Abstract
Due to its high dimensionality and high noise levels, analysis of a large brain functional network may not be powerful and easy to interpret; instead, decomposition of a large network into smaller subcomponents called modules may be more promising as suggested by some empirical evidence. For example, alteration of brain modularity is observed in patients suffering from various types of brain malfunctions. Although several methods exist for estimating brain functional networks, such as the sample correlation matrix or graphical lasso for a sparse precision matrix, it is still difficult to extract modules from such network estimates. Motivated by these considerations, we adapt a weighted gene co-expression network analysis (WGCNA) framework to resting-state fMRI (rs-fMRI) data to identify modular structures in brain functional networks. Modular structures are identified by using topological overlap matrix (TOM) elements in hierarchical clustering. We propose applying a new adaptive test built on the proportional odds model (POM) that can be applied to a high-dimensional setting, where the number of variables (p) can exceed the sample size (n) in addition to the usual p < n setting. We applied our proposed methods to the ADNI data to test for associations between a genetic variant and either the whole brain functional network or its various subcomponents using various connectivity measures. We uncovered several modules based on the control cohort, and some of them were marginally associated with the APOE4 variant and several other SNPs; however, due to the small sample size of the ADNI data, larger studies are needed.
Original language | English (US) |
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Pages (from-to) | 58-69 |
Number of pages | 12 |
Journal | Pacific Symposium on Biocomputing |
Volume | 0 |
DOIs | |
State | Published - 2017 |
Event | 22nd Pacific Symposium on Biocomputing, PSB 2017 - Kohala Coast, United States Duration: Jan 4 2017 → Jan 8 2017 |
Keywords
- Brain functional connectivity
- Functional MRI
- Proportional odds model
- Single nucleotide polymorphism
- WGCNA
- Weighted gene co-expression network analysis
- aSPU test
ASJC Scopus subject areas
- Biomedical Engineering
- Computational Theory and Mathematics