Highly effective verified lucid dream induction using combined cognitive-sensory training and wearable EEG: a multi-centre study

Jafarzadeh Esfahani, M.; Anna Christina Salvesen, L.; Picard-Deland, C.; Matzek, T.; Demsar, E.; van Buijtene, T.; Libucha, V.; Pedreschi, B.; Bernardi, G.; Zerr, P.; Adelhofer, N.; Schoch, S.; Carr, M.; Dresler, M.

doi:10.1101/2024.06.21.600133

bioRxiv · 10.1101/2024.06.21.600133

Highly effective verified lucid dream induction using combined cognitive-sensory training and wearable EEG: a multi-centre study

Jafarzadeh Esfahani, M.·Anna Christina Salvesen, L.·Picard-Deland, C.·Matzek, T.·Demsar, E.·van Buijtene, T.·Libucha, V.·Pedreschi, B.·Bernardi, G.·Zerr, P.·Adelhofer, N.·Schoch, S.·Carr, M.·Dresler, M.

View original source ↗Export metadata ↓

Abstract

Lucid dreaming occurs when one is aware of dreaming while asleep. While lucid dreaming can occur spontaneously, it remains rare. This multi-center study (Netherlands, Italy, Canada) tested a combined induction approach integrating senses-initiated lucid dreaming (SSILD) with targeted lucidity reactivation in a large sample of participants with varying lucid dreaming experience. Sixty participants (33 F; 26.5 {+/-}6.2 years old) completed two morning naps in the sleep laboratory. Participants received pre-sleep SSILD training paired with visual, auditory, and tactile cues that were then reintroduced in REM sleep, with stimulation and sham conditions counterbalanced. Lucidity and cue perception were verified using intentional eye movements from within the dream (signal-verified lucid dream, SVLD). SVLDs occurred in 31 participants (51.7%) across both naps. Subjective lucidity occurred in 63 naps (52.5%), including 40 SVLDs (33.3%) with no difference between stimulation (38.3%) and sham (28.3%) conditions. However, SVLD duration and number of predefined eye movements were higher with sensory cueing. Cues were perceived within sleep in most cued REM periods (71.1%), sometimes acting as lucidity signals (39.1% of stim SVLDs) and occasionally disrupting sleep (23.7% of cued periods). Overall, our combined cognitive-sensory induction method produced relatively high lucid dreaming rates, even in participants who rarely experience them. While cues were sometimes perceived as lucidity signals and may help prolong lucid episodes, our results do not show clear benefits of REM cueing over SSILD training alone. Further refinement of these methods may support clinical applications and deepen insight into consciousness and sensory processing during sleep.

Explore related subjects

neuroscience Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Jafarzadeh Esfahani, M., Anna Christina Salvesen, L., Picard-Deland, C., Matzek, T., Demsar, E., van Buijtene, T., Libucha, V., Pedreschi, B., Bernardi, G., Zerr, P., Adelhofer, N., Schoch, S., Carr, M., Dresler, M.. 2024-06-27. Highly effective verified lucid dream induction using combined cognitive-sensory training and wearable EEG: a multi-centre study. https://doi.org/10.1101/2024.06.21.600133

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗

bioRxiv2026

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗

bioRxiv2026

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗