Saturday, 11 July 2026
| Time | Type | Content | Speaker | Moderator |
|---|---|---|---|---|
| 08:50 – 09:00 | Opening Remark and Welcome Address | ||||
| Opening Remark and Welcome Address |
Rou-Shayn Chen
陳柔賢 醫師
AOC-IFCN President
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| 09:00 – 10:20 | Session 1 — Foundations of fMRI Processing with SPM | ||||
| Overview: From raw functional images to analyzable brain maps | ||||
| 09:00 – 09:30 |
Introduction to fMRI preprocessing Motion correction, normalization, smoothing Understanding outputs for analysis |
Chia-Feng Lu
盧家鋒 教授
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Jiann-Shing Jeng
鄭建興 醫師
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| 09:30 – 10:20 | Hand-on Practice Processing with real fMRI dataset using SPM Key output for downstream analysis |
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| 10:40 – 12:00 | Session 2 — From fMRI Matrix to Machine Learning in Parkinson's Disease | ||||
| Overview: Transforming imaging features into clinical prediction | ||||
| 10:40 – 11:10 |
Introduction to supervised machine learning Feature extraction from fMRI matrices |
Chung-Yao Chien
簡崇曜 醫師
|
Rou-Shayn Chen
陳柔賢 醫師
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| 11:10 – 12:00 | Hand-on Practice Building simple machine-learning models Application: detection of cognitive impairment in PD |
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| 12:00 – 13:20 | Lunch Symposium — Meet with Experts and Interactive Discussion | ||||
| 12:00 – 13:20 | Clinical Practice in Dopamine Agonist Prescription: Challenge and Solution |
Rou-Shayn Chen
陳柔賢 醫師
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| 13:30 – 14:50 | Session 3 — From PSG Signals to Deep Learning: An AI-Assisted Hands-on Approach | ||||
| Overview: Using time-series neurophysiological signals to build and interpret deep learning models | ||||
| 13:30 – 14:00 |
From PSG signals to RBD clinical labels: challenges and opportunities Introduction to deep learning for biomedical time-series |
Po-Yu Lin
林伯昱 醫師
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Jui-Cheng Chen
陳睿正 醫師
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| 14:00 – 14:50 | Hand-on Practice AI-assisted coding: building deep learning pipeline interactively Application: dentification of RBD as a prodromal marker of PD |
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