This paper represents a contribution to the study of the brain functional connectivity from the perspective of complex networks theory. More specifically, we apply graph theoretical analyses to provide evidence of the modular structure of the mouse brain and to shed light on its hierarchical organization. We propose a novel percolation analysis and we apply our approach to the analysis of a resting-state functional MRI data set from 41 mice. This approach reveals a robust hierarchical structure of modules persistent across different subjects. Importantly, we test this approach against a statistical benchmark (or null model) which constrains only the distributions of empirical correlations. Our results unambiguously show that the hierarchical character of the mouse brain modular structure is not trivially encoded into this lower-order constraint. Finally, we investigate the modular structure of the mouse brain by computing the Minimal Spanning Forest, a technique that identifies subnetworks characterized by the strongest internal correlations. This approach represents a faster alternative to other community detection methods and provides a means to rank modules on the basis of the strength of their internal edges.

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Primates adopt various strategies to interact with the environment. Yet, no study has examined the effects of behavioral strategies with regard to how movement inhibition is implemented at the neuronal level. We modified a classical approach to study movement control (stop-task) by adding an extra signal-termed the Ignore signal-which influenced movement inhibition only under a specific strategy. We simultaneously recorded multisite neuronal activity from the dorsal premotor (PMd) cortex of macaque monkeys during a task and applied a statespace approach. As a result, we found that movement generation is characterized by neuronal dynamics that evolve between subspaces. When the movement is halted, this evolution is arrested and inverted. Conversely, when the Ignore signal is presented, inversion of the evolution is observed briefly and only when a specific behavioral strategy is adopted. Moreover, neuronal signatures during the inhibitory process were predictive of how PMd processes inhibitory signals, allowing the classification of the resulting behavioral strategy. Our data corroborate the PMd as a critical node in movement inhibition. .

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Despite recent works have investigated functional and effective cortical networks in animal models, the dynamical information transfer among functional modules underneath cognitive control is still largely unknown. Here we addressed the issue by using transfer entropy and graph theory methods on neural activities recorded from a multielectrode (96 recording sites) array in the dorsal premotor cortex of rhesus monkeys. We focused our analysis on the decision time of a stop-signal (countermanding) task. When comparing trials with successful inhibition to those with generated movement we found evidence of heterogeneous interacting modules described by 4 main classes, hierarchically organized. Interestingly, the hierarchical organization resulted different in the two type of trials. Our results suggest that motor decisions are based on the local re-organization of the premotor cortical network