Multivariate pattern classification of facial expressions based on large-scale functional connectivity

It is an important question how human beings achieve efficient recognition of others’ facial expressions in cognitive neuroscience, and it has been identified that specific cortical regions show preferential activation to facial expressions in previous studies. However, the potential contributions of the connectivity patterns in the processing of facial expressions remained unclear. The present functional magnetic resonance imaging (fMRI) study explored whether facial expressions could be decoded from the functional connectivity (FC) patterns using multivariate pattern analysis combined with machine learning algorithms (fcMVPA).

Christian Hoffeldt

About Christian Hoffeldt

Talent Scout, Human Resource Management, Talent Management , Learning & Development, Organizational Development, Change Management, Psychology, Neuropsychology.

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