High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia

	High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia
	Plis, Sergey M.1; Sui, Jing 1,9; Lane, Terran 3; Roy, Sushmita 2; Clark, Vincent P.1; Potluru, Vamsi K.3; Huster, Rene J.8; Michael, Andrew 1; Sponheim, Scott R.5,6,7; Weisend, Michael P.1; Calhoun, Vince D.1,3,4
发表期刊	NEUROIMAGE
	2014-11-15
卷号	102 页码:35-48
文章类型	Article
摘要	Identifying the complex activity relationships present in rich, modern neuroimaging data sets remains a key challenge for neuroscience. The problem is hard because (a) the underlying spatial and temporal networks may be nonlinear and multivariate and (b) the observed data may be driven by numerous latent factors. Further, modern experiments often produce data sets containing multiple stimulus contexts or tasks processed by the same subjects. Fusing such multi-session data sets may reveal additional structure, but raises further statistical challenges. We present a novel analysis method for extracting complex activity networks from such multifaceted imaging data sets. Compared to previous methods, we choose a new point in the trade-off space, sacrificing detailed generative probability models and explicit latent variable inference in order to achieve robust estimation of multivariate, nonlinear group factors ("network clusters"). We apply our method to identify relationships of task-specific intrinsic networks in schizophrenia patients and control subjects from a large fMRI study. After identifying network-clusters characterized by within-and between-task interactions, we find significant differences between patient and control groups in interaction strength among networks. Our results are consistent with known findings of brain regions exhibiting deviations in schizophrenic patients. However, we also find high-order, nonlinear interactions that discriminate groups but that are not detected by linear, pairwise methods. We additionally identify high-order relationships that provide new insights into schizophrenia but that have not been found by traditional univariate or second-order methods. Overall, our approach can identify key relationships that are missed by existing analysis methods, without losing the ability to find relationships that are known to be important. (C) 2013 Elsevier Inc. All rights reserved.
关键词	Nonparametric Estimators Fmri High-order Interactions Multi-task Data
WOS标题词	Science & Technology ; Life Sciences & Biomedicine
关键词[WOS]	AUDITORY ODDBALL TASK ; WORKING-MEMORY TASK ; FMRI DATA ; INDEPENDENT COMPONENTS ; BIPOLAR DISORDER ; BLIND SEPARATION ; TIME-SERIES ; DATA FUSION ; MODEL ; CORTEX
收录类别	SCI
语种	英语
WOS研究方向	Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Neurosciences ; Neuroimaging ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000345390400005
引用统计	被引频次：18[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3154
专题	脑图谱与类脑智能实验室_脑网络组研究
作者单位	1.Mind Res Network, Albuquerque, NM 87106 USA 2.UW Madison, Wisconsin Inst Discovery, Dept Biostat & Med Informat, Madison, WI USA 3.Univ New Mexico, Dept Comp Sci, Albuquerque, NM 87131 USA 4.Univ New Mexico, Dept Elect & Comp Engn, Albuquerque, NM 87131 USA 5.Minneapolis VA Hlth Care Syst, Minneapolis, MN USA 6.Univ Minnesota, Dept Psychiat, Minneapolis, MN 55455 USA 7.Univ Minnesota, Dept Psychol, Minneapolis, MN 55455 USA 8.Carl von Ossietzky Univ Oldenburg, Expt Psychol Lab, D-26111 Oldenburg, Germany 9.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Plis, Sergey M.,Sui, Jing,Lane, Terran,et al. High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia[J]. NEUROIMAGE,2014,102:35-48.
APA	Plis, Sergey M..,Sui, Jing.,Lane, Terran.,Roy, Sushmita.,Clark, Vincent P..,...&Calhoun, Vince D..(2014).High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia.NEUROIMAGE,102,35-48.
MLA	Plis, Sergey M.,et al."High-order interactions observed in multi-task intrinsic networks are dominant indicators of aberrant brain function in schizophrenia".NEUROIMAGE 102(2014):35-48.