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