Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA.
Memory and Aging Center, Department of Neurology, University of California, San Francisco, CA, USA.
Neuroimage. 2022 Nov 1;261:119526. doi: 10.1016/j.neuroimage.2022.119526. Epub 2022 Jul 29.
The human brain exhibits a diverse yet constrained range of activity states. While these states can be faithfully represented in a low-dimensional latent space, our understanding of the constitutive functional anatomy is still evolving. Here we applied dimensionality reduction to task-free and task fMRI data to address whether latent dimensions reflect intrinsic systems and if so, how these systems may interact to generate different activity states. We find that each dimension represents a dynamic activity gradient, including a primary unipolar sensory-association gradient underlying the global signal. The gradients appear stable across individuals and cognitive states, while recapitulating key functional connectivity properties including anticorrelation, modularity, and regional hubness. We then use dynamical systems modeling to show that gradients causally interact via state-specific coupling parameters to create distinct brain activity patterns. Together, these findings indicate that a set of dynamic, intrinsic spatial gradients interact to determine the repertoire of possible brain activity states.
人类大脑表现出多样化但受约束的活动状态范围。虽然这些状态可以在低维潜在空间中忠实地表示，但我们对构成性功能解剖结构的理解仍在不断发展。在这里，我们应用降维方法对无任务和任务 fMRI 数据进行分析，以解决潜在维度是否反映内在系统，如果是这样，这些系统如何相互作用以产生不同的活动状态。我们发现每个维度都代表一个动态活动梯度，包括全局信号下的主要单极感觉关联梯度。这些梯度在个体和认知状态之间表现出稳定性，同时再现了关键的功能连接性质，包括反相关、模块性和区域中心性。然后，我们使用动力系统建模来表明梯度通过特定于状态的耦合参数相互作用，以产生不同的大脑活动模式。总之，这些发现表明，一组动态的、内在的空间梯度相互作用，以确定可能的大脑活动状态的范围。
