Is it normal to know your dreaming

It takes advantage of one of our types of memory, called prospective memory — basically our ability to remember to do things in the future. For example, if you need to go to the shops and buy bread, then you might say to yourself, "I need to remember to go to the shops and get some bread".

The MILD technique involves waking up after 5 hours of sleep, forming this prospective memory, and then going back to sleep to enter REM sleep. Instead of trying to remember to buy bread, the intention is to try and remember to realise you're dreaming in your next dream. Recall the last dream you just had, and after a couple of minutes, visualise you're back in that dream, and recite to yourself, "next time I'm dreaming, I want to remember that I'm dreaming" over and over.

By visualising the dream, you tie a visual component to a verbal affirmation, a combination of senses that makes memories stronger. We acknowledge Aboriginal and Torres Strait Islander peoples as the First Australians and Traditional Custodians of the lands where we live, learn, and work.

Laura from Melbourne has been able to lucid dream since she was about seven. Flying, falling, being chased: How our brain creates 'typical dreams'.

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No going back for universities as lectures ditched in favour of online learning Posted 30m ago 30 minutes ago Thu 11 Nov at pm. While it is plausible that the neurophysiological correlates of spontaneous frequent lucid dreaming are the same as frequent lucid dreaming that occurs as a result of training, this has not yet been studied.

Future longitudinal training studies would be valuable in order to evaluate within-subject changes in brain connectivity as a result of training to have lucid dreams and to compare how such changes relate to the functional network associated with frequent lucid dreaming identified here.

No significant differences were observed between groups in working memory capacity, or questionnaire assessments of prospective memory or trait mindfulness. It has been suggested that a sufficient level of working memory is required in order to become lucid during dreaming sleep 2 and thus it might be predicted that frequent lucid dreams could be associated with a higher baseline level of working memory capacity.

Likewise, an effective method of lucid dream induction, the Mnemonic Induction of Lucid Dreams MILD technique 63 , relies on the use of prospective memory to become lucid, and thus it might be predicted that frequent lucid dreams could be associated with increased prospective memory ability. However, spontaneous frequent lucid dreamers neither necessarily need to activate a pre-sleep intention nor use prospective memory to remember to recognize that they are dreaming; instead, their lucid dreams tend to occur spontaneously without engaging in specific methods for inducing them.

Thus, it remains plausible that there could be a relationship between working memory and prospective memory and successful training in lucid dreaming despite a lack of a relationship between these variables and spontaneous frequent lucid dreams. In future work it would be interesting to explore whether individuals with higher baseline scores on these measures show increased propensity in successfully training to have lucid dreams, as well as to quantify the association between potential improvements in these skills and lucid dream frequency as a result of training.

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 — 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 , We thank Stephen LaBerge for helpful discussion.

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Benjamin Baird, Email: ude. Giulio Tononi, Email: ude.

Supplementary information accompanies this paper at National Center for Biotechnology Information , U. Sci Rep. Published online Dec Author information Article notes Copyright and License information Disclaimer. Corresponding author. Received Jun 25; Accepted Nov This article has been cited by other articles in PMC. Abstract Humans typically lack awareness that they are dreaming while dreaming. Introduction For reasons not currently understood, humans are typically unaware that they are dreaming while dreaming.

Results Demographic and behavioral results The mean age for both groups was Table 1 Demographic, behavioral and questionnaire data for the frequent lucid dream group and control group. Open in a separate window. Figure 1. Table 2 Whole-brain seed-based resting-state functional connectivity for left aPFC between groups. Figure 2. Large-scale functional resting-state networks analysis We next tested whether connectivity within and between established LSNs differed between groups. Whole-brain graph-theoretic analysis To evaluate whole-brain differences in network and topological properties, we next parcellated the brain into regions according to the Lausanne atlas 37 , 38 and performed graph-theoretic analysis.

Figure 3. Discussion Summary of main findings To the best of our knowledge, the current study is the first to evaluate differences in brain structure and functional connectivity of individuals who experience lucid dreams with high frequency. Lucid dreaming and brain connectivity Becoming lucid during REM sleep dreaming involves making an accurate metacognitive judgment about the state of consciousness one is in, often by recognizing that the correct explanation for an anomaly in the dream is that one is dreaming 1 , 2.

Limitations, methodological considerations and future directions The measurement of individual differences in lucid dream frequency has been done in inconsistent ways and could be improved in future research. Individual differences in lucid dreaming and dream recall frequency Participants completed a questionnaire that measured their dream recall and lucid dreaming frequency Supplementary Methods: Dream and lucid dream frequency questionnaire.

Behavioral and questionnaire assessment 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. Large-scale networks LSNs analysis 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.

Graph-theoretic network analysis 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 , Electronic supplementary material Supplementary Information 2.

Acknowledgements We thank Stephen LaBerge for helpful discussion. Author Contributions B. Data Availability The data that support the findings of this study are available from the corresponding author on reasonable request.

Notes Competing Interests The authors declare no competing interests. Contributor Information Benjamin Baird, Email: ude. Electronic supplementary material Supplementary information accompanies this paper at References 1.

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. This results in poor sleep control, intense and persistent daytime sleepiness, difficulty sleeping at night, and dream-like hallucinations.

People with narcolepsy tend also to have more nightmares and better dream recall than people without the disorder. Some fascinating recent research conducted by scientists in the United Kingdom has also linked lucid dreaming to sleep paralysis, another striking sleep experience. Sleep paralysis occurs when we wake from sleep unable to move or to speak.

Both sleep paralysis and lucid dreaming appear to be related to transitions in and out of REM sleep. And REM sleep is a sleep stage characterized by vivid, active dreaming. This research showed an association between the frequency of sleep paralysis and the frequency of lucid dreaming. It also highlighted some important differences between the two sleep phenomena. Sleep paralysis was connected to higher stress and to lower sleep quality. On the other hand, lucid dreaming appeared to be a much more positive sleep experience.

In this study, lucid dreaming was not associated with stress or reduced sleep quality. It was linked to more positive waking daydreaming experiences, and to more vivid waking imagination. You might be asking: why would people want to encourage lucid dreaming? But dreams have long been thought to be vehicles for emotional processing, problem solving, idea exploring and creativity. Since ancient times, dreams have been thought to be a forum for both healing and discovery related to our waking lives.

In our modern age, rigorous scientific study has given us data to support all of these long-held thoughts about the usefulness of dreams.

Scientists, sleep experts and therapists including me! Working intentionally with lucid dreams can be effective in reducing the intensity, frequency and emotional disruption of nightmares. In a lucid dream setting the dreamer has the capacity to push back against negative and disturbing dream narratives, emotional content and events.

In a real sense, the dreamer may be able to re-script a cream to create more positive, empowering, calming outcomes. That makes lucid dreaming potentially useful in a range of psychological situations, including the treatment of waking-life phobias and traumas, issues with mood, and in relationships.

Lucid dreams have also long been sought as a way to enhance creativity—and that continues to be true today. People are understandably curious about how to mine their dream worlds to unlock creative powers, as well as to enhance other cognitive skills. The experience alone is enticing and desirable for many people. There are a number of techniques and tools being explored by scientists as ways to increase lucid dreaming.

Studies show that the drug galantamine , which is used to treat dementia, may be effective in increasing the frequency of lucid dreams. Galantamine works to stop the breakdown of acetylcholine, a brain chemical that is an important facilitator of reflective thinking, reasoning, and memory.

Acetylcholine also is involved the the regulation of REM sleep. Research is also exploring how sensory and environmental cues might be used to stimulate lucid dreams. Reality testing. This simple technique involves checking in with your surroundings throughout your waking day. As you observe your waking environment, ask yourself: am I awake or am I dreaming? This practice may spur the mind to ask this question inside your dreaming consciousness.