Final Project Descriptions
Summary: The class has been divided up into six groups, and we
have had six subjects scanned. 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 8/20 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:
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
cluster- and voxel-level inference. In general, cluster-level
P-values are the most
sensitive and least specific, while voxel-level is the more sensitive
and 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 two types of
inferences with yet a fourth, FDR voxel-level inferences.
Points to Address:
Resources: Tool to view how FDR threshold is determined, use
the FDRill function (a function in the spm2_local directory).
For each point above, 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:
For each point, evaluate as many subjects as possible.
Resources: The motion parameters can be found in the ra_img
directory, in the file realign.dat. You can load a datafile
such as this directly into an SPM prompt with
spm_load(spm_get); this will bring up a file selection
dialog, from which you can select the realign.dat file.
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:
For each point, evaluate as many subjects as possible.
Project V: Impact of Physiological Correctoins
Overview: The new preprocessing stream at the UM fMRI Lab
includes tools for reducing physiological effects of respriation and
the cardiac cyycle. In this project you will compare
results created with data preprocessed with the standard UM tools with
the preprocessed stream.
Points to Address:
For each point, evaluate as many subjects as possible.
Project VI: 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:
For each point, evaluate as many subjects as possible.
Resources: Gray Matter mask image:
avg152T1_GM.hdr
avg152T1_GM.img
Project VII: 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:
Note: SnPM won't be covered until Thursday; if you choose this
project, see Tom or Jun Ding for a crash course in SnPM.