腦功能連結分析
Advanced brain connectivity analysis
學期
110-1
學分
0
學分
當期課號
A655
永久課號
B2020
開課單位
神經科學研究所
授課教師
尼大衛、郭文瑞
校區
陽明
類別
選修
上課時間表
| 節 | 週一 |
|---|---|
2 09:00–09:50 | 腦功能連結分析 YL837（陽明） 3 節連堂 |
3 10:10–11:00 | |
4 11:10–12:00 |
* 根據陽明交大上課時間表所列
概述
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先修科目
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備註
無備註
教學方式
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評分方式
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課程大綱
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週次計畫
| 週次 | 主題 |
|---|---|
| 第 1 週 | Course orientation |
| 第 2 週 | Mid-Autumn Festival |
| 第 3 週 | Introduction to fMRI data preprocessing using SPM (I) ‧ What is SPM? Basic preprocessing steps of fMRI data ‧ Introduction to dataset ‧ Handout of dataset Homework for the next week: Install Matlab and SPM Import data into SPM Preprocess data of single subject |
| 第 4 週 | Introduction to fMRI data preprocessing using SPM (II) ‧ Student discussion of data preprocessing Homework for the next week: Preprocess data of 10 subjects |
| 第 5 週 | National Day |
| 第 6 週 | Conceptual introduction to functional brain connectivity Check preprocessed data ‧ How to quality check your data Homework for the next week: Hand out resting state papers about DMN (Buckner et al. 2019) and SAL (Menon et al., 2015). |
| 第 7 週 | Resting-state functional connectivity networks ‧ What is the default mode network? ‧ What is the salience network Homework for the next week: Hand out DPARSF and REST papers to read |
| 第 8 週 | Functional connectivity using DPARSF ‧ How to preprocess data in DPARSF (linear detrend, regression, filter) ‧ Define seed points ‧ Run FC analysis ‧ Consultation for papers and preprocessing Homework for the next week: Install DPARSF and preprocess data for next week Preprocess data of 10 subjects for seed-based analysis and calculate FC Choose a paper using DPARSF and seed analysis |
| 第 9 週 | Student presentation of papers Functional connectivity using DPARSF ‧ How to setup a contrast ‧ How to do 1-sample t-test in SPM Homework for the next week: Setup contrast and do 1-sample t-test on data with seed-point from chosen paper Prepare presentation of results |
| 第 10 週 | Present seed-point analysis and compare with paper Homework for the next week: Read paper about MATLAB toolbox (Zhou et al., 2009) |
| 第 11 週 | Other types of functional connectivity ‧ MATLAB toolbox ‧ Time lag ‧ Coherence ‧ Mutual information ‧ Show how to extract time series in DPARSF Homework for the next week: Extract time series for multiple ROIs and do further analysis Prepare presentation for next week |
| 第 12 週 | Students present their findings of time-series analysis The multiple comparisons issue ‧ What is it? ‧ Different approaches (FDR, FWE, cluster vs voxel, TFCE, AlphaSIm) Homework for the next week: Read GSR papers |
| 第 13 週 | Issues with global signal regression ‧ What is global signal regression? ‧ Why is it a problem? Homework for the next week: Recalculate FC with GSR and compare with old results |
| 第 14 週 | Break ‧ Do calculations with GSR ‧ Prepare presentation of GSR results ‧ Start writing your report |
| 第 15 週 | Student presentations of results and feedback ‧ With vs. without GSR Homework for the next week: Install GIFT toolbox Read ICA papers |
| 第 16 週 | Introduction to ICA ‧ What is ICA? ‧ Noise removal ‧ Identification of functional networks ‧ Introduction to GIFT toolbox Homework for the next week: Do ICA on smoothed data |
| 第 17 週 | Student presentation of ICA results ‧ Discussion of issues with data processing for the report Homework for the next week: Prepare report for ICA and seed-point analysis |
| 第 18 週 | (1) Hand in final report (2) Extra topics chosen by students |
教科書
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