Abstract
Applications of quantitative network analysis to functional brain connectivity have become popular in the last decade due to their ability to describe the general topological principles of brain networks. However, many issues arise when standard statistical analysis techniques are applied to functional magnetic resonance imaging (fMRI) connectivity maps. Frequently, summary measures of these maps, such as global efficiency and clustering coefficients, collapse the changing structures of graph topology from many scales to one. This can result in a loss of whole-brain spatio-temporal pattern information that may be significant in association and prediction analyses. Drawing from the electrical engineering field, the resistance perturbation distance is a quantification of similarity between graphs on the same vertex set that has been shown to identify changes in dynamic graphs, such as those from fMRI, while not being computationally expensive or result in a loss of information. This work proposes a novel kernel-based regression scheme that incorporates the resistance perturbation distance to better understand the association with biological phenotypes from fMRI using both simulated and real datasets.
Highlights
Since its introduction in the early 1990s [1] [2] [3], functional magnetic resonance imaging has rapidly grown to become the most popular technique to observe the living human brain [4]
Noninvasive, in-vivo techniques like functional magnetic resonance imaging (fMRI) can be used in biomedical research to examine localization of brain regions engaged by a particular task, determining brain networks, and predicting psychological or disease states [5]
While most fMRI studies initially focused on the examination of brain regions engaged during a specific task, increased attention has been paid in examining the connectivity of the entire brain at rest, commonly referred to as resting state fMRI [6]
Summary
Since its introduction in the early 1990s [1] [2] [3], functional magnetic resonance imaging (fMRI) has rapidly grown to become the most popular technique to observe the living human brain [4]. Noninvasive, in-vivo techniques like fMRI can be used in biomedical research to examine localization of brain regions engaged by a particular task, determining brain networks, and predicting psychological or disease states [5]. Analysis of rs-fMRI can help yield information about the strength of connections within and among brain regions that may be unique to clinical populations [6] [7]. All of these objectives can be achieved through the application of statistical.
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