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Spatial Analysis in Neuroimaging: Techniques and Applications

Explore the intricacies of surface-based analysis, intersubject registration, and smoothing techniques in neuroimaging research. Learn about coordinate systems, 3D and 2D analyses, and the importance of models of the cortical surface. Understand the benefits of spatial smoothing and clustering for group analysis. Discover how surface-based registration enhances data alignment and the impact of smoothing on activation maps. Surface-based analysis offers valuable insights into brain organization and function.

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Spatial Analysis in Neuroimaging: Techniques and Applications

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  1. Surface-based Analysis:Intersubject Registration and Smoothing

  2. Outline • Exploratory Spatial Analysis • Coordinate Systems • 3D (Volumetric) • 2D (Surface-based) • Intersubject registration • Volume-based • Surface-based • Surface-based smoothing • Surface-based clustering

  3. Exploratory Spatial Analysis • Don’t know where effect is going to be • vs ROI analysis • Analyze each voxel separately • Create a map • Find clusters

  4. Aging Exploratory Analysis Cortical Thickness vs Aging Salat, et al, 2004, Cerebral Cortex

  5. Aging Thickness Study Positive Age Correlation Negative Age Correlation N=40 p<.01

  6. Individual Exploratory Analysis • fMRI Words-vs-Fixation • Single subject (eg, presurgical planning or functional ROI) • Outlines are FreeSurfer cortical ROIs • Yellow and blue blobs are functional activation • Activation does not lie cleanly within a predefined ROI

  7. Exploratory Spatial Analysis • Generally requires spatial smoothing of data to increase SNR • For group analysis, requires that subjects’ brains be aligned to each other on a voxelwise basis. • Neither needed for an ROI analysis • Smoothing and intersubject registration can be performed in the volume or surface.

  8. Why Is a Model of the Cortical Surface Useful? Local functional organization of cortex is largely 2-dimensional! Eg, functional mapping of primary visual areas: From (Sereno et al, 1995, Science).

  9. y x z Coordinate Systems: 3D (Volumetric) • 3D Coordinate System • XYZ • RAS (Right-Anterior-Superior) • CRS (Column-Row-Slice) • Origin (XYZ=0, eg, AC) • MR Intensity at each XYZ

  10. central anterior f posterior sylvian q superior temporal calcarine • Curvature • SULCUS (+) • GYRUS (-) pial inflated Coordinate Systems: 2D (Surface) Sheet: 2D Coordinate System (X,Y) • Sphere: 2D Coordinate System • Latitude and Longitude (q, f) • Continuous, no cuts • Value at each point (eg, thickness) y x

  11. Intersubject Registration

  12. Volumetric Intersubject Registration • Affine/Linear • Translate • Rotate • Stretch • Shear • (12 DOF) • Match Intensity, Voxel-by-Voxel • Problems • Can use nonlinear volumetric (cf CVS)

  13. Subject 1 Subject 2 Surface-based Intersubject Registration • Curvature “Intensity” • SULCUS (+) • GYRUS (-) • Codes folding pattern • Translate, Rotate, Stretch, Shear (12 DOF) • Match Curvature, Vertex-by-Vertex • Nonlinear Stretching (“Morphing”) allowed (area regularization) • Actually done on sphere • “Spherical Morph”

  14. A Surface-Based Coordinate System Common space for group analysis (like Talairach)

  15. fsaverage • Has “subject” folder like individual FS subjects • “Buckner 40” subjects • Default registration space • MNI305 coordinates • ?h.average.curvature.filled.buckner40.tif

  16. Surface-based Intersubject Registration • Gray Matter-to-Gray Matter (it’s all gray matter!) • Gyrus-to-Gyrus and Sulcus-to-Sulcus • Some minor folding patterns won’t line up • Fully automated, no landmarking needed • Atlas registration is probabilistic, most variable regions get less weight. • Done automatically in recon-all • fsaverage

  17. Spatial Smoothing • Why should you smooth? • Might Improve CNR/SNR • Improve intersubject registration • How much smoothing? • Blob-size • Typically 5-20 mm FWHM • Surface smoothing more forgiving than volume-based

  18. 14mm FWHM 7mm FWHM Volume-based Smoothing • Smoothing is averaging of “nearby” voxels

  19. Volume-based Smoothing 14mm FWHM • 5 mm apart in 3D • 25 mm apart on surface! • Kernel much larger • Averaging with other tissue types (WM, CSF) • Averaging with other functional areas

  20. Full Max 2mm FWHM Half Max 5mm FWHM 10mm FWHM Spatial Smoothing • Spatially convolve image with Gaussian kernel. • Kernel sums to 1 • Full-Width/Half-max:FWHM = s/sqrt(log(256)) • s = standard deviation of the Gaussian Full-Width/Half-max 0 FWHM 5 FWHM 10 FWHM

  21. Effect of Smoothing on Activation • Working memory paradigm • FWHM: 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20

  22. central anterior posterior sylvian superior temporal calcarine Surface-based Smoothing • Smoothing is averaging of nearby vertices Sheet: 2D Coordinate System (X,Y) Sphere: 2D Coordinate System (q,f)

  23. Group fMRI Analysis: Volume vs Surface Surface-based Registration and smoothing Affine registration to MNI305 with volume smoothing Probe-vs-Fixation. Data from Functional Biomedical Informatics Research Network (fBIRN)

  24. Left > Right 5HT4 BP Asymmetry Study (N=16) p<10-3 p<10-2 p<10-2 Right > Left p<10-3 Surface Smoothing Volume Smoothing

  25. Surface-based Clustering • A cluster is a group of connected (neighboring) vertices above threshold • Neighborhood is 2D, not 3D • Cluster has a size (area in mm2) • Reduced search space (corrections for multiple comparisons)

  26. Summary • Why Surface-based Analysis? • Function has surface-based organization • Inter-subject registration: anatomy, not intensity • Smoothing • Clustering • Like 3D, but 2D Use FreeSurfer Be Happy

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