Developing and Validating Open Source Tools for Advanced Neuroimaging Research

Authors

Taylor Salo, Florida International UniversityFollow

Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Major/Program

Cognitive Neuroscience

First Advisor's Name

Angela R. Laird

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Matthew Sutherland

Second Advisor's Committee Title

Committee Member

Third Advisor's Name

Erica D. Musser

Third Advisor's Committee Title

Committee Member

Fourth Advisor's Name

Aaron Mattfeld

Fourth Advisor's Committee Title

Committee Member

Fifth Advisor's Name

Robert Laird

Fifth Advisor's Committee Title

Committee Member

Keywords

fMRI, meta-analysis, research software, open source software, neuroimaging, cognitive neuroscience, functional magnetic resonance imaging

Date of Defense

6-27-2022

Abstract

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.

Identifier

FIDC010771

ORCID

0000-0001-9813-3167

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.

Recommended Citation

Salo, Taylor, "Developing and Validating Open Source Tools for Advanced Neuroimaging Research" (2022). FIU Electronic Theses and Dissertations. 5010.
https://digitalcommons.fiu.edu/etd/5010