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This perspective discusses newly discovered mechanisms leading to cellular ionic imbalances, as well as underappreciated signaling cascades that mediate cell death and that may add to the traditional glutamatergic mechanisms to which ischemic brain injury is ascribed. An integrated consideration of such new mechanisms may aid in formulating better therapies.
There remains an urgent need to develop new strategies and therapies to help protect the brain from ischemic cell death. In this perspective, the authors suggest that learning more about the mechanisms that underlie brain self-preservation and developing multifaceted approaches that act on multiple pathways involved in both cell death and neuroprotection may advance our efforts to treat stroke.
The authors analyze a large corpus of the neuroscience literature and demonstrate that nearly half of the published studies considered incorrectly compared effect sizes by comparing their significance levels.
The immediate early gene product Arc has been broadly implicated in synaptic and experience-dependent plasticity. In this perspective, the authors synthesize disparate views of Arc in molecular signaling and its relevance to neurological disorders.
Parietal cortex has been implicated as a locus for decision making, and it has been suggested that decision encoding in this area is based on the movement used to report the decision. Here the authors discuss a complementary view that decisions represent more abstract information not linked to movements per se.
Progress in neural recording techniques has allowed the number of simultaneously recorded neurons to double approximately every 7 years, mimicking Moore's law. Emerging data analysis techniques should consider both the computational costs and the potential for more accurate models associated with this exponential growth of the number of recorded neurons.
Experimental work suggests that synaptic and intrinsic neuronal properties vary considerably across identified neurons in different animals. The authors propose that instead of building a single model that captures the average behavior of a neuron or circuit, one could construct a population of models with different underlying structure and similar behaviors, as a way of investigating compensatory mechanisms that contribute to neuron and network function.
