Controlling target brain regions by optimal selection of input nodes

Manjunatha, Karan Kabbur Hanumanthappa and Baron, Giorgia and Benozzo, Danilo and Silvestri, Erica and Corbetta, Maurizio and Chiuso, Alessandro and Bertoldo, Alessandra and Suweis, Samir and Allegra, Michele and Cagnan, Hayriye (2024) Controlling target brain regions by optimal selection of input nodes. PLOS Computational Biology, 20 (1). e1011274. ISSN 1553-7358

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The network control theory framework holds great potential to inform neurostimulation experiments aimed at inducing desired activity states in the brain. However, the current applicability of the framework is limited by inappropriate modeling of brain dynamics, and an overly ambitious focus on whole-brain activity control. In this work, we leverage recent progress in linear modeling of brain dynamics (effective connectivity) and we exploit the concept of target controllability to focus on the control of a single region or a small subnetwork of nodes. We discuss when control may be possible with a reasonably low energy cost and few stimulation loci, and give general predictions on where to stimulate depending on the subset of regions one wishes to control. Importantly, using the robustly asymmetric effective connectome instead of the symmetric structural connectome (as in previous research), we highlight the fundamentally different roles in- and out-hubs have in the control problem, and the relevance of inhibitory connections. The large degree of inter-individual variation in the effective connectome implies that the control problem is best formulated at the individual level, but we discuss to what extent group results may still prove useful.

Item Type: Article
Subjects: Eurolib Press > Biological Science
Depositing User: Managing Editor
Date Deposited: 23 Mar 2024 09:46
Last Modified: 23 Mar 2024 09:46

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