Publications by authors named "Shizhuo Mu"

Article Synopsis
  • - The study critiques common practices in brain connectomic analysis, particularly the mapping of functional networks (FNs) onto functional connectomes (FCs) without sufficient theoretical justification regarding their appropriateness.
  • - It introduces a framework based on Stochastic Block Models (SBMs) to evaluate the information-theoretic fitness of FNs when applied to individual FCs across different fMRI tasks, optimizing choices related to FC granularity, FN partition, and thresholding strategies.
  • - The research confirms that a commonly used threshold value of 0.25 is statistically valid for group-average FCs and suggests better methodologies for employing FNs and thresholding techniques in future individualized brain research.
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Alzheimer's disease (AD) is a critical national concern, affecting 5.8 million people and costing more than $250 billion annually. However, there is no available cure.

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Neurodegenerative processes are increasingly recognized as potential causative factors in Alzheimer's disease (AD) pathogenesis. While many studies have leveraged mediation analysis models to elucidate the underlying mechanisms linking genetic variants to AD diagnostic outcomes, the majority have predominantly focused on regional brain measure as a mediator, thereby compromising the granularity of the imaging data. In our investigation, using the imaging genetics data from a landmark AD cohort, we contrasted both region-based and voxel-based brain measurements as imaging endophenotypes, and examined their roles in mediating genetic effects on AD outcomes.

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Single-cell technologies have emerged as a transformative technology enabling high-dimensional characterization of cell populations at an unprecedented scale. The data's innate complexity and voluminous nature pose significant computational and analytical challenges, especially in comparative studies delineating cellular architectures across various biological conditions (i.e.

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