拓扑数据分析捕捉个体参与者的任务驱动功能磁共振成像档案:基于持久性的分类管道。

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Neuroinformatics Pub Date : 2024-01-01 Epub Date: 2023-11-04 DOI:10.1007/s12021-023-09645-3
Michael J Catanzaro, Sam Rizzo, John Kopchick, Asadur Chowdury, David R Rosenberg, Peter Bubenik, Vaibhav A Diwadkar
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引用次数: 0

摘要

基于BOLD的fMRI是研究大脑功能最广泛使用的方法。BOLD信号虽然很有价值,但却充满了独特的漏洞。其中最值得注意的是适度的信噪比,以及相对较低的时间和空间分辨率。然而,BOLD信号的高维复杂性也为功能发现提供了独特的机会。拓扑数据分析(TDA)是数学的一个分支,它被优化为在高维数据中搜索特定类别的结构,可以提供特别有价值的应用。在这项研究中,我们使用基本的运动控制范式获得了前扣带皮层(ACC)的fMRI数据。然后,对于每个参与者和三种任务条件中的每一种,使用两种方法总结ACC中的fMRI信号:a)基于TDA的持久同源性和持久性景观的方法,以及b)使用标准矢量化方案的基于非TDA的方法。最后,使用机器学习(使用支持向量分类器),在参与者中测试TDA和非TDA矢量化数据的分类准确性。在每个参与者中,基于TDA的分类优于基于非TDA的对应分类,这表明我们的TDA分析管道更好地描述了ACC中fMRI数据中任务和条件诱导的结构。我们的结果强调了TDA在表征区域fMRI信号中任务和情况诱导的结构方面的价值。除了为其他用户提供可供效仿的分析工具外,我们还讨论了基于TDA的方法在研究健康和临床大脑中功能性脑信号结构的个体差异中可以发挥的独特作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Topological Data Analysis Captures Task-Driven fMRI Profiles in Individual Participants: A Classification Pipeline Based on Persistence.

Topological Data Analysis Captures Task-Driven fMRI Profiles in Individual Participants: A Classification Pipeline Based on Persistence.

BOLD-based fMRI is the most widely used method for studying brain function. The BOLD signal while valuable, is beset with unique vulnerabilities. The most notable of these is the modest signal to noise ratio, and the relatively low temporal and spatial resolution. However, the high dimensional complexity of the BOLD signal also presents unique opportunities for functional discovery. Topological Data Analyses (TDA), a branch of mathematics optimized to search for specific classes of structure within high dimensional data may provide particularly valuable applications. In this investigation, we acquired fMRI data in the anterior cingulate cortex (ACC) using a basic motor control paradigm. Then, for each participant and each of three task conditions, fMRI signals in the ACC were summarized using two methods: a) TDA based methods of persistent homology and persistence landscapes and b) non-TDA based methods using a standard vectorization scheme. Finally, using machine learning (with support vector classifiers), classification accuracy of TDA and non-TDA vectorized data was tested across participants. In each participant, TDA-based classification out-performed the non-TDA based counterpart, suggesting that our TDA analytic pipeline better characterized task- and condition-induced structure in fMRI data in the ACC. Our results emphasize the value of TDA in characterizing task- and condition-induced structure in regional fMRI signals. In addition to providing our analytical tools for other users to emulate, we also discuss the unique role that TDA-based methods can play in the study of individual differences in the structure of functional brain signals in the healthy and the clinical brain.

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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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