Connectivity analyses for task-based fMRI

被引:2
|
作者
Huang, Shenyang [1 ,2 ]
De Brigard, Felipe [1 ,2 ,3 ]
Cabeza, Roberto [1 ,2 ]
Davis, Simon W. [1 ,3 ,4 ]
机构
[1] Duke Univ, Dept Psychol & Neurosci, Durham, NC 27708 USA
[2] Duke Univ, Ctr Cognit Neurosci, Durham, NC 27708 USA
[3] Duke Univ, Dept Philosophy, Durham, NC 27708 USA
[4] Duke Univ, Sch Med, Dept Neurol, Durham, NC 27708 USA
关键词
fMRI; Functional connectivity; Multivariate pattern analysis; Representational similarity analysis; Graph theory; RESTING-STATE FMRI; FUNCTIONAL CONNECTIVITY; PSYCHOPHYSIOLOGICAL INTERACTIONS; PATTERN-ANALYSIS; SEMANTIC REPRESENTATIONS; BRAIN NETWORKS; CORTEX; MEMORY; SIMILARITIES; ORGANIZATION;
D O I
10.1016/j.plrev.2024.04.012
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Functional connectivity is conventionally defined by measuring the similarity between brain signals from two regions. The technique has become widely adopted in the analysis of functional magnetic resonance imaging (fMRI) data, where it has provided cognitive neuroscientists with abundant information on how brain regions interact to support complex cognition. However, in the past decade the notion of " connectivity " has expanded in both the complexity and heterogeneity of its application to cognitive neuroscience, resulting in greater difficulty of interpretation, replication, and cross -study comparisons. In this paper, we begin with the canonical notions of functional connectivity and then introduce recent methodological developments that either estimate some alternative form of connectivity or extend the analytical framework, with the hope of bringing better clarity for cognitive neuroscience researchers.
引用
收藏
页码:139 / 156
页数:18
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