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FADTTS: Functional Analysis of Diffusion Tensor Tract Statistics. Hongtu Zhu, Ph.D. Department of Biostatistics and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill. Outline. Motivation Multivariate Varying Coefficient Models Simulation Studies

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fadtts functional analysis of diffusion tensor tract statistics

FADTTS: Functional Analysis of Diffusion Tensor Tract Statistics

Hongtu Zhu, Ph.D.

Department of Biostatistics and

Biomedical Research Imaging Center,

University of North Carolina at Chapel Hill

outline
Outline

Motivation

Multivariate Varying Coefficient Models

Simulation Studies

Real Data Analysis

motivation
Motivation

group 1

group 2

  • Structural Connectivity
  • Functional Connectivity

Anatomical MRI, DTI (HARDI)

EEG, fMRI, resting fMRI

neonatal brain development
Neonatal Brain Development

Motivation

PI: John H. Gilmore.

www.google.com

Knickmeyer RC, et al.J Neurosci, 2008 28: 12176-12182.

early brain development
Early Brain Development

Motivation

2 year

1 year

2 week

Knickmeyer RC, et al.J Neurosci, 2008 28: 12176-12182.

diffusion tensor tract statistics
Diffusion Tensor Tract Statistics

Motivation

FA

Tensor

1 year

2 year

1 year

2 year

2 week

2 week

motivation1
Motivation

MacaqueBrain Development

PI: Martin Styner

& Marc Niethammer.

Casey, B.J. et al. TRENDS in Cognitive Sciences, 2005 9(3): 104-110.

motivation2
Motivation

Casey, B.J. et al. TRENDS in Cognitive Sciences, 2005 9(3): 104-110.

motivation3
Motivation

Casey, B.J. et al. TRENDS in Cognitive Sciences, 2005 9(3): 104-110.

slide10

Functional Analysis of Diffusion Tensor Tract Statistics

Data

  • Diffusion properties (e.g., FA, RA)
  • Grids

(e)

  • Covariates (e.g., age, gender, diagnostic)
multivariate varying coefficient model
Multivariate Varying Coefficient Model

Decomposition:

High Frequency Noise

Varying Coefficients

Low Frequency Signal

Covariance operator:

weighted least squares estimate
Weighted Least Squares Estimate

Key Advantage

Low Frequency Signal

functional principal component analysis
Functional Principal Component Analysis

Smooth individual functions

Estimated covariance operator

Estimated eigenfunctions

statistical inferences
Statistical Inferences

Testing Linear Hypotheses

Grid Point

Whole Tract

Global Test Statistics

Local Test Statistics

confidence band
Confidence Band

Asymptotics

Critical point

Confidence band

comparisons
Comparisons
  • Pros
  • Directly smooth varying coefficient functions
  • Explicitly account for functional nature of tract statistics
  • Characterize low frequency signal
  • Drop high frequency noise
  • Increase statistical power
  • Cons
  • Complicated asymptotic results
  • Computationally intensive
simulation studies
Simulation Studies

Model

Setting

real data analysis
Real Data Analysis

Early Brain Development

Casey, B.J. et al. TRENDS in Cognitive Sciences, 2005 9(3): 104-110.

real data analysis1
Real Data Analysis

Splenium

128 subjects

Diffusion properties = Gender + Gestational age

confidence bands
Confidence Bands

Intercept

Gender

Age

FA

MD

fadtts gui toolbox1
FADTTS GUI Toolbox

Input: Raw data and test data.

Raw data include tract data, design data and diffusion data.

Test data include test matrix and vector.

All data is in .mat format.

Output: Basic plots and P-value plots

Basic plots include diffusion plot, coefficient plot, eigenvalue and eigenfunction plot, confidence band plot.

P-value plot include local p-value (in –log10 scale) plot with global p-value.

Download: FADTTS GUI Toolbox with related documents and sample data is free to download from

http://www.nitrc.org/projects/fadtts/

summary
Summary
  • From the statistical end, we have developed a new functional analysis pipeline for delineating the structure of the variability of multiple diffusion properties along major white matter fiber bundles and their association with a set of covariates of interest.
  • From the application end, FADTTS is demonstrated in a clinical study of neurodevelopment for revealing the complex inhomogeneous spatiotemporal maturation patterns as the apparent changes in fiber bundle diffusion properties.
  • We developed a GUI Tool box to facilitate the application of FADTTS.
future research
Future Research
  • extend FADTTS to the analysis of high angular resolution diffusion image (HARDI).
  • extend FADTTS to principal directions and full diffusion tensors on fiber bundles.
  • extend to more complex fiber structures, such as the medial manifolds of fiber tracts.
  • extend FADTTS to longitudinal studies and family studies.
references
References
  • Zhu, H.T., Kong, L.L., Li, R.Z., Styner, M., Gerig, G., Lin, W.L., Gilmore, J. H. (2011). FADTTS: Functional Analysis of Diffiusion Tensor Tract Statistics varying coefficient models for DTI tract statistics. Neuroimage, in press.
  • Zhu, H.T., Li, R. Z., Kong, L.L. (2011). Multivariate varying coefficient models for functional responses. Submitted.
  • Zhu, H., Styner, M., Li, Y., Kong, L., Shi, Y., Lin, W., Coe, C., and Gilmore, J. (2010). Multivariate varying coefficient models for DTI tract statistics. In Jiang, T., Navab, N., Pluim, J., and Viergever, M., editors, Medical Image Computing and Computer-Assisted Intervention MICCAI 2010, volume 6361 of Lecture Notes in Computer Science, pages 690-697. Springer Berlin / Heidelberg.
  • NICTR Toolbox (2011). FADTTS: Functional Analysis of Diffusion Tensor Tract Statistics. http://www.nitrc.org/projects/fadtts/