Madan Mohan Varun, Banerjee Arpan
National Brain Research Centre, Manesar, India.
Netw Neurosci. 2022 Oct 1;6(4):1275-1295. doi: 10.1162/netn_a_00260. eCollection 2022.
How communication among neuronal ensembles shapes functional brain dynamics is a question of fundamental importance to neuroscience. Communication in the brain can be viewed as a product of the interaction of node activities with the structural network over which these activities flow. The study of these interactions is, however, restricted by the difficulties in describing the complex dynamics of the brain. There is thus a need to develop methods to study these network-dynamical interactions and how they impact information flow, without having to ascertain dynamics a priori or resort to restrictive analytical approaches. Here, we adapt a recently established network analysis method based on perturbations, it to a neuroscientific setting to study how information flow in the brain can raise from properties of underlying structure. For proof-of-concept, we apply the approach on in silico whole-brain models. We expound on the functional implications of the distributions of metrics that capture network-dynamical interactions, termed and . We also study the network-dynamical interactions at the level of resting-state networks. An attractive feature of this method is its simplicity, which allows a direct translation to an experimental or clinical setting, such as for identifying targets for stimulation studies or therapeutic interventions.
神经元集群之间的通信如何塑造功能性脑动力学，这是神经科学中一个至关重要的基本问题。大脑中的通信可被视为节点活动与这些活动所流经的结构网络相互作用的产物。然而，对这些相互作用的研究受到描述大脑复杂动力学困难的限制。因此，需要开发方法来研究这些网络动力学相互作用以及它们如何影响信息流，而无需事先确定动力学或诉诸限制性的分析方法。在此，我们将一种最近基于微扰建立的网络分析方法应用于神经科学环境，以研究大脑中的信息流如何源自底层结构的特性。为了进行概念验证，我们将该方法应用于计算机模拟的全脑模型。我们阐述了捕获网络动力学相互作用的指标分布的功能含义，这些指标被称为 和 。我们还在静息态网络层面研究网络动力学相互作用。该方法的一个吸引人的特点是其简单性，这使得它可以直接转化为实验或临床环境，例如用于确定刺激研究或治疗干预的靶点。
