BME 499.098/Biostat 642:
Introduction to Functional MRI

Final Project Description



Summary: The class will be broken up into small groups. One member of each group will be scanned on Wednesday and Thursday of the first week and the group will analyze that data. Each group will work on one of the projects defined below and will present the results on the last day of class.

Project presentation: Presentations will last 15 minutes with 5 minutes for questions. The group should take time to explain the nature of their project and the motivation for studying the question at hand. The results should be clearly presented, preferably with projection from a PC.

Due Date: Friday 8/23 in class.

Project I: Increasing Sensitivity with a Temporal Derivative Covariate

Overview: Including a temporal derivative of a predictor allows for some uncertainty in the temporal delay of the modeled events. In this project you will evaluate the impact of fitting of temporal derivative in one or more single subject analyses.

Points to Address:

  • Is there statistical evidence that the temporal derivative predictors are useful?
    Use a summary of F-statistic images
  • Does the temporal derivative subjectively improve the fit?
    Plot different voxels of interest
  • Does there appear to be a systematic temporal delay across voxels? If there is any such bias, is it similar across subjects?
    Look at the images of temporal derivative coefficients; create histograms.
  • Does the temporal delay appear to be more important for block or event related designs?
    • For each point, evaluate as many subjects as possible.

    Project II: Relative Sensitivity of Levels of Inference & FDR

    Overview: SPM's tabular output provides corrected p-values for set-, cluster- and voxel-level inference. In general, set-level is the most sensitive and least specific, while voxel-level is the least sensitive by the most specific. This assessment, however, is based on general assumptions on the nature of the activations. In this project you will describe the relative sensitivity of these three types of inferences with yet a fourth, FDR voxel-level inferences.

    Points to Address:

  • On the class data, what is the relative ordering of the sensitivity of the four methods.
    Compare minimum corrected p-value, corrected intensity threshold, number of significant clusters, number of significant voxels.
  • Does the relative sensitivity (the order) of the different methods depend on smoothness?
    Try at least 3 different smoothnesses, e.g. 4, 8 and 16 FWHM.
    • Resources: Tool to view how FDR threshold is determined FDRill.m FDR.m abline.m For each point, evaluate as many subjects as possible.

    Project III: Increasing sensitivity and specificity with motion parameters covariates

    Overview: Subject motion is the largest source of nuisance variability in fMRI. Even after motion correction, there can be variability that is explained by subject motion. In this project you will examine the impact of including motion parameters in an analysis.

    Points to Address:

  • Do the motion parameters explain a significant amount of variance?
    Use a summary of an F-statistic image.
  • Do the motion parameters increase or decrease significance? Give an explanation why either case could occur?
  • Do the motion parameters appear to be related to the experimental paradigm
  • Is there any correspondence between overall movement and data quality? For example, does the subject with the largest movements have the worst results?
    • For each point, evaluate as many subjects as possible.

    Project IV: Impact of UM vs. SPM Preprocessing

    Overview: The UM fMRI Lab supplies investigators with preprocessed data, that is data that has had slice time and motion correction applied. These two corrections, however, are implemented with tools created outside of SPM*. In this project you will compare results created with data preprocessed with the standard UM tools with that preprocessed with SPM99's equivalent tools.

    * The UM slice time correction uses a locally windowed filter, while the SPM slice time correction uses a global filter (IFT(phaseshift(FT(X)))); the global filer may introduce artifacts. UM realignment (AIR) and SPM realignment both minimize the squared differences between the reference and transformed image, but AIR uses a more sophisticated optimization method.

    Points to Address:

  • Does the residual variance differ between the two processing paths?
    Compute ratios of ResMS images with ImCalc.
  • Do the activation results differ between the two methods? If so, how, and by how much?
  • If the results differ appreciably, find a particular voxel where the difference is striking. Plot the data for each one and try to suggest why they might differ.
    • For each point, evaluate as many subjects as possible.

    Project V: Increasing sensitivity with a Gray Matter Mask

    Overview: Each statistic image of the brain comprises as many as 100,000 hypothesis tests. The corrected thresholds which control for the multiple comparisons problem must (naturally) get more stringent as the number of voxels increase. One suggestion to allow for less string thresholds is to eliminate voxels where activations are not expected, in particular in white matter regions. In this project you will restrict your inferences to a gray matter mask and describe it's impact on your results.

    Points to Address:

  • How do the corrected thresholds, voxel counts and RESEL counts change after application of the GM mask? Compare the "Euler Characteristic Density" with and without thresholding
    The EC density reflects the "crinkliness" of the search volume; the first 3 elements are small when the search volume is smooth. The EC densities are saved in the variable R in SPM.mat. Ask for help interpreting R
  • How do the activation profiles change with the mask; are there regions that disappear? Are there significant regions that appear?
    • For each point, evaluate as many subjects as possible.

    Resources: Gray Matter mask image: avg152T1_GM.hdr avg152T1_GM.img

    Project VI: SnPM vs SPM

    Overview: The corrected p-values and thresholds in SPM are results from Random Field Theory (RFT). RFT depends on many assumptions on the data, most of them uncheckable. SnPM provides a nonparametric and data-driven method to obtain corrected thresholds. In this project you will apply SnPM to the group data and compare it to the results obtained with SPM.

    Points to Address:

  • Compare the intensity and cluster size thresholds of SPM and SnPM.
    Make sure the statistic values agree; the only thing different about SnPM is how corrected p-values and thresholds are obtained.
  • By smoothing the variance image the precision of local variance estimates can be increased by "borrowing strength" from neighbors. Try 10 mm FWHM variance smoothing; how does this change the results?
    • Last modified: Thu Aug 22 14:38:33 EDT 2002