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Neuroimage. 2017 Jul 15;155:549-564. doi: 10.1016/j.neuroimage.2017.04.061. Epub 2017 Apr 27.

Inference in the age of big data: Future perspectives on neuroscience.

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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. Electronic address:
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.


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.


Epistemology; High-dimensional statistics; Hypothesis testing; Machine learning; Sample complexity; Systems biology

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