Mutations of the parkin gene are the most frequent cause of early onset autosomal recessive parkinsonism (EO-AR). Here we show that inactivation of the parkin gene in mice results in motor and cognitive deficits, inhibition of amphetamine-induced dopamine release and inhibition of glutamate neurotransmission. The levels of dopamine are increased in the limbic brain areas of parkin mutant mice and there is a shift towards increased metabolism of dopamine by MAO. Although there was no evidence for a reduction of nigrostriatal dopamine neurons in the parkin mutant mice, the level of dopamine transporter protein was reduced in these animals, suggesting a decreased density of dopamine terminals, or adaptative changes in the nigrostriatal dopamine system. GSH levels were increased in the striatum and fetal mesencephalic neurons from parkin mutant mice, suggesting that a compensatory mechanism may protect dopamine neurons from neuronal death. These parkin mutant mice provide a valuable tool to better understand the preclinical deficits observed in patients with PD and to characterize the mechanisms leading to the degeneration of dopamine neurons that could provide new strategies for neuroprotection.

Connecting complex networks is known to exacerbate perturbations and lead to cascading failures, but natural networks of networks are surprisingly stable. A theory now proposes that network structure holds the key to understanding this paradox. Networks in nature do not act in isolation, but instead exchange information and depend on one another to function properly1,2,3. Theory has shown that connecting random networks may very easily result in abrupt failures3,4,5,6. This finding reveals an intriguing paradox7,8: if natural systems organize in interconnected networks, how can they be so stable? Here we provide a solution to this conundrum, showing that the stability of a system of networks relies on the relation between the internal structure of a network and its pattern of connections to other networks. Specifically, we demonstrate that if interconnections are provided by network hubs, and the connections between networks are moderately convergent, the system of networks is stable and robust to failure. We test this theoretical prediction on two independent experiments of functional brain networks (in task and resting states), which show that brain networks are connected with a topology that maximizes stability according to the theory.

Alcoholic liver disease: Utility of animal models

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Global integration of information in the brain results from complex interactions of segregated brain networks. Identifying the most influential neuronal populations that efficiently bind these networks is a fundamental problem of systems neuroscience. Here, we apply optimal percolation theory and pharmacogenetic interventions in vivo to predict and subsequently target nodes that are essential for global integration of a memory network in rodents. The theory predicts that integration in the memory network is mediated by a set of low-degree nodes located in the nucleus accumbens. This result is confirmed with pharmacogenetic inactivation of the nucleus accumbens, which eliminates the formation of the memory network, while inactivations of other brain areas leave the network intact. Thus, optimal percolation theory predicts essential nodes in brain networks. This could be used to identify targets of interventions to modulate brain function.