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" * * Data Analysis Lab
J. Rosenberg / A. Wall / J. Maisog
Room 150
In this laboratory exercise, you will be visiting three workstations, each with a different demonstration:
Station 1: Motion Correction and Spatial Normalization
Station 2: Spatial and Temporal Filtering
Station 3: Statistical Analysis (t-test) and Overlay
One of us (Rosenberg, Wall, Maisog) will be at each workstation to guide you through the demonstration and field questions. You may not be visiting the stations in the above order. Whichever workstation you visit first, you should make sure that you understand the Section 1 Orthogonal Views below; you dont need to repeat this section when you visit your 2nd or 3rd workstation.
Here is a nice review article on fMRI data analysis:
Smith SM, Overview of fMRI analysis, Br J Radiol. 2004;77 Spec No 2:S167-75.
EMBED Visio.Drawing.6 Sample fMRI Data for this session. Implicit reading task ADDIN EN.CITE Price19962108670639611996Jan-FebDemonstrating the implicit processing of visually presented words and pseudowords62-70Wellcome Department of Cognitive Neurology, Institute of Neurology, London, UK.Price, C. J.Wise, R. J.Frackowiak, R. S.Cereb CortexBrain/*physiology/radionuclide imagingCerebrovascular Circulation/*physiologyHumansPhotic StimulationResearch Support, Non-U.S. Gov'tTomography, Emission-ComputedVisual Pathways/*physiologyhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8670639[1] as well as a simple finger-tapping paradigm. All fMRI data are transverse (axial), with the left of the subject on the right side of the image (radiologic convention).
Processing Pipeline for Single-Subject fMRI Data On the next page is a block diagram illustrating the way single-subject fMRI data is processed at the Center for the Study of Learning. There is no single correct order in which to process data, but for our purposes we have found that this pipeline works well for us. This pipeline corresponds to the section entitled First-level analysis in the paper by Smith. Note that the two inputs are a set of raw functional MRI scans (usually on the order of 60 to 100 scans), and a template image defining a standard brain size and shape, and that the two outputs are a spatially normalized statistical map and a spatially normalized average scan.
Station 1: Motion Correction and Spatial Normalization Rigid-body resampling of images to minimize within-subject artifacts due to head-motion; and non-rigid body resampling of images to warp them into a standard anatomic shape such as Talairach or Montreal Neurological Institute (MNI) space.
The Problem of Head Motion reasons for doing motion correction (research reasons for motion correction, insert them here).
Head Motion before Motion Correction, Demonstration (AFNI) demonstrate AFNI cine loop on Subject # 10043, Visit 1, IR1 before motion correction.
Correcting Head Motion (AIR) perform motion correction on sample data.
(Apparent) Head Motion after Motion Correction, Demonstration demonstrate AFNI cine loop on Subject # 10043, Visit 1, IR1 after motion correction.
Stimulus-correlated motion a special case of motion artifact is stimulus-correlated head motion ADDIN EN.CITE Bullmore1999909882089711999Methods for diagnosis and treatment of stimulus-correlated motion in generic brain activation studies using fMRI38-48Department of Biostatistics & Computing, Institute of Psychiatry, London, UK. e.bullmore@iop.bpmf.ac.ukBullmore, E. T.Brammer, M. J.Rabe-Hesketh, S.Curtis, V. A.Morris, R. G.Williams, S. C.Sharma, T.McGuire, P. K.Hum Brain MappAdultAnalysis of VarianceBrain/*physiology/*physiopathology*Brain MappingComparative StudyHead Movements/*physiologyHumansMagnetic Resonance Imaging/methodsMaleReference ValuesRegression AnalysisResearch Support, Non-U.S. Gov'tSchizophrenia/*physiopathologySchizophrenic Psychology*Speechhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9882089Field2000110110032692182000SepFalse cerebral activation on BOLD functional MR images: study of low-amplitude motion weakly correlated to stimulus1388-96Division of Radiologic Sciences, Wake Forest University School of Medicine, Winston-Salem, NC 27157-1022, USA.Field, A. S.Yen, Y. F.Burdette, J. H.Elster, A. D.AJNR Am J NeuroradiolArtifactsBrain/*physiologyFalse Positive Reactions*Magnetic Resonance ImagingMotionOxygen/*bloodPhantoms, Imaginghttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11003269Gavrilescu2004120147555932122004FebChanges in effective connectivity models in the presence of task-correlated motion: an fMRI study49-63Howard Florey Institute, University of Melbourne, Melbourne, Australia. maria@pcomm.hfi.unimelb.edu.auGavrilescu, M.Stuart, G. W.Waites, A.Jackson, G.Svalbe, I. D.Egan, G. F.Hum Brain MappBrain Mapping/methodsChi-Square DistributionHumansMagnetic Resonance Imaging/*methods*Models, NeurologicalMultivariate AnalysisNonlinear DynamicsPsychomotor Performance/*physiologyResearch Support, Non-U.S. Gov'thttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14755593[2-4]. What is the effect on activation maps? How might one address this problem?
Spatial Normalization Demonstration using an MPRAGE from Subject # 10043, Visit 1, IR1. Three methods: Nonlinear AIR Warp ADDIN EN.CITE Woods19983094487802211998Jan-FebAutomated image registration: II. Intersubject validation of linear and nonlinear models153-65Department of Neurology, UCLA School of Medicine, USA.Woods, R. P.Grafton, S. T.Watson, J. D.Sicotte, N. L.Mazziotta, J. C.J Comput Assist TomogrAdultAlgorithms*Brain MappingFemaleHumans*Image Processing, Computer-AssistedLateralityLinear Models*Magnetic Resonance ImagingMaleMiddle AgedNonlinear DynamicsReference ValuesReproducibility of ResultsResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, Non-P.H.S.Research Support, U.S. Gov't, P.H.S.*Tomography, Emission-Computedhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=9448780[5], SPM ADDIN EN.CITE Ashburner19996010408769741999Nonlinear spatial normalization using basis functions254-66Functional Imaging Laboratory, Wellcome Department of Cognitive Neurology, Institute of Neurology, London, United Kingdom. j.ashburner@fil.ion.ucl.ac.ukAshburner, J.Friston, K. J.Hum Brain MappAlgorithmsBayes TheoremBrain/anatomy & histology/physiologyBrain Mapping/*methodsHumansMagnetic Resonance Imaging/*methods*Models, Neurological*Nonlinear DynamicsResearch Support, Non-U.S. Gov'tStereotaxic TechniquesTomography, Emission-Computed/*methodshttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10408769[6], piecewise linear (MEDx, ADDIN EN.CITE Talairach198881Talairach, J.Tournoux, Pierre1988Co-planar stereotaxic atlas of the human brain : an approach to medical cerebral imagingStuttgart and New YorkThieme Medical Publishersvii, 1220865772932 (Thieme Medical Publishers)Brain anatomy & histology atlasesStereotaxic Techniques atlases.[7]). Here, we will demonstrate using a nonlinear warp in AIR.
Station 2: Temporal and Spatial Filtering Filtering in both time and 3D space. ADDIN EN.CITE Hansen200222012161720942002Jul-AugDigital image processing for clinicians, part II: filtering429-37Section of Cardiology, Temple University School of Medicine, Philadelphia, PA 19140, USA.Hansen, C. L.J Nucl CardiolGamma CamerasHumans*Image Enhancement*Signal Processing, Computer-Assistedhttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12161720[8]. What does filtering mean?
Low Frequency Artifacts why do high-pass temporal filtering ADDIN EN.CITE Woolrich2001160117070931462001DecTemporal autocorrelation in univariate linear modeling of FMRI data1370-86Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, Department of Engineering Science, University of Oxford, Oxford OX3 9DU, United Kingdom.Woolrich, M. W.Ripley, B. D.Brady, M.Smith, S. M.NeuroimageArtifactsBrain/anatomy & histology/*physiologyCalibrationFourier AnalysisHumans*Image Enhancement*Image Processing, Computer-Assisted*Linear Models*Magnetic Resonance ImagingResearch Support, Non-U.S. Gov'thttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11707093Tanabe2002150119062301542002AprComparison of detrending methods for optimal fMRI preprocessing902-7Department of Radiology, University of Colorado Health Sciences Center, Denver, Colorado 80262, USA.Tanabe, J.Miller, D.Tregellas, J.Freedman, R.Meyer, F. G.NeuroimageAdultArtifactsBrain MappingCerebral Cortex/*physiologyComparative StudyFemaleFourier AnalysisHumansImage Processing, Computer-Assisted/*methodsMagnetic Resonance Imaging/*methodsMalePursuit, Smooth/*physiologyResearch Support, Non-U.S. Gov'thttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11906230[9, 10]?
High-Pass Temporal Filtering Demonstration #1 Example on synthetic time-series data (from MEDx tutorial).
High-Pass Temporal Filtering Demonstration #2 Example on real fMRI data.
Three reasons for doing low-pass spatial filtering (smoothing).
Spatial Smoothing Demonstrations.
Station 3: Statistics and Overlays . The data for this Station is from a simple 64-scan finger-tapping fMRI experiment. Most fMRI analyses use some method derived from the general linear model ADDIN EN.CITE Friston1995190Friston, KJHolmes, APWorsley, KJPoline, J-PFrith, CDFrackowiak, RSJ1995Statistical parametric maps in functional imaging: A general linear approachHuman Brain Mapping24189-210Bandettini199320083667973021993AugProcessing strategies for time-course data sets in functional MRI of the human brain161-73Biophysics Research Institute, Medical College of Wisconsin, Milwaukee 53226.Bandettini, P. A.Jesmanowicz, A.Wong, E. C.Hyde, J. S.Magn Reson MedBrain/*anatomy & histologyHumansImage Processing, Computer-Assisted/*methodsMagnetic Resonance Imaging/*methodsResearch Support, Non-U.S. Gov'tResearch Support, U.S. Gov't, P.H.S.http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=8366797[11, 12]; in this demonstration, well use simple t-tests (both single- as well as two-sample t-tests). Subjects alternated between a rest condition and a finger tapping condition in a classic block-design experiment.
Within-Subject Statistical Analysis: t-test First, we demonstrate the analysis of fMRI data within-subject. This will generate, among other things, a statistical Z map for this subject, as well as a contrast map for this subject. The contrast maps from 46 subjects will be used for the random effects group analysis in (b) below.
In MEDx, load the MEDx folder /export/w/Methods_Core/Methods-Tutorials/NCSI-525/Station3-StatisticsAndOverlays/SingleSubjectStatistics.f. This is data that has already been pre-processed using the techniques demonstrated in Stations 1 and 2.
This folder has two groups of scans, one for each condition. Select Page ( Page Manager, and go to the Group named Fix. These are the 32 scans that were acquired during the fixation crosshair (rest) condition of the finger-tapping experiment. On the other hand, the 32 scans in the Group named Tap were acquired during the finger-tapping task.
Select Toolbox ( Functional ( Group Statistics ( Between Groups. Select the Tap group as the Test Group, and the Fix group as the Control Group. Set the other options to Parametric and Unpaired t-test (the defaults). Then click on the OK button. This will perform the two-sample t-test, contrasting the Tap scans against the Fix scans.
Inspect the image named Tap vs Fix Mean Diff. This is the difference between the average of the Tap scans and the average of the Fix scans. This contrast map or mean difference image would be used as input in a random effects group analysis (analysis across subjects).
Inspect the image named Tap vs Fix Unpaired T Test ZScore. This is the statistical map showing areas of the brain associated by finger tapping. Note the strong area of activation on the left side of the brain, which is most probably motor cortex. There is also activation in the posterior occipital pole, due to the flashing circle that is present during the finger tapping blocks, but this is less strong than the activation in motor cortex.
Group Statistical Analysis: Random Effects Analysis Currently, the gold standard analysis for a group analysis (i.e., a statistical analysis across subjects) is a two-level random effects analysis ADDIN EN.CITE Friston1999230104938971041999OctMultisubject fMRI studies and conjunction analyses385-96The Wellcome Department of Cognitive Neurology, Institute of Neurology, Queen Square, London, WC1N 3BG, United Kingdom.Friston, K. J.Holmes, A. P.Price, C. J.Buchel, C.Worsley, K. J.NeuroimageAttentionBrain/anatomy & histology/*physiologyBrain Mapping/*methodsFixation, OcularHumansMagnetic Resonance Imaging/*methodsModels, StatisticalMotion Perception/physiologyProbabilityReproducibility of ResultsResearch Support, Non-U.S. Gov'thttp://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=10493897[13]. In the first level, the fMRI scans are processed within-subject, generating a contrast map for each subject. For example, this would be the mean difference map we looked at in (5,a,iv) above. In the second level, the contrast maps are passed on to a statistical test such as a t-test or ANOVA.
In MEDx, load the folder /export/w/Methods_Core/Methods-Tutorials/NCSI-525/Station3-StatisticsAndOverlays/GroupStatistics.f. These are mean difference images contrasting the Tap condition against the Fix condition, as was demonstrated on one subject in (5,a) above.
Select Toolbox ( Functional ( Group Statistics. This will pop up a window named Group Statistics. Select the tab labeled Within Group.
For the Group, select the MEDx page named Contrast Maps. This is a group of mean difference images for the contrast Tap minus Fix, from 46 subjects.
For Operation, select Single Group t-test. Leave Compare Mean To set to the default of 0. Click on the OK button. This performs the single-group t-test, and generates a Results group.
As with the within-subject t-test we performed in (a), this generates, among other things, a t-test map as well as a Z map. The t-test map is the first one in the Results group. Note that it indicates that the t-test has 45 degrees of freedom (DOF=45).
Inspect the Z map image; this is the image that has ZScore in its name. Note that it shows activation in left motor cortex as well as in the occipital lobe. A superior midline activation is likely SMA. There is a hint of right-sided cerebellar activation.
Save this Z map to disk as Analyze (AVW) format in the directory /export/w/Methods_Core/Methods-Tutorials/NCSI-525/Station3-StatisticsAndOverlays, as a file named Z.hdr. This will create both Z.hdr and Z.img files.
Overlays (Image Fusion) Well show voxels that were greater than 3.821 (activations) in yellow and red, and voxels that were less than -3.821 (deactivations) in blue and green (the +/- 3.821 threshold was computed by selecting a False Discovery Rate ADDIN EN.CITE Genovese2002240119062271542002AprThresholding of statistical maps in functional neuroimaging using the false discovery rate870-8Department of Statistics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA.Genovese, C. R.Lazar, N. A.Nichols, T.NeuroimageAdultArtifactsBrain Mapping/*methodsCerebral Cortex/*physiologyComputer SimulationFemaleHumans*Image Processing, Computer-AssistedMagnetic Resonance Imaging/*methodsMale*Mathematical ComputingMotor Activity/physiologyReference ValuesResearch Support, U.S. Gov't, Non-P.H.S.http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=11906227[14] of 0.05).
cd into the directory /export/w/Methods_Core/Methods-Tutorials/NCSI-525/Station3-StatisticsAndOverlays.
Type the command
ThresholdOverlay.csh mask.img Z.img -3.821 3.821
then hit the Enter or Return key (carriage return). This will overlay the group t-test image that we created onto a stripped structural MRI scan that is in MNI space, generating an Analyze format file named Overlay.Z._-3.821_3.821.hdr, along with an associated .img file. (The file mask.img is merely an image defining brain versus non-brain, pre-generated for this tutorial.) Now well load this into VolView for a 3D volume rendering.
We will use the program VolView (Kitware, Albany, NY) for 3D rendering. This program is installed on the machine named app1. So, on the machine app1, type the command volview, then hit the Enter or Return key (carriage return). Load the .hdr file we generated in the previous step.
The data is displayed with a default color and opacity look-up table (LUT). For a somewhat nicer display, load the following alternative LUT, which was custom-built for our data: /export/w/apps/tcl/DeactAndActOverlays.vvt.
Use the left-mouse button to rotate the display, the middle-mouse button to pan, and the right-mouse button to zoom/dezoom. Note that there is a hint of deactivation in the right motor cortex, opposite the activation in the left motor cortex. Try some of the other options, especially cropping.
ADDIN EN.REFLIST References.
1. Price CJ, Wise RJ, Frackowiak RS. Demonstrating the implicit processing of visually presented words and pseudowords. Cereb Cortex 1996; 6: 62-70.
2. Bullmore ET, Brammer MJ, Rabe-Hesketh S, Curtis VA, Morris RG, Williams SC, Sharma T, McGuire PK. Methods for diagnosis and treatment of stimulus-correlated motion in generic brain activation studies using fMRI. Hum Brain Mapp 1999; 7: 38-48.
3. Field AS, Yen YF, Burdette JH, Elster AD. False cerebral activation on BOLD functional MR images: study of low-amplitude motion weakly correlated to stimulus. AJNR Am J Neuroradiol 2000; 21: 1388-1396.
4. Gavrilescu M, Stuart GW, Waites A, Jackson G, Svalbe ID, Egan GF. Changes in effective connectivity models in the presence of task-correlated motion: an fMRI study. Hum Brain Mapp 2004; 21: 49-63.
5. Woods RP, Grafton ST, Watson JD, Sicotte NL, Mazziotta JC. Automated image registration: II. Intersubject validation of linear and nonlinear models. J Comput Assist Tomogr 1998; 22: 153-165.
6. Ashburner J, Friston KJ. Nonlinear spatial normalization using basis functions. Hum Brain Mapp 1999; 7: 254-266.
7. Talairach J, Tournoux P. Co-planar stereotaxic atlas of the human brain : an approach to medical cerebral imaging. Stuttgart and New York: Thieme Medical Publishers; 1988.
8. Hansen CL. Digital image processing for clinicians, part II: filtering. J Nucl Cardiol 2002; 9: 429-437.
9. Woolrich MW, Ripley BD, Brady M, Smith SM. Temporal autocorrelation in univariate linear modeling of FMRI data. Neuroimage 2001; 14: 1370-1386.
10. Tanabe J, Miller D, Tregellas J, Freedman R, Meyer FG. Comparison of detrending methods for optimal fMRI preprocessing. Neuroimage 2002; 15: 902-907.
11. Friston K, Holmes A, Worsley K, Poline J-P, Frith C, Frackowiak R. Statistical parametric maps in functional imaging: A general linear approach. Human Brain Mapping 1995; 2: 189-210.
12. Bandettini PA, Jesmanowicz A, Wong EC, Hyde JS. Processing strategies for time-course data sets in functional MRI of the human brain. Magn Reson Med 1993; 30: 161-173.
13. Friston KJ, Holmes AP, Price CJ, Buchel C, Worsley KJ. Multisubject fMRI studies and conjunction analyses. Neuroimage 1999; 10: 385-396.
14. Genovese CR, Lazar NA, Nichols T. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 2002; 15: 870-878.
NCSI 525
March 23, 2006
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