Speaker Profile

Executive Summary：
Dr. Yao-Chia Shih is an assistant professor serving for Graduate Institute of Medicine in Yuan Ze University. His research interests include the clinical applications of multiparametric MRI to neurodegenerative diseases, the identification of personalized imaging markers using AI techniques, and the investigation of neuro-modulatory effect using functional MRI. He certificated as PhD at Institute of Biomedical Engineering in 2019 from National Taiwan University. He was a postdoctoral fellow in the Radiological Sciences, Academic Clinical Programme in Duke-NUS medical school and an MR physicist in the Department of Diagnostic Radiology, Singapore General Hospital from 2019 to 2021. With ten years of experience in brain imaging, he has published >15 journal articles, >50 conference papers, and 1 national patent. In recent 5 years, he and his team also won the 1st and/or 2nd places of oral presentation in International Society for Magnetic Resonance in Medicine annual meeting 7 times and “2023國家新創獎”. He is currently investigating the brainstem or deep nuclear imaging markers in relation to clinical manifestations in patients with various movement disorders, and the generative AI models to synthesize MRI images, which are used for the disease prognosis and therapeutic guidance.

Lecture Abstract：
Substantia nigra pars compacta (SNpc) and locus coeruleus (LC) are located at the brainstem, where the dopaminergic and noradrenergic deficits play an important role in the disease developments and manifestations of Parkinson’s disease (PD). Pathologically, the accumulated Lewy bodies in these two brainstem nuclei contribute to the co-occurrence of complex motor and non-motor PD symptoms. Therefore, multiparametric quantitative magnetic resonance imaging (qMRI) emerges to characterize different neuropathological changes in these two nuclei as well as their projections, aid the better understanding of disease mechanisms across different stages, and help detemine reliably diagnostic imaging biomarkers. In this talk, I would like to introduce three novel qMRI techniques that are widely used in clinical research and radiological screening of PD around the world. The first is an iron sensitive imaging as known as quantitative susceptibility mapping (QSM) and susceptibility map-weighted imaging (SMWI), which are applied to quantify and visualize iron depositions in the SNpc; SMWI is excellent in qualitatively characterizing the nigrosome-1 that is a subregion of SNpc, considered as an early imaging marker with greater sensitivity to diagnose PD patients. The second is neuromelanin sensitive imaging (NMI) used to visualize and quantify the content of neuromelanin in both SNpc and LC. When PD patients suffer from dopaminergic and noradrenergic neuronal degeneration, decreased neuromelanin leads to the loss of hyperintense NMI signals in these two nuclei. The NMI can be also applied to differentiate atypical Parkinsonian disorders from PD. The last is the advanced diffusion MRI (dMRI), which is able to quantify microstructural integrity of these two nuclei and their corresponding white matter projections. I will demonstrate how to use dMRI to characterize pathological changes in the brainstem structures in patients with different PD subtypes. In conclusion, these qMRI techniques along with deep-learning-based approaches could help facilitate personized precision medicine of PD.

Executive Summary：
Prof. Duann is currently affiliated with the Institute of Education at National Yang Ming Chiao Tung University (NYCU) in Hsinchu, Taiwan, and the Institute for Neural Computation at the University of California San Diego in La Jolla, CA, USA. Before joining NYCU, he served as an associate professor at the Institute of Cognitive Neuroscience, National Central University (2016 ~ 2020), and held positions as associate director (2009 ~ 2014) and director (2014 ~ 2016) at the Biomedical Engineering Research Center of China Medical University and Hospital in Taichung, Taiwan. His expertise spans neuroimaging technologies such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), neuroimaging data analysis utilizing machine learning and deep learning, a nd biomedical engineering, including medical instrument design and development, usability studies, and clinical trials. He applies these interdisciplinary skills to various clinical applications. Currently, Prof. Duann's research focuses on applying neuroi maging technologies to investigate the freezing of gait and impulse control disorders in Parkinson’s disease patients, as well as epileptic networks in seizure patients. Additionally, he explores the relationship between learning and brain activity. For in stance, he and his team are identifying EEG correlates linked to comprehension and computation circuits involved in solving arithmetic word problems. Furthermore, Prof. Duann is pioneering new functional neuroimaging data analysis methods to uncover indivi dual differences in brain processes at the single trial level. This work aims to advance functional neuroimaging research and elucidate the complexity of brain activity.

Lecture Abstract：
In this study, we employed a motor-imagery fMRI paradigm, utilizing gait video clips that included conditions such as normal straight walking, normal turning, straight walking with freezing of gait (FOG) onset, and turning with FOG. These conditions were designed to artificially induce motor-related brain processes associated with distinct gait scenarios in Parkinson’s disease patients, both with and without FOG, as well as in normal control participants. We analyzed and compared brain BOLD activation patterns across different gait conditions at the group level. Our findings revealed that, in the comparison between FOG during turning and FOG during straight walking, there was significantly higher activation in the superior occipital gyrus, left precentral gyrus, and right postcentral gyrus in patients with FOG compared to those without. Similar patterns of activation, excluding the occipital gyrus, were observed when comparing freezers to normal controls. Furthermore, region-of-interest (ROI) analysis demonstrated a higher effect size in the locomotor regions of freezers compared to non-freezers during imagery of normal turning. These results suggest that freezers exhibit increased cortical and locomotor region activation, reflecting greater effort to overcome overinhibition of motor execution pathways, compared to non-freezers and normal controls. This effect appears to be more pronounced during complex walking tasks, such as turning, than during straight walking.

Executive Summary：
A. Jon Stoessl is Professor of Neurology at the University of British Columbia and Editor-in-Chief of Movement Disorders. He was previously Head of Neurology, Director of the Pacific Parkinson’s Research Centre, Co-Director of the Djavad Mowafaghian Centre for Brain Health and held a Tier 1 Canada Research Chair in Parkinson’s Disease. He has served on editorial boards including Lancet Neurology and Annals of Neurology, previously chaired the Scientific Advisory Boards of Parkinson’s Canada and the Parkinson’s Foundation, and was President of the World Parkinson Coalition. Dr. Stoessl uses positron emission tomography to study the causes and complications of Parkinson’s and its treatment, as well as dopamine function in the brain. He has been cited more than 30,000 times in the scientific literature with an ¬h-index of 79 (Google Scholar). He is a Member of the Order of Canada and a Fellow of the Canadian Academy of Health Sciences.

Lecture Abstract：
Neuroimaging tools serve numerous roles in the assessment of Parkinson’s disease. Diagnosis of dopamine denervation is best made by molecular imaging, but this is not specific to PD vs. atypical parkinsonism. Differential diagnosis is best accomplished by comprehensive MRI or glucose metabolic PET. Emerging biological definitions of PD require evidence of neurodegeneration, which can additionally be provided by demonstration of cardiac noradrenergic denervation. Assessment of disease progression can be accomplished to some degree by dopaminergic imaging, but this often correlates poorly with clinical progression and fails to take into consideration non-dopaminergic degeneration contributes to increasing aspects of disability over time. In addition to assessment of other neurotransmitter systems by molecular imaging, MRI assessment of neuromelanin, diffusion and microstructural damage may be useful and it is increasingly recognized that atrophy may not only track disease progression but may also predict disease trajectory. Disease complications may be predicted by structural and neurochemical changes in non-dopaminergic systems and changes in functional connectivity can additionally predict treatment-related complications. The capacity to quantitate the distribution of misfolded -synuclein would be of enormous help not only for early diagnosis, but also for tracking disease progression and target engagement by disease-modifying agents. While progress has been made in this direction, it has proved extremely challenging for a variety of reasons and there are additionally MRI-based approaches in development for this purpose. Other pathological processes that can be assessed include neuroinflammation and the density of synaptic vesicular glycoprotein 2A. As new techniques are developed, there is increasing appreciation for the importance of multi-modal imaging and for efforts to determine which changes reflect disease and which are related to compensatory mechanisms and resilience. Emerging approaches include hybrid PET-MRI, assessment of bioenergetics and of neurovascular coupling and cerebrovascular reactivity, assessment of glymphatic function, and mapping of structural changes to neurotransmitter and gene expression.