NCKU Moodle V1(Minor): 認知所(Cognitive Science)

Goal: The course is designed to give grad and interested undergrad students an advanced understanding of the basics of functional magnetic resonance imaging (fMRI), now a mainstay of cognitive neuroscience. Students will be introduced to the theoretical and technical basis of fMRI research, and given the opportunity of hands-on experience on data analysis, or better yet, the opportunity to design, implement, and analyze your own fMRI experiments, which will be the ambitious goal for this semester. In the course, students should learn

(1) To understand the basics (neurophysiology, physics, etc), advantages, and limitations (or issues of great research promises) of fMRI in investigating the neural basis of cognition.

(2) The basics of the fMRI methodology: including necessary and optional preprocessing steps, the standard general linear model (GLM), and some advanced analysis procedures.

(3) To analyze certain type of (given) fMRI data: either you can download sample datasets from online resources, get a copy of someone’s data, or get the fresh data from your experiment, for the experimental and exploratory analyses.

(4) The best thing: come up a research question, design an fMRI experiment to test the hypothesis, analyze the data so to see whether it is supported, revise it, etc.

Textbook: We will use this one as the textbook, published on Jan 2009:

Functional Magnetic Resonance Imaging, Second Edition

Scott A. Huettel, Allen W. Song, and Gregory McCarthy

Either you can buy new copies from the book dealer in Taiwan, or you can buy 2nd-handed from attendees from the last year.

Format: The course is divided into two parts: the 1^st half of the course is the lecture (from 3:10-4:25pm). After a 20-minute break, we will resume the class at 4:45pm in the computer room for exercises and assignments. All the lectures, exercises, and Q&As, will be communicated primarily in English. Powerpoint files will be available after the course, and students are encouraged to participate in the discussions provided in the moodle forum.

Grading: 20% from the report of your class assignments, 30% from the midterm exam, 40% on the final report, and 10% on your class performance and the discretion of the instructor.

Midterm: consisting 4-5 essay questions, covering the first 7 chapters of the textbook.

Assignments: A critical aspect of the fMRI learning is through doing-it-yourself. To reach this goal, the 2^nd part of the weekly class is devoted to online exercises, with 2-3 participants using one computer in the computing facility to familiarize yourself with the concepts, do assignments, etc. There will be 2-4 assignments in the semester, and you are required to turn in a less-than-5-page summary of your answer to the question.

Final report: you can either do a report on some of the advanced analyses on a given assigned topic, or do an summary of an empirical research project, with the concise background, the specific question, method, results, discussions, and references. The final paper is encouraged to write in English (using sized 12 Times New Roman font, double-spaced, on A4 papers), and the full paper (at least 10 pages) should be in my mailbox no later than 5pm, June 30 (Wed), 2010.

Schedule (tentative):

Week 1 (Feb 25, 2010): (a) Introduction (syllabus distribution), textbook issue, time-change inquiry, Q&A. (b) computing room walk-through, data analysis overview, Q&A, etc

Week 2 (Mar 4): (a) Chapter 1: An Introduction to fMRI; (b) Sample data download, File rename, building the functional and anatomical projects.

Week 3 (March 11): (a) Chapter 2: MRI Scanners; (b) protocol build-ups, and basic preprocessing (I): motion correction + slice time correction

Week 4 (March 18): (a) Chapter 3: Basic Principles of MR Signal Generation; (b) Basic preprocessing (II): spatial and temporal smoothing

Week 5 (March 25): (a) Chapter 4: Basic Principles of MR Image Formation; (b) Image co-registration and Talairaching.

Week 6 (April 1): No class. Assignment 1 due at 5pm: how does different order of preprocessing affects your results.

Week 7 (Apr 8): (a) Chapter 5: MR Contrast Mechanisms and Pulse Sequences; (b) General Linear Model (GLM)

Week 8 (April 15): (a) Chapter 6: From Neuronal to Hemodynamic Activity; (b) ROI analysis

Week 9 (April 22): Midterm exam

Week 10 (April 29): (a) Chapter 7: BOLD fMRI: Origins and Properties; (b) FFA: where it is; or visual retinotopic mapping (V1, V2, V3, etc)

Week 11 (May 6): (a) Chapter 8: Signal, Noise, and Preprocessing of fMRI Data; (b) The multi-voxel pattern analysis (MVPA).

Week 12 (May 13): (a) Chapter 9: Experimental Design; (b) Brain peeling: hemispheric inflation, cutting, and flattening; surface analysis.

Week 13 (May 20): (a) Chapter 10: Statistical Analysis: Basic Analyses; (b) group vs. individual analyses; assignment 2 due.

Week 14 (May 27): (a) Chapter 11: Statistical Analysis II: Advanced Approaches; (b) custom-made analyses: talking to Matlab

Week 15 (June 3): (a) Chapter 12: Advanced fMRI Methods; (b) using behavioral index to correlate with brain activities (I)

Week 16 (June 10): No class (HBM in Barcelona)

Week 17 (June 17): (a) Chapter 13: Combining fMRI with other Techniques; (b) student project report (1)

Week 18 (June 24): (a) Chapter 14: The Future of fMRI: Practical and Ethical Issues (b) student project report (2)

Final research paper due at Monday (June 30) 5pm (no exception allowed!)

教師: 龔俊嘉