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Abstract #3960

Cohesive parcellation of rsfMRI using constrained hierarchical clustering

Ajay Nemani1 and Mark Lowe1
1Imaging Sciences, Cleveland Clinic, Cleveland, OH, United States

Most connectivity-based parcellations of rsfMRI are synthesized from the dense connectome. However, the mean parcel signal is typically used for data reduction prior to network analysis. This results in representative parcel time series that are poorly correlated to their member voxels (poor parcel cohesion). We propose an adjacency-constrained, agglomerative hierarchical clustering framework that uses parcel cohesion as a linkage criteria. This results in parcels with mean time series that are significantly more representative of their member voxels. This parcellation is easily interpretable and well-suited for downstream analyses.

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