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Found 13 papers, 0 papers with code
Introduction to logistic regression
For random field theory based multiple comparison corrections In brain imaging, it is often necessary to compute the distribution of the supremum of a random field.
Gaussian kernel smoothing
Then from the central limit theorem, the weighted average should be more Gaussian.
Fast Polynomial Approximation of Heat Kernel Convolution on Manifolds and Its Application to Brain Sulcal and Gyral Graph Pattern Analysis
We also derive the closed-form expression of the spectral decomposition of the Laplace-Beltrami operator and use it to solve heat diffusion on a manifold for the first time.
Automatic Identification of Twin Zygosity in Resting-State Functional MRI
A key strength of twin studies arises from the fact that there are two types of twins, monozygotic and dizygotic, that share differing amounts of genetic information.
Heat Kernel Smoothing in Irregular Image Domains
We present the discrete version of heat kernel smoothing on graph data structure.
Mapping Heritability of Large-Scale Brain Networks with a Billion Connections {\em via} Persistent Homology
In many human brain network studies, we do not have sufficient number (n) of images relative to the number (p) of voxels due to the prohibitively expensive cost of scanning enough subjects.
Statistical Inference Models for Image Datasets With Systematic Variations
Statistical analysis of longitudinal or cross sectionalbrain imaging data to identify effects of neurodegenerative diseases is a fundamental task in various studies in neuroscience.
Unified Heat Kernel Regression for Diffusion, Kernel Smoothing and Wavelets on Manifolds and Its Application to Mandible Growth Modeling in CT Images
Starting with the heat kernel constructed from the eigenfunctions, we formulate a new bivariate kernel regression framework as a weighted eigenfunction expansion with the heat kernel as the weights.
Persistent Homology in Sparse Regression and Its Application to Brain Morphometry
Sparse systems are usually parameterized by a tuning parameter that determines the sparsity of the system.
Multivariate General Linear Models (MGLM) on Riemannian Manifolds with Applications to Statistical Analysis of Diffusion Weighted Images
Linear regression is a parametric model which is ubiquitous in scientific analysis.
Diffeomorphic Metric Mapping and Probabilistic Atlas Generation of Hybrid Diffusion Imaging based on BFOR Signal Basis
In this work, we show that the reorientation of the $q$-space signal due to spatial transformation can be easily defined on the BFOR signal basis.
Multi-resolution Shape Analysis via Non-Euclidean Wavelets: Applications to Mesh Segmentation and Surface Alignment Problems
In this paper, we adapt recent results in harmonic analysis, to derive NonEuclidean Wavelets based algorithms for a range of shape analysis problems in vision and medical imaging.
Wavelet based multi-scale shape features on arbitrary surfaces for cortical thickness discrimination
In contrast to hypothesis tests on point-wise measurements, in this paper, we make the case for performing statistical analysis on multi-scale shape descriptors that characterize the local topological context of the signal around each surface vertex.
