Unraveling the neural correlates of dream recall: Novel insights from deep convolutional nets
Arna Ghosh, Arthur Dehgan, Tarek Lajnef, Raphael Vallat, Jean-Baptiste Eichenlaub, Perrine Ruby, Karim Jerbi
Study at a glance
AI-extracted from the abstract| Characteristics | Observational cohort |
|---|---|
| Sample size | 36 |
| Population | Adults with high or low dream recall rates |
| Duration | Full-night sleep recording |
| Topics | Dreaming |
| Key findings | Deep convolutional networks trained on sleep EEG can classify individuals into high or low dream recall groups, with accuracy varying by sleep stage. |
Abstract
Dreams and our ability to recall them are among the most puzzling questions in sleep research. Specifically, putative differences in brain network dynamics between individuals with high versus low dream recall rates, are still poorly understood. In this study, we addressed this question as a classification problem where we applied deep convolutional networks (CNN) to sleep EEG recordings to predict whether subjects belonged to the high or low dream recall group (HDR and LDR resp.). More specifically, EEG was recorded during full-night sleep from 36 subjects (18 HDR and 18 LDR). We used a deep learning framework to discover features from the EEG data that are different between the two groups (as opposed to prior knowledge-driven hand-crafted feature analysis). For each sleep stage, we trained a separate CNN to classify EEG segments into HDR and LDR group. The classification accuracies indicated which sleep stages contained the best neural predictors of dream recall rate. While training the CNN, we added a subject discriminator network and used it as adversary to restrict the CNN from learning subject-specific features and instead learn features that are subject-agnostic but allow discrimination between HDR and LDR groups. These features were subsequently visualized using a method known as cue-combination for Class Activation Map (ccCAM), inspired by [1]. These findings are compared to results obtained in previous studies [2,3] and future research directions are discussed.
Comparable studies
Other observational and cohort studies on dreaming, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| Lucid Dreaming: a State of Consciousness with Features of Both Waking and Non-Lucid Dreaming Student volunteers trained to become lucid and signal lucidity with horizontal eye... | 2009 | Observational study | n = 6 |
| Nightmare Prevalence, Nightmare Distress, and Self-Reported Psychological Disturbance Young adults with a mean age of 20 years | 2002 | Prospective observational study | n = 116 |
| Long-term effects of extreme situational stress on sleep and dreaming Survivors of traumatic events who complained of sleep disturbances, compared with... | 1987 | Case-control study | n = 11 |
| The relationship of nightmare frequency and nightmare distress to well‐being Adults who reported dreams | 2004 | Observational cohort | n = 147 |
| The prevalence of nightmares and their independence from anxiety. Undergraduates | 1990 | Observational study using daily dream logs | n = 220 |