Controllability of Functional and Structural Brain Networks

IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Complexity Pub Date : 2024-09-18 DOI:10.1155/2024/7402894
Ali Moradi Amani, Amirhessam Tahmassebi, Andreas Stadlbauer, Uwe Meyer-Baese, Vincent Noblet, Frederic Blanc, Hagen Malberg, Anke Meyer-Baese
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Abstract

Normal and aberrant cognitive functions are the result of the dynamic interplay between large-scale neural circuits. Describing the nature of these interactions has been a challenging task yet important for neurodegenerative disease evolution. Fusing modern dynamic graph network theory techniques and control theory applied on complex brain networks creates a new framework for neurodegenerative disease research by determining disease evolution at the subject level, facilitating a predictive treatment response and revealing key mechanisms responsible for disease alterations. It has been shown that two types of controllability—the average and the modal controllability—are relevant for the mechanistic explanation of how the brain navigates between cognitive states. The average controllability favors highly connected areas which move the brain to easily reachable states, while the modal controllability favors weakly connected areas representative for difficult-to-reach states. We propose two different techniques to achieve these two types of controllability: a centrality measure based on a sensitivity analysis of the Laplacian matrix is employed to determine the average controllability, while graph distances form the basis of the modal controllability. The concepts of “choosing the best driver set” and “graph distances” are applied to measure the average controllability and the modal controllability, respectively. Based on these new techniques, we obtain important disease descriptors that visualize alterations in the disease trajectory by revealing densely connected hubs or sparser areas. Our results suggest that these two techniques can accurately describe the different node roles in controlling trajectories of brain networks.

Abstract Image

大脑功能和结构网络的可控性
正常和异常的认知功能是大规模神经回路之间动态相互作用的结果。描述这些相互作用的性质一直是一项具有挑战性的任务,但对于神经退行性疾病的演变却非常重要。将现代动态图网络理论技术与应用于复杂大脑网络的控制理论相融合,通过确定主体层面的疾病演变、促进预测性治疗反应和揭示导致疾病改变的关键机制,为神经退行性疾病研究创建了一个新框架。研究表明,两种可控性--平均可控性和模态可控性--与大脑如何在认知状态之间导航的机理解释相关。平均可控性有利于高连接区域,使大脑进入容易达到的状态,而模态可控性则有利于弱连接区域,使大脑进入难以达到的状态。我们提出了两种不同的技术来实现这两种可控性:基于拉普拉斯矩阵敏感性分析的中心度量被用来确定平均可控性,而图距离则是模态可控性的基础。选择最佳驱动集 "和 "图距离 "的概念分别用于测量平均可控性和模态可控性。基于这些新技术,我们获得了重要的疾病描述符,通过揭示连接密集的枢纽或稀疏的区域,直观地显示疾病轨迹的变化。我们的研究结果表明,这两种技术可以准确描述大脑网络控制轨迹中不同节点的作用。
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来源期刊
Complexity
Complexity 综合性期刊-数学跨学科应用
CiteScore
5.80
自引率
4.30%
发文量
595
审稿时长
>12 weeks
期刊介绍: Complexity is a cross-disciplinary journal focusing on the rapidly expanding science of complex adaptive systems. The purpose of the journal is to advance the science of complexity. Articles may deal with such methodological themes as chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory. Papers treating applications in any area of natural science or human endeavor are welcome, and especially encouraged are papers integrating conceptual themes and applications that cross traditional disciplinary boundaries. Complexity is not meant to serve as a forum for speculation and vague analogies between words like “chaos,” “self-organization,” and “emergence” that are often used in completely different ways in science and in daily life.
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