Arthur W Toga

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

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Publications (975)4076.56 Total impact

  • Arthur W Toga, Paul M Thompson
    Brain : a journal of neurology. 12/2014; 137(Pt 12):3104-6.
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    ABSTRACT: Memory impairment is the cardinal early feature of Alzheimer's disease, a highly prevalent disorder whose causes remain only partially understood. To identify novel genetic predictors, we used an integrative genomics approach to perform the largest study to date of human memory (n=14 781). Using a genome-wide screen, we discovered a novel association of a polymorphism in the pro-apoptotic gene FASTKD2 (fas-activated serine/threonine kinase domains 2; rs7594645-G) with better memory performance and replicated this finding in independent samples. Consistent with a neuroprotective effect, rs7594645-G carriers exhibited increased hippocampal volume and gray matter density and decreased cerebrospinal fluid levels of apoptotic mediators. The MTOR (mechanistic target of rapamycin) gene and pathways related to endocytosis, cholinergic neurotransmission, epidermal growth factor receptor signaling and immune regulation, among others, also displayed association with memory. These findings nominate FASTKD2 as a target for modulating neurodegeneration and suggest potential mechanisms for therapies to combat memory loss in normal cognitive aging and dementia.Molecular Psychiatry advance online publication 11 November 2014; doi:10.1038/mp.2014.142.
    Molecular Psychiatry 11/2014; · 15.15 Impact Factor
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    ABSTRACT: Alzheimer's disease (AD) is characterized by cortical atrophy and disrupted anatomic connectivity, and leads to abnormal interactions between neural systems. Diffusion-weighted imaging (DWI) and graph theory can be used to evaluate major brain networks and detect signs of a breakdown in network connectivity. In a longitudinal study using both DWI and standard magnetic resonance imaging (MRI), we assessed baseline white-matter connectivity patterns in 30 subjects with mild cognitive impairment (MCI, mean age 71.8 ± 7.5 years, 18 males and 12 females) from the Alzheimer's Disease Neuroimaging Initiative. Using both standard MRI-based cortical parcellations and whole-brain tractography, we computed baseline connectivity maps from which we calculated global "small-world" architecture measures, including mean clustering coefficient and characteristic path length. We evaluated whether these baseline network measures predicted future volumetric brain atrophy in MCI subjects, who are at risk for developing AD, as determined by 3-dimensional Jacobian "expansion factor maps" between baseline and 6-month follow-up anatomic scans. This study suggests that DWI-based network measures may be a novel predictor of AD progression. Copyright © 2014 Elsevier Inc. All rights reserved.
    Neurobiology of aging. 08/2014;
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    ABSTRACT: Dynamic changes in the brain's lateral ventricles on magnetic resonance imaging are powerful biomarkers of disease progression in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Ventricular measures can represent accumulation of diffuse brain atrophy with very high effect sizes. Despite having no direct role in cognition, ventricular expansion co-occurs with volumetric loss in gray and white matter structures. To better understand relationships between ventricular and cortical changes over time, we related ventricular expansion to atrophy in cognitively relevant cortical gray matter surfaces, which are more challenging to segment. In ADNI participants, percent change in ventricular volumes at 1-year (N = 677) and 2-year (N = 536) intervals was significantly associated with baseline cortical thickness and volume in the full sample controlling for age, sex, and diagnosis, and in MCI separately. Ventricular expansion in MCI was associated with thinner gray matter in frontal, temporal, and parietal regions affected by AD. Ventricular expansion reflects cortical atrophy in early AD, offering a useful biomarker for clinical trials of interventions to slow AD progression.
    Neurobiology of Aging 08/2014; · 6.17 Impact Factor
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    ABSTRACT: A significant portion of our risk for dementia in old age is associated with lifestyle factors (diet, exercise, and cardiovascular health) that are modifiable, at least in principle. One such risk factor, high-homocysteine levels in the blood, is known to increase risk for Alzheimer's disease and vascular disorders. Here, we set out to understand how homocysteine levels relate to 3D surface-based maps of cortical gray matter distribution (thickness, volume, and surface area) computed from brain magnetic resonance imaging in 803 elderly subjects from the Alzheimer's Disease Neuroimaging Initiative data set. Individuals with higher plasma levels of homocysteine had lower gray matter thickness in bilateral frontal, parietal, occipital, and right temporal regions and lower gray matter volumes in left frontal, parietal, temporal, and occipital regions, after controlling for diagnosis, age, and sex and after correcting for multiple comparisons. No significant within-group associations were found in cognitively healthy people, patients with mild cognitive impairment, or patients with Alzheimer's disease. These regional differences in gray matter structure may be useful biomarkers to assess the effectiveness of interventions, such as vitamin B supplements, that aim to prevent homocysteine-related brain atrophy by normalizing homocysteine levels. Copyright © 2014 Elsevier Inc. All rights reserved.
    Neurobiology of aging. 08/2014;
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    ABSTRACT: We compare a variety of different anatomic connectivity measures, including several novel ones, that may help in distinguishing Alzheimer's disease (AD) patients from controls. We studied diffusion-weighted magnetic resonance imaging from 200 subjects scanned as part of the Alzheimer's Disease Neuroimaging Initiative. We first evaluated measures derived from connectivity matrices based on whole-brain tractography; next, we studied additional network measures based on a novel flow-based measure of brain connectivity, computed on a dense 3-dimensional lattice. Based on these 2 kinds of connectivity matrices, we computed a variety of network measures. We evaluated the measures' ability to discriminate disease with a repeated, stratified 10-fold cross-validated classifier, using support vector machines, a supervised learning algorithm. We tested the relative importance of different combinations of features based on the accuracy, sensitivity, specificity, and feature ranking of the classification of 200 people into normal healthy controls and people with early or late mild cognitive impairment or AD.
    Neurobiology of Aging 08/2014; · 6.17 Impact Factor
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    ABSTRACT: In a previous report, we proposed a method for combining multiple markers of atrophy caused by Alzheimer's disease into a single atrophy score that is more powerful than any one feature. We applied the method to expansion rates of the lateral ventricles, achieving the most powerful ventricular atrophy measure to date. Here, we expand our method's application to tensor-based morphometry measures. We also combine the volumetric tensor-based morphometry measures with previously computed ventricular surface measures into a combined atrophy score. We show that our atrophy scores are longitudinally unbiased with the intercept bias estimated at 2 orders of magnitude below the mean atrophy of control subjects at 1 year. Both approaches yield the most powerful biomarker of atrophy not only for ventricular measures but also for all published unbiased imaging measures to date. A 2-year trial using our measures requires only 31 (22, 43) Alzheimer's disease subjects or 56 (44, 64) subjects with mild cognitive impairment to detect 25% slowing in atrophy with 80% power and 95% confidence.
    Neurobiology of Aging 08/2014; · 6.17 Impact Factor
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    ABSTRACT: Brain connectivity is progressively disrupted in Alzheimer's disease (AD). Here, we used a seemingly unrelated regression (SUR) model to enhance the power to identify structural connections related to cognitive scores. We simultaneously solved regression equations with different predictors and used correlated errors among the equations to boost power for associations with brain networks. Connectivity maps were computed to represent the brain's fiber networks from diffusion-weighted magnetic resonance imaging scans of 200 subjects from the Alzheimer's Disease Neuroimaging Initiative. We first identified a pattern of brain connections related to clinical decline using standard regressions powered by this large sample size. As AD studies with a large number of diffusion tensor imaging scans are rare, it is important to detect effects in smaller samples using simultaneous regression modeling like SUR. Diagnosis of mild cognitive impairment or AD is well known to be associated with ApoE genotype and educational level. In a subsample with no apparent associations using the general linear model, power was boosted with our SUR model-combining genotype, educational level, and clinical diagnosis.
    Neurobiology of Aging 08/2014; · 6.17 Impact Factor
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    ABSTRACT: Characterizing brain changes in Alzheimer's disease (AD) is important for patient prognosis and for assessing brain deterioration in clinical trials. In this diffusion weighted imaging study, we used a new fiber-tract modeling method to investigate white matter integrity in 50 elderly controls (CTL), 113 people with mild cognitive impairment, and 37 AD patients. After clustering tractography using a region-of-interest atlas, we used a shortest path graph search through each bundle's fiber density map to derive maximum density paths (MDPs), which we registered across subjects. We calculated the fractional anisotropy (FA) and mean diffusivity (MD) along all MDPs and found significant MD and FA differences between AD patients and CTL subjects, as well as MD differences between CTL and late mild cognitive impairment subjects. MD and FA were also associated with widely used clinical scores. As an MDP is a compact low-dimensional representation of white matter organization, we tested the utility of diffusion tensor imaging measures along these MDPs as features for support vector machine based classification of AD. Copyright © 2014. Published by Elsevier Inc.
    Neurobiology of aging. 08/2014;
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    ABSTRACT: Developmental dyslexia is a common reading disorder that negatively impacts an individual's ability to achieve literacy. Although the brain network involved in reading and its dysfunction in dyslexia has been well studied, it is unknown whether dyslexia is caused by structural abnormalities in the reading network itself or in the lower-level networks that provide input to the reading network. In this study, we acquired structural magnetic resonance imaging scans longitudinally from 27 Norwegian children from before formal literacy training began until after dyslexia was diagnosed. Thus, we were able to determine that the primary neuroanatomical abnormalities that precede dyslexia are not in the reading network itself, but rather in lower-level areas responsible for auditory and visual processing and core executive functions. Abnormalities in the reading network itself were only observed at age 11, after children had learned how to read. The findings suggest that abnormalities in the reading network are the consequence of having different reading experiences, rather than dyslexia per se, whereas the neuroanatomical precursors are predominantly in primary sensory cortices.
    Brain : a journal of neurology. 08/2014;
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    ABSTRACT: Segmentation methods for medical images may not generalize well to new data sets or new tasks, hampering their utility. We attempt to remedy these issues using deformable organisms to create an easily customizable segmentation plan. We validate our framework by creating a plan to locate the brain in 3D magnetic resonance images of the head (skull-stripping).
    Journal of Neuroscience Methods 08/2014; · 2.11 Impact Factor
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    ABSTRACT: Obesity is a crucial public health issue in developed countries, with implications for cardiovascular and brain health as we age. A number of commonly-carried genetic variants are associated with obesity. Here we aim to see whether variants in obesity-associated genes - NEGR1, FTO, MTCH2, MC4R, LRRN6C, MAP2K5, FAIM2, SEC16B, ETV5, BDNF-AS, ATXN2L, ATP2A1, KCTD15, and TNN13K - are associated with white matter microstructural properties, assessed by high angular resolution diffusion imaging (HARDI) in young healthy adults between 20-30 years of age from the Queensland Twin Imaging study (QTIM). We began with a multi-locus approach testing how a number of common genetic risk factors for obesity at the single nucleotide polymorphism (SNP) level may jointly influence white matter integrity throughout the brain and found a wide spread genetic effect. Risk allele rs2815752 in NEGR1 was most associated with lower white matter integrity across a substantial portion of the brain. Across the area of significance in the bilateral posterior corona radiata, each additional copy of the risk allele was associated with a 2.2% lower average FA. This is the first study to find an association between an obesity risk gene and differences in white matter integrity. As our subjects were young and healthy, our results suggest that NEGR1 has effects on brain structure independent of its effect on obesity.
    NeuroImage 07/2014; · 6.25 Impact Factor
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    ABSTRACT: Several common genetic variants influence cholesterol levels, which play a key role in overall health. Myelin synthesis and maintenance are highly sensitive to cholesterol concentrations, and abnormal cholesterol levels increase the risk for various brain diseases, including Alzheimer's disease. We report significant associations between higher serum cholesterol (CHOL) and high-density lipoprotein levels and higher fractional anisotropy in 403 young adults (23.8 ± 2.4 years) scanned with diffusion imaging and anatomic magnetic resonance imaging at 4 Tesla. By fitting a multi-locus genetic model within white matter areas associated with CHOL, we found that a set of 18 cholesterol-related, single-nucleotide polymorphisms implicated in Alzheimer's disease risk predicted fractional anisotropy. We focused on the single-nucleotide polymorphism with the largest individual effects, CETP (rs5882), and found that increased G-allele dosage was associated with higher fractional anisotropy and lower radial and mean diffusivities in voxel-wise analyses of the whole brain. A follow-up analysis detected white matter associations with rs5882 in the opposite direction in 78 older individuals (74.3 ± 7.3 years). Cholesterol levels may influence white matter integrity, and cholesterol-related genes may exert age-dependent effects on the brain.
    Neurobiology of aging. 06/2014;
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    ABSTRACT: Both traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) are common problems resulting from military service, and both have been associated with increased risk of cognitive decline and dementia resulting from Alzheimer's disease (AD) or other causes. This study aims to use imaging techniques and biomarker analysis to determine whether traumatic brain injury (TBI) and/or PTSD resulting from combat or other traumas increase the risk for AD and decrease cognitive reserve in Veteran subjects, after accounting for age. Using military and Department of Veterans Affairs records, 65 Vietnam War veterans with a history of moderate or severe TBI with or without PTSD, 65 with ongoing PTSD without TBI, and 65 control subjects are being enrolled in this study at 19 sites. The study aims to select subject groups that are comparable in age, gender, ethnicity, and education. Subjects with mild cognitive impairment (MCI) or dementia are being excluded. However, a new study just beginning, and similar in size, will study subjects with TBI, subjects with PTSD, and control subjects with MCI. Baseline measurements of cognition, function, blood, and cerebrospinal fluid biomarkers; magnetic resonance images (structural, diffusion tensor, and resting state blood-level oxygen dependent (BOLD) functional magnetic resonance imaging); and amyloid positron emission tomographic (PET) images with florbetapir are being obtained. One-year follow-up measurements will be collected for most of the baseline procedures, with the exception of the lumbar puncture, the PET imaging, and apolipoprotein E genotyping. To date, 19 subjects with TBI only, 46 with PTSD only, and 15 with TBI and PTSD have been recruited and referred to 13 clinics to undergo the study protocol. It is expected that cohorts will be fully recruited by October 2014. This study is a first step toward the design and statistical powering of an AD prevention trial using at-risk veterans as subjects, and provides the basis for a larger, more comprehensive study of dementia risk factors in veterans.
    Alzheimer's and Dementia 06/2014; 10(3):S226–S235. · 17.47 Impact Factor
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    ABSTRACT: Physical activity influences inflammation, and both affect brain structure and Alzheimer's disease (AD) risk. We hypothesized that older adults with greater reported physical activity intensity and lower serum levels of the inflammatory marker tumor necrosis factor α (TNFα) would have larger regional brain volumes on subsequent magnetic resonance imaging (MRI) scans. In 43 cognitively intact older adults (79.3 ± 4.8 years) and 39 patients with AD (81.9 ± 5.1 years at the time of MRI) participating in the Cardiovascular Health Study, we examined year-1 reported physical activity intensity, year-5 blood serum TNFα measures, and year-9 volumetric brain MRI scans. We examined how prior physical activity intensity and TNFα related to subsequent total and regional brain volumes. Physical activity intensity was measured using the modified Minnesota Leisure Time Physical Activities questionnaire at year 1 of the study, when all subjects included here were cognitively intact. Stability of measures was established for exercise intensity over 9 years and TNFα over 3 years in a subset of subjects who had these measurements at multiple time points. When considered together, more intense physical activity intensity and lower serum TNFα were both associated with greater total brain volume on follow-up MRI scans. TNFα, but not physical activity, was associated with regional volumes of the inferior parietal lobule, a region previously associated with inflammation in AD patients. Physical activity and TNFα may independently influence brain structure in older adults.
    Neuroscience 05/2014; · 3.12 Impact Factor
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    ABSTRACT: To understand factors that affect brain connectivity and integrity, it is beneficial to automatically cluster white matter (WM) fibers into anatomically recognizable tracts. Whole brain tractography, based on diffusion-weighted MRI, generates vast sets of fibers throughout the brain; clustering them into consistent and recognizable bundles can be difficult as there are wide individual variations in the trajectory and shape of WM pathways. Here we introduce a novel automated tract clustering algorithm based on label fusion - a concept from traditional intensity-based segmentation. Streamline tractography generates many incorrect fibers, so our top-down approach extracts tracts consistent with known anatomy, by mapping multiple hand-labeled atlases into a new dataset. We fuse clustering results from different atlases, using a mean distance fusion scheme. We reliably extracted the major tracts from 105-gradient high angular resolution diffusion images (HARDI) of 198 young normal twins. To compute population statistics, we use a point-wise correspondence method to match, compare, and average WM tracts across subjects. We illustrate our method in a genetic study of white matter tract heritability in twins.
    NeuroImage 05/2014; · 6.25 Impact Factor
  • Junning Li, Yonggang Shi, Arthur W Toga
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    ABSTRACT: Advances in diffusion weighted MR imaging have made it possible to non-invasively study the structral connectivity of human brains at high resolution. To model crossing fibers in white matter, a popular choice is the reconstruction of fiber orientation distributions (FODs) from diffusion data. For this sophiscated image representation of brain connectivity, classical image operations such as differentiation and interpolation must be redefined. In this paper, we introduce rotational gradient fields (RGF) as the spatial differential of FODs' orientations. By taking into accout the rotational effect of traveling in the RGF, an FOD at one location can be transported and aligned with the FOD at the target location. We propose a method for inducing RGFs with FOD metrics. We also show how RGFs can be used for interpolation, yielding intuitively more reasonable results than the Frechet mean on the unit sphere in a Hilbert space [1].
    04/2014; 2014:1051-1054.
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    ABSTRACT: In this paper we present a novel approach for the intrinsic mapping of anatomical surfaces and its application in brain mapping research. Using the Laplace-Beltrami eigensystem, we represent each surface with an isometry invariant embedding in a high dimensional space. The key idea in our system is that we realize surface deformation in the embedding space via the iterative optimization of a conformal metric without explicitly perturbing the surface or its embedding. By minimizing a distance measure in the embedding space with metric optimization, our method generates a conformal map directly between surfaces with highly uniform metric distortion and the ability of aligning salient geometric features. Besides pairwise surface maps, we also extend the metric optimization approach for group-wise atlas construction and multi-atlas cortical label fusion. In experimental results, we demonstrate the robustness and generality of our method by applying it to map both cortical and hippocampal surfaces in population studies. For cortical labeling, our method achieves excellent performance in a crossvalidation experiment with 40 manually labeled surfaces, and successfully models localized brain development in a pediatric study of 80 subjects. For hippocampal mapping, our method produces much more significant results than two popular tools on a multiple sclerosis study of 109 subjects.
    IEEE transactions on medical imaging. 03/2014;
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    ABSTRACT: Combining datasets across independent studies can boost statistical power by increasing the numbers of observations and can achieve more accurate estimates of effect sizes. This is especially important for genetic studies where a large number of observations are required to obtain sufficient power to detect and replicate genetic effects. There is a need to develop and evaluate methods for joint-analytical analyses of rich datasets collected in imaging genetics studies. The ENIGMA-DTI consortium is developing and evaluating approaches for obtaining pooled estimates of heritability through meta-and mega-genetic analytical approaches, to estimate the general additive genetic contributions to the intersubject variance in fractional anisotropy (FA) measured from diffusion tensor imaging (DTI). We used the ENIGMA-DTI data harmonization protocol for uniform processing of DTI data from multiple sites. We evaluated this protocol in five family-based cohorts providing data from a total of 2248 children and adults (ages: 9-85) collected with various imaging protocols. We used the imaging genetics analysis tool, SOLAR-Eclipse, to combine twin and family data from Dutch, Australian and Mexican-American cohorts into one large "mega-family". We showed that heritability estimates may vary from one cohort to another. We used two meta-analytical (the sample-size and standard-error weighted) approaches and a mega-genetic analysis to calculate heritability estimates across-population. We performed leave-one-out analysis of the joint estimates of heritability, removing a different cohort each time to understand the estimate variability. Overall, meta- and mega-genetic analyses of heritability produced robust estimates of heritability.
    NeuroImage 03/2014; · 6.25 Impact Factor
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    ABSTRACT: Previous studies have revealed an increased fractional anisotropy and greater thickness in the anterior parts of the corpus callosum in meditation practitioners compared with control subjects. Altered callosal features may be associated with an altered inter-hemispheric integration and the degree of brain asymmetry may also be shifted in meditation practitioners. Therefore, we investigated differences in gray matter asymmetry as well as correlations between gray matter asymmetry and years of meditation practice in 50 long-term meditators and 50 controls. We detected a decreased rightward asymmetry in the precuneus in meditators compared with controls. In addition, we observed that a stronger leftward asymmetry near the posterior intraparietal sulcus was positively associated with the number of meditation practice years. In a further exploratory analysis, we observed that a stronger rightward asymmetry in the pregenual cingulate cortex was negatively associated with the number of practice years. The group difference within the precuneus, as well as the positive correlations with meditation years in the pregenual cingulate cortex, suggests an adaptation of the default mode network in meditators. The positive correlation between meditation practice years and asymmetry near the posterior intraparietal sulcus may suggest that meditation is accompanied by changes in attention processing.
    Social Cognitive and Affective Neuroscience 03/2014; · 5.04 Impact Factor

Publication Stats

36k Citations
4,076.56 Total Impact Points

Institutions

  • 2014
    • Keck School of Medicine USC
      Los Angeles, California, United States
  • 2004–2014
    • University of Southern California
      • • Institute for Neuroimaging and Informatics (INI)
      • • Information Sciences Institute
      • • Department of Electrical Engineering
      • • Department of Psychology
      • • Department of Neurology
      Los Angeles, California, United States
    • University of Utah
      • Scientific Computing and Imaging Institute
      Salt Lake City, UT, United States
  • 1989–2014
    • University of California, Los Angeles
      • • Laboratory of Neuro Imaging
      • • Department of Neurology
      • • Department of Medicine
      • • Department of Neurosurgery
      Los Angeles, California, United States
  • 2013
    • Inha University Hospital
      Sinhyeon, South Gyeongsang, South Korea
  • 2011–2013
    • Arizona State University
      • School of Computing, Informatics, and Decision Systems Engineering
      Tempe, AZ, United States
    • University of California, Irvine
      • • Division of Neurology
      • • Department of Psychiatry & Human Behavior
      Irvine, California, United States
  • 2008–2013
    • Laureate Institute for Brain Research
      Tulsa, Oklahoma, United States
    • Charité Universitätsmedizin Berlin
      • Department of Psychiatry and Psychotherapy
      Berlin, Land Berlin, Germany
    • Massachusetts General Hospital
      • Department of Neurosurgery
      Boston, MA, United States
    • University of California, Berkeley
      Berkeley, California, United States
    • McGill University
      • McConnell Brain Imaging Centre
      Montréal, Quebec, Canada
  • 2007–2013
    • Yale University
      • • Department of Psychology
      • • Department of Psychiatry
      New Haven, CT, United States
    • California Institute of Technology
      • Beckman Institute
      Pasadena, California, United States
  • 2012
    • VTT Technical Research Centre of Finland
      Esbo, Southern Finland Province, Finland
    • National Yang Ming University
      • Department of Biomedical Engineering
      Taipei, Taipei, Taiwan
    • Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center
      Torrance, California, United States
  • 2005–2012
    • CSU Mentor
      Long Beach, California, United States
    • San Francisco VA Medical Center
      San Francisco, California, United States
    • Providence Health and Services
      Renton, Washington, United States
  • 1996–2012
    • Children's Hospital Los Angeles
      • DIvision of Neurology
      Los Angeles, California, United States
  • 2010–2011
    • Hospital of the University of Pennsylvania
      • Department of Pathology and Laboratory Medicine
      Philadelphia, PA, United States
    • University of Jinan (Jinan, China)
      Chi-nan-shih, Shandong Sheng, China
    • Tampere University of Technology
      • Signaalinkäsittelyn laitos
      Tampere, Western Finland, Finland
    • University of Texas Health Science Center at San Antonio
      • Research Imaging Institute
      San Antonio, Texas, United States
    • University of Illinois at Chicago
      • Department of Psychiatry (Chicago)
      Chicago, IL, United States
  • 2009–2011
    • University of Pennsylvania
      • Department of Criminology
      Philadelphia, Pennsylvania, United States
    • University of Iowa
      • Department of Psychiatry
      Iowa City, Iowa, United States
  • 2005–2011
    • University of California, San Francisco
      San Francisco, California, United States
  • 2006–2010
    • Johns Hopkins University
      • • Department of Biomedical Engineering
      • • Department of Neurology
      Baltimore, MD, United States
    • The University of Western Ontario
      • Department of Psychiatry
      London, Ontario, Canada
    • Universitätsklinikum Jena
      Jena, Thuringia, Germany
  • 2006–2009
    • University of Pittsburgh
      • • School of Medicine
      • • Department of Psychiatry
      • • Center for Alzheimer Disease Research
      Pittsburgh, PA, United States
  • 2007–2008
    • University of Queensland 
      • School of Psychology
      Brisbane, Queensland, Australia
  • 2004–2008
    • National Institute of Mental Health (NIMH)
      • Child Psychiatry Branch
      Bethesda, MD, United States
  • 2006–2007
    • University of California, Davis
      • • Center for Neuroscience
      • • Department of Neurology
      Davis, CA, United States
    • IRCCS Centro San Giovanni di Dio, Fatebenefratelli, Brescia
      • Laboratory of Epidemiology, Neuroimaging and Telemedicine - LENITEM
      Brescia, Lombardy, Italy
  • 2003
    • University of Nice-Sophia Antipolis
      Nice, Provence-Alpes-Côte d'Azur, France
    • Otto-von-Guericke-Universität Magdeburg
      Magdeburg, Saxony-Anhalt, Germany
  • 1999
    • Pacific Center for Neurological Disease
      Poway, California, United States
    • Pacific Neuropsychiatric Institute
      Seattle, Washington, United States