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Find video protocols related to scientific articles indexed in Pubmed.
Cortical thickness and surface area in neonates at high risk for schizophrenia.
Brain Struct Funct
PUBLISHED: 10-15-2014
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Schizophrenia is a neurodevelopmental disorder associated with subtle abnormal cortical thickness and cortical surface area. However, it is unclear whether these abnormalities exist in neonates associated with genetic risk for schizophrenia. To this end, this preliminary study was conducted to identify possible abnormalities of cortical thickness and surface area in the high-genetic-risk neonates. Structural magnetic resonance images were acquired from offspring of mothers (N = 21) who had schizophrenia (N = 12) or schizoaffective disorder (N = 9), and also matched healthy neonates of mothers who were free of psychiatric illness (N = 26). Neonatal cortical surfaces were reconstructed and parcellated as regions of interest (ROIs), and cortical thickness for each vertex was computed as the shortest distance between the inner and outer surfaces. Comparisons were made for the average cortical thickness and total surface area in each of 68 cortical ROIs. After false discovery rate (FDR) correction, it was found that the female high-genetic-risk neonates had significantly thinner cortical thickness in the right lateral occipital cortex than the female control neonates. Before FDR correction, the high-genetic-risk neonates had significantly thinner cortex in the left transverse temporal gyrus, left banks of superior temporal sulcus, left lingual gyrus, right paracentral cortex, right posterior cingulate cortex, right temporal pole, and right lateral occipital cortex, compared with the control neonates. Before FDR correction, in comparison with control neonates, male high-risk neonates had significantly thicker cortex in the left frontal pole, left cuneus cortex, and left lateral occipital cortex; while female high-risk neonates had significantly thinner cortex in the bilateral paracentral, bilateral lateral occipital, left transverse temporal, left pars opercularis, right cuneus, and right posterior cingulate cortices. The high-risk neonates also had significantly smaller cortical surface area in the right pars triangularis (before FDR correction), compared with control neonates. This preliminary study provides the first evidence that early development of cortical thickness and surface area might be abnormal in the neonates at genetic risk for schizophrenia.
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Functional-mixed effects models for candidate genetic mapping in imaging genetic studies.
Genet. Epidemiol.
PUBLISHED: 05-26-2014
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The aim of this paper is to develop a functional-mixed effects modeling (FMEM) framework for the joint analysis of high-dimensional imaging data in a large number of locations (called voxels) of a three-dimensional volume with a set of genetic markers and clinical covariates. Our FMEM is extremely useful for efficiently carrying out the candidate gene approaches in imaging genetic studies. FMEM consists of two novel components including a mixed effects model for modeling nonlinear genetic effects on imaging phenotypes by introducing the genetic random effects at each voxel and a jumping surface model for modeling the variance components of the genetic random effects and fixed effects as piecewise smooth functions of the voxels. Moreover, FMEM naturally accommodates the correlation structure of the genetic markers at each voxel, while the jumping surface model explicitly incorporates the intrinsically spatial smoothness of the imaging data. We propose a novel two-stage adaptive smoothing procedure to spatially estimate the piecewise smooth functions, particularly the irregular functional genetic variance components, while preserving their edges among different piecewise-smooth regions. We develop weighted likelihood ratio tests and derive their exact approximations to test the effect of the genetic markers across voxels. Simulation studies show that FMEM significantly outperforms voxel-wise approaches in terms of higher sensitivity and specificity to identify regions of interest for carrying out candidate genetic mapping in imaging genetic studies. Finally, FMEM is used to identify brain regions affected by three candidate genes including CR1, CD2AP, and PICALM, thereby hoping to shed light on the pathological interactions between these candidate genes and brain structure and function.
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UNC-Utah NA-MIC framework for DTI fiber tract analysis.
Front Neuroinform
PUBLISHED: 01-09-2014
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Diffusion tensor imaging has become an important modality in the field of neuroimaging to capture changes in micro-organization and to assess white matter integrity or development. While there exists a number of tractography toolsets, these usually lack tools for preprocessing or to analyze diffusion properties along the fiber tracts. Currently, the field is in critical need of a coherent end-to-end toolset for performing an along-fiber tract analysis, accessible to non-technical neuroimaging researchers. The UNC-Utah NA-MIC DTI framework represents a coherent, open source, end-to-end toolset for atlas fiber tract based DTI analysis encompassing DICOM data conversion, quality control, atlas building, fiber tractography, fiber parameterization, and statistical analysis of diffusion properties. Most steps utilize graphical user interfaces (GUI) to simplify interaction and provide an extensive DTI analysis framework for non-technical researchers/investigators. We illustrate the use of our framework on a small sample, cross sectional neuroimaging study of eight healthy 1-year-old children from the Infant Brain Imaging Study (IBIS) Network. In this limited test study, we illustrate the power of our method by quantifying the diffusion properties at 1 year of age on the genu and splenium fiber tracts.
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Environmental and genetic contributors to salivary testosterone levels in infants.
Front Endocrinol (Lausanne)
PUBLISHED: 01-01-2014
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Transient activation of the hypothalamic-pituitary-gonadal axis in early infancy plays an important role in male genital development and sexual differentiation of the brain, but factors contributing to individual variation in testosterone levels during this period are poorly understood. We measured salivary testosterone levels in 222 infants (119 males, 103 females, 108 singletons, 114 twins) between 2.70 and 4.80?months of age. We tested 16 major demographic and medical history variables for effects on inter-individual variation in salivary testosterone. Using the subset of twins, we estimated genetic and environmental contributions to salivary testosterone levels. Finally, we tested single nucleotide polymorphisms (SNPs) within ±5?kb of genes involved in testosterone synthesis, transport, signaling, and metabolism for associations with salivary testosterone using univariate tests and random forest (RF) analysis. We report an association between 5?min APGAR scores and salivary testosterone levels in males. Twin modeling indicated that individual variability in testosterone levels was primarily explained by environmental factors. Regarding genetic variation, univariate tests did not reveal any variants significantly associated with salivary testosterone after adjusting for false discovery rate. The top hit in males was rs10923844, an SNP of unknown function located downstream of HSD3B1 and HSD3B2. The top hits in females were two SNPs located upstream of ESR1 (rs3407085 and rs2295190). RF analysis, which reflects joint and conditional effects of multiple variants, indicated that genes involved in regulation of reproductive function, particularly LHCGR, are related to salivary testosterone levels in male infants, as are genes involved in cholesterol production, transport, and removal, while genes involved in estrogen signaling are related to salivary testosterone levels in female infants.
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Multivariate longitudinal shape analysis of human lateral ventricles during the first twenty-four months of life.
PLoS ONE
PUBLISHED: 01-01-2014
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Little is known about the temporospatial shape characteristics of human lateral ventricles (LVs) during the first two years of life. This study aimed to delineate the morphological growth characteristics of LVs during early infancy using longitudinally acquired MR images in normal healthy infants.
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Differential Reconstitution of T Cell Subsets following Immunodepleting Treatment with Alemtuzumab (Anti-CD52 Monoclonal Antibody) in Patients with Relapsing-Remitting Multiple Sclerosis.
J. Immunol.
PUBLISHED: 11-06-2013
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Alemtuzumab (anti-CD52 mAb) provides long-lasting disease activity suppression in relapsing-remitting multiple sclerosis (RRMS). The objective of this study was to characterize the immunological reconstitution of T cell subsets and its contribution to the prolonged RRMS suppression following alemtuzumab-induced lymphocyte depletion. The study was performed on blood samples from RRMS patients enrolled in the CARE-MS II clinical trial, which was recently completed and led to the submission of alemtuzumab for U.S. Food and Drug Administration approval as a treatment for RRMS. Alemtuzumab-treated patients exhibited a nearly complete depletion of circulating CD4(+) lymphocytes at day 7. During the immunological reconstitution, CD4(+)CD25(+)CD127(low) regulatory T cells preferentially expanded within the CD4(+) lymphocytes, reaching their peak expansion at month 1. The increase in the percentage of TGF-?1-, IL-10-, and IL-4-producing CD4(+) cells reached a maximum at month 3, whereas a significant decrease in the percentages of Th1 and Th17 cells was detected at months 12 and 24 in comparison with the baseline. A gradual increase in serum IL-7 and IL-4 and a decrease in IL-17A, IL-17F, IL-21, IL-22, and IFN-? levels were detected following treatment. In vitro studies have demonstrated that IL-7 induced an expansion of CD4(+)CD25(+)CD127(low) regulatory T cells and a decrease in the percentages of Th17 and Th1 cells. In conclusion, our results indicate that differential reconstitution of T cell subsets and selectively delayed CD4(+) T cell repopulation following alemtuzumab-induced lymphopenia may contribute to its long-lasting suppression of disease activity.
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Characteristics of magnetic resonance imaging biomarkers in a natural history study of golden retriever muscular dystrophy.
Neuromuscul. Disord.
PUBLISHED: 07-13-2013
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The goal of this study was to assess whether magnetic resonance imaging (MRI) biomarkers can quantify disease progression in golden retriever muscular dystrophy (GRMD) via a natural history study. The proximal pelvic limbs of ten GRMD and eight normal dogs were scanned at 3, 6, and 9-12months of age. Several MRI imaging and texture analysis biomarkers were quantified in seven muscles. Almost all MRI biomarkers readily distinguished GRMD from control dogs; however, only selected biomarkers tracked with longitudinal disease progression. The biomarkers that performed best were full-length muscle volume and a texture analysis biomarker, termed heterogeneity index. The biceps femoris, semitendinosus and cranial sartorius muscles showed differential progression in GRMD versus control dogs. MRI features in GRMD dogs showed dynamic progression that was most pronounced over the 3- to 6-month period. Volumetric biomarkers and water map values correlated with histopathological features of necrosis/regeneration at 6-months. In conclusion, selected MRI biomarkers (volume and heterogeneity index) in particular muscles (biceps femoris, semitendinosus, and cranial sartorius) adjusted for age effect allow distinction of differential longitudinal progression in GRMD dogs. These biomarkers may be used as surrogate outcome measures in preclinical GRMD trials.
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MOMENT-BASED METHOD FOR RANDOM EFFECTS SELECTION IN LINEAR MIXED MODELS.
Stat Sin
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The selection of random effects in linear mixed models is an important yet challenging problem in practice. We propose a robust and unified framework for automatically selecting random effects and estimating covariance components in linear mixed models. A moment-based loss function is first constructed for estimating the covariance matrix of random effects. Two types of shrinkage penalties, a hard thresholding operator and a new sandwich-type soft-thresholding penalty, are then imposed for sparse estimation and random effects selection. Compared with existing approaches, the new procedure does not require any distributional assumption on the random effects and error terms. We establish the asymptotic properties of the resulting estimator in terms of its consistency in both random effects selection and variance component estimation. Optimization strategies are suggested to tackle the computational challenges involved in estimating the sparse variance-covariance matrix. Furthermore, we extend the procedure to incorporate the selection of fixed effects as well. Numerical results show promising performance of the new approach in selecting both random and fixed effects and, consequently, improving the efficiency of estimating model parameters. Finally, we apply the approach to a data set from the Amsterdam Growth and Health study.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.