Executive Summary : | India, home to the third largest Alzheimer's disease (AD) population, faces challenges in early identification and treatment due to the lack of objective measures and the heterogeneity of the disease. The perturbation-response (PR) approach, which uses external perturbations like TMS and measures brain responses with EEG or fMRI, has shown success in distinguishing between healthy and diseased subjects. However, recent studies suggest that PR is sensitive to small changes in brain connectivity, making it a promising tool for detecting graded changes in AD progression. To address this issue, a whole-brain oscillator network model is proposed to simulate perturbation-response experiments. The model will be constructed using structural connectivity information from the Allen Mouse Brain Atlas and will predict neural responses to external perturbations in different states of consciousness. The model will be validated by comparing it to available experimental data.
The model will be extended to humans by incorporating human connectome data in Alzheimer's disease (AD) and healthy individuals. Simulations will be used to analyze changes in connectivity patterns during different stages of AD progression. To improve the PR approach's efficacy, simulations will be used to identify the most effective brain areas for perturbation that can reveal small connectivity changes related to AD in its early stages. The PR approach combined with a whole-brain model will generate experimentally testable predictions of biomarkers for early detection of neurodegenerative diseases, potentially leading to the development of a low-cost point of care system for early detection of cognitive decline and other neurological disorders. |