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Schlagwörter:
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Zusammenfassung:
Objective: The aim of the study was to assess the architecture of the social brain network for reading of emotional body language through integration of structural and effective connectivity.
Background: Understanding body language is indispensable for successful non-verbal communication and daily-life social interaction. Despite this significant clinical impact, the underlying social brain networks and their dysfunction after brain damage little understood.
Design/Methods: We performed functional MRI (fMRI) and diffusion tensor imaging (DTI) in 17 healthy right-handed male participants during recognition of emotions (happy, neutral and angry) conveyed by a point-light arm seen knocking on a door. Statistical Parametric Mapping (SPM12; The Wellcome Trust Centre for Neuroimaging, London, UK, http://www.fil.ion.ucl.ac.uk/spm) was used for data pre-processing, fMRI data and dynamic causal modelling (DCM) analysis of effective connectivity, and the FMRIB Software Library (FSL4, Oxford Centre for Functional MRI of the Brain, UK, http://www.fmrib.ox.ac.uk/fsl) for probabilistic tractography on the DTI data.
Results: The results show that the right superior temporal sulcus (STS) and caudate nucleus are preferentially activated by happy, and the left inferior insula, perigenual anterior cingulate cortex (ACC) and posterior midcingulate cortex (MCC) by angry as compared to neutral body motion. The cerebellar vermis (lobule IX) and right amygdala appear to signal a lack of emotional content. Measures of structural connection strength usefully inform effective connectivity analysis that reveals functional architecture within this network.
Conclusions: This study for the first time reveals the components, structural connections and functional interactions of the brain network for reading of emotional body language reading. The data contribute to better clinical consideration and understanding of socio-cognitive deficits after damage to this network. In addition, the developed effective-structural connectivity analysis may open new perspectives in task-related brain imaging assessment of different functional networks in normalcy and neuropsychiatric pathology, also outside the immediate field.
