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Measuring abnormal brains: building normative rules in neuroimaging using one-class support vector machines.

João Ricardo Sato¹, Jane Maryam Rondina, Janaina Mourão-Miranda

¹Center of Mathematics, Computation and Cognition, Universidade Federal do ABC Santo André, Brazil.

Frontiers in Neuroscience

|December 19, 2012

Summary

One-class support vector machines (OC-SVM) offer a novel approach for neuroimaging analysis. This unsupervised method establishes normative rules, interpreting deviations as abnormality scores for patient stratification and symptom quantification.

Keywords:

SVM machine learning neuroimaging one-class pattern recognition

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Area of Science:

Neuroimaging
Machine Learning
Pattern Recognition

Background:

Neuroimaging data is high-dimensional, requiring advanced analysis tools.
Current pattern recognition in neuroimaging often focuses on two-class classification (e.g., patient vs. control).

Purpose of the Study:

To highlight the potential of one-class support vector machines (OC-SVM) in neuroimaging.
To present OC-SVM as an unsupervised approach for establishing multivariate normative rules.

Main Methods:

Description of one-class classification concepts.
Explanation of the foundations of OC-SVM.
Discussion of OC-SVM's application in neuroimaging.

Main Results:

OC-SVM defines a characteristic boundary enclosing a specific class, unlike standard SVM's discriminating boundary.
Training OC-SVM with healthy control data allows for an abnormality score based on distance to the boundary.

Conclusions:

OC-SVM provides an unsupervised method for neuroimaging analysis.
Abnormality scores derived from OC-SVM can quantify symptom severity and identify patient subgroups.
This approach offers new insights for neuroimaging research.