scBIT: Integrating Single-cell Transcriptomic Data into fMRI-based Prediction for Alzheimer's Disease Diagnosis
Journal:
arXiv
Published Date:
Feb 4, 2025
Abstract
Functional MRI (fMRI) and single-cell transcriptomics are pivotal in
Alzheimer's disease (AD) research, each providing unique insights into neural
function and molecular mechanisms. However, integrating these complementary
modalities remains largely unexplored. Here, we introduce scBIT, a novel method
for enhancing AD prediction by combining fMRI with single-nucleus RNA (snRNA).
scBIT leverages snRNA as an auxiliary modality, significantly improving
fMRI-based prediction models and providing comprehensive interpretability. It
employs a sampling strategy to segment snRNA data into cell-type-specific gene
networks and utilizes a self-explainable graph neural network to extract
critical subgraphs. Additionally, we use demographic and genetic similarities
to pair snRNA and fMRI data across individuals, enabling robust cross-modal
learning. Extensive experiments validate scBIT's effectiveness in revealing
intricate brain region-gene associations and enhancing diagnostic prediction
accuracy. By advancing brain imaging transcriptomics to the single-cell level,
scBIT sheds new light on biomarker discovery in AD research. Experimental
results show that incorporating snRNA data into the scBIT model significantly
boosts accuracy, improving binary classification by 3.39% and five-class
classification by 26.59%. The codes were implemented in Python and have been
released on GitHub (https://github.com/77YQ77/scBIT) and Zenodo
(https://zenodo.org/records/11599030) with detailed instructions.
