Merényi, Erzsébet2019-05-172019-05-172018-052018-04-20May 2018O'Driscoll, Patrick. "Self-Organizing Maps for Segmentation of fMRI: Understanding the Genesis of Willed Movement Through A Multiple Subject Study." (2018) Diss., Rice University. <a href="https://hdl.handle.net/1911/105789">https://hdl.handle.net/1911/105789</a>.https://hdl.handle.net/1911/105789The neural process of executing willed movements, though fundamental to human activity, is not well understood. Analyzing the neural activity of human subjects performing willed movement encoded in functional Magnetic Resonance Imaging (fMRI) data may further our understanding of the genesis of willed movement. fMRI encodes the neural activity in the Blood Oxygen Level Dependence (BOLD) signal in each voxel, volume element, of the fMRI data. Model-free, data-driven methods to cluster voxels based on neural activity have become an increasingly common approach for generating interpretable brain maps. The hypothesis is: grouping voxels based upon the similarity of their time course should group voxels into functional regions of the brain. Furthermore, by examining the relationships between clusters, the relationships between brain regions can be revealed. Clustering is an ill-posed problem with many solutions and finding interpretable or good results is notoriously difficult. Self-Organizing Maps (SOMs) are adept at learning the data structure, and provide a means of visualizing and interpreting large, complex, high-dimensional datasets. SOMs learn the data manifold by placing prototypes into the dataspace to represent nearby datapoints. By clustering or grouping the SOM prototypes, one clusters the data represented by these prototypes. Currently, the most powerful SOM clustering methods rely on interactive visualizations and user expertise for cluster extraction. However, solutions that require less user time, increase reproducibility, and avoid the user bias and subjectivity are desirable. This work proposes a novel Combined Connectivity and Spatial Adjacency (CCSA) measure to be used in a Hierarchical Agglomerative Clustering (HAC) method to automatically extract clusters from SOMs. CCSA measure uses information available to the SOM in three distinct, latent dataspaces. Comparing HAC with CCSA to the most popular automated SOM clustering algorithms on synthetic datasets shows improved performance. HAC with CCSA extracts equivalent or better clusters than the interactive method on real fMRI multiple subject study of the genesis of willed movement. By examining the consistency across all subjects and by comparing the autocorrelation between the clusters extracted in each subject from the fMRI data, a medical model and the relationships between the brain regions can be understood.application/pdfengCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.ClusteringHierarchical Agglomerative ClusteringHACSelf-Organizing MapSOMfMRIMachine LearningClusteringwilled MovementThesis2019-05-17