Doctor of Philosophy (PhD)
First Advisor's Name
Angela R. Laird
First Advisor's Committee Title
Second Advisor's Name
Second Advisor's Committee Title
Third Advisor's Name
Erica D. Musser
Third Advisor's Committee Title
Fourth Advisor's Name
Fourth Advisor's Committee Title
Fifth Advisor's Name
Fifth Advisor's Committee Title
fMRI, meta-analysis, research software, open source software, neuroimaging, cognitive neuroscience, functional magnetic resonance imaging
Date of Defense
Almost all scientific research relies on software. This is particularly true for research that uses neuroimaging technologies, such as functional magnetic resonance imaging (fMRI). These technologies generate massive amounts of data per participant, which must be processed and analyzed using specialized software. A large portion of these tools are developed by teams of researchers, rather than trained software developers. In this kind of ecosystem, where the majority of software creators are scientists, rather than trained programmers, it becomes more important than ever to rely on community-based development, which may explain why most of this software is open source. It is in the development of this kind of research-oriented, open source software that I have focused much of my graduate training, as is reflected in this dissertation.
One software package I have helped to develop and maintain is tedana, a Python library for denoising multi-echo fMRI data. In chapter 2, I describe this library in a short, published software paper.
Another library I maintain as the primary developer is NiMARE, a Python library for performing neuroimaging meta-analyses and derivative analyses, such as automated annotation and functional decoding. In chapter 3, I present NiMARE in a hybrid software paper with embedded tutorial code exhibiting the functionality of the library. This paper is currently hosted as a Jupyter book that combines narrative content and code snippets that can be executed online.
In addition to research software development, I have focused my graduate work on performing reproducible, open fMRI research. To that end, chapter 4 is a repli- cation and extension of a recent paper on multi-echo fMRI denoising methods Power et al. (2018a). This replication was organized as a registered report, in which the introduction and methods were submitted for peer review before the analyses were performed.
Finally, chapter 5 is a conclusion to the dissertation, in which I reflect on the work I have done and the skills I have developed throughout my training.
Previously Published In
Salo et al., (2022). NiMARE: Neuroimaging Meta-Analysis Research Environment. NeuroLibre Reproducible Preprint Server, 1(1), 7, https://doi.org/10.55458/neurolibre.00007
DuPre, Salo et al., (2021). TE-dependent analysis of multi-echo fMRI with tedana. Journal of Open Source Software, 6(66), 3669, https://doi.org/10.21105/joss.03669
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Salo, Taylor, "Developing and Validating Open Source Tools for Advanced Neuroimaging Research" (2022). FIU Electronic Theses and Dissertations. 5010.
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