Evaluation of Effect of Preprocessing Algorithms on Resting State fMRI Data
Abstract
Graph theory modeling is a common modeling approach in neurobiology research studies. These models are useful since they describe patterns of connection for regions of interest in the brain using resting state fMRI images. The standard rule of thumb is to threshold the observed activation levels prior to model building. It is reasonable to assume that the use of this threshold affects the statistical distribution of commonly reported centrality metrics from the graph theory model, such as degree, betweenness, and closeness. In this study we examine the differential effect of using the standard approaches versus alternative direct thresholds and incorporation of thresholds through soft and hard covariance estimation. Along with the way it is viewed we care about the way we preprocess the data. Results indicate that direct thresholding is a more reliable preprocessing strategy, but soft thresholding of the covariance matrix may be a promising alternative in particular settings.
Subject Area
Statistics
Recommended Citation
Hernandez, Hortencia Josefina, "Evaluation of Effect of Preprocessing Algorithms on Resting State fMRI Data" (2022). ETD Collection for University of Texas, El Paso. AAI30242266.
https://scholarworks.utep.edu/dissertations/AAI30242266