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Background: Inhalational anesthetic agents are potent greenhouse gases with global warming potential that far exceed that of carbon dioxide. Traditionally, pediatric inhalation inductions are achieved with a volatile anesthetic delivered to the patient in oxygen and nitrous oxide at high fresh gas flows. While contemporary volatile anesthetics and anesthesia machines allow for a more environmentally conscious induction, practice has not changed. We aimed to reduce the environmental impact of our inhalation inductions by decreasing the use of nitrous oxide and fresh gas flows. Methods: Through a series of four plan‐do‐study‐act cycles, the improvement team used content experts to demonstrate the environmental impact of the current inductions and to provide practical ways to reduce this, by focusing on nitrous oxide use and fresh gas flows, with visual reminders introduced at point of delivery. The primary measures were the percentage of inhalation inductions that used nitrous oxide and the maximum fresh gas flows/kg during the induction period. Statistical process control charts were used to measure improvement over time. Results: 33 285 inhalation inductions were included over a 20‐month period. nitrous oxide use decreased from 80% to <20% and maximum fresh gas flows/kg decreased from a rate of 0.53 L/min/kg to 0.38 L/min/kg, an overall reduction of 28%. Reduction in fresh gas flows was greatest in the lightest weight groups. Induction times and behaviors remained unchanged over the duration of this project. Conclusions: Our quality improvement group decreased the environmental impact of inhalation inductions and created cultural change within our department to sustain change and foster the pursuit of future environmental efforts. [ABSTRACT FROM AUTHOR]

Precisely how humans process relational patterns of information in knowledge, language, music, and society is not well understood. Prior work in the field of statistical learning has demonstrated that humans process such information by building internal models of the underlying network structure. However, these mental maps are often inaccurate due to limitations in human information processing. The existence of such limitations raises clear questions: Given a target network that one wishes for a human to learn, what network should one present to the human? Should one simply present the target network as-is, or should one emphasize certain parts of the network to proactivelymitigate expected errors in learning? To investigate these questions, we study the optimization of network learnability in a computational model of human learning. Evaluating an array of synthetic and real-world networks, we find that learnability is enhanced by reinforcing connections within modules or clusters. In contrast, when networks contain significant core-periphery structure, we find that learnability is best optimized by reinforcing peripheral edges between low-degree nodes. Overall, our findings suggest that the accuracy of human network learning can be systematically enhanced by targeted emphasis and de-emphasis of prescribed sectors of information. [ABSTRACT FROM AUTHOR]

Recent advances in neuroscience have enabled the exploration of brain structure at the level of individual synaptic connections. These connectomics datasets continue to grow in size and complexity; methods to search for and identify interesting graph patterns offer a promising approach to quickly reduce data dimensionality and enable discovery. These graphs are often too large to be analyzed manually, presenting significant barriers to searching for structure and testing hypotheses. We combine graph database and analysis libraries with an easy-to-use neuroscience grammar suitable for rapidly constructing queries and searching for subgraphs and patterns of interest. Our approach abstracts many of the computer science and graph theory challenges associated with nanoscale brain network analysis and allows scientists to quickly conduct research at scale. We demonstrate the utility of these tools by searching for motifs on simulated data and real public connectomics datasets, and we share simple and complex structures relevant to the neuroscience community. We contextualize our findings and provide case studies and software to motivate future neuroscience exploration. [ABSTRACT FROM AUTHOR]

Humans are highly social beings that interact with each other on a daily basis. In these complex interactions, we get along by being able to identify others' actions and infer their intentions, thoughts and feelings. One of the major theories accounting for this critical ability assumes that the understanding of social signals is based on a primordial tendency to simulate observed actions by activating a mirror neuron system. If mirror neuron regions are important for action and emotion recognition, damage to regions in this network should lead to deficits in these domains. In the current behavioural and EEG study, we focused on the lateral prefrontal cortex including dorsal and ventral prefrontal cortex and utilized a series of task paradigms, each measuring a different aspect of recognizing others' actions or emotions from body cues. We examined 17 patients with lesions including (n = 8) or not including (n = 9) the inferior frontal gyrus, a core mirror neuron system region, and compared their performance to matched healthy control subjects (n = 18), in behavioural tasks and in an EEG observation-execution task measuring mu suppression. Our results provide support for the role of the lateral prefrontal cortex in understanding others' emotions, by showing that even unilateral lesions result in deficits in both accuracy and reaction time in tasks involving the recognition of others' emotions. In tasks involving the recognition of actions, patients showed a general increase in reaction time, but not a reduction in accuracy. Deficits in emotion recognition can be seen by either direct damage to the inferior frontal gyrus, or via damage to dorsal lateral prefrontal cortex regions, resulting in deteriorated performance and less EEG mu suppression over sensorimotor cortex. [ABSTRACT FROM AUTHOR]

Optimizing direct electrical stimulation for the treatment of neurological disease remains difficult due to an incomplete understanding of its physical propagation through brain tissue. Here, we use network control theory to predict how stimulation spreads through white matter to influence spatially distributed dynamics. We test the theory's predictions using a unique dataset comprising diffusion weighted imaging and electrocorticography in epilepsy patients undergoing grid stimulation. We find statistically significant shared variance between the predicted activity state transitions and the observed activity state transitions. We then use an optimal control framework to posit testable hypotheses regarding which brain states and structural properties will efficiently improve memory encoding when stimulated. Our work quantifies the role that white matter architecture plays in guiding the dynamics of direct electrical stimulation and offers empirical support for the utility of network control theory in explaining the brain's response to stimulation. • Tools from network control theory are extended to model empirical brain stimulation • A model of stimulation spreading along axon bundles predicts changes in activity • The model posits how brain activity and connectivity facilitate targeted stimulation Stiso et al. report evidence that network control theory can explain the propagation of electrical stimulation through the human brain and quantify how white matter connectivity is crucial for driving spatially distributed changes in activity. Furthermore, they use network control theory to predict stimulation outcome in specific cognitive contexts. [ABSTRACT FROM AUTHOR]