Biology subjects

Shinohara, R. T.

Publications and source records attributed to Shinohara, R. T..

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

Olfactory Deficits and Structural Anomalies of the Peripheral Olfactory System in 22q11.2 Deletion Syndrome

Background: Approximately 25% of individuals with 22q11.2 deletion syndrome (22q11DS) develop a psychotic disorder, and the syndrome shares key neurodevelopmental features with psychosis in the general population. The olfactory system develops early in utero, providing a window into gestational disruptions relevant to psychosis risk. We hypothesized that 22q11DS would show impairments in olfactory function and structural anomalies of the olfactory system relative to psychosis spectrum (PS) youth without a known genetic syndrome and typically developing controls (TD). Methods: Participants included individuals with 22q11DS (n=101), PS (n=56), and TD (n=52). Sinonasal symptoms, odor identification, and discrimination (Sniffin' Sticks) behaviorally. Structural 3T MRI measured olfactory bulb volume, sulcus depth, and sulcus length. Sample sizes vary per analysis. Results: Relative to TD, 22q11DS showed elevated sinonasal burden (p<.001), lower odor identification (p<.001), and lower odor discrimination (p<.001). PS showed elevated sinonasal burden (p<.001) and marginally lower odor identification (p=.06), but no discrimination deficit. Olfactory bulb volume was significantly smaller in 22q11DS versus TD (p=.007, r=.39); PS showed a non-significant trend (p=.07). Olfactory sulcus depth (p=.52) and length (p=.82) did not differ across groups. Conclusions: 22q11DS is associated with elevated sinonasal burden, robust olfactory identification and discrimination deficits exceeding those of PS and reduced olfactory bulb volume. The co-occurrence of functional deficits and reduced bulb volume without sulcal anomalies may distinguish the 22q11DS olfactory phenotype from that of PS. These hypothesis-generating findings are consistent with partially dissociable neurodevelopmental mechanisms and support the utility of 22q11DS as a genetic model for psychosis risk.

neuroscience↗

bioRxiv2026

An Open Test--Retest Dataset of Rapid Myelin Mapping Acquisitions

Myelin supports rapid and efficient neural communication and is implicated in brain development, learning, aging, and neurological disease. Although numerous magnetic resonance imaging (MRI) approaches have been proposed to characterize myelin in vivo, they have rarely been evaluated together within the same participants and across repeated acquisitions. To address these gaps, we introduce the Myelin Imaging Reliability and ReprOducibility Resource (MIRROR), an openly available multimodal test--retest MRI dataset of diffusion MRI (dMRI), inhomogeneous magnetization transfer imaging (ihMT), quantitative susceptibility mapping (QSM), MP2RAGE, T1w/T2w ratio mapping, and transverse relaxometry in 22 healthy adults. We evaluated gray--white matter differentiation, intermetric correspondence, test--retest reliability, and participant specificity across 27 myelin-sensitive metrics. Gray--white matter differentiation was strong for dMRI derivatives, MP2RAGE-derived R1, T1w/T2w, and ihMT, while being substantially weaker for QSM measures. Intermetric spatial correlations were generally strongest within acquisition or modeling families. Test--retest reliability was generally high, particularly for dMRI-derived metrics, but was more variable among QSM-, ihMT-, and R2*-derived measures. Regional spatial profiles were also highly participant-specific, with discriminability approaching its upper bound for most metrics. All imaging data, derived maps, and processing code are openly released. MIRROR provides a benchmark for characterizing the measurement properties and practical tradeoffs of scalable myelin-sensitive MRI methods, supporting principled acquisition selection and accelerating methodological development and validation.

neuroscience↗

bioRxiv2026

Charting neuroimaging-based head circumference and cranial shape across development

Quantitative assessment of head growth supports early detection of neurological disease, yet current clinical practice relies on manual measurements that are variable, anatomically limited, and difficult to scale. Here we introduce a fully automated imaging-native framework (CranioTrace) that transforms routine neuroimaging into standardized, population-referenced markers of cranial development. By integrating topology-constrained segmentation, contour regularization, and geometry-based quality control for automated slice selection, CranioTrace robustly extracts head circumference and cranial morphology from heterogeneous MRI and CT data without manual intervention. The framework enabled construction of sex-specific population growth models across pediatric development in a dataset comprising 9,685 scans spanning birth to early adulthood across 25 cohorts. Imaging-derived head circumference shows strong agreement with tape measurements across independent datasets, high reproducibility (intraclass correlation up to 0.99), and consistent performance across modalities. Beyond conventional head circumference, CranioTrace quantifies cranial shape and asymmetry, capturing developmental dynamics that are not accessible through conventional measurements. Population modeling reveals rapid early-life expansion followed by nonlinear deceleration and stable sex differences. Application to neurogenetic cohorts identifies disease-consistent shifts in growth trajectories: head circumference was higher in neurofibromatosis type 1 and 16p11.2 deletion and lower in 22q11.2 deletion and 16p11.2 duplication. By converting clinical imaging archives into scalable cranial phenotypes linked to probabilistic reference models, CranioTrace provides a foundation for imaging-based growth charting, integrated skull-brain phenotyping, and precise assessment of neurodevelopment.

neuroscience↗

bioRxiv2026

ComBatFamQC: Streamlining Interactive Batch-Effect Diagnostics and Harmonization for Neuroimaging Data in R

As multisite and multi-study data aggregation becomes increasingly common for improving statistical power and sample diversity, robust harmonization methods are needed to address biases introduced by batch variation, particularly in neuroimaging research. Although a variety of harmonization approaches are available, the lack of systematic guidance for diagnosing batch effects and selecting appropriate methods remains a major challenge. To address this gap, we introduce ComBatFamQC, a comprehensive R package designed to streamline batch-effect diagnosis, harmonization, and post-harmonization analysis. ComBatFamQC integrates a user-friendly Shiny app for interactive batch-effect diagnostics, state-of-the-art harmonization methods from the ComBat family, including ComBat, longitudinal ComBat, ComBat-GAM, and CovBat, and tools for downstream analysis after harmonization. The package provides qualitative visualizations, statistical tests for batch-effect assessment, and a consistent interface that supports both in-sample and out-of-sample harmonization through the Shiny app, the R console, or the command line. In addition, it includes functions for post-harmonization analyses to facilitate downstream modeling. Its modular design also supports the systematic incorporation of future harmonization methods and expanded downstream analysis capabilities.

bioinformatics↗

bioRxiv2026

Batch Effect Correction for Neuroimaging Data with Heterogeneous Spatial Correlations

Magnetic resonance imaging scans are effective tools for unveiling brain structures and understanding pathology for complex neurodevelopmental and aging processes. The spatial correlations among various brain regions reveal critical information and insights into the mechanisms of brain functions and their associations with cognitive abilities. Large-scale neuroimaging studies that acquire or aggregate imaging scans from multiple sites have become increasingly popular. Doing so enhances the diversity of study samples and robustness of study findings, and increases the statistical power of any analysis conducted for the biological hypotheses of interest. However, collecting images across different sites introduces non-biological variability attributed to differences in imaging protocol and configurations, known as batch effects. While there are methods to perform this batch effect correction, there are limited methods that directly account for the spatial patterns found in images of the brain. We develop Covariance-Aware Multivariate (CAM) ComBat that accounts for such spatial correlation across high-dimensional features, which could be heterogeneous across batches. We also propose a computationally efficient alternative of CAM-ComBat, Spatially-Informed Iterative Block (SIB) ComBat, that is scalable for very high dimension of features. We show that these methods outperform existing batch effect correction methods through simulation studies and an application to real neuroimaging data.

neuroscience↗

bioRxiv2026

White matter reflects the childhood exposome

The childhood environment is critical for brain development. However, most neuroimaging studies examine individual environmental measures (e.g., socioeconomic status) or a limited set of exposures, obscuring how the combination of complex, real-world exposures jointly influence brain development. Here we investigated how white matter shape and tissue properties are linked to the childhood exposome, a multidimensional measure capturing over 300 environmental exposures. Using multi-shell diffusion MRI from 8,183 children (ages 9-10) in the ABCD study, we quantified microstructural and macrostructural properties across 62 person-specific white matter tracts. The exposome showed widespread and highly replicable associations with both white matter microstructure and macrostructure: more advantaged environments were associated with larger tract macrostructure and lower orientation dispersion. Principal component analysis revealed that the dominant axis of exposome-white matter covariation aligns with the cortical sensorimotor-association hierarchy, such that tracts spanning this hierarchy exhibit the strongest associations with the exposome. Multivariate models demonstrated that patterns of white matter features explained 25% of the variance in the exposome in unseen individuals. Notably, white matter-based prediction of cognition was markedly reduced after accounting for the exposome ([~]82% reduction in explained variance), indicating that brain-cognition associations overlap substantially with variance captured by the exposome. These findings generalized to independent data from the Healthy Brain Network (n=869), which differs substantially from ABCD in MRI acquisition, participant selection, and childhood environments. Together, these results suggest that white matter architecture strongly reflects the childhood environment.

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↗

bioRxiv2025

Anatomical White Matter Tracts Span the Cortical Hierarchy to Support Cognitive Diversity

Long-range white matter (WM) tracts support cognition by enabling communication between distant cortical regions, which are organized along a hierarchy defined by the sensorimotor-to-association (S-A) axis. However, it remains unknown how WM tracts are positioned within the cortical hierarchy to support cognition. Here, we show that WM tracts are differentially positioned in the cortical hierarchy to support specific cognitive functions, and that tracts spanning the hierarchy connect regions with greater cognitive diversity. Moreover, tracts situated within the same hierarchical level connect biologically similar regions, while those crossing the hierarchy bridge distinct biological milieux to support diverse cognitive functions. The placement of tracts in the cortical hierarchy also reflects developmental variation in tract microstructure and individual differences in cognition. Together, these findings provide a framework that moves beyond conventional categories of association or projection tracts and links WM tract anatomy to cortical organization, cognitive function, cortical neurobiology, and neurodevelopment. We anticipate that this cortex-anchored framework for describing WM tracts may aid the interpretation of individual differences in WM structure related to development and behavior.

neuroscience↗

bioRxiv2025

An Open, Fully-processed, Longitudinal Data Resource to Study Brain Development and Transdiagnostic Executive Function

Executive function (EF) develops rapidly during adolescence. However, deficits in EF also emerge in adolescence, representing a transdiagnostic symptom associated with many forms of psychopathology. To promote transdiagnostic research on EF during development, we introduce a new data resource - the Penn Longitudinal Executive functioning in Adolescent Development study (Penn LEAD) - that combines longitudinal multimodal imaging data with rich clinical and cognitive phenotyping. These data include 225 imaging sessions from 132 individuals (8-16 years old at the time of enrollment) who are typically developing (27.3%), or meet criteria for attention-deficit hyperactivity disorder (20.5%) or the psychosis-spectrum (52.3%). In addition to phenotypic data from multiple cognitive tasks focused on EF, the study includes data from structural MRI, diffusion MRI, n-back task fMRI, resting-state fMRI, and arterial spin-labeled MRI. Notably, all raw data, fully-processed derived data, and detailed quality control recommendations are publicly shared on OpenNeuro. We anticipate that such analysis-ready data will accelerate research on EF development in psychiatry.

neuroscience↗

bioRxiv2025

Benchmarking Orientation Distribution Function Estimation Methods for Tractometry in Single-Shell Diffusion Magnetic Resonance Imaging - An Evaluation of Test-Retest Reliability and Predictive Capability

Deriving white matter (WM) bundles in-vivo has thus far mainly been applied in research settings, leveraging high angular resolution, multi-shell diffusion MRI (dMRI) acquisitions that enable advanced reconstruction methods. However, these advanced acquisitions are both time-consuming and costly to acquire. The ability to reconstruct WM bundles in the massive amounts of existing single-shelled, lower angular resolution data from legacy research studies and healthcare systems would offer much broader clinical applications and population-level generalizability. While legacy scans may offer a valuable, large-scale complement to contemporary research datasets, the reliability of white matter bundles derived from these scans remains unclear. Here, we leverage a large research dataset where each 64-direction dMRI scan was acquired as two independent 32-direction runs per subject. To investigate how recently developed bundle segmentation methods generalize to this data, we evaluated the test-retest reliability of the two 32-direction scans, of WM bundle extraction across three orientation distribution function (ODF) reconstruction methods: generalized q-sampling imaging (GQI), constrained spherical deconvolution (CSD), and single-shell three-tissue CSD (SS3T). We found that the majority of WM bundles could be reliably extracted from dMRI scans that were acquired using the 32-direction, single-shell acquisition scheme. The mean dice coefficient of reconstructed WM bundles was consistently higher within-subject than between-subject for all WM bundles and ODF reconstruction methods, illustrating preservation of person-specific anatomy. Further, when using features of the bundles to predict complex reasoning assessed using a computerized cognitive battery, we observed stable prediction accuracies (r: 0.15-0.36) across the test-retest data. Among the three ODF reconstruction methods, SS3T had a good balance between sensitivity and specificity in external validation, a high intra-class correlation of extracted features, more plausible bundles, and strong predictive performance. More broadly, these results demonstrate that bundle segmentation can achieve robust performance even on lower angular resolution, single-shell dMRI, with particular advantages for ODF methods optimized for single-shell data. This highlights the considerable potential for dMRI collected in healthcare settings and legacy research datasets to accelerate and expand the scope of WM research.

neuroscience↗

bioRxiv2025

Measuring Dependencies between Biological Signals with Temporal Self-supervision, and its Limitations

Measuring the statistical dependence between observed signals is a primary tool for scientific discovery. However, biological systems often exhibit complex non-linear interactions that currently cannot be captured without a priori knowledge regarding the nature of dependence. We introduce a self-supervised approach, concurrence, which is inspired by the observation that if two signals are dependent, then one should be able to distinguish between temporally aligned vs. misaligned segments extracted from them. Experiments with fMRI, physiological and behavioral signals show that, to our knowledge, concurrence is the first approach that can expose relationships across such a wide spectrum of signals and extract scientifically relevant differences without ad-hoc parameter tuning or reliance on a priori information, providing a potent tool for scientific discoveries across fields. However, depencencies caused by extraneous factors remain an open problem, thus researchers should validate that exposed relationships truely pertain to the question(s) of interest.

bioinformatics↗

bioRxiv2025

Charting structural brain asymmetry across the human lifespan

Lateralization is a fundamental principle of structural brain organization. In vivo imaging of brain asymmetry is essential for deciphering lateralized brain functions and their disruption in neurodevelopmental and neurodegenerative disorders. Here, we present a normative framework for benchmarking brain asymmetry across the lifespan, developed from an aggregated sample of 128 primary neuroimaging studies, including 177,701 scans from 138,231 individuals, jointly spanning the age range from 20 post menstrual weeks to 102 years. This resource includes comprehensive, hemisphere-specific brain growth charts for multiple neuroimaging phenotypes: regional cortical grey matter volume, thickness, surface area, and subcortical volumes. Our findings reveal distinct spatial patterns of asymmetry, with early leftward asymmetry observed in association cortices and late rightward asymmetry in sensory regions. These trajectories support theories of the neuroplasticity of asymmetry and the role of both genetic and environmental factors in shaping brain lateralization. Additionally, we provide tools to generate asymmetry centile scores, which allow the quantification of individual deviations from typical asymmetry throughout the lifespan and can be applied to unseen data or clinical populations. We demonstrate the utility of these models by highlighting group-level differences in asymmetry in autism spectrum disorder, schizophrenia, and Alzheimers disease, and exploring genetic correlations with hemispheric specialization. To facilitate further research, we have made this normative framework freely available as an interactive open-access resource (upon publication), offering an essential tool to advance both basic and clinical neuroscience.

neuroscience↗

bioRxiv2025

BARTharm: MRI Harmonization Using Image Quality Metrics and Bayesian Non-parametric

Image derived phenotypes (IDPs) harmonization from Magnetic Resonance Imaging (MRI) data is essential for reducing scanner-induced, non-biological variability and enabling accurate multi-site analysis. Existing methods like ComBat, while widely used, rely on linear assumptions and explicit scanner IDs - limitations that reduce their effectiveness in real-world scenarios involving complex scanner effects, non-linear biological variation, or anonymized data. We introduce BARTharm, a novel harmonization framework that uses Image Quality Metrics (IQMs) instead of Scanner IDs and models scanner and biological effects separately using Bayesian Additive Regression Trees (BART), allowing for flexible, data-driven adjustment of IDPs. Through extensive simulation studies, we demonstrate that IQMs provide a more informative and flexible representation of scanner-related variation than categorical Scanner IDs, enabling more accurate removal of non-biological effects. Leveraging this and its ability to model complex relationships, BARTharm, consistently outperforms ComBat across a range of challenging scenarios, including model misspecification and confounded scanner-biological relationships. Applied to real-world datasets, BARTharm successfully removes scanner-induced bias while preserving meaningful biological signals, resulting in stronger, more reliable associations with clinical outcomes. Overall, we find that BARTharm is a robust, data-driven improvement over traditional harmonization approaches, particularly suited for modern, large-scale neuroimaging studies.

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

Mapping individualized multi-scale hierarchical brain functional networks from fMRI by self-supervised deep learning

The brains multi-scale hierarchical organization supports functional segregation and integration. Characterizing the hierarchy of individualized multi-scale functional networks (FNs) is crucial for understanding these fundamental brain processes. It provides promising opportunities for both basic neuroscience and translational research in neuropsychiatric illness. However, current methods typically compute individualized FNs at a single scale and are not equipped to quantify any possible hierarchical organization. To address this limitation, we present a self-supervised deep learning (DL) framework that simultaneously computes multi-scale FNs and characterizes their across-scale hierarchical structure at the individual level. Our method learns intrinsic representations of fMRI data in a low-dimensional latent space to effectively encode multi-scale FNs and their hierarchical structure by optimizing functional homogeneity of FNs across scales jointly in an end-to-end learning manner. A DL model trained on fMRI scans from the Human Connectome Project successfully identified individualized multi-scale hierarchical FNs for unseen individuals and generalized to two external cohorts. Furthermore, the individualized hierarchical structure of FNs was significantly associated with biological phenotypes, including sex, brain development, and brain health. Our framework provides an effective method to compute multi-scale FNs and to characterize the inter-scale hierarchy of FNs for individuals, facilitating a comprehensive understanding of brain functional organization and its inter-individual variation.

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↗

bioRxiv2025

Uncovering functional connectivity patterns predictive of cognition in youth using interpretable predictive modeling

Functional MRI studies have identified functional connectivity (FC) patterns associated with behavioral traits using whole-brain or region-wise predictive models. However, whole-brain approaches often suffer from limited generalizability and interpretability due to the high-dimensionality of FC data. Conversely, region-wise models inherently isolate predictions, ineffective for characterizing contributions of the whole brain FC patterns in predicting a target trait. In this study, we propose an interpretable predictive model that learns fine-grained FC patterns predictive of behavioral traits, jointly at the regional and participant levels, to characterize the overall association of FC patterns with a target trait. Our model learns both a relevance score and a dedicated prediction model for each brain region, then integrates the regional predictions to generate a participant-level prediction, capturing the collective association of FC patterns with the trait. We validated our method using FC data from 6798 participants in the Adolescent Brain and Cognitive Development (ABCD) study for predicting cognition. Our interpretable predictive model identified the cingulo-parietal, retrosplenial-temporal, dorsal attention, salience, and cingulo-opercular networks as collectively predictive of cognitive traits. The interpretable model significantly improved prediction accuracy and facilitated the characterization of fine-grained differences in FC patterns across cognitive domains. Furthermore, the learned relevance scores enhanced region-wise predictions of longitudinal cognitive measures in the ABCD cohort and cognitive traits in an external Human Connectome Project Development (HCP-D) cohort. These findings suggest that our method effectively characterizes generalizable and fine-grained FC patterns linked to cognition in youth.

neuroscience↗