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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