PDF(Pubmed)
Abstract：
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 Untamed, as a tool for use in the fields of cognitive and clinical neuroscientific research. Parcellations created from the Human Connectome Project dataset using Untamed, along with the code to generate atlases with custom parcel numbers, are publicly available at https://untamed-atlas.github.io.
摘要：
皮质分裂长期以来一直是神经科学领域的基石，使大脑皮层被分割成不同的，促进复杂神经科学数据的解释和比较的非重叠区域。近年来,这些分组通常基于静息态功能磁共振成像(rsfMRI)数据的使用.并行,独立成分分析等方法长期以来一直被用于识别网络之间具有显著空间重叠的大规模功能网络。尽管两种形式的分解都利用了rsfMRI测量的相同的自发大脑活动，在不相交的皮质分区和全脑网络之间建立明确的关系方面仍然存在差距。为了解决这个问题，我们介绍了一个集成了NASCAR的新颖的分割框架，一种识别一系列功能性大脑网络的三维张量分解方法，通过最先进的图形表示学习来产生皮质分区，这些皮质分区表示与这些大脑网络一致的接近同质的功能区域。Further,通过使用张量分解，我们避免了传统方法在定义底层网络时假设统计独立性或正交性的局限性。我们的发现表明，就地块之间的功能连通性的同质性而言，这些分区与已建立的地图集具有可比性或优越性。任务对比度对齐，和建筑地图对齐。我们的方法管道是高度自动化的，允许在短短几分钟内快速适应新数据集和生成自定义分区，与需要大量手动输入的方法相比，这是一个显著的进步。我们描述了这种综合方法，我们称之为未驯服，作为认知和临床神经科学研究领域的工具。使用Untamed从HumanConnectomeProject数据集创建的分区，以及生成带有自定义包裹编号的地图集的代码，可在https://untamed-atlas上公开获得。github.io.
