Department of Computer Science, Georgia State University, Atlanta, GA 30332, USA.
Department of Computer Science, Georgia State University, Atlanta, GA 30332, USA; Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA 30303, USA.
J Neurosci Methods. 2022 Mar 1;369:109478. doi: 10.1016/j.jneumeth.2022.109478. Epub 2022 Jan 11.
Brain parcellation is an essential aspect of computational neuroimaging research and deals with segmenting the brain into (possibly overlapping) sub-regions employed to study brain anatomy or function. In the context of functional parcellation, brain organization which is often measured via temporal metrics such as coherence, is highly dynamic. This dynamic aspect is ignored in most research, which typically applies anatomically based, fixed regions for each individual, and can produce misleading results.
In this work, we propose a novel spatio-temporal-network (5D) brain parcellation scheme utilizing a deep residual network to predict the probability of each voxel belonging to a brain network at each point in time.
We trained 53 4D brain networks and evaluate the ability of these networks to capture spatial and temporal dynamics as well as to show sensitivity to individual or group-level variation (in our case with age).
The proposed system generates informative spatio-temporal networks that vary not only across individuals but also over time and space.
The dynamic 5D nature of the developed approach provides a powerful framework that expands on existing work and has potential to identify novel and typically ignored findings when studying the healthy and disordered brain.
脑区划分是计算神经影像学研究的一个重要方面，涉及将大脑分割成（可能重叠的）子区域，用于研究大脑解剖结构或功能。在功能分区的背景下，大脑组织通常通过诸如相干性等时间度量来测量，其具有高度动态性。这一动态方面在大多数研究中被忽略，这些研究通常针对每个个体应用基于解剖结构的固定区域，可能会产生误导性的结果。
在这项工作中，我们提出了一种新的时空网络（5D）脑区划分方案，利用深度残差网络来预测每个体素在每个时间点属于大脑网络的概率。
我们训练了 53 个 4D 大脑网络，并评估了这些网络捕捉空间和时间动态的能力，以及对个体或群体水平变化（在我们的案例中是年龄）的敏感性。
所提出的系统生成了信息丰富的时空网络，这些网络不仅在个体之间变化，而且在时间和空间上也变化。
所开发方法的动态 5D 性质提供了一个强大的框架，扩展了现有工作，并有可能在研究健康和紊乱的大脑时识别新的和通常被忽略的发现。
