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New computational tool could help optimize treatment of Alzheimer's disease

Novel modeling approach suggests how to personalize brain stimulation for individual patients

PLOS

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.

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In your coverage please use this URL to provide access to the freely available article in PLOS Computational Biology: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006136

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

Funding: LMSR acknowledges support from the Biomedical Engineering Graduate Program at the University of Calgary and the NSERC CREATE I3T Program. YIM is funded by a Banting postdoctoral fellowship (Government of Canada). SCM is funded by a predoctoral fellowship from the Spanish Ministry of Economy and Competitiveness (BES-2015-071253). Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck and Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (http://www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing Interests: The authors have declared that no competing interests exist.

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