Biology subjects

Shou, H.

Publications and source records attributed to Shou, H..

At least 19 recordsLinked to original sources

bioRxiv2026

Imputation-Based Harmonization Mitigates Site Effects Without Data Leakage in Machine Learning Studies

Neuroimaging studies that pool data across clinical sites often suffer from site effects --- variability in imaging measures that arises from technical heterogeneity across sites as opposed to true biological signal. While numerous harmonization methods have been proposed to remove site effects from imaging features, less attention has been placed on how to incorporate harmonization models into common machine-learning pipelines. We demonstrate that current approaches for integration either suffer from data leakage, fail to fully remove site effects from the target data, or attenuate true biological associations during the harmonization process. To address these issues, we propose Multiple Imputation for Removing Technical Heterogeneity (MIRTH), which uses imputed outcomes to harmonize the test data. We benchmark MIRTH's performance using both simulated and real-world volumetric data from the Alzheimer's Disease Neuroimaging Initiative and the Baltimore Longitudinal Study of Aging, demonstrating that MIRTH can achieve high predictive accuracy while avoiding inflated performance when the outcome is imbalanced across clinical sites.

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

A NOVEL MITOCHONDRIAL PEPTIDE ESSENTIAL FOR RESPIRATORY CAPACITY PROMOTES GROWTH, YIELD AND ABIOTIC STRESS TOLERANCE IN PLANTS

Efforts to boost crop productivity have focused on improving photosynthesis. However, plant respiration consumes over 90% of fixed carbon, fueling growth, stress responses, and resource acquisition. Despite its metabolic centrality, respiratory regulation remains underexplored as a yield target. Here, we identify AtMLSP1 (At4g17085), a 57-amino-acid mitochondrial inner membrane peptide in Arabidopsis thaliana. Loss-of-function atmlsp1 mutants show impaired growth, while AtMLSP1 overexpression boosts biomass, seed yield, and high light tolerance. No induction of alternative oxidase protein abundance or activity was observed in atmlsp1 plants, a response normally triggered by respiratory perturbation. Mitochondria isolated from atmlsp1 lines had an approximately 90% reduction in the amount of the intermembrane space protein Cytochrome c and a 30% reduction in all subunits of complex III, while other respiratory complexes remain unaffected. As expected, loss-of-function plants atmlsp1 reveal a significant decrease in total respiratory capacity and membrane potential, but mitochondrial integrity, and abundance of complex I, II and complex IV were unchanged. Overexpression of the orthologues of this gene in Oryza sativa (rice) and Glycine max (soybean) results in a significant increases in yield (seed number and size) under field conditions across different locations.

plant biology↗

bioRxiv2026

Mitochondrial Retrograde Signaling in Arabidopsis thaliana: heterogenous, spatial and polarised aspects

Organelle retrograde signaling studies have exclusively relied on bulk tissues, obscuring cell-type specificity, spatial organization, and heterogeneity within cell types. Here, we use single-nucleus and spatial RNA sequencing to define the cellular and spatial architecture of mitochondrial stress signaling in Arabidopsis thaliana. Inhibition of the mitochondrial electron transport chain by antimycin A and myxothiazol rapidly redistributed nuclei among transcriptional states within epidermal, leaf pavement, and mesophyll lineages, revealing that mitochondrial dysfunction reshapes cell identity trajectories rather than eliciting a uniform response. Stress-associated clusters were already present at low frequency in untreated samples, but the numbers of cells increased following stress treatments, indicating that stress increases the proportion of nuclei occupying pre-existing, primed transcriptional states. Gene-level analyses revealed distinct temporal dynamics and variable cell-state penetrance of the canonical mitochondrial stress markers and identified broadly responsive genes absent from earlier marker sets. Spatial transcriptomics resolved tissue-scale responses, spanning from pan-tissue induction to cell-type- and cluster-restricted activation, and uncovered pronounced adaxial-abaxial polarity in gene expression. Cell identity and spatial inferences were supported by extensive experimental validation using a custom 465-probe 10x Xenium panel and 52 promoter-GFP reporter lines. Together, these data provide a high-resolution framework for organelle-to-nucleus signaling and a resource of cell-type markers and spatial maps to dissect mitochondrial stress signaling and its integration with developmental and environmental pathways.

plant biology↗

bioRxiv2025

An open, fully-processed data resource for studying mood and sleep variability in the developing brain

Brain development during adolescence and early adulthood coincides with shifts in emotion regulation and sleep. Despite this, few existing datasets simultaneously characterize affective dynamics, sleep variation, and multimodal measures of brain development. Here, we describe the study protocol and initial release (n = 10) of an open data resource of neuroimaging paired with densely sampled behavioral measures in adolescents and young adults. All participants complete multi-echo functional MRI, compressed-sensing diffusion MRI, and advanced arterial spin-labeled MRI. Behavioral measures include ecological momentary assessment, actigraphy, extensive cognitive assessments, and detailed clinical phenotyping focused on emotion regulation. Raw and processed data are openly available without a data use agreement and will be regularly updated as accrual continues. Together, this resource will accelerate research on the links between mood, sleep, and brain development.

neuroscience↗

bioRxiv2025

INTACT: A method for integration of longitudinal physical activity data from multiple sources

Wearable devices and digital phenotyping are increasingly used in observational and interventional studies to measure real-time biosignals such as physical activity. However, integrating and comparing data across studies and cohorts remains challenging due to variability in device types, acquisition protocols, and preprocessing methods. A key challenge is removing unwanted study- or device-specific effects while preserving meaningful biological signals. These difficulties are exacerbated by the longitudinal and within-day correlations inherent in high-resolution time-varying data collected from wearable sensors. To address this, we propose INTACT (INtegration of Time-varying data from weArable sensors for physiCal acTivity), a novel method for harmonizing time-varying physical activity intensity data from accelerometers. INTACT models shared information through common eigenvalues and eigenfunctions while allowing for source-specific scale and rotation adjustments. We apply the proposed method to two real-world applications: (i) integration of accelerometer data from two waves of the National Health and Nutrition Examination Survey (NHANES), measured using different devices and reported in different units; and (ii) integration of NHANES accelerometry data with accelerometer and gyroscope measures from commercial devices. Across both applications, INTACT outperforms existing approaches in mitigating source effects while preserving biological variation, enabling more reliable cross-study comparisons of physical activity patterns.

bioinformatics↗

bioRxiv2024

SoyOD: An Integrated Soybean Multi-omics Database for Mining Genes and Biological Research

Soybean is a globally important crop for food, feed, oil, and nitrogen fixation. A variety of multi-omics research has been carried out generating datasets ranging from genotype to phenotype. To utilise this data, a soybean multi-omics database that has broad data coverage and comprehensive data analysis tools would be of value for basic and applied research. We present the soybean omics database (SoyOD), which integrates significant new datasets with existing public datasets for the most comprehensive collection of soybean multi-omics information. Compared to the existing soybean database, SoyOD incorporates an extensive collection of novel data derived from the deep-sequencing of 984 germplasms, 162 novel transcriptome datasets from seeds at different developmental stages, 53 phenotypic datasets, and over 2500 phenotypic images. In addition, SoyOD integrates existing data resources, including 59 assembled genomes, genetic variation data from 3904 soybean accessions, 225 sets of phenotypic data, and 1097 transcriptomic sequences covering 507 different tissues and treatment conditions. SoyOD is a novel tool, as it can be used to mine and analyze candidate genes for important agronomic traits, as shown in a case study on plant height. Additionally, powerful analytical and easy-to-use toolkits enable users to easily access the available multi-omics datasets, and to rapidly search genotypic and phenotypic data in a particular germplasm. The novelty, comprehensiveness, and user-friendly features of SoyOD make it a valuable resource for soybean molecular breeding and biological research. SoyOD is publicly accessible at https://bis.zju.edu.cn/soyod.

plant biology↗

bioRxiv2024

pNet: A toolbox for personalized functional networks modeling

Personalized functional networks (FNs) derived from functional magnetic resonance imaging (fMRI) data are useful for characterizing individual variations in the brain functional topography associated with the brain development, aging, and disorders. To facilitate applications of the personalized FNs with enhanced reliability and reproducibility, we develop an open-source toolbox that is user-friendly, extendable, and includes rigorous quality control (QC), featuring multiple user interfaces (graphics, command line, and a step-by-step guideline) and job-scheduling for high performance computing (HPC) clusters. Particularly, the toolbox, named personalized functional network modeling (pNet), takes fMRI inputs in either volumetric or surface type, ensuring compatibility with multiple fMRI data formats, and computes personalized FNs using two distinct modeling methods: one method optimizes the functional coherence of FNs, while the other enhances their independence. Additionally, the toolbox provides HTML-based reports for QC and visualization of personalized FNs. The toolbox is developed in both MATLAB and Python platforms with a modular design to facilitate extension and modification by users familiar with either programming language. We have evaluated the toolbox on two fMRI datasets and demonstrated its effectiveness and user-friendliness with interactive and scripting examples. pNet is publicly available at https://github.com/MLDataAnalytics/pNet.

neuroscience↗

bioRxiv2024

Residual Partial Least Squares Learning: Brain Cortical Thickness Simultaneously Predicts Eight Non-pairwise-correlated Behavioural and Disease Outcomes in Alzheimer's Disease

Alzheimers Disease (AD) is the leading cause of dementia, affecting brain structure, function, cognition, and behaviour. While previous studies have linked brain regions to univariate outcomes (e.g., disease status), the relationship between brain-wide changes and multiple disease and behavioural outcomes of AD is still not well understood. Here, we propose Residual Partial Least Squares (re-PLS) Learning, an explainable and generalisable framework that models high-dimensional brain features and multivariate outcomes, accounting for confounders. Using re-PLS, we map the many-to-many pathways between cortical thickness and multivariate AD outcomes; identify neural biomarkers that simultaneously predict multiple outcomes; control for confounding variables; conduct longitudinal AD prediction; and perform cross-cohort AD prediction. To evaluate its efficacy, we first carry out within-cohort cross-subject validation using ADNI data, and further examine its reproducibility via between-cohort cross-validation using ADNI and OASIS data. Together, our results unveil brain regions jointly but differentially predictive of distinctive cognitive-behavioural scores in AD.

neuroscience↗

bioRxiv2023

DeepComBat: A Statistically Motivated, Hyperparameter-Robust, Deep Learning Approach to Harmonization of Neuroimaging Data

Neuroimaging data from multiple batches (i.e. acquisition sites, scanner manufacturer, datasets, etc.) are increasingly necessary to gain new insights into the human brain. However, multi-batch data, as well as extracted radiomic features, exhibit pronounced technical artifacts across batches. These batch effects introduce confounding into the data and can obscure biological effects of interest, decreasing the generalizability and reproducibility of findings. This is especially true when multi-batch data is used alongside complex downstream analysis models, such as machine learning methods. Image harmonization methods seeking to remove these batch effects are important for mitigating these issues; however, significant multivariate batch effects remain in the data following harmonization by current state-of-the-art statistical and deep learning methods. We present DeepCombat, a deep learning harmonization method based on a conditional variational autoencoder architecture and the ComBat harmonization model. DeepCombat learns and removes subject-level batch effects by accounting for the multivariate relationships between features. Additionally, DeepComBat relaxes a number of strong assumptions commonly made by previous deep learning harmonization methods and is empirically robust across a wide range of hyperparameter choices. We apply this method to neuroimaging data from a large cognitive-aging cohort and find that DeepCombat outperforms existing methods, as assessed by a battery of machine learning methods, in removing scanner effects from cortical thickness measurements while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically-motivated deep learning harmonization methods.

neuroscience↗

bioRxiv2023

The Genetic Heterogeneity of Multimodal Human Brain Age

The complex biological mechanisms underlying human brain aging remain incompletely understood, involving multiple body organs and chronic diseases. In this study, we used multimodal magnetic resonance imaging and artificial intelligence to examine the genetic architecture of the brain age gap (BAG) derived from gray matter volume (GM-BAG, N=31,557 European ancestry), white matter microstructure (WM-BAG, N=31,674), and functional connectivity (FC-BAG, N=32,017). We identified sixteen genomic loci that reached genome-wide significance (P-value<5x10-8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG showed the highest heritability enrichment for genetic variants in conserved regions, whereas WM-BAG exhibited the highest heritability enrichment in the 5 untranslated regions; oligodendrocytes and astrocytes, but not neurons, showed significant heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several exposure variables on brain aging, such as type 2 diabetes on GM-BAG (odds ratio=1.05 [1.01, 1.09], P-value=1.96x10-2) and AD on WM-BAG (odds ratio=1.04 [1.02, 1.05], P-value=7.18x10-5). Overall, our results provide valuable insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at the MEDICINE knowledge portal: https://labs.loni.usc.edu/medicine.

genetics↗

bioRxiv2023

Establishing the Validity of Compressed Sensing Diffusion Spectrum Imaging

Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of twenty-six participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n=20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.

neuroscience↗

bioRxiv2023

Deconfounded Dimension Reduction via Partial Embeddings

Dimension reduction tools preserving similarity and graph structure such as t-SNE and UMAP can capture complex biological patterns in high-dimensional data. However, these tools typically are not designed to separate effects of interest from unwanted effects due to confounders. We introduce the partial embedding (PARE) framework, which enables removal of confounders from any distance-based dimension reduction method. We then develop partial t-SNE and partial UMAP and apply these methods to genomic and neuroimaging data. Our results show that the PARE framework can remove batch effects in single-cell sequencing data as well as separate clinical and technical variability in neuroimaging measures. We demonstrate that the PARE framework extends dimension reduction methods to highlight biological patterns of interest while effectively removing confounding effects.

neuroscience↗

bioRxiv2022

Genetic, clinical underpinnings of subtle early brain change along Alzheimer's dimensions

Alzheimers disease (AD) is associated with heterogeneous atrophy patterns. We employed a semi-supervised clustering technique known as Surreal-GAN, through which we identified two dominant dimensions of brain atrophy in symptomatic mild cognitive impairment (MCI) and AD patients: the "diffuse-AD" (R1) dimension shows widespread brain atrophy, and the "MTL-AD" (R2) dimension displays focal medial temporal lobe (MTL) atrophy. Critically, only R2 was associated with widely known sporadic AD genetic risk factors (e.g., APOE {varepsilon}4) in MCI and AD patients at baseline. We then independently detected the presence of the two dimensions in the early stages by deploying the trained model in the general population and two cognitively unimpaired cohorts of asymptomatic participants. In the general population, genome-wide association studies found 77 genes unrelated to APOE differentially associated with R1 and R2. Functional analyses revealed that these genes were overrepresented in differentially expressed gene sets in organs beyond the brain (R1 and R2), including the heart (R1) and the pituitary gland, muscle, and kidney (R2). These genes were enriched in biological pathways implicated in dendritic cells (R2), macrophage functions (R1), and cancer (R1 and R2). Several of them were "druggable genes" for cancer (R1), inflammation (R1), cardiovascular diseases (R1), and diseases of the nervous system (R2). The longitudinal progression showed that APOE {varepsilon}4, amyloid, and tau were associated with R2 at early asymptomatic stages, but this longitudinal association occurs only at late symptomatic stages in R1. Our findings deepen our understanding of the multifaceted pathogenesis of AD beyond the brain. In early asymptomatic stages, the two dimensions are associated with diverse pathological mechanisms, including cardiovascular diseases, inflammation, and hormonal dysfunction - driven by genes different from APOE - which may collectively contribute to the early pathogenesis of AD.

bioengineering↗

bioRxiv2022

Multiscale functional connectivity patterns of the aging brain learned from rsfMRI data of 4,259 individuals of the multi-cohort iSTAGING study

To learn multiscale functional connectivity patterns of the aging brain, we built a brain age prediction model of functional connectivity measures at seven scales on a large fMRI dataset, consisting of resting-state fMRI scans of 4259 individuals with a wide age range (22 to 97 years, with an average of 63) from five cohorts. We computed multiscale functional connectivity measures of individual subjects using a personalized functional network computational method, harmonized the functional connectivity measures of subjects from multiple datasets in order to build a functional brain age model, and finally evaluated how functional brain age gap correlated with cognitive measures of individual subjects. Our study has revealed that functional connectivity measures at multiple scales were more informative than those at any single scale for the brain age prediction, the data harmonization significantly improved the brain age prediction performance, and harmonization in the tangent space worked better than in the original space. Moreover, brain age gap scores of individual subjects derived from the brain age prediction model were significantly correlated with clinical and cognitive measures. Overall, these results demonstrated that multiscale functional connectivity patterns learned from a large-scale multi-site rsfMRI dataset were informative for characterizing the aging brain and the derived brain age gap was associated with cognitive and clinical measures.

bioengineering↗

bioRxiv2022

Suboptimal phenotypic reliability impedes reproducible human neuroscience

Summary ParagraphBiomarkers of behavior and psychiatric illness for cognitive and clinical neuroscience remain out of reach1-4. Suboptimal reliability of biological measurements, such as functional magnetic resonance imaging (fMRI), is increasingly cited as a primary culprit for discouragingly large sample size requirements and poor reproducibility of brain-based biomarker discovery1,5-7. In response, steps are being taken towards optimizing MRI reliability and increasing sample sizes8-11, though this will not be enough. Optimizing biological measurement reliability and increasing sample sizes are necessary but insufficient steps for biomarker discovery; this focus has overlooked the other side of the equation - the reliability of clinical and cognitive assessments - which are often suboptimal or unassessed. Through a combination of simulation analysis and empirical studies using neuroimaging data, we demonstrate that the joint reliability of both biological and clinical/cognitive phenotypic measurements must be optimized in order to ensure biomarkers are reproducible and accurate. Even with best-case scenario high reliability neuroimaging measurements and large sample sizes, we show that suboptimal reliability of phenotypic data (i.e., clinical diagnosis, behavioral and cognitive measurements) will continue to impede meaningful biomarker discovery for the field. Improving reliability through development of novel assessments of phenotypic variation is needed, but it is not the sole solution. We emphasize the potential to improve the reliability of established phenotypic methods through aggregation across multiple raters and/or measurements12-15, which is becoming increasingly feasible with recent innovations in data acquisition (e.g., web- and smart-phone-based administration, ecological momentary assessment, burst sampling, wearable devices, multimodal recordings)16-20. We demonstrate that such aggregation can achieve better biomarker discovery for a fraction of the cost engendered by large-scale samples. Although the current study has been motivated by ongoing developments in neuroimaging, the prioritization of reliable phenotyping will revolutionize neurobiological and clinical endeavors that are focused on brain and behavior.

neuroscience↗

bioRxiv2022

Similarity-Based Multimodal Regression

To better understand complex human phenotypes, large-scale studies have increasingly collected multiple data modalities across domains such as imaging, mobile health, and physical activity. The properties of each data type often differ substantially and require either separate analyses or extensive processing to obtain comparable features for a combined analysis. Multimodal data fusion enables certain analyses on matrix-valued and vector-valued data, but it generally cannot integrate modalities of different dimensions and data structures. For a single data modality, multivariate distance matrix regression provides a distance-based framework for regression accommodating a wide range of data types. However, no distancebased method exists to handle multiple complementary types of data. We propose a novel distance-based regression model, which we refer to as Similarity-based Multimodal Regression (SiMMR), that enables simultaneous regression of multiple modalities through their distance profiles. We demonstrate through simulation, imaging studies, and longitudinal mobile health analyses that our proposed method can detect associations in multimodal data of differing properties and dimensionalities, even with modest sample sizes. We perform experiments to evaluate several different test statistics and provide recommendations for applying our method across a broad range of scenarios.

neuroscience↗