fMRI Group

Natasha Matthews, Ashley Parks, Destiny Miller, Ziad Safadi, Dana Tudorascu, Julia Sacher. Adviser: Mark Wheeler

Aims

Learn to implement block and event-related fMRI experimental designs

Develop a thorough understanding of the major choice points in the analysis stream for fMRI data

Learn fMRI data post-processing: GLM and group analysis

Block ER

Design

Pros: High detection power due to

response summation Simple analysis

Con:Can’t look at effects of single events

(e.g., correct vs. incorrect trials; target present vs. absent)

Pros: Good estimation of time courses and

reasonable detection Enables post hoc sorting (e.g.,

correct vs. incorrect; target present vs. absent)

Con: Some loss of power for the contrast

between trial types.

Task: Median Nerve Stimulation

A nerve stimulation device is place on the subjects wrist above their median nerve

An electrical pulse is applied, resulting in activation of the nerve

Data AcquisitionScanner : Siemens 3T Trio

Number of subjects: 3

Anatomical Scan

T1 (MPRAGE)Slices : 38Functional Scan (Block design)

Whole Brain ScanSlices : 38Volumes:144

Voxel Size : 4 mm x 4 mm x 3.2mmInterleaved AcquisitionTR : 2sT2* Contrast

Scanner : Siemens 3T Trio

Number of subjects: 3

Anatomical Scan

T1 (MPRAGE)Functional Scan (Event-related design)

Whole Brain ScanSlices : 38Volumes run 1: 256Volumes run 2: 256Voxel Size : 4 mm x 4 mm x 3.2mmInterleaved AcquisitionTR : 2sT2* Contrast

Scanner : Siemens 3T TrioNumber of subjects: 3

Anatomical Scan

T1 (MPRAGE)Slices : 38Voxel Size: 0.5mm x 0.5mm x 1.0mm

Blocked Design

…

Time 0 s……....……….22s…….32s…………54s……64s…………86s……..96s……………. …278s Task

Volumes……………….11.…….16..…….……27.……32..…………43.……...48.……………. …139 (+5 extra)

Rest Rest Rest Rest

ZAP ZAP ZAP

Median Nerve Stimulation Localizer Task DesignGoal: Determine Motor Cortex Activation Region for Event

Related Task

Nerve Stimulation Localizer Task

Data Preprocessing for individual subjectsData Format: convert scanner images to format readable by AFNI

Dicom data, reformat to BRICK & .HEAD

Time Shift (slice timing correction): interpolation of time series to a specific point in TR (Fourier transformation: most accurate).

Spatial Registration (motion correction): of all images in the time series to a base image: we chose 6th image of run (close to structural image).

Smoothing: We investigated 4mm, 8mm, 12mm.

Spatial Smoothing

Box-car Model

Model Fit: No smoothing

Model Fit: 4mm smooth

Model Fit: 8mm smooth

Model Fit: 12 mm smooth

t-statistics in individual voxels in ROI

0

2

4

6

8

10

12

14

16

Nopreprocessing

0 4 8 12

41.36.27

44.37.29

Beta coefficients in individual voxels in ROI

0

5

10

15

20

25

Nopreprocessing

0 4 8 12

44.37.29

44.37.29

Data Preprocessing: Preparing for group analysis

Normalize time series: Regression coefficients now expressed as % signal change. For each run divide each time point by the mean and multiply by 100.

Original data Normalized data

Preparing data for group analysis

Registration to standard space: Can be done manually or automatically

Atlas TemplatesTT_N27+tlrc: AKA “Colin brain”. One subject (Colin) scanned 27 times and averaged.

TT_avg152T1+tlrc: Montreal Neurological Institute template, average volume of 152 normal brains.

Group t-test (zap > baseline)

Event-related Design

Event Related Design: Stimulation with 3 different frequencies

2 Hz

3.85 Hz

9 Hz

Zap ON 9.09 3.85 2.00

Inter-stimulus jitter - 2, 4, 6 seconds

Data from a representative subject2 Hz 3.8 Hz 9 Hz

Comparison of 9 Hz Event Related Design and Block Design

Bloc

k de

sign

Even

t rel

ated

des

ign

Creation of ROI masks to explore event-related data

ROI from functional results (localizer task)

ROI based on atlas coordinates

Time courses extracted from functionally defined ROI

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

1 2 3 4 5 6 7 8 9 10

Time

Beta values

average_9Hzaverage_4Hzaverage_2Hz

Peak of the activation

Area under the curve

Results of ANOVA on event related datausing the PEAK of the time course

Results of ANOVA on event related datausing the AREA UNDER THE CURVE

Thank you

Mark WheelerSeong-Gi KimTomika CohenRebecca Clark

Fellow MNTPers!