Aims: This study aims to track the variations in biological and imaging markers during the prodromal and clinical phases of neurodegenerative diseases, including Parkinson’s (PD) and Alzheimer’s (AD), and to develop a model for risk assessment, early diagnosis, and prognostic prediction using advanced technologies like machine learning. This approach would provide a theoretical foundation for new interventions to prevent or decelerate disease progression.
Scientific Rationale: Amidst economic and societal shifts and an aging demographic, the prevalence of neurodegenerative diseases like PD and AD is escalating. This severely impacts people’s health and quality of life and imposes substantial economic strains on families and society. These diseases exhibit a lengthy prodromal period, often leading to delayed diagnoses and treatment than neuropathologic occurrences. PD and AD still lack clear and effective therapies to slow the progression of the disease, which underscores the necessity for a deeper understanding of the biological and imaging markers for disease onset and progression. Establishing risk and prognostic prediction models for neurodegenerative disorders is crucial for formulating efficacious preventative and therapeutic strategies.
Project Duration: The project is projected to last three years. The initial year will be dedicated to collecting and organizing biological and neuroimaging data; the second year will involve a detailed data analysis; and the final year will include synthesizing the findings and formulating conclusions.
In conclusion, this study aims to investigate the biological and imaging markers pertinent to the risk assessment, early diagnosis, and prognostic judgment of neurodegenerative diseases. It seeks to develop methods for identifying risk factors and models for early diagnosis and prognosis prediction. It provides a theoretical framework for developing innovative, disease-modifying treatments to enhance patient quality of life.
