Courses:

Special software is required to use some of the files in this section: .dcm, .zip, .mnc, .mov, .gz, and .tar.

The lab portion of this class includes seven labs plus a matrix algebra primer.

• Lab 1: fMRI Acquisition
• Lab 2: Intro to fMRI Data and Analysis: Neurolens Tutorial I
• Lab 3: Improving fMRI signal detection using physiological data: Examples from the auditory system
• Lab 4: The life cycle of Medical Imaging data
• Lab 5: MRI Physics Labs
• Lab 6: Diffusion Tensor Imaging Analysis
• Lab 7: Statistical Analysis of fMRI Data

Instructor: Susan Whitfield-Gabrieli

Lab 1 presentation (PDF - 1.8 MB)

Lab 1 outline (PDF)

fMRI scan checklist (PDF)

Detailed explanation of scanning protocols (PDF)

Detailed explanation of fMRI task paradigms (PDF)

Northroff, G., et al. "Self-referential Processing in our Brain—A Meta-analysis of Imaging Studies on the Self." NeuroImage 31 (2006): 440-457.

Instructor: Divya S. Bolar

Lab 2 presentation (PDF)

• Supplemental video clip #1, for slide 8 (MOV - 8.4 MB) (includes video capture courtesy of Hyperionics Technology, LLC)
• Supplemental video clip #2, for slide 11 (MOV - 6.2 MB)

Download Neurolens fMRI Analysis software

NeuroLens tutorial (Courtesy for Rick Hoge. Used with permission.)

• Loading DICOM Files and doing an fMRI Analysis (PDF - 1.7 MB)
• Data for tutorial (ZIP - 23.4 MB) (The ZIP file contains: 218 .dcm, 2 .mnc files, and Stimulus Timing.rtf)

Lab 2 exercise (PDF)

Lab 2 solutions (PDF)

Instructor: Jennifer Melcher

This Lab examines two techniques that use physiological data to improve fMRI signal detection. One technique, called cardiac gating, is used to improve detection of activation in brainstem structures, auditory and non-auditory. The other, called clustered volume acquisition (CVA), is widely used in auditory studies to reduce the effects of scanner acoustic noise on activation.

Lab 3 Details (PDF) (Courtesy of Irina Sigalovsky. Used with permission.)

Instructor - Sonia Pujol, Ph.D.

fMRI provides powerful capabilities for minimally invasive studies of the brain response to various stimuli. Given the challenges in understanding the temporal and spatial changes observed in the brain, any fMRI study is highly interdisciplinary. Image analysis plays a decisive role in the extraction of diagnostic and metabolic information on the patient. This lab covers the different stages of the life cycle of medical imaging data, from data acquisition to statistical analysis.

This lab includes hands-on sessions using 3D Slicer, an open-source software for research in medical imaging analysis.

Slicer code download

Tutorial: Getting Started with Slicer2.6

Pre-computed dataset for Lab 4 (ZIP)

The lab report is a series of exercises based on the fMRI study presented in the class.

Lab 4 report questions (PDF)

Instructors: Cristina Triantafyllou, Larry Wald

The main goals of this three-part lab are to:

1. Become familiar with basic principles of MRI Physics and measurements (i.e. SNR, relaxation times, etc).
2. Understand the T1, T2 and T2* properties of various tissue compartments.
3. Acquire and evaluate phantom data.
4. Perform a human scanning experiment and investigate the various sources of noise in the fMRI time series.
5. Evaluate EPI distortions through field maps and by varying the readout properties.

All experiments will be performed on a human subject. The SNR measurements will also be run on a phantom for comparison. Some of the data analysis will be performed on the scanner console, however you will be asked to note the measurements obtained as you will need them to solve the exercises given in the lab report.

Lab 5 description (PDF)

Triantafyllou, C., et al. "Comparison of Physiological Noise at 1.5 T, 3 T and 7 T and Optimization of fMRI Acquisition Parameters." NeuroImage 26 (2005): 243-250.

Instructor: Sonia Pujol, Ph.D.

Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) measures in-vivo the anisotropic diffusion of water molecules in tissues. The study of the displacement distribution through advanced image processing techniques provides access to the microstructure, organization and connectivity of brain white matter. This lab covers the different stages of DT-MRI analysis, from data loading and tensor calculation to streamline tractography.

This lab includes hands-on sessions using 3D Slicer, an open-source software for research in medical imaging analysis.

See Lab 4 for Slicer code and tutorial information.

Pre-computed dataset for Lab 6 (GZ - 33.2 MB)

The lab report is a series of exercises based on the DT-MRI case study presented in the class.

Report questions:

1. Why are at least six different gradient directions necessary to compute the tensors?
2. Which parameters will you need to know in order to run a DTI analysis?
3. Why do the ventricles appear dark in the Diffusion Weighted Images?
4. Select an image from the Diffusion Weighted Imaging volume showing an artifact, and propose an explanation of its origin.
5. Which factors can affect the shape of the diffusion ellipsoid?

Instructor: Doug Greve

Notes (PDF)

Lab 7: Statistical Analysis of fMRI Data

Instructor: Anastasia Yendiki

This lab takes place over four separate class sessions.