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Related Concept Videos

Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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Updated: May 11, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Multivariate decoding of brain images using ordinal regression.

O M Doyle1, J Ashburner2, F O Zelaya1

  • 1King's College London, Department of Neuroimaging, Institute of Psychiatry (PO89), De Crespigny Park, London SE5 8AF, UK.

Neuroimage
|May 21, 2013
PubMed
Summary
This summary is machine-generated.

We introduce a novel whole brain probabilistic ordinal regression method for neuroimaging analysis. This approach accurately predicts ordered outcomes, outperforming traditional classification and regression models in predicting drug effects on brain activity.

Keywords:
Gaussian processesKetamineMultivariateOrdinal regressionPharmacological MRIScopolamine

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Biostatistics

Background:

  • Neuroimaging data analysis often involves predicting ordered outcomes, such as disease severity or drug response.
  • Conventional methods like classification and regression may not fully capture the ordinal nature of these predictions.
  • Existing techniques can ignore the inherent ranking of target variables or impose an inappropriate metric structure.

Purpose of the Study:

  • To propose a novel multivariate approach for neuroimaging data analysis: whole brain probabilistic ordinal regression using a Gaussian process framework.
  • To overcome limitations of conventional classification and regression methods when dealing with ordinal targets in neuroimaging.
  • To evaluate the performance of this new ordinal regression technique against established methods.

Main Methods:

  • Developed a whole brain probabilistic ordinal regression model within a Gaussian process framework.
  • Applied the ordinal regression technique to two pharmacological neuroimaging datasets from healthy volunteers.
  • Compared the performance of ordinal regression against multi-class classification and metric regression.

Main Results:

  • Ordinal regression significantly outperformed multi-class classification and metric regression in predicting ketamine's modulation by lamotrigine (accuracy, mean absolute error).
  • For ketamine's modulation by risperidone, ordinal regression outperformed metric regression and performed similarly to multi-class classification.
  • Ordinal regression outperformed both multi-class and metric regression for predicting scopolamine's effect on cerebral blood flow in the anterior cingulate cortex.

Conclusions:

  • Ordinal regression demonstrated superior or comparable performance across all tested neuroimaging datasets and prediction tasks.
  • The proposed probabilistic ordinal regression offers a powerful and flexible alternative for analyzing neuroimaging data with ordinal outcomes.
  • This method provides a fully probabilistic framework for model selection and enhances the predictive accuracy of neuroimaging studies.