Publications (17)54.2 Total impact
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Article: More insights into early brain development through statistical analyses of eigen-structural elements of diffusion tensor imaging using multivariate adaptive regression splines.
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ABSTRACT: The aim of this study was to characterize the maturational changes of the three eigenvalues (λ1 ≥ λ2 ≥ λ3) of diffusion tensor imaging (DTI) during early postnatal life for more insights into early brain development. In order to overcome the limitations of using presumed growth trajectories for regression analysis, we employed Multivariate Adaptive Regression Splines (MARS) to derive data-driven growth trajectories for the three eigenvalues. We further employed Generalized Estimating Equations (GEE) to carry out statistical inferences on the growth trajectories obtained with MARS. With a total of 71 longitudinal datasets acquired from 29 healthy, full-term pediatric subjects, we found that the growth velocities of the three eigenvalues were highly correlated, but significantly different from each other. This paradox suggested the existence of mechanisms coordinating the maturations of the three eigenvalues even though different physiological origins may be responsible for their temporal evolutions. Furthermore, our results revealed the limitations of using the average of λ2 and λ3 as the radial diffusivity in interpreting DTI findings during early brain development because these two eigenvalues had significantly different growth velocities even in central white matter. In addition, based upon the three eigenvalues, we have documented the growth trajectory differences between central and peripheral white matter, between anterior and posterior limbs of internal capsule, and between inferior and superior longitudinal fasciculus. Taken together, we have demonstrated that more insights into early brain maturation can be gained through analyzing eigen-structural elements of DTI.Brain Structure and Function 03/2013; · 5.63 Impact Factor -
Article: Diffusion tensor imaging based network analysis detects alterations of neuroconnectivity in patients with clinically early relapsing-remitting multiple sclerosis.
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ABSTRACT: Although it is inarguable that conventional MRI (cMRI) has greatly contributed to the diagnosis and assessment of multiple sclerosis (MS), cMRI does not show close correlation with clinical findings or pathologic features, and is unable to predict prognosis or stratify disease severity. To this end, diffusion tensor imaging (DTI) with tractography and neuroconnectivity analysis may assist disease assessment in MS. We, therefore, attempted this pilot study for initial assessment of early relapsing-remitting MS (RRMS). Neuroconnectivity analysis was used for evaluation of 24 early RRMS patients within 2 years of presentation, and compared to the network measures of a group of 30 age-and-gender-matched normal control subjects. To account for the situation that the connections between two adjacent regions may be disrupted by an MS lesion, a new metric, network communicability, was adopted to measure both direct and indirect connections. For each anatomical area, the brain network communicability and average path length were computed and compared to characterize the network changes in efficiencies. Statistically significant (P < 0.05) loss of communicability was revealed in our RRMS cohort, particularly in the frontal and hippocampal/parahippocampal regions as well as the motor strip and occipital lobes. Correlation with the 25-foot Walk test with communicability measures in the left superior frontal (r = -0.71) as well as the left superior temporal gyrus (r = -0.43) and left postcentral gyrus (r = -0.41) were identified. Additionally identified were increased communicability between the deep gray matter structures (left thalamus and putamen) with the major interhemispheric and intrahemispheric white matter tracts, the corpus callosum, and cingulum, respectively. These foci of increased communicability are thought to represent compensatory changes. The proposed DTI-based neuroconnectivity analysis demonstrated quantifiable, structurally relevant alterations of fiber tract connections in early RRMS and paves the way for longitudinal studies in larger patient groups. Hum Brain Mapp, 2012. © 2012 Wiley Periodicals, Inc.Human Brain Mapping 09/2012; · 5.88 Impact Factor -
Article: Longitudinal regression analysis of spatial-temporal growth patterns of geometrical diffusion measures in early postnatal brain development with diffusion tensor imaging.
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ABSTRACT: Although diffusion tensor imaging (DTI) has provided substantial insights into early brain development, most DTI studies based on fractional anisotropy (FA) and mean diffusivity (MD) may not capitalize on the information derived from the three principal diffusivities (e.g. eigenvalues). In this study, we explored the spatial and temporal evolution of white matter structures during early brain development using two geometrical diffusion measures, namely, linear (Cl) and planar (Cp) diffusion anisotropies, from 71 longitudinal datasets acquired from 29 healthy, full-term pediatric subjects. The growth trajectories were estimated with generalized estimating equations (GEE) using linear fitting with logarithm of age (days). The presence of the white matter structures in Cl and Cp was observed in neonates, suggesting that both the cylindrical and fanning or crossing structures in various white matter regions may already have been formed at birth. Moreover, we found that both Cl and Cp evolved in a temporally nonlinear and spatially inhomogeneous manner. The growth velocities of Cl in central white matter were significantly higher when compared to peripheral, or more laterally located, white matter: central growth velocity Cl=0.0465±0.0273/log(days), versus peripheral growth velocity Cl=0.0198±0.0127/log(days), p<10⁻⁶. In contrast, the growth velocities of Cp in central white matter were significantly lower than that in peripheral white matter: central growth velocity Cp=0.0014±0.0058/log(days), versus peripheral growth velocity Cp=0.0289±0.0101/log(days), p<10⁻⁶. Depending on the underlying white matter site which is analyzed, our findings suggest that ongoing physiologic and microstructural changes in the developing brain may exert different effects on the temporal evolution of these two geometrical diffusion measures. Thus, future studies utilizing DTI with correlative histological analysis in the study of early brain development are warranted.NeuroImage 07/2011; 58(4):993-1005. · 5.89 Impact Factor -
Article: SPHERE: SPherical Harmonic Elastic REgistration of HARDI data.
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ABSTRACT: In contrast to the more common Diffusion Tensor Imaging (DTI), High Angular Resolution Diffusion Imaging (HARDI) allows superior delineation of angular microstructures of brain white matter, and makes possible multiple-fiber modeling of each voxel for better characterization of brain connectivity. However, the complex orientation information afforded by HARDI makes registration of HARDI images more complicated than scalar images. In particular, the question of how much orientation information is needed for satisfactory alignment has not been sufficiently addressed. Low order orientation representation is generally more robust than high order representation, although the latter provides more information for correct alignment of fiber pathways. However, high order representation, when naïvely utilized, might not necessarily be conducive to improving registration accuracy since similar structures with significant orientation differences prior to proper alignment might be mistakenly taken as non-matching structures. We present in this paper a HARDI registration algorithm, called SPherical Harmonic Elastic REgistration (SPHERE), which in a principled means hierarchically extracts orientation information from HARDI data for structural alignment. The image volumes are first registered using robust, relatively direction invariant features derived from the Orientation Distribution Function (ODF), and the alignment is then further refined using spherical harmonic (SH) representation with gradually increasing orders. This progression from non-directional, single-directional to multi-directional representation provides a systematic means of extracting directional information given by diffusion-weighted imaging. Coupled with a template-subject-consistent soft-correspondence-matching scheme, this approach allows robust and accurate alignment of HARDI data. Experimental results show marked increase in accuracy over a state-of-the-art DTI registration algorithm.NeuroImage 03/2011; 55(2):545-56. · 5.89 Impact Factor -
Article: Early changes of tissue perfusion after tissue plasminogen activator in hyperacute ischemic stroke.
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ABSTRACT: it is hypothesized that tissue plasminogen activator rescues brain tissue by improving perfusion. In this study, we aimed to examine acute regional perfusion changes and how they influence infarction and clinical outcome. three sequential MR scans were performed in 15 tissue plasminogen activator-treated patients within 3.5 (tp1), at 6 hours (tp2), and at 1 month (tp3) after stroke onset. "Hypoperfusion" was defined if mean transit time prolongation was more than a threshold (4 thresholds: 3, 4, 5, and 6 seconds). Four regions of interest were classified: (1) "reperfusion"-hypoperfused at tp1, normal at tp2; (2) "nonreperfusion"-hypoperfused at tp1 and tp2; (3) "normal perfusion"-normal at tp1 and tp2; and (4) "new hypoperfusion"-normal at tp1 and hypoperfused at tp2. Risk of infarction was calculated within each region of interest. Associations between tissue perfusion changes and clinical variables were evaluated using stepwise multiple linear regressions. Moreover, the association between National Institutes of Health Stroke Scale changes and perfusion alterations was assessed using linear mixed effect models. regardless of the mean transit time threshold chosen, the risk of infarction in nonreperfused regions (40% to 68%, thresholds 3 to 6 seconds) was higher than reperfused regions (9% to 30%, P<0.05), and it was higher in new hypoperfusion regions (9% to 33%) than normal perfusion regions (3% to 4%, P<0.05). Volume of new hypoperfusion was significantly associated with onset-to-treatment time and initial hypoperfused volume. Overall relative reperfusion was significantly associated with National Institutes of Health Stroke Scale improvement. early tissue perfusion changes influenced final tissue fate. The development of new hypoperfusion may result from delay in tissue plasminogen activator and a large initial lesion.Stroke 01/2011; 42(1):65-72. · 5.73 Impact Factor -
Article: Development trends of white matter connectivity in the first years of life.
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ABSTRACT: The human brain is organized into a collection of interacting networks with specialized functions to support various cognitive functions. Recent research has reached a consensus that the brain manifests small-world topology, which implicates both global and local efficiency at minimal wiring costs, and also modular organization, which indicates functional segregation and specialization. However, the important questions of how and when the small-world topology and modular organization come into existence remain largely unanswered. Taking a graph theoretic approach, we attempt to shed light on this matter by an in vivo study, using diffusion tensor imaging based fiber tractography, on 39 healthy pediatric subjects with longitudinal data collected at average ages of 2 weeks, 1 year, and 2 years. Our results indicate that the small-world architecture exists at birth with efficiency that increases in later stages of development. In addition, we found that the networks are broad scale in nature, signifying the existence of pivotal connection hubs and resilience of the brain network to random and targeted attacks. We also observed, with development, that the brain network seems to evolve progressively from a local, predominantly proximity based, connectivity pattern to a more distributed, predominantly functional based, connectivity pattern. These observations suggest that the brain in the early years of life has relatively efficient systems that may solve similar information processing problems, but in divergent ways.PLoS ONE 01/2011; 6(9):e24678. · 4.09 Impact Factor -
Article: Simulation of brain mass effect with an arbitrary Lagrangian and Eulerian FEM.
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ABSTRACT: Estimation of intracranial stress distribution caused by mass effect is critical to the management of hemorrhagic stroke or brain tumor patients, who may suffer severe secondary brain injury from brain tissue compression. Coupling with physiological parameters that are readily available using MRI, eg, tissue perfusion, a non-invasive, quantitative and regional estimation of intracranial stress distribution could offer a better understanding of brain tissue's reaction under mass effect. A quantitative and sound measurement serving this particular purpose remains elusive due to multiple challenges associated with biomechanical modeling of the brain. One such challenge for the conventional Lagrangian frame based finite element method (LFEM) is that the mesh distortion resulted from the expansion of the mass effects can terminate the simulation prematurely before the desired pressure loading is achieved. In this work, we adopted an arbitrary Lagrangian and Eulerian FEM method (ALEF) with explicit dynamic solutions to simulate the expansion of brain mass effects caused by a pressure loading. This approach consists of three phases: 1) a Lagrangian phase to deform mesh like LFEM, 2) a mesh smoothing phase to reduce mesh distortion, and 3) an Eulerian phase to map the state variables from the old mesh to the smoothed one. In 2D simulations with simulated geometries, this approach is able to model substantially larger deformations compared to LFEM. We further applied this approach to a simulation with 3D real brain geometry to quantify the distribution of von Mises stress within the brain.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2010; 13(Pt 2):274-81. -
Article: Hierachical Spherical-Harmonics Based Deformable HARDI Registration.
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ABSTRACT: Diffusion Weighted Imaging (DWI) has gained considerable interest in the research communcity owing to its demonstrated capability of allowing in vivo probing of brain white matter microstructures. In terms of characterizing crossing fibers, High Angular Resolution Imaging (HARDI) affords more information than the more common Diffusion Tensor Imaging (DTI). But in the context of image registration, the question of how much information is needed for satifactory alignment remains unanswered. Low order representation of the diffusivity information is generally more robust than the higher order representaton, but the latter gives more information for correct fiber tract alignment. Higher order representation, however, when naively utilized might not necessarily be conducive to improving registration accuracy since similar tissue structures with significant orientation differences prior to proper alignment might be mistakenly considered as non-matching structures. We propose in this paper a hierarchical spherical-harmonics based registration algorithm which utilized the wealth of information provided by HARDI in a more principled means. The image volumes are first registered using robust relatively direction invariant features derived from the diffusion-attenuation profile, and their alignment is then refined using spherical harmonic (SH) representation of gradually increasing order. The progression of represention from non-directional, single-directional to multi-directional representation presents a systematic means of extracting directional information from the HARDI data. Experimental results show a marked increase in registration accuracy (as high as 23.32%) over a state-of-the-art DTI registration algorithm.Lecture Notes in Computer Science 01/2010; 6326:228-236. -
Article: Evaluation of MR-derived cerebral oxygen metabolic index in experimental hyperoxic hypercapnia, hypoxia, and ischemia.
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ABSTRACT: A noninvasive MRI method to measure cerebral oxygen metabolism has the potential to assess tissue viability during cerebral ischemia. The purposes of this study were to validate MR oxygenation measurements across a wide range of global cerebral oxygenation and to examine the spatiotemporal evolution of oxygen metabolism during focal middle cerebral artery occlusion in rats. A group of rats (n=28) under normal, hyperoxic hypercapnia and hypoxia were studied to compare MR-measured cerebral oxygen saturation (O(2)Sat(MRv)) with blood gas oximetry measurements in the jugular vein (O(2)Sat(JV)) and superior sagittal sinus (O(2)Sat(SSS)). In a separate group of rats (n=31), MR-measured cerebral oxygen metabolic index (MR_COMI) was acquired at multiple time points during middle cerebral artery occlusion. Histogram analysis was performed on the normalized MR_COMI (rMR_COMI) to examine evolution of oxygen metabolism during acute ischemia. Highly linear relationships were obtained between O(2)Sat(MRv) and O(2)Sat(JV)/O(2)Sat(SSS) in rats under global cerebral oxygenation alterations. In the focal ischemia study, rMR_COMI values were significantly lower within the areas of eventual infarction than other regions. Moreover, the rMR_COMI values within the ischemic territory decreased with time, concomitant with an increase in the number of voxels with severely impaired oxygen metabolism. Accurate estimates of O(2)Sat(MRv) can be obtained across a broad and physiologically relevant range of cerebral oxygenation. Furthermore, this method demonstrates a dynamic alteration of cerebral oxygen metabolism during acute ischemia in rats.Stroke 05/2009; 40(6):2165-72. · 5.73 Impact Factor -
Article: White matter abnormalities revealed by diffusion tensor imaging in non-demented and demented HIV+ patients.
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ABSTRACT: HIV associated dementia (HAD) is the most advanced stage of central nervous system disease caused by HIV infection. Previous studies have demonstrated that patients with HAD exhibit greater cerebral and basal ganglia atrophy than non-demented HIV+ (HND) patients. However, the extent to which white matter is affected in HAD patients compared to HND patients remains elusive. This study is designed to address the potential white matter abnormalities through the utilization of diffusion tensor imaging (DTI) in both HND and HAD patients. DTI and T1-weighted images were acquired from 18 healthy controls, 21 HND and 8 HAD patients. T1 image-based registration was performed to 1) parcellate the whole brain white matter into major white matter regions, including frontal, parietal, temporal and occipital white matter, corpus callosum and internal capsule for statistical comparisons of the mean DTI values, and 2) warp all DTI parametric images towards the common template space for voxel-based analysis. The statistical comparisons were performed with four DTI parameters including fractional anisotropy (FA), mean (MD), axial (AD), and radial (RD) diffusivities. With Whitney U tests on the mean DTI values, both HND and HAD demonstrated significant differences from the healthy control in multiple white matter regions. In addition, HAD patients exhibited significantly elevated MD and RD in the parietal white matter when compared to HND patients. In the voxel-based analysis, widespread abnormal regions were identified for both HND and HAD patients, although a much larger abnormal volume was observed in HAD patients for all four DTI parameters. Furthermore, both region of interest (ROI) based and voxel-based analyses revealed that RD was affected to a much greater extent than AD by HIV infection, which may suggest that demyelination is the prominent disease progression in white matter.NeuroImage 05/2009; 47(4):1154-62. · 5.89 Impact Factor -
Article: Mapping growth patterns and genetic influences on early brain development in twins.
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ABSTRACT: Despite substantial progress in understanding the anatomical and functional development of the human brain, little is known on the spatial-temporal patterns and genetic influences on white matter maturation in twins. Neuroimaging data acquired from longitudinal twin studies provide a unique platform for scientists to investigate such issues. However, the interpretation of neuroimaging data from longitudinal twin studies is hindered by the lacking of appropriate image processing and statistical tools. In this study, we developed a statistical framework for analyzing longitudinal twin neuroimaging data, which is consisted of generalized estimating equation (GEE2) and a test procedure. The GEE2 method can jointly model imaging measures with genetic effect, environmental effect, and behavioral and clinical variables. The score test statistic is used to test linear hypothesis such as the association between brain structure and function with the covariates of interest. A resampling method is used to control the family-wise error rate to adjust for multiple comparisons. With diffusion tensor imaging (DTI), we demonstrate the application of our statistical methods in quantifying the spatiotemporal white matter maturation patterns and in detecting the genetic effects in a longitudinal neonatal twin study. The proposed approach can be easily applied to longitudinal twin data with multiple outcomes and accommodate incomplete and unbalanced data, i.e., subjects with different number of measurements.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2009; 12(Pt 2):232-9. -
Article: Intrinsic Regression Models for Positive-Definite Matrices With Applications to Diffusion Tensor Imaging.
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ABSTRACT: The aim of this paper is to develop an intrinsic regression model for the analysis of positive-definite matrices as responses in a Riemannian manifold and their association with a set of covariates, such as age and gender, in a Euclidean space. The primary motivation and application of the proposed methodology is in medical imaging. Because the set of positive-definite matrices do not form a vector space, directly applying classical multivariate regression may be inadequate in establishing the relationship between positive-definite matrices and covariates of interest, such as age and gender, in real applications. Our intrinsic regression model, which is a semiparametric model, uses a link function to map from the Euclidean space of covariates to the Riemannian manifold of positive-definite matrices. We develop an estimation procedure to calculate parameter estimates and establish their limiting distributions. We develop score statistics to test linear hypotheses on unknown parameters and develop a test procedure based on a resampling method to simultaneously assess the statistical significance of linear hypotheses across a large region of interest. Simulation studies are used to demonstrate the methodology and examine the finite sample performance of the test procedure for controlling the family-wise error rate. We apply our methods to the detection of statistical significance of diagnostic effects on the integrity of white matter in a diffusion tensor study of human immunodeficiency virus. Supplemental materials for this article are available online.Journal of the American Statistical Association 01/2009; 104(487):1203-1212. · 1.99 Impact Factor -
Article: Human cortical representation of oral temperature.
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ABSTRACT: The temperature of foods and fluids is a major factor that determines their pleasantness and acceptability. Studies of nonhuman primates have shown that many neurons in cortical taste areas receive and process not only chemosensory inputs, but oral thermosensory (temperature) inputs as well. We investigated whether changes in oral temperature activate these areas in humans, or middle or posterior insular cortex, the areas most frequently identified for the encoding of temperature information from the human hand. In the fMRI study we identified areas of activation in response to innocuous, temperature-controlled (cooled and warmed, 5, 20 and 50 degrees C) liquid introduced into the mouth. The oral temperature stimuli activated the insular taste cortex (identified by glucose taste stimuli), a part of the somatosensory cortex, the orbitofrontal cortex, the anterior cingulate cortex, and the ventral striatum. Brain regions where activations correlated with the pleasantness ratings of the oral temperature stimuli included the orbitofrontal cortex and pregenual cingulate cortex. We conclude that a network of taste- and reward-responsive regions of the human brain is also activated by intra-oral thermal stimulation, and that the pleasant subjective states elicited by oral thermal stimuli are correlated with the activations in the orbitofrontal cortex and pregenual cingulate cortex. Thus the pleasantness of oral temperature is represented in brain regions shown in previous studies to represent the pleasantness of the taste and flavour of food. Bringing together these different oral representations in the same brain regions may enable particular combinations to influence the pleasantness of foods.Physiology & Behavior 01/2008; 92(5):975-84. · 2.87 Impact Factor -
Article: Neuroimaging in human immunodeficiency virus infection.
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ABSTRACT: Human immunodeficiency virus (HIV) is associated with central nervous system (CNS) changes that may affect cerebral blood flow (CBF), metabolism, structure, and diffusion. Each of the available neuroimaging techniques offers unique insight into the neural mechanisms underlying HIV, as well as a potential means of monitoring disease progression and treatment response. The purpose of the article is to provide a review of experimental studies evaluating changes related to HIV with imaging techniques, including single-photon emission computed tomography (SPECT), positron emission tomography (PET), volumetric magnetic resonance imaging (MRI), functional MRI (fMRI), magnetic resonance spectroscopy (MRS), diffusion tensor imaging (DTI), and perfusion MRI (pMRI).Journal of Neuroimmunology 01/2005; 157(1-2):153-62. · 2.96 Impact Factor -
Article: Practical consideration for 3T imaging.
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ABSTRACT: In the past 10 to 15 years, 1.5T has been one of the most commonly used field strengths for day-to-day clinical operations. However, recent advances in high field technology and the increased availability of high field (> 1.5T) human scanners have opened the doors for a variety of exciting improvements in clinical and research applications of MR imaging. In particular, 3T has continued to gain wide acceptance as one of the main field strengths for clinical and research studies. Therefore, in this article the authors focus on the pros and cons of 3T imaging and comparisons between results obtained at 3T and 1.5T.Magnetic Resonance Imaging Clinics of North America 11/2003; 11(4):615-39, vi. · 1.63 Impact Factor -
Article: RADTI: regression analyses of diffusion tensor images
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ABSTRACT: Diffusion tensor image (DTI) is a powerful tool for quantitatively assessing the integrity of anatomical connectiv-ity in white matter in clinical populations. The prevalent methods for group-level analysis of DTI are statistical analyses of invariant measures (e.g., fractional anisotropy) and principal directions across groups. The invariant measures and principal directions, however, do not capture all information in full diffusion tensor, which can decrease the statistical power of DTI in detecting subtle changes of white matters. Thus, it is very desirable to develop new statistical methods for analyzing full diffusion tensors. In this paper, we develop a set of toolbox, called RADTI, for the analysis of the full diffusion tensors as responses and establish their association with a set of covariates. The key idea is to use the recent development of log-Euclidean metric and then transform diffusion tensors in a nonlinear space into their matrix logarithms in a Euclidean space. Our regression model is a semiparametric model, which avoids any specific parametric assumptions. We develop an estimation procedure and a test procedure based on score statistics and a resampling method to simultaneously assess the statistical significance of linear hypotheses across a large region of interest. Monte Carlo simulations are used to examine the finite sample performance of the test procedure for controlling the family-wise error rate. We apply our methods to the detection of statistical significance of diagnostic and age effects on the integrity of white matter in a diffusion tensor study of human immunodeficiency virus. -
Article: LSTGEE: longitudinal analysis of neuroimaging data
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ABSTRACT: Longitudinal imaging studies are essential to understanding the neural development of neuropsychiatric disorders, substance use disorders, and normal brain. Using appropriate image processing and statistical tools to analyze the imaging, behavioral, and clinical data is critical for optimally exploring and interpreting the findings from those imaging studies. However, the existing imaging processing and statistical methods for analyzing imaging longitudinal measures are primarily developed for cross-sectional neuroimaging studies. The simple use of these cross-sectional tools to longitudinal imaging studies will significantly decrease the statistical power of longitudinal studies in detecting subtle changes of imaging measures and the causal role of time-dependent covariate in disease process. The main objective of this paper is to develop longitudinal statistics toolbox, called LSTGEE, for the analysis of neuroimaging data from longitudinal studies. We develop generalized estimating equations for jointly modeling imaging measures with behavioral and clinical variables from longitudinal studies. We develop a test procedure based on a score test statistic and a resampling method to test linear hypotheses of unknown parameters, such as associations between brain structure and function and covariates of interest, such as IQ, age, gene, diagnostic groups, and severity of disease. We demonstrate the application of our statistical methods to the detection of the changes of the fractional anisotropy across time in a longitudinal neonate study. Particularly, our results demonstrate that the use of longitudinal statistics can dramatically increase the statistical power in detecting the changes of neuroimaging measures. The proposed approach can be applied to longitudinal data with multiple outcomes and accommodate incomplete and unbalanced data, i.e., subjects with different number of measurements.
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Institutions
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2009–2013
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University of North Carolina at Chapel Hill
- • Biomedical Research Imaging Center
- • Department of Radiology
Chapel Hill, NC, USA
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