But how much of this is true and how much of it is, like dreams themselves, a creation of our brains? Roth adds. Again, the data is lacking and figuring out exactly how lucid dreams occur is tricky, notes Dr. But they occur mostly during REM sleep. Roth noted. But studies have focused on a handful of specific methods that participants suggest work. Just take it all with a grain of salt. Some examples include looking at your reflection in the mirror, pushing against solid objects or even trying to breathe through a pinched nose.
The MILD approach involves leveraging your intention to remember to do something in the future; in this case, remembering your dreaming. Typically combined with the MILD approach, this technique involves sleep disruption. One study found that people who are experiencing a lucid dream exhibit brain activity that seems to be a hybrid of both REM sleep and wakefulness. Other studies have found that certain areas of the prefrontal cortex appear to exhibit increased activity during lucid dreaming compared to standard REM sleep.
The research shows that the anterior prefrontal cortex, a part of the brain associated with higher levels of self-reflection, is larger in people who report having frequent lucid dreams. The researchers suggest that people who are more likely to engage in such self-reflection during normal waking life are also more readily able to take control of their dreams.
There are a few things that you can do to help increase your chances of experiencing a lucid dream. Some strategies that can help include:. There are a number of factors that can play a role in whether or not you experience lucid dreaming. While lucid dreaming may have some mental health benefits, there is some evidence to suggest that it may also have some downside. Some things to remember:. So while there are things that you can do that may help make it more likely that you will spontaneously experience a lucid dream, it is impossible to guarantee that you will be able to induce the experience.
Ever wonder what your personality type means? Sign up to find out more in our Healthy Mind newsletter. Lucid dreaming verified by volitional communication during REM sleep. Percept Mot Skills. LaBerge S. Lucid dreaming in Western literature. In: Gackenbach J. Springer, Boston, MA. Lucid dreaming incidence: A quality effects meta-analysis of 50years of research. Conscious Cogn. Vallat R, Ruby PM. Is it a good idea to cultivate lucid dreaming? Front Psychol. Schredl M, Erlacher D. Frequency of lucid dreaming in a representative German sample.
Harb G. Posttraumatic nightmares and imagery rehearsal: the possible role of lucid dreaming. Applications of lucid dreams: An online study. International Journal of Dream Research. Metacognitive mechanisms underlying lucid dreaming. Frontiers in Neuroscience. Neider, M. Consciousness and Cognition, 20 2 , — Voss, U. Sleep, 32 9 , — Aspy, D.
Frontiers in Psychology. Erlacher, D. Vallat, R. Rak, M. Increased Lucid Dreaming Frequency in Narcolepsy. Sleep, 38 5 , — University of Adelaide. Want to control your dreams?
Science Daily. Mota-Rolim, S. Tai, M. Sleep, 40 1 , A Learn more about Dreams. Dreams By Eric Suni October 30, By Austin Meadows November 11, By Sarah Shoen October 7, By Sarah Shoen July 22, By Danielle Pacheco July 16, By Sarah Shoen July 15, By Danielle Pacheco July 14, Can Blind People Dream? By Tom Ryan June 29, How Do Dreams Affect Sleep?
By Danielle Pacheco October 30, Load More Articles. Furthermore, no differences were observed between frequent lucid dream and control groups in behavioral or questionnaire measures of working memory capacity, prospective memory, mind-wandering frequency or trait mindfulness.
The current results suggest that increased functional integrity during wakefulness between aPFC and temporoparietal association areas—all regions that show suppressed activity in REM sleep and increased activity during lucid REM sleep—is associated with the tendency to have frequent lucid dreams.
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. Given the link to metacognition, it has been speculated that lucid dreaming is linked to neural systems that regulate executive control processes, in particular the frontoparietal control network FPCN 27 , The FPCN is a large-scale brain network that is interconnected with both the default mode network DMN , which is linked to internal aspects of cognition, such as autobiographical memory 43 , 44 , spontaneous thought 45 , 46 , and self-referential processing 47 , and the dorsal attention network DAN , which is involved in visuospatial perceptual attention 48 , Being spatially interposed between these two systems, the FPCN is postulated to integrate information coming from the opposing DMN and DAN systems by switching between competing internally and externally directed processes Based on a parcellation of 17 resting-state networks in the human brain, which distinguished potentially separable FPCN networks 35 , a recent study found that the FPCN could be fractionated using hierarchical clustering and machine learning classification into two distinct subsystems: FPCNa, which is more strongly connected to the DMN than the DAN and is linked to introspective processes, and FPCNb, which is more strongly connected to the DAN than the DMN and is linked to regulation of perceptual attention The current results show that frequent lucid dreams are associated with increased functional connectivity between aPFC and a network of regions that showed substantial overlap with the FPCN sub-network corresponding most closely to FPCNa 35 , However, neither connectivity within FPCN broadly defined through meta-analysis nor connectivity within FPCN sub-networks as defined through parcellation of resting-state networks was significantly associated with frequent lucid dreaming in the current study.
This may be attributed to both the partial overlap of the regions that showed increased aPFC connectivity in lucid dreamers with FPCN networks, as well as the fact that lucid dream frequency was associated with increased connectivity between these regions and aPFC in the left hemisphere, but not to increased connectivity between these regions and right aPFC, or broadly increased connectivity between other regions of FPCN to each other outside of aPFC.
The strongest increase in functional connectivity in the frequent lucid dream group was observed between left aPFC and IPL, which localized to a dorsal segment of the anterior subdivision of the angular gyrus PGa bilaterally, as measured by overlap with cytoachitectonic probability maps. While many neuroimaging studies have treated the regions that comprise IPL as a homogenous region, cytoarchitectonic mapping studies have shown that these regions can be subdivided 51 , 52 , and these subdivisions show distinct patterns of structural and functional connectivity Specifically, PGa shows increased functional connectivity with the caudate, anterior cingulate, and bilateral frontal poles compared to PGp, whereas PGp shows increased connectivity with regions of the DMN, including precuneus, medial prefrontal cortex and parahippocampal and hippocampal gyri Cognitive or clinical correlates of altered functional connectivity between the frontal pole and this specific subdivision of AG PGa have to our knowledge not yet been identified, since much of the cognitive neuroscience literature on this region lacks anatomical specificity.
However, a meta-analysis of neuroimaging studies of language and semantic processes found that the left AG had the densest concentration of activation foci across studies, with a significant clustering of activation foci also in MTG The authors also note that these regions are greatly expanded in humans compared to non-human primates, suggesting a role in the development of language. Moreover, PGa is more closely linked to the semantic system that PGp, and analysis of the connectivity and cognitive functions associated with this region suggests that it is positioned at the top of a processing hierarchy for concept retrieval and conceptual integration Specifically, non-lucid dreams exhibit reduced working memory function, reduced ability to engage in behavioral control and planning, and reduced reflective consciousness 55 , 56 , The distinction between primary and higher-order consciousness is thought to depend on the linguistic abilities that separate humans from other species While language processes also occur during non-lucid dreams 60 , 61 , they are nevertheless linked to the remembered present and apparently lack the conceptual structure that allows for full self-awareness.
The measurement of individual differences in lucid dream frequency has been done in inconsistent ways and could be improved in future research. Indeed, lucid dreams can range from a realization about the fact that one is dreaming followed by a loss of lucidity shortly thereafter to more extended lucid dreams in which an individual can maintain lucidity for prolonged periods of time Likewise, lucid dreams can be characterized by varying degrees of clarity of thought.
Evaluating the duration of lucid dreams as well as the degree of awareness during lucid dreams will be valuable to relating brain structural and functional measures to lucid dream frequency in future studies. An extended discussion of this issue is beyond the scope of the present article; however, overall these remarks emphasize the need for the development of standardized measures that can be used to assess individual differences in frequency of lucid dreams that simultaneously measure the duration and degree of lucidity during dreams.
Another limitation of the current study is that our measurement of lucid dream frequency relied on questionnaire responses and participant interviews. There are established methods for the objective validation of individual lucid dreams in a sleep laboratory setting using volitional eye-movement signals 4 , but there are no protocols for physiologically validating the frequency of lucid dreams.
While questionnaire measures of lucid dream frequency have shown high test-retest reliability 64 , one way to further validate participant questionnaire responses would be to attempt to physiologically validate at least one lucid dream in the sleep laboratory for each participant. We think that additional validations such as this would potentially be valuable to incorporate in future studies.
Nevertheless, it is important to note that the estimated frequency of lucid dreaming would still depend on questionnaire assessment. Thus, approaches such as this do not obviate the reliance on questionnaire assessment as used in the current study. An intriguing, though ambitious, method for deriving a measure of lucid dream frequency not dependent on questionnaire assessment would be to utilize home-based EEG recording systems to collect longitudinal sleep polysomnography data, from which estimates of lucid dreaming frequency could be derived from the frequency of signal-verified lucid dreams collected over many nights.
However, this approach would only measure the frequency of signal-verified lucid dreams, and instances in which participants achieved lucidity but did not make the eye signal due to factors such as awakening or forgetting the intention would be missed by this procedure. In contrast to the observed differences in functional connectivity described above, in the current study we did not observe any significant differences in brain structure gray matter density between groups.
As noted in the introduction, a limitation of that study is that the high-lucidity group was not a sample of frequent lucid dreamers, but rather individuals from a database that scored above the group median on a composite measure of dreaming, which measured not only frequency of lucid dreams but also different dimensions of dream content.
However, the fact that the study found that these aPFC regions also showed increased BOLD activity during the monitoring component of a thought-monitoring task lends additional plausibility to the results. It is important to note that issues of statistical power could also account for the discrepant findings of these two studies. Unfortunately, no statistics or estimates of effect size have been reported for this effect and as a result we were unable to perform a power analysis to determine the adequate sample size for testing this effect.
However, a single study that fails to reject the null hypothesis does not provide good evidence for the absence of an effect, especially with relatively small sample sizes. Overall, therefore, more research addressing this question using larger sample sizes will be needed before firm conclusions can be drawn. Here we studied individuals who reported spontaneous lucid dreaming with high frequency without engaging in training to have lucid dreams.
In our questionnaire samples, the proportion of individuals who reported spontaneous lucid dreams on close to a nightly basis constituted approximately 1 in 1, respondents.
While frequent spontaneous lucid dreams are uncommon, evidence indicates that lucid dreaming is a learnable skill that can be developed by training in strategies such as metacognitive monitoring i. 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 , 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.
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