An Introduction to Neuro-Imaging for Neuroscience (PhD Program) Dr M A Oghabian `

Transcription

1 An Introduction to Neuro-Imaging for Neuroscience (PhD Program) Dr M A Oghabian `

2 تصويربرداري بر مبناي نوع اطالعات 1( تصويربرداري اطالعات اناتوميك 2( تصويربرداري اطالعات فيزيولوزيك 3( تصويربرداري اطالعات مولكولي )تغييرات شيميائي( 4( تصويربرداري اطالعات عملكردي

3 Structural & Functional Imaging STRUCTURE CT MRI FUNCTION PET SPECT Nuclear Medicine is to physiology as Radiology is to anat

4 Spin-Echo Images TR = 500 ms TE = 20 ms TR = 2000 ms TE = 40 ms TR = 2000 ms TE = 80 ms

5 Contrasts: PD, T1, T2 and T2* weighting Which is best for brain morphometry/freesurfer? PD-weighting (proton/spin density) + T1-weighting (gray/white contrast) + T2-weighting (bright CSF/tumor) FLASH 5 MPRAGE T2-SPACE

6 Brain Structural Analysis 1- Cortical (surface-based) Analysis. 2- Volume Analysis. 3- Voxel Based morphometry 4- Fiber Tracking

7 Surface and Volume Analysis Cortical Reconstruction and Automatic Labeling Inflation Surface Flattening Automatic Subcortical Gray Matter Labeling Automatic Gyral White Matter Labeling

8 DTI (Diffusion Tensor Imaging)

9 DTI Fiber Tracking

10 DTI Fiber Tracking and fmri

11 Functional Neuro-Imaging Methods

12 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

13 Spatial and Temporal Resolution of Various functional imaging methods

14 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,

15 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 (K trans )

16 Pre-surgical risk evaluation Perfusion MRI (CBV/CBF/MTT/TTP) FMRI Presurgical Planning MA Oghabian: NeuroImaging and Analysis Lab- Tehran Unive of Medical Sci

17 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

18 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.

19 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

20 در تصویر طیف پروتون در TE= 30ms در حضور میدان مغناطیسی 7Tدر مطالعهاي بر روي مغز انسان در میدان 14 ترکیب متفاوت بر روي طیف قابل مشاهده بودهاند

21 Pre-surgical risk evaluation MRS (Cholin increase in tumour FMRI Presurgical Planning MA Oghabian: NeuroImaging and Analysis Lab- Tehran Unive of Medical Sci

22 fmri (BOLD imaging)

23 The Brain Before fmri (1957) Polyak, in Savoy, 2001, Acta Psychologica

24 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

25 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 O 2 which is carried by the blood; increased blood flow and resulting hemodynamics are foundation to fmri

26 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

27 Language task (WG) on left-handed patient with Temporoparietal mass FMRI Presurgical Planning MA Oghabian: NeuroImaging and Analysis Lab- Tehran Unive of Medical Sci

28 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

29 Necessary Equipment 4T magnet RF Coil gradient coil (inside) Magnet Gradient Coil RF Coil Source: Joe Gati, photos

30 fmri Setup

31 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 T 2 * Two types Exogenous: Externally applied, non-biological compounds (e.g., Gd-DTPA) Endogenous: Internally generated biological compound (e.g., deoxyhemoglobin, dhb)

32 BOLD contrast 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

33 Disadvantage of T2*: Susceptibility Artifacts T1-weighted image T2*-weighted image sinuses ear canals -In T2* images, artifacts occur near junctions between air (sinuses, ear canals) and tissue FMRI Week 6 BOLD fmri Scott Huettel, Duke University

34 Blood Deoxygenation affects T 2 * Decay Thulborn et al., 1982

35 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

36 Vasculature FMRI Week 6 BOLD fmri Source: Menon & Kim, TICS Scott Huettel, Duke University

37 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

38 BOLD signal Source: Doug Noll s primer FMRI Week 6 BOLD fmri Scott Huettel, Duke University

39 Stimulus to BOLD Source: Arthurs & Boniface, 2002, Trends in Neurosciences FMRI Week 6 BOLD fmri Scott Huettel, Duke University

40 Basic Form of Hemodynamic Response

41 BOLD Time Course for repeated stimulus

42 A Simple Experiment Lateral Occipital Complex responds when subject views objects Blank Screen Intact Objects 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) Scrambled Objects

43 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

44 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 PREDICTED ACTIVATION IN OBJECT AREA BOXCAR SHIFTED CONVOLVED WITH HRF

45 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 0, 0 Even where there s no brain, there s noise Slice 9, Voxel 1, 0 Slice 9, Voxel 22, 7 Slice 9, Voxel 9, 27 Here s a voxel that responds well whenever there s visual stimulation Slice 9, Voxel 18, 36 Here s a couple that sort of show the right pattern but is it real? Slice 9, Voxel 13, 41 Here s one that responds well whenever there s intact objects Slice 9, Voxel 14, 42

46 BOLD signal in each voxel M xy Signal M o sin S task S control T 2 * task T 2 * control S TE optimum time

47 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

48 Effect of Thresholds r =.80 64% of variance p < r =.50 25% of variance p < r =.40 16% of variance p < r =.24 6% of variance p <.05 r = 0 0% of variance p < 1

49 Does our stat test indicate that the region is active? No Yes Types of Errors Is the region truly active? Yes No HIT Type I Error p value: probability of a Type I error e.g., p <.05 Type II Error Correct Rejection There is less than a 5% probability that a voxel our stats have declared as active is in reality NOT active Slide modified from Duke course

50 Complications Not only is it hard to determine what s real, but there are all sorts of statistical problems What s wrong with these data? Potential problems: 1. data may be contaminated by artifacts (e.g., head motion, breathing artifacts) r =.24 6% of variance p < * 49,152 = 2457 significant voxels by chance alone 3. adjacent voxels uncorrelated with each other; adjacent time points uncorrelated with one another) are false

51 Source: Karl Friston

52 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:

53 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

54 Example of ICA hardest part is sorting the many components into meaningful ones vs. artifacts fingerprints may help

55 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

56 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

57 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

58 System requirement

59 1.5T 2% signal change 3T 4% signal change

60 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

61 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 3T) up to 50 slices TR ~ 2 to 3 s Parallel imaging techniques

62 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)

63 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

64 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

65 Thank You M A Oghabian FMRI Week 6 BOLD fmri Scott Huettel, Duke University