Evidence map›Paper›PMID 21335092›Full record

ArticleNeuroImage2011

FADTTS: functional analysis of diffusion tensor tract statistics.

Hongtu Zhu, Linglong Kong, Runze Li, Martin Styner, Guido Gerig, Weili Lin, John H Gilmore

Abstract read
In one paragraph

Article in NeuroImage, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

53 citing papers in PubMed.

  1. Article
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  11. High-Dimensional Spatial Quantile Function-on-Scalar Regression.Journal of the American Statistical Association · 2022
    Article
  12. Article
  13. Article
  14. (TS)NeuroImage · 2020
    Article
  15. Trajectories from Distribution-valued Functional Curves: A Unified Wasserstein FrameworkMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2020
    Article
  16. Article
  17. Article
  18. Article
  19. FSEM: Functional Structural Equation Models for Twin Functional Data.Journal of the American Statistical Association · 2019
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Hongtu ZhuDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. htzhu@email.unc.edu
Linglong Kong
Runze Li
Martin Styner
Guido Gerig
Weili Lin
John H Gilmore

Funding

UNC CLINICAL AND TRANSLATIONAL SCIENCE AWARD: BPCAUL1RR025747 · NCRR · UNIV OF NORTH CAROLINA CHAPEL HILL · PI RUNGE, MARSCHALL S. · 2008 to 2011
$44.6M
Washington University ConsoritumU54EB005149 · NIBIB · BRIGHAM AND WOMEN'S HOSPITAL · PI KIKINIS, RON · 2004 to 2013
$39.3M
UNC MRDD Research CenterP30HD003110 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BELGER, AYSENIL · 1985 to 2012
$28.7M
THE REFINEMENT &APPLICATION OF MEDICAL DECISION MAKING IN PREVENTION SCIENCESP50DA010075 · NIDA · PENNSYLVANIA STATE UNIVERSITY-UNIV PARK · PI LANZA, STEPHANIE T · 1996 to 2014
$24.5M
Statistical Methods for Cancer Clinical TrialsP01CA142538 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI DAVIDIAN, MARIE, KOSOROK, MICHAEL R · 2010 to 2019
$21.3M
Prospective study of the onset of schizophreniaP50MH064065 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI GILMORE, JOHN HORACE · 2002 to 2011
$19.6M
PRENATAL COCAINE AND NEUROBEHAVIORAL FACTORS UNDERLYING MOTHER-INFANT INTERACTIONP01DA022446 · NIDA · UNIV OF NORTH CAROLINA CHAPEL HILL · PI JOHNS, JOSEPHINE M. · 2008 to 2015
$10.2M
Early Brain Development in TwinsR01MH070890 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI GILMORE, JOHN HORACE · 2004 to 2014
$5.3M
Early Brain Development in One and Two Year OldsR01HD053000 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI GILMORE, JOHN HORACE · 2007 to 2018
$4.4M
Early Brain Development in TwinsU01MH070890 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI GILMORE, JOHN HORACE · 2015 to 2019
$2.9M
Statistical Analysis of Biomedical Imaging Data in Curved SpaceR01MH086633 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI IBRAHIM, JOSEPH G · 2010 to 2018
$2.9M
Characterization of Normal Brain Development Using Parallel MRIR01NS055754 · NINDS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LIN, WEILI · 2007 to 2011
$2.5M
NCI NIH HHS P01 CA142538NCI NIH HHS P01CA142538-01NCRR NIH HHS RR025747-01NCRR NIH HHS UL1 RR025747NIA NIH HHS AG033387NIA NIH HHS R21 AG033387NIBIB NIH HHS R01EB5-34816NIBIB NIH HHS U54 EB005149NIBIB NIH HHS U54 EB005149-01NICHD NIH HHS HD 03110NICHD NIH HHS HD053000NICHD NIH HHS P30 HD003110NICHD NIH HHS R01 HD053000NIDA NIH HHS P01 DA022446NIDA NIH HHS P50 DA010075NIDA NIH HHS P50-DA10075NIDA NIH HHS R21 DA024260NIDA NIH HHS R21-DA024260NIMH NIH HHS MH064065NIMH NIH HHS MH070890NIMH NIH HHS MH086633NIMH NIH HHS P50 MH064065NIMH NIH HHS R01 MH070890NIMH NIH HHS R01 MH086633NIMH NIH HHS R01 MH091645NIMH NIH HHS U01 MH070890NINDS NIH HHS R01 NS055754NINDS NIH HHS R01NS055754NINDS NIH HHS R41 NS059095NINDS NIH HHS R42 NS059095
6 · The paper itself

Abstract

The aim of this paper is to present a functional analysis of a diffusion tensor tract statistics (FADTTS) pipeline for delineating the association between multiple diffusion properties along major white matter fiber bundles with a set of covariates of interest, such as age, diagnostic status and gender, and the structure of the variability of these white matter tract properties in various diffusion tensor imaging studies. The FADTTS integrates five statistical tools: (i) a multivariate varying coefficient model for allowing the varying coefficient functions in terms of arc length to characterize the varying associations between fiber bundle diffusion properties and a set of covariates, (ii) a weighted least squares estimation of the varying coefficient functions, (iii) a functional principal component analysis to delineate the structure of the variability in fiber bundle diffusion properties, (iv) a global test statistic to test hypotheses of interest, and (v) a simultaneous confidence band to quantify the uncertainty in the estimated coefficient functions. Simulated data are used to evaluate the finite sample performance of FADTTS. We apply FADTTS to investigate the development of white matter diffusivities along the splenium of the corpus callosum tract and the right internal capsule tract in a clinical study of neurodevelopment. FADTTS can be used to facilitate the understanding of normal brain development, the neural bases of neuropsychiatric disorders, and the joint effects of environmental and genetic factors on white matter fiber bundles. The advantages of FADTTS compared with the other existing approaches are that they are capable of modeling the structured inter-subject variability, testing the joint effects, and constructing their simultaneous confidence bands. However, FADTTS is not crucial for estimation and reduces to the functional analysis method for the single measure.

Indexed as

AlgorithmsAnisotropyBrainComputer SimulationDiffusion Tensor ImagingFemaleGestational AgeHumansImage Processing, Computer-AssistedInfantInternal CapsuleLeast-Squares AnalysisMaleMonte Carlo MethodMultivariate AnalysisNeural Pathways

Identifiers

PMID21335092
PMCPMC3085665

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.