Description: We here employed methods for large-scale data mining to perform a connectivity-derived parcellation of the human amygdala based on whole-brain coactivation patterns computed for each seed voxel. Using this approach, connectivity-based parcellation divided the amygdala into three distinct clusters that are highly consistent with earlier microstructural distinctions. Meta-analytic connectivity modelling and functional characterization further revealed that the amygdala's laterobasal nuclei group was associated with coordinating high-level sensory input, whereas its centromedial nuclei group was linked to mediating attentional, vegetative, and motor responses. The results of this model-free approach support the concordance of structural, connectional, and functional organization in the human amygdala. This dataset was automatically imported from the ANIMA <http://anima.modelgui.org/> database. Version: 1
Related article: http://doi.org/10.1002/hbm.22138
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