腦功能連結分析

Course orientation

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

Introduction to fMRI data preprocessing using SPM (II) ‧Student discussion of data preprocessing Homework for the next week: Preprocess data of 10 subjects

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).

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

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

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

Present seed-point analysis and compare with paper Homework for the next week: Read paper about MATLAB toolbox (Zhou et al., 2009)

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

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

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

Break ‧Do calculations with GSR ‧Prepare presentation of GSR results ‧Start writing your report

Student presentations of results and feedback ‧With vs. without GSR

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

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

(1) Hand in final report (2) Extra topics chosen by students