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.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12753431 |
| Date | 03 August 2020 |
| Creators | Michael Siyuan Wang (9193706) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Relation | https://figshare.com/articles/thesis/Evaluating_Tangent_Spaces_Distances_and_Deep_Learning_Models_to_Develop_Classifiers_for_Brain_Connectivity_Data/12753431 |
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