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Front Neurosci. 2017 Oct 6;11:543. doi: 10.3389/fnins.2017.00543. eCollection 2017.

Classical Statistics and Statistical Learning in Imaging Neuroscience.

Bzdok D1,2,3.

Author information

1
Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany.
2
Translational Brain Medicine, Jülich-Aachen Research Alliance (JARA), Aachen, Germany.
3
Parietal Team, Institut National de Recherche en Informatique et en Automatique (INRIA), Gif-sur-Yvette, France.

Abstract

Brain-imaging research has predominantly generated insight by means of classical statistics, including regression-type analyses and null-hypothesis testing using t-test and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity especially for applications in rich and complex data, including cross-validated out-of-sample prediction using pattern classification and sparsity-inducing regression. This concept paper discusses the implications of inferential justifications and algorithmic methodologies in common data analysis scenarios in neuroimaging. It is retraced how classical statistics and statistical learning originated from different historical contexts, build on different theoretical foundations, make different assumptions, and evaluate different outcome metrics to permit differently nuanced conclusions. The present considerations should help reduce current confusion between model-driven classical hypothesis testing and data-driven learning algorithms for investigating the brain with imaging techniques.

KEYWORDS:

Rosetta Stone; data science; epistemology; machine learning; neuroimaging; p-value; statistical inference

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