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Application of machine learning and complex network measures to an EEG dataset from DMT experiments

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.