1
|
Jiang Z, Sullivan PF, Li T, Zhao B, Wang X, Luo T, Huang S, Guan PY, Chen J, Yang Y, Stein JL, Li Y, Liu D, Sun L, Zhu H. The pivotal role of the X-chromosome in the genetic architecture of the human brain. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.08.30.23294848. [PMID: 37693466 PMCID: PMC10491353 DOI: 10.1101/2023.08.30.23294848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Genes on the X-chromosome are extensively expressed in the human brain. However, little is known for the X-chromosome's impact on the brain anatomy, microstructure, and functional network. We examined 1,045 complex brain imaging traits from 38,529 participants in the UK Biobank. We unveiled potential autosome-X-chromosome interactions, while proposing an atlas outlining dosage compensation (DC) for brain imaging traits. Through extensive association studies, we identified 72 genome-wide significant trait-locus pairs (including 29 new associations) that share genetic architectures with brain-related disorders, notably schizophrenia. Furthermore, we discovered unique sex-specific associations and assessed variations in genetic effects between sexes. Our research offers critical insights into the X-chromosome's role in the human brain, underscoring its contribution to the differences observed in brain structure and functionality between sexes.
Collapse
|
2
|
Sakly H, Said M, Radhouane S, Tagina M. Medical decision making for 5D cardiac model: Template matching technique and simulation of the fifth dimension. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 191:105382. [PMID: 32066046 DOI: 10.1016/j.cmpb.2020.105382] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 01/18/2020] [Accepted: 02/02/2020] [Indexed: 06/10/2023]
Abstract
The purpose of this paper is to develop a 5D cardiac model which is inspired from the 5D model for the lungs. This model depends on five variables: the anatomical structure of the 3D heart, temporal dimension and the function of blood flow as the fifth dimension. To test this hypothesis, we took the same mathematical modeling as a reference for the fifth dimension of pulmonary flow where r→ρ(t)=r→v(t)+rf→(t) wherer→v(t) is the displacement vectors with approximate magnitudes by linear functions of the tidal volume and rf→(t) is the blood flow. The scans were acquired for 10 patients,in the 404 series for a total of 18,483 images studied in three cases: healthy patient, case of heart failure and aortic stenosis. Where r→vand r→f are the unit vectors along the volume of ejection and the blood flow axes, indicating the direction of motion of the object due to heart volume ejection and blood flow variations, respectively. The quantities of α and β coefficients are determined from real-time patient image data. The alpha and beta coefficients are derived from the following dimension equations[mm / ml] [mm*ms / ml] . Since the cardiac system has two diastolic and systolic phases, we have calculated α1 and β1 for telediatolic volume and α2 and β2 for telesystolic volume throughout the cardiac cycle as a function of the location of the cuts chosen randomly. Fifth-dimensional experiments are used to track, simulate the behavior of blood flow to detect preliminary indications for the identification of stenosis or valve leakage. The average discrepancy was tabulated as the global fraction of systolic ejection. The results shown in Fig. 3 detect a correspondence between the hunting chamber cut and the flow sequence through the orifice of aorta for this patient with suspicious of having an aortic stenosis disease and an ejection fraction about 71% with a maximum of velocity (Vmax) detected=250 (cm / ms) = 2.5 (m / 10-3 s). In this case this patient has a minor stenosis in the aorta. It should be referred that the normalization of this measures is classified such as : Minor stenosis: area 1.5 cm2, Vmax <3 m / moderate stenosis: area 1.0 - 1.5 cm2, Vmax 3 - 4 m / severe stenosis: area <1.0 cm2, Vmax> 4 m / s. For a patient who has an aortic stenosis the cloud of the points is accumulated comparing to the origin of the axis while the patient with a symptom of insufficiency the points are widened with a remarkable gap in the trajectory. To solve the issue of the bad prediction, the inaccuracy of the clouds points of the model 5D, the lack of the exact measurements to estimate the degree of cardiac insufficiency (leakage or stenosis), a solution of 5D imagery was depicted. Our main contribution is to test the validity of the template-matching algorithm and the fifth dimension simulation to provide more clues to detect the aortic stenosis and cardiac insufficiency in the context of medical decision support.
Collapse
Affiliation(s)
- Houneida Sakly
- COSMOS Laboratory -National Institute of Computer Sciences (ENSI), University of Mannouba, Tunisia.
| | - Mourad Said
- RSNA Member and Chief of the Radiology and Medical Imaging Unit within the International Center Carthage Medical, Tourist Area "JINEN EL OUEST"-5000 Monastir, Tunisia.
| | - Syrine Radhouane
- Private Higher School of Engineering and Technology (Esprit), Technological Pole, Tunisia.
| | - Moncef Tagina
- COSMOS Laboratory -National Institute of Computer Sciences (ENSI), University of Mannouba, Tunisia.
| |
Collapse
|
3
|
Yue C, Zipunnikov V, Bazin PL, Pham D, Reich D, Crainiceanu C, Caffo B. Parametrization of white matter manifold-like structures using principal surfaces. J Am Stat Assoc 2016; 111:1050-1060. [PMID: 28090127 PMCID: PMC5224707 DOI: 10.1080/01621459.2016.1164050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2013] [Revised: 02/01/2016] [Indexed: 10/22/2022]
Abstract
In this manuscript, we are concerned with data generated from a diffusion tensor imaging (DTI) experiment. The goal is to parameterize manifold-like white matter tracts, such as the corpus callosum, using principal surfaces. The problem is approached by finding a geometrically motivated surface-based representation of the corpus callosum and visualized fractional anisotropy (FA) values projected onto the surface. The method also applies to any other diffusion summary. An algorithm is proposed that 1) constructs the principal surface of a corpus callosum; 2) flattens the surface into a parametric 2D map; 3) projects associated FA values on the map. The algorithm is applied to a longitudinal study containing 466 diffusion tensor images of 176 multiple sclerosis (MS) patients observed at multiple visits. For each subject and visit the study contains a registered DTI scan of the corpus callosum at roughly 20,000 voxels. Extensive simulation studies demonstrate fast convergence and robust performance of the algorithm under a variety of challenging scenarios.
Collapse
Affiliation(s)
- Chen Yue
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, 21205
| | - Vadim Zipunnikov
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, 21205
| | - Pierre-Louis Bazin
- Department of Neurophysics, Max Planck Institute, Leipzig, Germany, 04103
| | - Dzung Pham
- Center for Neuroscience and Regenerative Medicine, Henry Jackson Foundation, Bethesda, MD 20892
| | - Daniel Reich
- Department of Radiology and Imaging Sciences, National Institute of Health, Bethesda, MD 20892
| | | | - Brian Caffo
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, 21205
| |
Collapse
|
4
|
Gao Q, Ahn M, Zhu H. Cook's Distance Measures for Varying Coefficient Models with Functional Responses. Technometrics 2015; 57:268-280. [PMID: 26257438 PMCID: PMC4524573 DOI: 10.1080/00401706.2014.914978] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
The aim of this paper is to develop Cook's distance measures for assessing the influence of both atypical curves and observations under varying coefficient model for functional responses. Our Cook's distance measures include Cook's distances for deleting multiple curves and for deleting multiple grid points, and their scaled Cook's distances. We systematically investigate some theoretical properties of these diagnostic measures. Simulation studies are conducted to evaluate the finite sample properties of these Cook's distances under different scenarios. A real diffusion tensor tract data set is analyzed to illustrate the use of our diagnostic measures.
Collapse
Affiliation(s)
- Qibing Gao
- Department of Statistics Nanjing Normal University Nanjing 210023, China
| | - Mihye Ahn
- Department of Biostatistics and Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Department of Biostatistics and Biomedical Research Imaging Center University of North Carolina at Chapel Hill Chapel Hill, NC 27599, USA
| |
Collapse
|
5
|
Lin YC, Daducci A, Meskaldji DE, Thiran JP, Michel P, Meuli R, Krueger G, Menegaz G, Granziera C. Quantitative Analysis of Myelin and Axonal Remodeling in the Uninjured Motor Network After Stroke. Brain Connect 2014; 5:401-12. [PMID: 25296185 DOI: 10.1089/brain.2014.0245] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Contralesional brain connectivity plasticity was previously reported after stroke. This study aims at disentangling the biological mechanisms underlying connectivity plasticity in the uninjured motor network after an ischemic lesion. In particular, we measured generalized fractional anisotropy (GFA) and magnetization transfer ratio (MTR) to assess whether poststroke connectivity remodeling depends on axonal and/or myelin changes. Diffusion-spectrum imaging and magnetization transfer MRI at 3T were performed in 10 patients in acute phase, at 1 and 6 months after stroke, which was affecting motor cortical and/or subcortical areas. Ten age- and gender-matched healthy volunteers were scanned 1 month apart for longitudinal comparison. Clinical assessment was also performed in patients prior to magnetic resonance imaging (MRI). In the contralesional hemisphere, average measures and tract-based quantitative analysis of GFA and MTR were performed to assess axonal integrity and myelination along motor connections as well as their variations in time. Mean and tract-based measures of MTR and GFA showed significant changes in a number of contralesional motor connections, confirming both axonal and myelin plasticity in our cohort of patients. Moreover, density-derived features (peak height, standard deviation, and skewness) of GFA and MTR along the tracts showed additional correlation with clinical scores than mean values. These findings reveal the interplay between contralateral myelin and axonal remodeling after stroke.
Collapse
Affiliation(s)
- Ying-Chia Lin
- 1 Department of Computer Science, University of Verona , Verona, Italy
| | - Alessandro Daducci
- 2 STI/IEL/LTS5 , Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Djalel Eddine Meskaldji
- 3 Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland .,4 Department of Radiology and Medical Informatics, University of Geneva , Geneva, Switzerland
| | - Jean-Philippe Thiran
- 2 STI/IEL/LTS5 , Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Patrik Michel
- 5 Stroke Center, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, University of Lausanne , Lausanne, Switzerland
| | - Reto Meuli
- 6 Department of Radiology, Centre Hospitalier Universitaire Vaudois, University of Lausanne , Lausanne, Switzerland
| | - Gunnar Krueger
- 7 Healthcare Sector IM&WS S, Siemens Schweiz AG, Lausanne, Switzerland .,8 Advanced Clinical Imaging Technology Group, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Gloria Menegaz
- 1 Department of Computer Science, University of Verona , Verona, Italy
| | - Cristina Granziera
- 2 STI/IEL/LTS5 , Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland .,8 Advanced Clinical Imaging Technology Group, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland .,9 Laboratoire de Recherche en Neuroimagerie and Neuroimmunology Unit, Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois and University of Lausanne , Lausanne, Switzerland
| |
Collapse
|
6
|
Abstract
Many longitudinal imaging studies have been/are being widely conducted to use diffusion tensor imaging (DTI) to better understand white matter maturation in normal controls and diseased subjects. There is an urgent demand for the development of statistical methods for analyzing diffusion properties along major fiber tracts obtained from longitudinal DTI studies. Jointly analyzing fiber-tract diffusion properties and covariates from longitudinal studies raises several major challenges including (i) infinite-dimensional functional response data, (ii) complex spatial-temporal correlation structure, and (iii) complex spatial smoothness. To address these challenges, this article is to develop a longitudinal functional analysis framework (LFAF) to delineate the dynamic changes of diffusion properties along major fiber tracts and their association with a set of covariates of interest (e.g., age and group status) and the structure of the variability of these white matter tract properties in various longitudinal studies. Our LFAF consists of a functional mixed effects model for addressing all three challenges, an efficient method for spatially smoothing varying coefficient functions, an estimation method for estimating the spatial-temporal correlation structure, a test procedure with a global test statistic for testing hypotheses of interest associated with functional response, and a simultaneous confidence band for quantifying the uncertainty in the estimated coefficient functions. Simulated data are used to evaluate the finite sample performance of LFAF and to demonstrate that LFAF significantly outperforms a voxel-wise mixed model method. We apply LFAF to study the spatial-temporal dynamics of white-matter fiber tracts in a clinical study of neurodevelopment.
Collapse
|
7
|
Sharma A, Durrleman S, Gilmore JH, Gerig G. LONGITUDINAL GROWTH MODELING OF DISCRETE-TIME FUNCTIONS WITH APPLICATION TO DTI TRACT EVOLUTION IN EARLY NEURODEVELOPMENT. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2013; 2012:1945-1400. [PMID: 24443681 DOI: 10.1109/isbi.2012.6235829] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
We present a new framework for spatiotemporal analysis of parameterized functions attributed by properties of 4D longitudinal image data. Our driving application is the measurement of temporal change in white matter diffusivity of fiber tracts. A smooth temporal modeling of change from a discrete-time set of functions is obtained with an extension of the logistic growth model to time-dependent spline functions, capturing growth with only a few descriptive parameters. An unbiased template baseline function is also jointly estimated. Solution is demonstrated via energy minimization with an extension to simultaneous modeling of trajectories for multiple subjects. The new framework is validated with synthetic data and applied to longitudinal DTI from 15 infants. Interpretation of estimated model growth parameters is facilitated by visualization in the original coordinate space of fiber tracts.
Collapse
Affiliation(s)
- Anuja Sharma
- School of Computing, SCI Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Stanley Durrleman
- INRIA-Asclepios project, 2004 route des Lucioles, 06902 Sophia Antipolis, France
| | - John H Gilmore
- UNC Chapel Hill, Department of Psychiatry, Chapel Hill, NC 27599-7160, USA
| | - Guido Gerig
- School of Computing, SCI Institute, University of Utah, Salt Lake City, UT 84112, USA
| |
Collapse
|
8
|
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 2013; 84:753-64. [PMID: 24076225 DOI: 10.1016/j.neuroimage.2013.09.020] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2013] [Revised: 09/09/2013] [Accepted: 09/10/2013] [Indexed: 11/29/2022] Open
Abstract
Many longitudinal imaging studies have collected repeated diffusion tensor magnetic resonance imaging data to understand white matter maturation and structural connectivity pattern in normal controls and diseased subjects. There is an urgent demand for the development of statistical methods for the analysis of diffusion properties along fiber tracts and clinical data obtained from longitudinal studies. Jointly analyzing repeated fiber-tract diffusion properties and covariates (e.g., age or gender) raises several major challenges including (i) infinite-dimensional functional response data, (ii) complex spatial-temporal correlation structure, and (iii) complex spatial smoothness. To address these challenges, this article is to develop a functional mixed effects modeling (FMEM) framework to delineate the dynamic changes of diffusion properties along major fiber tracts and their association with a set of covariates of interest and the structure of the variability of these white matter tract properties in various longitudinal studies. Our FMEM consists of a functional mixed effects model for addressing all three challenges, 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. Simulated data are used to evaluate the finite sample performance of FMEM and to demonstrate that FMEM significantly outperforms the standard pointwise mixed effects modeling approach. We apply FMEM to study the spatial-temporal dynamics of white-matter fiber tracts in a clinical study of neurodevelopment.
Collapse
Affiliation(s)
- Ying Yuan
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, USA
| | | | | | | | | | | | | |
Collapse
|
9
|
Bayesian scalar-on-image regression with application to association between intracranial DTI and cognitive outcomes. Neuroimage 2013; 83:210-23. [PMID: 23792220 DOI: 10.1016/j.neuroimage.2013.06.020] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2012] [Revised: 06/02/2013] [Accepted: 06/03/2013] [Indexed: 11/21/2022] Open
Abstract
Diffusion tensor imaging (DTI) measures water diffusion within white matter, allowing for in vivo quantification of brain pathways. These pathways often subserve specific functions, and impairment of those functions is often associated with imaging abnormalities. As a method for predicting clinical disability from DTI images, we propose a hierarchical Bayesian "scalar-on-image" regression procedure. Our procedure introduces a latent binary map that estimates the locations of predictive voxels and penalizes the magnitude of effect sizes in these voxels, thereby resolving the ill-posed nature of the problem. By inducing a spatial prior structure, the procedure yields a sparse association map that also maintains spatial continuity of predictive regions. The method is demonstrated on a simulation study and on a study of association between fractional anisotropy and cognitive disability in a cross-sectional sample of 135 multiple sclerosis patients.
Collapse
|
10
|
Ullah S, Finch CF. Applications of functional data analysis: A systematic review. BMC Med Res Methodol 2013; 13:43. [PMID: 23510439 PMCID: PMC3626842 DOI: 10.1186/1471-2288-13-43] [Citation(s) in RCA: 86] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2012] [Accepted: 03/04/2013] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Functional data analysis (FDA) is increasingly being used to better analyze, model and predict time series data. Key aspects of FDA include the choice of smoothing technique, data reduction, adjustment for clustering, functional linear modeling and forecasting methods. METHODS A systematic review using 11 electronic databases was conducted to identify FDA application studies published in the peer-review literature during 1995-2010. Papers reporting methodological considerations only were excluded, as were non-English articles. RESULTS In total, 84 FDA application articles were identified; 75.0% of the reviewed articles have been published since 2005. Application of FDA has appeared in a large number of publications across various fields of sciences; the majority is related to biomedicine applications (21.4%). Overall, 72 studies (85.7%) provided information about the type of smoothing techniques used, with B-spline smoothing (29.8%) being the most popular. Functional principal component analysis (FPCA) for extracting information from functional data was reported in 51 (60.7%) studies. One-quarter (25.0%) of the published studies used functional linear models to describe relationships between explanatory and outcome variables and only 8.3% used FDA for forecasting time series data. CONCLUSIONS Despite its clear benefits for analyzing time series data, full appreciation of the key features and value of FDA have been limited to date, though the applications show its relevance to many public health and biomedical problems. Wider application of FDA to all studies involving correlated measurements should allow better modeling of, and predictions from, such data in the future especially as FDA makes no a priori age and time effects assumptions.
Collapse
Affiliation(s)
- Shahid Ullah
- Flinders Centre for Epidemiology and Biostatistics, School of Medicine, Faculty of Health Sciences, Flinders University, Adelaide, SA, 5001, Australia
| | - Caroline F Finch
- Centre for Healthy and Safe Sports (CHASS), University of Ballarat, SMB Campus, Ballarat, VIC, 3353, Australia
| |
Collapse
|
11
|
Yuan Y, Zhu H, Styner M, Gilmore JH, Marron JS. VARYING COEFFICIENT MODEL FOR MODELING DIFFUSION TENSORS ALONG WHITE MATTER TRACTS. Ann Appl Stat 2013; 7:102-125. [PMID: 24533040 DOI: 10.1214/12-aoas574] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Diffusion tensor imaging provides important information on tissue 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 co-efficient 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.
Collapse
Affiliation(s)
- Ying Yuan
- University of North Carolina at Chapel Hill
| | - Hongtu Zhu
- University of North Carolina at Chapel Hill
| | | | | | - J S Marron
- University of North Carolina at Chapel Hill
| |
Collapse
|
12
|
Yuan Y, Zhu H, Lin W, Marron JS. Local Polynomial Regression for Symmetric Positive Definite Matrices. J R Stat Soc Series B Stat Methodol 2012; 74:697-719. [PMID: 23008683 PMCID: PMC3448376 DOI: 10.1111/j.1467-9868.2011.01022.x] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
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. Simulation studies are further used to compare the estimators under the two metrics and to examine their finite sample performance. We use our method to detect diagnostic differences between diffusion tensors along fiber tracts in a study of human immunodeficiency virus.
Collapse
Affiliation(s)
- Ying Yuan
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Hongtu Zhu
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Weili Lin
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - J. S. Marron
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| |
Collapse
|
13
|
Hua Z, Dunson DB, Gilmore JH, Styner MA, Zhu H. Semiparametric Bayesian local functional models for diffusion tensor tract statistics. Neuroimage 2012; 63:460-74. [PMID: 22732565 DOI: 10.1016/j.neuroimage.2012.06.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2012] [Revised: 05/25/2012] [Accepted: 06/15/2012] [Indexed: 10/28/2022] Open
Abstract
We propose a semiparametric Bayesian local functional model (BFM) for the analysis of multiple diffusion properties (e.g., fractional anisotropy) along white matter fiber bundles with a set of covariates of interest, such as age and gender. BFM accounts for heterogeneity in the shape of the fiber bundle diffusion properties among subjects, while allowing the impact of the covariates to vary across subjects. A nonparametric Bayesian LPP2 prior facilitates global and local borrowings of information among subjects, while an infinite factor model flexibly represents low-dimensional structure. Local hypothesis testing and credible bands are developed to identify fiber segments, along which multiple diffusion properties are significantly associated with covariates of interest, while controlling for multiple comparisons. Moreover, BFM naturally group subjects into more homogeneous clusters. Posterior computation proceeds via an efficient Markov chain Monte Carlo algorithm. A simulation study is performed to evaluate the finite sample performance of BFM. We apply BFM 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 in new born infants.
Collapse
Affiliation(s)
- Zhaowei Hua
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | | | | | | | | |
Collapse
|
14
|
Shi Y, Short SJ, Knickmeyer RC, Wang J, Coe CL, Niethammer M, Gilmore JH, Zhu H, Styner MA. Diffusion tensor imaging-based characterization of brain neurodevelopment in primates. ACTA ACUST UNITED AC 2012; 23:36-48. [PMID: 22275483 DOI: 10.1093/cercor/bhr372] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Primate neuroimaging provides a critical opportunity for understanding neurodevelopment. Yet the lack of a normative description has limited the direct comparison with changes in humans. This paper presents for the first time a cross-sectional diffusion tensor imaging (DTI) study characterizing primate brain neurodevelopment between 1 and 6 years of age on 25 healthy undisturbed rhesus monkeys (14 male, 11 female). A comprehensive analysis including region-of-interest, voxel-wise, and fiber tract-based approach demonstrated significant changes of DTI properties over time. Changes in fractional anisotropy (FA), mean diffusivity, axial diffusivity (AD), and radial diffusivity (RD) exhibited a heterogeneous pattern across different regions as well as along fiber tracts. Most of these patterns are similar to those from human studies yet a few followed unique patterns. Overall, we observed substantial increase in FA and AD and a decrease in RD for white matter (WM) along with similar yet smaller changes in gray matter (GM). We further observed an overall posterior-to-anterior trend in DTI property changes over time and strong correlations between WM and GM development. These DTI trends provide crucial insights into underlying age-related biological maturation, including myelination, axonal density changes, fiber tract reorganization, and synaptic pruning processes.
Collapse
Affiliation(s)
- Yundi Shi
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599-7160, USA.
| | | | | | | | | | | | | | | | | |
Collapse
|
15
|
Lebel C, Benner T, Beaulieu C. Six is enough? Comparison of diffusion parameters measured using six or more diffusion-encoding gradient directions with deterministic tractography. Magn Reson Med 2011; 68:474-83. [DOI: 10.1002/mrm.23254] [Citation(s) in RCA: 80] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2010] [Revised: 07/13/2011] [Accepted: 09/22/2011] [Indexed: 11/06/2022]
|
16
|
Colby JB, Soderberg L, Lebel C, Dinov ID, Thompson PM, Sowell ER. Along-tract statistics allow for enhanced tractography analysis. Neuroimage 2011; 59:3227-42. [PMID: 22094644 DOI: 10.1016/j.neuroimage.2011.11.004] [Citation(s) in RCA: 157] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2011] [Revised: 10/19/2011] [Accepted: 11/02/2011] [Indexed: 02/07/2023] Open
Abstract
Diffusion imaging tractography is a valuable tool for neuroscience researchers because it allows the generation of individualized virtual dissections of major white matter tracts in the human brain. It facilitates between-subject statistical analyses tailored to the specific anatomy of each participant. There is prominent variation in diffusion imaging metrics (e.g., fractional anisotropy, FA) within tracts, but most tractography studies use a "tract-averaged" approach to analysis by averaging the scalar values from the many streamline vertices in a tract dissection into a single point-spread estimate for each tract. Here we describe a complete workflow needed to conduct an along-tract analysis of white matter streamline tract groups. This consists of 1) A flexible MATLAB toolkit for generating along-tract data based on B-spline resampling and compilation of scalar data at different collections of vertices along the curving tract spines, and 2) Statistical analysis and rich data visualization by leveraging tools available through the R platform for statistical computing. We demonstrate the effectiveness of such an along-tract approach over the tract-averaged approach in an example analysis of 10 major white matter tracts in a single subject. We also show that these techniques easily extend to between-group analyses typically used in neuroscience applications, by conducting an along-tract analysis of differences in FA between 9 individuals with fetal alcohol spectrum disorders (FASDs) and 11 typically-developing controls. This analysis reveals localized differences between FASD and control groups that were not apparent using a tract-averaged method. Finally, to validate our approach and highlight the strength of this extensible software framework, we implement 2 other methods from the literature and leverage the existing workflow tools to conduct a comparison study.
Collapse
Affiliation(s)
- John B Colby
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
| | | | | | | | | | | |
Collapse
|
17
|
Goldsmith J, Crainiceanu CM, Caffo BS, Reich DS. Penalized functional regression analysis of white-matter tract profiles in multiple sclerosis. Neuroimage 2011; 57:431-9. [PMID: 21554962 PMCID: PMC3114268 DOI: 10.1016/j.neuroimage.2011.04.044] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2011] [Revised: 03/23/2011] [Accepted: 04/20/2011] [Indexed: 10/18/2022] Open
Abstract
Diffusion tensor imaging (DTI) enables noninvasive parcellation of cerebral white matter into its component fiber bundles or tracts. These tracts often subserve specific functions, and damage to the tracts can therefore result in characteristic forms of disability. Attempts to quantify the extent of tract-specific damage have been limited in part by substantial spatial variation of imaging properties from one end of a tract to the other, variation that can be compounded by the effects of disease. Here, we develop a "penalized functional regression" procedure to analyze spatially normalized tract profiles, which powerfully characterize such spatial variation. The central idea is to identify and emphasize portions of a tract that are more relevant to a clinical outcome score, such as case status or degree of disability. The procedure also yields a "tract abnormality score" for each tract and MRI index studied. Importantly, the weighting function used in this procedure is constrained to be smooth, and the statistical associations are estimated using generalized linear models. We test the method on data from a cross-sectional MRI and functional study of 115 multiple-sclerosis cases and 42 healthy volunteers, considering a range of quantitative MRI indices, white-matter tracts, and clinical outcome scores, and using training and testing sets to validate the results. We show that attention to spatial variation yields up to 15% (mean across all tracts and MRI indices: 6.4%) improvement in the ability to discriminate multiple sclerosis cases from healthy volunteers. Our results confirm that comprehensive analysis of white-matter tract-specific imaging data improves with knowledge and characterization of the normal spatial variation.
Collapse
Affiliation(s)
- Jeff Goldsmith
- Department of Biostatistics, Johns Hopkins School of Public Health, 615 N Wolfe St, Baltimore, MD 21205
| | - Ciprian M. Crainiceanu
- Department of Biostatistics, Johns Hopkins School of Public Health, 615 N Wolfe St, Baltimore, MD 21205
| | - Brian S. Caffo
- Department of Biostatistics, Johns Hopkins School of Public Health, 615 N Wolfe St, Baltimore, MD 21205
| | - Daniel S. Reich
- Departments of Radiology and Neurology, Johns Hopkins School of Medicine, 600 N Wolfe St, Baltimore, MD 20892
| |
Collapse
|
18
|
Reiss PT, Mennes M, Petkova E, Huang L, Hoptman MJ, Biswal BB, Colcombe SJ, Zuo XN, Milham MP. Extracting information from functional connectivity maps via function-on-scalar regression. Neuroimage 2011; 56:140-8. [PMID: 21296165 DOI: 10.1016/j.neuroimage.2011.01.071] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2010] [Revised: 01/27/2011] [Accepted: 01/28/2011] [Indexed: 11/18/2022] Open
Abstract
Functional connectivity of an individual human brain is often studied by acquiring a resting state functional magnetic resonance imaging scan, and mapping the correlation of each voxel's BOLD time series with that of a seed region. As large collections of such maps become available, including multisite data sets, there is an increasing need for ways to distill the information in these maps in a readily visualized form. Here we propose a two-step analytic strategy. First, we construct connectivity-distance profiles, which summarize the connectivity of each voxel in the brain as a function of distance from the seed, a functional relationship that has attracted much recent interest. Next, these profile functions are regressed on predictors of interest, whether categorical (e.g., acquisition site or diagnostic group) or continuous (e.g., age). This procedure can provide insight into the roles of multiple sources of variation, and detect large-scale patterns not easily available from conventional analyses. We illustrate the proposed methods with a resting state data set pooled across four imaging sites.
Collapse
Affiliation(s)
- Philip T Reiss
- Department of Child and Adolescent Psychiatry, New York University School of Medicine, NY, USA.
| | | | | | | | | | | | | | | | | |
Collapse
|
19
|
Zhu H, Kong L, Li R, Styner M, Gerig G, Lin W, Gilmore JH. FADTTS: functional analysis of diffusion tensor tract statistics. Neuroimage 2011; 56:1412-25. [PMID: 21335092 DOI: 10.1016/j.neuroimage.2011.01.075] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2010] [Revised: 01/19/2011] [Accepted: 01/28/2011] [Indexed: 11/18/2022] Open
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.
Collapse
Affiliation(s)
- Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
| | | | | | | | | | | | | |
Collapse
|
20
|
Greven S, Crainiceanu C, Caffo B, Reich D. Longitudinal functional principal component analysis. Electron J Stat 2010; 4:1022-1054. [PMID: 21743825 PMCID: PMC3131008 DOI: 10.1214/10-ejs575] [Citation(s) in RCA: 106] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
We introduce models for the analysis of functional data observed at multiple time points. The dynamic behavior of functional data is decomposed into a time-dependent population average, baseline (or static) subject-specific variability, longitudinal (or dynamic) subject-specific variability, subject-visit-specific variability and measurement error. The model can be viewed as the functional analog of the classical longitudinal mixed effects model where random effects are replaced by random processes. Methods have wide applicability and are computationally feasible for moderate and large data sets. Computational feasibility is assured by using principal component bases for the functional processes. The methodology is motivated by and applied to a diffusion tensor imaging (DTI) study designed to analyze differences and changes in brain connectivity in healthy volunteers and multiple sclerosis (MS) patients. An R implementation is provided.87.
Collapse
Affiliation(s)
- Sonja Greven
- Department of Statistics, Ludwig-Maximilians-University Munich,
Ludwigstr. 33, 80539 Munich, Germany
| | - Ciprian Crainiceanu
- Department of Biostatistics, Johns Hopkins University, 615 N. Wolfe
Street, Baltimore, MD 21205, USA
| | - Brian Caffo
- Department of Biostatistics, Johns Hopkins University, 615 N. Wolfe
Street, Baltimore, MD 21205, USA
| | - Daniel Reich
- Translational Neuroradiology Unit, Neuroimmunology Branch, National
Institute of Neurological Disorders and Stroke, National Institutes of
Health, Bethesda, MD 20814, USA. Departments of Radiology and Neurology,
Johns Hopkins Hospital, 600 N. Wolfe Street, Baltimore, MD 21287, USA
| |
Collapse
|
21
|
Zhu H, Styner M, Li Y, Kong L, Shi Y, Lin W, Coe C, Gilmore JH. Multivariate varying coefficient models for DTI tract statistics. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2010; 13:690-7. [PMID: 20879291 PMCID: PMC2964931 DOI: 10.1007/978-3-642-15705-9_84] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/12/2023]
Abstract
Diffusion tensor imaging (DTI) is important for characterizing the structure of white matter fiber bundles as well as detailed tissue properties along these fiber bundles in vivo. There has been extensive interest in the analysis of diffusion properties measured along fiber tracts as a function of age, diagnostic status, and gender, while controlling for other clinical variables. However, the existing methods have several limitations including the independent analysis of diffusion properties, a lack of method for accounting for multiple covariates, and a lack of formal statistical inference, such as estimation theory and hypothesis testing. This paper presents a statistical framework, called VCMTS, to specifically address these limitations. The VCMTS framework consists of four integrated components: a varying coefficient model for characterizing the association between fiber bundle diffusion properties and a set of covariates, the local polynomial kernel method for estimating smoothed multiple diffusion properties along individual fiber bundles, global and local test statistics for testing hypotheses of interest along fiber tracts, and a resampling method for approximating the p-value of the global test statistic. The proposed methodology is applied to characterizing the development of four diffusion properties along the splenium and genu of the corpus callosum tract in a study of neurodevelopment in healthy rhesus monkeys. Significant time effects on the four diffusion properties were found.
Collapse
Affiliation(s)
- Hongtu Zhu
- Department of Biostatistics, Radiology, Psychiatry and Computer Science, and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, USA
| | | | | | | | | | | | | | | |
Collapse
|
22
|
Abstract
Semiparametrically structured models are defined as a class of models for which the predictors may contain parametric parts, additive parts of covariates with an unspecified functional form, and interactions which are described as varying coefficients. In the case of an ordinal response the complexity of the predictor is determined by different sorts of effects. Global effects and category-specific effects are distinguished; the latter allow the effect to vary across response categories. A general framework is developed in which global as well as category-specific effects may have unspecified functional form. The framework extends various existing methods of modeling ordinal responses. The Wilkinson-Rogers notation is extended to incorporate smooth model parts and varying coefficient terms, the latter being important for the smooth specification of category-specific effects.
Collapse
Affiliation(s)
- Gerhard Tutz
- Ludwig-Maximilians-Universität München, Akademiestrasse 1, 80799 München, Germany.
| |
Collapse
|