Ben Messaoud Remy, Le Du Vincent, Bousfiha Camile, Corsi Marie-Constance, Gonzalez-Astudillo Juliana, Kaufmann Brigitte Charlotte, Venot Tristan, Couvy-Duchesne Baptiste, Migliaccio Lara, Rosso Charlotte, Bartolomeo Paolo, Chavez Mario, De Vico Fallani Fabrizio
Inria Paris, Paris, France.
Sorbonne Université, Paris Brain Institute, CNRS, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France.
PLoS Comput Biol. 2025 Jan 7;21(1):e1012691. doi: 10.1371/journal.pcbi.1012691. eCollection 2025 Jan.
Identifying the driver nodes of a network has crucial implications in biological systems from unveiling causal interactions to informing effective intervention strategies. Despite recent advances in network control theory, results remain inaccurate as the number of drivers becomes too small compared to the network size, thus limiting the concrete usability in many real-life applications. To overcome this issue, we introduced a framework that integrates principles from spectral graph theory and output controllability to project the network state into a smaller topological space formed by the Laplacian network structure. Through extensive simulations on synthetic and real networks, we showed that a relatively low number of projected components can significantly improve the control accuracy. By introducing a new low-dimensional controllability metric we experimentally validated our method on N = 6134 human connectomes obtained from the UK-biobank cohort. Results revealed previously unappreciated influential brain regions, enabled to draw directed maps between differently specialized cerebral systems, and yielded new insights into hemispheric lateralization. Taken together, our results offered a theoretically grounded solution to deal with network controllability and provided insights into the causal interactions of the human brain.
识别网络中的驱动节点在生物系统中具有至关重要的意义，从揭示因果相互作用到为有效的干预策略提供信息。尽管网络控制理论最近取得了进展，但由于与网络规模相比驱动节点的数量变得太少，结果仍然不准确，从而限制了其在许多实际应用中的具体可用性。为了克服这个问题，我们引入了一个框架，该框架整合了谱图理论和输出可控性的原理，将网络状态投影到由拉普拉斯网络结构形成的较小拓扑空间中。通过对合成网络和真实网络的大量模拟，我们表明相对较少数量的投影组件可以显著提高控制精度。通过引入一种新的低维可控性度量，我们在从英国生物银行队列获得的N = 6134个人类连接组上通过实验验证了我们的方法。结果揭示了以前未被重视的有影响力的脑区，能够绘制不同专门化脑系统之间的定向图谱，并对半球侧化产生了新的见解。综上所述，我们的结果提供了一个理论基础的解决方案来处理网络可控性，并为人类大脑的因果相互作用提供了见解。
