Mars Rogier B, Jbabdi Saad, Rushworth Matthew F S
Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, United Kingdom; email:
Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, 6525 HR Nijmegen, The Netherlands.
Annu Rev Neurosci. 2021 Jul 8;44:69-86. doi: 10.1146/annurev-neuro-100220-025942. Epub 2021 Feb 3.
Comparative neuroscience is entering the era of big data. New high-throughput methods and data-sharing initiatives have resulted in the availability of large, digital data sets containing many types of data from ever more species. Here, we present a framework for exploiting the new possibilities offered. The multimodality of the data allows vertical translations, which are comparisons of different aspects of brain organization within a single species and across scales. Horizontal translations compare particular aspects of brain organization across species, often by building abstract feature spaces. Combining vertical and horizontal translations allows for more sophisticated comparisons, including relating principles of brain organization across species by contrasting horizontal translations, and for making formal predictions of unobtainable data based on observed results in a model species.
比较神经科学正在进入大数据时代。新的高通量方法和数据共享计划使得包含来自越来越多物种的多种类型数据的大型数字数据集得以出现。在此,我们提出一个利用这些新可能性的框架。数据的多模态允许进行垂直翻译,即在单个物种内和跨尺度上对大脑组织的不同方面进行比较。水平翻译则通常通过构建抽象特征空间来比较不同物种间大脑组织的特定方面。将垂直翻译和水平翻译相结合可以进行更复杂的比较,包括通过对比水平翻译来关联不同物种间的大脑组织原理,以及根据模型物种中的观察结果对无法获得的数据进行形式化预测。