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Daniel Marcus : Curriculum Vitae

CURRICULUM VITAE – Daniel S. Marcus, PhD

 

PRESENT POSITION:         

Assistant Professor

Mallinckrodt Institute of Radiology

Washington University School of Medicine, St. Louis, MO

 

EDUCATION: 

1991 - 1994                            

Washington University, St. Louis

B.A. in Biology & English Literature

 

1995 - 2001                            

Washington University School of Medicine

Ph.D. in Neuroscience

 

ACADEMIC POSITIONS / EMPLOYMENT:

2001 - 2003                            

Bioinformatics Specialist I                 

Howard Hughes Medical Institute at Washington University          

 

2003 - 2005                            

Research Assistant Professor of Psychology

Washington University

 

2003 - 2005                            

Bioinformatics Specialist II

Howard Hughes Medical Institute at Washington University

 

2006 - 2010                            

Research Assistant Professor of Radiology

Washington University School of Medicine

 

2010 - present                                   

Assistant Professor of Radiology

Washington University School of Medicine

 

BIBLIOGRAPHY:

Archie, K.A. & Marcus, D.S., 2012. DicomBrowser: software for viewing and modifying DICOM metadata. Journal of digital imaging: the official journal of the Society for Computer Applications in Radiology, 25(5), pp.635–645.
Bateman, R.J. et al., 2012. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. The New England journal of medicine, 367(9), pp.795–804.
Buckner, R.L. et al., 2004. A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume. NeuroImage, 23(2), pp.724–738.
Burns, J.M. et al., 2005. White matter lesions are prevalent but differentially related with cognition in aging and early Alzheimer disease. Archives of neurology, 62(12), pp.1870–1876.
Cash, D.M. et al., 2013. The pattern of atrophy in familial Alzheimer disease Volumetric MRI results from the DIAN study. Neurology, p.10.1212/WNL.0b013e3182a841c6. Available at: http://www.neurology.org/content/early/2013/09/18/WNL.0b013e3182a841c6 [Accessed September 25, 2013].
Chhatwal, J.P. et al., 2013. Impaired default network functional connectivity in autosomal dominant Alzheimer disease. Neurology, 81(8), pp.736–744.
Clark, K. et al., 2013. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. Journal of digital imaging.
Erickson, B.J., Pan, T. & Marcus, D.S., 2012. Whitepapers on imaging infrastructure for research: Part 1: General workflow considerations. Journal of digital imaging: the official journal of the Society for Computer Applications in Radiology, 25(4), pp.449–453.
von Eschenbach, A.C. & Buetow, K., 2006. Cancer informatics vision: caBIG. Cancer informatics, 2, pp.22–24.
Fagan, A.M., Mintun, M.A., et al., 2009. Cerebrospinal fluid tau and ptau(181) increase with cortical amyloid deposition in cognitively normal individuals: implications for future clinical trials of Alzheimer’s disease. EMBO molecular medicine, 1(8-9), pp.371–380.
Fagan, A.M., Head, D., et al., 2009. Decreased cerebrospinal fluid Abeta(42) correlates with brain atrophy in cognitively normal elderly. Annals of neurology, 65(2), pp.176–183.
Gadde, S. et al., 2012. XCEDE: an extensible schema for biomedical data. Neuroinformatics, 10(1), pp.19–32.
Gutman, D.A. et al., 2013. Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data. Journal of the American Medical Informatics Association: JAMIA.
Keator, D.B. et al., 2008. A national human neuroimaging collaboratory enabled by the Biomedical Informatics Research Network (BIRN). IEEE transactions on information technology in biomedicine: a publication of the IEEE Engineering in Medicine and Biology Society, 12(2), pp.162–172.
Keator, D.B. et al., 2009. Derived Data Storage and Exchange Workflow for Large-Scale Neuroimaging Analyses on the BIRN Grid. Frontiers in neuroinformatics, 3, p.30.
Marcus, D.S. et al., 2011. Informatics and data mining tools and strategies for the human connectome project. Frontiers in neuroinformatics, 5, p.4.
Marcus, D.S., Wang, T.H., et al., 2007. Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Journal of cognitive neuroscience, 19(9), pp.1498–1507.
Marcus, D.S. et al., 2009. Open Access Series of Imaging Studies: Longitudinal MRI Data in Nondemented and Demented Older Adults. Journal of Cognitive Neuroscience, 22(12), pp.2677–2684. Available at: http://dx.doi.org/10.1162/jocn.2009.21407.
Marcus, D.S., Olsen, T.R., et al., 2007. The Extensible Neuroimaging Archive Toolkit: an informatics platform for managing, exploring, and sharing neuroimaging data. Neuroinformatics, 5(1), pp.11–34.
Marcus, D.S., Archie, K.A., et al., 2007. The open-source neuroimaging research enterprise. Journal of digital imaging: the official journal of the Society for Computer Applications in Radiology, 20 Suppl 1, pp.130–138.
Marcus, D.S., Erickson, B.J. & Pan, T., 2012. Imaging infrastructure for research. Part 2. Data management practices. Journal of digital imaging: the official journal of the Society for Computer Applications in Radiology, 25(5), pp.566–569.
Marcus, D.S. & Van Essen, D.C., 2002. Scene segmentation and attention in primate cortical areas V1 and V2. Journal of neurophysiology, 88(5), pp.2648–2658.
Milchenko, M. & Marcus, D., 2013. Obscuring surface anatomy in volumetric imaging data. Neuroinformatics, 11(1), pp.65–75.
Mills, S.M. et al., 2013. Preclinical trials in autosomal dominant AD: Implementation of the DIAN-TU trial. Revue neurologique.
Morris, J.C. et al., 2012. Developing an international network for Alzheimer research: The Dominantly Inherited Alzheimer Network. Clinical investigation, 2(10), pp.975–984.
Pan, T., Erickson, B.J. & Marcus, D.S., 2012. Whitepapers on imaging infrastructure for research part three: security and privacy. Journal of digital imaging: the official journal of the Society for Computer Applications in Radiology, 25(6), pp.692–702.
Poline, J.-B. et al., 2012. Data sharing in neuroimaging research. Frontiers in neuroinformatics, 6, p.9.
Prior, F.W., Erickson, B.J. & Tarbox, L., 2007. Open source software projects of the caBIG In Vivo Imaging Workspace Software special interest group. Journal of digital imaging, 20 Suppl 1, pp.94–100.
Roe, C.M. et al., 2010. Alzheimer disease identification using amyloid imaging and reserve variables: proof of concept. Neurology, 75(1), pp.42–48.
Roe, C.M. et al., 2011. Cerebrospinal fluid biomarkers, education, brain volume, and future cognition. Archives of neurology, 68(9), pp.1145–1151.
Roe, C.M. et al., 2011. Improving CSF biomarker accuracy in predicting prevalent and incident Alzheimer disease. Neurology, 76(6), pp.501–510.
Schwartz, Y. et al., 2012. PyXNAT: XNAT in Python. Frontiers in neuroinformatics, 6, p.12.
Van Essen, D.C. et al., 2012. The Human Connectome Project: a data acquisition perspective. NeuroImage, 62(4), pp.2222–2231.
Zinn, P.O. et al., 2012. A Novel Volume-Age-KPS (VAK) Glioblastoma Classification Identifies a Prognostic Cognate microRNA-Gene Signature. PLoS ONE, 7(8), p.e41522. Available at: http://dx.doi.org/10.1371/journal.pone.0041522 [Accessed September 25, 2013].