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Introduction to Neuroimaging

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This slide includes various neuroimaging methods. Firstly, brief backgrounds of positron emission tomography (PET), diffusion tensor MRI, voxel-based morphometry will be introduced. Secondly, a theoretical explanation of BOLD fMRI and preprocessing will be introduced.

http://skyeong.net

Introduction to Neuroimaging

  1. 1. 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-
  2. 2. 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
  3. 3. Positron Emission Tomography:
 Two photo detector
  4. 4. Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 4 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.
  5. 5. Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 5 Coincidence Detection
  6. 6. 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
  7. 7. 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
  8. 8. Blood Oxygen Level Dependent Signal for 
 functional MRI
  9. 9. 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
  10. 10. 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  scanner magnetic field due to solenoid
  11. 11. 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.
  12. 12. 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
  13. 13. 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.

  14. 14. 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 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.
  15. 15. Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 15 Mechanism of BOLD fMRI Time BOLDsignal T2* task T2* control TEoptimal ΔS ↑ Neural Activity ↑ Blood Flow ↑ Oxyhemoglobin ↑ T2* ↑ MR Signal
  16. 16. 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
  17. 17. Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 17 Block Designed fMRI MRI Language Area Motor Area
  18. 18. 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
  19. 19. steps in the spatial preprocessing fMRI preprocessing
  20. 20. Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 20 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 …
  21. 21. Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p Realignment 21 ... motion parameters mean-fMRI sagittal coronal axial 100 dynamic images
  22. 22. Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p Coregistration 22 BeforeCoregAfterCoreg • High Resolution T1 data is registered to mean-fMRI • Rigid-body transformation only
 (translation & rotation) T1 mean-­‐fMRI T1 mean-­‐fMRI
  23. 23. 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
  24. 24. Resting State 
 Functional Connectivity Michael  D.  Fox  (2005)  PNAS     Seed-ROI based connectivity analysis Graph theoretical analysis
  25. 25. fMRI preprocessing steps in the temporal preprocessing
  26. 26. Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 26 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
  27. 27. 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 50 100 150 200 250 300 350 400 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
  28. 28. Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 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)  
  29. 29. 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 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
  30. 30. Graph Theory and Matrix
  31. 31. Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 31 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
  32. 32. Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 32 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
  33. 33. 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
  34. 34. Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 34 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)
  35. 35. 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
  36. 36. 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).
  37. 37. 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 Template IB = 1 × [1 / 2] = 0.5 IB = 1 × [4 / 2] = 2 Modulation ModulationNormalisation Normalisation IB = IA × [VA / VB] Larger Brain Smaller Brain
  38. 38. 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”.
  39. 39. Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 39 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.
  40. 40. Diffusion Tensor Imaging
  41. 41. 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 + +
  42. 42. 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 @ 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
  43. 43. 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)2 p 2 p 2 1 + 2 2 + 2 3

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