Summary: A new computational approach could help personalize brain stimulation for individual Alzheimer’s patients, researchers say.
Scientists have developed a novel computational approach that incorporates individual patients’ brain activity to calculate optimal, personalized brain stimulation treatment for Alzheimer’s disease. Lazaro Sanchez-Rodriguez of the University of Calgary, Canada, and colleagues present their new framework in PLOS Computational Biology.
Electrical stimulation of certain parts of the brain could help promote healthy activity in neural circuits impaired by Alzheimer’s disease, a neurodegenerative condition. This experimental treatment has shown some promise in clinical trials. However, all patients currently receive identical treatment protocols, potentially leading to different outcomes according to individual variations in brain signaling.
To investigate the possibility of personalized brain stimulation, Sanchez-Rodriguez and colleagues took a theoretical approach. They built a computational tool that incorporates patients’ MRI scans and physiological brain signaling measurements to calculate optimal brain stimulation signals, with the goal of delivering efficient, effective personalized treatment.
The new approach is based on a computational strategy known as the state-dependent Riccati equation control (SDRE), which has been applied in other fields–such as aerospace engineering–to optimize input signals that control dynamic, nonlinear systems like the human brain. This strategy enabled the new tool to identify specific brain regions that would not benefit from brain stimulation.
The researchers also used their new framework to show that certain parts of the brain, the limbic system and basal ganglia structures, could serve as particularly powerful targets for brain stimulation in Alzheimer’s disease. Moreover, they found that patients whose neural structures are highly integrated in the brain network may be the most suitable candidates for stimulation.
“With our new framework, we are getting closer to erasing the knowledge gap between theory and application in brain stimulation,” Sanchez-Rodriguez says. “I think we will soon see a boom in the application of our framework and similar tools to study other diseases involving impaired brain activity, such as epilepsy and Parkinson’s.”
Next, the researchers plan to refine their tool so that it accounts for additional variation in brain activity between patients. The approach will need to be tested in animals before it enters clinical trials.
Source: Lazaro Sanchez-Rodriguez – PLOS
Publisher: Organized by NeuroscienceNews.com.
Image Source: NeuroscienceNews.com image is credited to Lazaro Sanchez-Rodriguez, partially using BrainNet Viewer.
Original Research: Open access research for “Design of optimal nonlinear network controllers for Alzheimer’s disease” by Lazaro M. Sanchez-Rodriguez, Yasser Iturria-Medina, Erica A. Baines, Sabela C. Mallo, Mehdy Dousty, Roberto C. Sotero, on behalf of The Alzheimer’s Disease Neuroimaging Initiative in PLOS Computational Biology. Published May 24 2018.
Design of optimal nonlinear network controllers for Alzheimer’s disease
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.