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Schizophreniais 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.

There is a growing interest in the medical use of psychedelic substances as preliminary studies using them for psychiatric disorders have shown positive results. In particularly, one of these substances is N,N-dimethyltryptamine (DMT) an agonist serotonergic psychedelic that can induce profound alterations in state of consciousness.In this work, we propose a computational method based on machine learning as an exploratory tool to reveal DMT-induced changes in brain activity using EEG data and provide new insights into the mechanisms of action of this psychedelic substance. To answer these questions, we propose a two-class classification based on (A) the connectivity matrix or (B) complex network measures derived from it as input to a support vector machine We found that both approaches were able to automatically detect changes in the brain activity, with case (B) showing the highest AUC (89%), indicating that complex network measurements best capture the brain changes that occur due to DMT use. In a second step, we ranked the features that contributed most to this result. For case (A) we found that differences in the high alpha, low beta, and delta frequency band were most important to distinguish between the state before and after DMT inhalation, which is consistent with results described in the literature. Further, the connection between the temporal (TP8) and central cortex (C3) and between the precentral gyrus (FC5) and the lateral occipital cortex (T8) contributed most to the classification result. The connection between regions TP8 and C3 has been found in the literature associated with finger movements that might have occurred during DMT consumption. However, the connection between cortical regions FC5 and P8 has not been found in the literature and is presumably related to emotional, visual, sensory, perceptual, and mystical experiences of the volunteers during DMT consumption. For case (B) closeness centrality was the most important complex network measure. Moreover, we found larger communities and a longer average path length with the use of DMT and the opposite in its absence indicating that the balance between functional segregation and integration was disrupted. This findings supports the idea that cortical brain activity becomes more entropic under psychedelics.Overall, a robust computational workflow has been developed here with an interpretability of how DMT (or other psychedelics) modify brain networks and insights into their mechanism of action. Finally, the same methodology applied here may be useful in interpreting EEG time series from patients who consumed other psychedelic drugs and can help obtain a detailed understanding of functional changes in the neural network of the brain as a result of drug administration.