Abstract #1666

Data-driven functional sub-division of the sensory-motor network using hierarchical clustering for resting-state fMRI data.

Yanlu Wang¹ and Tie-Qiang Li^1,2

¹Clinical Sciences, Intervention and Technology, Karolinska Institute, Stockholm, Sweden, ²Medical Physics, Karolinska University Hospital, Stockholm, Sweden

A data-driven analysis method based on hierarchical clustering was used to analyze the sensory-motor resting-state network from resting-state fMRI data. It was used to analyze the network’s functional sub-division, and intra-network functional organization, in different levels of detail. Sub-network for the sensory-motor network as obtained by hierarchical clustering is anatomically and functionally sensible. Further sub-division of the paracentral lobule network hub successfully revealed its functional sub-division in great detail. The intra-network organization of intrinsic functional connectivity derived from spontaneous activity of the brain at rest reflects consistently, the functional and neural anatomic connectivity topography of the resting-state network.

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