A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives

John-Dylan Haynes

doi:10.1016/j.neuron.2015.05.025

A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives

Neuron. 2015 Jul 15;87(2):257-70. doi: 10.1016/j.neuron.2015.05.025.

Author

John-Dylan Haynes¹

Affiliation

¹ Bernstein Center for Computational Neuroscience, Charité - Universitätsmedizin, 10117 Berlin, Germany; Berlin Center for Advanced Neuroimaging, Charité - Universitätsmedizin, 10117 Berlin, Germany; Berlin School of Mind and Brain, Humboldt Universität zu Berlin, 10117 Berlin, Germany; Department of Neurology, Charité - Universitätsmedizin, 10117 Berlin, Germany; Department of Psychology, Humboldt Universität zu Berlin, 10117 Berlin, Germany; Cluster of Excellence NeuroCure, Charité - Universitätsmedizin, 10117 Berlin, Germany; SFB 940, Volition and Cognitive Control, Technische Universität Dresden, 01069 Dresden, Germany. Electronic address: haynes@bccn-berlin.de.

PMID: 26182413
DOI: 10.1016/j.neuron.2015.05.025

Abstract

Human fMRI signals exhibit a spatial patterning that contains detailed information about a person's mental states. Using classifiers it is possible to access this information and study brain processes at the level of individual mental representations. The precise link between fMRI signals and neural population signals still needs to be unraveled. Also, the interpretation of classification studies needs to be handled with care. Nonetheless, pattern-based analyses make it possible to investigate human representational spaces in unprecedented ways, especially when combined with computational modeling.

Publication types

Research Support, Non-U.S. Gov't
Review

MeSH terms

Brain / blood supply*
Brain Mapping
Computer Simulation
Humans
Image Interpretation, Computer-Assisted
Magnetic Resonance Imaging*
Mental Processes*
Models, Neurological*
Oxygen / blood

Substances

Oxygen