Classical Statistics and Statistical Learning in Imaging Neuroscience

TL;DR

This paper compares classical statistical methods and modern statistical learning techniques in neuroimaging, highlighting their differences, similarities, and implications for neurobiological research.

Contribution

It clarifies the conceptual relationship between classical statistics and statistical learning in neuroimaging analysis, illustrating their roles and differences through common scenarios.

Findings

Classical statistics and statistical learning form a continuum in neuroimaging analysis.

They differ in origins, assumptions, and evaluation metrics.

Understanding their relationship helps clarify neuroimaging data interpretation.

Abstract

Neuroimaging research has predominantly drawn conclusions based on classical statistics, including null-hypothesis testing, t-tests, and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity, including cross-validation, pattern classification, and sparsity-inducing regression. These two methodological families used for neuroimaging data analysis can be viewed as two extremes of a continuum. Yet, they originated from different historical contexts, build on different theories, rest on different assumptions, evaluate different outcome metrics, and permit different conclusions. This paper portrays commonalities and differences between classical statistics and statistical learning with their relation to neuroimaging research. The conceptual implications are illustrated in three common analysis scenarios. It is thus tried to resolve possible confusion between classical hypothesis testing and data-guided model estimation by discussing their ramifications for the neuroimaging access to neurobiology.