Characterization of near death experiences using text mining analyses: A preliminary study.

The notion that death represents a passing to an afterlife, where we are reunited with loved ones and live eternally in a utopian paradise, is common in the reports of people who have encountered a "Near-Death Experience" (NDE). NDEs are thoroughly portrayed by the media but empirical studies are rather recent. The definition of the phenomenon as well as the identification of NDE experiencers is still a matter of debate. To date, NDEs' identification and description in studies have mostly derived from answered items in questionnaires. However, questionnaires' content could be restricting and subject to personal interpretation. We believe that in addition to their use, user-independent statistical text examination of freely expressed NDEs narratives is of prior importance to help capture the phenomenology of such a subjective and complex phenomenon. Towards that aim, we included 158 participants with a firsthand retrospective narrative of their self-reported NDE that we analyzed using an automated text-mining method. The output revealed the top words expressed by experiencers. In a second step, a hierarchical clustering analysis was conducted to visualize the relationships between these words. It revealed three main clusters of features: visual perceptions, emotions and spatial components. We believe the user-independent and data-driven text mining approach used in this study is promising by contributing to the building a rigorous description and definition of NDEs.

Multimodal Neuroimaging Approach to Variability of Functional Connectivity in Disorders of Consciousness: A PET/MRI Pilot Study.

Behavioral assessments could not suffice to provide accurate diagnostic information in individuals with disorders of consciousness (DoC). Multimodal neuroimaging markers have been developed to support clinical assessments of these patients. Here we present findings obtained by hybrid fludeoxyglucose (FDG-)PET/MR imaging in three severely brain-injured patients, one in an unresponsive wakefulness syndrome (UWS), one in a minimally conscious state (MCS), and one patient emerged from MCS (EMCS). Repeated behavioral assessment by means of Coma Recovery Scale-Revised and neurophysiological evaluation were performed in the two weeks before and after neuroimaging acquisition, to ascertain that clinical diagnosis was stable. The three patients underwent one imaging session, during which two resting-state fMRI (rs-fMRI) blocks were run with a temporal gap of about 30 min. rs-fMRI data were analyzed with a graph theory approach applied to nine independent networks. We also analyzed the benefits of concatenating the two acquisitions for each patient or to select for each network the graph strength map with a higher ratio of fitness. Finally, as for clinical assessment, we considered the best functional connectivity pattern for each network and correlated graph strength maps to FDG uptake. Functional connectivity analysis showed several differences between the two rs-fMRI acquisitions, affecting in a different way each network and with a different variability for the three patients, as assessed by ratio of fitness. Moreover, combined PET/fMRI analysis demonstrated a higher functional/metabolic correlation for patients in EMCS and MCS compared to UWS. In conclusion, we observed for the first time, through a test-retest approach, a variability in the appearance and temporal/spatial patterns of resting-state networks in severely brain-injured patients, proposing a new method to select the most informative connectivity pattern.