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结合感兴趣功能区和白质束的实用指南。

A practical guide for combining functional regions of interest and white matter bundles.

作者信息

Meisler Steven L, Kubota Emily, Grotheer Mareike, Gabrieli John D E, Grill-Spector Kalanit

机构信息

Program in Speech and Hearing Bioscience and Technology, Harvard Medical School, Boston, MA, United States.

Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States.

出版信息

Front Neurosci. 2024 Aug 16;18:1385847. doi: 10.3389/fnins.2024.1385847. eCollection 2024.

Abstract

Diffusion-weighted imaging (DWI) is the primary method to investigate macro- and microstructure of neural white matter . DWI can be used to identify and characterize individual-specific white matter bundles, enabling precise analyses on hypothesis-driven connections in the brain and bridging the relationships between brain structure, function, and behavior. However, cortical endpoints of bundles may span larger areas than what a researcher is interested in, challenging presumptions that bundles are specifically tied to certain brain functions. Functional MRI (fMRI) can be integrated to further refine bundles such that they are restricted to functionally-defined cortical regions. Analyzing properties of these Functional Sub-Bundles (FSuB) increases precision and interpretability of results when studying neural connections supporting specific tasks. Several parameters of DWI and fMRI analyses, ranging from data acquisition to processing, can impact the efficacy of integrating functional and diffusion MRI. Here, we discuss the applications of the FSuB approach, suggest best practices for acquiring and processing neuroimaging data towards this end, and introduce the , a flexible open-source software for creating FSuBs. We demonstrate our processing code and the on an openly-available dataset, the Natural Scenes Dataset.

摘要

扩散加权成像(DWI)是研究神经白质宏观和微观结构的主要方法。DWI可用于识别和表征个体特异性白质束,从而能够对大脑中基于假设的连接进行精确分析,并在脑结构、功能和行为之间建立联系。然而,束的皮质端点可能跨越比研究人员感兴趣的更大区域,这对束与某些脑功能有特定关联的假设提出了挑战。功能磁共振成像(fMRI)可以与之整合,进一步细化束,使其局限于功能定义的皮质区域。分析这些功能子束(FSuB)的特性可提高研究支持特定任务的神经连接时结果的精确性和可解释性。从数据采集到处理,DWI和fMRI分析的几个参数都会影响功能磁共振成像和扩散磁共振成像整合的效果。在此,我们讨论FSuB方法的应用,为此目的提出获取和处理神经影像数据的最佳实践,并介绍,这是一个用于创建FSuB的灵活开源软件。我们在一个公开可用的数据集——自然场景数据集上展示我们的处理代码和。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/666c/11363198/7b94671cf578/fnins-18-1385847-g001.jpg

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