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![Sunghyon Kyeong (Yonsei Univ) Introduction to Neuroimaging: Methods and Preprocessing steps p 37
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Signal intensity ensures that total amount of GM in a subject’s temporal lobe is the
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IB = 1 × [1 / 2] = 0.5
IB = 1 × [4 / 2] = 2
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ModulationNormalisation
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IB = IA × [VA / VB]
Larger Brain
Smaller Brain](https://image.slidesharecdn.com/tutorialfmri0neuroimagingmethods-150510110847-lva1-app6892/75/Introduction-to-Neuroimaging-37-2048.jpg)
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Introduction to Neuroimaging
The document provides an introduction to neuroimaging techniques, specifically focusing on PET, fMRI, VBM, and DTI methods. It outlines the principles of signal generation, preprocessing steps, and the detection mechanisms for different imaging modalities, including the effects of blood oxygen levels on fMRI signals. Additionally, it covers the processes of spatial and temporal preprocessing, functional connectivity, and the construction of morphometric brain networks.
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Introduction to Neuroimaging
- 1. Sunghyon Kyeong sunghyon.kyeong@gmail.com Institute ofBehavioural Science in Medicine, Yonsei University College of Medicine Introduction to Neuroimaging -PET, fMRI, VBM, and DTI-
- 2. Sunghyon Kyeong (YonseiUniv) 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. Positron Emission Tomography: Twophoto detector
- 4. Sunghyon Kyeong (YonseiUniv) 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. Sunghyon Kyeong (YonseiUniv) Introduction to Neuroimaging: Methods and Preprocessing steps p 5 Coincidence Detection
- 6. Sunghyon Kyeong (YonseiUniv) 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. Sunghyon Kyeong (YonseiUniv) 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. Blood Oxygen LevelDependent Signal for functional MRI
- 9. Sunghyon Kyeong (YonseiUniv) 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. Sunghyon Kyeong (YonseiUniv) 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. Sunghyon Kyeong (YonseiUniv) 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. Sunghyon Kyeong (YonseiUniv) 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. Sunghyon Kyeong (YonseiUniv) 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. Sunghyon Kyeong (YonseiUniv) 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. Sunghyon Kyeong (YonseiUniv) 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. Sunghyon Kyeong (YonseiUniv) 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. Sunghyon Kyeong (YonseiUniv) Introduction to Neuroimaging: Methods and Preprocessing steps p 17 Block Designed fMRI MRI Language Area Motor Area
- 18. Sunghyon Kyeong (YonseiUniv) 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. steps in thespatial preprocessing fMRI preprocessing
- 20. Sunghyon Kyeong (YonseiUniv) 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. Sunghyon Kyeong (YonseiUniv) Introduction to Neuroimaging: Methods and Preprocessing steps p Realignment 21 ... motion parameters mean-fMRI sagittal coronal axial 100 dynamic images
- 22. Sunghyon Kyeong (YonseiUniv) 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. Sunghyon Kyeong (YonseiUniv) 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. Resting State FunctionalConnectivity Michael D. Fox (2005) PNAS Seed-ROI based connectivity analysis Graph theoretical analysis
- 25. fMRI preprocessing steps inthe temporal preprocessing
- 26. Sunghyon Kyeong (YonseiUniv) 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. Sunghyon Kyeong (YonseiUniv) 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. Sunghyon Kyeong (YonseiUniv) 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. Sunghyon Kyeong (YonseiUniv) 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. Graph Theory andMatrix
- 31. Sunghyon Kyeong (YonseiUniv) 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. Sunghyon Kyeong (YonseiUniv) 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. Morphometric Brain Network Hippocampus Posterior Hipp timeas 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. Sunghyon Kyeong (YonseiUniv) 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. Sunghyon Kyeong (YonseiUniv) 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. Sunghyon Kyeong (YonseiUniv) 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. Sunghyon Kyeong (YonseiUniv) 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. Sunghyon Kyeong (YonseiUniv) 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. Sunghyon Kyeong (YonseiUniv) 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.
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- 41. Sunghyon Kyeong (YonseiUniv) Introduction to Neuroimaging: Methods and Preprocessing steps p 41 6 directional encoding b=0 Tensor FA Construct Structural Network Fiber Tracking: DT-MRI + +
- 42. Sunghyon Kyeong (YonseiUniv) 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. Sunghyon Kyeong (YonseiUniv) 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