Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 1: In vivo small-animal imaging

Abstract

Diffusion MRI; Diffusion tensor; Small animal

Resonancia magnética de difusión; Tensor de difusión; Animal pequeño

Ressonància magnètica de difusió; Tensor de difusió; Animal petit

Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected considerations and recommendations from the diffusion community on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We, then, give recommendations for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including preprocessing, model-fitting, and tractography. Finally, we provide an online resource that lists publicly available preclinical dMRI datasets and software packages to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. Although we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.

We acknowledge financial support from the National Institutes of Health (K01EB032898, R01AG057991, R01NS 125020, R01EB017230, R01EB019980, R01EB031954, R01 CA160620, R01NS109090, R01NS119605, U54AG054349, P50MH096889); the National Institute of Biomedical Imaging and Bioengineering (R01EB031765, R56EB031765); the National Institute on Drug Abuse (P30DA048742); the Secretary of Universities and Research (Government of Catalonia) Beatriu de Pinós postdoctoral fellowship (2020 BP 00117); “la Caixa” Foundation (ID 100010434); the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 847648; Junior Leader fellowship codes (LCF/BQ/PR22/11920010; CF/BQ/PI20/11760029); the European Union's Horizon Europe Programme for Research Infrastructures under the specific grant agreement no. 101147319 (EBRAINS 2.0); the Research Council of Norway under grant agreement no. 333157 (INCF Norwegian Node 2022-2027); the Research Foundation Flanders (FWO: 12M3119N); the Belgian Science Policy Prodex (Grant ISLRA 2009–1062); the μNEURO Research Center of Excellence of the University of Antwerp, the Institutional research chair in Neuroinformatics (Sherbrooke, Canada); the NSERC Discovery Grant; the European Research Council Consolidator grant (101044180); the Canada Research Chair in Quantitative Magnetic Resonance Imaging (950-230815); the Canadian Institute of Health Research (CIHR FDN-143263); the Canada Foundation for Innovation (32454, 34824); the Fonds de Recherche du Québec - Santé (322736); the Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-07244); the Canada First Research Excellence Fund (IVADO and TransMedTech); the Courtois NeuroMod project; the Quebec BioImaging Network (5886, 35450); the Mila-Tech Transfer Funding Program; and the Swiss National Science Foundation (Eccellenza Fellowship PCEFP2_194260).