TY - JOUR
T1 - Detecting overlapped functional clusters in resting state fMRI with Connected Iterative Scan
T2 - A graph theory based clustering algorithm
AU - Yan, Xiaodan
AU - Kelley, Stephen
AU - Goldberg, Mark
AU - Biswal, Bharat B.
N1 - Funding Information:
This study is based upon work partially supported by the U.S. National Science Foundation (NSF) under Grant Nos. IIS-0324947 and by the U.S. Department of Homeland Security (DHS) through the Center for Dynamic Data Analysis for Homeland Security administered through ONR grant number N00014-07-1-0150 to Rutgers University. The content of this paper does not necessarily reflect the position or policy of the U.S. government, no official endorsement should be inferred or implied. We thank Institute of Psychology of the Chinese Academy of Sciences for providing the fMRI dataset. We also thank Dr. John Rinzel at the Center for Neural Science, New York University for his encouragement and valuable comments on this study.
PY - 2011/7/15
Y1 - 2011/7/15
N2 - The brain is a complex neural network with interleaving functional connectivity among anatomical regions. However, current functional parcellation algorithms usually emphasize independence or orthogonality between the spatial components, with the interleaving nature underrepresented. This study investigates a method, Connected Iterative Scan (CIS), for identifying functionally overlapped anatomical groups with resting state fMRI. CIS iteratively optimizes a grouping of vertexes in a weighted graph, using a density metric computed based on the input and output weights of a candidate cluster. In this study, CIS is able to detect the overlapped clusters in a simulated dataset. CIS also detects that the default mode network and the task positive network, which were known as two anti-correlated networks, are overlapped at the posterior cingulate cortex and the lateral parietal cortex. CIS also detects the conventional functional clusters in the whole brain neural network (e.g., the visual cluster, the motor cluster, the frontal cluster, etc.), as well as meaningful overlaps, and also revealed the possible existence of an emotional memory functional cluster. CIS was able to identify several hub regions actively participating in many clusters. With the ability to reveal overlapping functional clusters, CIS is potentially useful in revealing the delicate architecture of the brain neural network.
AB - The brain is a complex neural network with interleaving functional connectivity among anatomical regions. However, current functional parcellation algorithms usually emphasize independence or orthogonality between the spatial components, with the interleaving nature underrepresented. This study investigates a method, Connected Iterative Scan (CIS), for identifying functionally overlapped anatomical groups with resting state fMRI. CIS iteratively optimizes a grouping of vertexes in a weighted graph, using a density metric computed based on the input and output weights of a candidate cluster. In this study, CIS is able to detect the overlapped clusters in a simulated dataset. CIS also detects that the default mode network and the task positive network, which were known as two anti-correlated networks, are overlapped at the posterior cingulate cortex and the lateral parietal cortex. CIS also detects the conventional functional clusters in the whole brain neural network (e.g., the visual cluster, the motor cluster, the frontal cluster, etc.), as well as meaningful overlaps, and also revealed the possible existence of an emotional memory functional cluster. CIS was able to identify several hub regions actively participating in many clusters. With the ability to reveal overlapping functional clusters, CIS is potentially useful in revealing the delicate architecture of the brain neural network.
KW - Brain network
KW - Clustering
KW - Graph theory
KW - Hub
KW - Resting state fMRI
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U2 - 10.1016/j.jneumeth.2011.05.001
DO - 10.1016/j.jneumeth.2011.05.001
M3 - Article
C2 - 21565220
AN - SCOPUS:79958078214
SN - 0165-0270
VL - 199
SP - 108
EP - 118
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
IS - 1
ER -