fRAT: an interactive, Python-based tool forregion-of-interest summaries of functional imaging data

Howley, Elliot; Francis, Susan; Schluppeck, Denis

Abstract

Functional magnetic resonance imaging (fMRI) is widely used to address basic cognitive as well as clinical neuroscience questions. The specific choice of imaging sequence and parameters used for image acquisition can have a marked effect on the acquired data. The choice of different parameter settings used for the echo-planar imaging (EPI) acquisition include the trade-offs between an increase in-plane parallel acceleration factor (SENSE, GRAPPA, ASSET) to reduce geometric distortions and increase in Multiband factor to reduce scan time, both of which lead to spatially dependent noise amplification. In addition, the T2* varies across the brain influencing the optimal echo time (Clare et al., 2001). Assessing how these parameters affect data quality over brain regions is crucial. Such data quality metrics, in particular temporal signal-to-noise ratio (tSNR) of the fMRI scan, can be used to optimise fMRI scan parameters for a chosen set of regions-of-interest (ROI). Reporting of data quality metrics, such as tSNR and image SNR (iSNR), is often performed over the whole brain or for a small number of manually defined voxels. Importantly, this can obscure important inter-regional differences in data quality metrics. There is currently a lack of easy-to-use tools to analyse these data quality metrics, as well as functional statistical measures (such as beta maps or similar) simultaneously across multiple ROIs.

The goal of the fMRI ROI Analysis Tool (fRAT), presented here, is to provide a toolset to address this gap, and provide a straightforward method for conducting ROI-wise analyses of fMRI metrics.

Citation

Howley, E., Francis, S., & Schluppeck, D. (2023). fRAT: an interactive, Python-based tool forregion-of-interest summaries of functional imaging data. Journal of Open Source Software, 8(85), Article 5200. https://doi.org/10.21105/joss.05200

• Journal Article Type: Article
• Acceptance Date: May 11, 2023
• Online Publication Date: May 11, 2023
• Publication Date: May 11, 2023
• Deposit Date: Jun 29, 2023
• Publicly Available Date: Jun 29, 2023
• Electronic ISSN: 2475-9066
• Publisher: Open Journals
• Peer Reviewed: Peer Reviewed
• Volume: 8
• Issue: 85
• Article Number: 5200
• DOI: https://doi.org/10.21105/joss.05200
• Keywords: General Earth and Planetary Sciences; General Environmental Science
• Public URL: https://nottingham-repository.worktribe.com/output/20567082
• Publisher URL: https://joss.theoj.org/papers/10.21105/joss.05200