Computational neuroanatomy the methods /
Computational neuroanatomy is an emerging field that utilizes various non-invasive brain imaging modalities, such as MRI and DTI, in quantifying the spatiotemporal dynamics of the human brain structures in both normal and clinical populations. This discip.
| Основен автор: | Chung, Moo K. |
|---|---|
| Формат: | Електронен |
| Език: | English |
| Публикувано: |
Singapore ; Hackensack, NJ :
World Scientific Pub. Co.,
℗♭2013.
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| Предмети: | |
| Онлайн достъп: |
http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=533854 |
| Подобни документи: |
Print version::
Computational neuroanatomy. |
Съдържание:
- Preface; Contents; 1. Statistical Preliminary; 1.1 General Linear Models; 1.2 Random Fields; 1.2.1 Covariance Functions; 1.2.2 Gaussian Random Fields; 1.2.3 Differentiation and Integration of Fields; 1.2.4 Statistical Inference on Fields; 1.3 Multiple Comparisons; 1.3.1 Bonferroni Correction; 1.3.2 Random Fields Theory; 1.3.3 Poisson Clumping Heuristic; 1.3.4 Euler Characteristic Method; 1.3.5 Intrinsic Volume; 1.3.6 Euler Characteristic Density; 1.4 Statistical Power Analysis; 1.4.1 Statistical Power at a Voxel; 1.4.2 Statistical Power under Multiple Comparisons.
- 2. Deformation-Based Morphometry2.1 Image Registration; 2.2 Deformation-Based Morphometry; 2.3 Displacement Vector Fields; 2.3.1 Dynamic Model on Displacement; 2.3.2 Local Inference via Hotelling's T2-Field; 2.3.3 Detecting Local Brain Growth; 2.4 Global Inference via Integral Statistic; 2.4.1 Karhunen-Lo eve Expansion; 2.4.2 Mercer's Theorem; 2.4.3 Integral Statistic on Displacement; 3. Tensor-Based Morphometry; 3.1 Jacobian Determinant; 3.2 Distributional Assumptions; 3.3 Local Volume Changes; 3.4 Longitudinal Modeling; 3.4.1 Normal Brain Development in Children.
- 3.5 Global Inference via Divergence Theorem3.6 Second Order Tensor Fields; 3.6.1 Membrane Spline Energy; 3.6.2 Vorticity Tensor Fields; 3.6.3 Generalized Variance Field; 4. Voxel-Based Morphometry; 4.1 Image Segmentation; 4.1.1 Mumford-Shah Model; 4.1.2 Level Sets; 4.1.3 Active Contours; 4.1.4 Deformable Surface Models; 4.1.5 Thin-Plate Spline Thresholding; 4.2 Mixture Models; 4.2.1 Bayesian Segmentation; 4.2.2 Mixture Models; 4.2.3 Expectation Maximization Algorithm; 4.2.4 Two Components Gaussian Mixtures; 4.3 Voxel-Based Morphometry; 4.3.1 ROI Volume Estimation in VBM.
- 4.3.2 Limitations of Witelson Partition4.3.3 General Linear Models on Tissue Densities; 4.3.4 2D VBM Applied to Corpus Callosum; 5. Geometry of Cortical Manifolds; 5.1 Surface Parameterization; 5.1.1 B-Spline Parameterization; 5.1.2 B-Spline Curves; 5.1.3 Quadratic Parameterization; 5.1.4 Fourier Descriptors; 5.2 Surface Normals and Curvatures; 5.2.1 Surface Normals; 5.2.2 Gaussian and Mean Curvatures; 5.2.3 Curvatures of Polynomial Surfaces; 5.3 Laplace-Beltrami Operator; 5.3.1 Eigenfunctions of Laplace-Beltrami Operator; 5.3.2 Multiplicity of Eigenfunctions.
- 5.3.3 Laplace-Beltrami Shape Descriptors5.3.4 Second Eigenfunctions; 5.3.5 Dirichlet Energy; 5.3.6 Fiedler's Vector; 5.4 Finite Element Methods; 5.4.1 Pieacewise Linear Functions; 5.4.2 Mass and Stiffness Matrices; 6. Smoothing on Cortical Manifolds; 6.1 Gaussian Kernel Smoothing; 6.1.1 Isotropic Gaussian Kernel; 6.1.2 Anisotropic Gaussian Kernel; 6.2 Diffusion Smoothing; 6.2.1 Diffusion in Euclidean Space; 6.2.2 Diffusion in 1D; 6.2.3 Diffusion on Triangular Mesh; 6.2.4 Finite Difference Scheme; 6.3 Heat Kernel Smoothing; 6.3.1 Heat Kernel; 6.3.2 Heat Kernel Smoothing.