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Proal E.,New York University | Proal E.,Autonomous University of Barcelona | Reiss P.T.,New York University | Reiss P.T.,Nathan Kline Institute for Psychiatric Research | And 15 more authors.
Archives of General Psychiatry | Year: 2011

Context: Volumetric studies have reported relatively decreased cortical thickness and gray matter volumes in adults with attention-deficit/hyperactivity disorder (ADHD) whose childhood status was retrospectively recalled. We present, to our knowledge, the first prospective study combining cortical thickness and voxel-based morphometry in adults diagnosed as having ADHD in childhood. Objectives: To test whether adults with combinedtype childhood ADHD exhibit cortical thinning and decreased gray matter in regions hypothesized to be related to ADHD and to test whether anatomic differences are associated with a current ADHD diagnosis, including persistent vs remitting ADHD. Design: Cross-sectional analysis embedded in a 33-year prospective follow-up at a mean age of 41.2 years. Setting: Research outpatient center. Participants: We recruited probands with ADHD from a cohort of 207 white boys aged 6 to 12 years. Male comparison participants (n=178) were free of ADHD in childhood. Weobtained magnetic resonance images in 59 probands and 80 comparison participants (28.5% and 44.9% of the original samples, respectively). Main Outcome Measures: Whole-brain voxel-based morphometry and vertexwise cortical thickness analyses. Results: The cortex was significantly thinner in ADHD probands than in comparison participants in the dorsal attentional network and limbic areas (false discovery rate <0.05, corrected). In addition, gray matter was significantly decreased in probands in the right caudate, right thalamus, and bilateral cerebellar hemispheres. Probands with persistent ADHD (n=17) did not differ significantly from those with remitting ADHD (n=26) (false discovery rate <0.05). At uncorrected P <.05, individuals with remitting ADHD had thicker cortex relative to those with persistent ADHD in the medial occipital cortex, insula, parahippocampus, and prefrontal regions. Conclusions: Anatomic gray matter reductions are observable in adults with childhood ADHD, regardless of the current diagnosis. The most affected regions underpin top-down control of attention and regulation of emotion and motivation. Exploratory analyses suggest that diagnostic remission may result from compensatory maturation of prefrontal, cerebellar, and thalamic circuitry. ©2011 American Medical Association. All rights reserved.

Nguyen T.M.,University of Windsor | Nguyen T.M.,Biospective Inc | Wu Q.M.J.,University of Windsor
IEEE Transactions on Medical Imaging | Year: 2016

The finite Gaussian mixture model with kernel correlation is a flexible tool that has recently received attention for point set registration. While there are many algorithms for point set registration presented in the literature, an important issue arising from these studies concerns the mapping of data with nonlinear relationships and the ability to select a suitable kernel. Kernel selection is crucial for effective point set registration. We focus here on multiple kernel point set registration. We make several contributions in this paper. First, each observation is modeled using the Student's t-distribution, which is heavily tailed and more robust than the Gaussian distribution. Second, by automatically adjusting the kernel weights, the proposed method allows us to prune the ineffective kernels. This makes the choice of kernels less crucial. After parameter learning, the kernel saliencies of the irrelevant kernels go to zero. Thus, the choice of kernels is less crucial and it is easy to include other kinds of kernels. Finally, we show empirically that our model outperforms state-of-the-art methods recently proposed in the literature. © 2016 IEEE.

Nguyen T.M.,University of Windsor | Nguyen T.M.,Biospective Inc | Jonathan Wu Q.M.,University of Windsor
IEEE Transactions on Fuzzy Systems | Year: 2015

Fuzzy c-means (FCM) clustering has been successfully applied in various pattern recognition areas. While FCM is gaining attention, an important issue arising from these studies is the need to determine which attributes of the data should be used. Answering this question is difficult, because there is no labeled training data available in clustering to guide the search. We present a feature selection for FCM. The advantage of our method is that it is intuitively appealing, avoiding combinatorial searches, and allowing us to prune the feature set. Our method is also adaptable and can change through complex scenes in an online environment. We do not have to wait until all data have been generated before learning begins. Finally, to estimate the model parameters, the gradient method is adopted to minimize the fuzzy objective function with the Kullback-Leibler divergence information. Numerical experiments are presented to demonstrate the robustness and accuracy of our method. © 2015 IEEE.

Choi S.R.,Avid Radiopharmaceuticals | Schneider J.A.,Rush University | Bennett D.A.,Rush University | Beach T.G.,Banner Sun Health Research Institute | And 10 more authors.
Alzheimer Disease and Associated Disorders | Year: 2012

Background: Florbetapir F 18 ( 18F-AV-45) is a positron emission tomography imaging ligand for the detection of amyloid aggregation associated with Alzheimer disease. Earlier data showed that florbetapir F 18 binds with high affinity to β-amyloid (Aβ) plaques in human brain homogenates (Kd =3.7 nM) and has favorable imaging pharmacokinetic properties, including rapid brain penetration and washout. This study used human autopsy brain tissue to evaluate the correlation between in vitro florbetapir F 18 binding and Aβ density measured by established neuropathologic methods. Methods: The localization and density of florbetapir F 18 binding in frozen and formalin-fixed paraffin-embedded sections of postmortem brain tissue from 40 patients with a varying degree of neurodegenerative pathology was assessed by standard florbetapir F 18 autoradiography and correlated with the localization and density of Aβ identified by silver staining, thioflavin S staining, and immunohistochemistry. Results: There were strong quantitative correlations between florbetapir F 18 tissue binding and both Aβ plaques identified by light microscopy (Silver staining and thioflavin S fluorescence) and by immunohistochemical measurements of Aβ using 3 antibodies recognizing different epitopes of the Aβ peptide. Florbetapir F 18 did not bind to neurofibrillary tangles. Conclusions: Florbetapir F 18 selectively binds Aβ in human brain tissue. The binding intensity was quantitatively correlated with the density of Aβ plaques identified by standard neuropathologic techniques and correlated with the density of Aβ measured by immunohistochemistry. As Aβ plaques are a defining neuropathologic feature for Alzheimer disease, these results support the use of florbetapir F 18 as an amyloid positron emission tomography ligand to identify the presence of Alzheimer disease pathology in patients with signs and symptoms of progressive late-life cognitive impairment. Copyright © 2012 by Lippincott Williams & Wilkins.

Clark C.M.,Avid Radiopharmaceuticals | Clark C.M.,University of Pennsylvania | Schneider J.A.,Rush University Medical Center | Bedell B.J.,Biospective Inc. | And 22 more authors.
JAMA - Journal of the American Medical Association | Year: 2011

Context: The ability to identify and quantify brain β-amyloid could increase the accuracy of a clinical diagnosis of Alzheimer disease. Objective: To determine if florbetapir F 18 positron emission tomographic (PET) imaging performed during life accurately predicts the presence of β-amyloid in the brain at autopsy. Design, Setting, and Participants: Prospective clinical evaluation conducted February 2009 through March 2010 of florbetapir-PET imaging performed on 35 patients from hospice, long-term care, and community health care facilities near the end of their lives (6 patients to establish the protocol and 29 to validate) compared with immunohistochemistry and silver stain measures of brain β-amyloid after their death used as the reference standard. PET images were also obtained in 74 young individuals (18-50 years) presumed free of brain amyloid to better understand the frequency of a false-positive interpretation of a florbetapir-PET image. Main Outcome Measuresβ Correlation of florbetapir-PET image interpretation (based on the median of 3 nuclear medicine physicians' ratings) and semiautomated quantification of cortical retention with postmortem β-amyloid burden, neuritic amyloid plaque density, and neuropathological diagnosis of Alzheimer disease in the first 35 participants autopsied (out of 152 individuals enrolled in the PET pathological correlation study). Results: Florbetapir-PET imaging was performed a mean of 99 days (range, 1-377 days) before death for the 29 individuals in the primary analysis cohort. Fifteen of the 29 individuals (51.7%) met pathological criteria for Alzheimer disease. Both visual interpretation of the florbetapir-PET images and mean quantitative estimates of cortical uptake were correlated with presence and quantity of β-amyloid pathology at autopsy as measured by immunohistochemistry (Bonferroni ρ, 0.78 [95% confidence interval, 0.58-0.89]; P<.001]) and silver stain neuritic plaque score (Bonferroni ρ, 0.71 [95% confidence interval, 0.47-0.86]; P<.001). Florbetapir-PET images and postmortem results rated as positive or negative for β-amyloid agreed in 96% of the 29 individuals in the primary analysis cohort. The florbetapir-PET image was rated as amyloid negative in the 74 younger individuals in the nonautopsy cohort. Conclusions: Florbetapir-PET imaging was correlated with the presence and density of β-amyloid. These data provide evidence that a molecular imaging procedure can identify β-amyloid pathology in the brains of individuals during life. Additional studies are required to understand the appropriate use of florbetapir-PET imaging in the clinical diagnosis of Alzheimer disease and for the prediction of progression to dementia. ©2011 American Medical Association. All rights reserved.

Vunckx K.,Catholic University of Leuven | Atre A.,Catholic University of Leuven | Baete K.,Catholic University of Leuven | Reilhac A.,Biospective Inc. | And 4 more authors.
IEEE Transactions on Medical Imaging | Year: 2012

In emission tomography, image reconstruction and therefore also tracer development and diagnosis may benefit from the use of anatomical side information obtained with other imaging modalities in the same subject, as it helps to correct for the partial volume effect. One way to implement this, is to use the anatomical image for defining the a priori distribution in a maximum-a-posteriori (MAP) reconstruction algorithm. In this contribution, we use the PET-SORTEO Monte Carlo simulator to evaluate the quantitative accuracy reached by three different anatomical priors when reconstructing positron emission tomography (PET) brain images, using volumetric magnetic resonance imaging (MRI) to provide the anatomical information. The priors are: 1) a prior especially developed for FDG PET brain imaging, which relies on a segmentation of the MR-image (Baete , 2004); 2) the joint entropy-prior (Nuyts, 2007); 3) a prior that encourages smoothness within a position dependent neighborhood, computed from the MR-image. The latter prior was recently proposed by our group in (Vunckx and Nuyts, 2010), and was based on the prior presented by Bowsher (2004). The two latter priors do not rely on an explicit segmentation, which makes them more generally applicable than a segmentation-based prior. All three priors produced a compromise between noise and bias that was clearly better than that obtained with postsmoothed maximum likelihood expectation maximization (MLEM) or MAP with a relative difference prior. The performance of the joint entropy prior was slightly worse than that of the other two priors. The performance of the segmentation-based prior is quite sensitive to the accuracy of the segmentation. In contrast to the joint entropy-prior, the Bowsher-prior is easily tuned and does not suffer from convergence problems. © 2011 IEEE.

Tomei S.,CNRS Research Center for Image Acquisition and Processing for Health | Reilhac A.,CH Lyon Sud | Visvikis D.,French Institute of Health and Medical Research | Boussion N.,CERMEP | And 3 more authors.
IEEE Transactions on Nuclear Science | Year: 2010

The purpose of this paper is to generate and distribute a database of simulated whole body 18F-FDG positron emission tomography (PET) oncology images. As far as we know, this database is the first addressing the need for simulated 18F-FDG PET oncology images by providing a series of realistic whole-body patient images with well-controlled inserted lesions of calibrated uptakes. It also fulfills the requirements of detection performance studies by including normal and pathological cases. The originality of the database is based on three points. First, we built a complex model of 18F-FDG patient based on the Zubal phantom in combination with activity distributions in the main organs of interest derived from a series of 70 clinical cases. Secondly, we proposed a model of lesions extent corresponding to real lymphoma patients. The lesion contrast levels were derived from a human observer detection study so as to cover the entire range of detectability. Lastly, the simulated database was generated with the PET-SORTEO Monte Carlo simulation tool that was fully validated against the geometry of the ECAT EXACT HR+ (CTI/Siemens Knoxville). The oncoPET-DB database is composed of 100 whole-body PET simulated images, including 50 normal cases coming from different realizations of noise of the healthy model and 50 pathological cases including lesions of calibrated uptakes and various diameters. Such a database will be useful to evaluate algorithms that may impact quantification or contrast recovery, to perform observer studies or to assess computer-aided diagnosis methods. Perspectives include enriching the present database with new pathological and normal cases accounting for interindividual variability of anatomy and FDG uptake. © 2010 IEEE.

Carbonell F.,Biospective Inc. | Charil A.,Biospective Inc. | Zijdenbos A.P.,Biospective Inc. | Evans A.C.,Biospective Inc. | And 3 more authors.
Journal of Cerebral Blood Flow and Metabolism | Year: 2014

Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI). © 2014 ISCBFM.

Carbonell F.,Biospective Inc. | Charil A.,Biospective Inc. | Zijdenbos A.P.,Biospective Inc. | Evans A.C.,Biospective Inc. | And 3 more authors.
Journal of Cerebral Blood Flow and Metabolism | Year: 2014

Positron emission tomography (PET) studies using [18F]2-fluoro-2- deoxyglucose (FDG) have identified a well-defined pattern of glucose hypometabolism in Alzheimer's disease (AD). The assessment of the metabolic relationship among brain regions has the potential to provide unique information regarding the disease process. Previous studies of metabolic correlation patterns have demonstrated alterations in AD subjects relative to age-matched, healthy control subjects. The objective of this study was to examine the associations between β-amyloid, apolipoprotein E ε4 (APOE ε4) genotype, and metabolic correlations patterns in subjects diagnosed with mild cognitive impairment (MCI). Mild cognitive impairment subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study were categorized into β-amyloid-low and β-amyloid-high groups, based on quantitative analysis of [18F]florbetapir PET scans, and APOE ε4 non-carriers and carriers based on genotyping. We generated voxel-wise metabolic correlation strength maps across the entire cerebral cortex for each group, and, subsequently, performed a seed-based analysis. We found that the APOE ε4 genotype was closely related to regional glucose hypometabolism, while elevated, fibrillar β-amyloid burden was associated with specific derangements of the metabolic correlation patterns. © 2014 ISCBFM.

Bellec P.,University of Montréal | Lavoie-Courchesne S.,University of Montréal | Lavoie-Courchesne S.,Montreal Neurological Institute | Dickinson P.,University of Montréal | And 5 more authors.
Frontiers in Neuroinformatics | Year: 2012

The analysis of neuroimaging databases typically involves a large number of inter-connected steps called a pipeline. The pipeline system for Octave and Matlab (PSOM) is a flexible framework for the implementation of pipelines in the form of Octave or Matlab scripts. PSOM does not introduce new language constructs to specify the steps and structure of the workflow. All steps of analysis are instead described by a regular Matlab data structure, documenting their associated command and options, as well as their input, output and cleaned-up files. The PSOM execution engine provides a number of automated services: (1) it executes jobs in parallel on a local computing facility as long as the dependencies between jobs allow for it and suffcient resources are available; (2) it generates a comprehensive record of the pipeline stages and the history of execution, which is detailed enough to fully reproduce the analysis; (3) if an analysis is started multiple times, it executes only the parts of the pipeline that need to be reprocessed. PSOM is distributed under an opensource MIT license and can be used without restriction for academic or commercial projects. The package has no external dependencies besides Matlab or Octave, is straightforward to install and supports of variety of operating systems (Linux, Windows, Mac). We ran several benchmark experiments on a public database including 200 subjects, using a pipeline for the preprocessing of functional magnetic resonance images. The benchmark results showed that PSOM is a powerful solution for the analysis of large databases using local or distributed computing resources. © 2012 Bellec, Lavoie-courchesne, Dickinson, Lerch, Zijdenbos and Evans.

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