Department of Neurobiology, University of Chicago, Chicago, Illinois.
Department of Electrical Engineering and Computer Science, University of Cincinnati, Cincinnati, Ohio.
J Neurophysiol. 2022 Jul 1;128(1):197-217. doi: 10.1152/jn.00411.2021. Epub 2022 Jun 8.
Brain parcellations play a crucial role in the analysis of brain imaging data sets, as they can significantly affect the outcome of the analysis. In recent years, several novel approaches for constructing MRI-based brain parcellations have been developed with promising results. In the absence of ground truth, several evaluation approaches have been used to evaluate currently available brain parcellations. In this article, we review and critique methods used for evaluating functional brain parcellations constructed using fMRI data sets. We also describe how some of these evaluation methods have been used to estimate the optimal parcellation granularity. We provide a critical discussion of the current approach to the problem of identifying the optimal brain parcellation that is suited for a given neuroimaging study. We argue that the criteria for an optimal brain parcellation must depend on the application the parcellation is intended for. We describe a teleological approach to the evaluation of brain parcellations, where brain parcellations are evaluated in different contexts and optimal brain parcellations for each context are identified separately. We conclude by discussing several directions for further research that would result in improved evaluation strategies.
脑区划分在脑影像数据集的分析中起着至关重要的作用,因为它们会显著影响分析的结果。近年来,已经开发出了几种新的基于 MRI 的脑区划分方法,取得了有前景的结果。在没有真实数据的情况下,已经使用了几种评估方法来评估现有的脑区划分。在本文中,我们回顾和评价了用于评估基于 fMRI 数据集构建的功能脑区划分的方法。我们还描述了如何使用其中一些评估方法来估计最佳的分区粒度。我们对当前识别适合特定神经影像学研究的最佳脑区划分的问题的方法进行了批判性讨论。我们认为,最佳脑区划分的标准必须取决于该划分的应用目的。我们描述了一种目的论的脑区划分评估方法,其中在不同的背景下评估脑区划分,并分别确定每个背景下的最佳脑区划分。最后,我们讨论了几个进一步研究的方向,这将导致改进的评估策略。