Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, 52072 Aachen, Germany; JARA-BRAIN, Jülich-Aachen Research Alliance, Germany; IRTG2150 - International Research Training Group, Germany; Parietal team, INRIA, Neurospin, bat 145, CEA Saclay, 91191 Gif-sur-Yvette, France.
Department of Electrical and Computer Engineering, National University of Singapore, 119077 Singapore; Clinical Imaging Research Centre, National University of Singapore, 117599 Singapore; Singapore Institute for Neurotechnology, National University of Singapore, 117456 Singapore; Memory Networks Programme, National University of Singapore, 119077 Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA.
Neuroimage. 2017 Jul 15;155:549-564. doi: 10.1016/j.neuroimage.2017.04.061. Epub 2017 Apr 27.
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.
神经科学正在经历比以往任何时候都更快的变化。在过去的 100 年里，我们的领域定性地描述和侵入性地操纵单个或少数生物体，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广度（例如，微观解剖学、突触连接和光遗传学脑行为测定）和规模（例如，认知、脑成像和遗传学）的定量数据集。虽然越来越多的数据可用性和信息粒度已经被充分讨论，但我们将注意力转向一个研究较少的问题：前所未有的数据丰富度将如何塑造数据分析实践？统计推理对于从健康和病理性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，大规模数据分析将使用更多的非参数、生成式和混合频率论和贝叶斯方面的统计模型，同时用样本外预测来补充经典的假设检验。
