2009 multimodal neuroimaging training program fmri module: experimental design, image processing,...
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2009 Multimodal Neuroimaging Training Program
fMRI Module: Experimental Design, Image Processing, & Data Analysis
Courtney M. Bell, Gina D’Angelo, Huiqiong Deng, Arava Kallai, Kamrun Nahar
Ikechukwu Onyewuenyi, William Ottowitz
Mark Wheeler, Instructor, Elisabeth Ploran, TA
OVERVIEW
Introduction Experimental Design Preprocessing Data Analysis
Blocked Design ExampleFinger Tapping
Event Related Design ExampleCategorization
BLOCKED VS. EVENT RELATED
Blocked Design
Event Related Design
FMRI: DESIGN CONSIDERATIONS
Blocked Design Event Related Design
• Advantages:• High detection power• Simple analysis• Cost effective
• Disadvantages:• Inability to estimate changes in activation over time •No trial sorting•Possible anticipation effects
•Advantages:• Increased estimation power over time• Enables trial sorting
•Disadvantages:• Lower detection power•Costly (money & time)•Careful planning
DATA ACQUISITION & PARAMETER SELECTION
Scanner : Siemens 3T Anatomical Scans
T1 (MPRAGE) Slices : 176 Voxel Size: 0.5mm x 0.5mm x 1.0mm Rationale
Functional Scans Finger Tapping Task & Categorization Task
Whole Brain Scan Slices : 38 Voxel Size : 3.2mm x 3.2mm x 3.2mm Interleaved Acquisition TR : 2s T2* Contrast Rationale
N = 7 (Males = 3; Females = 4) 6 R; 1L
PREPROCESSING STEPS
ReformatTime ShiftMotion CorrectionSmoothingScaling
DATA TRANSFORMATION
Background Images from scanner collected in DICOM format DICOM format cannot be interpreted by AFNI
AFNI : Analysis software
Purpose: Convert DICOM files to AFNI format
TIME SHIFTING
Background: Slices acquired in interleaved fashion to prevent
“bleeding” Odd slices collected first; even slices collected
second Data from consecutive slices taken at half TR
May get hemodynamic response that is slightly phase shifted
Purpose: To “guess” (interpolate) what BOLD response
would look like if occurred at the same time across all slices
MOTION CORRECTION
Background Subjects move during data acquisition
Therefore, voxel timeseries not referring to the same position over time
Creates need to select “base” image for voxel realignment
Purpose Reposition voxels in accordance with the selected
base image Criteria for selecting base image
Point at which have least likelihood of scanner “drift”
Point at which have maximal participant and scanner stability Early vs. middle images
MOTION CORRECTION – FIRST RUN
MOTION CORRECTION – LAST RUN
SMOOTHING Background
fMRI signal is noisy Different subjects can have slightly different
areas of activation Purpose
To improve signal to noise ratio by removing noise
To improve detection power in group analysis Current Project
Tested 0, 4, and 6 mm FWHM Gaussian smoothing kernel
Disadvantages Changes the data Results in correlated voxels
SMOOTHING
3.2mm - No Smoothing 4mm Smoothing 6mm Smoothing
SCALING
Background Data represented as BOLD signal intensity Arbitrary raw signal Need relative comparison to make data
meaningful
Purpose Goal is to scale a voxel time series by its mean in
order to do group analysis
DATA ANALYSIS
Project Specific AnalysesPossible data analysis
Define regressorsAssume shape of BOLD
response (?)Perform statistical analysesGenerate significance maps Use predefined ROIs
BLOCK DESIGN IMPLEMENTATION
Finger-tapping Task
Localization Task
DIGIT 1 VS. DIGIT 5: AN FMRI STUDY OF FINGER-TAPPING
TOPOGRAPHY
MOTOR HOMUNCULUS
Huettel et al. 2009
FINGER-TAPPING MOTOR TASK
Multi-finger sequential tapping task (3 mins) D1 and D5 responses are evoked in separate
blocks Visual pacing stimulus (externally guided)
20s20s
20s20s
20s
x 2
DATA ANALYSIS
Conditions Tap vs. Rest D1 vs. Rest D5 vs. Rest D1 vs. D5
Creating regressors for AFNI Rest periods were identified as “0”; tap periods
as “1” D1 is “1” when tapping D1 and “0” otherwise D5 is “1” when tapping D5 and “0” otherwise
DATA ANALYSIS
General Linear ModelRed - Assumed HRF ModelBlack - Regressor
TAPPING (D1 + D5) VS. REST
Tap (D1+D5) vs. Rest
Finger-tapping relative to rest produced significant lateralized activation in the left precentral gyrus (BA4; -38, -20, 55).
α = 0.01.
R
D1 VS. REST – GROUP ANALYSIS
D1 vs. Rest
Left precentral gyrus (-54, -9, 32)
α = 0.01.
R
D5 VS. REST – GROUP ANALYSIS
D5 vs. Rest
Left precentral gyrus (-60, -5, 32)
α = 0.01.
R
D1 VS. D5 - INDIVIDUAL ANALYSIS
D1 vs. D5
D1 is anterior to D5 which is consistent with the electrode studies
R
D1 VS. D5 - GROUP ANALYSIS
Blue regions indicates increased activity to D1 tapping; red is for D5 response.
Activation for D1 was localized in left BA4 (-56, -17, 35); however, a distinct motor area was not identified for D5.
α = 0.05
R
SUMMARY
Localized finger-tapping region in primary motor cortex
Group analysis only identified distinctive motor cortex areas for D1 - not D5
Efficiency of group analysis for this dataset
Variation in the anatomical location of D1 and D5
Limited significance in group activation
EVENT RELATED DESIGN IMPLEMENTATION
Categorization Task
CATEGORIZATION TASK
Hard Face
Easy Object
Hard Object
Easy Face
Event related design used for increased estimation power & trial sorting
3 runs x 213 TRs (80 stimuli, 20 of each type)
+
+
+
+Jitter(2s, 4s, or 6s)
HYPOTHESES
Face vs. Object activation map Different locations in
Fusiform Gyrus
Hard vs. Easy Frontal activation during decision
making
INDIVIDUAL CATEGORIZATION DATA (α= 0.01)
Face
Object
GROUP CATEGORIZATION DATAFACE VS. OBJECT (α= 0.01)
Face
Talairach coordinates:X = 43, Y = -54, z = -7Right Fusiform GyrusBA: 37
Talairach coordinates:X = 16, Y = -23, z = -9Right Parahippocampal GyrusBA: 35
Object
GROUP CATEGORIZATION DATAEASY VS. HARD (α = 0.01)
Talairach coordinates:X = 4, Y = 23, Z = 10
(4 mm from) Right ACCBA: 24
* Note: On white matter
Easy > Hard
SUMMARY OF CATEGORIZATION
Group results Faces more prominent than objects
Faces vs. Objects : FFA (BA 37) and PPA
Easy vs. Hard : Anterior Cingulate Cortex (ACC)
Relatively consistent with individual results
Some individual results showed both face vs. object and easy vs. hard activations
Overall Summary
Learned basic concepts associated with fMRI Physics Design Data Collection Preprocessing Analysis
Applied basic concepts using small sample Discussed possible limitations and future
directions