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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. and 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. Available at: https://doi.org/10.1007/s10278-012-9462-x.

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. Available at: https://doi.org/10.1056/NEJMoa1202753.

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. Available at: https://doi.org/10.1016/j.neuroimage.2004.06.018.

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. Available at: https://doi.org/10.1001/archneur.62.12.1870.

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: https://doi.org/10.1212/WNL.0b013e3182a841c6.

Chhatwal, J.P. et al. (2013) ‘Impaired default network functional connectivity in autosomal dominant Alzheimer disease’, Neurology, 81(8), pp. 736–744. Available at: https://doi.org/10.1212/WNL.0b013e3182a1aafe.

Clark, K. et al. (2013) ‘The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository’, Journal of digital imaging [Preprint]. Available at: https://doi.org/10.1007/s10278-013-9622-7.

Erickson, B.J., Pan, T. and 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. Available at: https://doi.org/10.1007/s10278-012-9490-6.

von Eschenbach, A.C. and 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. Available at: https://doi.org/10.1002/emmm.200900048.

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. Available at: https://doi.org/10.1002/ana.21559.

Gadde, S. et al. (2012) ‘XCEDE: an extensible schema for biomedical data’, Neuroinformatics, 10(1), pp. 19–32. Available at: https://doi.org/10.1007/s12021-011-9119-9.

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 [Preprint]. Available at: https://doi.org/10.1136/amiajnl-2012-001469.

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. Available at: https://doi.org/10.1109/TITB.2008.917893.

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. Available at: https://doi.org/10.3389/neuro.11.030.2009.

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. Available at: https://doi.org/10.1162/jocn.2007.19.9.1498.

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. Available at: https://doi.org/10.1007/s10278-007-9066-z.

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: https://doi.org/i: 10.1162/jocn.2009.21407</p>.

Marcus, D.S. et al. (2011) ‘Informatics and data mining tools and strategies for the human connectome project’, Frontiers in neuroinformatics, 5, p. 4. Available at: https://doi.org/10.3389/fninf.2011.00004.

Marcus, D.S., Erickson, B.J. and 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. Available at: https://doi.org/10.1007/s10278-012-9502-6.

Marcus, D.S. and Van Essen, D.C. (2002) ‘Scene segmentation and attention in primate cortical areas V1 and V2’, Journal of neurophysiology, 88(5), pp. 2648–2658. Available at: https://doi.org/10.1152/jn.00916.2001.

Milchenko, M. and Marcus, D. (2013) ‘Obscuring surface anatomy in volumetric imaging data’, Neuroinformatics, 11(1), pp. 65–75. Available at: https://doi.org/10.1007/s12021-012-9160-3.

Mills, S.M. et al. (2013) ‘Preclinical trials in autosomal dominant AD: Implementation of the DIAN-TU trial’, Revue neurologique [Preprint]. Available at: https://doi.org/10.1016/j.neurol.2013.07.017.

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. Available at: https://doi.org/10.4155/cli.12.93.

Pan, T., Erickson, B.J. and 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. Available at: https://doi.org/10.1007/s10278-012-9493-3.

Poline, J.-B. et al. (2012) ‘Data sharing in neuroimaging research’, Frontiers in neuroinformatics, 6, p. 9. Available at: https://doi.org/10.3389/fninf.2012.00009.

Prior, F.W., Erickson, B.J. and 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. Available at: https://doi.org/10.1007/s10278-007-9061-4.

Roe, C.M. et al. (2010) ‘Alzheimer disease identification using amyloid imaging and reserve variables: proof of concept’, Neurology, 75(1), pp. 42–48. Available at: https://doi.org/10.1212/WNL.0b013e3181e620f4.

Roe, Catherine M et al. (2011) ‘Cerebrospinal fluid biomarkers, education, brain volume, and future cognition’, Archives of neurology, 68(9), pp. 1145–1151. Available at: https://doi.org/10.1001/archneurol.2011.192.

Roe, C M et al. (2011) ‘Improving CSF biomarker accuracy in predicting prevalent and incident Alzheimer disease’, Neurology, 76(6), pp. 501–510. Available at: https://doi.org/10.1212/WNL.0b013e31820af900.

Schwartz, Y. et al. (2012) ‘PyXNAT: XNAT in Python’, Frontiers in neuroinformatics, 6, p. 12. Available at: https://doi.org/10.3389/fninf.2012.00012.

Van Essen, D.C. et al. (2012) ‘The Human Connectome Project: a data acquisition perspective’, NeuroImage, 62(4), pp. 2222–2231. Available at: https://doi.org/10.1016/j.neuroimage.2012.02.018.

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: https://doi.org/10.1371/journal.pone.0041522.