ReviewNeuroImage2018
Deconstructing multivariate decoding for the study of brain function.
Review in NeuroImage, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 145 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
145 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Brain-wide decoding of numbers and letters: Converging evidence from multivariate fMRI analysis and probabilistic meta-analysis.Cortex; a journal devoted to the study of the nervous system and behavior · 2025Pooled it
- Disentangling objects' contextual associations from perceptual and conceptual attributes using time-resolved neural decoding.Journal of vision · 2026Article
- Evaluating the effects of regularization and cross-validation parameters on the performance of SVM-based decoding of EEG data.Cortex; a journal devoted to the study of the nervous system and behavior · 2026Article
- Optimising Analysis Choices for Multivariate Decoding: Creating Pseudotrials Using Trial Averaging and Resampling.The European journal of neuroscience · 2026Article
- Content-Independent Pointers Mediate Working Memory Storage for Both Visual and Verbal Stimuli.Journal of cognitive neuroscience · 2026Article
- From prediction to control: Age-related dissociations between implicit regularity encoding and attentional modulation during perception-action integration.Developmental cognitive neuroscience · 2026Article
- App-based mindfulness meditation training enhances cognitive flexibility and modulates ACC and medial frontal gyrus activation during task switching in adolescent OCD.Neuroimage. Reports · 2026Article
- The anatomical basis of morpho-syntactic computation reflected in gray matter volume and pattern of the inferior frontal gyrus.Brain structure & function · 2026Article
- Evaluating the effects of regularization and cross-validation parameters on the performance of SVM-based decoding of EEG data.bioRxiv : the preprint server for biology · 2026Article
- Neural fingerprint of the dark triad: Resting state BOLD power (fALFF) alterations in executive and default mode networks.Cognitive, affective & behavioral neuroscience · 2026Article
- Article
- Role of the left intraparietal sulcus in the comprehension of action intensity described in sentences.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- A Novel Approach to Map the Causal Impact of Brain Stimulation on Semantic Processing With Language Models.Neurobiology of language (Cambridge, Mass.) · 2026Article
- Cortical Representation of Auditory Selective Attention in a Dichotic Listening Task: A Functional Near-Infrared Spectroscopy Study.Brain topography · 2025Article
- EEG neurosubtyping of infants predicts language trajectories.Journal of neural transmission (Vienna, Austria : 1996) · 2025Article
- Article
- Article
- Largely Intact But Less Reliable and Distributed Neural Representations of Subjective Value in Human Opioid Addiction.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Selective Attention Dynamically Modulates the Hierarchical Order of Perceptual and Conceptual Representations.Human brain mapping · 2025Article
- In silico discovery of representational relationships across visual cortex.Nature human behaviour · 2025Article
85 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the study of brain function was firmly embedded in a statistical philosophy grounded on univariate methods of data analysis. In this way, multivariate decoding for brain interpretation grew out of two established frameworks: multivariate decoding for predictions in real-world applications, and classical univariate analysis based on the study and interpretation of brain activation. We argue that this led to two confusions, one reflecting a mixture of multivariate decoding for prediction or interpretation, and the other a mixture of the conceptual and statistical philosophies underlying multivariate decoding and classical univariate analysis. Here we attempt to systematically disambiguate multivariate decoding for the study of brain function from the frameworks it grew out of. After elaborating these confusions and their consequences, we describe six, often unappreciated, differences between classical univariate analysis and multivariate decoding. We then focus on how the common interpretation of what is signal and noise changes in multivariate decoding. Finally, we use four examples to illustrate where these confusions may impact the interpretation of neuroimaging data. We conclude with a discussion of potential strategies to help resolve these confusions in interpreting multivariate decoding results, including the potential departure from multivariate decoding methods for the study of brain function.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.