Paul Taylor, Ph.D.
Scientific and Statistical Computing Core
Dr. Taylor is currently the Acting Director of the Scientific and Statistical Computing Core (SSCC). He completed his BS in Physics (with a double major in Classics) from Boston College in 2004, and completed his doctorate in Astrophysics in 2009 from the University of Oxford in the UK. He then spent one year as a teaching assistant at the African Institute for Mathematical Sciences (AIMS) in South Africa, before starting a postdoctoral fellowship in the Biswal Lab at the New Jersey Medical School, focused combining (resting state) FMRI with DTI-based information. This project led to writing a set of programs that eventually comprised FATCAT: the Functional And Tractographic Connectivity Analysis Toolbox. FATCAT was added to the SSCC’s AFNI code base in 2012, and further collaboration and program development continued remotely. He then started a postdoc in the Meintjes Lab at the University of Cape Town, SA, focusing on applications of neuroimaging to the brain development of neonates with fetal alcohol exposure and HIV exposure and treatment. During this time, Dr. Taylor was also a regular visiting lecturer across the AIMS network, teaching introductory programming and signal processing (which he continues to do). In 2015 Dr. Taylor formally joined the SSCC as a Staff Scientist, working on methods development, as well as teaching and research mentoring, related to various MRI modalities.
The primary function of the SSCC is to support MRI-related neuroimaging research at NIMH and NIH, including structural, functional, and diffusion-based imaging. We also facilitate multimodal studies, such as with EEG, ECoG, CT, PET, tDCS/TMS and more. Our overarching goal is to help brain researchers stay close to their data. One way we perform these duties is by developing the open source, publicly available software distribution, AFNI (Analysis of Functional NeuroImages), which was started in 1994 and continues to be widely used worldwide. A large part of our work is teaching, training and mentoring many students, trainees and other researchers across NIMH and NIH, as well as globally. We actively develop new analysis and visualization methods, typically implementing these within AFNI. Many of these are created to directly address research needs and experimental designs of collaborators, and others are based on our own experience to improve analysis techniques within the field. These research, development and educational interests include: experiment and study design; data visualization and quality control; processing methods and quantitative analysis; utilizing appropriate statistical modeling and validation; promoting understandable and reproducible science; and improving both scientific approaches and results reporting across the field.
Chen G, Pine DS, Brotman MA, Smith AR, Cox RW, Taylor PA, Haller SP (2022). Hyperbolic trade-off: the importance of balancing trial and subject sample sizes in neuroimaging. Neuroimage 247:118786. [Pubmed Link]
Jung B, Taylor PA, Seidlitz J, Sponheim C, Perkins P, Ungerleider LG, Glen D, Messinger A (2020). A comprehensive macaque fMRI pipeline and hierarchical atlas. NeuroImage 235:117997. [Pubmed Link]
Chen G, Padmala S, Chen Y, Taylor PA, Cox RW, Pessoa L (2020). To pool or not to pool: Can we ignore cross-trial variability in FMRI? NeuroImage 225:117496. [Pubmed Link]
Chen GC, Taylor PA, Cox RW (2017). Is the Statistic Value All We Should Care about in Neuroimaging? Neuroimage 147:952-959. [Pubmed Link]
Taylor PA, Chen G, Cox RW, Saad ZS (2016). Open Environment for Multimodal Interactive Connectivity Visualization and Analysis. Brain Connectivity 6(2):109-21. [Pubmed Link]
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