Brain activation detection by modified neighborhood one-class SVM on fMRI data

- A modified neighborhood one-class SVM algorithm is proposed to detect brain functional activation on fMRI data.
- The algorithm is based on the modified probability distribution assumption and the modified neighborhood consistency hypothesis.
- The idea of ReHo and the multivariate RV measure are combined to select the features of each voxel.
- Our method is effective for the brain activation detection in the whole brain.

The one-class support vector machine (OC-SVM) is a data-driven machine learning method that has been applied as a novel technique for brain activation detection. Several researchers have obtained positive preliminary results using OC-SVMs. Nevertheless, existing algorithms are either too complicated or oversimplified and their performance needs to be further improved. In this study, a modified neighborhood one-class support vector machine (MNOC-SVM) algorithm is proposed to detect brain functional activation on functional magnetic resonance imaging (fMRI) data. This method is based on two basic assumptions: (a) For task-related fMRI data, time series of only a few voxels are related to a particular functional activity or functional area, and these voxels should be identified as activated voxels, i.e., the outliers. In contrast, for resting-state fMRI data, only a small number of voxels are unrelated to any resting-state functional networks. These voxels should instead be taken as non-activated voxels, i.e., the outliers. (b) Close voxels have similarly activated or non-activated states. To improve detection accuracy, we apply the following features to each voxel: the RV coefficient between each voxel and its 26 neighborhood voxels (or fewer than 26 for voxels on the edge of the brain), a flag for isolated voxels and a flag for isolated areas. For both task-related and resting-state fMRI data, our MNOC-SVM method effectively detects activated functional areas in the whole brain.