CAT: a computational anatomy toolbox for the analysis of structural MRI data.

IF 11.8 2区生物学 Q1 MULTIDISCIPLINARY SCIENCES

GigaScience Pub Date : 2024-01-02 DOI:10.1093/gigascience/giae049

Christian Gaser, Robert Dahnke, Paul M Thompson, Florian Kurth, Eileen Luders, The Alzheimer's Disease Neuroimaging Initiative

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引用次数: 0

Abstract

A large range of sophisticated brain image analysis tools have been developed by the neuroscience community, greatly advancing the field of human brain mapping. Here we introduce the Computational Anatomy Toolbox (CAT)-a powerful suite of tools for brain morphometric analyses with an intuitive graphical user interface but also usable as a shell script. CAT is suitable for beginners, casual users, experts, and developers alike, providing a comprehensive set of analysis options, workflows, and integrated pipelines. The available analysis streams-illustrated on an example dataset-allow for voxel-based, surface-based, and region-based morphometric analyses. Notably, CAT incorporates multiple quality control options and covers the entire analysis workflow, including the preprocessing of cross-sectional and longitudinal data, statistical analysis, and the visualization of results. The overarching aim of this article is to provide a complete description and evaluation of CAT while offering a citable standard for the neuroscience community.

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CAT：用于分析核磁共振成像结构数据的计算解剖工具箱。

神经科学界开发了大量复杂的大脑图像分析工具，极大地推动了人脑绘图领域的发展。这里我们介绍计算解剖工具箱（CAT）--一套功能强大的脑形态分析工具，具有直观的图形用户界面，也可作为 shell 脚本使用。CAT 适合初学者、普通用户、专家和开发人员使用，提供了一套全面的分析选项、工作流程和集成管道。可用的分析流--以一个示例数据集为例--允许进行基于体素、基于表面和基于区域的形态计量分析。值得注意的是，CAT 集成了多种质量控制选项，涵盖了整个分析工作流程，包括横断面和纵向数据的预处理、统计分析和结果可视化。本文的总体目标是对 CAT 进行完整的描述和评估，同时为神经科学界提供一个可引用的标准。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

GigaScience MULTIDISCIPLINARY SCIENCES-

CiteScore

15.50

自引率

1.10%

发文量

119

审稿时长

1 weeks

期刊介绍： GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.