Richard M Leahy

University of California, Los Angeles, Los Ángeles, California, United States

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Publications (345)599.56 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Diffusion MRI provides quantitative information about microstructural properties which can be useful in neuroimaging studies of the human brain. Echo planar imaging (EPI) sequences, which are frequently used for acquisition of diffusion images, are sensitive to inhomogeneities in the primary magnetic (B0) field that cause localized distortions in the reconstructed images. We describe and evaluate a new method for correction of susceptibility-induced distortion in diffusion images in the absence of an accurate B0 fieldmap. In our method, the distortion field is estimated using a constrained non-rigid registration between an undistorted T1-weighted anatomical image and one of the distorted EPI images from diffusion acquisition. Our registration framework is based on a new approach, INVERSION (Inverse contrast Normalization for VERy Simple registratION), which exploits the inverted contrast relationship between T1- and T2-weighted brain images to define a simple and robust similarity measure. We also describe how INVERSION can be used for rigid alignment of diffusion images and T1-weighted anatomical images. Our approach is evaluated with multiple in vivo datasets acquired with different acquisition parameters. Compared to other methods, INVERSION shows robust and consistent performance in rigid registration and shows improved alignment of diffusion and anatomical images relative to normalized mutual information for non-rigid distortion correction. Copyright © 2015 Elsevier Inc. All rights reserved.
    NeuroImage 03/2015; DOI:10.1016/j.neuroimage.2015.03.050 · 6.13 Impact Factor
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    ABSTRACT: Late preterm birth is increasingly recognized as a risk factor for cognitive and social deficits. The prefrontal cortex is particularly vulnerable to injury in late prematurity because of its protracted development and extensive cortical connections. Our study examined children born late preterm without access to advanced postnatal care to assess structural and functional connectivity related to the prefrontal cortex. Thirty-eight preadolescents [19 born late preterm (34-36 /7 weeks gestational age) and 19 at term] were recruited from a developing community in Brazil. Participants underwent neuropsychological testing. Individuals underwent three-dimensional T1-weighted, diffusion-weighted, and resting state functional MRI. Probabilistic tractography and functional connectivity analyses were carried out using unilateral seeds combining the medial prefrontal cortex and the anterior cingulate cortex. Late preterm children showed increased functional connectivity within regions of the default mode, salience, and central-executive networks from both right and left frontal cortex seeds. Decreased functional connectivity was observed within the right parahippocampal region from left frontal seeding. Probabilistic tractography showed a pattern of decreased streamlines in frontal white matter pathways and the corpus callosum, but also increased streamlines in the left orbitofrontal white matter and the right frontal white matter when seeded from the right. Late preterm children and term control children scored similarly on neuropsychological testing. Prefrontal cortical connectivity is altered in late prematurity, with hyperconnectivity observed in key resting state networks in the absence of neuropsychological deficits. Abnormal structural connectivity indicated by probabilistic tractography suggests subtle changes in white matter development, implying disruption of normal maturation during the late gestational period.
    Neuroreport 01/2015; 26(1):22-6. DOI:10.1097/WNR.0000000000000296 · 1.64 Impact Factor
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    ABSTRACT: High EEG frontal alpha power (FAP) is thought to represent a state of low arousal in the brain, which has been related in past research to antisocial behavior (ASB). We investigated a longitudinal sample of 900 twins in two assessments in late childhood and mid-adolescence to verify whether relationships exist between FAP and both aggressive and nonaggressive ASB. ASB was measured by the Child Behavioral Checklist, and FAP was calculated using connectivity analysis methods that used principal components analysis to derive power of the most dominant frontal activation. Significant positive predictive relationships emerged in males between childhood FAP and adolescent aggressive ASB using multilevel mixed modeling. No concurrent relationships were found. Using bivariate biometric twin modeling analysis, the relationship between childhood FAP and adolescent aggressive ASB in males was found to be entirely due to genetic factors, which were correlated r=0.22. Copyright © 2014. Published by Elsevier B.V.
    Biological Psychology 11/2014; 105. DOI:10.1016/j.biopsycho.2014.11.010 · 3.47 Impact Factor
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    ABSTRACT: To enable high-quality correction of susceptibility-induced geometric distortion artifacts in diffusion magnetic resonance imaging (MRI) images without increasing scan time. A new method for distortion correction is proposed based on subsampling a generalized version of the state-of-the-art reversed-gradient distortion correction method. Rather than acquire each q-space sample multiple times with different distortions (as in the conventional reversed-gradient method), we sample each q-space point once with an interlaced sampling scheme that measures different distortions at different q-space locations. Distortion correction is achieved using a novel constrained reconstruction formulation that leverages the smoothness of diffusion data in q-space. The effectiveness of the proposed method is demonstrated with simulated and in vivo diffusion MRI data. The proposed method is substantially faster than the reversed-gradient method, and can also provide smaller intensity errors in the corrected images and smaller errors in derived quantitative diffusion parameters. The proposed method enables state-of-the-art distortion correction performance without increasing data acquisition time. Magn Reson Med, 2013. © 2013 Wiley Periodicals, Inc.
    Magnetic Resonance in Medicine 11/2014; 72(5). DOI:10.1002/mrm.25026 · 3.40 Impact Factor
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    ABSTRACT: Several studies comparing adult musicians and non-musicians have provided compelling evidence for functional and anatomical differences in the brain systems engaged by musical training. It is not known, however, whether those differences result from long-term musical training or from pre-existing traits favoring musicality. In an attempt to begin addressing this question, we have launched a longitudinal investigation of the effects of childhood music training on cognitive, social and neural development. We compared a group of 6- to 7-year old children at the start of intense after-school musical training, with two groups of children: one involved in high intensity sports training but not musical training, another not involved in any systematic training. All children were tested with a comprehensive battery of cognitive, motor, musical, emotional, and social assessments and underwent magnetic resonance imaging and electroencephalography. Our first objective was to determine whether children who participate in musical training were different, prior to training, from children in the control groups in terms of cognitive, motor, musical, emotional, and social behavior measures as well as in structural and functional brain measures. Our second objective was to determine whether musical skills, as measured by a music perception assessment prior to training, correlates with emotional and social outcome measures that have been shown to be associated with musical training. We found no neural, cognitive, motor, emotional, or social differences among the three groups. In addition, there was no correlation between music perception skills and any of the social or emotional measures. These results provide a baseline for an ongoing longitudinal investigation of the effects of music training.
    Frontiers in Human Neuroscience 09/2014; 8:690. DOI:10.3389/fnhum.2014.00690 · 2.90 Impact Factor
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    ABSTRACT: Background / Purpose: The main goal of this study was to develop a method to reliably estimate functional connectivity in the presence of cross-talk with interference in EEG and MEG data. Main conclusion: We show LC is invariant to linear mixing when only two signals are present whereas C, IC and PLI change as the degree of mixing changes. However, this bivariate framework ignores the interference that occurs when additional sources mix into the two signals of interest. By regressing out reference signals from the interfering regions using real regression coefficients we aimed to improve estimation of true interaction between these two signals. The resulting method (PLC) uses L1-regularization to control the degree of signal suppression in the regression.
    20th Annual Meeting of the Organization for Human Brain Mapping (OHBM) 2014; 08/2014
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    ABSTRACT: We investigate using dual time-point PET data to perform Patlak modeling. This approach can be used for whole body dynamic PET studies in which we compute voxel-wise estimates of Patlak parameters using two frames of data for each bed position. Our approach directly uses list-mode arrival times for each event to estimate the Patlak parametric image. We use a penalized likelihood method in which the penalty function uses spatially variant weighting to ensure a count independent local impulse response. We evaluate performance of the method in comparison to fractional changes in SUV values (%DSUV) between the two frames using Cramer Rao analysis and Monte Carlo simulation. Receiver operating characteristic (ROC) curves are used to compare performance in differentiating tumors relative to background based on the dynamic data sets. Using area under the ROC curve as a performance metric, we show superior performance of Patlak relative to %DSUV over a range of dynamic data sets and parameters. These results suggest that Patlak analysis may be appropriate for analysis of dual time-point whole body PET data and could lead to superior detection of tumors relative to %DSUV metrics.
    IEEE Transactions on Medical Imaging 04/2014; 33(4):913-924. DOI:10.1109/TMI.2014.2298868 · 3.80 Impact Factor
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    ABSTRACT: Obesity is a global health problem, particularly in the U.S. where one third of adults are obese. A reliable and accurate method of quantifying obesity is necessary. Visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) are two measures of obesity that reflect different associated health risks, but accurate measurements in humans or rodent models are difficult. In this paper we present an automatic, registration-based segmentation method for mouse adiposity studies using microCT images. We co-register the subject CT image and a mouse CT atlas. Our method is based on surface matching of the microCT image and an atlas. Surface-based elastic volume warping is used to match the internal anatomy. We acquired a whole body scan of a C57BL6/J mouse injected with contrast agent using microCT and created a whole body mouse atlas by manually delineate the boundaries of the mouse and major organs. For method verification we scanned a C57BL6/J mouse from the base of the skull to the distal tibia. We registered the obtained mouse CT image to our atlas. Preliminary results show that we can warp the atlas image to match the posture and shape of the subject CT image, which has significant differences from the atlas. We plan to use this software tool in longitudinal obesity studies using mouse models.
    Proceedings of SPIE - The International Society for Optical Engineering 03/2014; DOI:10.1117/12.2043744 · 0.20 Impact Factor
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    ABSTRACT: We present a method based on spectral theory for the shape analysis of carpal bones of the human wrist. We represent the cortical surface of the carpal bone in a coordinate system based on the eigensystem of the two-dimensional Helmholtz equation. We employ a metric-global point signature (GPS)-that exploits the scale and isometric invariance of eigenfunctions to quantify overall bone shape. We use a fast finite-element-method to compute the GPS metric. We capitalize upon the properties of GPS representation-such as stability, a standard Euclidean (ℓ(2)) metric definition, and invariance to scaling, translation and rotation-to perform shape analysis of the carpal bones of ten women and ten men from a publicly-available database. We demonstrate the utility of the proposed GPS representation to provide a means for comparing shapes of the carpal bones across populations.
    Physics in Medicine and Biology 02/2014; 59(4):961-73. DOI:10.1088/0031-9155/59/4/961 · 2.92 Impact Factor
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    ABSTRACT: Time-of-flight (TOF) information improves the signal-to-noise ratio in positron emission tomography (PET). The computation cost in processing TOF-PET sinograms is substantially higher than for nonTOF data because the data in each line of response is divided among multiple TOF bins. This additional cost has motivated research into methods for rebinning TOF data into lower dimensional representations that exploit redundancies inherent in TOF data. We have previously developed approximate Fourier methods that rebin TOF data into either three-dimensional (3D) nonTOF or 2D nonTOF formats. We refer to these methods respectively as FORET-3D and FORET-2D. Here we describe maximum a posteriori (MAP) estimators for use with FORET rebinned data. We first derive approximate expressions for the variance of the rebinned data. We then use these results to rescale the data so that the variance and mean are approximately equal allowing us to use the Poisson likelihood model for MAP reconstruction. MAP reconstruction from these rebinned data uses a system matrix in which the detector response model accounts for the effects of rebinning. Using these methods we compare the performance of FORET-2D and 3D with TOF and nonTOF reconstructions using phantom and clinical data. Our phantom results show a small loss in contrast recovery at matched noise levels using FORET compared to reconstruction from the original TOF data. Clinical examples show FORET images that are qualitatively similar to those obtained from the original TOF-PET data but with a small increase in variance at matched resolution. Reconstruction time is reduced by a factor of 5 and 30 using FORET3D+MAP and FORET2D+MAP respectively compared to 3D TOF MAP, which makes these methods attractive for clinical applications.
    Physics in Medicine and Biology 02/2014; 59(4):925-949. DOI:10.1088/0031-9155/59/4/925 · 2.92 Impact Factor
  • Yu-Teng Chang, Dimitrios Pantazis, Richard M Leahy
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    ABSTRACT: A wealth of methods has been developed to identify natural divisions of brain networks into groups or modules, with one of the most prominent being modularity. Compared with the popularity of methods to detect community structure, only a few methods exist to statistically control for spurious modules, relying almost exclusively on resampling techniques. It is well known that even random networks can exhibit high modularity because of incidental concentration of edges, even though they have no underlying organizational structure. Consequently, interpretation of community structure is confounded by the lack of principled and computationally tractable approaches to statistically control for spurious modules. In this paper we show that the modularity of random networks follows a transformed version of the Tracy-Widom distribution, providing for the first time a link between module detection and random matrix theory. We compute parametric formulas for the distribution of modularity for random networks as a function of network size and edge variance, and show that we can efficiently control for false positives in brain and other real-world networks.
    NeuroImage 01/2014; DOI:10.1016/j.neuroimage.2014.01.010 · 6.13 Impact Factor
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    Joyita Dutta, Richard M Leahy, Quanzheng Li
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    ABSTRACT: Dynamic positron emission tomography (PET), which reveals information about both the spatial distribution and temporal kinetics of a radiotracer, enables quantitative interpretation of PET data. Model-based interpretation of dynamic PET images by means of parametric fitting, however, is often a challenging task due to high levels of noise, thus necessitating a denoising step. The objective of this paper is to develop and characterize a denoising framework for dynamic PET based on non-local means (NLM). NLM denoising computes weighted averages of voxel intensities assigning larger weights to voxels that are similar to a given voxel in terms of their local neighborhoods or patches. We introduce three key modifications to tailor the original NLM framework to dynamic PET. Firstly, we derive similarities from less noisy later time points in a typical PET acquisition to denoise the entire time series. Secondly, we use spatiotemporal patches for robust similarity computation. Finally, we use a spatially varying smoothing parameter based on a local variance approximation over each spatiotemporal patch. To assess the performance of our denoising technique, we performed a realistic simulation on a dynamic digital phantom based on the Digimouse atlas. For experimental validation, we denoised [Formula: see text] PET images from a mouse study and a hepatocellular carcinoma patient study. We compared the performance of NLM denoising with four other denoising approaches - Gaussian filtering, PCA, HYPR, and conventional NLM based on spatial patches. The simulation study revealed significant improvement in bias-variance performance achieved using our NLM technique relative to all the other methods. The experimental data analysis revealed that our technique leads to clear improvement in contrast-to-noise ratio in Patlak parametric images generated from denoised preclinical and clinical dynamic images, indicating its ability to preserve image contrast and high intensity details while lowering the background noise variance.
    PLoS ONE 12/2013; 8(12):e81390. DOI:10.1371/journal.pone.0081390 · 3.53 Impact Factor
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    ABSTRACT: The estimation and analysis of kinetic parameters in dynamic PET is frequently confounded by tissue heterogeneity and partial volume effects. We propose a new constrained model of dynamic PET to address these limitations. The proposed formulation incorporates an explicit mixture model in which each image voxel is represented as a mixture of different pure tissue types with distinct temporal dynamics.We use Cram´er-Rao lower bounds to demonstrate that the use of prior information is important to stabilize parameter estimation with this model. As a result, we propose a constrained formulation of the estimation problem that we solve using a two-stage algorithm. In the first stage, a sparse signal processing method is applied to estimate the rate parameters for the different tissue compartments from the noisy PET time series. In the second stage, tissue fractions and the linear parameters of different time activity curves are estimated using a combination of spatial-regularity and fractional mixture constraints. A block coordinate descent algorithm is combined with a manifold search to robustly estimate these parameters. The method is evaluated with both simulated and experimental dynamic PET data.
    11/2013; 33(1). DOI:10.1109/TMI.2013.2283229
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    ABSTRACT: Coherence is a widely used measure to investigate interaction between electrophysiological signals obtained from EEG or MEG. However spurious coherence between locations can be introduced through cross-talk or linear mixing between signals that occurs as a result of diffuse lead-field sensitivity (in sensor data) or limited spatial resolution (in cortical current density maps). Measures including the imaginary part of the coherence (IC), phase lag index (PLI) and Lagged coherence (LC) have all been proposed to overcome this problem. Each of these metrics use the fact that cross-talk will produce instantaneous or zero phase-lag interactions between signals. By constructing measures such as these, all of which have zero value in the case of instantaneous mixing only, we can reduce sensitivity to cross-talk. However, none of these measures considers the effect of interference from external sources. In this paper we first investigate the relative performance of IC, PLI and LC. We then propose and evaluate a novel measure, partial lagged coherence (PLC), which is more robust to cross-talk in the presence of interfering signals.
    2013 Asilomar Conference on Signals, Systems and Computers; 11/2013
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    ABSTRACT: Measurements of electrophysiological activity can be used to infer interactions between different regions of the human brain. In this work, we consider the use of an autoregressive conditional heteroscedasticity (ARCH) model to estimate causality in variance between different brain regions in simulation and continuously measured EEG data. We propose an efficient new algorithm for ARCH model estimation and demonstrate that the proposed approach provides promising results that are distinct from the causality estimates obtained from simpler and more conventional signal causality models.
    2013 Asilomar Conference on Signals, Systems and Computers; 11/2013
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    ABSTRACT: Background / Purpose: Sulcal landmarks have been used extensively for cortical registration, and we have recently seen increasing interest in analyzing the geometry of sulcal anatomy in the study of disease progression, aging, brain asymmetry and various studies of differences in neuropsychological groupings. We present a method for automated generation and analysis of sulcal curves. We also present a novel invariant representation of curves termed the Anisotropic Global Point signature (AGPS) that allows quantitative comparison of the shapes of curves. The AGPS representation is applied to analysis of sulcal shape symmetry and sulcal shape inheritability in a twins study. Main conclusion: We have developed a method for automated shape analysis of sulcal curves allowing us to perform quantitative morphometric analysis of the cortical shapes.This cortical morphometry allows us to analyze the shape of the sulcal curves on the cortex in a quantitative manner.
    19th Annual Meeting of the Organization for Human Brain Mapping (OHBM) 2013; 07/2013
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    ABSTRACT: Estimating and modeling functional connectivity in the brain is a challenging problem with potential applications in the understanding of brain organization and various neurological and neuropsychological conditions. An important objective in connectivity analysis is to determine the connections between regions of interest in the brain. However, traditional functional connectivity analyses have frequently focused on modeling interactions between time series recordings at individual sensors, voxels, or vertices despite the fact that a single region of interest will often include multiple such recordings. In this paper, we present a novel measure of interaction between regions of interest rather than individual signals. The proposed measure,termed canonical Granger causality, combines ideas from canonical correlation and Granger causality analysis to yield a measure that reflects directed causality between two regions of interest. In particular, canonical Granger causality uses optimized linear combinations of signals from each region of interest to enable accurate causality measurements from substantially less data compared to alternative multivariate methods that have previously been proposed for this scenario. The optimized linear combinations are obtained using a variation of a technique developed for optimization on the Steifel manifold. We demonstrate the advantages of canonical Granger causality in comparison to alternative causality measures for a range of different simulated datasets. We also apply the proposed measure to local field potential data recorded in a macaque brain during a visuomotor task. Results demonstrate that canonical Granger causality can be used to identify causal relationships between striate and prestriate cortex in cases where standard Granger causality is unable to identify statistically significant interactions.
    NeuroImage 06/2013; DOI:10.1016/j.neuroimage.2013.06.056 · 6.13 Impact Factor
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    19th Annual Meeting of the Organization for Human Brain Mapping, Seattle, WA, USA; 06/2013
  • Sergul Aydore, Dimitrios Pantazis, Richard M Leahy
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    ABSTRACT: We investigate the properties of the Phase Locking Value (PLV) and the Phase Lag Index (PLI) as metrics for quantifying interactions in bivariate local field potential (LFP), electroencephalography (EEG) and magnetoencephalography (MEG) data. In particular we describe the relationship between nonparametric estimates of PLV and PLI and the parameters of two distributions that can both be used to model phase interactions. The first of these is the von Mises distribution, for which the sample PLV is a maximum likelihood estimator. The second is the relative phase distribution associated with bivariate circularly symmetric complex Gaussian data. We derive an explicit expression for the PLV for this distribution and show that it is a function of the cross-correlation between the two signals. We compare the bias and variance of the sample PLV and the PLV computed from the cross-correlation. We also show that both the von Mises and Gaussian models are suitable for representing relative phase in application to LFP data from a visually-cued motor study in macaque. We then compare results using the two different PLV estimators and conclude that, for this data, the sample PLV provides equivalent information to the cross-correlation of the two complex time series.
    NeuroImage 02/2013; DOI:10.1016/j.neuroimage.2013.02.008 · 6.13 Impact Factor
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    ABSTRACT: The ability to use electrophysiological brain signals to decode various parameters of voluntary movement is a central question in Brain Machine Interface (BMI) research. Invasive BMI systems can successfully decode movement trajectories from the spiking activity of neurons in primary motor cortex and posterior parietal cortex. It has long been assumed that non-invasive techniques do not provide sufficient signal resolution to decode the kinematics of complex time-varying movements. This view stems from the hypothesis that movement parameters such as direction, position, velocity, or acceleration are primarily encoded by neuronal firing in motor cortex. Consequently, the fact that such signals cannot be detected using non-invasive techniques such as Electroencephalography (EEG) or Magnetoencephalography (MEG) has led to the claim that fine movement properties cannot be decoded with non-invasive methods. However, this view has been proven wrong by numerous studies in recent years. First, a growing body of research over the last decade has shown that the local field potential (LFP) signal, which represents the summed activity of a neuronal population, can encode movement parameters at a level comparable to unit recordings. These findings were confirmed in humans by the successful use of electrocorticography (ECoG) to achieve continuous movement decoding via invasive human BMI approaches. Very recently, a number of non-invasive studies were able to provide striking evidence that even surface-level MEG or EEG data can contain sufficient information on hand movement in order to infer movement direction and hand kinematics from brain signals recorded using non-invasive methods. Here we provide a brief review of this recent literature and discuss its importance on the future of BMI research and its implications on the development of novel motor rehabilitation strategies.
    ITBM-RBM 02/2013; 32:8-18. DOI:10.1016/j.irbm.2010.12.004

Publication Stats

11k Citations
599.56 Total Impact Points

Institutions

  • 1989–2014
    • University of California, Los Angeles
      • • Department of Electrical Engineering
      • • Department of Molecular and Medical Pharmacology
      • • Laboratory of Neuro Imaging
      Los Ángeles, California, United States
  • 1985–2014
    • University of Southern California
      • • Department of Electrical Engineering
      • • Department of Radiology
      Los Angeles, California, United States
  • 2010
    • Florida Atlantic University
      • Center for Complex Systems and Brain Sciences
      Boca Raton, Florida, United States
  • 2003–2009
    • University of California, Davis
      • Department of Biomedical Engineering
      Davis, California, United States
  • 2006
    • The University of Tokushima
      • Institute of Technology and Science
      Tokusima, Tokushima, Japan
  • 2004
    • French National Centre for Scientific Research
      Lutetia Parisorum, Île-de-France, France
  • 1990–2003
    • Los Alamos National Laboratory
      Los Alamos, California, United States
  • 2002
    • University of Michigan
      • Department of Biostatistics
      Ann Arbor, MI, United States
  • 1993
    • University of Pennsylvania
      Philadelphia, Pennsylvania, United States
  • 1992
    • Philadelphia University
      Filadelfia, Pennsylvania, United States
  • 1990–1992
    • University of Houston
      • Department of Electrical & Computer Engineering
      Houston, TX, United States
  • 1983–1984
    • Newcastle University
      • School of Electrical and Electronic Engineering
      Newcastle-on-Tyne, England, United Kingdom