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f MRI. Sonia Poltoratski Vanderbilt University. unbridled joy. brain picture. knowledge. data. intro psych. analysis. ...is the wild wild west. what is BOLD?. crippling depression. Outline:. MR Physics BOLD signal Basics of Analysis Evolution Good & Bad Practices. MR Physics.

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sonia poltoratski vanderbilt university

fMRI

Sonia Poltoratski

Vanderbilt University

slide2

unbridled

joy

brain

picture

knowledge

data

intro psych

analysis

...is the wild wild west

what is

BOLD?

crippling depression

outline
Outline:
  • MR Physics
  • BOLD signal
  • Basics of Analysis
  • Evolution
  • Good & Bad Practices
mr physics
MR Physics
  • MR in humans = proton nuclear magnetic resonance, which detects the presence of hydrogen nuclei
  • Since the single proton of hydrogen in unbalanced, normal thermal energy causes it to spin about itself

electron

-

+

proton

spins
Spins

μ J

  • The proton’s positive charge generates an electrical current
  • In a magnetic field, this loop current induces torque, called the magnetic moment (μ)
  • The proton’s odd-numbered atomic mass gives it an angular momentum (J)

+

+

+

+

+

+

+

+

+

+

proton

net magnetization m
Net magnetization (M)

Negligible under normal conditions

proton precession
Proton Precession
  • Spinning objects respond to applied forces by moving their axes perpendicular to the applied force
proton precession1
Proton Precession
  • Spinning objects respond to applied forces by moving their axes perpendicular to the applied force

precession

axis

magnetic field

spin

axis

proton precession2
Proton Precession

magnetic field

parallel state

(low energy level)

anti-parallel state

(high energy level)

net magnetization m1
Net Magnetization (M)

longitudinal

M

magnetic field

transverse

net magnetization m2
Net Magnetization (M)

Increasing magnetic field  increase in net magnetization

The Zeeman Effect

high energy state

energy

ΔE

low energy state

magnetic field strength

signal generation
Signal Generation

excitation B1

magnetic field B0

photons: electromagnetic fields oscillating at the resonate (Larmor) frequency of hydrogen

signal generation net m
Signal Generation: Net M

excitation B1

M

magnetic field B0

flip angle

θ

signal reception
Signal Reception

reception

magnetic field B0

decaying, time-varying signal that depends on the molecular environment of the spins

signal reception1
Signal Reception

T1 recovery(longitudinal relaxation):

Individual spins return to their low-energy state, and net M becomes again parallel to the main field

T2 decay (transverse relaxation):

Immediately after excitation, spins precess in phase

This coherence is gradually lost

Images depict the spatial distribution of these properties

- BOLD

t1 relaxation times
T1 Relaxation Times

Fat

White Matter

Grey Matter

CSF

t2 decay times
T2 Decay Times

Fat

White Matter

CSF

Grey Matter

image formation
Image Formation
  • Magnetic gradient: spatially varying magnetic field
  • Adding a second gradient field causes spins at different locations to precess at different frequencies in a predictable manner

Paul C. Lauterbur and Sir Peter Mansfield at the 2003 Nobel Prize Ceremony

image formation1
Image Formation

longitudinal magnetization

slice excitation

transverse magnetization

2D spatial encoding

acquired MR signal in k-space

2D inverse Fourier

transform

2D MR

image

slice excitation
Slice Excitation

ƒ

resonant frequency vs. position

slice direction

slice excitation1
Slice Excitation

ƒ

resonant frequency

vs. position

when gradient is applied

frequency range of RF pulse

slice direction

excited slice

spatial e ncoding
Spatial Encoding

2D

A gradient field that differs along two dimensions results in a unique frequency assigned to each location in the space, influencing the location’s spin phase

  • Phase encoding gradient: turned on before data acquisition so that spins accumulate differential phase offset over space
  • Frequency encoding gradient: turned on during data acquisition so that the frequency of spin precession changes over space

Resulting data is in units of spatial frequency, which can be converted into units of distance via inverse Fourier transform

Echo Planar Imaging(EPI) allows us to collect an entire imagine in milliseconds, either following 1 excitation (single-shot) or several (multi-shot)

slide24
T1-Weighted Image

T2-Weighted Image

pop quiz
Pop Quiz!

MRI data acquisition

The experimental data were collected at the Vanderbilt University Institute for Imaging Science using a 3T Philips InteraAchieva MRI scanner with an eight-channel head coil. The functional data were acquired using standard gradient-echo echoplanar T2*-weighted imaging with 28 slices, aligned approximately perpendicular to the calcarine sulcus and covering the entire occipital lobe as well as the posterior parietal and posterior temporal cortex (TR, 2 s; TE, 35 ms; flip angle, 80°; FOV, 192 x192; slice thickness 3 mm with no gap; in-plane resolution, 3 x3 mm). In addition to the functional images, we collected a T1-weighted anatomical image for every subject (1 mm isotropic voxels). A custom bite bar system was used to minimize the subject’s head motion.

Keitzmann, Swisher, Konig, & Tong (2012)

pop quiz1
Pop Quiz!

MRI data acquisition

The experimental data were collected at the Vanderbilt University Institute for Imaging Science using a 3TPhilips InteraAchieva MRI scanner with an eight-channel head coil. The functional data were acquired using standard gradient-echo echoplanarT2*-weighted imaging with 28 slices, aligned approximately perpendicular to the calcarine sulcus and covering the entire occipital lobe as well as the posterior parietal and posterior temporal cortex (TR, 2 s; TE, 35 ms; flip angle, 80°; FOV, 192 x192; slice thickness 3 mm with no gap; in-plane resolution, 3 x3 mm). In addition to the functional images, we collected a T1-weighted anatomical image for every subject (1 mm isotropic voxels). A custom bite bar system was used to minimize the subject’s head motion.

Keitzmann, Swisher, Konig, & Tong (2012)

outline1
Outline:
  • MR Physics
  • BOLD signal
  • Basics of Analysis
  • Evolution
  • Good & Bad Practices
bold signal
BOLD signal

Blood-Oxygen-Level-Dependent Contrast (Thulborn et al., 1982; Ogawa, 1990)

  • Deoxygenated
  • Hemoglobin
  • Paramagnetic (significant magnetic moment)
  • 20% greater magnetic susceptibility, which impacts T2 decay

Oxygenated

Hemoglobin

Diamagnetic (no unpaired electrons or magnetic moment)

bold signal1
BOLD signal

Blood-Oxygen-Level-Dependent Contrast (Thulborn et al., 1982; Ogawa, 1990)

  • Deoxygenated
  • Hemoglobin
  • Paramagnetic (significant magnetic moment)
  • 20% greater magnetic susceptibility, which impacts T2 decay

Oxygenated

Hemoglobin

Diamagnetic (no unpaired electrons or magnetic moment)

The more deoxygenated blood is present, the shorter the T2

Difference emerges at ~ 1.5T

ogawa 1990
Ogawa (1990)
  • Blood oxygen content in rodents reflected in T2-weighted images
  • Metabolic demand for oxygen (confirmed by concurrent EEG) is necessary for BOLD contrast

During an MRI experiment with an anesthetized mouse, I saw most of the dark lines disappear when the breathing air was switched to pure O2 in order to rescue the mouse as it appeared to start choking. This observation rang a bell.

fmri vs other methods
fMRI vs. Other Methods

MEG & ERP

PET

brain

map

column

layer

neuron

dendrite

synapse

fMRI

Optical Imaging

Natural Lesions

TMS

Induced Lesions

Multi-unit recording

log size

Single Unit

Patch Clamp

Light Microscopy

millisecond second minute hour day

log time

outline2
Outline:
  • MR Physics
  • BOLD signal
  • Basics of Analysis
  • Evolution
  • Good & Bad Practices
voxels
Voxels

1mm x 1mm x 1.5mm voxels

7mm x 7mm x 10mm voxels

(Smith, 2004)

preprocessing stages
Preprocessing Stages
  • Slice-timing correction: correcting for differences in acquisition times within a TR
  • Motion correction: re-alignment of images across the session
  • Spatial smoothing: blurring of neighboring data points, akin to low-pass filtering.
preprocessing stages1
Preprocessing Stages
  • Mean intensity adjustment: normalization of signal to account for global drifts over time
  • Temporal high-pass filtering: removal of low-frequency drifts in time course
hemodynamic response function
Hemodynamic Response Function

peak

stimulus

percent MR signal change

undershoot

initial dip

-10 -5 0 5 10 15 20 25

time (s)

modeling the waveform
Modeling the Waveform

J

J

J

block design

HRF

fit this model to the time series of each voxel

general linear modeling
General Linear Modeling

Y= X. β+ ε

estimated parameters

error

observed data at a single voxel

design matrix

test if the slope of β is different from zero

slide39

=

t stat at each voxel

anatomical scan image

my FFA!

outline3
Outline:
  • MR Physics
  • BOLD signal
  • Basics of Analysis
  • Evolution
  • Good & Bad Practices
voxel resolution
Voxel Resolution

Kanwisher, McDermott, & Chun (1997):

3.25 x 3.25 x 6 mm

McGugin et al. (2013):

1.25 x 1.25 x 1.25 mm

tr duration
TR Duration

(not my) unpublished data removed for web use

(Tong Lab data)

7Tesla, TR = 200ms

outline4
Outline:
  • MR Physics
  • BOLD signal
  • Basics of Analysis
  • Evolution
  • Good & Bad Practices
the seductive allure of neuroimaging
‘The Seductive Allure of Neuroimaging’

Non-experts judge explanations with neuroscience information as more satisfying than explanations without neuroscience, especially bad explanations.

(Weisberg et al., J Cog Neuro 2008)

pitfalls in fmri
Pitfalls in fMRI
  • Study Design
    • What is your contrast?
    • What conclusions can we draw from fMRI activation?
  • Statistical Analysis

vs

puzzlingly high correlations in fmri studies of emotion personality social cognition vul et al 2009
Puzzlingly High Correlations in fMRI Studies of Emotion, Personality, & Social CognitionVul et al. (2009)

Voodoo Correlations in Social Neuroscience

  • Noticed R > 0.8 correlations, seemingly higher than possible under constraints of fMRI and variability of personality measures
  • Non-independence error:
    • Selecting a small number of voxels based on some trait
    • Only reporting the correlation of the trait to those voxels
  • 54% of surveyed papers, including those published in Science, Nature, and Neuron
pitfalls in fmri1
Pitfalls in fMRI
  • Study Design
    • What is your contrast?
    • What conclusions can we draw from fMRI activation?
  • Statistical Analysis
    • Correction for Multiple Comparisons
    • Independently-selected ROI’s
    • Software & Human Error

Act carefully and critically at all stages

of fMRI research!

the finer things in fmri
The Finer Things in fMRI

Event-Related

Design

fMRI-A:

Adaptation

Multi-Voxel Pattern Analysis

event related design
Event Related Design
  • Allows us to mix events of different types, avoiding effects related to blocking
  • Events can be categorized or defined post-hoc based on subject’s responses
  • In slow ERD, the BOLD response is allowed to return to baseline between events

J

J

J

block design

J

J

J

event-related design

rapid event related design
Rapid Event Related Design

(BAD)

J

Q

I

J

Q

I

J

Q

I

events:

individual HRFs:

summed HRFs:

rapid event related design1
Rapid Event Related Design

(GOOD)

J

Q

Q

I

J

I

events:

jittered order

& ISI

individual HRFs:

summed HRFs:

fmri a adaptation
fMRI-A: Adaptation
  • Neuronal population is adapted by repetition of a stimulus
  • Some property of the stimulus is changed
  • Recovery from adaptation is assessed:
    • Signal remains adapted = neurons are invariant
    • Signal recovers = neurons are sensitive to the changed property

The resolution of fMRI makes it difficult to distinguish between homogenous and heterogenous populations:

(Grill-Spector & Malach, 2001)

example face viewpoint invariance
Example: Face Viewpoint Invariance

Adapt to identical view

Change the property of interest

In both cases, signal is reduced

In (L) case, signal recovers

(Grill-Spector & Malach, 2001)

multi voxel pattern analysis
Multi-Voxel Pattern Analysis

(re: Kamitani & Tong, 2005)

multi voxel pattern analysis1
Multi-Voxel Pattern Analysis
  • AKA: fMRI decoding, MVPA, multivariate analysis
  • In univariate analysis described so far, we:
    • Assume independence of each voxel
    • Test whether each voxel responds more to one condition than the other
  • MVPA is designed to test whether 2+ conditions can be distinguished based on activity pattern in a set of voxels
  • Critically, MVPA can sometimes identify differences in conditions when average activity is equal

(review: Pratte & Tong, 2012)

multi voxel pattern analysis2
Multi-Voxel Pattern Analysis

Subjects view stimuli from two categories & feature selective voxels are selected

Data is divided into training and test runs; Training voxel patterns are decomposed and tagged by category

Training runs are input to a classifier function

The classifier defines a multi-dimensional decision boundary, and category membership for the test run is predicted

(review: Norman et al., 2006)