Finally, the finding that there was no significant difference in mindfulness in frequent lucid dreamers is consistent with other research, which has found that outside of meditators, there does not appear to be an association between trait mindfulness and lucid dream frequency in the facets of mindfulness studied here decentering and curiosity 34 , 67 , If so, this would suggest that it may be possible to bias these networks toward increased metacognitive awareness of dreaming during REM sleep, for example through techniques to increase activation of these regions.
Notably, a recent double blind, placebo-controlled study found that cholinergic enhancement with galantamine, an acetylcholinesterease inhibitor AChEI , increased the frequency of lucid dreams in a dose-related manner when taken late in the sleep cycle and combined with training in the mental set for lucid dream induction While the relationship between cholinergic modulation and frontoparietal activation is complex and depends on the task context and population under study see ref.
Given that frontoparietal activity is typically suppressed during REM sleep, an intriguing follow-up to these findings based on the current results would be to examine whether AChEIs, and galantamine in particular, may facilitate lucid dreaming through increasing activation within the network of fronto-temporo-parietal areas observed here. In line with the above ideas, several studies have attempted to induce lucid dreams through electrical stimulation of the frontal cortex during REM sleep.
One study tested whether transcranial direct current stimulation tDCS applied to the frontal cortex would increase lucid dreaming While tDCS resulted in a small numerical increase in self-ratings of the unreality of dream objects, it did not significantly increase the number of lucid dreams as rated by judges or confirmed through the eye-signaling method.
Specifically, lucid dreams were not dreams that participants self-reported as lucid, nor dreams that were objectively verified to be lucid through the eye-movement signaling method.
Instead, dreams were inferred to be lucid based on higher scores to questionnaire items measuring the amount of insight or dissociation Given that dissociation i. Furthermore, mean ratings in the insight subscale increased from approximately 0. In summary, it remains unclear whether electrical brain stimulation techniques could be effective for inducing lucid dreams see refs 19 , 62 for further discussion.
Nevertheless, given the current findings, stimulation of aPFC and temporoparietal association areas appears to be a worthwhile direction for future research attempting to induce lucid dreaming. Future studies might consider testing a wider range of stimulation parameters, particularly applied to aPFC, as well as combining stimulation with training in the appropriate attentional set for lucid dream induction. Participants were recruited via mass emails sent to University of Wisconsin-Madison faculty, staff and students.
The study was described broadly as a study on brain structure and dreaming. Exclusion criteria for all participants included pregnancy, severe mental illness or any contraindications for MRI e. To determine study eligibility, participants completed a questionnaire that measured their dream recall and lucid dreaming frequency described below. For the frequent lucid dream group, we recruited individuals who reported a minimum of 3—4 lucid dreams per week, or approximately one lucid dream every other night without engaging in training to have lucid dreams.
We recruited control participants who were 1-to-1 matched to participants in the frequent lucid dream group on age, gender and dream recall frequency variables but who reported lucid dreams never or rarely.
Signed informed consent was obtained from all participants before the experiment, and ethical approval for the study was obtained from the University of Wisconsin—Madison Institutional Review Board. The study protocol was conducted in accordance with the Declaration of Helsinki. Participants completed a questionnaire that measured their dream recall and lucid dreaming frequency Supplementary Methods: Dream and lucid dream frequency questionnaire.
Dream recall was measured with a pt scale ranging from 0 never to 15 more than one dream per night. Lucid dream frequency was measured with a pt scale ranging from 0 no lucid dreams to 15 multiple lucid dreams per night. Participants were also provided with a short excerpt of a written report of a lucid dream see Supplementary Methods for full text of the definition and example of lucid dreaming provided on the questionnaire measure.
Several additional checks were made to ensure that participants had a clear understanding of the meaning of lucid dreaming. First, participants were asked to provide a written example of one of their lucid dreams, including how they knew they were dreaming.
Second, participants were interviewed by the experimenters before being enrolled in the study to ensure that they had a clear understanding of the meaning of lucid dreaming. During the interview participants described several recent lucid dreams and confirmed the frequency with which they experienced lucid dreams through follow-up questions. Only participants who demonstrated unambiguous understanding of lucidity and met the frequency criteria as confirmed by both written and oral responses were enrolled in the frequent lucid dream group.
The frequent lucid dream group also reported several additional variables related to their experiences with lucid dreaming, including the number of lucid dreams they had in the last six months, the most lucid dreams they had ever had in a six-month period, whether they had engaged in training to have lucid dreams and their general interest in the topic.
As noted above, we aimed to match dream recall between the frequent lucid dream group and control group as closely as possible in order to control for this potentially confounding variable. However, it was not always possible to recruit a matched control participant that was exactly matched on age, gender and dream recall. For each participant in the frequent lucid dream group, we therefore sought to recruit the closet matched pair control participant of the same age and gender, with the constraint that dream recall had to be within at least 3 rank order values on the questionnaire measure.
In 7 cases, we were able to obtain an exact match between control participants and frequent lucid dream participants on dream recall, in 5 cases within 1 rank value, in 1 case within 2 rank values and in 1 case within 3 rank values. In 4 out of the 5 cases that were within 1 rank value, the difference in reported dream recall frequency was between 7 dreams recalled per week and 5—6 dreams recalled per week, and in the remaining case the difference was between 3—4 dreams recalled per week and 5—6 dreams recalled per week.
Overall this method ensured that the frequent lucid dream group and control group were closely matched on dream recall frequency. Participants completed several additional assessments that measured cognitive variables which have been hypothesized to be associated with lucid dreaming and have been linked to PFC function, including working memory capacity WMC , trait mindfulness and prospective memory e.
These tasks have been validated to yield a reliable measure of WMC 75 , In brief, each task presents to-be-remembered stimuli in alternation with an unrelated processing task. Following standard scoring procedures, span scores were calculated as the total number of items recalled in correct serial order across all trials Participants also completed a questionnaire battery that assessed several additional variables of interest: their mind-wandering frequency, memory function in everyday life and trait mindfulness.
Memory function was assessed with the Prospective and Retrospective Memory Questionnaire PRMQ 78 , which measures self-report scores of the frequency of both prospective and retrospective memory errors in everyday life see ref.
The TMS measures two factor-analytically derived components of mindfulness: Curiosity and Decentering. Resting-state functional MRI scans were collected on a 3. During the resting-state scan, participants were instructed to stay awake and relax, to hold as still as possible, and to keep their eyes open. A diffeomorphic non-linear registration algorithm diffeomorphic anatomical registration through exponentiated lie algebra; DARTEL 81 was used to iteratively register the images to their average.
The resulting flow fields were combined with an affine spatial transformation to generate Montreal Neurological Institute MNI template spatially normalized and smoothed Jacobian-scaled gray matter images. We additionally evaluated average gray matter density between groups in the two regions of prefrontal cortex and bilateral hippocampus observed by ref. Total hippocampal volume was also extracted from an updated routine for automated segmentation of the hippocampal subfields implemented in FreeSurfer version 6.
Resting-state fMRI data were processed based on a workflow described previously To remove potential scanner instability effects, the first four volumes of each EPI sequence were removed. Brain mask, cerebrospinal fluid CSF mask and white matter WM mask were parcellated using FreeSurfer 87 , 88 , 89 , 90 and transformed into EPI space and eroded by 2 voxels in each direction to reduce partial volume effects.
Realigned timeseries were masked using the brain mask. Differences in global mean intensity between functional sessions were removed by normalizing the mean of all voxels across each run to This was followed by nuisance regression of motion-related artifacts using a GLM with six rigid-body motion registration parameters and outlier scans as regressors.
Principal components of physiological noise were estimated using the CompCor method Timeseries were then denoised using a GLM model with 10 CompCor components as simultaneous nuisance regressors. Note that global signal regression was not performed because this processing step can induce negative correlations in group-level results Although aPFC functional connectivity was the main target of the current investigation, we also performed supplementary seed-based functional connectivity analysis on other regions identified in ref.
Translated ROIs were restricted within the cortical ribbon mask. Full brain connectivity correlation maps were calculated using AFNI Voxelwise independent samples t -tests were performed between groups. Whole-brain analyses were conducted, correcting for multiple comparisons using topological FDR 93 at the cluster level.
Cytoarchitectonic mapping studies have shown that AG can be divided into anterior PGa and posterior PGp subdivisions and IPS can be divided into three distinct subdivisions hlP1 on the posterior lateral bank, hlP2 which is anterior to hIP1, and hlP3 which is posterior and medial to both subdivisions 51 , The subdivisions of AG and IPS have been shown to have distinct structural and functional connectivity patterns We performed a follow-up analysis on the functional clusters identified in our seed based functional connectivity analysis in order to characterize the overlap between these clusters and the anatomical subdivisions of these regions.
MPMs create non-overlapping regions of interest from the inherently overlapping cytoarchitectonic probability maps 94 , The anatomical boundaries of these maps are described in detail in previous publications 51 , 52 , Mean connectivity values from each binarized mask were exacted using the MarsBar toolbox In order to compare whether connectivity within and between established large scale resting-state brain networks showed differences between groups, we extracted timecourses from a set of nodes from a meta-analysis by Power, et al.
For each network, we calculated the mean correlation between all nodes within the network within-network connectivity as well as the mean correlation between all nodes of a given network and all the nodes of each other network between-network connectivity.
We also evaluated the overlap between our seed-based functional connectivity results and a network parcellation of human brain connectivity networks We followed up this network overlap analysis by evaluating the connectivity between all nodes within the frontoparietal control subsystem that showed the largest overlap with the functional connectivity results, based on a node parcellation of the 17 functional networks To construct functional networks for graph-theoretic analysis, anatomical scans were segmented using FreeSurfer and parcellated into regions according to the Lausanne atlas included in the connectome mapping toolkit 37 , Resting-state fMRI data pre-processing was identical to the procedures described above see Resting-state fMRI data processing with the exception that no spatial smoothing was applied, as spatial smoothing can distort network measures derived from average timeseries within parcellated regions e.
All network metrics were computed in Matlab v 9. For each node in the network we analyzed the degree k , strength s , betweenness centrality BC and eigenvector centrality EC. These metrics are described in detail elsewhere see refs 98 , 99 for reviews. In brief, k quantifies the total number of connections of a node, while s quantifies the sum of the weights of all connections to a node.
BC and EC are different measures of centrality of nodes: BC is the fraction of all shortest paths in the network that contain a given node and EC quantifies nodes connected to other densely connected nodes as having high centrality. In order to compare network and topological properties between groups it is important to ensure that graphs contain the same number of edges Following recommended practice 99 , rather than apply a single threshold to graphs, which would limit any findings to a single arbitrary connection density, we thresholded graphs over a range of connection densities 0.
To test the null hypothesis of no difference in AUC between groups, we used a nonparametric bootstrapping procedure in which we randomly reassigned groups with replacement 10, times and computed a bootstrapped t -value for each node. This statistical approach has been used in previous studies and allows for strong control over type I error , The data that support the findings of this study are available from the corresponding author on reasonable request.
LaBerge, S. Lucid dreaming: The power of being awake and aware in your dreams Jeremy P. Tarcher, Kihlstrom, J. Lucid dreaming: Metaconsciousness during paradoxical sleep in Dream research: Contributions to clinical practice ed. Kramer, M. Lucid dreaming verified by volitional communication during REMsleep. Motor Skills 52 , — Erlacher, D. Motor area activation during dreamed hand clenching: A pilot study on EEG alpha band. Sleep Hypnosis 5 , — Google Scholar. Voluntary control of respiration during REM sleep.
Sleep Res. Psychophysiological correlates of the initiation of lucid dreaming. Dresler, M. Dreamed movement elicits activation in the sensorimotor cortex. Sleep 35 , — Smooth tracking of visual targets distinguishes lucid REM sleep dreaming and waking perception from imagination. Saunders, D. Lucid dreaming incidence: A quality effects meta-analysis of 50 years of research.
Article PubMed Google Scholar. Snyder, T. Individual differences associated with lucid dreaming in Conscious mind, sleeping brain eds LaBerge, S. Braun, A. Regional cerebral blood flow throughout the sleep-wake cycle.
Brain , — Maquet, P. Functional neuroanatomy of human rapid-eye-movement sleep and dreaming. Nature , — Nir, Y. Dreaming and the brain: From phenomenology to neurophysiology. Trends Cog. Article Google Scholar. Hobson, J. The cognitive neuroscience of sleep: Neuronal systems, consciousness and learning. Holzinger, B. Psychophysiological correlates of lucid dreaming.
Dreaming 16 , 88—95 Voss, U. Lucid dreaming: A state of consciousness with features of both waking and non-lucid dreaming. Sleep 32 , — Baird, B. Consciousness and meta-consciousness during sleep in Elsevier handbook of sleep research in press. Signal-verfied lucid dreaming proves that REM sleep can support reflective consciousness. Dream Res. McCaig, R. Improved modulation of rostrolateral prefrontal cortex using real-time fMRI training and meta-cognitive awareness. NeuroImage 55 , — Christoff, K.
Evaluating self-generated information: Anterior prefrontal contributions to human cognition. Fleming, S.
Relating introspective accuracy to individual differences in brain structure. Science , — Medial and lateral networks in anterior prefrontal cortex support metacognitive ability for memory and perception. Joseph, R. Frontal lobe psychopathology: Mania, depression, confabulation, catatonia, perseveration, obsessive compulsions, and schizophrenia. Psychiatry 62 , — Schmitz, T. Neural correlates of self-evaluative accuracy after traumatic brain injury.
Neuropsychologia 44 , — Neural correlates of insight in dreaming and psychosis. Sleep Med. Volitional components of consciousness vary across wakefulness, dreaming and lucid dreaming. Spoormaker, V. Lucid and non-lucid dreaming: Thinking in networks. Filevich, E. Metacognitive mechanisms underlying lucid dreaming. Ashburner, J. Voxel-based morphometry—the methods.
NeuroImage 11 , — Power, J. Functional network organization of the human brain. Neuron 72 , — Neider, M. Lucid dreaming and ventromedial versus dorsolateral prefrontal task performance. Stumbrys, T. Meta-awareness during day and night: The relationship between mindfulness and lucid dreaming.
The science community is divided on the subject of dream control. The majority of scientists say that it's not possible. But there are some scientists who argue that there's so much we don't know about the human mind that we can't make any conclusive judgments one way or the other.
Some researchers have suggested that out-of-body experiences OBEs are a type of lucid dream. During an out-of-body experience, a person sees his physical body as if he's located somewhere outside it. A person undergoing an operation might feel as if he's actually floating above his body and looking down on it.
If you'd really like to try to have a lucid dream, there are different suggested approaches. Dream recall is important. You may have heard of people keeping dream journals. As soon as you wake up from a dream, you record as many things as you can remember, even if you've woken up in the middle of the night.
The idea is that by focusing on your dreams each day, you'll get into the habit of remembering them and start to see certain rhythms in how you dream -- once you're attuned to your process of dreaming, you'll become a better observer of your own dreams. When you wake up from a dream, try your best to remember it fully. When you go back to sleep, keep telling yourself that you're going to remember that you're dreaming during your next dream. The next step is to picture yourself back in the dream that you just had and look for a sign that the dream is a dream and not reality, like the fact that you're flying through the air with wings LaBerge calls these dreamsigns.
At this point, remind yourself that you're dreaming and continue the visualization. Keep doing this until you fall asleep. Another method that might help involves napping. You wake up extra early, stay awake for a half hour or so, and then go back to sleep. Something about the interruption of sleep seems to blur the border between being asleep and being awake. Reality testing, or reminding yourself throughout the day that you're conscious, is another approach.
It also has connections to the Buddhist concept of mindfulness. This repeated acknowledgement of the state you're in is supposed to help you explore the other extreme -- the more you realize what consciousness is like and when you're conscious, the more likely you'll be to recognize when you're in a dream state. After all, how do you know you're conscious? Your actions have a logical reaction -- you flip on a light switch and the light turns on. When you flip the switch down, the light turns off.
In dreams, actions don't tend to follow a logical pattern. As far as gadgets go, the most notable might be the NovaDreamer , another Lucidity Institute innovation, which looks like a cross between a sleep mask and goggles. It's supposed to help you with lucid dreaming by letting you know when you're in REM sleep. Sensors track your eye movements and trigger a light that shines on your eyes. When you see the light in your dream, you'll know you're dreaming.
LaBerge has also experimented with the use of galantamine , a drug sometimes used to treat Alzheimer's that's supposed to help the ability to think and remember. Sign up for our Newsletter! Mobile Newsletter banner close. Mobile Newsletter chat close. Mobile Newsletter chat dots. Mobile Newsletter chat avatar. Mobile Newsletter chat subscribe. Life Science. Inside the Mind.
The Human Brain. How Lucid Dreaming Works. If you could fly, where would you go? Image found here. World of Lucid Dreaming. I have never given it a try because it always freaked me out and I almost saw it as dangerous. But it was also clearly stated that you should never have too much of a good thing. Your blog about lucid dream fascinated me. I tried it once and honestly I was completely scared. I tried to train my body into be completely aware that I was in a dream but as I felt a weird sensation in my body I panicked and woke up immediately.
It is amazing the power of the mind and all the mysteries of it and it is completely scientific in regards to popular mystical belief. Sites at Penn State.
0コメント