StandardRat: A multi-center consensus protocol to enhance functional connectivity specificity in the rat brain

Abstract

Task-free functional connectivity in animal models provides an experimental framework to examine connectivity phenomena under controlled conditions and allows comparison with invasive or terminal procedures. To date, animal acquisitions are performed with varying protocols and analyses that hamper result comparison and integration. We introduce StandardRat, a consensus rat functional MRI acquisition protocol tested across 20 centers. To develop this protocol with optimized acquisition and processing parameters, we initially aggregated 65 functional imaging datasets acquired in rats from 46 centers. We developed a reproducible pipeline for the analysis of rat data acquired with diverse protocols and determined experimental and processing parameters associated with a more robust functional connectivity detection. We show that the standardized protocol enhances biologically plausible functional connectivity patterns, relative to pre-existing acquisitions. The protocol and processing pipeline described here are openly shared with the neuroimaging community to promote interoperability and cooperation towards tackling the most important challenges in neuroscience.

Rodent Automated Bold Improvement of EPI Sequences (RABIES): A standardized image processing and data quality platform for rodent fMRI

“…Despite partial associations with acquisition parameters, the sources leading to analysis failures remained largely unclear. These findings were also recently reproduced in rats, incorporating datasets from 46 different sites and identifying expected connectivity in 40.8% of all scans (Grandjean et al, 2022). The authors found, however, that by standardizing key aspects of scan acquisition across 20 sites, this could be improved to 61.8%, highlighting the importance of improving standards for acquisition and data quality evaluation.…”

“…Motivated by the current variability in results from network analysis between mouse fMRI sites (Grandjean et al, 2020(Grandjean et al, , 2022, we further develop new tools for data quality assessment and provide novel insights into the underlying sources of data quality failures leading to downstream issues with network analyses. These tools are building upon an extensive body of human fMRI literature on confounds and data quality metrics.…”

“…Previous initiatives, such as fMRIPrep, have already been transformative in those regards for human work. The RABIES analysis quality control framework will be crucial for the development of high quality data acquisition protocols (Grandjean et al, 2022). Providing these reports, automatically generated from RABIES, along with publications will improve transparency of results and prevent false positives/negatives.…”

“…Reproducibility is a major concern for the neuroimaging community, leading to elegant solutions for human research applications (Botvinik-Nezer et al, 2020;Eklund et al, 2016;Poldrack et al, 2017;Turner et al, 2018), but complete tailored solutions have not yet emerged for rodents. Reproducibility concerns are now emerging within the rodent community (Grandjean et al, 2020(Grandjean et al, , 2022Ioanas et al, 2021;Mandino et al, 2020), echoing the human litterature on aspects of image processing, analysis and data quality assessment, but also raising additional challenges pertaining to rodent acquisition, including whether to scan animals awake or sedated and the selection of anesthetic type (Ioanas et al, 2021;Mandino et al, 2020). Without careful consideration, each stage of the study design can introduce confounds into downstream analysis, and left uncorrected, these factors can bias downstream statistical inference (Grandjean et al, 2020;Power et al, 2012).…”

“…In light of these current issues, we present here Rodent Automated Bold Improvement of EPI Sequences (RABIES; publicly available to the community https://github.com/CoBrALab/ RABIES), a novel software platform that integrates the latest computational framework to deliver a solution for preprocessing, confound correction, and analysis, tailored for rodent neuroimaging. RABIES was recently applied to successfully process and analyze datasets from 46 rat fMRI acquisition sites (Grandjean et al, 2022). Built around principles of reproducibility and transparency, RABIES aims to provide rodent fMRI research with an accessible and reliable image processing platform complemented with data diagnostic tools encouraging best practices for quality control.…”