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FATCAT:(一个高效的)功能和轨迹连通性分析工具箱。

FATCAT: (an efficient) Functional and Tractographic Connectivity Analysis Toolbox.

机构信息

1 African Institute for Mathematical Sciences , Muizenberg, Western Cape, South Africa .

出版信息

Brain Connect. 2013;3(5):523-35. doi: 10.1089/brain.2013.0154.

Abstract

We present a suite of software tools for facilitating the combination of functional magnetic resonance imaging (FMRI) and diffusion-based tractography from a network-focused point of view. The programs have been designed for investigating functionally derived gray matter networks and related structural white matter networks. The software comprises the Functional and Tractographic Connectivity Analysis Toolbox (FATCAT), now freely distributed with AFNI. This toolbox supports common file formats and has been designed to integrate as easily as possible with existing standard FMRI pipelines and diffusion software, such as AFNI, FSL, and TrackVis. The programs are efficient, run by commandline for facilitating group processing, and produce several visualizable outputs. Here, we present the programs and their underlying methods, and we also provide a test example of resting-state FMRI analysis combined with tractography. Tractography results are compared with existing methods, showing significantly reduced runtime and generally similar connectivity, but with important differences such as more circumscribed tract regions and more physiologically identifiable paths produced between several region-of-interest pairs. Currently, FATCAT uses only diffusion tensor-based tractography (one direction per voxel), but higher-order models will soon be included.

摘要

我们提出了一套软件工具,用于从网络角度促进功能磁共振成像(fMRI)和基于扩散的束追踪的组合。这些程序旨在研究功能衍生的灰质网络和相关的结构白质网络。软件包括功能和束追踪连通性分析工具箱(FATCAT),现在与 AFNI 一起免费分发。该工具箱支持常见的文件格式,并设计为尽可能轻松地与现有的标准 fMRI 管道和扩散软件(如 AFNI、FSL 和 TrackVis)集成。这些程序效率高,通过命令行运行,便于进行组处理,并产生多个可视化输出。在这里,我们介绍了这些程序及其底层方法,还提供了一个静息态 fMRI 分析与束追踪相结合的测试示例。将追踪结果与现有方法进行比较,结果表明运行时间显著缩短,连通性大致相似,但存在一些重要差异,例如在几个感兴趣区对之间产生了更局限的束区域和更具生理可识别的路径。目前,FATCAT 仅使用基于扩散张量的束追踪(每个体素一个方向),但很快将包括更高阶模型。

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