Design of optimal nonlinear network controllers for Alzheimer's disease
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|xmlui.metadata.dc.title:||Design of optimal nonlinear network controllers for Alzheimer's disease
|xmlui.metadata.dc.contributor.author:||Sanchez-Rodriguez, Lazaro M
Baines, Erica A.
Mallo López, Sabela Carme
Sotero, Roberto C.
|xmlui.metadata.dc.contributor.affiliation:||Universidade de Santiago de Compostela. Departamento de Psicoloxía Evolutiva e da Educación
|xmlui.metadata.dc.subject:||Alzheimer's disease | Neural networks | Electroencephalography | Linear regression analysis | Centrality | Functional electrical stimulation | Nonlinear dynamics | Nonlinear systems ||
|xmlui.metadata.dc.identifier.citation:||Sanchez-Rodriguez LM, Iturria-Medina Y, Baines EA, Mallo SC, Dousty M, Sotero RC, et al. (2018) Design of optimal nonlinear network controllers for Alzheimer's disease. PLoS Comput Biol 14(5): e1006136. https://doi.org/10.1371/journal.pcbi.1006136
|xmlui.metadata.dc.description.abstract:||Brain stimulation can modulate the activity of neural circuits impaired by Alzheimer's disease (AD), having promising clinical benefit. However, all individuals with the same condition currently receive identical brain stimulation, with limited theoretical basis for this generic approach. In this study, we introduce a control theory framework for obtaining exogenous signals that revert pathological electroencephalographic activity in AD at a minimal energetic cost, while reflecting patients' biological variability. We used anatomical networks obtained from diffusion magnetic resonance images acquired by the Alzheimer's Disease Neuroimaging Initiative (ADNI) as mediators for the interaction between Duffing oscillators. The nonlinear nature of the brain dynamics is preserved, given that we extend the so-called state-dependent Riccati equation control to reflect the stimulation objective in the highdimensional neural system. By considering nonlinearities in our model, we identified regions for which control inputs fail to correct abnormal activity. There are changes to the way stimulated regions are ranked in terms of the energetic cost of controlling the entire network, from a linear to a nonlinear approach. We also found that limbic system and basal ganglia structures constitute the top target locations for stimulation in AD. Patients with highly integrated anatomical networks±namely, networks having low average shortest path length, high global efficiency±are the most suitable candidates for the propagation of stimuli and consequent success on the control task. Other diseases associated with alterations in brain dynamics and the self-control mechanisms of the brain can be addressed through our framework|
|xmlui.metadata.dc.rights:||© 2018 Sanchez-Rodriguez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Except where otherwise noted, this item's license is described as © 2018 Sanchez-Rodriguez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited