Title :
Cluster-based analysis for characterizing dynamic functional connectivity
Author :
Shakil, Sadia ; Magnuson, Matthew E. ; Keilholz, Shella D. ; Chin-Hui Lee
Author_Institution :
Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
Abstract :
Different regions in the resting brain exhibit non-stationary functional connectivity (FC) over time. In this paper, a simple and efficient framework of clustering the variability in FC of a rat´s brain at rest is proposed. This clustering process reveals areas that are always connected with a chosen region, called seed voxel, along with the areas exhibiting variability in the FC. This addresses an issue common to most dynamic FC analysis techniques, which is the assumption that the spatial extent of a given network remains constant over time. We increase the voxel size and reduce the spatial resolution to analyze variable FC of the whole resting brain. We hypothesize that the adjacent voxels in resting state functional magnetic resonance imaging (rsfMRI), just as in task-based fMRI, exhibit similar intensities, so they can be averaged to obtain larger voxels without any significant loss of information. Sliding window correlation is used to compute variable patterns of the rat´s whole brain FC with the seed voxel in the sensorimotor cortex. These patterns are grouped based on their spatial similarities using binary transformed feature vectors in k-means clustering, not only revealing the variable and nonvariable portions of FC in the resting brain but also detecting the extent of the variability of these patterns.
Keywords :
biomedical MRI; brain; medical image processing; pattern clustering; binary transformed feature vectors; cluster-based analysis; clustering process; dynamic FC analysis techniques; dynamic functional connectivity; k-means clustering; rats brain; resting state functional magnetic resonance imaging; rsfMRI; seed voxel; sensorimotor cortex; sliding window correlation; task-based fMRI; voxel size; Brain; Correlation; Magnetic resonance imaging; Pediatrics; Rats; Silicon; Time series analysis; Clustering; Dunn´s Index; Functional Connectivity; Functional MRI; Resting State Functional MRI; Sliding Window Correlation; k-means;
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE
Conference_Location :
Chicago, IL
DOI :
10.1109/EMBC.2014.6943757