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

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...

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Veröffentlicht in:Neuron (Cambridge, Mass.) Mass.), 2015-07, Vol.87 (2), p.257-270
1. Verfasser: Haynes, John-Dylan
Format: Artikel
Sprache:eng
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Zusammenfassung: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. This review by Haynes provides an introduction to multivoxel pattern analysis (MVPA) of fMRI data. After a methodological overview, Haynes discusses limitations and pitfalls of MVPA techniques and presents emerging directions, such as encoding/decoding models and representational similarity analyses.
ISSN:0896-6273
1097-4199
DOI:10.1016/j.neuron.2015.05.025