Imaging Genetics Center

Allenstown Elementary School, United States

Imaging Genetics Center

Allenstown Elementary School, United States
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Shi J.,Arizona State University | Lepore N.,Childrens Hospital Los Angeles | Gutman B.A.,University of Southern California | Thompson P.M.,Imaging Genetics Center | And 3 more authors.
Human Brain Mapping | Year: 2014

The apolipoprotein E (APOE) e4 allele is the most prevalent genetic risk factor for Alzheimer's disease (AD). Hippocampal volumes are generally smaller in AD patients carrying the e4 allele compared to e4 noncarriers. Here we examined the effect of APOE e4 on hippocampal morphometry in a large imaging database-the Alzheimer's Disease Neuroimaging Initiative (ADNI). We automatically segmented and constructed hippocampal surfaces from the baseline MR images of 725 subjects with known APOE genotype information including 167 with AD, 354 with mild cognitive impairment (MCI), and 204 normal controls. High-order correspondences between hippocampal surfaces were enforced across subjects with a novel inverse consistent surface fluid registration method. Multivariate statistics consisting of multivariate tensor-based morphometry (mTBM) and radial distance were computed for surface deformation analysis. Using Hotelling's T2 test, we found significant morphological deformation in APOE e4 carriers relative to noncarriers in the entire cohort as well as in the nondemented (pooled MCI and control) subjects, affecting the left hippocampus more than the right, and this effect was more pronounced in e4 homozygotes than heterozygotes. Our findings are consistent with previous studies that showed e4 carriers exhibit accelerated hippocampal atrophy; we extend these findings to a novel measure of hippocampal morphometry. Hippocampal morphometry has significant potential as an imaging biomarker of early stage AD. © 2014 Wiley Periodicals, Inc.


Rajagopalan P.,Imaging Genetics Center | Refsum H.,University of Oslo | Hua X.,Imaging Genetics Center | Toga A.W.,Imaging Genetics Center | And 4 more authors.
Neurobiology of Aging | Year: 2013

Poor kidney function is associated with increased risk of cognitive decline and generalized brain atrophy. Chronic kidney disease impairs glomerular filtration rate, and this deterioration is indicated by elevated blood levels of kidney biomarkers such as creatinine and cystatin C. Here we hypothesized that impaired renal function would be associated with brain deficits in regions vulnerable to neurodegeneration. Using tensor-based morphometry, we related patterns of brain volumetric differences to creatinine, cystatin C levels, and glomerular filtration rate in a large cohort of 738 (mean age, 75.5 ± 6.8 years; 438 men, 300 women) elderly Caucasian subjects scanned as part of the Alzheimer's Disease Neuroimaging Initiative. Elevated kidney biomarkers were associated with volume deficits in the white matter region of the brain. All 3 renal parameters in our study showed significant associations consistently with a region that corresponds with the anterior limb of internal capsule, bilaterally. This is the first study to report a marked profile of structural alterations in the brain associated with elevated kidney biomarkers, helping us to explain the cognitive deficits. © 2013 Elsevier Inc.


Guillaume B.,University of Liège | Guillaume B.,University of Warwick | Guillaume B.,Glaxosmithkline | Hua X.,Imaging Genetics Center | And 4 more authors.
NeuroImage | Year: 2014

Despite the growing importance of longitudinal data in neuroimaging, the standard analysis methods make restrictive or unrealistic assumptions (e.g., assumption of Compound Symmetry-the state of all equal variances and equal correlations-or spatially homogeneous longitudinal correlations). While some new methods have been proposed to more accurately account for such data, these methods are based on iterative algorithms that are slow and failure-prone. In this article, we propose the use of the Sandwich Estimator method which first estimates the parameters of interest with a simple Ordinary Least Square model and second estimates variances/covariances with the "so-called" Sandwich Estimator (SwE) which accounts for the within-subject correlation existing in longitudinal data. Here, we introduce the SwE method in its classic form, and we review and propose several adjustments to improve its behaviour, specifically in small samples. We use intensive Monte Carlo simulations to compare all considered adjustments and isolate the best combination for neuroimaging data. We also compare the SwE method to other popular methods and demonstrate its strengths and weaknesses. Finally, we analyse a highly unbalanced longitudinal dataset from the Alzheimer's Disease Neuroimaging Initiative and demonstrate the flexibility of the SwE method to fit within- and between-subject effects in a single model. Software implementing this SwE method has been made freely available at http://warwick.ac.uk/tenichols/SwE. © 2014.


Nir T.M.,Imaging Genetics Center | Jahanshad N.,Imaging Genetics Center | Jahanshad N.,University of California at Los Angeles | Busovaca E.,University of California at San Francisco | And 4 more authors.
Human Brain Mapping | Year: 2014

People with HIV are living longer as combination antiretroviral therapy (cART) becomes more widely available. However, even when plasma viral load is reduced to untraceable levels, chronic HIV infection is associated with neurological deficits and brain atrophy beyond that of normal aging. HIV is often marked by cortical and subcortical atrophy, but the integrity of the brain's white matter (WM) pathways also progressively declines. Few studies focus on older cohorts where normal aging may be compounded with HIV infection to influence deficit patterns. In this relatively large diffusion tensor imaging (DTI) study, we investigated abnormalities in WM fiber integrity in 56 HIV+ adults with access to cART (mean age: 63.9 ± 3.7 years), compared to 31 matched healthy controls (65.4 ± 2.2 years). Statistical 3D maps revealed the independent effects of HIV diagnosis and age on fractional anisotropy (FA) and diffusivity, but we did not find any evidence for an age by diagnosis interaction in our current sample. Compared to healthy controls, HIV patients showed pervasive FA decreases and diffusivity increases throughout WM. We also assessed neuropsychological (NP) summary z-score associations. In both patients and controls, fiber integrity measures were associated with NP summary scores. The greatest differences were detected in the corpus callosum and in the projection fibers of the corona radiata. These deficits are consistent with published NP deficits and cortical atrophy patterns in elderly people with HIV. © 2013 Wiley Periodicals, Inc.


Roussotte F.F.,Imaging Genetics Center | Daianu M.,Imaging Genetics Center | Jahanshad N.,Imaging Genetics Center | Leonardo C.D.,Imaging Genetics Center | And 2 more authors.
Brain Imaging and Behavior | Year: 2014

Neuroimaging offers a powerful means to assess the trajectory of brain degeneration in a variety of disorders, including Alzheimer's disease (AD). Here we describe how multi-modal imaging can be used to study the changing brain during the different stages of AD. We integrate findings from a range of studies using magnetic resonance imaging (MRI), positron emission tomography (PET), functional MRI (fMRI) and diffusion weighted imaging (DWI). Neuroimaging reveals how risk genes for degenerative disorders affect the brain, including several recently discovered genetic variants that may disrupt brain connectivity. We review some recent neuroimaging studies of genetic polymorphisms associated with increased risk for late-onset Alzheimer's disease (LOAD). Some genetic variants that increase risk for drug addiction may overlap with those associated with degenerative brain disorders. These common associations offer new insight into mechanisms underlying neurodegeneration and addictive behaviors, and may offer new leads for treating them before severe and irreversible neurological symptoms appear. © 2013 Springer Science+Business Media New York.


Dennis E.L.,Imaging Genetics Center
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention | Year: 2012

The human connectome has recently become a popular research topic in neuroscience, and many new algorithms have been applied to analyze brain networks. In particular, network topology measures from graph theory have been adapted to analyze network efficiency and 'small-world' properties. While there has been a surge in the number of papers examining connectivity through graph theory, questions remain about its test-retest reliability (TRT). In particular, the reproducibility of structural connectivity measures has not been assessed. We examined the TRT of global connectivity measures generated from graph theory analyses of 17 young adults who underwent two high-angular resolution diffusion (HARDI) scans approximately 3 months apart. Of the measures assessed, modularity had the highest TRT, and it was stable across a range of sparsities (a thresholding parameter used to define which network edges are retained). These reliability measures underline the need to develop network descriptors that are robust to acquisition parameters.


Couvy-Duchesne B.,QIMR Berghofer Medical Research Institute | Couvy-Duchesne B.,University of Queensland | Blokland G.A.M.,QIMR Berghofer Medical Research Institute | Blokland G.A.M.,University of Queensland | And 6 more authors.
NeuroImage | Year: 2014

Head motion (HM) is a critical confounding factor in functional MRI. Here we investigate whether HM during resting state functional MRI (RS-fMRI) is influenced by genetic factors in a sample of 462 twins (65% fema≤ 101 MZ (monozygotic) and 130 DZ (dizygotic) twin pairs; mean age: 21 (SD=3.16), range 16-29). Heritability estimates for three HM components-mean translation (MT), maximum translation (MAXT) and mean rotation (MR)-ranged from 37 to 51%. We detected a significant common genetic influence on HM variability, with about two-thirds (genetic correlations range 0.76-1.00) of the variance shared between MR, MT and MAXT. A composite metric (HM-PC1), which aggregated these three, was also moderately heritable (h2=42%). Using a sub-sample (N=35) of the twins we confirmed that mean and maximum translational and rotational motions were consistent "traits" over repeated scans (r=0.53-0.59); reliability was even higher for the composite metric (r=0.66). In addition, phenotypic and cross-trait cross-twin correlations between HM and resting state functional connectivities (RS-FCs) with Brodmann areas (BA) 44 and 45, in which RS-FCs were found to be moderately heritable (BA44: h2-=0.23 (sd=0.041), BA45: h2-=0.26 (sd=0.061)), indicated that HM might not represent a major bias in genetic studies using FCs. Even so, the HM effect on FC was not completely eliminated after regression. HM may be a valuable endophenotype whose relationship with brain disorders remains to be elucidated. © 2014 Elsevier Inc.


Dennis E.L.,Imaging Genetics Center | Thompson P.M.,Imaging Genetics Center
Dialogues in Clinical Neuroscience | Year: 2013

In the course of development, the brain undergoes a remarkable process of restructuring as it adapts to the environment and becomes more efficient in processing information. A variety of brain imaging methods can be used to probe how anatomy, connectivity, and function change in the developing brain. Here we review recent discoveries egarding these brain changes in both typically developing individuals and individuals with neurodevelopmental disorders. We begin with typical development, summarizing research on changes in regional brain volume and tissue ensity, cortical thickness, white matter integrity, and functional connectivity. Space limits preclude the coverage of all neurodevelopmental disorders; instead, we cover a representative selection of studies examining neural correlates of autism, attention deficit/hyperactivity disorder, Fragile X, 22q11.2 deletion syndrome, Williams syndrome, own syndrome, and Turner syndrome. Where possible, we focus on studies that identify an age by diagnosis interaction, suggesting an altered developmental trajectory. The studies we review generally cover the developmental period from infancy to early adulthood. Great progress has been made over the last 20 years in mapping how the brain matures with MR technology. With ever-improving technology, we expect this progress to accelerate, offering a deeper understanding of brain development, and more effective interventions for neurodevelopmental disorders. © 2013, AICH.


Peng D.X.,The Interdisciplinary Center | Kelley R.G.,The Interdisciplinary Center | Quintin E.-M.,The Interdisciplinary Center | Raman M.,The Interdisciplinary Center | And 3 more authors.
Human Brain Mapping | Year: 2014

Individuals with fragile X syndrome (FXS) exhibit frontal lobe-associated cognitive and behavioral deficits, including impaired general cognitive abilities, perseverative behaviors, and social difficulties. Neural signals related to these functions are communicated through frontostriatal circuits, which connect with distinct regions of the caudate nucleus (CN). Enlargement of the CN is the most robust and reproduced neuroanatomical abnormality in FXS, but very little is known on how this affects behavioral/cognitive outcomes in this condition. Here, we investigated topography within focal regions of the CN associated with prefrontal circuitry and its link with aberrant behavior and intellect in FXS. Imaging data were acquired from 48 individuals with FXS, 28 IQ-matched controls without FXS (IQ-CTL), and 36 typically developing controls (TD-CTL). Of the total participant count, cognitive and behavioral assessment data were obtained from 44 individuals with FXS and 27 participants in the IQ-CTL group. CN volume and topography were compared between groups. Correlations were performed between CN topography and cognitive as well as behavioral measures within FXS and IQ-CTL groups. As expected, the FXS group had larger CN compared with both IQ-CTL and TD-CTL groups. Correlations between focal CN topography and frontal lobe-associated cognitive and behavioral deficits in the FXS group supported the hypothesis that CN enlargement is related to abnormal orbitofrontal-caudate and dorsolateral-caudate circuitry in FXS. These findings deepen our understanding of neuroanatomical mechanisms underlying cognitive-behavioral problems in FXS and hold promise for informing future behavioral and psychopharmacological interventions targeting specific neural pathways. © 2013 Wiley Periodicals, Inc.


Xiang S.,Arizona State University | Yuan L.,Arizona State University | Fan W.,Huawei | Wang Y.,Arizona State University | And 2 more authors.
NeuroImage | Year: 2014

Bio-imaging technologies allow scientists to collect large amounts of high-dimensional data from multiple heterogeneous sources for many biomedical applications. In the study of Alzheimer's Disease (AD), neuroimaging data, gene/protein expression data, etc., are often analyzed together to improve predictive power. Joint learning from multiple complementary data sources is advantageous, but feature-pruning and data source selection are critical to learn interpretable models from high-dimensional data. Often, the data collected has block-wise missing entries. In the Alzheimer's Disease Neuroimaging Initiative (ADNI), most subjects have MRI and genetic information, but only half have cerebrospinal fluid (CSF) measures, a different half has FDG-PET; only some have proteomic data. Here we propose how to effectively integrate information from multiple heterogeneous data sources when data is block-wise missing. We present a unified "bi-level" learning model for complete multi-source data, and extend it to incomplete data. Our major contributions are: (1) our proposed models unify feature-level and source-level analysis, including several existing feature learning approaches as special cases; (2) the model for incomplete data avoids imputing missing data and offers superior performance; it generalizes to other applications with block-wise missing data sources; (3) we present efficient optimization algorithms for modeling complete and incomplete data. We comprehensively evaluate the proposed models including all ADNI subjects with at least one of four data types at baseline: MRI, FDG-PET, CSF and proteomics. Our proposed models compare favorably with existing approaches. © 2013 Elsevier Inc.

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