Inference in the age of big data: Future perspectives on neuroscience
Inference in the age of big data: Future perspectives on neuroscience
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DOI：
10.1016/j.neuroimage.2017.04.061
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发表时间：
2017-07-15
期刊：
影响因子：
5.7
通讯作者：
Yeo, B. T. Thomas
中科院分区：
文献类型：
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作者：
Bzdok, Danilo;Yeo, B. T. Thomas
神经科学正在经历比以往任何时候都快的变化。100多年来，我们的领域定性地描述和侵入性地操纵单个或几个生物体，以获得解剖学，生理学和药理学的见解。在过去的10年里，神经科学产生了前所未有的量化数据集（例如，显微解剖学、突触连接和光遗传学脑行为测定）和大小（例如，认知、脑成像和遗传学）。虽然不断增长的数据可用性和信息粒度已经得到了充分的讨论，我们直接关注一个较少探讨的问题：前所未有的数据丰富性将如何塑造数据分析实践？统计推理对于从健康和病理大脑测量中提取神经生物学知识变得越来越重要。我们认为，大规模的数据分析将使用更多的统计模型，是非参数的，生成的，并混合频率论和贝叶斯方面，同时补充经典的假设检验与样本外的预测。
Neuroscience is undergoing faster changes than ever before. Over 100 years our field qualitatively described and invasively manipulated single or few organisms to gain anatomical, physiological, and pharmacological insights. In the last 10 years neuroscience spawned quantitative datasets of unprecedented breadth (e.g., microanatomy, synaptic connections, and optogenetic brain-behavior assays) and size (e.g., cognition, brain imaging, and genetics). While growing data availability and information granularity have been amply discussed, we direct attention to a less explored question: How will the unprecedented data richness shape data analysis practices? Statistical reasoning is becoming more important to distill neurobiological knowledge from healthy and pathological brain measurements. We argue that large-scale data analysis will use more statistical models that are non-parametric, generative, and mixing frequentist and Bayesian aspects, while supplementing classical hypothesis testing with out-of-sample predictions.