Preprocessing in fMRI

Chun-Chia Kung

Oct 5, 2013

NCKU MRI center

The Black Box

• The danger of automated processing and fancy images is that you can get blobs without every really looking at the real data

• The more steps done at once, the greater the chance of problems

Raw

Data

Big Black Box

of automated

software

Pretty pictures

Know Thy Data

• Look at raw functional images

– Where are the artifacts and distortions?

– How well do the functionals and anatomicals correspond

• Look at the movies

– Is there any evidence of head motion?

– Is there any evidence of scanner artifacts (e.g., spikes)

• Look at the time courses

– Is there anything unexpected (e.g., abrupt signal changes at the start of the run)?

– What do the time courses look like in the unactivatable areas (ventricles, white matter, outside head)?

• Look at individual subjects

• Double check effects of various transformations

– Make sure left and right didn’t get reversed

– Make sure functionals line up well with anatomicals following all transformations

• Think as you go. Investigate suspicious patterns.

Data Preprocessing Options

1. artifact screening• ensure the data is free from scanner and subject artifacts

• done by eyeballing and manual correction

2. slice scan time correction• correct for sampling of different slices at different times

3. motion correction• correct for sampling of different slices at different times

4. spatial filtering• smooth the spatial data

5. temporal filtering• remove low frequency drifts (e.g., linear trends)

• remove high frequency noise (not recommended because it increases temporal

autocorrelation and artificially inflates statistics)

6. spatial normalization• put data in standard space (Talairach or MNI Space)

Sample ArtifactsGhosts

Spikes

Metallic Objects (e.g., hair tie)Hardware Malfunctions

Longitudinal saturation effect

Freq distribution of physiological noise

A Map of Noise

• voxels with high variability shown in white

Linear Drift (or scanner drift)

Distribution of physiological noise

2. slice-scan time correction

3. Motion-induced intensity Changes

Slide modified from Duke course

Motion Spurious Activation at Edges

time1 time2

lateral

motion in

x direction

motion in

z direction

(e.g., padding sinks)

time 1 > time 2

time 1 < time 2

brain

position

stat

map

Spurious Activation at Edges

Motion Correction Algorithms

• Most algorithms assume a rigid body (i.e., that brain doesn’t deform with movement)

• Align each volume of the brain to a target volume using six parameters: three translations and three rotations

• Target volume: the functional volume that is closest in time to the anatomical image

x translation

z t

ransla

tion

y t

ransla

tion

pitch roll yaw

Head Motion: relatively good

… and catastrophically bad

Slide from Duke course

Problems with Motion Correction

• lose information from top and bottom of image

– possible solution: prospective motion correction• calculate motion prior to volume collection and change slice plan

accordingly

we’re missing data here

we have extra data here

Time 1 Time 2

Different motions; different effectsDrift within run Movement

between runs

Uncorrelated

abrupt movement

within run

Correlated abrupt

movement within a

run

Motion correction

Spurious activations okay, corrected

by LTR

okay minor problem huge problem can reduce

problems

Increased residuals okay, corrected

by LTR

okay problem problem can reduce

problems; may be

improved by

including motion

parameters as

predictors of no

interest

Regions move problem minor-major

problem depending

on size of

movement

problem problem can reduce

problems; if

algorithm is fooled

by physics

artifacts, problem

can be made

worse by MC

Physics artifacts not such a

problem

because effects

are gradual

okay problem huge problem can’t fix problem;

may be misled by

artifacts

The Fridge Rule

• When it doubt, throw it out!

Head Restraint

Head Vise(more comfortable than it

sounds!)

Bite Bar

Often a whack of foam padding works as well as anything

Vacuum Pack

Thermoplastic mask

Even the mock scanner…

Prevention is the Best Remedy

• Tell your subjects how to be good subjects– “Don’t move” is too vague

• Make sure the subject is comfy going in– avoid “princess and the pea” phenomenon

• Emphasize importance of not moving at all during beeping– do not change posture

– if possible, do not swallow

– do not change posture

– do not change mouth position

– do not tense up at start of scan

• Discourage any movements that would displace the head between scans

• Do not use compressible head support

4. Spatial Smoothing

• Application of Gaussian kernel

– Usually expressed in #mm FWHM

– “Full Width – Half Maximum”

– Typically ~2 times voxel size

Slide from Duke course

Reduction of false-positive rate by spatial smoothing

Effects of Spatial Smoothing on Activity

Unsmoothed Data

Smoothed Data (kernel width 5 voxels)

Slide from Duke course

Should you spatially smooth?

• Advantages– Increases Signal to Noise Ratio (SNR)

• Matched Filter Theorem: Maximum increase in SNR by filter with same shape/size as signal

– Reduces number of comparisons• Allows application of Gaussian Field Theory

– May improve comparisons across subjects• Signal may be spread widely across cortex, due to intersubject variability

• Disadvantages– Reduces spatial resolution – Challenging to smooth accurately if size/shape of signal is not

known

Slide from Duke course

5. Time Course Filtering

Low and High Frequency Noise

Source: Smith chapter in Functional MRI: An Introduction to Methods

Preprocessing Options

Before LTR:

After LTR:

Preprocessing Options

High pass filter•pass the high frequencies, block the low frequencies•a linear trend is really just a very very low frequency so LTR may not be strictly necessary if HP filtering is performed (though it doesn’t hurt)

Before High-pass

linear drift

~1/2 cycle/time course

~2 cycles/time course

After High-pass

Preprocessing Options

• Gaussian filtering

– each time point gets averaged with adjacent time points

– has the effect of being a low pass filter

• passes the low frequencies, blocks the high frequencies

– You better know it clearly what you are doing

After Gaussian (Low Pass) filteringBefore Gaussian (Low Pass) filtering

Take home Messages

• Look at your data

• Work with your physicist to minimize physical noise

• Design your experiments to minimize physiological noise

• Motion is the worst problem: When in doubt, triple-check

• Preprocessing is not always a “one size fits all” exercise