Biology subjects

Zhu, A. H.

Publications and source records attributed to Zhu, A. H..

13 recordsLinked to original sources

bioRxiv2026

FastEBM: Fast, Scalable, and Uncertainty-Aware Event-Based Disease Progression Modeling

Event-based models (EBMs) are used to infer ordering of biomarker alteration patterns with respect to disease progression. However, EBM approaches rely on computationally expensive permutation-based inference, assumptions of feature independence, and likelihood optimization that can limit scalability and stability in high-dimensional settings. Here, we introduce Fast Event-Based Model (FastEBM), a scalable, uncertainty aware, Markov- chain-based framework that reformulates disease progression inference as a subject-ordering problem on a data-driven diffusion manifold. The progression uncertainty, used to derive positional variance diagrams, is quantified using first-passage-time variability derived directly from the inferred Markov process. Using synthetic experiments varying feature dimensionality, cohort size, noise level, and feature-correlation structure, we compared FastEBM with established methods, including Gaussian mixture model EBM (GMM-EBM), kernel density estimation EBM (KDE-EBM), and discriminative EBM (DEBM). FastEBM achieved the best accuracy and runtime. In low-subject/high-dimensional stress tests, FastEBM retained event-order recovery. FastEBM remained robust in simulations containing correlated and redundant features after decorrelation and feature-group handling. We applied FastEBM to real-world data to characterize biomarker progression in Alzheimers disease. First, we evaluated a low-dimensional multi- modal dataset from The Alzheimers Disease Prediction Of Longitudinal Evolution (TAD- POLE) challenge. Second, to demonstrate high-dimensional disease progression mapping, we applied FastEBM to regional cortical tau-PET data from the Alzheimers Disease Neuroimaging Initiative (ADNI). In both cases, FastEBM recovered progression patterns broadly consistent with the literature, also revealing lateralized progression trends. These results show that diffusion-based Markov geometry provides a scalable and robust alternative to conventional event-based modeling. FastEBM is available at: https://github.com/sjusc07/FastEBM.

bioinformatics↗

bioRxiv2025

NeuroDISK: An AI Approach to Automate Continuous Inquiry-Driven Discoveries in Neuroimaging Genetics

Collaborative and multi-site neuroimaging studies have greatly accelerated the rate at which new and existing data can be aggregated to answer a neuroscientific question. New research initiatives are continuously collecting more data, allowing opportunities to refine previous published findings through continuous and dynamic updates. Yet, we lack a practical framework for researchers to systematically, automatically, and continuously update published findings. We developed NeuroDISK, an automated artificial intelligence based framework that: 1) performs automated and inquiry-driven analyses, and 2) continuously updates these analyses as new data becomes available. NeuroDISK was evaluated using published results from the ENIGMA consortiums work on the genetic architecture of the cerebral cortex. We incorporate both meta-analysis and meta-regression options to showcase our framework on the effect of specific genotypes and moderators on select brain regions. Initial NeuroDISK meta-analysis results replicate the original publication, and we show result updates after adding new data. The NeuroDISK framework can be generalized for users to define question(s), run corresponding workflow(s) and access results interactively and continuously.

bioinformatics↗

bioRxiv2024

Lifespan Normative Modeling of Brain Microstructure

Normative models of brain metrics based on large populations could be extremely valuable for detecting brain abnormalities in patients with a variety of disorders, including degenerative, psychiatric and neurodevelopmental conditions, but no such models exist for the brains white matter (WM) microstructure. Here we present a large-scale normative model of brain WM microstructure - based on 19 international diffusion MRI datasets covering almost the entire lifespan (totaling N=54,583 individuals; age: 4-91 years). We extracted regional diffusion tensor imaging (DTI) metrics using a standardized analysis and quality control protocol and used hierarchical Bayesian regression (HBR) to model the statistical distribution of derived WM metrics as a function of age and sex. We extracted the average lifespan trajectories and corresponding centile curves for each WM region. We illustrate the utility of the method by applying it to detect and visualize profiles of WM microstructural deviations in a variety of contexts: in mild cognitive impairment, Alzheimers disease, and 22q11.2 deletion syndrome - a neurogenetic condition that markedly increases risk for schizophrenia. The resulting large-scale model provides a common reference to identify disease effects on the brains microstructure in individuals or groups, and to compare disorders, and discover factors affecting WM abnormalities. The derived normative models are a valuable resource publicly available to the community, adaptable and extendable to future datasets as the available data expands.

neuroscience↗

bioRxiv2024

A Surface-based deep learning approach for cortical shape analysis

Advances in deep learning hold promise for predicting clinical factors from human brain images. In this study, we applied a spherical harmonics-based convolutional neural network approach (SPHARM-Net) to MRI-derived brain shape metrics to predict age, sex, and Alzheimers disease (AD) diagnosis. MRI-derived brain features included vertex-wise cortical curvature, convexity, thickness, and surface area. SPHARM-Net performs convolutions using the spherical harmonic transforms, eliminating the need to explicitly define neighborhood size, and achieving rotational equivariance. Sex classification and age regression were carried out in a large sample of healthy adults (UK Biobank; N=32,979), and AD classification performance was tested in a large, publicly available sample (ADNI; N=1,213). SPHARM-Net showed strong performance for sex classification (accuracy=0.91; balanced accuracy= 0.91; AUC=0.97), and age regression (average absolute error=2.97 years; R-squared=0.77; Pearsons coefficient=0.9). AD classification also performed well (accuracy=0.86; balanced accuracy=0.83; AUC=0.9). Our experiments demonstrate promising preliminary performance using the SPHARM-Net for two widely studied benchmarking tasks and for AD classification. Future work will include comparisons of shape-based methods and extending these analysis to more challenging tasks such as mood disorder classification.

neuroscience↗

bioRxiv2024

The Genetic Architecture of the Human Corpus Callosum and its Subregions

The corpus callosum (CC) is the largest set of white matter fibers connecting the two hemispheres of the brain. In humans, it is essential for coordinating sensorimotor responses, performing associative/executive functions, and representing information in multiple dimensions. Understanding which genetic variants underpin corpus callosum morphometry, and their shared influence on cortical structure and susceptibility to neuropsychiatric disorders, can provide molecular insights into the CCs role in mediating cortical development and its contribution to neuropsychiatric disease. To characterize the morphometry of the midsagittal corpus callosum, we developed a publicly available artificial intelligence based tool to extract, parcellate, and calculate its total and regional area and thickness. Using the UK Biobank (UKB) and the Adolescent Brain Cognitive Development study (ABCD), we extracted measures of midsagittal corpus callosum morphometry and performed a genome-wide association study (GWAS) meta-analysis of European participants (combined N = 46,685). We then examined evidence for generalization to the non-European participants of the UKB and ABCD cohorts (combined N = 7,040). Post-GWAS analyses implicate prenatal intracellular organization and cell growth patterns, and high heritability in regions of open chromatin, suggesting transcriptional activity regulation in early development. Results suggest programmed cell death mediated by the immune system drives the thinning of the posterior body and isthmus. Global and local genetic overlap, along with causal genetic liability, between the corpus callosum, cerebral cortex, and neuropsychiatric disorders such as attention-deficit/hyperactivity and bipolar disorders were identified. These results provide insight into variability of corpus callosum development, its genetic influence on the cerebral cortex, and biological mechanisms related to neuropsychiatric dysfunction.

neuroscience↗

bioRxiv2024

Reference curves for harmonizing multi-site regional diffusion MRI metrics across the lifespan

Age-related white matter (WM) microstructure maturation and decline occur throughout the human lifespan, complementing the process of gray matter development and degeneration. Here, we create normative lifespan reference curves for global and regional WM microstructure by harmonizing diffusion MRI (dMRI)-derived data from ten public datasets (N = 40,898 subjects; age: 3-95 years; 47.6% male). We tested three harmonization methods on regional diffusion tensor imaging (DTI) based fractional anisotropy (FA), a metric of WM microstructure, extracted using the ENIGMA-DTI pipeline. ComBat-GAM harmonization provided multi-study trajectories most consistent with known WM maturation peaks. Lifespan FA reference curves were validated with test-retest data and used to assess the effect of the ApoE4 risk factor for dementia in WM across the lifespan. We found significant associations between ApoE4 and FA in WM regions associated with neurodegenerative disease even in healthy individuals across the lifespan, with regional age-by-genotype interactions. Our lifespan reference curves and tools to harmonize new dMRI data to the curves are publicly available as eHarmonize (https://github.com/ahzhu/eharmonize).

neuroscience↗

bioRxiv2022

Multisite Test-Retest Reliability and Compatibility of Brain Metrics derived from FreeSurfer Versions 7.1, 6.0, and 5.3

Automatic neuroimaging segmentation and parcellation tools provide convenient and systematic methods for extracting numerous features from brain MRI scans, and are becoming standard practice for large-scale coordinated studies. One such tool, FreeSurfer, provides an easy-to-use pipeline to extract metrics describing cortical and subcortical morphometry. Over the past two decades, there have been over 25 stable releases of FreeSurfer, and different versions are used across published works. Despite this, the reliability and compatibility of metrics derived from the most recent major version releases have yet to be assessed empirically. Here, we use test-retest data from three public brain MRI datasets to assess within-version reliability and between-version compatibility across 42 regional outputs from three versions of FreeSurfer: the latest, v7.1, and two previous stable releases - v5.3, and v6.0. We find v7.1 was less compatible with older versions for measuring cortical thickness. In particular, the thickness of the cingulate gyrus had low compatibility (intraclass correlation coefficient (ICC) between 0.37 and 0.61) between versions. Temporal and frontal poles, and the medial orbitofrontal surface area metrics, also showed low to moderate compatibility with v7.1. While our work compares all three versions, our sub-comparisons between the older versions (v5.3 and v6.0) replicates earlier findings of low compatibility of pallidum and putamen volumes. Low between-version compatibility was not always indicative of low within-version reliability - all versions showed good to excellent reliability across most regional measures (ICC>0.8). Age associations, quality control metrics, and Dice coefficients in an independent sample of 106 individual scans, processed with all three versions of FreeSurfer, revealed differences in results of downstream statistical analysis. As neuroimaging studies adopt more recently released software, we provide researchers with a reference to highlight the regions and metrics that may yield findings inconsistent with published works using older FreeSurfer software. An interactive viewer for the results is provided at http://data.brainescience.org/Freesurfer_Reliability/

neuroscience↗

bioRxiv2021

Genetic map of regional sulcal morphology in the human brain

The human brain is a complex organ underlying many cognitive and physiological processes, affected by a wide range of diseases. Genetic associations with macroscopic brain structure are emerging, providing insights into genetic sources of brain variability and risk for functional impairments and disease. However, specific associations with measures of local brain folding, associated with both brain development and decline, remain under-explored. Here we carried out detailed large-scale genome-wide associations of regional brain cortical sulcal measures derived from magnetic resonance imaging data of 40,169 individuals in the UK Biobank. Combining both genotyping and whole-exome sequencing data ([~]12 million variants), we discovered 388 regional brain folding associations across 77 genetic loci at p<5x10-8, which replicated at p<0.05. We found genes in associated loci to be independently enriched for expression in the cerebral cortex, neuronal development processes and differential regulation in early brain development. We integrated coding associations and brain eQTLs to refine genes for various loci and demonstrated shared signal in the pleiotropic KCNK2 locus with a cortex-specific KCNK2 eQTL. Genetic correlations with neuropsychiatric conditions highlighted emerging patterns across distinct sulcal parameters and related phenotypes. We provide an interactive 3D visualisation of our summary associations, making complex association patterns easier to interpret, and emphasising the added resolution of regional brain analyses compared to global brain measures. Our results offer new insights into the genetic architecture underpinning brain folding and provide a resource to the wider scientific community for studies of pathways driving brain folding and their role in health and disease.

genomics↗

bioRxiv2020

Sex-Dependent Age Trajectories of Subcortical Brain Structures: Analysis of Large-Scale Percentile Models and Shape Morphometry

Modeling of structural brain variation over the lifespan is important to better understand factors contributing to healthy aging and risk for neurological conditions such as Alzheimers disease. Even so, we lack normative data on brain morphometry across the adult lifespan in large, well-powered samples. Here, in a large population-based sample of 26,440 adults from the UK Biobank (age: 44-81 yrs.), we created normative percentile charts for MRI-derived subcortical volumes. Next, we investigated associations between these morphometric measures and the strongest known genetic risk factor for late-onset Alzheimers disease (APOE genotype) and mapped the spatial distribution of age-by-sex interactions using computational surface mesh modeling and shape analysis. Vertex-wise shape mapping supplements traditional gross volumetric approaches to reveal finer-grained variations across functionally important brain subcompartments. Normative curves revealed volumetric loss with age, as expected, for all subcortical brain structures except for the lateral ventricles, which expanded with age. Surprisingly, no volumetric associations with APOE genotype were detected, despite the very large sample size. Age-related trajectories for volumes differed in women versus men, and surface-based statistical maps revealed the spatial distribution of the age-by-sex interaction. Subcortical volumes declined faster in men than women over the full age range, but after age 60, fewer structures showed sex-dependent trajectories, indicating similar volumetric changes in older men and women. Large-scale statistical modeling of age effects on brain structures may drive new insights into individual differences in brain aging and help to identify factors that promote healthy brain aging and risk for disease.

neuroscience↗

bioRxiv2020

Exogenous sex hormone effects on brain microstructure in women: a diffusion MRI study in the UK Biobank

Changes in estrogen levels in women have been associated with increased risk for age-related neurodegenerative diseases, including Alzheimers disease, but the impact of exogenous estrogen exposure on the brain is poorly understood. Oral contraceptives (OC) and hormone therapy (HT) and are both common sources of exogenous estrogen for women in reproductive and post-menopausal years, respectively. Here we examined the association of exogenous sex hormone exposure with the brains white matter (WM) aging trajectories in postmenopausal women using and not using OC and HT (HT users: n=3,033, non-users n=5,093; OC users: n=6,964; non-users n=1,156), while also investigating multiple dMRI models. Cross-sectional brain dMRI data was analyzed from the UK Biobank using conventional diffusion tensor imaging (DTI), the tensor distribution function (TDF), and neurite orientation dispersion and density imaging (NODDI). Mean skeletonized diffusivity measures were extracted across the whole brain, and fractional polynomial regressions were used to characterize age-related trajectories for WM microstructural measures. Advanced dMRI model NODDI revealed a steeper WM aging trajectory in HT users relative to non-users, and for those using unopposed estrogens relative to combined estrogens treatment. By contrast, no interaction was detected between OC status and age effects on the diffusivity measures we examined. Exogenous sex hormone exposure may negatively impact WM microstructure aging in postmenopausal women. We also present normative reference curves for white matter microarchitectural parameters in women, to help identify individuals with microstructural anomalies.

neuroscience↗

bioRxiv2020

Advanced diffusion-weighted MRI metrics detect sex differences in aging among 15,000 adults in the UK Biobank

A comprehensive characterization of the brains white matter is critical for improving our understanding of healthy and diseased aging. Here we used diffusion-weighted magnetic resonance imaging (dMRI) to estimate age and sex effects on white matter microstructure in a cross-sectional sample of 15,628 adults aged 45-80 years old (47.6% male, 52.4% female). Microstructure was assessed using the following four models: a conventional single-shell model, diffusion tensor imaging (DTI); a more advanced single-shell model, the tensor distribution function (TDF); an advanced multi-shell model, neurite orientation dispersion and density imaging (NODDI); and another advanced multi-shell model, mean apparent propagator MRI (MAPMRI). Age was modeled using a data-driven statistical approach, and normative centile curves were created to provide sex-stratified white matter reference charts. Participant age and sex substantially impacted many aspects of white matter microstructure across the brain, with the advanced dMRI models TDF and NODDI detecting such effects the most sensitively. These findings and the normative reference curves provide an important foundation for the study of healthy and diseased brain aging.

neuroscience↗

bioRxiv2020

Accurate brain age prediction using recurrent slice-based networks

BrainAge (a subjects apparent age predicted from neuroimaging data) is an important biomarker of brain aging. The deviation of BrainAge from true age has been associated with psychiatric and neurological disease, and has proven effective in predicting conversion from mild cognitive impairment (MCI) to dementia. Conventionally, 3D convolutional neural networks and their variants are used for brain age prediction. However, these networks have a larger number of parameters and take longer to train than their 2D counterparts. Here we propose a 2D slice-based recurrent neural network model, which takes in an ordered sequence of sagittal slices as input to predict the brain age. The model consists of two components: a 2D convolutional neural network (CNN), which encodes the relevant features from the slices, and a recurrent neural network (RNN) that learns the relationship between slices. We compare our method to other recently proposed methods, including 3D deep convolutional regression networks, information theoretic models, and bag-of-features (BoF) models (such as BagNet) - where the classification is based on the occurrences of local features, without taking into consideration their global spatial ordering. In our experiments, our proposed model performs comparably to, or better than, the current state of the art models, with nearly half the number of parameters and a lower convergence time.

neuroscience↗

bioRxiv2018

Altered cortical brain structure and increased risk for disease seen decades after perinatal exposure to maternal smoking: A study of 9,000 adults in the UK Biobank

Secondhand smoke exposure is a major public health risk that is especially harmful to the developing brain, but it is unclear if early exposure affects brain structure during middle age and older adulthood. Here we analyzed brain MRI data from the UK Biobank in a population-based sample of individuals (ages 44-80) who were exposed (n=2,510) or unexposed (n=6,079) to smoking around birth. We used robust statistical models, including quantile regressions, to test the effect of perinatal smoke exposure (PSE) on cortical surface area (SA), thickness, and subcortical volume. We hypothesized that PSE would be associated with cortical disruption in primary sensory areas compared to unexposed (PSE-) adults. After adjusting for multiple comparisons, SA was significantly lower in the pericalcarine (PCAL), inferior parietal (IPL), and regions of the temporal and frontal cortex of PSE+ adults; these abnormalities were associated with increased risk for several diseases, including circulatory and endocrine conditions. Sensitivity analyses conducted in a hold-out group of healthy participants (exposed, n=109, unexposed, n=315) replicated the effect of PSE on SA in the PCAL and IPL. Collectively our results show a negative, long term effect of PSE on sensory cortices that may increase risk for disease later in life.

neuroscience↗