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

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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.