Brain Mapping 2015
DOI: 10.1016/b978-0-12-397025-1.00212-8
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Functional Connectivity

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Cited by 50 publications
(28 citation statements)
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“…Thus, the functional coupling between face-processing areas and the cerebellum may represent the processes of integration of the emotional meaning of facial expressions. However, we must keep in mind that functional connectivity does not necessarily imply a causal relationship between two areas (Eickhoff & Müller, 2015). Therefore, there are other possible interpretations in which the cerebellum receives the input from FFAs and OFA or in which there exists exchange of information between these areas.…”
Section: Discussionmentioning
confidence: 99%
“…Thus, the functional coupling between face-processing areas and the cerebellum may represent the processes of integration of the emotional meaning of facial expressions. However, we must keep in mind that functional connectivity does not necessarily imply a causal relationship between two areas (Eickhoff & Müller, 2015). Therefore, there are other possible interpretations in which the cerebellum receives the input from FFAs and OFA or in which there exists exchange of information between these areas.…”
Section: Discussionmentioning
confidence: 99%
“…Recently, the communication and interaction between them and anatomically separated neural regions came into focus of affective and stress-related neuroimaging research. Temporally dependent activation patterns in spontaneous activity of two different areas might indicate an exchange of information between functionally linked neural regions (Eickhoff and Müller, 2015; Friston et al, 1993). The amygdala interacts with multiple brain regions involved in the regulation of social, affective and stress-related information (Robinson et al, 2011; Roy et al, 2010; Stein et al, 2007), including lateral and medial frontal areas, posterior cingulate cortex, hippocampus, hypothalamus and brainstem regions.…”
Section: Introductionmentioning
confidence: 99%
“…ICA is a model-free analysis technique that allows one to characterize the spatio-temporal structure of the data. ICA identifies mutually independent sets of regions (component) exhibiting high within-component functional connectivity36.…”
Section: Methodsmentioning
confidence: 99%