Características funcionais da conectividade cerebral em estados alterados de consciência

Abstract

Introduction: Altered states of consciousness (ASCs) cause changes in thinking, memory, perception of time and emotional expression; generate depersonalisation, transcendental mystical experiences, expansion of consciousness; perceptual distortions, including hallucinations, pseudo-hallucinations, sharpened perceptions, among others. ASCs are part of the human behavioural repertoire and can be classified into three groups: spontaneous, induced, and pathological. Considering the complexity of these phenomena, one of the biggest gaps in research in this field is the lack of a comprehensive characterisation that encompasses the different types of ASCs in a brain dynamic approach. In this way, a characterisation of these states using Brain Functional Networks (BFNs) will be able to access brain nuances that cannot be achieved with pre-established static methods. Methodology: To achieve the proposed objectives, we evaluated the brain activity by electroencephalography (EEG) of individuals in the different types of ASCs: Pathological - Schizophrenia (SCZ) and Major Depressive Disorder (MDD); Pharmacologically Induced - N, N-dimethyltryptamine (DMT) and Ketamine (KTM), and Psychologically Spontaneous - Meditation (MD) and mediumistic trance during Psychography (PSYC) in a mediumship group (MG). The public EEG datasets used were: EQZ and TDM groups (were compared with healthy controls (HC)); DMT and KTM (were paired with without DMT or KTM) and, from meditations (MD) (Himalayan Yoga Traditions (HY) and Isha Shoonya Yoga (ISHA) were paired with active thinking activity (AT)). Our own EEG datasets are the data from the Brahma Kumaris' Raja Yoga (RY) and Gurdjieff's (GD) meditations (both paired with relaxation (RL)), and the mediumship trance with PSYC and your control groups. The mediumship group (MG) were compared during writing in wakefulness (WW) and psychography (PSYC), and the CT group between WW and creative writing (CW). To the brain signature, we compared MG group during WW, PSYC and RL, and to the CT group, between WW, CW and RL. Both groups (MG and CT) were tested to psychologically using the Neupsilin, SCID-5, PANSS and Dissociative Experience Scale (DES) scales. Neuropsychological tests indicated that both groups, MG and CT, did not show mental disorders. After cleaning the EEG data artefacts, parameters were defined for constructing the BFNs, which were standardised due to the different characteristics of the data, and the networks were generated using time-varying graphs (TVGs) and the motif synchronisation method (MS). We then evaluated the indices of the networks considering time, both globally and locally: weighted degree (WD), probability of hub formation (PHF), path length (PL), E-I index (E-I), clustering coefficient (CC), coefficient of variation of edge (CVE), coefficient of variation of clustering (CVC) and full synchronisation time (FST). In addition, we propose a new index to analyse the interaction between brain regions (IBR) in ASC. To assess the brain pattern / brain signature, we investigated the distance between the weighted matrices. Results: In general, for SCZ compared to HC, we found differences for the WD, CVE, PHF and IBR indices. For MDD compared to HC, we found differences for WW, PHF, PL, FST, CVE, CVC and IBR. For the comparison between DMT and without DMT, we saw that WD, E-I, PHF, PL, CVE, CVC, FST and IBR showed significant differences. The evaluation of the networks during the use of the KTM showed differences for WD, E-I, PL, CC, FST and, CVC compared to without KTM. For the evaluation between the meditations and their control activities: RY, GD vs. RL and ISHA, HY vs. AT, only GD (vs. RL) showed differences for the follow indices WD, PHF and, CC; and ISHA vs. AT for E-I, CVE. When we compared the meditations each other (RY vs. GD vs. ISHA vs. HY), we observed differences for E-I, CVE, and IBR. For comparisons between MG vs. CT, we found no inter-group differences, only intra-group differences to MG between PSYC vs. WW. Neuropsychological tests indicated that both groups, MG and CT, did not show mental disorders. The MG scored for hallucinatory perceptions, but they did not have a schizophrenic disorder. The DES indicated two individuals from the MG with a tendency to dissociation, and none one of the CT. The results for the BFNs indices exhibited differences between PSYC vs. WW for CC, PL, CVE, CVC and IBR. The brain signature results revealed altered brain patterns for the MG, both in terms of intra-individual delta distance and between PSYC tasks compared to WW and RL. These results were the opposite for the CT group, which showed no changes in the intra-individual delta distance, nor for the comparisons between the tasks (WW, CW and RL). In order to relate all the results of the groups studied to the BFNs indices, we carried out principal component analysis (PCA) which revealed that the indices that most differentiated the groups were WD and PFH (PC1) and, CVE and CC for PC2. Conclusion: We were able to identify differences between the ASCs studied, considering their respective control states, less for the RY vs. RL and between HY vs. AT meditations. Through the BFNs, we saw that the ASCs promoted a different brain physiology from the usual state. Evaluation by PCA revealed that ASCs such as the trance experienced by MG, SCZ, DMT and KTM shared aspects of the BFNs in common. Despite the limitations found and the exploratory comparisons, we highlight the similarities between the cerebral characteristics of mediumistic trance, schizophrenia and DMT without, however, presenting associated pathological characteristics for MG. Mediumship trance promotes changes in the brain signature. This finding highlights the need for further research to understand the variations in individual brain patterns during different altered states of consciousness.