Lecture fMRI data analysis

Question 1

Terminology - Subjects

Answer 1

Participants that participate in the experiment

Question 2

Terminology - Session

Answer 2

Measurement

Question 3

Terminology - Run

Answer 3

Image you receive of the measurement

Question 4

Terminology - Volume, slices & voxels

Answer 4

The brain (image) is made out of a volume, that is made up of slices, that is made out of voxels (3D pixel = volume element)

Question 5

Field of View (FOX)

Answer 5

Field of the image that you see, you can see different slices inside the FOV.

Thinner slices -> higher resolution

Question 6

fMRI signal - Functional images (T2*)

Answer 6

The grey value in a voxel is the BOLD signal. There are about 20-25.000 voxels.

The spatial resolution is about 2-4 mm and the temporal resolution is seconds (time series; every few seconds)

Question 7

fMRI signal - Anatomical image (T1)

Answer 7

High(er) resolution (~1 mm) and be able to distinguish different tissue types

Used for normalization, localization (analysis)

Question 8

EEG signal

Answer 8

Signal from an electrode on the scalp (compared to a voxel in fMRI); 32/64/128 electrodes

Low spatial resolution (cm) and time series with high temporal resolution (ms)

Question 9

fMRI analysis

Answer 9

Preprocessing:
- Registration
- Normalize, smooth

Model specification/estimation

Statistical inference

Question 10

fMRI analysis - Preprocessing

Answer 10

• (Slice-timing correction)
• Motion correction (realignment)
• Co-registration anatomy-functional scans
• Normalisation to standard brain (MNI space)
• (spatial) Smoothing

→Data ready for analysis

Question 11

fMRI analysis - Preprocessing: slice-timing correction

Answer 11

If you want to correct the analysis to the fact that the signal of the slices is not acquired at the same time (this is only a problem if you have a quick design)

Question 12

fMRI analysis - Preprocessing 1: registration

Answer 12

Realignment aka registration
- Motion correction (within the same image)
- Intrasubject registration (within subject)
- Correct with 3 translations (x, y, z) and with 3 rotations (pitch, roll, yaw)
-> Problematic when movement is bigger than voxel (displacement: rotation around anterior commissure)

Coregistration
- Intrasubject, intermodality registration
- Coregister anatomical MRI with functional MRI (different images with different contrasts)

Question 12

fMRI analysis - Preprocessing 2: normalize, smooth

Answer 12

Spatial normalization: bring all participants into the same space
- Intersubject registration (between subjects)
- Register subject anatomy to atlas space:
MNI- or Talairach space
- Localization

Spatial smoothing:
- Blur data with Gaussian kernel (6-10 mm
FWHM)
- To satisfy random field theory assumptions
- Averages out noise/fluctuations (>SNR)
- For intersubject analyses

Question 12

fMRI analysis - Model specification: design matrix

Answer 12

Design matrix: a mathematical description of your experiment
E.g. ‘stimulus on = 1’ , ‘stimulus off = 0’ : a
simple ‘block design’

Visual depiction of what the task looked like. We use a linear model for each voxel. Use expected shape of BOLD response.

Model: Y = X * B + E, in which Y is the observation, X the predictor, B(eta) is a parameter estimate and E is the error

Question 13

fMRI analysis - Model specification

Answer 13

fMRI data are voxel time series (3D-time=4D).

You should reflect the hemodynamic response function (HRF) induced by neuronal events. The HRF is convolved with the design matrix, so this is an estimate.

Estimate for each voxel how much variance of the
fMRI signal (BOLD response) our convolved
parameters can explain.

-> Express in a Statistical Parametric Map (SPM)