Course Description

Methods for Functional Neuroimaging provides a graduate level introduction to functional neuroimaging methods. The lectures will focus on the fundamentals of task design, preprocessing, statistical modeling, artifact detection, and visualization of data associated with simple fMRI experimental designs of the type routinely used in clinical neuroimaging research. Lectures will be linked with a series of interactive laboratory exercises that will provide students with experience using SPM8 and MRIcron to combine structural and functional neuroimaging data in the service of detecting differential patterns of task-related activity. Course prerequisites include an introductory statistics course. Some knowledge of imaging physics will also be useful. Students are expected to have access to a PC with MATLAB, SPM8 and MRIcron software.

Lectures will be held Fridays from 4-6PM.

Laboratories will be held Fridays from 6-8PM

Evaluations will be weighted as follows:
120 points: final exam grade
80 points: homework grades (each assignment is worth a maximum of 10 points)

Homework assignments are due one week after their corresponding laboratory and should be turned in to Corinna at cbauer@bu.edu .

Course Web Site: The site includes general information about the course, activities and links to the other sites of interest. Students are encouraged to visit this page regularly and make suggestions for improvements. Resources and readings are listed below. Required materials are marked with an asterix (*).

Tentative Schedule

22JAN

Lecture - Experimental Functional Neuroimaging Introduction

Research questions
Determination of study type
Observational, experimental or quasi-experimental
Hypothesis specification
Participant selection
Sample size / power analysis
Ethical considerations

Reading:

Laboratory - Introduction to SPM, MATLAB, Image Conversion

• SPM8 Manual Chapter 20 DICOM Import*
• MATLAB 7 Getting Started Guide*
• MATLAB Tutorials

29JAN

Lecture - Introduction to First-Level Analysis

Reading:

• Overview of fMRI Analysis*

Laboratory - Preprocessing Steps

05FEB

Lecture - none

Laboratory - First-Level Analysis

• Introduction to Statistical Parametric Mapping*
• SPM8 Manual Chapter 28 Auditory fMRI Data*
• Auditory data
  

19FEB

Lecture - none

Laboratory - First-Level Analysis

• SPM8 Manual Chapter 29 Face fMRI Data*
• Repetition priming for faces data

26FEB

Lecture - Image Acquisition

Structural imaging
MPRAGE/SPGR acquisition
Functional imaging
EPI acquisition
Field strength
Echo time
Repetition time
Slice acquisition order
Slice angle
Voxel dimensions
Field map

Reading:

• Maintaining acquisition and analysis data quality*

Laboratory – Multimodal visualization

Coregistration of structural and functional images
Multiplanar views
Volume rendering
Surface rendering

• xjview*
• MRIcron*

05MAR

Lecture - Preprocessing

Geometric distortion correction
Slice timing correction
Realignment
Spatial filtering
Temporal filtering
Anatomical space conversion
Direct transformation to a common anatomical space
Indirect transformation to a common anatomical space

Reading:

• Slice Timing*
• Rigid Body Registration*
• Spatial Normalization*
• Image Segmentation*

Laboratory - Preprocessing

• SPM8 Manual Chapter 1 - Slice TimingGeometric distortion correction*
• SPM8 Manual Chapter 2 - Realign*
• SPM8 Manual Chapter 3 - Realign and Unwarp*
• SPM8 Manual Chapter 4 - Coregistration*
• SPM8 Manual Chapter 5 - Segment*
• SPM8 Manual Chapter 6 - Normalize*
• SPM8 Manual Chapter 7 - Smooth*

19MAR

Lecture - Quality Assurance

Instrumental and physiological noise sources
Artifact prevention
Artifact detection
Artifact effect mitigation

Laboratory - Quality Assurance

• Artifact Detection Tools*

26MAR

Lecture - Task Design

Design fundamentals
Task timing
Block design
Stochastic design
Mixed design
Self-paced
Parametric modulators
Sparse temporal sampling
Session length

Reading:

• Design efficiency*

Laboratory - Task Design

• fMRI Simulator*

02APR

Lecture - Statistical Background for Functional Neuroimaging

Matrix algebra for statistics
Correlation and regression
ANOVA
ANCOVA
Classical and Bayesian estimation
Regression diagnostics

Reading:

• The General Linear Model
• Variance Components

Laboratory - Statistical Model Specification

• SPM8 Manual Chapter 8 - fMRI Model Specification*

09APR

Lecture - Single-Subject Analysis

Within-session design
Across-session design
Covariates

Reading:

• Contrasts and Classical Inference
• Analysis of fMRI Time Series

Laboratory - Single-Subject Analysis

• SPM8 Manual Chapter 9 - fMRI Model Estimation*

M16APR

Lecture - Group Analysis

Single group
Multiple group
Longitudinal design
Covariates

Reading:

• ANOVA and SPM*
• Random Effects Analysis*
• Hierarchical Models

Laboratory - Group Analysis

• SPM8 Manual Chapter 10 - Factorial Design Specification*
• SPM8 Manual Chapter 30 - Face Group fMRI Data*
• Multi-subject event-related fMRI - Repetition priming data*

23APR

Lecture - Inference

Hypothesis testing
Parameter confidence intervals
Critical threshold
Correction for multiple comparisons
Gaussian random field theory

Reading:

• Introduction to Random Field Theory*

Laboratory - Anatomical labeling

Common anatomical spaces
Probabilistic atlases
Manual labeling
Automated labeling

• Reporting Activity*
• Talairach Software*
• SPM Anatomy Atlas

30APR

Lecture – Structural analysis

Voxel-based morphometry
Acquisition
Preprocessing
Estimation
Inference
Surface-based morphometry
Region of interest morphometry

Reading:

• Voxel-Based Morphometry of the Human Brain*

Laboratory – Structural analysis

• VBM Toolbox*
• VBM Using SPM*

Final exam distribution

07MAY

Final exam due