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216 Research products

  • Neuroinformatics
  • Open Access
  • National Institutes of Health
  • UC Davis Alzheimer's Core Center

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Eskildsen, S.F.; Coupe, P.; Fonov, V.; Manjon, J.V.; +8 Authors

    Brain extraction is an important step in the analysis of brain images. The variability in brain morphology and the difference in intensity characteristics due to imaging sequences make the development of a general purpose brain extraction algorithm challenging. To address this issue, we propose a new robust method (BEaST) dedicated to produce consistent and accurate brain extraction. This method is based on nonlocal segmentation embedded in a multi-resolution framework. A library of 80 priors is semi-automatically constructed from the NIH-sponsored MRI study of normal brain development, the International Consortium for Brain Mapping, and the Alzheimer's Disease Neuroimaging Initiative databases. In testing, a mean Dice similarity coefficient of 0.9834 ± 0.0053 was obtained when performing leave-one-out cross validation selecting only 20 priors from the library. Validation using the online Segmentation Validation Engine resulted in a top ranking position with a mean Dice coefficient of 0.9781 ± 0.0047. Robustness of BEaST is demonstrated on all baseline ADNI data, resulting in a very low failure rate. The segmentation accuracy of the method is better than two widely used publicly available methods and recent state-of-the-art hybrid approaches. BEaST provides results comparable to a recent label fusion approach, while being 40 times faster and requiring a much smaller library of priors. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., as well as non-profit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30AG010129, K01 AG030514, and the Dana Foundation.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ HAL-Inserm; Hal-Dide...arrow_drop_down
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    Other literature type . Article . 2012
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Other literature type . 2012
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    NeuroImage
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    NeuroImage
    Article . 2011
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    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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    Article . 2012
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    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ HAL-Inserm; Hal-Dide...arrow_drop_down
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      Other literature type . Article . 2012
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      NeuroImage
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      NeuroImage
      Article . 2011
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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      Article . 2012
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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      Article . 2012
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Suping Cai; Liyu Huang; Jia Zou; Longlong Jing; +6 Authors

    We used resting-state functional magnetic resonance imaging (fMRI) to investigate changes in the thalamus functional connectivity in early and late stages of amnestic mild cognitive impairment. Data of 25 late stages of amnestic mild cognitive impairment (LMCI) patients, 30 early stages of amnestic mild cognitive impairment (EMCI) patients and 30 well-matched healthy controls (HC) were analyzed from the Alzheimer's disease Neuroimaging Initiative (ADNI). We focused on the correlation between low frequency fMRI signal fluctuations in the thalamus and those in all other brain regions. Compared to healthy controls, we found functional connectivity between the left/right thalamus and a set of brain areas was decreased in LMCI and/or EMCI including right fusiform gyrus (FG), left and right superior temporal gyrus, left medial frontal gyrus extending into supplementary motor area, right insula, left middle temporal gyrus (MTG) extending into middle occipital gyrus (MOG). We also observed increased functional connectivity between the left/right thalamus and several regions in LMCI and/or EMCI including left FG, right MOG, left and right precuneus, right MTG and left inferior temporal gyrus. In the direct comparison between the LMCI and EMCI groups, we obtained several brain regions showed thalamus-seeded functional connectivity differences such as the precentral gyrus, hippocampus, FG and MTG. Briefly, these brain regions mentioned above were mainly located in the thalamo-related networks including thalamo-hippocampus, thalamo-temporal, thalamo-visual, and thalamo-default mode network. The decreased functional connectivity of the thalamus might suggest reduced functional integrity of thalamo-related networks and increased functional connectivity indicated that aMCI patients could use additional brain resources to compensate for the loss of cognitive function. Our study provided a new sight to understand the two important states of aMCI and revealed resting-state fMRI is an appropriate method for exploring pathophysiological changes in aMCI.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
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    Europe PubMed Central
    Article . 2015
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    PLoS ONE
    Article . 2015 . Peer-reviewed
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    Article . 2015
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      Article . 2015 . Peer-reviewed
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      Article . 2015
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Zhang, N.; Song, X.; Zhang, Y.;

    Background: Several structural brain changes have been associated with Alzheimer's disease (AD). This study investigated the prediction of AD by combining multiple brain changes with the hallmark medial temporal lobe atrophy (MTA). Methods: High-resolution magnetic resonance imaging (MRI) data of people with mild AD (n = 39), mild cognitive impairment (MCI; n = 82), and of healthy controls (HC; n = 58) were obtained at baseline. Among these people, 26 AD, 53 MCI, and 46 HC subjects had 24-month follow-up MRI scans. Bilateral MTA was evaluated using a medial temporal lobe atrophy scale (MTAS). Common changes in the aging brain were summarized using a brain atrophy and lesion index (BALI). The performance of the MTAS, BALI, and a score combining both, using a logistic regression model, were assessed. Results: The MTAS and BALI scores were closely correlated (r2 > 0.56); each differed between the diagnostic groups. Having an unfavorable MTAS score was associated with an increased risk of MCI-AD conversion (OR = 3.71, p = 0.039), adjusted for age, sex, and education; having an unfavorable BALI score marginally contributed to such risks (OR = 4.08, p = 0.080). Combining MTAS and BALI components resulted in a greater OR (8.99, p = 0.007) and an improved predictive accuracy (75.9%, p = 0.002). Conclusion: Multiple structural changes have an additive effect on AD. The data support potential future roles of combining multiple coexisting structural changes to benefit AD diagnosis, progression monitoring, and/or treatment effect evaluation. Copyright © 2012 S. Karger AG, Basel.

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      Other literature type . 2012
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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    Authors: Yongfu Hao; Tianyao Wang; Xinqing Zhang; Yunyun Duan; +4 Authors

    AbstractAutomatic and reliable segmentation of subcortical structures is an important but difficult task in quantitative brain image analysis. Multi‐atlas based segmentation methods have attracted great interest due to their promising performance. Under the multi‐atlas based segmentation framework, using deformation fields generated for registering atlas images onto a target image to be segmented, labels of the atlases are first propagated to the target image space and then fused to get the target image segmentation based on a label fusion strategy. While many label fusion strategies have been developed, most of these methods adopt predefined weighting models that are not necessarily optimal. In this study, we propose a novel local label learning strategy to estimate the target image's segmentation label using statistical machine learning techniques. In particular, we use a L1‐regularized support vector machine (SVM) with a k nearest neighbor (kNN) based training sample selection strategy to learn a classifier for each of the target image voxel from its neighboring voxels in the atlases based on both image intensity and texture features. Our method has produced segmentation results consistently better than state‐of‐the‐art label fusion methods in validation experiments on hippocampal segmentation of over 100 MR images obtained from publicly available and in‐house datasets. Volumetric analysis has also demonstrated the capability of our method in detecting hippocampal volume changes due to Alzheimer's disease. Hum Brain Mapp 35:2674–2697, 2014. © 2013 Wiley Periodicals, Inc.

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    Human Brain Mapping
    Article . 2013 . Peer-reviewed
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      Human Brain Mapping
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    Authors: Cecily G. Swinford; Shannon L. Risacher; Arnaud Charil; Adam J. Schwarz; +2 Authors

    Introduction Relationship between self– and informant memory concerns and tau aggregation was assessed in adults at risk for Alzheimer's disease (AD). Methods Regional mean standardized uptake value ratios were extracted from [18F]flortaucipir positron emission tomography (PET) scans of 82 at-risk adults in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Associations between self– and informant ECog memory scores and tau aggregation were analyzed on both regional and voxelwise bases. Analyses were completed both on the whole sample and restricted to amyloid-positive individuals only. Results Memory concerns were associated with tau aggregation. Self-perception was more associated with frontal tau. In contrast, informant scores were more associated with parietal tau. This source-by-region interaction was more prominent in amyloid-positive participants and observed in both regional and voxelwise analyses. Discussion Quantitative assessment of perceived memory functioning may be useful for screening older adults at risk for Alzheimer's disease. Individuals and their informants may provide complementary information relating to the anatomical distribution of tau. Highlights • Both self– and informant memory concerns are associated with tau aggregation in at-risk adults. • Self-concerns are more associated with frontal tau compared to a parietal pattern for informant concerns. • This pattern is strongest in amyloid-positive participants.

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    Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring
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      Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring
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    Authors: Junghi, Kim; Wei, Pan;

    There has been increasing interest in developing more powerful and flexible statistical tests to detect genetic associations with multiple traits, as arising from neuroimaging genetic studies. Most of existing methods treat a single trait or multiple traits as response while treating an SNP as a predictor coded under an additive inheritance mode. In this paper, we follow an earlier approach in treating an SNP as an ordinal response while treating traits as predictors in a proportional odds model (POM). In this way, it is not only easier to handle mixed types of traits, e.g., some quantitative and some binary, but it is also potentially more robust to the commonly adopted additive inheritance mode. More importantly, we develop an adaptive test in a POM so that it can maintain high power across many possible situations. Compared to the existing methods treating multiple traits as responses, e.g., in a generalized estimating equation (GEE) approach, the proposed method can be applied to a high dimensional setting where the number of phenotypes (p) can be larger than the sample size (n), in addition to a usual small P setting. The promising performance of the proposed method was demonstrated with applications to the Alzheimer's Disease Neuroimaging Initiative (ADNI) data, in which either structural MRI driven phenotypes or resting-state functional MRI (rs-fMRI) derived brain functional connectivity measures were used as phenotypes. The applications led to the identification of several top SNPs of biological interest. Furthermore, simulation studies showed competitive performance of the new method, especially for p>n.

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    Genetic Epidemiology
    Article . 2017 . Peer-reviewed
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      Genetic Epidemiology
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    Authors: Toledo, Jon B; Korff, Ané; Shaw, Leslie M; Trojanowski, John Q; +1 Authors

    Introduction Alzheimer’s disease (AD) is characterized by the deposition of tau and amyloid in the brain. Although the core cerebrospinal fluid (CSF) AD biomarkers amyloid β peptide 1–42 (Aβ1–42), total tau (t-tau) and phosphorylated tau 181 (p-tau181) show good diagnostic sensitivity and specificity, additional biomarkers that can aid in preclinical diagnosis or better track disease progression are needed. Activation of the complement system, a pivotal part of inflammation, occurs at very early stages in the AD brain. Therefore, CSF levels of complement proteins that could be linked to cognitive and structural changes in AD may have diagnostic and prognostic value. Methods Using xMAP® technology based assays we measured complement 3 (C3) and factor H (FH) in the CSF of 110 controls (CN), 187 mild cognitive impairment (MCI) and 92 AD subjects of the AD Neuroimaging Initiative (ADNI) at baseline. All ADNI participants underwent clinical follow-up at 12 month intervals and MCI subjects had additional visits at 6 and 18 months. The association between CSF biomarkers and different outcome measures were analyzed using Cox proportional hazard models (conversion from MCI to AD), logistic regression models (classification of clinical groups) and mixed-effects models adjusted for age, gender, education, t-tau/Aβ1–42 and APOE ϵ4 presence (baseline and longitudinal association between biomarkers and cognitive scores). Results Although no association was found between the complement proteins and clinical diagnosis or cognitive measures, lower levels of C3 (β = −0.12, p = 0.041) and FH (β = −0.075, p = 0.041) were associated with faster cognitive decline in MCI subjects as measured by the AD Assessment Scale-cognitive subscale (ADAS-Cog) test. Furthermore, lower FH levels were associated with larger lateral ventricular volume (p = 0.024), which is indicative of brain atrophy. Conclusions Our study confirms a lack of suitability of CSF C3 and FH as diagnostic biomarkers of AD, but points to their modest potential as prognostic biomarkers and therapeutic targets in cognitively impaired patients.

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    Alzheimer’s Research & Therapy
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    Authors: Cassidy M. Fiford; Jennifer M. Nicholas; Geert Jan Biessels; Christopher A. Lane; +2 Authors

    Abstract Introduction Understanding relationships among blood pressure (BP), cognition, and brain volume could inform Alzheimer's disease (AD) management. Methods We investigated Alzheimer's Disease Neuroimaging Initiative (ADNI) participants: 200 controls, 346 mild cognitive impairment (MCI), and 154 AD. National Alzheimer's Co‐ordinating Center (NACC) participants were separately analyzed: 1098 controls, 2297 MCI, and 4845 AD. Relationships between cognition and BP were assessed in both cohorts and BP and atrophy rates in ADNI. Multivariate mixed linear‐regression models were fitted with joint outcomes of BP (systolic, diastolic, and pulse pressure), cognition (Mini‐Mental State Examination, Logical Memory, and Digit Symbol) and atrophy rate (whole‐brain, hippocampus). Results ADNI MCI and AD patients with greater baseline systolic BP had higher hippocampal atrophy rates ([r, P value]; 0.2, 0.005 and 0.2, 0.04, respectively). NACC AD patients with lower systolic BP had lower cognitive scores (0.1, 0.0003). Discussion Higher late‐life BP may be associated with faster decline in cognitively impaired elders.

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    Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring
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      Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring
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    Authors: Katrina L. Grasby*† and Neda Jahanshad*† et al.;

    The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson’s disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

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      ZENODO
      Article . 2020
      License: CC BY
      Data sources: ZENODO
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    Authors: Jason L. Stein; Derrek P. Hibar; Sarah K. Madsen; M. Khamis; +11 Authors

    The caudate is a subcortical brain structure implicated in many common neurological and psychiatric disorders. To identify specific genes associated with variations in caudate volume, structural magnetic resonance imaging and genome-wide genotypes were acquired from two large cohorts, the Alzheimer's Disease NeuroImaging Initiative (ADNI; N=734) and the Brisbane Adolescent/Young Adult Longitudinal Twin Study (BLTS; N=464). In a preliminary analysis of heritability, around 90% of the variation in caudate volume was due to genetic factors. We then conducted genome-wide association to find common variants that contribute to this relatively high heritability. Replicated genetic association was found for the right caudate volume at single-nucleotide polymorphism rs163030 in the ADNI discovery sample (P=2.36 × 10⁻⁶) and in the BLTS replication sample (P=0.012). This genetic variation accounted for 2.79 and 1.61% of the trait variance, respectively. The peak of association was found in and around two genes, WDR41 and PDE8B, involved in dopamine signaling and development. In addition, a previously identified mutation in PDE8B causes a rare autosomal-dominant type of striatal degeneration. Searching across both samples offers a rigorous way to screen for genes consistently influencing brain structure at different stages of life. Variants identified here may be relevant to common disorders affecting the caudate.

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    Europe PubMed Central
    Article . 2011
    Data sources: PubMed Central
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    Molecular Psychiatry
    Article . 2011 . Peer-reviewed
    License: Springer TDM
    Data sources: Crossref
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      Molecular Psychiatry
      Article . 2011 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Eskildsen, S.F.; Coupe, P.; Fonov, V.; Manjon, J.V.; +8 Authors

    Brain extraction is an important step in the analysis of brain images. The variability in brain morphology and the difference in intensity characteristics due to imaging sequences make the development of a general purpose brain extraction algorithm challenging. To address this issue, we propose a new robust method (BEaST) dedicated to produce consistent and accurate brain extraction. This method is based on nonlocal segmentation embedded in a multi-resolution framework. A library of 80 priors is semi-automatically constructed from the NIH-sponsored MRI study of normal brain development, the International Consortium for Brain Mapping, and the Alzheimer's Disease Neuroimaging Initiative databases. In testing, a mean Dice similarity coefficient of 0.9834 ± 0.0053 was obtained when performing leave-one-out cross validation selecting only 20 priors from the library. Validation using the online Segmentation Validation Engine resulted in a top ranking position with a mean Dice coefficient of 0.9781 ± 0.0047. Robustness of BEaST is demonstrated on all baseline ADNI data, resulting in a very low failure rate. The segmentation accuracy of the method is better than two widely used publicly available methods and recent state-of-the-art hybrid approaches. BEaST provides results comparable to a recent label fusion approach, while being 40 times faster and requiring a much smaller library of priors. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics, Johnson and Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc, F. Hoffman-La Roche, Schering-Plough, Synarc, Inc., as well as non-profit partners the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer's Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30AG010129, K01 AG030514, and the Dana Foundation.

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    Other literature type . Article . 2012
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    Other literature type . 2012
    Data sources: RiuNet
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    NeuroImage
    Article
    License: CC BY NC ND
    Data sources: UnpayWall
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    NeuroImage
    Article . 2011
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    NARCIS
    Article . 2012
    Data sources: NARCIS
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