Project Overview
Alzheimer’s disease (AD) is one of the most pressing global health challenges, characterized by progressive cognitive decline and significant societal impact. Early diagnosis—particularly during the stage of Mild Cognitive Impairment (MCI)—is critical for improving patient outcomes, enabling timely intervention, and reducing long-term healthcare burdens. However, identifying reliable and sensitive biomarkers for early-stage detection remains a major challenge in modern neuroscience and clinical practice.
This project aims to address this challenge by exploring advanced data-driven approaches for analyzing brain activity patterns derived from resting-state functional magnetic resonance imaging (rs-fMRI). By studying the complex organization of brain networks, the research seeks to uncover meaningful patterns associated with the progression from normal aging to Alzheimer’s disease.
Objectives
The primary objective of this project is to develop a robust computational framework for identifying early indicators of Alzheimer’s disease based on brain network characteristics. Specifically, the project focuses on:
- Investigating alterations in functional brain networks across different stages of cognitive decline
- Identifying potential neuroimaging biomarkers associated with early and progressive stages of Alzheimer’s disease
- Supporting the prediction of cognitive status and disease severity using brain-derived features
- Contributing to the development of reliable, non-invasive tools for early diagnosis and monitoring
Ultimately, this research aims to bridge the gap between advanced computational analysis and clinical application, contributing to precision health solutions for neurodegenerative diseases. By enabling earlier and more accurate detection, the project supports better clinical decision-making and improved quality of life for patients.