Measuring neural representations with fMRI: practices and pitfalls
Abstract
Recently, there has been a dramatic increase in the number of functional magnetic resonance imaging studies seeking to answer questions about how the brain represents information. Representational questions are of particular importance in connecting neuroscientific and cognitive levels of analysis because it is at the representational level that many formal models of cognition make distinct predictions. This review discusses techniques for univariate, adaptation, and multivoxel analysis, and how they have been used to answer questions about content specificity in different regions of the brain, how this content is organized, and how representations are shaped by and contribute to cognitive processes. Each of the analysis techniques makes different assumptions about the underlying neural code and thus differ in how they can be applied to specific questions. We also discuss the many pitfalls of representational analysis, from the flexibility in data analysis pipelines to emergent nonrepresentational relationships that can arise between stimuli in a task.
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This is an important issue with all MVPA analyses and fMRI results in general.
“This is an important issue with all MVPA analyses and fMRI results in general61”
From Separate neural representations for physical pain and social rejection (2014) · cites this paper as [61]
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Schematic Influences on Category Learning and Recognition Memory.
Sakamoto, Y., & Love, B. C. (2004) · Journal of Experimental Psychology General · doi:10.1037/0096-3445.133.4.534 · Cited by 110 · see citing works
Selective Amplification of Stimulus Differences during Categorical Processing of Speech
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Self-citationChallenges for molecular neuroimaging with MRI
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Mixture Models of Categorization
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Multiple systems of category learning
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Spatiotemporal activity estimation for multivoxel pattern analysis with rapid event-related designs
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Self-citationPredicting judged similarity of natural categories from their neural representations
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de Bruijn cycles for neural decoding
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Comparing the similarity and spatial structure of neural representations: A pattern-component model
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Davis, T. H., Love, B. C., & Preston, A. R. (2012) · Journal of Experimental Psychology Learning Memory and Cognition · doi:10.1037/a0027865 · Cited by 80 · see citing works
Self-citationMur, M., Ruff, D. A., Bodurka, J., Bandettini, P. A., & Kriegeskorte, N. (2010) · Cerebral Cortex · doi:10.1093/cercor/bhp272 · Cited by 67 · see citing works
A comparison of fMRI adaptation and multivariate pattern classification analysis in visual cortex
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Exploratory analysis and data modeling in functional neuroimaging
Sommer, F. T., & Wichert, A. (2003) · Cited by 60 · see citing works
Quantifying the Internal Structure of Categories Using a Neural Typicality Measure
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Self-citationThe advantage of brief fMRI acquisition runs for multi-voxel pattern detection across runs
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Targeting the functional properties of cortical neurons using fMR-adaptation
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Pattern‐information fMRI: New questions which it opens up and challenges which face it
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The multifaceted nature of unsupervised category learning
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Generalized Sparse Classifiers for Decoding Cognitive States in fMRI
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Dynamic Norm-based Encoding for Unfamiliar Shapes in Human Visual Cortex
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Environment and Goals Jointly Direct Category Acquisition
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Using fMRI to Constrain Theories of Cognition
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Self-citationRules-plus-exception tasks: A problem for exemplar models?
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Separate neural representations for physical pain and social rejection
Woo, C.-W., Koban, L., Kross, E., Lindquist, M. A., Banich, M. T., Ruzic, L., Andrews-Hanna, J. R., & Wager, T. D. (2014) · Nature Communications · doi:10.1038/ncomms6380 · Cited by 310 · see citing works
The impact of study design on pattern estimation for single-trial multivariate pattern analysis
Mumford, J. A., Davis, T., & Poldrack, R. A. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.09.026 · Cited by 237 · see citing works
Consciousness and the fallacy of misplaced objectivity
Ellia, F., Hendren, J., Grasso, M., Kozma, C., Mindt, G., P. Lang, J., M. Haun, A., Albantakis, L., Boly, M., & Tononi, G. (2021) · Neuroscience of Consciousness · doi:10.1093/nc/niab032 · Cited by 57 · see citing works
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Pushing the Boundaries of Psychiatric Neuroimaging to Ground Diagnosis in Biology
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Dynamical Emergence Theory (DET): A Computational Account of Phenomenal Consciousness
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Fuelscher, I., Caeyenberghs, K., Enticott, P. G., Kirkovski, M., Farquharson, S., Lum, J., & Hyde, C. (2019) · European Journal of Neuroscience · doi:10.1111/ejn.14370 · Cited by 13 · see citing works
- Authors
- Davis, T., & Poldrack, R. A.
- Affiliations
- Imaging Research Center
- The University of Texas at Austin (US)
- Corresponding author
- Davis, Tyler
- ORCID
- Poldrack, Russell A.
- Venue
- Annals of the New York Academy of Sciences
- Publisher
- Wiley
- Published
- 2013-06-05
- Volume
- 1296
- Issue
- 1
- Pages
- 108-134
- ISSN
- 0077-8923 (ISSN-L)
- 1749-6632
- OpenAlex
- W2133129196
- Cited by
- 132
- References
- 147
- Citations by year
- 2026: 6 · 2025: 4 · 2024: 7 · 2023: 5 · 2022: 8 · 2021: 12 · 2020: 11 · 2019: 12 · 2018: 14 · 2017: 12 · 2016: 11 · 2015: 17 · 2014: 12 · 2013: 1
- FWCI
- 1.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annals of the New York Academy of Sciences (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- neural representations, representational analysis, multi-voxel pattern analysis, univariate analysis, content specificity, neural code, cognitive models, data analysis pipeline, functional MRI
- MeSH terms
- Humans, Magnetic Resonance Imaging, Nervous System, Photic Stimulation, Body Size, Neuroimaging
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.
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