Design of optimal nonlinear network controllers for Alzheimer's disease

• Published in: PLoS computational biology 2018-05, Vol.14 (5), p.e1006136-e1006136
• Main Authors: Sanchez-Rodriguez, Lazaro M, Iturria-Medina, Yasser, Baines, Erica A, Mallo, Sabela C, Dousty, Mehdy, Sotero, Roberto C
• Format: Article
• Language: English
• Subjects: Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Alzheimers disease; Basal ganglia; Biology and Life Sciences; Biomedical engineering; Brain; Brain - diagnostic imaging; Brain - physiopathology; Brain stimulation; Care and treatment; Computer and Information Sciences; Computer Simulation; Control theory; Diffusion Magnetic Resonance Imaging - methods; Disease control; Duffing oscillators; EEG; Electroencephalography - methods; Ganglia; Humans; Image acquisition; Image Processing, Computer-Assisted; Internet; Limbic system; Magnetic resonance; Magnetic resonance imaging; Medical imaging; Medicine and Health Sciences; Metabolism; Methods; Networks; Neural networks; Neurodegenerative diseases; Neuroimaging; Neuroimaging - methods; Neurology; Nonlinear control; Nonlinear Dynamics; Oscillators; Patients; Physical Sciences; Research and Analysis Methods; Riccati equation; Shortest-path problems; Signal Processing, Computer-Assisted; Stimulation
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1006136

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 high-dimensional 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.