neuroimaging, dementia, PET, MRI, machine learning
Phi Kappa Phi National Honor Society
Graduate Dean's List Award - CSULB Department of Engineering and Computer Science
Dr. David Keator, M.S., Ph.D. has an extensive background in data science, applied machine learning, statistics, and informatics methods for structured data exchange and reproducibility in neuroimaging. Since 1996 Dr. Keator has been the technical director (now operations director) of the UCI Neuroscience Imaging Center (formerly UCI Brain Imaging Center) responsible for image quality control, reconstruction, and statistical analysis. Dr. Keator has been the chair of the NeuroInformatics Working group FBIRN (Functional Biomedical Informatics Research Network), leading technical personnel from member sites in developing a federated database for storing and managing subject demographics, clinical assessments, imaging, and genetics information along with the supporting metadata representations. Dr. Keator has co-developed the Neuroimaging Data Model (NIDM) a semantic web enabled metadata format for neuroimaging currently being incorporated into popular software packages such as SPM, FSL, and AFNI. Dr. Keator is an active part of the International NeuroInformatics Coordinating Facility (INCF) Neuroimaging Task Force and chairs the NIDM working group. Dr. Keator directs the UCI Conte Center Informatics core, which is developing a center-wide informatics platform, based on semantic web technologies and using the NIDM standard to wrap source data across center projects and cores. Visit me on GitHub (https://github.com/dbkeator) and Twitter (@brainjunky)!
Keator D.B., Phelan MJ, Taylor LM, Doran E, Krinsky-McHale S, Price J, Ballard E, Kreisl WC, Hom C, Nguyen D, Pulsifer M, Lai F, Rosas DH, Brickman AM, Schupf N, Yassa MA, Silverman W, Lott IT. Down syndrome: distribution of brain amyloid in mild cognitive impairment. Alzheimer’s & Dementia: Diagnosis, Assessment and Disease Monitoring. Special Collection on Biomarkers of Alzheimer’s Disease among Adults with Down Syndrome, In Press, 2020 DOI: 10.1002/dad2.12013.
Kennedy D.N., Abraham S.A., Bates J.F., Crowley A., Ghosh S., Gillespie T., Gonclaves M., Grethe J., Halchenko Y.O., Hanke M., Haselgrove C., Hodge S.M., Jarecka D., Kaczmarzyk J., Keator D.B., Meyer K., Martone M.E., Padhy S., Poline J.B., Preuss N., Sincomb T., Travers M. Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging. Frontiers in Neuroinformatics, 2019 vol. 13. DOI: https://doi.org/10.3389/fninf.2019.00001.
Keator D.B., Doran E., VanErp T.G.M., Phelan M., Tseung K., Yassa M., Potkin S.G., Lott I. [18F]-Florbetapir PET: Towards Predicting Dementia in Adults with Down Syndrome. bioRxiv preprint: https://doi.org/10.1101/235440. 2018.
Ramones A., Pita A., Keator D.B., Wu J. Case report: Significant quantitative MRI brain volumetric finding associated with electrical brain injury. Burn Open. 2018, vol. 2, issue 3. DOI: https://doi.org/10.1016/j.burnso.2018.03.002.
Keator D.B., Chen J., Nichols N., Fana F., Stern H., Baram T.Z., Small S.L. A Semantic Cross-Species Derived Data Management Application. Data Science Journal. 2017 16, p.45. DOI: http://doi.org/10.5334/dsj-2017-045.
Ghosh SS, Poline JB, Keator DB, Halchenko YO, Thomas AG, Kessler DA, Kennedy DN. A very simple, re-executable neuroimaging publication. F1000Research. 2017 Feb 10;6.
Amen DG, Trujillo M, Keator D., Taylor DV, Willeumier K, Meysami S, Raji CA. Gender-Based Cerebral Perfusion Differences in 46,034 Functional Neuroimaging Scans. Journal of Alzheimer's Disease. 2017 60(2):605-614. doi: http://doi.org/10.3233/JAD-170432.
Doran E, Keator D.B., Head E., Phelan M.J., Kim R., Totoiu M., Barrio J., Small G., Potkin S.G., Lott I. Down Syndrome, Partial Trisomy 21, and Absence of Alzheimer’s Disease: The role of APP. Journal of Alzheimer’s Disease. 56.2 (2017): 459-470.
Maumet C., Auer T., Bowring A., Chen G., Das S., Flandin G., Ghosh S., Glatard T., Gorgolewski K., Helmer K., Jenkinson M., Keator D.B., Nichols N, Poline J.B., Reynolds R., Sochat V., Turner J., Nichols T. Sharing brain mapping statistical results with the neuroimaging data model. Scientific Data. Scientific Data, 2016, 3, 160102. http://doi.org/10.1038/sdata.2016.102.
Gorgolewski K.J.,Auer T.,Calhoun V.,Craddock C.,Das S.,Duff E.,Flandin G.,Ghosh S.,Halchenko Y.,Handwerker D.,Hanke M.,Keator D.B.,Li X.,Maumet C.,Michael Z.,Nichols B.N.,Nichols T.,Poline J.B.,Roken A.,Schaefer G.,Sochat V.,Turner J.A.,Varoquaux G.,Poldrack R. The Brain Imaging Data Structure: a standard for organizing and describing outputs of neuroimaging experiments. Data Science Journal. 2016.
Full CV: https://drive.google.com/open?id=0B3KAfE6L3piOakMyWVVKb0Vfckk
Sub-Award PI: WA00433491
Center for Reproducible Neuroimaging Computation (CRNC)
The Center for Reproducible Neuroimaging Computation (CRNC), a Biomedical Technology Research Center, is dedicated to establishing and promoting reproducible neuroimaging research through the use of semantic web and software container technologies.
NIA 5U01AG051412-03 Sub-Award Co-I: 3U01AG051412-03S1
Biomarkers of Alzheimer's Disease in Down Syndrome
Goals: The overall aim of this project is to identify biomarkers associated with the progression of Alzheimer's disease (AD) from its prodromal stages to frank dementia in adults with Down syndrome (DS).
NIMH R01 MH108155 (PI: Rao), Co-I:
Effects of Childhood Maltreatment on Neurocircuitry in Adolescent Depression
NIDA R01 DA040966 (PI: Rao), Co-I:
Prevention of Adolescent Risky Behaviors: Neural Markers of Intervention Effects
NIMH P50MH096889-06A1 Silvio O. Conte Center (PI: Baram)
Fragmented early-life experiences, aberrant circuit maturation, emotional vulnerabilities
Environmental and Biological Variation The major goals of this project are to probe the effects of fragmented early life experience on neuronal network structure and function using magnetic resonance brain imaging (MRI) of rats and humans.
NIMH 1RF1MH120021-01 (PI: Keator)
In this project we develop human neuroimaging domain-specific controlled vocabularies through community engagement and to provide tools for their use in BRAIN Initiative projects. The proposed work will provide a controlled vocabulary for use by the newer BRAIN Initiative projects, incorporating such annotations into the BIDS format and hosted through the BRAIN Initiative archives such as OpenNeuro. This project will greatly improve the ability to search across and reuse datasets
Organization of Human Brain Mapping