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Abstract #1301

Hierarchical intra-network organization of the visual network from resting-state fMRI data

Yanlu Wang 1 and Tie-Qiang Li 1,2

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

We have previously extracted functional connectivity networks from resting-state fMRI data using hierarchical clustering at voxel-level while retaining full-brain coverage. Hierarchical clustering algorithm is not only a data-driven analysis method, but also naturally stratifies data in a hierarchical structure. Using this inherent property of the algorithm, we investigated the intra-network hierarchical organization of the visual network and showed that the intra-network connectivity conforms to the two-stream hypothesis of visual processing. This suggests that functional sub-division of resting-state functional connectivity networks through hierarchical clustering reflects the intra-network organization of resting-state functional connectivity networks.

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