Introduction to Neuroimaging
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Transcript of Introduction to Neuroimaging
Sunghyon [email protected]
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
with Ctrl-key, select multiple regions
with Ctrl-key, select multiple regions
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
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 4
Positron Emission Tomography
gamma raydetectors Unstable parent
nucleus
Proton decays to neutron in positron and neutrino emitted
Positron combines with electron and annihilates
Two anti-parallel 511 keVphotons produced
p� n + �+ + ⇥ebeta decay process :
NaI(Tl), bismuth germanate oxide (BGO), gadolinium oxyorthosilicate (GSO), lutetium oxyorthosilicate (LSO) are used for the crystal.
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 5
Coincidence Detection
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 6
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
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 7
• 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
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 9
2D iFFT
Raw Data
k-Space Image
Complex Data in Image Domain
M = |R + iI|
P = tan�1(I/R)
fMRI Data Acquisition
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 10
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 scan
ner
magnetic field due to solenoid
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 11
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.
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 12
• 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
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 13
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.
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 14
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
dHbHb
deoxyhemoglobin (paramagnetic) oxyhemoglobin (non-magnetic)
• BOLD contrast measures inhomogeneities in magnetic field due to changes in the level of O2 in the blood.
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 15
Mechanism of BOLD fMRI
Time
BO
LD s
igna
l 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
BOLD
Sig
nal C
hang
e
Time (second)
0 5 10 15 20
• BOLD signal은 자극 이 제시되고 5~6초 후에 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
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 17
Block Designed fMRI
MR
I
Language Area Motor Area
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p
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
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 20
Summary of PreprocessInput Output
EPI1.niiEPI2.nii
…
aEPI1.niiaEPI2.nii
…
aEPI1.niiaEPI2.nii
…
meanaEPI.nii aEPI1.nii (realigned) aEPI2.nii (realigned)
rp_EPI.txt …
meanaEPI.nii anat.nii
meanaEPI.nii anat.nii (coregistered)
anat.nii aEPI1.niiaEPI2.nii
…
wanat.nii waEPI1.nii waEPI1.nii
…
waEPI1.niiwaEPI2.nii
…
Slice Timing
Realignment
CoregistrationT1 → 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 SPMGroup-wise GLM analysisone 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.niiswaEPI1.nii
…
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p
Realignment
21
...
motion parameters mean-fMRI
sagittal
coronal
axial
100 dynamic images
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p
Coregistration
22
Bef
ore
Cor
egA
fter C
oreg
• High Resolution T1 data is registered to mean-fMRI
• Rigid-body transformation only (translation & rotation)
T1 mean-‐fMRI
T1 mean-‐fMRI
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 23
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 preprocessingsteps in the temporal preprocessing
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 26
0 100 200 300 400 500 600 700 800720
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
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p
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 40065
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
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p
0 0.05 0.1 0.15 0.2 0.250
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)
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p
Functional Connectivity
29
0 50 100 150 200 250 300 350 400−30
−20
−10
0
10
20average time course within a node
computing the pair-wire correlation coefficients for functional connectivity
AAL atlas
weighted undirected
Adjacency Matrix (Aij)
Thresholding
Graph
Graph Theory and Matrix
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 31
Types of Graph
Binary Undirected
BinaryDirected
Weighted Directed
1
3
6
5
2
4
0 1 1 0 0 01 0 1 0 1 01 1 0 0 0 00 0 0 0 1 00 0 0 1 0 10 0 0 0 1 0
Aij =Matrix
k2 = 3k3 = 2k4 = 1
Degree
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 32
Graph Visualisation
degree strengthclustering coefficientnode betweenness centralitynode efficiency
edge strengthedge betweenness centrality
modular architecture
Network Properties
Node Properties
Edge Properties
Modular Structure
Network Visualisation
계산된 네트워크의 노드 속성값을가시화 과정에서 노드 크기로 표현함.
계산된 네트워크의 엣지 속성값을가시화 과정에서 엣지의 두께로 표편함.
계산된 네트워크의 모듈구조를가시화 과정에서 노드의 색깔로 표현함.
1
2
Morphometric Brain Network
Hippocampus
Posterior Hipp
time as taxi driver (month)
adju
sted
VBM
resp
onse
spo
ster
ior h
ippo
cam
pus
ante
rior h
ippo
cam
pal c
ross
-se
ctio
nal a
rea
(mm
2 )
Posterior Hipp
Anterior Hipp
Taxi drivers' brains 'grow' on the job
Maguire, E.A. (2000) PNAS
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 34
1. Tissue segmentation2. Create Template & Normalisation3. Modulation4. Smoothing5. Network Construction
The data are pre-processed to sensitise the statistical tests to *regional* tissue volumes
Analysis Steps
Voxel-based Morphometry (VBM)
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 35
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
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 36
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).
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 37
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
TemplateIB = 1 × [1 / 2] = 0.5
IB = 1 × [4 / 2] = 2
Modulation
ModulationNormalisation
Normalisation
IB = IA × [VA / VB]
Larger Brain
Smaller Brain
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 38
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”.
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 39
Age, VTIV
ROI index (i)
Subject index (j)
After Regression
MijMij is a GMV for a Subject i and ROI j
−1 −0.5 0 0.5 10
200
400
600
800
1000
1200
What’s the meaning of positive and negative associations in the morphometric network?
ROI Based MorphometryRegressors
Adjacency Matrix (Aij) Distribution of Correlation Values
Morphometric network is a part of structural network, and representing group level network.
Diffusion Tensor Imaging
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 41
6 directional encoding b=0
Tensor
FA
Construct Structural Network Fiber
Tracking:
DT-MRI
+
+
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 42
DTI & Tractography• Diffusion Tensor: at least 6 directional DWIs + non-DWI are required.
• Diagonalization using Singular Value Decomposition
D =
0
@D
xx
Dxy
Dzy
Dyx
Dyy
Dyz
Dzx
Dzy
Dzz
1
A
D = (e1 e2 e3)T
0
@�1 0 00 �2 00 0 �3
1
A (e1 e2 e3) =3X
k=1
�kekeTk
Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 43
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)2p
2p
�21 + �2
2 + �23