A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives
Open accessAbstract
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.
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Decoding and other types of multivariate pattern analysis have helped reveal the content of regional representations, providing evidence that brain-computational models must incorporate.
“Decoding and other types of multivariate pattern analysis have helped reveal the content of regional representations55,56,58,59, providing evidence that brain-computational models must incorporate.”
From Cognitive computational neuroscience (2018) · cites this paper as [59]
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The Neural Representation of Voluntary Task-Set Selection in Dynamic Environments
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Closed-loop brain training: the science of neurofeedback
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Electrophysiological mechanisms of human memory consolidation
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Studying the neural representations of uncertainty
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Concepts in the Brain: The View From Cross-linguistic Diversity
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Suppression weakens unwanted memories via a sustained reduction of neural reactivation
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Brain network dynamics during spontaneous strategy shifts and incremental task optimization
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- Authors
- Haynes, J.-D.
- Affiliations
- Humboldt-Universität zu Berlin (DE)
- Corresponding author
- Haynes, John-Dylan
- ORCID
- Haynes, John-Dylan
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2015-07
- Volume
- 87
- Issue
- 2
- Pages
- 257-270
- ISSN
- 0896-6273 (ISSN-L)
- OpenAlex
- W1597053271
- Cited by
- 402
- References
- 128
- Citations by year
- 2026: 13 · 2025: 22 · 2024: 26 · 2023: 37 · 2022: 33 · 2021: 40 · 2020: 49 · 2019: 45 · 2018: 53 · 2017: 51 · 2016: 29 · 2015: 4
- FWCI
- 4.49
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Bundesministerium für Bildung und Forschung
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- representational similarity, brain decoding, mental representation
- MeSH terms
- Brain, Brain Mapping, Computer Simulation, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Mental Processes, Models, Neurological, Oxygen
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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