A systematic review of the relationship between magnetic resonance imaging based resting-state and structural networks in the rodent brain

Front Neurosci. 2023 Jul 24:17:1194630. doi: 10.3389/fnins.2023.1194630. eCollection 2023.

Abstract

Recent developments in rodent brain imaging have enabled translational characterization of functional and structural connectivity at the whole brain level in vivo. Nevertheless, fundamental questions about the link between structural and functional networks remain unsolved. In this review, we systematically searched for experimental studies in rodents investigating both structural and functional network measures, including studies correlating functional connectivity using resting-state functional MRI with diffusion tensor imaging or viral tracing data. We aimed to answer whether functional networks reflect the architecture of the structural connectome, how this reciprocal relationship changes throughout a disease, how structural and functional changes relate to each other, and whether changes follow the same timeline. We present the knowledge derived exclusively from studies that included in vivo imaging of functional and structural networks. The limited number of available reports makes it difficult to draw general conclusions besides finding a spatial and temporal decoupling between structural and functional networks during brain disease. Data suggest that when overcoming the currently limited evidence through future studies with combined imaging in various disease models, it will be possible to explore the interaction between both network systems as a disease or recovery biomarker.

Keywords: DTI; MRI; connectivity; graph theory; mouse; rat; rs-fMRI.

Publication types

  • Systematic Review

Grants and funding

This work was funded by the Friebe Foundation (Germany)—Project-ID T0498/28960/16 and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 431549029—SFB 1451. Open access publication funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—Project-ID 491111487.