DeepAI AI Chat
Log In Sign Up

The Future of Data Analysis in the Neurosciences

by   Danilo Bzdok, et al.

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 big-sample datasets on microanatomy, synaptic connections, optogenetic brain-behavior assays, and high-level cognition. While growing data availability and information granularity have been amply discussed, we direct attention to a routinely neglected question: How will the unprecedented data richness shape data analysis practices? Statistical reasoning is becoming more central to distill neurobiological knowledge from healthy and pathological brain recordings. We believe that large-scale data analysis will use more models that are non-parametric, generative, mixing frequentist and Bayesian aspects, and grounded in different statistical inferences.


A paradigm shift in neuroscience driven by big data: State of art, challenges, and proof of concept

A recent editorial in Nature noted that cognitive neuroscience is at a c...

Guidelines for data analysis scripts

Unorganized heaps of analysis code are a growing liability as data analy...

Statistical learning methods for neuroimaging data analysis with applications

The aim of this paper is to provide a comprehensive review of statistica...

ICABiDAS: Intuition Centred Architecture for Big Data Analysis and Synthesis

Humans are expert in the amount of sensory data they deal with each mome...

MCMC Sampling of Directed Flag Complexes with Fixed Undirected Graphs

Constructing null models to test the significance of extracted informati...

Secrets of the Brain: An Introduction to the Brain Anatomical Structure and Biological Function

In this paper, we will provide an introduction to the brain structure an...