Functional Analysis of Diffusion Tensor Releases

psc 1.0

aaaaabbbbb1355@stage.nitrcce.org (Tengfei Li) — Mon, 23 Apr 2018 21:51:00 GMT

Add code of Mapping Population-based Structural Connectomes (PSC)

Our developed PSC framework is a workflow which can simultaneously characterize a large number of white matter bundles within and across different subjects for group analysis. Given the DWI and T1 images it has three major components: (i) reliable construction of the structural connectome for the whole brain, including a robust tractography algorithm and streamline post-processing techniques, such as dilation of gray matter regions, streamline cutting, and outlier streamline removal are applied to improve the robustness of the extracted structural connectomes; (ii) low-dimensional representation of streamlines in each connection, including a shape analysis framework to separate the variation of streamlines in each cell of the streamlines, and an encoding and decoding procedure to efﬁciently compress the streamlines; (iii) multi-level connectome analysis, including the groupwise connectome analysis at three different levels, the streamline level, the weighted network level; and the binary network level.

Reference
1. Zhengwu Zhang, Maxime Descoteaux, Jingwen Zhang, Gabriel Girard, Maxime Chamberland, David Dunson, Anuj Srivastava, and Hongtu Zhu. "Mapping population-based structural connectomes." NeuroImage 172 (2018): 130-145.

fadtts SIVC1.0

aaaaabbbbb1355@stage.nitrcce.org (Tengfei Li) — Mon, 09 May 2016 2:11:00 GMT

Notes:
Add code of Single-index varying coefficient model for functional responses

Regression using imaging responses and some clinical vector covariate is an important issue in the brain imaging research, and has drawn a lot of attention in biostatistics and neuroscience nowadays. One great challenge usually comes from the smoothness of the function structure, or that for those voxels close to each other, they have strong correlations. Motivated by the analysis of a real-diffusion weighted imaging analysis from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, this paper here propose a nonparametric Single-Index Varying Coefficient model and here establish this Matlab GUI software. The aim of this software is to implement a functional analysis
pipeline, for the joint analysis of functional data and clinical data, for example age, gender and disease status. The model consists of a nonparametric structure called functional single index to characterize the association of functional response as well as a complex spatial-temporal correlation structure to characterize the proximity and spatially smooth varying coefficient functions. The software provides
estimation of regression coefficients, nonparametric structure as well as the spatial-temporal correlation structure. Furthermore, it can output a simultaneous confidence band for estimations, and make predictions on any given test set of design covariate matrix.

Reference
1. Xinchao Luo, Lixing Zhu, Hongtu Zhu. "Single-index varying coefficient model for functional responses", Annals of Applied Statistics, Biometrics (2016). Online ISSN: 1541-0420.

fadtts FADTTS, LPR, VCDTI, and FMPM V5.01

linglong_unc@stage.nitrcce.org (Linglong Kong) — Thu, 21 May 2015 20:57:00 GMT

Fixed a bug to calculate and display p-values for FADTTS

fadtts FADTTS, LPR, VCDTI, and FMPM release

linglong_unc@stage.nitrcce.org (Linglong Kong) — Mon, 13 Oct 2014 22:20:00 GMT

FMPM represents Functional Mixed Processes Models. The aim of this tool is to implement a functional analysis pipeline, for the joint analysis of longitudinally measured functional data and clinical data, for example age, gender and disease status. FMPM consists of a functional mixed effects model for characterizing the association of functional response with covariates of interest by incorporating complex spatial–temporal correlation structure, an efficient method for spatially smoothing varying coefficient functions, an estimation method for estimating the spatial– temporal correlation structure, a test procedure with local and global test statistics for testing hypotheses of interest associated with functional response, and a simultaneous confidence band for quantifying the uncertainty in the estimated coefficient functions.

Yuan Y, Gilmore JH, Geng X, Martin S, Chen K, Wang JL, Zhu H. FMEM: functional mixed effects modeling for the analysis of longitudinal white matter Tract data. Neuroimage 84(2014):753-64, 2013.

fadtts FADTTS, Lprspdm, and VCDTI for spdm

linglong_unc@stage.nitrcce.org (Linglong Kong) — Thu, 24 May 2012 4:07:00 GMT

[color=#333333; font-family: arial, helvetica, sans-serif; line-height: 16px; font-size: medium]Add code of Varying Coefficient Model For Modeling Diffusion Tensors Along White Matter Tracts

Diffusion tensor imaging provides important information on tis-sue structure and orientation of fiber tracts in brain white matter in vivo. It results in diffusion tensors, which are 3*3 symmetric positive definite (SPD) matrices, along fiber bundles. This paper develops a functional data analysis framework to model diffusion tensors along fiber tracts as functional data in a Riemannian manifold with a set of covariates of interest, such as age and gender. We propose a statistical model with varying coefficient functions to characterize the dynamic association between functional SPD matrix-valued responses and covariates. We calculate weighted least squares estimators of the varying coefficient functions for the Log-Euclidean metric in the space of SPD matrices. We also develop a global test statistic to test specific hypotheses about these coefficient functions and construct their simultaneous confidence bands. Simulated data are further used to examine the finite sample performance of the estimated varying coefficient functions. We apply our model to study potential gender differences and find a statistically significant aspect of the development of diffusion tensors along the right internal capsule tract in a clinical study of neurodevelopment.

Reference
1. Ying Yuan, Hongtu Zhu, Martin Styner, John H. Gilmore and J. S. Marron. "Varying Coefficient Model For Modeling Diffusion Tensors Along White Matter Tracts", Annals of Applied Statistics, Accepted.[/color][color=#333333; font-family: arial, helvetica, sans-serif; line-height: 16px; font-size: medium]
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fadtts FADTTS, Lprspdm, and FADTTS for spdm

linglong_unc@stage.nitrcce.org (Linglong Kong) — Thu, 24 May 2012 3:26:00 GMT

Add code of Varying Coefficient Model For Modeling Diffusion Tensors Along White Matter Tracts

Diffusion tensor imaging provides important information on tis-sue structure and orientation of fiber tracts in brain white matter in vivo. It results in diffusion tensors, which are 3*3 symmetric positive definite (SPD) matrices, along fiber bundles. This paper develops a functional data analysis framework to model diffusion tensors along fiber tracts as functional data in a Riemannian manifold with a set of covariates of interest, such as age and gender. We propose a statistical model with varying coefficient functions to characterize the dynamic association between functional SPD matrix-valued responses and covariates. We calculate weighted least squares estimators of the varying coefficient functions for the Log-Euclidean metric in the space of SPD matrices. We also develop a global test statistic to test specific hypotheses about these coefficient functions and construct their simultaneous confidence bands. Simulated data are further used to examine the finite sample performance of the estimated varying coefficient functions. We apply our model to study potential gender differences and find a statistically significant aspect of the development of diffusion tensors along the right internal capsule tract in a clinical study of neurodevelopment.

Reference
1. Ying Yuan, Hongtu Zhu, Martin Styner, John H. Gilmore and J. S. Marron. "Varying Coefficient Model For Modeling Diffusion Tensors Along White Matter Tracts", Annals of Applied Statistics, under revision.

fadtts fadtts and lprspdm V1.14

linglong_unc@stage.nitrcce.org (Linglong Kong) — Mon, 13 Feb 2012 19:52:00 GMT

Add data for Lprspdm package

fadtts fadtts and lprspdm V1.13

linglong_unc@stage.nitrcce.org (Linglong Kong) — Thu, 26 Jan 2012 17:54:00 GMT

fadtts fadtts and lprspdm V1.12

linglong_unc@stage.nitrcce.org (Linglong Kong) — Thu, 26 Jan 2012 5:34:00 GMT

add lprspdm package, see Ying Yuan, Hongtu Zhu, Weili Lin, J. S. Marron (2011). Local Polynomial Regression for Symmetric Positive Definite Matrices. JRSSB


Local polynomial regression has received extensive attention for the nonparametric estimation of regression functions when both the response and the covariate are in Euclidean space. However, little has been done when the response is in a Riemannian manifold. We develop an intrinsic local polynomial regression estimate for the analysis of symmetric
positive definite (SPD) matrices as responses that lie in a Riemannian manifold with covariate in Euclidean space. The primary motivation and application of the proposed methodology is in computer vision and medical imaging. We examine two commonly used metrics, including the trace metric and the Log- Euclidean metric on the space of SPD matrices.
For each metric, we develop a cross-validation bandwidth selection
method, derive the asymptotic bias, variance, and normality of the
intrinsic local constant and local linear estimators, and compare their
asymptotic mean square errors

fadtts fadtts and lprspdm V1.11 release

linglong_unc@stage.nitrcce.org (Linglong Kong) — Wed, 25 Jan 2012 15:44:00 GMT

add lprspdm package, see Ying Yuan, Hongtu Zhu, Weili Lin, J. S. Marron (2011). Local Polynomial Regression for Symmetric Positive
Definite Matrices. JRSSB


Local polynomial regression has received extensive attention for the
nonparametric estimation of regression functions when both the response
and the covariate are in Euclidean space. However, little has been done
when the response is in a Riemannian manifold. We develop an intrinsic
local polynomial regression estimate for the analysis of symmetric
positive definite (SPD) matrices as responses that lie in a Riemannian
manifold with covariate in Euclidean space. The primary motivation and
application of the proposed methodology is in computer vision and
medical imaging. We examine two commonly used metrics, including the
trace metric and the Log- Euclidean metric on the space of SPD matrices.
For each metric, we develop a cross-validation bandwidth selection
method, derive the asymptotic bias, variance, and normality of the
intrinsic local constant and local linear estimators, and compare their
asymptotic mean square errors