Modular organization of mouse brain functional connectivity

Resting state fMRI (rsfMRI) has been widely employed to investigate the intrinsic organizational structure of the brain. A strong rationale exists for the translational implementation of analogous measurements in small laboratory animals, such as the laboratory mouse. We recently described the presence of robust distributed resting-state fMRI networks in the mouse brain displaying striking analogy with those observed in humans and primates, comprising plausible homologues for the salience and default-mode network. Here we capitalised on this recent work to investigate the topological architecture of mouse brain rsfMRI networks. Specifically, we used graph theoretical analysis to investigate the modular organisation of mouse brain functional connectivity networks. To this purpose, rsfMRI signals were parcellated into anatomical clusters to cover the entire brain, and the corresponding correlation matrix partitioned into functional connectivity communities using an algorithm that relies on the optimization of the mathematical representation of modularity.We show that this approach can identify sub-networks whose distributions indicate compelling functional subdivisions in the mouse brain that can be related to known partitions of the brain.