Analysis of functional connectivity using machine learning and deep learning in multimodal data from patients with schizophrenia

Schizophrenia is a severe mental disorder associated with persistent or recurrent psychosis, hallucinations, delusions, and thought disorders that affect approximately 26 million people worldwide, according to the World Health Organization (WHO). Several studies encompass machine learning and deep learning algorithms to automate the diagnosis of this mental disorder. Others study schizophrenia brain networks to get new insights into the dynamics of information processing in patients suffering from the condition. In this paper, we offer a rigorous approach with machine learning and deep learning techniques for evaluating connectivity matrices and measures of complex networks to establish an automated diagnosis and comprehend the topology and dynamics of brain networks in schizophrenia patients. For this purpose, we employed an fMRI and EEG dataset in a multimodal fashion. In addition, we combined EEG measures, i.e., Hjorth mobility and complexity, to complex network measurements to be analyzed in our model for the first time in the literature. When comparing the schizophrenia group to the control group, we found a high positive correlation between the left superior parietal lobe and the left motor cortex and a positive correlation between the left dorsal posterior cingulate cortex and the left primary motor. In terms of complex network measures, the diameter, which corresponds to the longest shortest path length in a network, may be regarded as a biomarker because it is the most important measure in a multimodal fashion. Furthermore, the schizophrenia brain networks exhibit less segregation and lower distribution of information. As a final result, EEG measures outperformed complex networks in capturing the brain alterations associated with schizophrenia. As a result, our model achieved an AUC of 100%, an accuracy of 98% for the fMRI, an AUC of 95 %, and an accuracy of 95% for the EEG data set. These are excellent classification results. Furthermore, we investigated the impact of specific brain connections and network measures for these results, which helped us better describe changes in the diseased brain.