The evidence that antipsychotics improve brain function and reduce symptoms in schizophrenia is unmistakable, but how antipsychotics change brain function is poorly understood, especially within neuronal systems. In this review, we investigated the hypothesized normalization of the functional magnetic resonance imaging (fMRI) blood oxygen level dependent signal in the context of antipsychotic treatment. First, we conducted a systematic PubMed search to identify eight fMRI investigations that met the following inclusion criteria: case-control, longitudinal design; pre- and post-treatment contrasts with a healthy comparison group; and antipsychotic-free or antipsychotic-naïve patients with schizophrenia at the start of the investigation. We hypothesized that aberrant activation patterns or connectivity between patients with schizophrenia and healthy comparisons at the first imaging assessment would no longer be apparent or “normalize” at the second imaging assessment. The included studies differed by analysis method and fMRI task but demonstrated normalization of fMRI activation or connectivity during the treatment interval. Second, we reviewed putative mechanisms from animal studies that support normalization of the BOLD signal in schizophrenia. We provided several neuronal-based interpretations of these changes of the BOLD signal that may be attributable to long-term antipsychotic administration.

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The explosion of network science has permitted an understanding of how the structure of social networks affects the dynamics of social contagion. In community-based interventions with spill-over effects, identifying influential spreaders may be harnessed to increase the spreading efficiency of social contagion, in terms of time needed to spread all the largest connected component of the network. Several strategies have been proved to be efficient using only data and simulation-based models in specific network topologies without a consensus of an overall result. Hence, the purpose of this paper is to benchmark the spreading efficiency of seeding strategies related to network structural properties and sizes. We simulate spreading processes on empirical and simulated social networks within a wide range of densities, clustering coefficients, and sizes. We also propose three new decentralized seeding strategies that are structurally different from well-known strategies: community hubs, ambassadors, and random hubs. We observe that the efficiency ranking of strategies varies with the network structure. In general, for sparse networks with community structure, decentralized influencers are suitable for increasing the spreading efficiency. By contrast, when the networks are denser, centralized influencers outperform. These results provide a framework for selecting efficient strategies according to different contexts in which social networks emerge.Information, behaviors, diseases, emotions, and even the adoption of technological innovations spread through social networks 1-5 . Recently, the explosion of network science across disciplines has produced many important advances in understanding how the structure of social networks affects the dynamics of social contagion. Specifically, the study of social networks has provided an opportunity to potentiate interventions with spill-over effects aimed to increase population well-being. For example, several studies have examined the spreading processes efficiency related to the topological properties of networks 4,6-8 . Other studies have analyzed the role of homophily in spreading processes 9-11 , while others have focused on identifying influential spreaders in networks and how they may be harnessed to increase the efficiency of public health and poverty reduction interventions [12][13][14][15] .A key point for designing interventions with spill-over effects is to allocate resources for the intervention targeting in a wisely way. Thus, it is crucial to have an appropriate methodological framework for selecting seednodes with the best spreading ability. Several complex networks studies have proposed selecting seednodes by ranking network nodes based on centrality measures 12,15-28 . Particularly, nodes with high degree, closeness, and betweenness coefficients have been identified as influential or high-risk individuals during a spreading process 16,23,29 . Furthermore, there are random-walk based seeding strategies, such as Page-Rank, that have been shown more efficient than ce...

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