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. 2009 Apr;30(4):1068-76.
doi: 10.1002/hbm.20569.

An fMRI normative database for connectivity networks using one-class support vector machines

João Ricardo Sato  1 , Maria da Graça Morais Martin, André Fujita, Janaina Mourão-Miranda, Michael John Brammer, Edson Amaro Jr
Affiliations

An fMRI normative database for connectivity networks using one-class support vector machines

João Ricardo Sato et al. Hum Brain Mapp. 2009 Apr.

Abstract

The application of functional magnetic resonance imaging (fMRI) in neuroscience studies has increased enormously in the last decade. Although primarily used to map brain regions activated by specific stimuli, many studies have shown that fMRI can also be useful in identifying interactions between brain regions (functional and effective connectivity). Despite the widespread use of fMRI as a research tool, clinical applications of brain connectivity as studied by fMRI are not well established. One possible explanation is the lack of normal patterns and intersubject variability-two variables that are still largely uncharacterized in most patient populations of interest. In the current study, we combine the identification of functional connectivity networks extracted by using Spearman partial correlation with the use of a one-class support vector machine in order construct a normative database. An application of this approach is illustrated using an fMRI dataset of 43 healthy subjects performing a visual working memory task. In addition, the relationships between the results obtained and behavioral data are explored.

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Figures

Figure 1
Figure 1
Illustration of one‐class SVM with an RBF kernel.
Figure 2
Figure 2
Group activation maps for the memory task experiment (p‐cluster <0.05).
Figure 3
Figure 3
Estimated group connectivity network. The numbers in the arrows indicate values of Spearman partial correlation. (M/L/R)F: Medial, left and right frontal regions, respectively. (L/R)P: Left and right parietal cortex, respectively.
Figure 4
Figure 4
Kernel estimates of marginal densities for each feature variable (partial correlation). The numbers indicate the index and location of misclassified samples, which were considered in the tails of multivariate distribution (below p‐quantile). Extreme observations are highlighted with a circle.
Figure 5
Figure 5
Kernel estimates density of laterality score in the data. The numbers indicate the index and location of misclassified samples using one‐class SVM.
See this image and copyright information in PMC

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