TY  - INPR
Y1  - 2016/05//
UR  - http://eprints.imtlucca.it/3225/
A1  - Lin, Ying-Chia
A1  - Gili, Tommaso
A1  - Tsaftaris, Sotirios A.
A1  - Gabrielli, Andrea
A1  - Iorio, Mariangela
A1  - Spalletta, Gianfranco
A1  - Caldarelli, Guido
T2  - 24th Annual Meeting ISMRM
AV  - none
M2  - Singapore
ID  - eprints3225
N2  - Network analysis of resting-state fMRI (rsfMRI) has been widely utilized to investigate the functional architecture of the whole brain. Here we propose a robust parcellation method that first divides cortical and sub-cortical regions into sub-regions by clustering the rsfMRI data for each subject independently, and then merges those individual parcellations to obtain a global whole brain parcellation. To do so our method relies on majority voting (to merge parcellations of multiple subjects) and enforces spatial constraints within a hierarchical agglomerative clustering framework to define parcels that are spatially homogeneous.
TI  - A cortical and sub-cortical parcellation clustering by intrinsic functional connectivity
ER  -