Inria, CEA, Université Paris-Saclay, 91120, Palaiseau, France.
Department of Computer Science, Western University, London, ON, Canada.
Brain Struct Funct. 2024 Jan;229(1):161-181. doi: 10.1007/s00429-023-02723-x. Epub 2023 Nov 27.
The analysis and understanding of brain characteristics often require considering region-level information rather than voxel-sampled data. Subject-specific parcellations have been put forward in recent years, as they can adapt to individual brain organization and thus offer more accurate individual summaries than standard atlases. However, the price to pay for adaptability is the lack of group-level consistency of the data representation. Here, we investigate whether the good representations brought by individualized models are merely an effect of circular analysis, in which individual brain features are better represented by subject-specific summaries, or whether this carries over to new individuals, i.e., whether one can actually adapt an existing parcellation to new individuals and still obtain good summaries in these individuals. For this, we adapt a dictionary-learning method to produce brain parcellations. We use it on a deep-phenotyping dataset to assess quantitatively the patterns of activity obtained under naturalistic and controlled-task-based settings. We show that the benefits of individual parcellations are substantial, but that they vary a lot across brain systems.
分析和理解大脑特征通常需要考虑区域级别的信息，而不是体素采样数据。近年来，已经提出了基于个体的分割，因为它们可以适应个体的大脑组织，从而提供比标准图谱更准确的个体总结。然而，为了适应而付出的代价是数据表示缺乏群体水平的一致性。在这里，我们研究了由个体化模型带来的良好表示是否仅仅是循环分析的一种效果，即个体大脑特征可以通过个体的总结更好地表示，或者这是否会扩展到新的个体，即是否可以实际上将现有的分割适应于新的个体，并在这些个体中仍然获得良好的总结。为此，我们采用了一种字典学习方法来生成脑分割。我们在一个深度表型数据集上使用它，以定量评估在自然和基于控制任务的设置下获得的活动模式。我们表明，个体分割的好处是显著的，但在不同的大脑系统中变化很大。
