ATPP: A Pipeline for Automatic Tractography-Based Brain Parcellation

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.

Delineating functional segregations of the human middle temporal gyrus with resting‐state functional connectivity and coactivation patterns

“…How to determine the optimal parcellation solutions for brain areas is very hard. In this study, we used hierarchy index (HI) which reflects the hierarchical structure of the different solutions by the average probability that a given cluster in k solution has only one “parent‐cluster” in k − 1 solution (Kahnt, Chang, Park, Heinzle, & Haynes, ; Li et al, ) to select the final parcellation results. Specifically, HI is defined as , where , here x is a matrix whose elements x ij reflects the number of voxels in cluster j i = 1 … k stemming from cluster j j = 1 … k − 1 in k −1 solution ( K > 2).…”

MonkeyCBP: A Toolbox for Connectivity-Based Parcellation of Monkey Brain

“…By applying this fixed threshold, it enables the images not only to have fewer spurious connections but also to retain enough sensitivity to the true connections (Heiervang et al, 2006;Johansen-Berg et al, 2007). For monkey data, compared with the parameter settings from previous process from ATPP in this step (Li et al, 2017), MonkeyCBP re-recommended new parameter values (e.g., number of samples -default = 10,000; Steplength in mm -default = 0.2) (Thomas et al, 2014;Folloni et al, 2019;Sani et al, 2019). To facilitate individual brain parcellation, the down-sampling rate of the connectivity profile in subsequent steps is also suggested to be modified (e.g., 3-mm isotropic voxels) (Johansen-Berg et al, 2004).…”

Multi-modal human brain mapping and Brainnetome study

“…A: Surface rendering of the parcellations; B: 3D volume rendering; C: three slices in the sagittal view, coronal view and axial view, respectively 认可. 中国科学院自动化研究所脑网络组研究中心自 主开发了系列软件, 包括基于磁共振影像的脑网络组 图谱构建软件 [41] (图2A)、脑图谱分区查看软件(图 2B)、功能磁共振分析软件BRANT [80] 、 扩散磁共振 分析软件DiffusionKit [13] , 另外还有北京师范大学开发 的神经影像脑网络可视化BrainNet Viewer软件 [81] 、静 息态磁共振数据分析软件REST [82] 和DPARSF [83] , 以及 中国科学院心理研究所开发的CCS [84] 和DPABI软件 [85] 等. EEG脑信号分析方面, 电子科技大学开发了参考电 极标准化技术REST工具包 [86] , 可以配合EEG专有工具 包EEGLAB使用 [87] , 另外该团队还开发了EEG和fMRI 信号融合的脑网络分析工具包 [88] . 以及白质网络的效率与智商的关系 [71] .…”

“…图 2 脑网络组图谱软件. A: 基于磁共振影像的脑网络组图谱构建软件 [41] ; B: 脑图谱分区查看软件 Figure 2 The softwares for Brainnetome atlas. A: The software for constructing the Brainnetome atlas based on MRI data [41] ; B: the Brainnetome atlas viewer 退化慢的老年人其智商也退化得较慢 [93] .…”