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

Abstract : 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.
Document type :
Journal articles
Complete list of metadatas

Cited literature [228 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01516891
Contributor : Danilo Bzdok <>
Submitted on : Tuesday, May 9, 2017 - 5:44:27 PM
Last modification on : Monday, February 10, 2020 - 6:13:43 PM
Long-term archiving on: Thursday, August 10, 2017 - 12:18:49 PM

Files

big_analysis_NIMG_R4_manuscrip...
Files produced by the author(s)

Identifiers

Citation

Danilo Bzdok, B Yeo. Inference in the age of big data: Future perspectives on neuroscience. NeuroImage, Elsevier, 2017, ⟨10.1016/j.neuroimage.2017.04.061⟩. ⟨hal-01516891⟩

Share

Metrics

Record views

1736

Files downloads

361