ArticleFrontiers in neuroscience2012
What makes a pattern? Matching decoding methods to data in multivariate pattern analysis.
Article in Frontiers in neuroscience, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Article
- Revealing the co-existence of written and spoken language coding neural populations in the visual word form area.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Emotion regulation in bipolar disorder type-I: multivariate analysis of fMRI data.International journal of bipolar disorders · 2023Article
- Do sparse brain activity patterns underlie human cognition?NeuroImage · 2022Article
- Sex differences in brain aging among adults with family history of Alzheimer's disease and APOE4 genetic risk.NeuroImage. Clinical · 2021Article
- Putative protective neural mechanisms in prereaders with a family history of dyslexia who subsequently develop typical reading skills.Human brain mapping · 2020Article
- He who is well prepared has half won the battle: an FMRI study of task preparation.Cerebral cortex (New York, N.Y. : 1991) · 2015Article
- Distinguishing multi-voxel patterns and mean activation: why, how, and what does it tell us?Cognitive, affective & behavioral neuroscience · 2013Article
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Authors and funding
3 authors.
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Abstract
Research in neuroscience faces the challenge of integrating information across different spatial scales of brain function. A promising technique for harnessing information at a range of spatial scales is multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data. While the prevalence of MVPA has increased dramatically in recent years, its typical implementations for classification of mental states utilize only a subset of the information encoded in local fMRI signals. We review published studies employing multivariate pattern classification since the technique's introduction, which reveal an extensive focus on the improved detection power that linear classifiers provide over traditional analysis techniques. We demonstrate using simulations and a searchlight approach, however, that non-linear classifiers are capable of extracting distinct information about interactions within a local region. We conclude that for spatially localized analyses, such as searchlight and region of interest, multiple classification approaches should be compared in order to match fMRI analyses to the properties of local circuits.
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Registered trials
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