Functional connectivity based Diagnosis of Bipolar Disorder by using resting state fMRI

The well-timed and correct diagnosis of Bipolar Disorder (BD) followed by proper treatment is vital for avoiding the progress of the illness. Although using resting-state functional magnetic resonance imaging (rs-fMRI) data and the features extracted from them may have an important role in diagnosing this kind of brain disorder, few researches have been conducted on this illness and the obtained results are not accurate. In this research we used a new approach to diagnose BD I. By using seed-based correlation we used the following 4 regions of interest in order to extract the connectivity maps for each subject: the posterior cingulate cortex (PCC) to probe the default mode network (DMN), the amygdala and the subgenual cingulate cortex (sgACC) to probe the salience network (SN) and the dorsolateral prefrontal cortex (dlPFC) to probe the frontoparietal network (FPN). After computing the connectivity maps for each subject we extracted the most important connectivities using different threshold on the t-value from the t-test that we applied on them and then we used a support vector machine (SVM) using only four combined features and a leave one out cross-validation (LOOCV) method to classify the two groups. The proposed method was done on rs-fMRI data from 49 healthy control subjects and 34 BD I patients and an accuracy of higher than 90% was obtained in differentiating the two groups from each other. Also there were no hyper-connectivity between the 4 ROIs and the rest of the brain regions for the BD I groups in relation with the healthy controls. The regions that had most of the hypo-connectivity with the 4 ROI’s that we used were: the angular gyrus (Ag) and the orbitofrontal cortex (OFC) with the PCC, the anterior cingulate cortex with the amygdala and the dlPFC and the inferior temporal gyrus (ITG) with the sgACC.