Large Sample Group Independent Component Analysis of Functional Magnetic Resonance Imaging Using Anatomical Atlas-Based Reduction and Bootstrapped Clustering.
Large Sample Group Independent Component Analysis of Functional Magnetic Resonance Imaging Using Anatomical Atlas-Based Reduction and Bootstrapped Clustering.
复制标题
DOI：
10.1002/ima.20286
复制
发表时间：
2011-06
影响因子：
3.3
通讯作者：
Cohen, Mark S.
中科院分区：
文献类型：
--
作者：
Anderson, Ariana;Bramen, Jennifer;Douglas, Pamela K.;Lenartowicz, Agatha;Cho, Andrew;Culbertson, Chris;Brody, Arthur L.;Yuille, Alan L.;Cohen, Mark S.
Independent component analysis (ICA) is a popular method for the analysis of functional magnetic resonance imaging (fMRI) signals that is capable of revealing connected brain systems of functional significance. To be computationally tractable, estimating the independent components (ICs) inevitably requires one or more dimension reduction steps. Whereas most algorithms perform such reductions in the time domain, the input data are much more extensive in the spatial domain, and there is broad consensus that the brain obeys rules of localization of function into regions that are smaller in number than the number of voxels in a brain image. These functional units apparently reorganize dynamically into networks under different task conditions. Here we develop a new approach to ICA, producing group results by bagging and clustering over hundreds of pooled single-subject ICA results that have been projected to a lower-dimensional subspace. Averages of anatomically based regions are used to compress the single subject-ICA results prior to clustering and resampling via bagging. The computational advantages of this approach make it possible to perform group-level analyses on datasets consisting of hundreds of scan sessions by combining the results of within-subject analysis, while retaining the theoretical advantage of mimicking what is known of the functional organization of the brain. The result is a compact set of spatial activity patterns that are common and stable across scan sessions and across individuals. Such representations may be used in the context of statistical pattern recognition supporting real-time state classification.
登录
查看更多内容
影响因子：
5.7
作者：
Jenkinson, M;Bannister, P;Smith, S
通讯作者：
Smith, S
影响因子：
7.8
作者：
Hyvärinen, A;Oja, E
通讯作者：
Oja, E
DOI：
10.1073/pnas.0905267106
发表时间：
2009-08-04
影响因子：
11.1
作者：
Smith, Stephen M.;Fox, Peter T.;Beckmann, Christian F.
通讯作者：
Beckmann, Christian F.
影响因子：
5.7
作者：
Beckmann, CF;Smith, SM
通讯作者：
Smith, SM
影响因子：
5.7
作者：
Calhoun VD;Liu J;Adali T
通讯作者：
Adali T