Degree-based statistic and center persistency for brain connectivity analysis.
Brain connectivity analyses have been widely performed to investigate the organization and functioning of the brain, or to observe changes in neurological or psychiatric conditions. However, connectivity analysis inevitably introduces the problem of mass-univariate hypothesis testing. Although, several cluster-wise correction methods have been suggested to address this problem and shown to provide high sensitivity, these approaches fundamentally have two drawbacks: the lack of spatial specificity (localization power) and the arbitrariness of an initial cluster-forming threshold. In this study, we propose a novel method, degree-based statistic (DBS), performing cluster-wise inference. DBS is designed to overcome the above-mentioned two shortcomings. From a network perspective, a few brain regions are of critical importance and considered to play pivotal roles in network integration. Regarding this notion, DBS defines a cluster as a set of edges of which one ending node is shared. This definition enables the efficient detection of clusters and their center nodes. Furthermore, a new measure of a cluster, center persistency (CP) was introduced. The efficiency of DBS with a known "ground truth" simulation was demonstrated. Then they applied DBS to two experimental datasets and showed that DBS successfully detects the persistent clusters. In conclusion, by adopting a graph theoretical concept of degrees and borrowing the concept of persistence from algebraic topology, DBS could sensitively identify clusters with centric nodes that would play pivotal roles in an effect of interest. DBS is potentially widely applicable to variable cognitive or clinical situations and allows us to obtain statistically reliable and easily interpretable results. Hum Brain Mapp 38:165-181, 2017. © 2016 Wiley Periodicals, Inc.
Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.|KI for Health Science and Technology, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
Kwangsun
K
Yoo
Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.|KI for Health Science and Technology, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
Peter
P
Lee
Department of Biostatistics and Medical Informatics and Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin, Madison, Wisconsin 53706.
Moo K
MK
Chung
Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
William S
WS
Sohn
Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Sun Ju
SJ
Chung
Department of Neurology, Samsung Medical Center, Sungkyunkwan University, School of Medicine, Seoul, Republic of Korea.|Neuroscience Center, Samsung Medical Center, Sungkyunkwan University, School of Medicine, Seoul, Republic of Korea.
Duk L
DL
Na
Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
Daheen
D
Ju
Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.|KI for Health Science and Technology, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
Yong
Y
Jeong
D000368:Aged; D000544:Alzheimer Disease / Q000000981:diagnostic imaging / Q000473:pathology*; D001921:Brain / Q000000981:diagnostic imaging / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D016022:Case-Control Studies; D005260:Female; D007839:Functional Laterality; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology; D009434:Neural Pathways / Q000000981:diagnostic imaging / Q000473:pathology*; D010300:Parkinson Disease / Q000000981:diagnostic imaging / Q000473:pathology*
center persistency (CP); cluster-wise correction; connectivity analysis; degree-based statistic (DBS); family-wise error (FWE); multiple testing problem (MTP)