Arthur W Toga

Keck School of Medicine USC, Los Ángeles, California, United States

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Publications (985)4293.54 Total impact

  • Arthur W Toga · Ivo D Dinov
    12/2015; 2(1). DOI:10.1186/s40537-015-0016-1
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    ABSTRACT: This article investigates late-onset cognitive impairment using neuroimaging and genetics biomarkers for Alzheimer's Disease Neuroimaging Initiative (ADNI) participants. Eight-hundred and eight ADNI subjects were identified and divided into three groups: 200 subjects with Alzheimer's disease (AD), 383 subjects with mild cognitive impairment (MCI), and 225 asymptomatic normal controls (NC). Their structural magnetic resonance imaging (MRI) data were parcellated using BrainParser, and the 80 most important neuroimaging biomarkers were extracted using the global shape analysis Pipeline workflow. Using Plink via the Pipeline environment, we obtained 80 SNPs highly-associated with the imaging biomarkers. In the AD cohort, rs2137962 was significantly associated bilaterally with changes in the hippocampi and the parahippocampal gyri, and rs1498853, rs288503, and rs288496 were associated with the left and right hippocampi, the right parahippocampal gyrus, and the left inferor temporal gyrus. In the MCI cohort, rs17028008 and rs17027976 were significantly associated with the right caudate and right fusiform gyrus, rs2075650 (TOMM40) was associated with the right caudate, and rs1334496 and rs4829605 were significantly associated with the right inferior temporal gyrus. In the NC cohort, Chromosome 15 [rs734854 (STOML1), rs11072463 (PML), rs4886844 (PML), and rs1052242 (PML)] was significantly associated with both hippocampi and both insular cortices, and rs4899412 (RGS6) was significantly associated with the caudate. We observed significant correlations between genetic and neuroimaging phenotypes in the 808 ADNI subjects. These results suggest that differences between AD, MCI, and NC cohorts may be examined by using powerful joint models of morphometric, imaging and genotypic data.
    Journal of Alzheimer's disease: JAD 10/2015; DOI:10.3233/JAD-150335 · 4.15 Impact Factor
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    ABSTRACT: The Function Biomedical Informatics Research Network (FBIRN) developed methods and tools for conducting multi-scanner functional magnetic resonance imaging (fMRI) studies. Method and tool development were based on two major goals: 1) to assess the major sources of variation in fMRI studies conducted across scanners, including instrumentation, acquisition protocols, challenge tasks, and analysis methods, and 2) to provide a distributed network infrastructure and an associated federated database to host and query large, multi-site, fMRI and clinical datasets. In the process of achieving these goals the FBIRN test bed generated several multi-scanner brain imaging data sets to be shared with the wider scientific community via the BIRN Data Repository (BDR). The FBIRN Phase 1 dataset consists of a traveling subject study of 5 healthy subjects, each scanned on 10 different 1.5 to 4 Tesla scanners. The FBIRN Phase 2 and Phase 3 datasets consist of subjects with schizophrenia or schizoaffective disorder along with healthy comparison subjects scanned at multiple sites. In this paper, we provide concise descriptions of FBIRN's multi-scanner brain imaging data sets and details about the BIRN Data Repository instance of the Human Imaging Database (HID) used to publicly share the data.
    NeuroImage 09/2015; DOI:10.1016/j.neuroimage.2015.09.003 · 6.36 Impact Factor
  • Journal of Parkinson's Disease 09/2015; 5(3):581-594. DOI:10.3233/JPD-150570 · 1.91 Impact Factor
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    ABSTRACT: The MGH-USC CONNECTOM MRI scanner housed at the Massachusetts General Hospital (MGH) is a major hardware innovation of the Human Connectome Project (HCP). The 3T CONNECTOM scanner is capable of producing magnetic field gradient of up to 300 mT/m strength for in vivo human brain imaging, which greatly shortens the time spent on diffusion encoding, and decreases the signal loss due to T2 decay. To demonstrate the capability of the novel gradient system, data of healthy adult participants were acquired for this MGH-USC Adult Diffusion Dataset (N=35), minimally preprocessed, and shared through the Laboratory of Neuro Imaging Image Data Archive (LONI IDA) and the WU-Minn Connectome Database (ConnecomeDB). Another purpose of sharing the data is to facilitate methodological studies of diffusion MRI (dMRI) analyses utilizing high diffusion contrast, which perhaps is not easily feasible with standard MR gradient system. In addition, acquisition of the MGH-Harvard-USC Lifespan Dataset is currently underway to include 120 healthy participants ranging from 8 to 90 years old, which will also be shared through LONI IDA and ConnectomeDB. Here we describe the efforts of the MGH-USC HCP consortium in acquiring and sharing the ultra-high b-value diffusion MRI data and provide a report on data preprocessing and access. We conclude with a demonstration of the example data, along with results of standard diffusion analyses, including q-ball Orientation Distribution Function (ODF) reconstruction and tractography.
    NeuroImage 09/2015; DOI:10.1016/j.neuroimage.2015.08.075 · 6.36 Impact Factor
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    ABSTRACT: Brain amyloid deposition is thought to be a seminal event in Alzheimer's disease. To identify genes influencing Alzheimer's disease pathogenesis, we performed a genome-wide association study of longitudinal change in brain amyloid burden measured by (18)F-florbetapir PET. A novel association with higher rates of amyloid accumulation independent from APOE (apolipoprotein E) ε4 status was identified in IL1RAP (interleukin-1 receptor accessory protein; rs12053868-G; P = 1.38 × 10(-9)) and was validated by deep sequencing. IL1RAP rs12053868-G carriers were more likely to progress from mild cognitive impairment to Alzheimer's disease and exhibited greater longitudinal temporal cortex atrophy on MRI. In independent cohorts rs12053868-G was associated with accelerated cognitive decline and lower cortical (11)C-PBR28 PET signal, a marker of microglial activation. These results suggest a crucial role of activated microglia in limiting amyloid accumulation and nominate the IL-1/IL1RAP pathway as a potential target for modulating this process. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email:
    Brain 08/2015; DOI:10.1093/brain/awv231 · 9.20 Impact Factor
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    ABSTRACT: Modern biomedical data collection is generating exponentially more data in a multitude of formats. This flood of complex data poses significant opportunities to discover and understand the critical interplay among such diverse domains as genomics, proteomics, metabolomics, and phenomics, including imaging, biometrics, and clinical data. The Big Data for Discovery Science Center is taking an "-ome to home" approach to discover linkages between these disparate data sources by mining existing databases of proteomic and genomic data, brain images, and clinical assessments. In support of this work, the authors developed new technological capabilities that make it easy for researchers to manage, aggregate, manipulate, integrate, and model large amounts of distributed data. Guided by biological domain expertise, the Center's computational resources and software will reveal relationships and patterns, aiding researchers in identifying biomarkers for the most confounding conditions and diseases, such as Parkinson's and Alzheimer's. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email:
    Journal of the American Medical Informatics Association 07/2015; DOI:10.1093/jamia/ocv077 · 3.50 Impact Factor
  • Arthur W Toga · Karen L Crawford
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    ABSTRACT: The Informatics Core of the Alzheimer's Disease Neuroimaging Initiative has coordinated data integration and dissemination for a continually growing and complex data set in which both data contributors and recipients span institutions, scientific disciplines, and geographic boundaries. This article provides an update on the accomplishments and future plans. Copyright © 2015 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.
    Alzheimer's & dementia: the journal of the Alzheimer's Association 07/2015; 11(7):832-9. DOI:10.1016/j.jalz.2015.04.004 · 12.41 Impact Factor
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    ABSTRACT: The Alzheimer's Disease Neuroimaging Initiative (ADNI) was established in 2004 to facilitate the development of effective treatments for Alzheimer's disease (AD) by validating biomarkers for AD clinical trials. We searched for ADNI publications using established methods. ADNI has (1) developed standardized biomarkers for use in clinical trial subject selection and as surrogate outcome measures; (2) standardized protocols for use across multiple centers; (3) initiated worldwide ADNI; (4) inspired initiatives investigating traumatic brain injury and post-traumatic stress disorder in military populations, and depression, respectively, as an AD risk factor; (5) acted as a data-sharing model; (6) generated data used in over 600 publications, leading to the identification of novel AD risk alleles, and an understanding of the relationship between biomarkers and AD progression; and (7) inspired other public-private partnerships developing biomarkers for Parkinson's disease and multiple sclerosis. ADNI has made myriad impacts in its first decade. A competitive renewal of the project in 2015 would see the use of newly developed tau imaging ligands, and the continued development of recruitment strategies and outcome measures for clinical trials. Copyright © 2015 The Alzheimer's Association. All rights reserved.
    Alzheimer's & dementia: the journal of the Alzheimer's Association 07/2015; 11(7):865-84. DOI:10.1016/j.jalz.2015.04.005 · 12.41 Impact Factor
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    ABSTRACT: Genetic data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) have been crucial in advancing the understanding of Alzheimer's disease (AD) pathophysiology. Here, we provide an update on sample collection, scientific progress and opportunities, conceptual issues, and future plans. Lymphoblastoid cell lines and DNA and RNA samples from blood have been collected and banked, and data and biosamples have been widely disseminated. To date, APOE genotyping, genome-wide association study (GWAS), and whole exome and whole genome sequencing data have been obtained and disseminated. ADNI genetic data have been downloaded thousands of times, and >300 publications have resulted, including reports of large-scale GWAS by consortia to which ADNI contributed. Many of the first applications of quantitative endophenotype association studies used ADNI data, including some of the earliest GWAS and pathway-based studies of biospecimen and imaging biomarkers, as well as memory and other clinical/cognitive variables. Other contributions include some of the first whole exome and whole genome sequencing data sets and reports in healthy controls, mild cognitive impairment, and AD. Numerous genetic susceptibility and protective markers for AD and disease biomarkers have been identified and replicated using ADNI data and have heavily implicated immune, mitochondrial, cell cycle/fate, and other biological processes. Early sequencing studies suggest that rare and structural variants are likely to account for significant additional phenotypic variation. Longitudinal analyses of transcriptomic, proteomic, metabolomic, and epigenomic changes will also further elucidate dynamic processes underlying preclinical and prodromal stages of disease. Integration of this unique collection of multiomics data within a systems biology framework will help to separate truly informative markers of early disease mechanisms and potential novel therapeutic targets from the vast background of less relevant biological processes. Fortunately, a broad swath of the scientific community has accepted this grand challenge. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
    Alzheimer's & dementia: the journal of the Alzheimer's Association 07/2015; 11(7):792-814. DOI:10.1016/j.jalz.2015.05.009 · 12.41 Impact Factor
  • Junning Li · Yonggang Shi · Arthur W Toga
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    ABSTRACT: Thresholding statistical maps with appropriate correction of multiple testing remains a critical and challenging problem in brain mapping. Since the false discovery rate (FDR) criterion was introduced to the neuroimaging community a decade ago, various improvements have been proposed. However, a highly desirable feature, transformation invariance, has not been adequately addressed, especially for voxel-based FDR. Thresholding applied after spatial transformation is not necessarily equivalent to transformation applied after thresholding in the original space. We find this problem closely related to another important issue: spatial correlation of signals. A Gaussian random vector-valued image after normalization is a random map from a Euclidean space to a high-dimension unit-sphere. Instead of defining the FDR measure in the image's Euclidean space, we define it in the signals' hyper-spherical space whose measure not only reflects the intrinsic "volume" of signals' randomness but also keeps invariant under images' spatial transformation. Experiments with synthetic and real images demonstrate that our method achieves transformation invariance and significantly minimizes the bias introduced by the choice of template images.
    Information processing in medical imaging: proceedings of the ... conference 07/2015; 9123:125-136. DOI:10.1007/978-3-319-19992-4_10
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    ABSTRACT: Development of the fetal hippocampal formation has been difficult to fully describe because of rapid changes in its shape during the fetal period. The aims of this study were to: (1) segment the fetal hippocampal formation using 7.0T MR images from 41 specimens with gestational ages ranging from 14 to 22 weeks; and (2) reveal the developmental course of the fetal hippocampal formation using volume and shape analyses. Differences in hemispheric volume were observed, with the right hippocampi being larger than the left. Absolute volume changes showed a linear increase, while relative volume changes demonstrated an inverted-U shape trend during this period. Together these exhibited a variable developmental rate among different regions of the fetal brain. Different sub-regional growth of the fetal hippocampal formation was specifically observed using shape analysis. The fetal hippocampal formation possessed a prominent medial-lateral bidirectional shape growth pattern during its rotation process. Our results provide additional insight into 3D hippocampal morphology in the assessment of fetal brain development and can be used as a reference for future hippocampal studies. Copyright © 2015. Published by Elsevier Inc.
    NeuroImage 06/2015; 119. DOI:10.1016/j.neuroimage.2015.06.055 · 6.36 Impact Factor
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    ABSTRACT: Preventive neuroradiology is a new concept supported by growing literature. The main rationale of preventive neuroradiology is the application of multimodal brain imaging toward early and subclinical detection of brain disease and subsequent preventive actions through identification of modifiable risk factors. An insightful example of this is in the area of age-related cognitive decline, mild cognitive impairment, and dementia with potentially modifiable risk factors such as obesity, diet, sleep, hypertension, diabetes, depression, supplementation, smoking, and physical activity. In studying this link between lifestyle and cognitive decline, brain imaging markers may be instrumental as quantitative measures or even indicators of early disease. The purpose of this article is to provide an overview of the major studies reflecting how lifestyle factors affect the brain and cognition aging. In this hot topics review, we will specifically focus on obesity and physical activity. © 2015 American Society of Neuroradiology.
    American Journal of Neuroradiology 06/2015; DOI:10.3174/ajnr.A4409 · 3.59 Impact Factor
  • Stroke 06/2015; 46(7). DOI:10.1161/STROKEAHA.115.009479 · 5.72 Impact Factor
  • Scott C Neu · Karen L Crawford · Arthur W Toga
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    ABSTRACT: The Global Alzheimer's Association Interactive Network (GAAIN) aims to be a shared network of research data, analysis tools, and computational resources for studying the causes of Alzheimer's disease. Central to its design are policies that honor data ownership, prevent unauthorized data distribution, and respect the boundaries of contributing institutions. The results of data queries are displayed in graphs and summary tables, which protects data ownership while providing sufficient information to view trends in aggregated data and discover new data sets. In this article we report on our progress in sharing data through the integration of geographically-separated and independently-operated Alzheimer's disease research studies around the world. Copyright © 2015. Published by Elsevier Inc.
    NeuroImage 06/2015; DOI:10.1016/j.neuroimage.2015.05.082 · 6.36 Impact Factor
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    ABSTRACT: The Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [18F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.
    Alzheimer's & dementia: the journal of the Alzheimer's Association 06/2015; 11(6):e1-e120. DOI:10.1016/j.jalz.2014.11.001 · 12.41 Impact Factor
  • Karen L Crawford · Scott C Neu · Arthur W Toga
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    ABSTRACT: The LONI Image and Data Archive (IDA)(1) is a repository for sharing and long-term preservation of neuroimaging and biomedical research data. Originally designed to archive strictly medical image files, the IDA has evolved over the last ten years and now encompasses the storage and dissemination of neuroimaging, clinical, biospecimen, and genetic data. In this article, we report upon the genesis of the IDA and how it currently securely manages data and protects data ownership. Copyright © 2015 Elsevier Inc. All rights reserved.
    NeuroImage 05/2015; DOI:10.1016/j.neuroimage.2015.04.067 · 6.36 Impact Factor
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    ABSTRACT: This article investigates subjects aged 55 to 65 from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to broaden our understanding of early-onset (EO) cognitive impairment using neuroimaging and genetics biomarkers. Nine of the subjects had EO-AD (Alzheimer's disease) and 27 had EO-MCI (mild cognitive impairment). The 15 most important neuroimaging markers were extracted with the Global Shape Analysis (GSA) Pipeline workflow. The 20 most significant single nucleotide polymorphisms (SNPs) were chosen and were associated with specific neuroimaging biomarkers. We identified associations between the neuroimaging phenotypes and genotypes for a total of 36 subjects. Our results for all the subjects taken together showed the most significant associations between rs7718456 and L_hippocampus (volume), and between rs7718456 and R_hippocampus (volume). For the 27 MCI subjects, we found the most significant associations between rs6446443 and R_superior_frontal_gyrus (volume), and between rs17029131 and L_Precuneus (volume). For the nine AD subjects, we found the most significant associations between rs16964473 and L_rectus gyrus (surface area), and between rs12972537 and L_rectus_gyrus (surface area). We observed significant correlations between the SNPs and the neuroimaging phenotypes in the 36 EO subjects in terms of neuroimaging genetics. However, larger sample sizes are needed to ensure that the effects will be detectable for a reasonable false-positive error rate using the GSA and Plink Pipeline workflows.
    Psychiatry investigation 05/2015; 12(1):125-35. DOI:10.4306/pi.2015.12.1.125 · 1.28 Impact Factor
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    Naveen Ashish · Arthur W Toga
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    ABSTRACT: This paper presents a system for declaratively transforming medical subjects' data into a common data model representation. Our work is part of the "GAAIN" project on Alzheimer's disease data federation across multiple data providers. We present a general purpose data transformation system that we have developed by leveraging the existing state-of-the-art in data integration and query rewriting. In this work we have further extended the current technology with new formalisms that facilitate expressing a broader range of data transformation tasks, plus new execution methodologies to ensure efficient data transformation for disease datasets.
    Frontiers in Neuroinformatics 02/2015; 9:1. DOI:10.3389/fninf.2015.00001 · 3.26 Impact Factor
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    Brain 02/2015; 138(Pt 9). DOI:10.1093/brain/awv037 · 9.20 Impact Factor

Publication Stats

46k Citations
4,293.54 Total Impact Points


  • 2014–2015
    • Keck School of Medicine USC
      Los Ángeles, California, United States
  • 2007–2015
    • University of Southern California
      • • Department of Neurology
      • • Institute for Neuroimaging and Informatics (INI)
      • • Information Sciences Institute
      • • Department of Psychology
      Los Ángeles, California, United States
    • University of California, San Francisco
      San Francisco, California, United States
    • University of California, Davis
      • Center for Neuroscience
      Davis, CA, United States
    • University of Queensland
      Brisbane, Queensland, Australia
  • 2010–2014
    • CSU Mentor
      • Department of Neurology
      Long Beach, California, United States
  • 1989–2014
    • University of California, Los Angeles
      • • Department of Neurology
      • • Laboratory of Neuro Imaging
      • • Department of Medicine
      • • Department of Neurosurgery
      Los Angeles, California, United States
    • Columbia University
      • College of Physicians and Surgeons
      New York City, New York, United States
  • 1988–2013
    • Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center
      • Department of Medicine
      Torrance, California, United States
  • 2012
    • University of California, Irvine
      • Department of Psychiatry & Human Behavior
      Irvine, California, United States
  • 2007–2012
    • California State University
      • Department of Neurology
      Long Beach, California, United States
  • 2011
    • Johns Hopkins University
      Baltimore, Maryland, United States
  • 2009
    • University of Iowa
      • Department of Psychiatry
      Iowa City, Iowa, United States
  • 2004–2008
    • Università degli Studi di Brescia
      Brescia, Lombardy, Italy
    • University of Virginia
      • Department of Neurosurgery
      Charlottesville, Virginia, United States
  • 2002–2008
    • University of California, San Diego
      • Department of Psychiatry
      San Diego, California, United States
  • 1996–2008
    • McGill University
      • • McConnell Brain Imaging Centre
      • • Montreal Neurological Institute
      Montréal, Quebec, Canada
  • 2006–2007
    • University of Pittsburgh
      • Department of Psychiatry
      Pittsburgh, Pennsylvania, United States
  • 2001–2006
    • National Institute of Mental Health (NIMH)
      • Child Psychiatry Branch
      Maryland, United States
  • 2005
    • Harvard University
      Cambridge, Massachusetts, United States
  • 1999–2005
    • Pacific Neuropsychiatric Institute
      Seattle, Washington, United States
  • 2003
    • University of Nice-Sophia Antipolis
      Nice, Provence-Alpes-Côte d'Azur, France
    • Humboldt-Universität zu Berlin
      Berlín, Berlin, Germany
    • University of Melbourne
      Melbourne, Victoria, Australia
  • 2000
    • University of Washington Seattle
      • Department of Neurological Surgery
      Seattle, Washington, United States
  • 1980–1987
    • Washington University in St. Louis
      • Department of Neurology
      San Luis, Missouri, United States
  • 1979
    • Saint Louis University
      • Division of Nephrology
      Saint Louis, Michigan, United States