Introduction to Neuroimaging

Sunghyon Kyeong
sunghyon.kyeong@gmail.com
Institute of Behavioural Science in Medicine,  
Yonsei University College of Medicine
Introduction to Neuroimaging 
-PET, fMRI, VBM, and DTI-

Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 2
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Outline
• Positron Emission Topography (PET) Imaging
• Principles of BOLD signal generation
• Review on fMRI preprocessing steps
• Functional Network Construction
• Morphometric Brain Network
• Network from Diffusion Tensor Imaging

Positron Emission Tomography: 
Two photo detector

Positron Emission Tomography
gamma ray 
detectors Unstable parent 
nucleus
Proton decays to 
neutron in positron  
and neutrino emitted
Positron combines with 
electron and annihilates
Two anti-parallel 511 keV 
photons produced
p n + +
+ ⇥ebeta decay process :
NaI(Tl), bismuth germanate oxide (BGO),  
gadolinium oxyorthosilicate (GSO),  
lutetium oxyorthosilicate (LSO) are used for the crystal.

Coincidence Detection

Types of Coincidence Events
• A scattered coincidence is one in which at least one of the detected photons had undergone
at least one Compton scattering event prior to detection
• Random coincidence occur when two photons not arising from the same annihilation event
are incident on the detectors with the coincident time window of the system

• Unstable positron-emitting isotopes are synthesised in a cyclotron
by bombarding elements such as oxygen, carbon, or ﬂuorine with
protons.
• Isotopes : 15O(half-life 2min), 18F(110 min), 11C(20min)
• When the radio-labeled compounds are injected into the blood
stream, they distribute according to the physiological state of the
brain, accumulating preferentially in more metabolically active
areas.
• The structure of F-18-FDG is similar to the glucose, so it can used
to diagnosis the abnormality of glucose metabolism.
Isotope in PET imaging

Blood Oxygen Level Dependent Signal for  
functional MRI

2D iFFT
Raw Data
k-Space Image
Complex Data in
Image Domain
M = |R + iI|
P = tan 1
(I/R)
fMRI Data Acquisition

Detection of MRI Signal
• Applying RF pulse to tip down bulk magnetisation (Mz) to
the transverse plane.
• Mz tends to align the external magnetic ﬁeld as time goes
on (T1 recovery).
• Mz decays in the transverse plane as time goes on (T2
decay).
Good Contrast
Good Contrast
B0
MR
scanner
magnetic ﬁeld due to solenoid

Tissue T1 (ms) T2 (ms)
Gray matter (GM) 950 100
White matter (WM) 600 80
Muscle 900 50
Cerebrospinal fluid (CSF) 4500 2200
Fat 250 60
Blood 1200 100~300
Tissue Specific T1 and T2
B0 = 1.5 T
T = 37 C
obtained
at
• T1 recovery and T2 decay time ranges from tens to thousands of
milliseconds for protons in human tissue over the main field. Typical values
for various tissues are shown in following table.
• Applying the pulse sequences, we can discriminate brain tissues; The
different sequences should be applied to obtain the specific image, for
example, anatomic, functional, angio images.

• The abbreviation BOLD fMRI stands for Blood Oxygen Level
Dependent functional MRI.
• The BOLD contrast mechanism alters the T2* parameter mainly
through neural activity–dependent changes in the relative
concentration of oxygenated and deoxygenated blood.
• Deoxyhemoglobin is paramagnetic and inﬂuences the MR signal
unlike oxygenated hemoglobin.
Detecting BOLD fMRI Signal

Contrast Agents for fMRI ?
• Deﬁnition : Substances that alter magnetic susceptibility of tissue of
blood, leading to changes in MR signal 
- Affects local magnetic homogeneity: decrease in T2*
• Two types 
- Exogenous : Externally applied, non-biological compounds. 
- Endogenous : Internally generated biological compound (e.g., dHb)
• BOLD functional magnetic imaging method doesn’t need the external
contrast agents.

O2 Ratios in Blood
High ratio deoxy : 
→ deoxygenated blood  
→ fast decrease in MRI signal
Low ratio deoxy : 
→ oxygenated blood
→ slow decrease in MRI signal
Normal blood flow High blood flow
BOLD signal =
HB
dHB
dHb
Hb
deoxyhemoglobin (paramagnetic) oxyhemoglobin (non-magnetic)
• BOLD contrast measures inhomogeneities in magnetic field due to changes
in the level of O2 in the blood.

Mechanism of BOLD fMRI
Time
BOLDsignal
T2* task
T2* control
TEoptimal
ΔS
↑ Neural Activity ↑ Blood Flow ↑ Oxyhemoglobin
↑ T2*
↑ MR Signal

Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p
Hemodynamic Response
16
BOLDSignalChange
Time (second)
0 5 10 15 20
• BOLD signal은 자극
이 제시되고 5~6초 후
에 최대 반응을 보임
• Fast event related
+ jittered ISI is the
optimal design
Reference for FMRI Experimental Design, http://afni.nimh.nih.gov/pub/dist/HOWTO/howto/ht03_stim/html/stim_background.html

Block Designed fMRI
MRI
Language Area Motor Area

Resting State fMRI
• Resting state fMRI measures “low-frequency (0.01~0.08 Hz)” slow oscillation.
• Resting state means “Keep eyes closed resting state but not sleep for
several minutes”.
• Resting state functional connectivity considered as “intrinsic connectivity”.
• Modular structure in RSFC were found in many studies.
• Default mode network (DMN) alteration in Psychiatric patients (e.g.
schizophrenia).
18

steps in the spatial preprocessing
fMRI preprocessing

Summary of Preprocess
Input Output
EPI1.nii 
EPI2.nii
…
aEPI1.nii 
aEPI2.nii
…
aEPI1.nii 
aEPI2.nii
…
meanaEPI.nii
aEPI1.nii (realigned) 
aEPI2.nii (realigned)
rp_EPI.txt
…
meanaEPI.nii
anat.nii
meanaEPI.nii
anat.nii (coregistered)
anat.nii
aEPI1.nii 
aEPI2.nii
…
wanat.nii
waEPI1.nii
waEPI1.nii
…
waEPI1.nii 
waEPI2.nii
…
Slice Timing
Realignment
Coregistration 
T1 → meanEPI
Normalisation
Smoothing
Event related fMRI analysis
Resting state fMRI analysis
Preprocessing
• Specify 1st-level in SPM 
Individual GLM with Stimulus onset and
rp_EPI.txt as regressors
• Specify 2nd-level in SPM 
Group-wise GLM analysis 
one sample, two sample, factorial design,
flexible design
• Linear detrending of EPI time series at each
voxel.
• bandpass filtering (0.009~0.08Hz) to capture
Low-frequency fluctuation
• regression nuisance parameters such as head
motion, white matter, ventricle, and global signal
• Functional connectivity analysis and Complex
network analysis
swaEPI1.nii
swaEPI1.nii
…

Realignment
21
...
motion parameters mean-fMRI
sagittal
coronal
axial
100 dynamic images

Coregistration
22
BeforeCoregAfterCoreg
• High Resolution T1
data is registered to
mean-fMRI
• Rigid-body
transformation only 
(translation & rotation)
T1 mean-‐fMRI
T1 mean-‐fMRI

coregistered
T1 T1
template
normalized
T1
(wT1)
fMRI
images
...
...
normalised
fMRI
(wfMRI)
images
...
smoothed
fMRI
(swfMRI)
images
Nonlinear
normalisation
(T1→Template)
w
w
spatial
gaussian
?ilter
(FWHM=6
or
8mm)
S
Normalisation and Smoothing

Resting State  
Functional Connectivity
Michael
D.
Fox
(2005)
PNAS

Seed-ROI based connectivity analysis Graph theoretical analysis

fMRI preprocessing
steps in the temporal preprocessing

0 100 200 300 400 500 600 700 800
720
730
740
750
760
770
780
790
time course at voxel i 
(before linear detrending)
increasing trend due to heat
0 100 200 300 400 500 600 700 800
−25
−20
−15
−10
−5
0
5
10
15
20
25
after detrending (i.e. removing
long term increasing trend)
time course with linear function
Linear Detrending

Nuisance parameter regression
27
0 200 400 600 800
YGS
YCSF
YWM

0 200 400 600 800
−0.8
−0.6
−0.4
−0.2
0
0.2
0.4
0.6
0.8
x
translation

y
translation

z
translation
0 200 400 600 800
−0.02
−0.015
−0.01
−0.005
0
0.005
0.01
0.015
0.02
pitch

roll

yaw
GM WM CSF
Tx
Ty
Tz
Rx
Ry
Rz
0 50 100 150 200 250 300 350 400
65
70
75
80
85
90
0 50 100 150 200 250 300 350 400
−10
−5
0
5
10
Volume
(inter-‐volume
interval
=
2
sec)

Y
=
β1Tx
+
β2Ty
+
β3Tz
+
β4Rx
+
β5Ry
+
β6Rz
+
β7YGS
+
β8YCSF
+
β9YWM
+
ε
Head motions were regressed out to remove spin-history artefact.
Before After

0 0.05 0.1 0.15 0.2 0.25
0
100
200
300
400
500
600
Bandpass
Filtering
Region 
(0.01
-‐
0.08
Hz)
Bandpass Filtering
28
0 50 100 150 200 250 300 350 400
−4
−3
−2
−1
0
1
2
3
4
Bandpass
Ailtering
(0.01-‐0.08
Hz)
 
:
removing
vary
slow
wave,
cardiac
&

respiratory
noise
• very
low
frequency
regions
are

related
to
drift
(<0.01
Hz) 
• high
frequency
regions
are
related
to

respiratory
&

cardiac
noise
Frequency
(Hz)

Volume
(inter-‐volume
interval
=
2
sec)

Functional Connectivity
29
0 50 100 150 200 250 300 350 400
−30
−20
−10
0
10
20
average time course within a node
computing the pair-wire
correlation coefﬁcients for
functional connectivity
AAL atlas
weighted  
undirected
Adjacency  
Matrix (Aij)
Thresholding
Graph

Types of Graph
Binary
Undirected
Binary
Directed
Weighted
Directed
1
3
6
5
2
4
0 1 1 0 0 0
1 0 1 0 1 0
1 1 0 0 0 0
0 0 0 0 1 0
0 0 0 1 0 1
0 0 0 0 1 0
Aij
=Matrix
k2 = 3
k3 = 2
k4 = 1
Degree

Graph Visualisation
degree strength
clustering coefﬁcient
node betweenness centrality
node efﬁciency
edge strength
edge betweenness centrality
modular architecture
Network Properties
Node Properties
Edge Properties
Modular Structure
Network Visualisation
계산된 네트워크의 노드 속성값을 
가시화 과정에서 노드 크기로 표현함.
계산된 네트워크의 엣지 속성값을 
가시화 과정에서 엣지의 두께로 표편함.
계산된 네트워크의 모듈구조를 
가시화 과정에서 노드의 색깔로 표현함.
1
2

Morphometric Brain
Network
Hippocampus
Posterior Hipp
time as taxi driver (month)
adjustedVBMresponses 
posteriorhippocampus
anteriorhippocampalcross-
sectionalarea(mm2)
Posterior Hipp
Anterior Hipp
Taxi drivers' brains 'grow' on
the job
Maguire, E.A. (2000) PNAS

1. Tissue segmentation
2. Create Template & Normalisation
3. Modulation
4. Smoothing
5. Network Construction
The data are pre-
processed to sensitise
the statistical tests to
*regional* tissue volumes
Analysis Steps
Voxel-based Morphometry (VBM)

Segmentation
Probability maps
Mixture model
CSF GM WM
• Individual T1 weighted images are partitioned into
- grey matter / white matter / cerebrospinal ﬂuid
• Segmentation is achieved by combining with
- probability maps / Bayesian Priors (based on general knowledge about
normal tissue distribution)
- mixture model cluster analysis (which identiﬁes voxel intensity
distributions of particular tissue types in the original image)
GM WM CSF
T1 weighted image

Modulation
* Jacobian determinants of the deformation ﬁeld
• Is optional processing step but tends to be applied
• Corrects for changes in brain VOLUME caused by non-
linear spatial normalisation
• Multiplication of the spatially normalised GM (or other
tissue class) by its relative volume before and after
warping*, i.e. IB = IA×(VA/VB).

Example
IB
=
?
IA
=
1

VA
=
1 VB
=
2
IA
=
1

VA
=
4
IB
=
?VB
=
2
Template
Signal intensity ensures that total amount of GM in a subject’s temporal lobe is the
same before and after spatial normalisation and can be distinguished between subjects
Template
IB = 1 × [1 / 2] = 0.5
IB = 1 × [4 / 2] = 2
Modulation
ModulationNormalisation
Normalisation
IB = IA × [VA / VB]
Larger Brain
Smaller Brain

What is GM density
• The exact interpretation of GM concentration or density is
complicated.
• It is not interpretable as (i) neuronal packing density or (ii)
other cytoarchitectonic tissue properties, though
changes in these microscopic properties may lead to
macro- or mesoscopic VBM-detectable differences.
• Modulated data are more “concrete”.

Age, VTIV
ROI
index
(i)
Subject
index
(j)
After Regression
Mij
Mij is a GMV for a
Subject i and ROI j
−1 −0.5 0 0.5 1
0
200
400
600
800
1000
1200
What’s the meaning of
positive and negative
associations in the
morphometric network?
ROI Based Morphometry
Regressors
Adjacency Matrix (Aij) Distribution of Correlation Values
Morphometric network is a part of structural network, and representing group level network.

6 directional encoding b=0
Tensor
FA
Construct

Structural
Network

Fiber
 
Tracking:
DT-MRI
+
+

DTI & Tractography
• Diffusion Tensor: at least 6 directional DWIs + non-DWI are required.
• Diagonalization using Singular Value Decomposition
D =
0
@
Dxx Dxy Dzy
Dyx Dyy Dyz
Dzx Dzy Dzz
1
A
D = (e1 e2 e3)T
0
@
1 0 0
0 2 0
0 0 3
1
A (e1 e2 e3) =
3X
k=1
kekeT
k

Useful Quantities
• Mean ADC (apparent diffusion coefﬁcient)
• FA (fractional anisotropy)
• PDD (principal diffusion direction) 
what direction is greatest diffusion along? 
the orientation of ﬁner tract
Trace(D) = hDi =
1 + 2 + 3
3
FA =
p
( 1 2)2 + ( 2 3)2 + ( 3 1)2
p
2
p
2
1 + 2
2 + 2
3

Introduction to Neuroimaging

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