introduction to functional mri jody culham brain and mind institute department of psychology...

Introduction to Functional MRI

Jody CulhamBrain and Mind Institute

Department of PsychologyUniversity of Western Ontario

http://www.fmri4newbies.com/

MAIN SOURCES:

FMRI Graduate Course (NBIO 381, PSY 362)Dr. Scott HuettelDuke-UNC Brain Imaging & Analysis Center (BIAC)

http://www.biac.duke.edu/education/courses/

Karla L. MillerFMRIB CentreOxford University

http://www.fmrib.ox.ac.uk/~karla/

E = mc2

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The First “Brain Imaging Experiment”

“[In Mosso’s experiments] the subject to be observed lay on a delicately balanced table which could tip downward either at the head or at the foot if the weight of either end were increased. The moment emotional or intellectual activity began in the subject, down went the balance at the head-end, in consequence of the redistribution of blood in his system.”

-- William James, Principles of Psychology (1890)

Angelo MossoItalian physiologist

(1846-1910)

… and probably the cheapest one too!

FMRI – Week 1 – IntroductionScott Huettel, Duke University

Timeline of MR Imaging

1920 1930 1940 1950 1960 1970 1980 1990 2000

1924 - Pauli suggests that nuclear particles may have angular momentum (spin).

1937 – Rabi measures magnetic moment of nucleus. Coins “magnetic resonance”.

1944 – Rabi wins Nobel prize in Physics.

1946 – Purcell shows that matter absorbs energy at a resonant frequency.

1946 – Bloch demonstrates that nuclear precession can be measured in detector coils.

1952 – Purcell and Bloch share Nobel prize in Physics.

1972 – Damadian patents idea for large NMR scanner to detect malignant tissue.

1959 – Singer measures blood flow using NMR (in mice).

1973 – Lauterbur publishes method for generating images using NMR gradients.

1973 – Mansfield independently publishes gradient approach to MR.

1975 – Ernst develops 2D-Fourier transform for MR.

NMR becomes MRI

MRI scanners become clinically prevalent.

1990 – Ogawa and colleagues create functional images using endogenous, blood-oxygenation contrast.

1985 – Insurance reimbursements for MRI exams begin.

M R

FMRI – Week 1 – Introduction Scott Huettel, Duke University

Timeline of MR Imaging

1920 1930 1940 1950 1960 1970 1980 1990 2000

1924 - Pauli suggests that nuclear particles may have angular momentum (spin).

1937 – Rabi measures magnetic moment of nucleus. Coins “magnetic resonance”.

1944 – Rabi wins Nobel prize in Physics.

1946 – Purcell shows that matter absorbs energy at a resonant frequency.

1946 – Bloch demonstrates that nuclear precession can be measured in detector coils.

1952 – Purcell and Bloch share Nobel prize in Physics.

1972 – Damadian patents idea for large NMR scanner to detect malignant tissue.

1959 – Singer measures blood flow using NMR (in mice).

1973 – Lauterbur publishes method for generating images using NMR gradients.

1973 – Mansfield independently publishes gradient approach to MR.

1975 – Ernst develops 2D-Fourier transform for MR.

NMR becomes MRI

MRI scanners become clinically prevalent.

1990 – Ogawa and colleagues create functional images using endogenous, blood-oxygenation contrast.

1985 – Insurance reimbursements for MRI exams begin.

M R I f

The Rise of fMRI…

Friston, 2010,Science

Schleim & Roiser, 2009, Front. Hum. Neurosci.

… and the Decline of PET

FMRI – Week 1 – Introduction Scott Huettel, Duke University

Spatial vs. Temporal Resolution of Selected Brain Imaging Methods

The Brain Before fMRI (1957)

Polyak, in Savoy, 2001, Acta Psychologica fMRI for Dummies

moving bodiessocial cognition

faces objectsstatic bodies

grasping

motion perception

motion near head

orientation selectivitymemory

scenes

motorcontrol

reaching and pointing

touch

retinotopic visual maps eyemovements

executive control

The Brain After fMRI (Incomplete)

Magnetic Resonance ImagingScanner

fMRI Setup

[Source: Mouser.com]

K-Space

Source: Traveler’s Guide to K-space (C.A. Mistretta)

K-Space

• Data gathered in k-space (Fourier domain of image)• Image is Fourier transform of acquired data• How k-space is sampled has implications for image

k-space image space

Fouriertransform

ky

kx

ky

kx

Reduces TE (sacrifices some functional contrast)

Must acquire slightly more than half (Hermetian symmetry is approximate)

Slight blurring added to image

Partial k-space EPI

EPI acquires one imageper TR

Due to symmetry, canactually collect less thanfull k-space

Spiral FMRI

• Currently, only serious alternative to EPI

• Short apparent TE (center of k-space acquired early)

• Fast and efficient use of gradient hardware

• Different artifacts than EPI (not necessarily better)

• Coil sensitivity encodes spatial information

• Can “leave out” large parts of k-space– Theory: For n coils, only need 1/n of k-space– Practice: Need at least ~1/3 of k-space– In general, incurs loss of SNR

• More coverage, higher resolution, faster imaging, etc.

Parallel imaging (SENSE, SMASH, etc.)

Single coil 8-channel array

Surface coils

FMRI – Week 1 – IntroductionScott Huettel, Duke University

MR Safety• Pacemaker malfunctions leading to death

– At least 5 as of 1998 (Schenck, JMRI, 2001)– E.g., in 2000 an elderly man died in Australia after being twice asked if he

had a pacemaker

• Blinding due to movements of metal in the eye– At least two incidents (1985, 1990)

• Dislodgment of aneurysm clip (1992)

• Projectile injuries (most common incident type)– Injuries (e.g., cranial fractures) from oxygen canister (1991, 2001)– Scissors hit patient in head, causing wounds (1993)

• Gun pulled out of policeman’s hand, hitting wall and firing– Rochester, NY (2000)

MRI fMRI

series of 3D volumes (i.e., 4D data)(e.g., every 2 sec for 5 mins)

high spatial

resolution(1 mm)

low spatial resolution(~3-5 mm)

…

MRI vs. fMRI

one 3D volume(collected over several minutes)

PET and fMRI Activation

Source: Posner & Raichle, Images of Mind

fMRI Experiment Stages: Prep

1) Prepare subject• Consent form• Safety screening• Instructions and practice trials if appropriate

2) Shimming • putting body in magnetic field makes it non-uniform• adjust 3 orthogonal weak magnets to make magnetic field as homogenous as

possible

3) SagittalsTake images along the midline to use to plan slices

[Source: Wikipedia]

fMRI Experiment Stages: Anatomicals4) Take anatomical (T1) images

• high-resolution images (e.g., 0.75 x 0.75 x 3.0 mm)• 3D data: 3 spatial dimensions, sampled at one point in time• 64 anatomical slices takes ~4 minutes

64 axial slices (3 mm)

Slice Thicknesse.g., 6 mm

Gap, here 0 mm

Number of Slicese.g., 10

Slice prescription (on SAG slice)IN-PLANE SLICE

Field of View (FOV)e.g., 19.2 cm

VOXEL(Volumetric Pixel)

3 mm

3 mm6 mm

Slice Terminology

Matrix Sizee.g., 64 x 64

In-plane resolutione.g., 192 mm / 64

= 3 mm

fMRI Experiment Stages: Functionals5) Take functional (T2*) images

• images are indirectly related to neural activity• usually low resolution images (e.g. here 3 x 3 x 6 mm)• all slices at one time = a volume (sometimes also called an image)• sample many volumes (time points) (e.g., 1 volume every 2 seconds for 136

volumes = 272 sec = 4:32)• 4D data: 3 spatial, 1 temporal

…

fMRI Simplified

Time

Condition 1

Condition 2

~2s

...

~ 5 min

Region of interest (ROI)

Time

fMRISignal

Intensity

ROI Time Course

Condition

Ignoring: HRF, subject motion, multiple comparisons

BOLD Time CourseBlood Oxygenation Level-Dependent Signal

Positive BOLD response

InitialDip

OvershootPost-stimulusUndershoot

0

1

2

3

BO

LD R

espo

nse

(% s

igna

l cha

nge)

Time

Stimulus

The metabolic signal we track is actually time-lagged from neural activity…

“HRF”(Hemodynamic response function)

The Canonical FMRI Experiment

• Subject is given sensory stimulation or task, interleaved with control or rest condition

• Acquire timeseries of BOLD-sensitive images during stimulation

• Analyse image timeseries to determine where signal changed in response to stimulation

PredictedBOLD signal

time

Stimuluspattern

on

off

on

off

on

off

on

offoff

BOLD/Signal Time Courses

TIME

MR

SIG

NA

L(A

RB

ITR

AR

Y U

NIT

S)

BOLD signal has arbitrary units:

varies from coil to coil, voxel to voxel, day to day, subject to subject

Predicted HRF

Observed signal

BOLD/Signal Time Courses

TIME

MR

SIG

NA

L(%

Cha

nge)

Observed signal

Predicted HRF

Signal usually converted into units of % change:

Typically on the order of ½ - 4 %.

Final Statistics

• For convenience/summarization, time course at each voxel can be converted to a scalar measure

• Most common: parametric test of significance (e.g., t-test)

• “statistical parametric map”: voxel-wise parametric test results

Stats on Anatomical

2D 3D

Multiple Comparisons Problem

• Voxel-level statistics assume independence of all voxels (not true)

• Also, by virtue of the number of tests involved, conventional p-values are far too loose– p < 0.05 implies 5% chance of a false positive– Acceptable for one test, but with 100,000 tests (~ ½

brain size) that would be 5,000 false positives!…

• Many options: Bonferroni (conservative), familywise error rate (FWE), false discovery rate (FDR), cluster level significance, and others

Limitations of Neuroimaging

• Physical Limitations– spatial limitations (~1 mm)– temporal limitations (~50 ms to several seconds)

• Physiological Limitations– noise

• head motion• artifacts (respiration, cardiac pulse)

– localization of BOLD response• vasculature

• Current Conceptual Limitations– how can we analyze highly complex data sets?

• brain networks

– how are neural changes manifested in fMRI activation?

Limitations of Neuroimaging

Logothetis, 2008, Nature

Canonical cerebral microcircuit(excitatory in red, inhibitory in black)

BOLD signal mayIncrease or decrease,and this doesn’tnecessarily tell whatthe “neural input”or “neural output”was.

More complicated than just “taskrelated neuronsfiring”

BOLD Signal Dropout

BOLDNon-BOLD

Dephasing near air-tissue boundaries (e.g., sinuses)

BOLD contrast (using long TE) coupled to signal loss (“black holes”)

Additional Topics

• Practical steps for image analysis (next)• Biological sources of the BOLD signal• Physics and underpinnings of MR signal• Computational background on image processing steps• Experimental design