1 research outputs found
A Review on MR Based Human Brain Parcellation Methods
Brain parcellations play a ubiquitous role in the analysis of magnetic
resonance imaging (MRI) datasets. Over 100 years of research has been conducted
in pursuit of an ideal brain parcellation. Different methods have been
developed and studied for constructing brain parcellations using different
imaging modalities. More recently, several data-driven parcellation methods
have been adopted from data mining, machine learning, and statistics
communities. With contributions from different scientific fields, there is a
rich body of literature that needs to be examined to appreciate the breadth of
existing research and the gaps that need to be investigated. In this work, we
review the large body of in vivo brain parcellation research spanning different
neuroimaging modalities and methods. A key contribution of this work is a
semantic organization of this large body of work into different taxonomies,
making it easy to understand the breadth and depth of the brain parcellation
literature. Specifically, we categorized the existing parcellations into three
groups: Anatomical parcellations, functional parcellations, and structural
parcellations which are constructed using T1-weighted MRI, functional MRI
(fMRI), and diffusion-weighted imaging (DWI) datasets, respectively. We provide
a multi-level taxonomy of different methods studied in each of these
categories, compare their relative strengths and weaknesses, and highlight the
challenges currently faced for the development of brain parcellations.Comment: 31 pages, 3 figures, 2 table