Multi-voxel pattern analysis

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Multi-voxel pattern analysis
Abbreviations: MVPA
Variations:
Category: Multi-voxel pattern analysis
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Neuroimaging analysis
Machine learning

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Multi-voxel pattern analysis is a (usually) multivariate method to classify or predict neuroimaging scans. It has been applied for sets of positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) scans.

In neuroimaging classification a machine learning algorithm classifies neuroimages according to some criteria, e.g., healthy/disease, brain stimulation/rest.

Contents

[edit] Tools

  1. 3dsvm
  2. BrainVoyager [1]
  3. Lyngby Toolbox
  4. Princeton MVPA Toolbox
  5. PRoNTo
  6. PyMVPA

[edit] History

Early work on PET with principal component analysis and artificial neural networks goes back to the middle of the 1990's.[1][2][3]

Prediction in Alzheimer.[4][5]

See further references at [2]

A review from 2006 is available.[6]

[edit] Papers

[edit] Reviews

  1. Decoding mental states from brain activity in humans (2006)
  2. Machine learning classifiers and fMRI: a tutorial overview (2009)

[edit] Original papers

  1. A critique of multi-voxel pattern analysis
  2. Bayesian decoding of brain images
  3. Beyond mind-reading: multi-voxel pattern analysis of fMRI data
  4. Category-specific cortical activity precedes retrieval during memory search
  5. Classifying brain states and determining the discriminating activation patterns: support vector machine on functional MRI data
  6. Decoding the visual and subjective contents of the human brain
  7. Functional magnetic resonance imaging (fMRI) 'brain reading': detecting and classifying distributed patterns of fMRI activity in human visual cortex
  8. Stanislas Dehaene, Gurvan Le Clec'H, Laurent Cohen, Jean-Baptiste Poline, Pierre-Francois van de Moortele, Denis Le Bihan (1998). "Inferring behavior from functional brain images". Nature Neuroscience 1: 549. doi: 10.1038/2785.
  9. Inverse retinotopy: inferring the visual content of images from brain activation patterns
  10. Benny Lautrup, Lars Kai Hansen, Ian Law, Niels Mørch, Claus Svarer(1994). "Massive weight sharing: a cure for extremely ill-posed problems". Supercomputing in Brain Research: From Tomography to Neural Networks.
  11. Neuroinformatics in functional neuroimaging
  12. Support vector machines for temporal classification of block design fMRI data
  13. The quantitative evaluation of functional neuroimaging experiments: mutual information learning curves
  14. Using FMRI brain activation to identify cognitive states associated with perception of tools and dwellings
  15. Visualization and analysis of 3D functional brain images

[edit] Reference

  1. Massive weight sharing: a cure for extremely ill-posed problems
  2. Extremely ill-posed learning
  3. Visualization of neural networks using saliency maps
  4. Correlation between cognitive function scores and the response of a neural network classifier for SPECT data in patients with alzheimer's disease
  5. A neural network classifier for SPECT in Alzheimer's disease: correlation with cognitive function
  6. Beyond mind-reading: multi-voxel pattern analysis of fMRI data
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