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On the neural networks of empathy: A principal component analysis of an fMRI study

Jason S Nomi1,2, Dag Scherfeld1, Skara Friederichs1, Ralf Schäfer3, Matthias Franz3, Hans-Jörg Wittsack4, Nina P Azari2, John Missimer5 and Rüdiger J Seitz1,6,7*

Author Affiliations

1 Department of Neurology, University Hospital Düsseldorf, Moorenstrasse 5, 40225, Düsseldorf, Germany

2 Department of Psychology, University of Hawaii at Hilo, College of Arts and Sciences, 200 W. Kawili Street, Hilo, Hawaii 96720-4091, USA

3 Clinical Institute of Psychosomatic Medicine and Psychotherapy, University Hospital Düsseldorf, Moorenstrasse 5, 40225 Düsseldorf, Germany

4 Institute of Diagnostic Radiology, University Hospital Düsseldorf, Moorenstrasse 5, 40225 Düsseldorf, Germany

5 Paul Scherrer Institute, 5232 Villigen, Switzerland

6 Biomedical Research Centre, Heinrich-Heine-University Düsseldorf, Moorenstrasse 5, 40225 Düsseldorf, Germany

7 Brain Imaging Centre West, 52407 Jülich, Germany

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Behavioral and Brain Functions 2008, 4:41 doi:10.1186/1744-9081-4-41

Published: 17 September 2008

Abstract

Background

Human emotional expressions serve an important communicatory role allowing the rapid transmission of valence information among individuals. We aimed at exploring the neural networks mediating the recognition of and empathy with human facial expressions of emotion.

Methods

A principal component analysis was applied to event-related functional magnetic imaging (fMRI) data of 14 right-handed healthy volunteers (29 +/- 6 years). During scanning, subjects viewed happy, sad and neutral face expressions in the following conditions: emotion recognition, empathizing with emotion, and a control condition of simple object detection. Functionally relevant principal components (PCs) were identified by planned comparisons at an alpha level of p < 0.001.

Results

Four PCs revealed significant differences in variance patterns of the conditions, thereby revealing distinct neural networks: mediating facial identification (PC 1), identification of an expressed emotion (PC 2), attention to an expressed emotion (PC 12), and sense of an emotional state (PC 27).

Conclusion

Our findings further the notion that the appraisal of human facial expressions involves multiple neural circuits that process highly differentiated cognitive aspects of emotion.