We consider a data-driven approach for the subdivision of an individual subject’s functional Magnetic Resonance Imaging (fMRI) scan into regions of interest, i.e., brain parcellation. The approach is based on a computational technique for calculating resolution from inverse problem theory, which we apply to neighborhood selection for brain connectivity networks. This can be efficiently calculated even for very large images, and explicitly incorporates regularization in the form of spatial smoothing and a noise cutoff. We demonstrate the reproducibility of the method on multiple scans of the same subjects, as well as the variations between subjects.
Keith Dillon, Yu-Ping Wang, ” A regularized clustering approach to brain parcellation from functional MRI data“, Proc. SPIE 10394, Wavelets and Sparsity XVII, 103940E (2017/08/24); doi: 10.1117/12.2274846; http://dx.doi.org/10.1117/12.2274846
A regularized clustering approach to brain parcellation from functional MRI data