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Hybridizing behavioral models: a possible solution to some problems in neurophenotyping research?

机译:混合行为模型:对神经表型研究中某些问题的可能解决方案?

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摘要

The use of batteries of single-domain tests for neurophenotyping research is a common strategy to achieve higher data density and explore different behavioral domains. This approach, however, is accompanied by several methodological challenges, briefly discussed here. As an alternative, this paper advocates the wider use of extensive "hybrid" protocols that assess multiple domains in parallel, or logically/logistically combine experimental paradigms, in a way that disproportionately maximizes the number of tested phenotypes per experimental manipulation. Several examples of this approach are given in this paper, demonstrating the potential to reduce time, cost and subject requirements for the experiments. Offering behavioral analyses that are lacking in the standard single-domain tests, such "hybrid" models enable innovative modeling of neuropsychiatric disorders by more thorough and broader investigation of complex phenotypical characteristics.
机译:使用单域测试电池进行神经表型研究是实现更高数据密度并探索不同行为域的常见策略。但是,这种方法伴随着一些方法上的挑战,这里简要讨论一下。作为替代方案,本文提倡更广泛地使用广泛的“混合”协议,这些协议以并行或逻辑/逻辑组合实验范式的方式评估多个域,从而最大程度地使每个实验操作的测试表型数量最大化。本文提供了这种方法的几个示例,展示了减少实验时间,成本和主题要求的潜力。通过提供标准单域测试中缺少的行为分析,此类“混合”模型可以通过更全面,更广泛地研究复杂的表型特征,对神经精神疾病进行创新建模。

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