between crosshair fixation and movie watching, with a smaller subset of reproducible network reassignments which were overwhelmingly border shifts between neighboring networks. This suggests largely conserved global structure of functional network topography between crosshair fixation and movie watching, though border shifts can compound into variable estimates of cortical network size particularly for movie positive (language, salience, and parietal memory) and movie negative networks (default mode, action mode). Functional network assignments derived from movies and crosshair were also similarly individual-specific and consistent within common neuromodulation targets including dorsolateral prefrontal cortex and somatomotor cortex. Key results were replicated in independent pediatric consortia data (Healthy Brain Network, n = 466, 6-22 years) and reproduced across independent PFM data sessions. Acknowledging individual differences and level of analysis, we conclude that movies are a viable alternative to resting state crosshair fixation for individual network definitions.

neuroscience↗

bioRxiv2026

Inflections in adolescent cortical maturation signal psychopathology risk

Cerebral cortical maturation supports emotional, social, and cognitive development during adolescence1. Neurodevelopmental theories of psychiatric illnesses, which peak in onset around age 182, implicate subtle alterations in adolescent cortical development3. However, mechanistic links between cortical maturation and emergent psychopathology have remained elusive. This gap likely reflects a need for more nuanced understanding of cortical development4, larger, longitudinal samples powered to detect small effects,5 and more uniform image quality control.6 Using 29,482 structural MRI scans from 11,720 adolescents in the Adolescent Brain Cognitive Development (ABCD) study, we demonstrate reproducible differences in the contour and tempo of maturation across the cortical surface. Regional trajectories are robust to image quality variance and align with increasing functional specialization over time. Thicker cortex in early-maturing sensory regions predicts psychiatric symptoms as early as four years prior to their emergence, a pattern that subsequently spreads to association cortex. Overlap with spatially and temporally organized gene co-expression networks implicates prenatal programming of this sequence of maturation and its links to psychopathology. These findings present a novel, versatile neurodevelopmental framework for cortical maturation during adolescence that sequentially implicates sensory and association cortex in risk for emergent psychiatric symptoms.

neuroscience↗

bioRxiv2026

Functional Organization of the Neonatal Basal Ganglia and Thalamus

The basal ganglia and thalamus are key nodes in subcortico-cortical loops involved in sensory, motor, and cognitive function. In adults, posterior regions of the subcortex link to cortical sensorimotor networks and anterior regions link to association networks. Alterations in the size, strength, and selectivity of these subcortical regional network representations are implicated in several neuropsychiatric disorders, many of which originate early in development. However, the organization of these network representations at birth remains incompletely understood, limiting our ability to devise normative and atypical developmental models of subcortico-cortical interactions. Using resting-state fMRI, we characterized the size, strength, and selectivity of cortical network representations in the basal ganglia and thalamus in a set of neonates (n=261) and compared results to children (age range 9-11 years, n=69) and adults (n=120). We found that the broad anterior-posterior organization of the subcortex is present at birth, yet representations of somatomotor networks were larger at birth compared to children and adults (p<0.001). The strength and selectivity of subcortico-cortical functional connectivity (FC) exhibited interactions between age group and network (all p<0.001), such that subcortical representations of sensorimotor networks exhibited stronger FC and higher selectivity in neonates, while subcortical representations of association networks exhibited stronger FC and higher selectivity in older cohorts. In parallel, data-driven clustering revealed areas with integration of multiple networks in the neonatal subcortex. These results suggest that subcortico-cortical FC evolves over development largely in a sensorimotor-association manner and provide a baseline for normative and disordered subcortical development. HIGHLIGHTSO_LIThe basic anterior-posterior layout of association to sensorimotor network representation in the basal ganglia and thalamus is present at term birth. C_LIO_LISensorimotor systems are overrepresented in neonates compared to children and adults. C_LIO_LISensorimotor representations exhibit greater functional connectivity strength and selectivity than association representations at birth, while association representations are stronger and more selective than sensorimotor representations in adults. C_LIO_LIThe neonatal subcortex exhibits substantial integration of multiple cortical networks. C_LI GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/736181v1_ufig1.gif" ALT="Figure 1"> View larger version (48K): org.highwire.dtl.DTLVardef@1d3bfa3org.highwire.dtl.DTLVardef@ca904dorg.highwire.dtl.DTLVardef@c7c219org.highwire.dtl.DTLVardef@983020_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2026

Functional brain network correlates of pubertal timing and depressive symptoms in preadolescence

BACKGROUNDVariation in pubertal maturation relative to same-age, same-sex peers (pubertal timing) has been linked to increased risk for depressive symptoms during adolescence. This developmental period is also characterized by substantial reorganization of functional brain networks. However, how pubertal timing relates to resting-state functional connectivity (rsFC) changes and depression risk remains unclear. METHODSWe examined pubertal timing and rsFC associations in preadolescents aged 9-11 years from the Adolescent Brain Cognitive Development (ABCD) Study. Pubertal timing was estimated using a puberty age gap approach based on parent-reported physical development. Linear mixed-effects and Bayesian multilevel models were used to assess cross-sectional and longitudinal associations between pubertal timing and rsFC across large-scale functional brain networks. We also tested whether rsFC differences explained associations between pubertal timing and later depressive symptoms. RESULTSEarlier pubertal timing was associated with heterogeneous rsFC patterns, with stronger and more widespread effects in females. In females, earlier pubertal timing was associated with rsFC increases and decreases across sensory-motor and association networks, whereas in males, associations were more limited and localized to sensorimotor and cerebellar systems. Longitudinally, earlier pubertal timing in females predicted reductions in rsFC at the 2-year follow-up, with no significant associations in males. rsFC differences did not explain the pubertal timing and later depressive symptoms association. CONCLUSIONSPubertal timing is associated with sex-specific patterns of brain functional connectivity during early adolescence, with greater heterogeneity and broader network involvement in females. These findings suggest that pubertal maturation contributes to early reorganization of functional brain networks, although these changes did not explain subsequent depressive symptoms.

neuroscience↗

bioRxiv2026

Mapping developmental patterns of intrinsic timescale

Intrinsic timescale is a commonly used measure of spontaneous neural dynamics that quantifies the temporal window of processing of neuronal populations. Intrinsic timescale displays a hierarchical cortical organization across multiple species and imaging modalities, with shorter timescales in sensorimotor cortex compared to association cortex. However, less is known about how intrinsic timescale evolves during human brain development and whether its cortical maturation patterns generalize to independent developmental samples. Here we estimate the intrinsic timescale in two independent cross-sectional datasets of youth (HCPD: n=565; HBN: n=729; age range 8-22 years) and investigate its neurodevelopmental patterns. We find that developmental patterns in the intrinsic timescale follow a hierarchical pattern that recapitulates an axis spanning sensorimotor to association cortices (S-A axis). Our analysis of an independent healthy young adult dataset (HCPYA: n=973, age range 22-37 years) underscores the specificity of these developmental findings, suggesting that the intrinsic timescale develops along the S-A axis in youth and stabilizes in adulthood. Together, these results reveal convergence between major axes of cortical organization and development, highlighting intrinsic timescale as a principled marker of hierarchical brain maturation in youth.

neuroscience↗

bioRxiv2026

Functional organization underlying superior performance in a memory champion

Memory athletes can achieve superior performance (e.g., memorizing 339 digits in 5 minutes) with extensive daily training, by converting abstract information into vivid scenes, and placing them along a mental path, that is later retraced (Method of Loci). Understanding the brain mechanisms underlying such training-derived mastery would increase our understanding of the brains memory systems and could suggest novel approaches to improving cognition in other domains. As memory athletes use personalized training techniques, it has been challenging to study them with standard group paradigms. Fortunately, precision functional mapping (PFM) enables detailed investigation of individual brains through repeated sampling of resting-state functional connectivity and task fMRI. Here, we precisely mapped the brain organization of a 6-time U.S. Memory Champion (>13 hours fMRI). Relative to controls, the Memory Champions network functional connectivity (FC) was strengthened with the retrosplenial, extrastriate visual, and dorsal frontal cortex (area 55b), as well as with the caudate nucleus. The Memory Champion had modules related to scene and semantic processing not seen in controls, alongside stronger connectivity between the caudate and classical memory networks. During rote memorization, the Champions task fMRI patterns were typical, with the hippocampus active during encoding. This pattern was reversed when he used his Method of Loci technique, with greater hippocampal activity during recall than encoding. Hence, intense practice at converting abstract information into more memorable formats can develop a procedural memory skill that utilizes brain regions typically reserved for navigation, language, social cognition, and associative learning.

neuroscience↗

bioRxiv2025

Model-based EEG phenotyping uncovers distinct neurocomputational mechanisms underlying learning impairments across psychopathologies

BackgroundMajor depressive disorder (MDD), bipolar disorder (BP), and schizophrenia (SCZ) involve learning impairments with poorly understood mechanisms. Understanding both the similarities and differences in these mechanisms is important to guide the development of new, targeted interventions. Methods255 participants diagnosed with MDD (n=54), BP (n=47), SCZ (n=67) or without any diagnoses (CTRL; n=87) performed an associative learning task. Computational modeling quantified the mechanistic interplay between working memory (WM) and reinforcement learning (RL). The latent RL and WM signatures in the EEG dynamics showed shared and distinct neurocognitive mechanisms underlying learning. ResultsAll clinical groups showed learning impairments at the behavioral level. Model-based EEG analyses linked these impairments to distinct patterns in the dynamic interplay between latent RL and WM mechanisms, contrasting with the typical patterns observed in CTRL. SCZ was characterized by reduced neural markers of WM, weakening the cooperative influence of WM onto RL (reduced WM recruitment), and reduced integration of negative feedback. Conversely, MDD was characterized by reduced reciprocal influence of RL onto WM, reducing the tendency to upregulate WM contribution with reward history (impaired WM management). Finally, BP was characterized by deficits in both WM and RL recruitment, along with higher WM decay. ConclusionsBehavioral learning impairments that appear similar across clinical groups can be linked to distinct neurocognitive mechanisms via integrative neurocomputational modeling. Our approach provides insights into the interplay of underlying learning mechanisms and how they manifest differently across psychopathologies. CitationThis manuscript is a preprint version of the later manuscript accepted for publication in Biological Psychiatry: Global Open Science. The content may differ from the final published version following peer review and editorial revisions. Ging-Jehli, N.R., Rac-Lubashevsky, R., Bera, K., Roberts, A., Loder, A., Boudewyn, M.A., Carter, C.S., Erickson, M., Gold, J., Luck, S.J., Ragland, J.D., Yonelinas, A.P., MacDonald III, A.W., Barch, D.M., & Frank, M.J. (2025). Model-based EEG phenotyping uncovers distinct neurocomputational mechanisms underlying learning impairments across psychopathologies. Preprint at bioRxiv.

pathology↗

bioRxiv2025

Refining RDoC Using Individual-Level Task fMRI Factor Models Reveals Reproducible Brain-wide Motifs

The Research Domain Criteria (RDoC) framework was introduced to guide psychiatric research using biologically grounded, dimensional constructs of mental function. However, its hierarchical domain structure remains largely unvalidated against individual-level brain and behavioral data. Building on prior group-level work, we applied a multi-stage validation framework to Human Connectome Project (HCP) task-fMRI data to test whether individual-level, data-driven models more accurately capture the organization of brain activity and behavior than RDoC-based models. Using confirmatory factor analysis in two independent cohorts, we found that data-driven bifactor models consistently outperformed RDoC-based models across multiple fit indices. The general factor derived from these models revealed a reproducible, low-dimensional axis spanning visual-attentional to default mode-auditory systems, aligning with canonical macroscale cortical gradients. Community detection further identified reproducible spatial motifs whose centroids corresponded to interpretable functional systems and whose alignment predicted individual performance on working memory and relational reasoning tasks. To assess whether these findings extended beyond neural data, we analyzed behavioral measures in HCP and in an independent transdiagnostic dataset (LA5c). In both datasets, data-driven behavioral models outperformed RDoC-based models, although the relative support for bifactor versus specific factor structure differed by dataset. Extending the neural analyses to LA5c, which included healthy controls and individuals with ADHD, bipolar disorder, and schizophrenia, showed that data-driven bifactor models generalized across diagnostic groups and that alignment with data-driven community centroids related to symptom severity, whereas RDoC-based representations showed weaker or no associations. Finally, topological analysis of task-evoked brain activity revealed that data-driven representations better captured the global organization of brain states than RDoC domains. Together, these findings demonstrate that individual-level, empirically derived models provide a more accurate, generalizable, and behaviorally relevant account of brain organization than the current RDoC framework. By integrating neural, behavioral, and clinical validation, this work advances precision neuroscience and supports the empirical refinement of dimensional psychiatric frameworks.

neuroscience↗

bioRxiv2025

Commonality and Variability in Functional Networks In Young Children Under 5 Years Old

Functional brain networks support human cognition, yet how individualized network architecture emerges in early childhood remains poorly understood. Averaging across participants can obscure age-specific organization and person-to-person differences, particularly in slowly developing association cortices. We developed an age-appropriate functional reference that captured common structure across toddlers without averaging away individual variability, enabling estimation of each childs networks from resting-state fMRI. Across cohorts of 8-60-month-old children, we found individualized network organization--including finer-scale subdivisions and emerging language lateralization--well before age five. Network layouts showed longitudinal stability, with greater consistency in sensory than association regions. Within-network connectivity was stronger and explained age-related variance when networks were defined using individualized rather than group-consensus topography. Left-lateralization of language networks tracked age-normalized verbal ability, linking early functional architecture to emerging cognition. These findings show that behaviorally relevant brain networks arise far earlier than previously recognized, providing a foundation for studying typical development and early biomarkers.

neuroscience↗

bioRxiv2025

Replicable, Transdiagnostic Behavioral and Neural Correlates of Sensory Over-responsivity

ObjectiveSensory over-responsivity (SOR), characterized by strong negative reactions to typically innocuous stimuli, is considered a symptom of autism spectrum disorder. However, SOR also affects 15-20% of children overall, including a majority of children with common psychiatric conditions. Despite its prevalence, the clinical specificity and neurobiological bases of SOR remain poorly understood. Our study aims to determine the specific clinical significance of SOR across diverse child samples and establish whether SOR is associated with replicable patterns of functional connectivity (FC). MethodWe analyzed data from 15,728 children (ages 6-17.9 years) across five datasets: three community samples, including the Adolescent Brain Cognitive Development [ABCD] and Healthy Brain Network [HBN] studies, and two autism-enriched samples. Bivariate and multivariate models examined associations between SOR and symptoms of anxiety, attention-deficit/hyperactivity disorder, depression, conduct disorder, and oppositional defiant disorder, as well as autistic traits. Analysis of resting-state functional MRI (fMRI) data from the ABCD study (n=4195) identified candidate brain-wide and circuit-specific FC correlates of mild SOR and tested replication of effects in an independent ABCD subsample (n=4190). Additional analyses tested extension to children with severe SOR in ABCD and replication in smaller samples [HBN (n=356), ABCD subset (n=356)]. ResultsMultivariate analyses revealed that SOR is associated with a remarkably consistent profile across samples: greater levels of both autistic traits and anxiety symptoms and, in community samples, lower levels of conduct disorder symptoms. Across samples, SOR is not reliably associated with symptoms of any other analyzed psychiatric conditions. Mild SOR is associated with brain-wide FC patterns and, specifically, reduced FC between cingulo-parietal network and bilateral caudate nucleus; these patterns show robust replication across independent ABCD subsamples and extension to severe SOR in ABCD but are not significant in smaller samples, indicating that large sample size is needed to reliably detect brain effects. ConclusionResults suggest that SOR may constitute a latent trait associated with both specific clinical risk and protection, and with replicable cortico-subcortical neural correlates. These findings advance our understanding of the neurobiology and clinical relevance of SOR. They may also inform clinical practice and future research aimed at understanding and supporting individuals with sensory challenges.

neuroscience↗

bioRxiv2025

Ambient Pollution Components and Sources Associated with Hippocampal Architecture and Memory in Pre-Adolescents

BackgroundAmbient air pollution poses significant risks to brain health. The hippocampus may be particularly vulnerable, yet the extent to which it is impacted in children remains unclear. MethodsUsing partial least squares correlation, we cross-sectionally analyzed air pollution, brain, and cognitive data from the Adolescent Brain Cognitive Development Study to examine how multi-pollutant exposure influences hippocampal structure and memory in 9-11-year-olds (n= 7,940). Annual average air pollution exposures included PM2.5 (total mass, 15 components, and 6 source factors), NO2, and 8-hour maximum O3. Hippocampal outcomes included microstructure measured using Restriction Spectrum Imaging and hippocampus longitudinal-axis (i.e., head, body, tail) volumes. We examined hippocampal-dependent list-learning using the Rey Auditory Verbal Learning Test. Models were adjusted for demographic, socioeconomic, and neuroimaging factors. FindingsPM2.5 total mass was associated with hippocampal microstructure, but not long-axis volume or list-learning ability. Component and source analyses provided greater specificity: higher bromine, sulfate, and vanadium exposure was related to microstructure (72% shared variance), while higher copper and zinc exposure correlated with smaller left head and right body and tail volumes (75% shared variance). Source models implicated biomass burning and traffic pollution in microstructure (61% and 32% shared variance) and industrial and traffic sources in smaller hippocampal volumes (77% shared variance). Higher exposure to several components were also linked to poorer list-learning (67% shared variance). DiscussionCo-exposure to multiple pollutants is linked to differences in hippocampal structure and memory, showing that associations are driven not only by PM2.5 total mass but also by specific components and sources. This evidence underscores the necessity of targeting source-specific (e.g., biomass burning, traffic, and industrial emissions) and constituent components (e.g., metals) of air pollution during critical developmental windows to safeguard brain health.

neuroscience↗

bioRxiv2025

Stimulant medications affect arousal and reward, not attention

Prescription stimulants such as methylphenidate are being used by an increasing portion of the population, primarily children. These potent norepinephrine and dopamine reuptake inhibitors promote wakefulness, suppress appetite, enhance physical performance, and are purported to increase attentional abilities. Prior functional magnetic resonance imaging (fMRI) studies have yielded conflicting results about the effects of stimulants on the brains attention, action/motor, and salience regions that are difficult to reconcile with their proposed attentional effects. Here, we utilized resting-state fMRI (rs-fMRI) data from the large Adolescent Brain Cognitive Development (ABCD) Study to understand the effects of stimulants on brain functional connectivity (FC) in children (n = 11,875; 8-11 years old) using network level analysis (NLA). We validated these brain-wide association study (BWAS) findings in a controlled, precision imaging drug trial (PIDT) with highly-sampled (165-210 minutes) healthy adults receiving high-dose methylphenidate (Ritalin, 40 mg). In both studies, stimulants were associated with altered FC in action and motor regions, matching patterns of norepinephrine transporter expression. Connectivity was also changed in the salience (SAL) and parietal memory networks (PMN), which are important for reward-motivated learning and closely linked to dopamine, but not the brains attention systems (e.g. dorsal attention network, DAN). Stimulant-related differences in FC closely matched the rs-fMRI pattern of getting enough sleep, as well as EEG- and respiration-derived brain maps of arousal. Taking stimulants rescued the effects of sleep deprivation on brain connectivity and school grades. The combined noradrenergic and dopaminergic effects of stimulants may drive brain organization towards a more wakeful and rewarded configuration, explaining improved task effort and persistence without direct effects on attention networks.

neuroscience↗

bioRxiv2025

Two Axes of White Matter Development

Despite decades of neuroimaging research, how white matter develops along the length of major tracts in humans remains unknown. Here, we identify fundamental patterns of white matter maturation by examining developmental variation along major, long-range cortico-cortical tracts in youth ages 5-23 years using diffusion MRI from three large-scale, cross-sectional datasets (total N = 2,716). Across datasets, we delineate two replicable axes of human white matter development. First, we find a deep-to-superficial axis, in which superficial tract regions near the cortical surface exhibit greater age-related change than deep tract regions. Second, we demonstrate that the development of superficial tract regions aligns with the cortical hierarchy defined by the sensorimotor-association axis, with tract ends adjacent to sensorimotor cortices maturing earlier than those adjacent to association cortices. These results reveal developmental variation along tracts that conventional tract-average analyses have previously obscured, challenging the implicit assumption that white matter tracts mature uniformly along their length. Such developmental variation along tracts may have functional implications, including mitigating ephaptic coupling in densely packed deep tract regions and tuning neural synchrony through hierarchical development in superficial tract regions - ultimately refining neural transmission in youth.

neuroscience↗

bioRxiv2024

Brain functional connectivity, but not neuroanatomy, captures the interrelationship between sex and gender in preadolescents

Understanding sex differences in the adolescent brain is crucial, as these differences are linked to neurological and psychiatric conditions that vary between males and females. Predicting sex from adolescent brain data may offer valuable insights into how these variations shape neurodevelopment. Recently, attention has shifted toward exploring socially-identified gender, distinct from sex assigned at birth, recognizing its additional explanatory power. This study evaluates whether resting-state functional connectivity (rsFC) or cortical thickness more effectively predicts sex and sex/gender alignment (the congruence between sex and gender) and investigates their interrelationship in preadolescents. Using data from the Adolescent Brain Cognitive Development (ABCD) Study, we employed machine learning to predict both sex (assigned at birth) and sex/gender alignment from rsFC and cortical thickness. rsFC predicted sex with significantly higher accuracy (86%) than cortical thickness (75%) and combining both did not improve the rsFC models accuracy. Brain regions most effective in predicting sex belonged to association (default mode, dorsal attention, and parietal memory) and visual (visual and medial visual) networks. The rsFC sex classifier trained on sex/gender aligned youth was significantly more effective in classifying unseen youth with sex/gender alignment than in classifying unseen youth with sex/gender unalignment. In females, the degree to which their brains rsFC matched a sex profile (female or male), was positively associated with the degree of sex/gender alignment. Lastly, neither rsFC nor cortical thickness predicted sex/gender alignment. These findings highlight rsFCs predictive power in capturing the relationship between sex and gender and the complexity of the interplay between sex, gender, and the brains functional connectivity and neuroanatomy.

neuroscience↗

bioRxiv2024

Predicting Task Activation Maps from Resting-State Functional Connectivity using Deep Learning

Deep learning models have demonstrated the potential to predict task-evoked brain activation from resting-state fMRI, offering a pathway toward individualized brain mapping without requiring task-based data. In this study, we systematically evaluate architectural strategies for improving the efficiency and scalability of such models. Using data from the Human Connectome Project, we replicate the BrainSurfCNN framework and introduce two extensions: BrainSERF, which incorporates channel-wise attention through squeeze-and-excitation modules, and BrainSurfGCN, a graph-based model that leverages cortical mesh topology for efficient message passing. Across multiple evaluation metrics, including spatial correlation, Dice score, Dice AUC, and subject identification accuracy, all models achieve comparable predictive performance. Despite similar accuracy, the proposed models offer distinct advantages. BrainSERF provides modest improvements in capturing individual-specific features, while BrainSurfGCN achieves substantial reductions in model size and training time, highlighting a favorable trade-off between performance and computational efficiency. Beyond architectural comparisons, we investigate factors driving variability in prediction accuracy. We find that behavioral task performance, resting-state data quality, and inter-subject variability in task activation jointly constrain prediction fidelity. In particular, contrasts with lower signal reliability and higher variability exhibit reduced predictability across all models. Together, these findings demonstrate that incorporating topological and functional structural priors can improve the efficiency of deep learning models without sacrificing accuracy, while also emphasizing that prediction performance is fundamentally limited by the reliability of the underlying neural signals.

neuroscience↗

bioRxiv2024

Early Life Neuroimaging: The Generalizability of Cortical Area Parcellations Across Development

The cerebral cortex consists of distinct areas that develop through intrinsic embryonic patterning and postnatal experiences. Accurate parcellation of these areas in neuroimaging studies improves statistical power and cross-study comparability. Given significant brain changes in volume, microstructure, and connectivity during early life, we hypothesized that cortical areas in 1- to 3-year-olds would differ markedly from neonates and increasingly resemble adult patterns as development progresses. Here, we parcellated the cerebral cortex into putative areas using local functional connectivity gradients in 92 toddlers at 2 years old. We demonstrate high reproducibility of these cortical regions across 1- to 3-year-olds in two independent datasets. The area boundaries in 1- to 3-year-olds were more similar to those in adults than those in neonates. While the age-specific group area parcellation better fit the underlying functional connectivity in individuals during the first 3 years, adult area parcellations might still have some utility in developmental studies, especially in children older than 6 years. Additionally, we provide connectivity-based community assignments of the parcels, showing fragmented anterior and posterior components based on the strongest connectivity, yet alignment with adult systems when weaker connectivity was included.

neuroscience↗

bioRxiv2024

A sensorimotor-association axis of thalamocortical connection development

Human cortical development follows a sensorimotor-to-association sequence during childhood and adolescence1-6. The brains capacity to enact this sequence over decades indicates that it relies on intrinsic mechanisms to regulate inter-regional differences in the timing of cortical maturation, yet regulators of human developmental chronology are not well understood. Given evidence from animal models that thalamic axons modulate windows of cortical plasticity7-12, here we evaluate the overarching hypothesis that structural connections between the thalamus and cortex help to coordinate cortical maturational heterochronicity during youth. We first introduce, cortically annotate, and anatomically validate a new atlas of human thalamocortical connections using diffusion tractography. By applying this atlas to three independent youth datasets (ages 8-23 years; total N = 2,676), we reproducibly demonstrate that thalamocortical connections develop along a maturational gradient that aligns with the cortexs sensorimotor-association axis. Associative cortical regions with thalamic connections that take longest to mature exhibit protracted expression of neurochemical, structural, and functional markers indicative of higher circuit plasticity as well as heightened environmental sensitivity. This work highlights a central role for the thalamus in the orchestration of hierarchically organized and environmentally sensitive windows of cortical developmental malleability.

neuroscience↗