Evaluating Tangent Spaces, Distances, and Deep Learning Models to Develop Classifiers for Brain Connectivity Data
thesisposted on 03.08.2020, 17:52 by Michael Siyuan Wang
A better, more optimized processing pipeline for functional connectivity (FC) data will likely accelerate practical advances within the field of neuroimaging. When using correlation-based measures of FC, researchers have recently employed a few data-driven methods to maximize its predictive power. In this study, we apply a few of these post-processing methods in both task, twin, and subject identification problems. First, we employ PCA reconstruction of the original dataset, which has been successfully used to maximize subject-level identifiability. We show there is dataset-dependent optimal PCA reconstruction for task and twin identification. Next, we analyze FCs in their native geometry using tangent space projection with various mean covariance reference matrices. We demonstrate that the tangent projection of the original FCs can drastically increase subject and twin identification rates. For example, the identification rate of 106 MZ twin pairs increased from 0.487 of the original FCs to 0.943 after tangent projection with the logarithmic Euclidean reference matrix. We also use Schaefer’s variable parcellation sizes to show that increasing parcellation granularity in general increases twin and subject identification rates. Finally, we show that our custom convolutional neural network classifier achieves an average task identification rate of 0.986, surpassing state-of-the-art results. These post-processing methods are promising for future research in functional connectome predictive modeling and, if optimized further, can likely be extended into clinical applications.