PSY
992 Current Methods in Cognitive
Neuroscience
Fall
2011
Instructor: Taosheng Liu PhD
Meetings: Tuesday 4:30-5:50; Thursday 4:00-5:20
[NOTE DIFFERENT STARTING TIMES]
Office hours: Wednesday noon-2 and by appointment
Course
description and objectives
Cognitive neuroscience attempts to give a causal
account of how the brain implements computations underlying cognition. A basic
requirement in this endeavor is to be able to measure brain activity with appropriate
methods. This course is aimed at informing students of these methods. The
objective is to introduce students to several different neuroimaging modalities
and associated analysis technique. The course will focus mainly on the design
and analysis of functional magnetic resonance imaging (fMRI) experiments, with
additional coverage of electroencephalography (EEG/ERP) and transcranial
magnetic stimulation (TMS). At the end of the course, students should be able
to implement a cognitive neuroscience study with appropriately designed
experiments and potentially analyze the data generated from such experiments.
Prerequisite
This
is a method course that focuses on data analysis/manipulation. Background
knowledge in the following areas will be helpful: cognitive
psychology, neuroanatomy and neurophysiology, math (e.g.,
linear algebra, probability and statistics), programming. Matlab will used to
analyze some data. However, proficiency with Matlab is not required as the
course will introduce students to basic Matlab programming concepts. Due to the
diversity of student background, I will adjust the level of coverage to
accommodate students’ interest and capability.
Text
and Readings
Readings will be from handouts,
tutorials, and primary literature. In addition, the following text is useful
for background in fMRI:
Huettel, S. A., Song, A. W., McCarthy, G.
(2008) Functional Magnetic Resonance
Imaging, Second Edition, Sinauer Associates:
Sunderland, MA
Course
requirement and assessment
Class participation 20%
Lab assignments 42%
Final paper/project 38%
I fully expect everyone will attend
every class session. The lecture sessions will introduce theoretical concept
via readings, during which actively participation in the discussion is
encouraged.
The lab/data analysis sessions will be
devoted to running the experiment and analyzing data. You should write a lab
report for each lab sessions.
You will work on a final project paper
which will count 40% of the grade. First, select a topic of your interest; this
should be done in consultation with me and should be finalized by the middle of
the semester. Then read some background literature and analyze different
perspectives/theories/models. Then you should propose an experiment to test a
new hypothesis about the topic. We will have more discussions about each person’s
project during class sessions.
Class schedule
(tentative schedule, subject to change)
|
Wk |
Date |
Topic |
Reading |
|
1 |
9/1 |
Introduction and Linux |
Thomas (2009) Chap 4 & 5 |
|
2 |
9/6 |
Matlab overview; Linear algebra I |
Matlab slides, Maloney Chap 1-3 |
|
|
9/8 |
Linear algebra II |
Maloney Chap 4-5 |
|
3 |
9/13 |
Linear algebra III |
Linear system tutorial, FFT tutorial |
|
|
9/15 |
MR physics and fMRI BOLD |
Wager 09 |
|
4 |
9/20 |
Anatomical data analysis |
fMRITutorial1, FreeSurfer |
|
|
9/22 |
Lab 1: retinotopy |
|
|
5 |
9/27 |
Retinotopic mapping: theory |
Engel 97; Warnking 02; retsimu |
|
|
9/29 |
Data analysis: retinotopy |
|
|
6 |
10/4 |
Event-related design: basics |
Least square tutorial, fMRITutorial2 |
|
|
10/6 |
Optimizing event-related design |
Buracas 02; Wager 03 |
|
7 |
10/11 |
Lab 2: event-related fMRI |
|
|
|
10/13 |
Data analysis: event-related |
|
|
8 |
10/18 |
Functional connectivity (D. Zhu) |
Fox 07 |
|
|
10/20 |
Diffusion tensor imaging (D. Zhu) |
Bihan 01 |
|
9 |
10/25 |
Lab 3: data analysis connectivity (D. Zhu) |
|
|
|
10/27 |
Pattern classification: theory |
Duda Chap 5, Burges, 98 |
|
10 |
11/1 |
Multivoxel pattern analysis in fMRI |
Haxby 01; Kamitani 05; Haynes 05 |
|
|
11/3 |
Lab 4: MVPA |
|
|
11 |
11/8 |
Data analysis: MVPA |
|
|
|
11/10 |
Data analysis: MVPA |
|
|
12 |
11/15 |
EEG/ERP: theory (J. Moser) |
Fabiani chap, Nieuwenhuis 11 |
|
|
11/17 |
Lab 5: ERP P-300 (J. Moser) |
|
|
13 |
11/22 |
Data analysis: ERP (J. Moser) |
|
|
14 |
11/29 |
TMS: theory (F. Kagerer) |
TBA |
|
|
12/1 |
Lab 6: TMS MEP demo (F. Kagerer) |
|
|
15 |
12/6 |
Data analysis: TMS (F. Kagerer) |
|
|
15 |
12/8 |
Student projects presentation/discussion |
|