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

O VERVIEW Introduction Experimental Design Preprocessing Data Analysis Blocked Design Example Finger Tapping Event Related Design Example Categorization

B LOCKED VS. E VENT R ELATED Blocked Design Event Related Design

F MRI: D ESIGN C ONSIDERATIONS Blocked DesignEvent 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

D ATA A CQUISITION & P ARAMETER S ELECTION 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

P REPROCESSING S TEPS Reformat Time Shift Motion Correction Smoothing Scaling

D ATA T RANSFORMATION 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

T IME S HIFTING 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

M OTION C ORRECTION 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

M OTION CORRECTION – F IRST R UN

M OTION CORRECTION – LAST RUN

S MOOTHING 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

S MOOTHING 3.2mm - No Smoothing4mm Smoothing6mm Smoothing

S CALING 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

D ATA A NALYSIS Project Specific Analyses Possible data analysis Define regressors Assume shape of BOLD response (?) Perform statistical analyses Generate significance maps Use predefined ROIs

B LOCK D ESIGN I MPLEMENTATION Finger-tapping Task Localization Task

D IGIT 1 VS. D IGIT 5: A N F MRI S TUDY OF F INGER - TAPPING T OPOGRAPHY

M OTOR H OMUNCULUS Huettel et al. 2009

F INGER -T APPING M OTOR T ASK Multi-finger sequential tapping task (3 mins) D1 and D5 responses are evoked in separate blocks Visual pacing stimulus (externally guided) 20s x 2

D ATA A NALYSIS 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

D ATA A NALYSIS General Linear Model Red - Assumed HRF Model Black - Regressor

T APPING (D1 + D5) VS. R EST Tap (D1+D5) vs. Rest Finger-tapping relative to rest produced significant lateralized activation in the left precentral gyrus (BA4; -38, -20, 55). α = R

D1 VS. R EST – GROUP ANALYSIS D1 vs. Rest Left precentral gyrus (-54, -9, 32) α = R

D5 VS. R EST – GROUP ANALYSIS D5 vs. Rest Left precentral gyrus (-60, -5, 32) α = R

D1 VS. D5 - I NDIVIDUAL A NALYSIS D1 vs. D5 D1 is anterior to D5 which is consistent with the electrode studies R

D1 VS. D5 - G ROUP A NALYSIS 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

S UMMARY 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

E VENT R ELATED D ESIGN I MPLEMENTATION Categorization Task

C ATEGORIZATION T ASK 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

FAST EVENT RELATED ANALYSIS Trials are overlapping. Look at TRs 0-9 (20 sec.) from each stimuli (averaged across conditions).

INDIVIDUAL CATEGORIZATION DATA (α= 0.01) Face Object

GROUP CATEGORIZATION DATA FACE VS. OBJECT (α= 0.01) Face Talairach coordinates: X = 43, Y = -54, z = -7 Right Fusiform Gyrus BA: 37 Talairach coordinates: X = 16, Y = -23, z = -9 Right Parahippocampal Gyrus BA: 35 Object

GROUP CATEGORIZATION DATA EASY VS. HARD (α = 0.01) Talairach coordinates: X = 4, Y = 23, Z = 10 (4 mm from) Right ACC BA: 24 * Note: On white matter Easy > Hard

S UMMARY OF C ATEGORIZATION 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

W HAT W E H AVE L EARNED How to design an fMRI experiment and data collection Steps in data preprocessing and data analysis How to do individual subject analysis using the GLM Blocked vs. Event-related designs. Discussed possible limitations and future directions Ideas about how to incorporate fMRI into research using our current modalities (EEG, PET) when we return home (...)

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