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An Introduction to Neuro-Imaging
for Neuroscience
(PhD Program)
Dr M A Oghabian
`
www.oghabian.net
1 )ناتوميكتصو يربرداري اطلاعات ا
2) يربرداري اطلاعات فيزيولوزيكتصو
3 )(تغييرات شيميائي)يربرداري اطلاعات مولكولي تصو
4 )يربرداري اطلاعات عملكرديتصو
تصويربرداري بر مبناي نوع اطلاعات
PET SPECT FUNCTION
STRUCTURE CT MRI
Structural & Functional Imaging
Nuclear Medicine is to physiology as Radiology is to anatomy
TR = 500 ms TE = 20 ms
Spin-Echo Images
TR = 2000 ms TE = 40 ms
TR = 2000 ms TE = 80 ms
Contrasts: PD, T1, T2 and T2* weighting
PD-weighting
(proton/spin density)
+ T1-weighting
(gray/white contrast)
+ T2-weighting
(bright CSF/tumor)
Which is best for brain morphometry/FreeSurfer?
FLASH 5° MPRAGE T2-SPACE
Brain Structural Analysis
1- Cortical (surface-based) Analysis.
2- Volume Analysis.
3- Voxel Based morphometry
4- Fiber Tracking
Surface and Volume Analysis
Cortical Reconstruction
and Automatic Labeling Inflation
Surface Flattening
Automatic Subcortical
Gray Matter Labeling
Automatic Gyral White
Matter Labeling
DTI
(Diffusion
Tensor
Imaging)
DTI
Fiber
Tracking
DTI Fiber Tracking and fMRI
Functional Neuro-Imaging
Methods
fMRI vs. PET
• fMRI does not require exposure to radiation
– fMRI can be repeated
• fMRI has better spatial and temporal resolution
– requires less averaging
– can resolve brief single events
• MRI is becoming very common; PET is specialized
• MRI can obtain anatomical and functional images within same
session
• PET can resolve some areas of the brain better
• in PET, isotopes can tagged to many possible tracers (e.g., glucose
or dopamine)
• PET can provide more direct measures about metabolic processes
Spatial and Temporal Resolution of
Various functional imaging methods
Perfusion measurement • Goal of perfusion MRI is to measure the blood
flow
• This flow corresponds to microcirculatory
tissue perfusion rather than the flow of the
main vascular axes
• This is expressed in milliliters per 100 gram of
tissue per minute.
• Gadolinium chelates do not cross the normal
blood-brain barrier. But in pathological
conditions they cross, and therefore produces
extravasation,
Perfusion parameters • These parameters are extracted by post-processing the signal
curves, and are:
• TA: time of arrival of the contrast agent in the slice after injection
• TTP (Time To Peak): time corresponding to the maximum
contrast variation
• MTT (mean transit time)
• Peak amplitude: percentage loss of intensity of the maximal
signal
• rCBV (regional cerebral blood volume): The area below the
decreasing signal curve
• rCBF (regional cerebral blood flow): The ratio rCBV/MTT.
• RCBV and rCBF are relative measurements, giving the
calculation of ratios between pathological zone and healthy zone
(which serves as a reference).
• Permeability (Ktrans)
FMRI – Presurgical Planning MA Oghabian: NeuroImaging and Analysis Lab- Tehran Unive of Medical Sci
Pre
-surg
ical risk e
valu
ation
Perf
usio
n M
RI
(CB
V/C
BF
/MT
T/T
TP
)
Perfusion MRI
• Perfusion MRI is a technique for evaluation of microscopic
blood flow in cerebral capillaries and venules.
• (A) Perfusion of a high grade brain tumor demonstrates areas of
increased capillary blood volume in tumor (red) corresponding to tumor
neovascularity.
• (B) Perfusion map superimposed on FLAIR image demonstrates area of
tumor with highest malignancy potential to aid biopsy planning
Diffusion MRI in acute stroke
• (a) Day 1 diffusion MRI demonstrates a small cortical a
sub-cortical infarct.
• (b) Day 2 DWI demonstrates enlargement of the stroke
(limiting Diffusion).
• (c) Perfusion MRI demonstrates reduced blood flow
corresponding to final infarct size.
MRI Spectroscopy (MRS)
• MRS is a special technique used for
characterization of the biochemistry of
tumors, infarcts, and other pathology.
•
• MRS is useful for demonstrating
aspects of physiology such as tumor
aggressiveness and anaerobic
metabolism.
• 2D MRS of the brain demonstrates
the characteristic internal biochemical
makeup of glioblastoma multiforme, a
highly aggressive brain tumor
در حضور میدان مغناطیسی TE= 30msتصویر طیف پروتون در در
7T ترکیب متفاوت بر روي 14مطالعه اي بر روي مغز انسان در میدان در
طیف قابل مشاهده بوده اند
FMRI – Presurgical Planning MA Oghabian: NeuroImaging and Analysis Lab- Tehran Unive of Medical Sci
Pre
-surg
ical risk e
valu
ation
MR
S (
Cholin
incre
ase in tum
our
• fMRI (BOLD imaging)
The Brain Before fMRI (1957)
Polyak, in Savoy, 2001, Acta Psychologica
Solution: Brain Functional studies • Imaging
– CBF
– CBV
– Perfusion study
– Diffusion study
– Metabolite evaluation (MRS)
• Creatine
• Lactate
• Phosphor (ATP)
– ……
– BOLD
• Signal measurement
• Post-Synaptic Potentials
• EEG (ERPs) and MEG
• Action Potentials
• Electrophysiology
What is fMRI?
• Functional Magnetic Resonance Imaging (fMRI): uses MRI to indirectly measure brain activity
• Known for over 100 yrs. that blood flow and blood oxygenation are linked to neural activity– only since the early 1990’s was fMRI developed (Ogawa & Kwong)
• Based on the assumption that neuronal activity requires O2
which is carried by the blood; increased blood flow and resulting hemodynamics are foundation to fMRI
Why Use fMRI?
• Clinical uses- pre-surgical planning, id’ing brain pathologies, and many future potential uses
• Research Purposes- many researchers from several disciplines are using fMRI to better understand brain function in animals and humans
Language task (WG) on left-handed
patient with Temporoparietal mass
FMRI – Presurgical Planning MA Oghabian: NeuroImaging and Analysis Lab- Tehran Unive of Medical Sci
History of fMRI
MRI
-1971: MRI Tumor detection (Damadian)
-1973: Lauterbur suggests NMR could be used to form images
-1977: clinical MRI scanner patented
-1977: Mansfield proposes echo-planar imaging (EPI) to acquire images faster
fMRI
-1990: Ogawa observes BOLD effect with T2*
blood vessels became more visible as blood oxygen decreased
-1991: Belliveau observes first functional images using a contrast agent
-1992: Ogawa et al. and Kwong et al. publish first functional images using BOLD signal
Ogawa
Necessary Equipment
Magnet Gradient Coil RF Coil
Source: Joe Gati, photos
RF Coil
4T magnet
gradient coil
(inside)
fMRI Setup
Contrast Agents in MRI
• Definition: Substances that alter magnetic
susceptibility of tissue or blood, leading to
changes in MR signal
– Affects local magnetic homogeneity: decrease in T2*
• Two types
– Exogenous: Externally applied, non-biological
compounds (e.g., Gd-DTPA)
– Endogenous: Internally generated biological
compound (e.g., deoxyhemoglobin, dHb)
BOLD contrast
FMRI – Week 6 – BOLD fMRI Scott Huettel, Duke University
Oxygenated blood?
No signal loss…
Deoxygenated blood?
Signal loss!!!
Images from Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging
FMRI – Week 6 – BOLD fMRI Scott Huettel, Duke University
Disadvantage of T2*: Susceptibility Artifacts
-In T2* images, artifacts occur near junctions between
air (sinuses, ear canals) and tissue
sinuses
ear
canals
T1-weighted image T2*-weighted image
Blood Deoxygenation affects T2* Decay
Thulborn et al., 1982
BOLD Endogenous Contrast
• Blood Oxyenation Level Dependent Contrast – Deoxyhemoglobin is paramagnetic
– Magnetic susceptibility of blood increases linearly with increasing Deoxygenation
• Oxygen is extracted during passage through capillary bed – Brain arteries are fully oxygenated
– During activation Venous (and capillary) blood has increased proportion of Doxyhemoglobin
– Then oxygen is compensated in veins
– Difference between oxy and deoxy states becomes greater for veins BOLD sensitive to venous changes
FMRI – Week 6 – BOLD fMRI Scott Huettel, Duke University
Vasculature
Source: Menon & Kim, TICS
Measuring Deoxyhemoglobin
• fMRI measurements are of amount of
oxyhemoglobin per voxels in Venus pool
• We assume that amount of deoxygenated
hemoglobin in vein (and oxyhemoglobin in
later stage) is predictive of neuronal
activity
FMRI – Week 6 – BOLD fMRI Scott Huettel, Duke University
BOLD signal
Source: Doug Noll’s primer
FMRI – Week 6 – BOLD fMRI Scott Huettel, Duke University
Stimulus to BOLD
Source: Arthurs & Boniface, 2002, Trends in Neurosciences
Basic Form of Hemodynamic Response
A Simple Experiment
Intact
Objects
Scrambled
Objects
Blank
Screen
TIME
Condition changes every 16 seconds (8 volumes per Block), 17 block
One volume (12 slices) every 2 seconds
for 272 seconds (4 minutes, 32 seconds)
Lateral Occipital Complex
• responds when subject
views objects
What data do we start with
• 12 slices * 64 voxels x 64 voxels = 49,152 voxels
• Each voxel has 136 time points
• Therefore, for each run, we have 6.7 million data points
• We often have several runs for each experiment
…
Predicted Responses • It takes about 5 sec for the blood to catch up with the brain
• We can model the predicted activation in one of following ways: – Block activation model stimuli
1. shift the boxcar by approximately 5 seconds (2 images x 2 seconds/image = 4 sec, close enough)
2. convolve the boxcar with the hemodynamic response to model the shape of the true function as well as the delay
– Event related stimuli
– Rest activation (non-model based) response
PREDICTED ACTIVATION IN VISUAL AREA
BOXCAR
SHIFTED
CONVOLVED
WITH HRF
PREDICTED ACTIVATION IN OBJECT AREA
Why do we need stats? • move the cursor over different areas and see if any of the
time courses look interesting: Here for visual activation
Slice 9, Voxel 1, 0 Slice 9, Voxel 0, 0
Even where there’s no brain, there’s noise
Slice 9, Voxel 9, 27
Here’s a voxel that responds well whenever there’s visual stimulation
Slice 9, Voxel 13, 41
Here’s one that responds well whenever there’s intact objects
Slice 9, Voxel 14, 42
Here’s a couple that sort of show the right pattern but is it “real”?
Slice 9, Voxel 18, 36
Slice 9, Voxel 22, 7
BOLD signal in each voxel
time
Mxy
Signal
Mo sin
T2* task
T2* control
TEoptimum
Stask
Scontrol
S
Statistical Approaches in a Nutshell t-tests
• compare activation levels between two conditions
correlations
• model activation and see whether any areas show a similar pattern
Fourier analysis
• Do a Fourier analysis to see if there is energy at your paradigm frequency
Fourier analysis images
from Huettel, Song &
McCarthy, 2004,
Functional Magnetic
Resonance Imaging
Effect of Thresholds
r = 0
0% of variance
p < 1
r = .24
6% of variance
p < .05
r = .50
25% of variance
p < .000001
r = .40
16% of variance
p < .000001
r = .80
64% of variance
p < 10-33
Types of Errors
Slide modified from Duke course
Is the region truly active?
Do
es o
ur
stat
tes
t in
dic
ate
that
th
e re
gio
n i
s ac
tive?
Yes
N
o
Yes No
HIT Type I
Error
Type II
Error
Correct
Rejection
p value:
probability of a Type I error
e.g., p <.05
“There is less than a 5%
probability that a voxel our
stats have declared as
“active” is in reality NOT
active
Complications
• Not only is it hard to determine what’s real,
but there are all sorts of statistical problems
r = .24
6% of variance
p < .05
What’s wrong with these data? Potential problems: 1. data may be contaminated
by artifacts (e.g., head
motion, breathing artifacts)
2. .05 * 49,152 = 2457
“significant” voxels by
chance alone
3. adjacent voxels uncorrelated
with each other; adjacent
time points uncorrelated
with one another) are false
Source: Karl Friston
Estimating Parameters of Statistical Model
• FSL and SPM uses the Generalized Linear Model (GLM) to make parameter estimates.
Y = X . β + ε
Observed data Design matrix – model
formed of several
components which explain
the observed data
Parameters defining the
contribution of each
component of the
design matrix to the
model.
These are estimated so
as to minimize the
error, and are used to
generate the contrasts
between conditions
Error - the
difference
between the
observed
data and the
model
defined by
Xβ.
Source: http://www.fil.ion.ucl.ac.uk/spm/doc/mfd/GLM.ppt
Hypothesis vs. Data-Driven Approaches
Hypothesis-driven Examples: t-tests, correlations, general linear model (GLM)
• a priori model of activation is suggested
• data is checked to see how closely it matches components of the model
• most commonly used approach
Data-driven Example: Independent Component Analysis (ICA)
• no prior hypotheses are necessary
• multivariate techniques determine the patterns across all voxels
• can be used to validate a model
• can be inspected to see if there are things happening in your data that
you didn’t predict
• can be used to identify confounds (e.g., head motion)
Example of ICA
• hardest part is sorting the many components into meaningful ones vs. artifacts
• “fingerprints” may help
Study Design & Experimental Setup •Study Design
- Normative data in normal subjects in necessary
•Experimental Setup
- Blocked versus Event-Related Design
– Habituation, expectation, fatigue
•Data Acquisition
-Stimulus Pacing
-Matching difficulty level
•Post-Processing
-Performance monitoring
•Statistical Analysis and Validation
•Interpreting Results
- eg; Presurgical fMRI:
•Report
Major components of post-processing
and Analysis 1. Quality control (data free from noise and artifacts)
2. Motion correction
3. Slice timing correction
4. Spatial normalization (alignment into common spatial
framework)
5. Spatial smoothing
6. Temporal filtering
7. Statistical modeling (GLM & data fitting)
8. Statistical Inference (estimation of statistical
significance)
9. Visualization
Software
package for
fMRI analysis • SPM: include connectivity modeling tools, psychophysioligical
interaction, Dynamic Causal Moleding
• FSL: novel modeling techniques (eg RANDOMISE modules), ICA
for resting-state, DTI analysis, FSLview & probabilistic Atlases,
enable computing clusters
• AFNI: Powerful visualization abilities, integrating volume and
cortical surfaces
• BrainVoyager: commercial on all computing platforms
• Freesurfer: for cortical surfaces and anatomical
parcellations; can incorporate fMRI data from SPM/FSL
System requirement
1.5T
2%
signal
change
3T
4%
signal
change
Echo Planar Imaging: EPI Properties:
- One single excitation pulse ⇒ low rf energy deposition
(low SAR values).
- A train of gradient echoes, generated by very fast
switching of the defasing/refasing frequency encoding
gradients, at different values of the phase encoding
Gradient
- every echo generates one line of the image
- MR image acquired in less than 60 ms (128x 128 matrix)
- Sensitive to susceptibility effects
MRI Basis: T2*- contrast
Fast Imaging WHOLE HEAD
– SMALL voxels
• about 2.5 x 2.5 x 2.5 mm3
– High bandwidth
– EchoPlanarImaging:
• 128x128 (64x64) matrix
• TE ~ 50 ms (~ 30 ms @ 3T)
• up to 50 slices
• TR ~ 2 to 3 s
– Parallel imaging techniques
EPI Imaging
Important features of EPI
encoding:
• Adjacent points along kx are
acquired with a short Dt (= 5 μs).
(high bandwidth),
•Adjacent points along ky are
taken with a long Dt (= 500 μs)
(low bandwidth)
EPI: Practical issues
• Sensitive to field gradient artifacts
(eddy currents, susceptibility interfaces)
• geometric distortion (anatomical localization of activation
foci)
• signal drop-out (frontal and temporal poles)
5 mT/m 250 ms 10 mT/m 130 ms 16 mT/m 80 ms
Pre-requisities for fMRI analysis
• Probability and Statistics
• Computer programming: ATHLAB/python/UNIX
shell scripting
• Linear Algebra: GLM/image processing
• MRI: data acquisition/artifacts
• Neurophysiology & biophysics: Neuron activities
& blood flow/hemodynamic response
• Signal & Image processing: Fourier analysis based
processing
Thank You
M A Oghabian
FMRI – Week 6 – BOLD fMRI Scott Huettel, Duke University
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