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 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.
Highlights
Alzheimer’s disease (AD) is the most common cause of dementia, with classic biomarkers including vascular and glucose metabolism dysregulation, amyloid-β and tau deposition, white matter degeneration, functional impairment, and grey matter atrophy [1]
Amid the current efforts for developing personalized medicine, we introduce a framework for producing tailored stimulation signals, based on individual neuroimaging data and innovative modeling
Our approach leads to the identification of potential target regions and subjects to successfully respond to brain stimulation therapies and yields various disease-reverting signals
Summary
Alzheimer’s disease (AD) is the most common cause of dementia, with classic biomarkers including vascular and glucose metabolism dysregulation, amyloid-β and tau deposition, white matter degeneration, functional impairment, and grey matter atrophy [1]. An ongoing clinical trial has reported reversion of impaired glucose metabolism in the temporal and parietal association cortices along with slowing of cognitive indicators for the progression of AD by applying deep brain stimulation (DBS) in the fornix [4,5]. Unlike the case of Parkinson’s disease where a decrease in tremor constitutes a short-term measure for the success of the therapy, these AD studies lacked such a biomarker. They were unable to guarantee that their stimulation parameters were the optimal for their purposes.
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