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Xiaoke Hao

As seen in: Nature, Frontiers, BioRxiv
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Articles by Xiaoke Hao

Genome-wide analysis identifies novel shared loci between depression and white matter microstructure - Molecular Psychiatry

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By Qiyu Zhao, Shuo Wang, Xiaoke Hao, Shen Li
| Nature Verified
Abstract Depression, a complex and heritable psychiatric disorder, is associated with alterations in white matter microstructure, yet their shared genetic basis remains largely unclear.
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Genomic and transcriptomic profiles influence on brain morphology and their interactions with pain sensitivity individual differences

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By Yiwen Pan, Zhiguo Zhang, Xiaoke Hao, Gan Huang
| BioRxiv
Abstract Pain sensitivity varies widely among individuals and is influenced by a complex interplay of multi-omics factors, including genetic variations, gene expression, and brain morphology. While previous studies have identified associations between pain sensitivity and brain morphology, the exact mechanisms by which genetic profiles interact with brain structure to influence individual pain sensitivity remain unclear.
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Genomic and transcriptomic profiles influence on brain morphology and their interactions with pain sensitivity

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By Yiwen Pan, Zhiguo Zhang, Xiaoke Hao, Gan Huang
| BioRxiv
Abstract Pain sensitivity varies widely among people and is influenced by multi-omics factors, including genotypes, gene expression, and brain morphology. Though previous studies have reported the association between pain sensitivity and brain morphology, it remains unclear how genetic profiles interact with the brain to influence individual pain sensitivity.
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Spatial-temporal data-augmentation-based functional brain network analysis for brain disorders identification

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By Lingyun Guo, Xiaoke Hao, Xiaoqi Sheng
| Frontiers
Qinghua Liu†, Yangyang Zhang†, Lingyun Guo and ZhengXia Wang* School of Computer Science and Technology, Hainan University, Haikou, China Introduction: Due to the lack of devices and the difficulty of gathering patients, the small sample size is one of the most challenging problems in functional brain network (FBN) analysis. Previous studies have attempted to solve this problem of sample limitation through data augmentation methods, such as sample transformation and noise addition.
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