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Abstract
There is a longstanding effort to parcellate brain into areas based on micro-structural, macro-structural, or connectional features, forming various brain atlases. Among them, connectivity-based parcellation gains much emphasis, especially with the considerable progress of multimodal magnetic resonance imaging in the past two decades. The Brainnetome Atlas published recently is such an atlas that follows the framework of connectivity-based parcellation. However, in the construction of the atlas, the deluge of high resolution multimodal MRI data and time-consuming computation poses challenges and there is still short of publically available tools dedicated to parcellation. In this paper, we present an integrated open source pipeline (https://www.nitrc.org/projects/atpp), named Automatic Tractography-based Parcellation Pipeline (ATPP) to realize the framework of parcellation with automatic processing and massive parallel computing. ATPP is developed to have a powerful and flexible command line version, taking multiple regions of interest as input, as well as a user-friendly graphical user interface version for parcellating single region of interest. We demonstrate the two versions by parcellating two brain regions, left precentral gyrus and middle frontal gyrus, on two independent datasets. In addition, ATPP has been successfully utilized and fully validated in a variety of brain regions and the human Brainnetome Atlas, showing the capacity to greatly facilitate brain parcellation.
Highlights
From the well-known Brodmann atlas (Brodmann, 1909), which was released over 100 years ago, to the recently published Brainnetome Atlas (Fan et al, 2016) and Human Connectome Project (HCP) parcellation (Glasser et al, 2016), brain parcellations or atlases are in transition from purely ex vivo histology-based printed atlases to powerful neuroimaging-based digital brain maps with multimodal in vivo information
In the course of building the human Brainnetome Atlas, we developed an integrated pipeline, named Automatic Tractography-based Parcellation Pipeline (ATPP), as an implementation of the framework of connectivity-based parcellation
With the given region of interest (ROI) and configurations, ATPP can automatically process all the above 13 steps, which consist of registration, tractography, clustering, labeling, and validation, and accelerate the progress by massive parallel computing within and across
Summary
From the well-known Brodmann atlas (Brodmann, 1909), which was released over 100 years ago, to the recently published Brainnetome Atlas (Fan et al, 2016) and HCP parcellation (Glasser et al, 2016), brain parcellations or atlases are in transition from purely ex vivo histology-based printed atlases to powerful neuroimaging-based digital brain maps with multimodal in vivo information. Structural connectivity-based parcellation for a specific brain region or the entire cortex, such as in the human Brainnetome Atlas, requires processing substantial amount of data, including high resolution multimodal MRI raw data and intermediate results.
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