Abstract
Parcellations used in resting-state fMRI (rs-fMRI) analyses are derived from group-level information, and thus ignore both subject-level functional differences and the downstream task. In this paper, we introduce RefineNet, a Bayesian-inspired deep network architecture that adjusts region boundaries based on individual functional connectivity profiles. RefineNet uses an iterative voxel reassignment procedure that considers neighborhood information while balancing temporal coherence of the refined parcellation. We validate RefineNet on rs-fMRI data from three different datasets, each one geared towards a different predictive task: (1) cognitive fluid intelligence prediction using the HCP dataset (regression), (2) autism versus control diagnosis using the ABIDE II dataset (classification), and (3) language localization using an rs-fMRI brain tumor dataset (segmentation). We demonstrate that RefineNet improves the performance of existing deep networks from the literature on each of these tasks. We also show that RefineNet produces anatomically meaningful subject-level parcellations with higher temporal coherence.
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This work was supported by the National Science Foundation CAREER award 1845430 (PI: Venkataraman) and the Research & Education Foundation Carestream Health RSNA Research Scholar Grant RSCH1420.
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Nandakumar, N., Manzoor, K., Agarwal, S., Sair, H.I., Venkataraman, A. (2022). RefineNet: An Automated Framework to Generate Task and Subject-Specific Brain Parcellations for Resting-State fMRI Analysis. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13431. Springer, Cham. https://doi.org/10.1007/978-3-031-16431-6_30
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