CRII: RI: Learning novel multi-resolution representations of graphs: Applications to Brain Connectivity analysis for Alzheimer's Disease

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This project aims to identify disease-specific changes in human brain connectivity in early stages by developing a novel Deep Learning framework applicable to data with arbitrary structure such as graphs. This is important because regional brain variations often do not manifest as cognitive changes until significant brain pathology has accumulated, and a better understanding of the brain may be possible by characterizing changes in connectivity defined by relationships between different brain regions. Recent techniques with Deep Learning have demonstrated successful results with human-level precision in various image analysis tasks such as image classification, object detection, and image segmentation, but they cannot be directly applied to analyze brain connectivity because of its arbitrary structure. The key is to derive effective representation of the data; however, it is still unclear how to derive sophisticated representations for complex data such as graphs and it often requires large-scale datasets. A novel Graph Deep Learning technique that can detect subtle changes in brain connectivity with small numbers of samples is therefore necessary. Success of this project will facilitate understanding of the relationship between brain connectivity and neurodegenerative disease, mechanisms for early diagnosis, and discovery of new treatments. Technically, the overarching goal of this project is to design a Convolution Neural Network (CNN) model for graph data and to determine the extent to which it yields new scientific findings in neuroscience. To meet the goal, this project will focus on: 1) Developing a novel transform for graphs (e.g., brain networks) for their novel multi-resolution representations that are theoretically described by convolution, 2) Developing an efficient deep learning architecture for graphs that operates within a small sample-size regime to improve performance of disease diagnosis and sensitivity of statistical inferences, and 3) Validating the developed models on a simulation study as well as real brain network datasets for Alzheimer’s Disease to characterize disease-specific patterns in the brain connectivity. The developed framework will benefit various areas of neuroimaging research with functional and structural brain connectivity that are locally carried at small scales, and spur development of further studies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.