An automated framework for emotional fMRI data analysis using covariance matrix.

Classifying a particular affective state from the patterns of fMRI data is challenging. This is because of difficulty in finding discriminative and computationally inexpensive features and methods. In the present work, we employed a kernel-based machine learning framework utilizing covariance features and support vector machine (SVM) to classify 3 emotional states: pleasant (Ple), neutral (Neu), and unpleasant (Unp). We examined task functional magnetic resonance imaging (fMRI) of 5 healthy subjects who passively viewed a series of relevant pictures. Kernels (covariance matrix) per subject were extracted, mean-centered, and normalized by standard deviation. A standard leave-one-out cross validation was employed for generalization error. A balanced accuracy was computed to report overall accuracy. Discrimination maps derived from SVM weights were used to evaluate contributions of the input voxels. Best accuracy was achieved in Ple-vs-Neu with 88 %, followed by Ple-vs-Unp (79 %), and and Unp-vs-Neu (68 %). Discrimination and t-maps suggested highly contributed regions close to dorsal attention network. The proposed machine learning framework is efficient and can be generalized to differentiate other cognitive tasks and emotions in fMRI.