Beyond Functional Connectivity: Investigating Networks of Multivariate Representations

被引:90
作者
Anzellotti, Stefano [1 ]
Coutanche, Marc N. [2 ]
机构
[1] MIT, Dept Brain & Cognit Sci, E25-618, Cambridge, MA 02139 USA
[2] Univ Pittsburgh, Dept Psychol, Pittsburgh, PA 15260 USA
关键词
VOXEL PATTERN-ANALYSIS; HUMAN BRAIN; TEMPORAL-LOBE; FMRI; INFORMATION; INTEGRATION; ACTIVATION; EXPERTISE; IDENTITY; OBJECTS;
D O I
10.1016/j.tics.2017.12.002
中图分类号
B84 [心理学]; C [社会科学总论]; Q98 [人类学];
学科分类号
03 ; 0303 ; 030303 ; 04 ; 0402 ;
摘要
For over two decades, interactions between brain regions have been measured in humans by asking how the univariate responses in different regions co-vary ('Functional Connectivity'). Thousands of Functional Connectivity studies have been published investigating the healthy brain and how it is affected by neural disorders. The advent of multivariate fMRI analyses showed that patterns of responses within regions encode information that is lost by averaging. Despite this, connectivity methods predominantly continue to focus on univariate responses. In this review, we discuss the recent emergence of multivariate and nonlinear methods for studying interactions between brain regions. These new developments bring sensitivity to fluctuations in multivariate information, and offer the possibility to ask not only whether brain regions interact, but how they do so.
引用
收藏
页码:258 / 269
页数:12
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