Evidence map›Paper›PMID 23401720›Full record

ReviewComputational and mathematical methods in medicine2012

Multivoxel pattern analysis for FMRI data: a review.

Abdelhak Mahmoudi, Sylvain Takerkart, Fakhita Regragui, Driss Boussaoud, Andrea Brovelli

Abstract readReview
In one paragraph

Review in Computational and mathematical methods in medicine, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 89 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
89citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

89 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Neural Representations and Functional Connections Underlie Subjective Positive Affect.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026
    Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Dynamic Neural Deactivation Bridges Direct and Competitive Inhibition Processes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. Regulation of craving for real-time fMRI neurofeedback based on individual classification.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024
    Article

29 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Abdelhak MahmoudiUniversité Mohammed V-Agdal, Rabat, Morocco. abdelhak.mahmoudi@gmail.com
Sylvain Takerkart
Fakhita Regragui
Driss Boussaoud
Andrea Brovelli

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Functional magnetic resonance imaging (fMRI) exploits blood-oxygen-level-dependent (BOLD) contrasts to map neural activity associated with a variety of brain functions including sensory processing, motor control, and cognitive and emotional functions. The general linear model (GLM) approach is used to reveal task-related brain areas by searching for linear correlations between the fMRI time course and a reference model. One of the limitations of the GLM approach is the assumption that the covariance across neighbouring voxels is not informative about the cognitive function under examination. Multivoxel pattern analysis (MVPA) represents a promising technique that is currently exploited to investigate the information contained in distributed patterns of neural activity to infer the functional role of brain areas and networks. MVPA is considered as a supervised classification problem where a classifier attempts to capture the relationships between spatial pattern of fMRI activity and experimental conditions. In this paper , we review MVPA and describe the mathematical basis of the classification algorithms used for decoding fMRI signals, such as support vector machines (SVMs). In addition, we describe the workflow of processing steps required for MVPA such as feature selection, dimensionality reduction, cross-validation, and classifier performance estimation based on receiver operating characteristic (ROC) curves.

Indexed as

Area Under CurveBrainBrain MappingComputational BiologyHumansKineticsLeast-Squares AnalysisLinear ModelsMagnetic Resonance ImagingModels, BiologicalNeuronsNormal DistributionOxygenROC CurveSupport Vector MachineTime FactorsOxygen

Identifiers

PMID23401720
PMCPMC3529504

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.