Data_Sheet_1_Fantasy Proneness Correlates With the Intensity of Near-Death Experience.docx

<p>Little is known about the personality characteristics of those who have experienced a “Near-Death Experience” (NDE). One interesting candidate is fantasy proneness. We studied this trait in individuals who developed NDEs in the presence (i.e., classical NDEs) or absence (i.e., NDEs-like) of a life-threatening situation. We surveyed a total of 228 individuals. From those, 108 qualified as NDE experiencers (i.e., Greyson NDE scale total score ≥7): 51 had their NDEs in the context of a life-threatening situation; 57 had their NDEs not related to a life-threatening situation. From those who did not meet the criteria to be considered “experiencers,” 20 had their NDE in the absence of a life-threatening situation; 50 had faced death but did not recall a NDE and finally, 50 were healthy people without a history of life threat and/or NDE. All participants completed a measure of NDE intensity (the Greyson NDE scale) and a measure of fantasy proneness (the Creative Experiences Questionnaire). People reporting NDEs-like scored higher on fantasy proneness than those reporting classical NDEs, individuals whose experiences did not meet the NDE criteria and matched controls. By contrast, individuals reporting classical NDEs did not show different engagement in fantasy as matched controls. The reported intensity of the experiences was positively correlated with engagement in fantasy. Our findings support the view that strong engagement in fantasy by individuals recalling NDEs-like might make these persons more likely to report such subjective experiences when exposed to suitable physiological and/or psychological conditions (e.g., meditation, syncope).</p

Tinnitus Population.

<p>Tinnitus Population.</p

Experimental Design

<p>All participants underwent four fMRI scanning sessions (I–IV) within a half-day. In scanning session (I), they performed an auditory oddball task during which they mentally counted the number of deviant tones interspersed in a flow of repeated tones. Participants were then trained during 30 min outside of the scanner (training), either to the spatial memory navigation task (red path), or to the procedural memory SRT task (blue path). Immediately after the end of the training session, they were scanned again (II) while performing the auditory oddball task. They were then allowed a further 30-min break outside of the scanner without any further practice (rest). They were scanned once again (III) while performing the auditory oddball task. Afterwards, participants' memory of the learned task was tested outside of the scanner (retest). Finally, participants underwent a fourth fMRI session (IV), during which they explored virtual environments (red path) or practiced motor sequences in the SRT task (blue path), to determine the set of brain areas associated with task practice. The procedure was repeated 2 wk later using the other learning task.</p

Practice-Related Activations

<p>(A) Brain activity during exploration of the virtual environment (Session IV). Cross hair shows hippocampus activation (22 −26 −6 mm, <i>p</i>^corr < 0.005) superimposed on participants' average anatomical T1-weighted MRI image. Color bars indicate magnitude of effect size. (B) Brain activity during practice of the procedural serial RT task (Session IV). Cross hair shows cerebellum activation (12 −74 −22 mm, <i>p</i>^corr < 0.05). </p

Post-Training Modulation of Neuronal Activity and Behavioral Performance

<div><p>(A) Activations are superimposed on one participant's T1-weighted normalized MRI image. Left side: Plots of the correlation between changes in spatial performance (distance left to target in learning minus test sessions) and brain response during intervening oddball Session II (versus I; [B]) in the hippocampus ([24 −24 −2 mm], Z = 3.75, <i>p</i>^svc(10mm) < 0.05) around an a priori location [26 −24 −8 mm]). Each point represents one participant. Part C shows the non-significant correlation ( <i>p</i> > 0.8) at the same location during Session III (versus II). Right side: Plots of the correlation between individual levels of sequence knowledge (RT for novel minus learned sequence) at the end of the Learning phase and brain response during (B) Session II (versus I), showing the non-significant correlation in the left caudate nucleus, and (C) intervening oddball Session III (versus II[C]) in the same location ([−12 −2 20 mm], Z = 4.48, <i>p</i>^svc < 0.005). </p> <p> <i>r</i> = correlation coefficient. </p></div

Discriminative power across subjects of nine measures, as measured by area under the ROC curve (AROC).

<p>Each symbol represents a state pair as indicated. The AROC is computed from the mean scores for the 7 subjects, obtained from the broadband signal from 25 electrodes from the whole cortex. The closer the AROC is to 0 or 1, the better the measure is at discriminating the given state-pair, close to 1 signifying that the measure tends to be greater for the more conscious state and close to 0 signifying that the measure tends to be greater for the less conscious state. When the AROC is 0.5 there is no discriminative power; hit rate equals false alarm rate for all classification thresholds. LZc, ACE and SCE have nearly maximal discriminative power for state pairs LOC/MS and LOC/WR. The measure sumCov fails to discriminate LOC/MS yet has strong (inverse) discriminative power for LOC/WR and MS/WR. Normalized delta band power discriminates LOC/WR and LOC/MS strongly yet MS/WR poorly.</p

Regions of the auditory resting state network identified in controls and chronic tinnitus patients.

<p>Regions of the auditory resting state network identified in controls and chronic tinnitus patients.</p

Complexity measures for increasingly regular activation/synchrony matrices.

<p>Given a random binary matrix of activations (for ACE and LZc) or synchronies (for SCE), with increasing number of duplicated channels, the complexity measures monotonically decrease with the number of equal channels. See text for details.</p

Memory Characteristics Questionnaire (MCQ [10]) total scores for each assessed memory (median and interquartile ranges; *p<0.05).

<p>Note that NDEs show significantly more characteristics as compared to the assessed target memories in the coma, “no memory” and control groups.</p

Task-Specific Modulation of Regional Brain Responses by Prior Learning

<p>Spatial learning-related offline activity: (A) Higher brain responses after spatial than after procedural learning in Session II (versus I). Blue cross hair on hippocampus (26 −24 −8 mm) activation superimposed on participants' average anatomical T1-weighted MRI image. (B) Higher brain responses in the parahippocampal gyrus (26 −32 −18 mm) after a further 30-min break during Session III (versus II). (C) Co-occurring decreased brain responses in the hippocampus (22 −22 −10 mm, blue cross hair) during Session III (versus II), more after spatial than after procedural learning. Procedural learning-related offline activity: (D) Higher brain response in the medial cerebellum (2 −60 −28 mm) after procedural than after spatial learning in Session II (versus I). (E) Co-occurring decreased brain responses in the putamen (−20 2 10 mm, blue cross hair), lateral cerebellum, SMA, and other neocortical areas during Session II (versus I), more after procedural than after spatial learning. (F) Higher brain response after a further 30-min break during Session III (versus II) in the caudate nucleus (top: −16 0 16 mm) and the SMA (bottom: 10 2 56 mm). Color bars indicate the magnitude of the effect size, in the yellow range for increased post-training brain response, and in the blue range for decreased post-training brain response.</p