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Abstract Self-induced cognitive trance (SICT) is a voluntary non-ordinary state of consciousness characterized by a lucid yet narrowed awareness of the external surroundings. It involves a hyper-focused immersive experience of flow, expanded inner imagery, modified somatosensory processing, and an altered perception of self and time. SICT is gaining attention due to its potential clinical applications. Similar states of non-ordinary state of consciousness, such as meditation, hypnosis, and psychedelic experiences, have been reported to induce changes in the autonomic nervous system. However, the functioning of the autonomic nervous system during SICT remains poorly understood. In this study, we aimed to investigate the impact of SICT on the cardiac and respiratory signals of 25 participants proficient in SICT. To accomplish this, we measured various metrics of heart rate variability (HRV) and respiration rate variability (RRV) in three conditions: resting state, SICT, and a mental imagery task. Subsequently, we employed a machine learning framework utilizing a linear discriminant analysis classifier and a cross-validation scheme to identify the features that exhibited the best discrimination between these three conditions. The results revealed that during SICT, participants experienced an increased heart rate and a decreased level of high-frequency (HF) HRV compared to the control conditions. Additionally, specific increases in respiratory amplitude, phase ratio, and RRV were observed during SICT in comparison to the other conditions. These findings suggest that SICT is associated with a reduction in parasympathetic activity, indicative of a hyperarousal state of the autonomic nervous system during SICT.

Abstract Verbal fluency (VF) is a heterogeneous cognitive function that requires executive as well as language abilities. The purpose of this study was to elucidate the specificity of the resting state MEG correlates of the executive and language components. To this end, we administered a VF test, another verbal test (Vocabulary), and another executive test (Trail Making Test), and we recorded 5-min eyes-open resting-state MEG data in 28 healthy participants. We used source-reconstructed spectral power estimates to compute correlation/anticorrelation MEG clusters with the performance at each test, as well as with the advantage in performance between tests, across individuals using cluster-level statistics in the standard frequency bands. By obtaining conjunction clusters between verbal fluency scores and factor loading obtained for verbal fluency and each of the two other tests, we showed a core of slow clusters (delta to beta) localized in the right hemisphere, in adjacent parts of the premotor, pre-central and post-central cortex in the mid-lateral regions related to executive monitoring. We also found slow parietal clusters bilaterally and a cluster in the gamma 2 and 3 bands in the left inferior frontal gyrus likely associated with phonological processing involved in verbal fluency.