Speaker Profile

Executive Summary：
After completing his neurology residency in 2007, he went on to train as a neuroepidemiology fellow at The Parkinson’s Institute under Dr. Carlie Tanner’s supervision. He obtained Epidemiology MS degree at Stanford in 2009 and then PhD degree at UC Berkeley in 2012, mentored by Dr. William Jagust. Raymond has been working on population-based studies of Parkinson’s disease and dystonia, longitudinal effects of vascular risks in Alzheimer’s disease, and tracking cognitive decline by imaging biomarkers. He is currently Associate Editor for Movement Disorders, past chair for MDS-Epidemiology Study Group, and an active reviewer for several journals. His clinical and research interest is mainly in the field of Geriatric Neurology, covering epidemiology, comorbidity, diagnostic complexity, risk perception in dementia and parkinsonism. Lately he moved to Taitung St. Mary's Hospital and established Center for Dementia Care, which aims to educate and serve the poor and indigenous peoples of Easter Taiwan.

Lecture Abstract：
Throughout years of editorial work, I have seen hundreds of PD epidemiology manuscript submissions. Many of them shared similar methodological flaws or design issues. The 30-min talk will cover some of the challenges often seen in epidemiological studies for PD, including misconception in confounding, comorbidity effect, reverse causation, to name a few. Some tips and tricks are provided for future authors to approach these challenges.

Lecture Abstract：
Magnetic Resonance Imaging (MRI) has widely used in the study of Parkinson's Disease, playing a critical role in everything from diagnosis to prognosis. Traditionally, neuroimaging has been used to rule out concomitant neurological disorders. Morphological MRI has identified varying degrees of atrophy in specific brain regions, such as the basal ganglia. However, the diagnostic performance of basal ganglia volume alone has proven to be limited.

This talk will update the latest advancements in MRI for the study of Parkinson's Disease, with a particular focus on emerging techniques like diffusion MRI and nigrosome imaging. We will also discuss the growing role of Artificial Intelligence in Parkinson's Disease research. This includes accelerated MRI image acquisition, image-based diagnosis through topographical differences, and deep learning-based diagnostic approaches for Parkinson's Disease.

Moreover, we will explore how machine learning tools are being used to enhance subtype classification and accurately stage the severity of the disease. We will demonstrate how AI can meet the clinical needs for rapid measurement, automated analysis, and reliable diagnostic and prognostic assessments. These advancements have the potential to greatly increase clinicians' diagnostic confidence, add substantial value to routine neuroimaging exams, and reduce the burden of repeated examinations on patients. Ultimately, this integration of AI in MRI represents a significant step forward in improving both the accuracy and efficiency of Parkinson's Disease diagnosis and management.