no code implementations • 28 Aug 2020 • Moo. K. Chung
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.
no code implementations • 19 Jul 2020 • Moo. K. Chung
Then from the central limit theorem, the weighted average should be more Gaussian.
no code implementations • 7 Nov 2019 • Shih-Gu Huang, Ilwoo Lyu, Anqi Qiu, Moo. K. Chung
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.
no code implementations • 30 Jun 2018 • Andrey Gritsenko, Martin A. Lindquist, Gregory R. Kirk, Moo. K. Chung
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.
no code implementations • 21 Oct 2017 • Moo. K. Chung, Yanli Wang, Gurong Wu
We present the discrete version of heat kernel smoothing on graph data structure.
no code implementations • 15 Sep 2015 • Moo. K. Chung, Victoria Vilalta-Gil, Paul J. Rathouz, Benjamin B. Lahey, David H. Zald
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.
no code implementations • CVPR 2015 • Won Hwa Kim, Barbara B. Bendlin, Moo. K. Chung, Sterling C. Johnson, Vikas Singh
Statistical analysis of longitudinal or cross sectionalbrain imaging data to identify effects of neurodegenerative diseases is a fundamental task in various studies in neuroscience.
no code implementations • 23 Sep 2014 • Moo. K. Chung, Anqi Qiu, Seongho Seo, Houri K. Vorperian
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.
no code implementations • 31 Aug 2014 • Moo. K. Chung, Jamie L. Hanson, Jieping Ye, Richard J. Davidson, Seth D. Pollak
Sparse systems are usually parameterized by a tuning parameter that determines the sparsity of the system.
no code implementations • CVPR 2014 • Hyunwoo J. Kim, Nagesh Adluru, Maxwell D. Collins, Moo. K. Chung, Barbara B. Bendlin, Sterling C. Johnson, Richard J. Davidson, Vikas Singh
Linear regression is a parametric model which is ubiquitous in scientific analysis.
no code implementations • 25 Sep 2013 • Jia Du, A. Pasha Hosseinbor, Moo. K. Chung, Barbara B. Bendlin, Gaurav Suryawanshi, Andrew L. Alexander, Anqi Qiu
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.
no code implementations • CVPR 2013 • Won Hwa Kim, Moo. K. Chung, Vikas Singh
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.
no code implementations • NeurIPS 2012 • Won H. Kim, Deepti Pachauri, Charles Hatt, Moo. K. Chung, Sterling Johnson, Vikas Singh
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.