PSY 992Current 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 studentsí 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 personís 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