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Controlling Alzheimer's disease by deep brain stimulation based on a data-driven cortical network model.
SiLu Yan1, XiaoLi Yang1, ZhiXi Duan1
1School of Mathematics and Statistics, Shaanxi Normal University, Xi'an, 710062 People's Republic of China.
Cognitive Neurodynamics
|November 18, 2024
Summary
Deep brain stimulation (DBS) shows promise for Alzheimer's disease (AD). Neurocomputational models reveal DBS can alleviate AD symptoms by optimizing brain network activity and targeting specific brain regions.
Area of Science:
- Neuroscience
- Computational Biology
- Medical Engineering
Background:
- Alzheimer's disease (AD) is characterized by cognitive decline and specific electrophysiological changes, such as EEG slowing.
- Current treatments for AD have limitations, necessitating exploration of novel therapeutic approaches like deep brain stimulation (DBS).
Purpose of the Study:
- To investigate the neurocomputational effects of DBS on a modeled cortical network representing Alzheimer's disease.
- To identify optimal DBS targets and stimulation parameters for alleviating AD-related pathological symptoms.
- To explore the relationship between brain network topology and DBS efficacy in AD.
Main Methods:
- Construction of a data-driven cortical network model using Diffusion Tensor Imaging (DTI) data.
- Simulation of AD-related EEG slowing by reducing synaptic connectivity.
- Application of simulated DBS to key brain targets (hippocampus, nucleus accumbens, olfactory tubercle) with varying parameters.
- Analysis of simulated EEG changes (power spectra, dominant frequency) and network properties.
Main Results:
- Simulated DBS induced changes in EEG power spectra (increased alpha, decreased theta) and dominant frequency, consistent with therapeutic effects.
- The hippocampus emerged as a superior target for DBS compared to the olfactory tubercle and nucleus accumbens.
- DBS efficacy correlated with the topological importance (nodal degree) of stimulated brain regions.
- Optimal DBS parameter adjustment was found to alleviate simulated AD pathological symptoms.
Conclusions:
- DBS demonstrates potential as a therapeutic strategy for Alzheimer's disease, offering a neurocomputational basis for its efficacy.
- The hippocampus is a promising target for DBS in AD treatment, with stimulation parameters significantly influencing outcomes.
- Brain network structure plays a crucial role in determining the effectiveness of DBS, providing guidance for personalized treatment strategies.
