Liu Yijun, Li Jian, Wisnowski Jessica L, Leahy Richard M
Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA.
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA.
bioRxiv. 2024 Jan 17:2024.01.05.574423. doi: 10.1101/2024.01.05.574423.
Cortical parcellation has long been a cornerstone in the field of neuroscience, enabling the cerebral cortex to be partitioned into distinct, non-overlapping regions that facilitate the interpretation and comparison of complex neuroscientific data. In recent years, these parcellations have frequently been based on the use of resting-state fMRI (rsfMRI) data. In parallel, methods such as independent components analysis have long been used to identify large-scale functional networks with significant spatial overlap between networks. Despite the fact that both forms of decomposition make use of the same spontaneous brain activity measured with rsfMRI, a gap persists in establishing a clear relationship between disjoint cortical parcellations and brain-wide networks. To address this, we introduce a novel parcellation framework that integrates NASCAR, a three-dimensional tensor decomposition method that identifies a series of functional brain networks, with state-of-the-art graph representation learning to produce cortical parcellations that represent near-homogeneous functional regions that are consistent with these brain networks. Further, through the use of the tensor decomposition, we avoid the limitations of traditional approaches that assume statistical independence or orthogonality in defining the underlying networks. Our findings demonstrate that these parcellations are comparable or superior to established atlases in terms of homogeneity of the functional connectivity across parcels, task contrast alignment, and architectonic map alignment. Our methodological pipeline is highly automated, allowing for rapid adaptation to new datasets and the generation of custom parcellations in just minutes, a significant advancement over methods that require extensive manual input. We describe this integrated approach, which we refer to as , as a tool for use in the fields of cognitive and clinical neuroscientific research. Parcellations created from the Human Connectome Project dataset using , along with the code to generate atlases with custom parcel numbers, are publicly available at https://untamed-atlas.github.io.
长期以来，皮质分区一直是神经科学领域的基石，它能将大脑皮质划分为不同的、不重叠的区域，便于解释和比较复杂的神经科学数据。近年来，这些分区常常基于静息态功能磁共振成像（rsfMRI）数据。与此同时，诸如独立成分分析等方法长期以来一直被用于识别大规模功能网络，这些网络之间存在显著的空间重叠。尽管这两种分解形式都利用了通过rsfMRI测量的相同自发脑活动，但在建立不相交的皮质分区与全脑网络之间的明确关系方面仍存在差距。为了解决这个问题，我们引入了一种新颖的分区框架，该框架将NASCAR（一种识别一系列功能性脑网络的三维张量分解方法）与先进的图表示学习相结合，以生成代表与这些脑网络一致的近乎同质功能区域的皮质分区。此外，通过使用张量分解，我们避免了传统方法在定义基础网络时假设统计独立性或正交性的局限性。我们的研究结果表明，这些分区在各分区功能连接的同质性、任务对比对齐和结构图谱对齐方面与已建立的图谱相当或更优。我们的方法流程高度自动化，能够快速适应新数据集，并在几分钟内生成自定义分区，这比需要大量人工输入的方法有了显著进步。我们将这种综合方法（我们称之为 ）描述为一种用于认知和临床神经科学研究领域的工具。使用 从人类连接组计划数据集创建的分区，以及生成具有自定义分区编号图谱的代码，可在https://untamed-atlas.github.io上公开获取。
