Biology subjects

Gao, J.-H.

Publications and source records attributed to Gao, J.-H..

5 recordsLinked to original sources

bioRxiv2023

Brain-Age Prediction: Systematic Evaluation of Site Effects, and Sample Age Range and Size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of site harmonization, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2,101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 years and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1,600 participants. These findings have been incorporated into CentileBrain [https://centilebrain.org/#/brainAGE2], an open-science, web-based platform for individualized neuroimaging metrics.

bioinformatics↗

bioRxiv2023

Dual long-axis reorganization of hippocampus in youth

The reorganization of human hippocampus, especially its interaction with cortex, remains largely undefined in youth. The organization of a single hippocampal long-axis has been predominantly characterized as monotonic1-6, despite recent indications of nonmonotonic features in neuron density7 and geometric eigenmodes8. While the human cortical hierarchy has been well recognized for significant developmental and evolutionary advantages9-12, hippocampus has been typically considered an evolutionarily conserved brain structure1,13,14, and overlooked regarding its integrative role of cortical hierarchical processing during development. Here, we corroborated the presence and significance of a dual long-axis representation of the hippocampal connectome and geometry including both linear and quadratic gradients along its long-axis in youth. This finding was robust across two independent large-scale developmental cohorts. Charting development of the dual long-axis gradients underscored their specific contributions to the cortical hierarchy maturation from the frontoparietal and salience/ventral attention networks. The observed developmental variability in spontaneous brain activities in youth parallels the gradients of myelin content. During childhood through adolescence to early adulthood, the hippocampus reorganized the dual long-axis by gradually relaxing its geometric constraints on the intrinsic network organization of cortical spontaneous activity for refined executive functions. Molecular processes underlying such reorganization of the dual long-axis in hippocampus are linked to neural growth, stress hormone regulation, and neuroactive signaling. Our findings enrich the understanding of hippocampal-cortical reorganizational principles across structural, functional, and molecular dimensions as well as its maturation, and define the plasticity distribution within the human hippocampus at systems level, holding potentials to enhance and translate neurodevelopment and neuropsychiatric healthcare.

neuroscience↗

bioRxiv2023

SACNet: A Multiscale Diffeomorphic Convolutional Registration Network with Prior Neuroanatomical Constraints for Flexible Susceptibility Artifact Correction in Echo Planar Imaging

Susceptibility artifacts (SAs), inevitable in brain diffusion MR (dMRI) scans acquired using single-shot echo planar imaging (EPI), severely compromise the accurate detection of human brain structure. Existing SA correction (SAC) methods offer inadequate correction quality and limited applicability across diverse datasets with varied acquisition protocols. To address these challenges, we proposed SACNet, a SAC framework based on unsupervised registration convolutional networks, featuring: i) a novel diffeomorphism regularization function to avoid unnatural SAC warps, modified from a potential well function; ii) an integration with prior neuroanatomical constraints and coarse-to-fine processing strategy to enables multi-scale geometric and intensity recoveries in severe distorted areas; iii) a unified registration framework that incorporates multiple phase-encoding (PE) EPI images and structural images, ensuring compatibility with both single- and inverse-PE protocols, with or without field maps. Utilizing simulated dMRI images and over 2000 brain scans from neonatal, child, adult and traveling participants, our method consistently demonstrates state-of-the-art correction performance. Notably, SACNet effectively reduces SAs-related multicenter effects compared to existing methods. We have developed user-friendly tools using containerization techniques, hope to facilitate SAC correction quality across extensive neuroimaging studies.

neuroscience↗

bioRxiv2023

Functional connectome through the human life span

The lifespan growth of the functional connectome remains unknown. Here, we assemble task-free functional and structural magnetic resonance imaging data from 33,250 individuals aged 32 postmenstrual weeks to 80 years from 132 global sites. We report critical inflection points in the nonlinear growth curves of the global mean and variance of the connectome, peaking in the late fourth and late third decades of life, respectively. After constructing a fine-grained, lifespan-wide suite of system-level brain atlases, we show distinct maturation timelines for functional segregation within different systems. Lifespan growth of regional connectivity is organized along a primary-to-association cortical axis. These connectome-based normative models reveal substantial individual heterogeneities in functional brain networks in patients with autism spectrum disorder, major depressive disorder, and Alzheimers disease. These findings elucidate the lifespan evolution of the functional connectome and can serve as a normative reference for quantifying individual variation in development, aging, and neuropsychiatric disorders.

neuroscience↗

bioRxiv2022

Structural connectome architecture shapes the maturation of cortical morphology from childhood to adolescence

Cortical thinning is an important hallmark of the maturation of brain morphology during childhood and adolescence. However, the connectome-based wiring mechanism that underlies cortical maturation remains unclear. Using neuroimaging, connectome, transcriptome, and computational modeling, we mapped cortical thinning patterns primarily located in lateral frontal and parietal heteromodal nodes during childhood and adolescence, which is structurally constrained by white matter network architecture and is particularly represented using a network-based diffusion model. Furthermore, connectome-based constraints are regionally heterogeneous, with the largest constraints residing in frontoparietal nodes, and are associated with gene expression signatures of microstructural neurodevelopmental events. These results are highly reproducible while using another independent dataset. Our findings advance our understanding of network-level mechanisms and the associated genetic basis that underlies the maturational process of cortical morphology during childhood and adolescence.

neuroscience↗