A Non-Parametric Approach for the Activation Detection of Block Design fMRI Simulated Data Using Self-Organizing Maps and Support Vector Machine

Functional magnetic resonance imaging (fMRI) is a popular method to probe the functional organizationof the brain using hemodynamic responses. In this method, volume images of the entire brain areobtained with a very good spatial resolution and low temporal resolution. However, they always sufferfrom high dimensionality in the face of classification algorithms. In this work, we combine a supportvector machine (SVM) with a self-organizing map (SOM) for having a feature-based classification byusing SVM. Then, a linear kernel SVM is used for detecting the active areas. Here, we use SOM forfeature extracting and labeling the datasets. SOM has two major advances: (i) it reduces dimension ofdata sets for having less computational complexity and (ii) it is useful for identifying brain regions withsmall onset differences in hemodynamic responses. Our non-parametric model is compared withparametric and non-parametric methods. We use simulated fMRI data sets and block design inputsin this paper and consider the contrast to noise ratio (CNR) value equal to 0.6 for simulated datasets.fMRI simulated dataset has contrast 1–4% in active areas. The accuracy of our proposed method is93.63% and the error rate is 6.37%.