Dr. Valerio Zerbi in the group of Prof. Nicole Wenderoth has for the first time applied a novel method (FIX) for the analysis of resting state functional MRI in mice. In their paper, that will be published in the December issue of NeuroImage, Zerbi and colleagues show that FIX is an effective method for removing artifacts from rs-fMRI signals in mice and were able to define 23 meaningful resting state networks.
Resting state fMRI (rs-fMRI) is a powerful tool to assess brain connectivity and investigate neuropathology in mouse models. The method gives information about the functional connectivity in the brain in absence of performance of specific tasks and can provide information about neuropathology. But despite these advantages, obtaining relevant resting-state fMRI signals is a challenge because of the high background noise from non-neuronal sources, such as effects of breathing, head movements, blood pressure and scanner artefacts.
Fixing signals from neural sources
Valerio Zerbi and colleagues have shown for the first time that these background noises can be effectively eliminated in mouse rs-fMRI by applying FIX. Developed in the Oxford University Centre for Functional MRI of the Brain (FMRIB), FIX is a novel fully automatic solution for cleaning (both task and resting) fMRI data of various types of structured noise. Using this method, Zerbi was able to identify 23 resting state networks, each involving different functional areas, e.g. somatosensory, visual or auditory areas. Two of these had default mode network (DMN)-like topography. These networks, that receive higher order network information and for which the term “intelligence oversight system” has been coined, were first demonstrated in mice in 2014. The data by Zerbi et al. confirm and further explore the existence of such networks in mice.
Mapping the mouse brain with rs-fMRI: An optimized pipeline for functional network identification. Zerbi V, Grandjean J, Rudin M, Wenderoth N. Neuroimage 2015; 123:11-21 PubMed Abstract
Image: Resting state fMRI network in the mouse brain, Valerio Zerbi