introduction to fmri

Introduction to MRI/fMRI

PSY101 . Lab 3 . Fall 2014

Scanners at the PNI

Siemens Skyra Siemens Prisma

The magnetic field

• MRI scanners create strong magnetic fields (between 1.5 and 7 Tesla)

• 1 Tesla = 10,000 Gauss

• The strength of the magnetic field of the earth ranges from 0.25 -0.65 Gauss

Risks associated with the magnetic field

• magnetic field can pick up even large magnetic objects and pull them into

the scanner bore with great velocity

• Rotation of metal objects that are located in the body

• Malfunction of electronic devices (e.g. pacemakers)

• Electric burns can arise from electrically conductive objects in the magenetic

field

Two types of scans

MRI functional MRI

voxel

MRI physics (strongly oversimplified)

• a person goes into a strong magnet

• atomic nuclei reorient themselves along the magnetic field

• a radiofrequency pulse (1) flips the nuclei from the oriented position and (2)

synchronizes the precession of their spin axis

• a receiver measures the time until the nuclei return to their original

orientation (structural scans) or desynchronize (functional scans)

outside scanner inside scanner

precession

What is measured in fMRI?

• neurons consume oxygen and nutrients

• increased neural activity requires increased

supply of oxygen

• oxygen is bound to hemoglobin

(oxyhemoglobin vs. de-oxyhemoglobin)

• to supply neurons with oxygen and glucose,

blood flow is increased locally

• the local increase in blood flow leads to a

displacement of de-oxyhemoglobin

• MR signal is higher for oxygenated compared

to de-oxygenated blood

the blood oxygen level dependent (BOLD) response

300 10 20

0

time [s]

peak

undershoot

hemodynamic

lag

stimulus

time [s]

BO

LD

stimulus

the BOLD response over time

A [BOLD] response is measured for every voxel

…

1 Volume

= 1 image of entire brain

(in this case 36 horizontal slices)

1 Run (147 TRs)

TR* 1TR 2

TR 2 TR 147

*TR = Time of Repetition = time it takes to acquire one volume

4-D datasets

Outline of a scan session

1 Task instruction + safety screening

2 Put subject in the scanner

3 Localizer scan

4 Anatomical scan

5 Shimming

6 Test scan

7 Data collection

8 [Field map]

Today’s experiments

PSY101 . Lab 3 . Fall 2014

Face/Scene Localizer

scenes faces scrambled scenes scrambled faces

Stimuli presented in blocked design

Task: 1-back task

posterior

Category-selective visual cortex

anterior

LHRH

PPA

FFA

>

>

Beyond faces and scenes:

The FFA and PPA as ROIs for studying other cognitive functions

0

1

2

3

4

5

6

FFA PPA

attend faces

attend scenes

Attend faces:

female vs. male

Attend scenes:

indoor vs. outdoor

Attention enhances responses to task-relevant information!

Identical physical stimulation during

the two attention conditions

Introduction to fMRI analysis

PSY101 . Lab 3 . Fall 2014

Before we get started …

1. Log on to a Lab PC using the VMUser account

Username: .\VMUser

Password: @psychLAB

2. Open VMWare Player

3. Within VMWare Player, open FSLVm6_64

Analysis occurs in two main steps

1 Preprocessing

2 Statistical analysis

Preprocessing

1 Slice timing correction

Correct for the timing difference in the sequential acquisition of slices

2 Motion correction

Correct for subject’s head movements

3 Spatial smoothing

Increase signal-to-noise ratio

4 Temporal smoothing

Remove unwanted temporal components

5 Registration

Align functional to anatomical data

Align functional and anatomical data with a standard space (“Normalization”)

Slice-timing correction

• In our experiment we measured one

functional image (volume) of the brain every 2

seconds

• Each volume was acquired in 36 interleaved

horizontal slices

• This means that every slice was acquired at a

different time during the 2s TR

Sp

ace

[slic

es]

time [s]

volume (TR) volume (TR)

1

36

18

Slice-timing correction

To correct for this difference in timing, time-

series in each slice is phase-shifted so that it

appears as if all slices were acquired at the

same time

Sp

ace

[slic

es]

time [s]

volume (TR) volume (TR)

1

36

18

Motion correction

• Data are acquired in absolute spatial coordinates. If head movement occurs,

the time course of a voxel is derived from different parts of the brain

• Head movement is often correlated with the task

• Head motion decreases statistical power

before movement after movement

Dealing with head motion

During data acquisition

• Tell participants to be still in the scanner

• Head restraints

During preprocessing

• Remove time points with excessive movement

• Spatially align functional data to one reference image

• Adjust 6 parameters:

translational rotational

yaw pitch rollx, y, z

roll [°] pitch [°] yaw [°] I-S [mm] R-L [mm] A-P [mm]

Rotation Translation

Motion correction output

• Apply a Gaussian filter to effectively spread the intensity at each voxel to

neighboring voxels.

• Increases signal-to-noise ratio: blurring reduces high frequency noise while

retaining signal

Spatial smoothing

FWHM

= full width at

half maximum

before smoothingwith smoothing

FWHM 4mm

Temporal filtering

The MRI signal can contain unwanted temporal components:

• High-frequency noise

• Low-frequency drifts

Remove unwanted temporal components using filters

• High pass: remove frequencies below cut-off frequency

• Low pass: remove frequencies above cut-off frequency

rawfiltered

Normalization

Individual brains differ largely in size, form, and location of brain areas

Projecting data from different individual into a common standard space, allows

for

• combining data across subjects

• making comparisons across studies

Statistical analysis: regression

Core idea: observed data can be explained by a combination of weighted

regressors

Example: Explain miles per gallon (mpg) of a car, based on the car’s weight

and the driver’s height.

Observed data: mpg

Regressors: car’s weight, driver’s height

Weights: βcar’s weight = high; βdriver’s height = low

0

5

10

15

20

25

30

2 2.5 3 3.5 4 4.5 5

0

5

10

15

20

25

30

1.4 1.6 1.8 2 2.2

mp

g

mp

g

Driver’s height [m]Car’s weight [t]

observed data = BOLD response extracted from an individual voxels

Regression in fMRI

Regressors: timing of conditions combined with

assumptions about the shape of the BOLD

response

faces scenesscr scenes scr faces fixation scenesfixation

faces

scenes

scr(ambled) scenes

scr(ambled) faces

time

scr(ambled) scenes

scr(ambled) faces


faces

scenes

time

Regressors: timing of conditions combined with

assumptions about the shape of the BOLD

response

Regressors are combined into a single model

time


Regressors that account for a lot of variance in the

signal receive high beta values

time


model

data

weights

Weights are plotted as statistical parametric maps

Contrasts: intact vs. scrambled objects

>

Significance thresholds

p = 1p = 0.03

p = 0.4p = 0.01

Before you leave …

1. Grab all the files you still need

2. Delete the firstlevel_R1.feat directory you created during your

analysis

3. Power off the virtual machine

4. Log off the Lab PC

introduction to fmri

Education