Bio

Bio


Audrey is a second year radiology resident who received her MD-PhD from UNC Chapel Hill. For her Neurobiology PhD Audrey helped to develop a pipeline of tools to analyze diffusion weighted images, which she then used to perform DTI and quantitative tractography to investigate the episodic memory circuitry in cigarette smokers and nonsmokers. She hopes to continue neuroimaging research during a fellowship in Neuroradiology. Outside of radiology, Audrey is also passionate about quality improvement, mentorship, advocacy, and leadership. She is a part of the Radiology Diversity Committee, GME Diversity Committee, and GME Women in Medicine Leadership Council. Audrey is also an active mentor through the First Generation Mentorship Program, the Women in Medicine Mentorship Program, is the GME Diversity Mentorship Chair, and founder of the mentoring program Navigating Medicine. She also is excited to mentor through the new group, the Stanford Women Association of Physician Scientists (SWAPS) to help MD-PhDs in training.

Publications

All Publications


  • Aqueductal stenosis secondary to Whipple's disease. JBR-BTR : organe de la Societe royale belge de radiologie (SRBR) = orgaan van de Koninklijke Belgische Vereniging voor Radiologie (KBVR) Chiang, F., Verde, A. R., Sossa, D. E., Sossa, D. G., Castillo, M. ; 96 (6): 395

    View details for PubMedID 24617193

  • Transfusional iron overload presenting as choroid plexus hemosiderosis. JBR-BTR : organe de la Societe royale belge de radiologie (SRBR) = orgaan van de Koninklijke Belgische Vereniging voor Radiologie (KBVR) Sossa, D. E., Chiang, F., Verde, A. R., Sossa, D. G., Castillo, M. ; 96 (6): 391

    View details for PubMedID 24617189

  • Antenatal depression, treatment with selective serotonin reuptake inhibitors, and neonatal brain structure: A propensity-matched cohort study. Psychiatry research. Neuroimaging Jha, S. C., Meltzer-Brody, S., Steiner, R. J., Cornea, E., Woolson, S., Ahn, M., Verde, A. R., Hamer, R. M., Zhu, H., Styner, M., Gilmore, J. H., Knickmeyer, R. C. 2016; 253: 43?53

    Abstract

    The aim of this propensity-matched cohort study was to evaluate the impact of prenatal SSRI exposure and a history of maternal depression on neonatal brain volumes and white matter microstructure. SSRI-exposed neonates (n=27) were matched to children of mothers with no history of depression or SSRI use (n=54). Additionally, neonates of mothers with a history of depression, but no prenatal SSRI exposure (n=41), were matched to children of mothers with no history of depression or SSRI use (n=82). Structural magnetic resonance imaging and diffusion weighted imaging scans were acquired with a 3T Siemens Allegra scanner. Global tissue volumes were characterized using an automatic, atlas-moderated expectation maximization segmentation tool. Local differences in gray matter volumes were examined using deformation-based morphometry. Quantitative tractography was performed using an adaptation of the UNC-Utah NA-MIC DTI framework. SSRI-exposed neonates exhibited widespread changes in white matter microstructure compared to matched controls. Children exposed to a history of maternal depression but no SSRIs showed no significant differences in brain development compared to matched controls. No significant differences were found in global or regional tissue volumes. Additional research is needed to clarify whether SSRIs directly alter white matter development or whether this relationship is mediated by depressive symptoms during pregnancy.

    View details for PubMedID 27254086

    View details for PubMedCentralID PMC4930375

  • UNC-Utah NA-MICframework for DTI fiber tract analysis FRONTIERS IN NEUROINFORMATICS Verde, A. R., Budin, F., Berger, J., Gupta, A., Farzinfar, M., Kaiser, A., Ahn, M., Johnson, H., Matsui, J., Hazlett, H. C., Sharma, A., Goodlett, C., Shi, Y., Gouttard, S., Vachet, C., Piven, J., Zhu, H., Gerig, G., Styner, M. 2014; 7: 51

    Abstract

    Diffusion tensor imaging has become an important modality in the field of neuroimaging to capture changes in micro-organization and to assess white matter integrity or development. While there exists a number of tractography toolsets, these usually lack tools for preprocessing or to analyze diffusion properties along the fiber tracts. Currently, the field is in critical need of a coherent end-to-end toolset for performing an along-fiber tract analysis, accessible to non-technical neuroimaging researchers. The UNC-Utah NA-MIC DTI framework represents a coherent, open source, end-to-end toolset for atlas fiber tract based DTI analysis encompassing DICOM data conversion, quality control, atlas building, fiber tractography, fiber parameterization, and statistical analysis of diffusion properties. Most steps utilize graphical user interfaces (GUI) to simplify interaction and provide an extensive DTI analysis framework for non-technical researchers/investigators. We illustrate the use of our framework on a small sample, cross sectional neuroimaging study of eight healthy 1-year-old children from the Infant Brain Imaging Study (IBIS) Network. In this limited test study, we illustrate the power of our method by quantifying the diffusion properties at 1 year of age on the genu and splenium fiber tracts.

    View details for DOI 10.3389/fninf.2013.00051

    View details for Web of Science ID 000348103400001

    View details for PubMedID 24409141

    View details for PubMedCentralID PMC3885811

  • Diffusion imaging quality control via entropy of principal direction distribution NEUROIMAGE Farzinfar, M., Oguz, I., Smith, R. G., Verde, A. R., Dietrich, C., Gupta, A., Escolar, M. L., Piven, J., Pujol, S., Vachet, C., Gouttard, S., Gerig, G., Dager, S., McKinstry, R. C., Paterson, S., Evans, A. C., Styner, M. A., IBIS Network 2013; 82: 1?12

    Abstract

    Diffusion MR imaging has received increasing attention in the neuroimaging community, as it yields new insights into the microstructural organization of white matter that are not available with conventional MRI techniques. While the technology has enormous potential, diffusion MRI suffers from a unique and complex set of image quality problems, limiting the sensitivity of studies and reducing the accuracy of findings. Furthermore, the acquisition time for diffusion MRI is longer than conventional MRI due to the need for multiple acquisitions to obtain directionally encoded Diffusion Weighted Images (DWI). This leads to increased motion artifacts, reduced signal-to-noise ratio (SNR), and increased proneness to a wide variety of artifacts, including eddy-current and motion artifacts, "venetian blind" artifacts, as well as slice-wise and gradient-wise inconsistencies. Such artifacts mandate stringent Quality Control (QC) schemes in the processing of diffusion MRI data. Most existing QC procedures are conducted in the DWI domain and/or on a voxel level, but our own experiments show that these methods often do not fully detect and eliminate certain types of artifacts, often only visible when investigating groups of DWI's or a derived diffusion model, such as the most-employed diffusion tensor imaging (DTI). Here, we propose a novel regional QC measure in the DTI domain that employs the entropy of the regional distribution of the principal directions (PD). The PD entropy quantifies the scattering and spread of the principal diffusion directions and is invariant to the patient's position in the scanner. High entropy value indicates that the PDs are distributed relatively uniformly, while low entropy value indicates the presence of clusters in the PD distribution. The novel QC measure is intended to complement the existing set of QC procedures by detecting and correcting residual artifacts. Such residual artifacts cause directional bias in the measured PD and here called dominant direction artifacts. Experiments show that our automatic method can reliably detect and potentially correct such artifacts, especially the ones caused by the vibrations of the scanner table during the scan. The results further indicate the usefulness of this method for general quality assessment in DTI studies.

    View details for DOI 10.1016/j.neuroimage.2013.05.022

    View details for Web of Science ID 000324568400001

    View details for PubMedID 23684874

    View details for PubMedCentralID PMC3798052

  • DTI Quality Control Assessment via Error Estimation From Monte Carlo Simulations Farzinfar, M., Li, Y., Verde, A. R., Oguz, I., Gerig, G., Styner, M. A., Ourselin, S., Haynor, D. R. SPIE-INT SOC OPTICAL ENGINEERING. 2013

    View details for DOI 10.1117/12.2006925

    View details for Web of Science ID 000322020600081

  • UNC-Utah NA-MIC DTI framework: Atlas Based Fiber Tract Analysis with Application to a Study of Nicotine Smoking Addiction Verde, A. R., Berger, J., Gupta, A., Farzinfar, M., Kaiser, A., Chanon, V. W., Boettiger, C., Johnson, H., Matsui, J., Sharma, A., Goodlett, C., Shi, Y., Zhu, H., Gerig, G., Gouttard, S., Vachet, C., Styner, M., Ourselin, S., Haynor, D. R. SPIE-INT SOC OPTICAL ENGINEERING. 2013

    Abstract

    The UNC-Utah NA-MIC DTI framework represents a coherent, open source, atlas fiber tract based DTI analysis framework that addresses the lack of a standardized fiber tract based DTI analysis workflow in the field. Most steps utilize graphical user interfaces (GUI) to simplify interaction and provide an extensive DTI analysis framework for non-technical researchers/investigators.We illustrate the use of our framework on a 54 directional DWI neuroimaging study contrasting 15 Smokers and 14 Controls.At the heart of the framework is a set of tools anchored around the multi-purpose image analysis platform 3D-Slicer. Several workflow steps are handled via external modules called from Slicer in order to provide an integrated approach. Our workflow starts with conversion from DICOM, followed by thorough automatic and interactive quality control (QC), which is a must for a good DTI study. Our framework is centered around a DTI atlas that is either provided as a template or computed directly as an unbiased average atlas from the study data via deformable atlas building. Fiber tracts are defined via interactive tractography and clustering on that atlas. DTI fiber profiles are extracted automatically using the atlas mapping information. These tract parameter profiles are then analyzed using our statistics toolbox (FADTTS). The statistical results are then mapped back on to the fiber bundles and visualized with 3D Slicer.This framework provides a coherent set of tools for DTI quality control and analysis.This framework will provide the field with a uniform process for DTI quality control and analysis.

    View details for DOI 10.1117/12.2007093

    View details for Web of Science ID 000322020600082

    View details for PubMedID 24386543

    View details for PubMedCentralID PMC3877245

  • Software-based Diffusion MR Human Brain Phantom for Evaluating Fiber-tracking Algorithms Shi, Y., Roger, G., Vachet, C., Budin, F., Maltbie, E., Verde, A., Hoogstoel, M., Berger, J., Styner, M., Ourselin, S., Haynor, D. R. SPIE-INT SOC OPTICAL ENGINEERING. 2013

    Abstract

    Fiber tracking provides insights into the brain white matter network and has become more and more popular in diffusion MR imaging. Hardware or software phantom provides an essential platform to investigate, validate and compare various tractography algorithms towards a "gold standard". Software phantoms excel due to their flexibility in varying imaging parameters, such as tissue composition, SNR, as well as potential to model various anatomies and pathologies. This paper describes a novel method in generating diffusion MR images with various imaging parameters from realistically appearing, individually varying brain anatomy based on predefined fiber tracts within a high-resolution human brain atlas. Specifically, joint, high resolution DWI and structural MRI brain atlases were constructed with images acquired from 6 healthy subjects (age 22-26) for the DWI data and 56 healthy subject (age 18-59) for the structural MRI data. Full brain fiber tracking was performed with filtered, two-tensor tractography in atlas space. A deformation field based principal component model from the structural MRI as well as unbiased atlas building was then employed to generate synthetic structural brain MR images that are individually varying. Atlas fiber tracts were accordingly warped into each synthetic brain anatomy. Diffusion MR images were finally computed from these warped tracts via a composite hindered and restricted model of diffusion with various imaging parameters for gradient directions, image resolution and SNR. Furthermore, an open-source program was developed to evaluate the fiber tracking results both qualitatively and quantitatively based on various similarity measures.

    View details for PubMedID 24357914

  • tRNA regulation of gene expression: Interactions of an mRNA 5 '-UTR with a regulatory tRNA (vol 12, pg 1254, 2006) RNA-A PUBLICATION OF THE RNA SOCIETY Nelson, A. R., Henkin, T. M., Agris, P. F. 2006; 12 (8): 1601

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