Sleep for Enhanced Learning

Overseen By Rishi Krishnamurthy, BA

Age: 18 - 65

Sex: Any

Trial Phase: Academic

Sponsor: University of Pennsylvania

Must not be taking: Antidepressants, Sedatives

Disqualifiers: MR contraindications, Claustrophobia, Pregnancy, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: 3

105 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial explores how sleep can enhance learning and memory by examining the brain's ability to connect and store new information. Researchers focus on the hippocampus, a brain region crucial for memory, to understand how it helps recognize patterns and categories in learning. They test various learning methods, such as associative inference and category learning, and investigate how sleep might reinforce these memories using brain imaging and monitoring brain activity during naps. Individuals with normal vision and hearing, fluent in English, and without major psychiatric or neurological disorders may be suitable for this trial. As an unphased trial, this study offers a unique opportunity to contribute to groundbreaking research on sleep and memory.

Will I have to stop taking my current medications?

If you are currently taking antidepressants or sedatives, you will need to stop taking them to participate in this trial.

Is there any evidence suggesting that this trial's treatments are likely to be safe?

Research shows that the treatments tested in the Sleep for Enhanced Learning trial are generally well-tolerated. For the study's associative inference and category learning sections, researchers use a brain scan called functional magnetic resonance imaging (fMRI). Studies have found that fMRI is safe, commonly used in research, and carries a very low risk of serious side effects.

For the sleep portion of the study, researchers use electroencephalography (EEG) to record brain activity. Research has shown that EEG is safe and frequently used in sleep studies. EEG devices, including wearable ones, have been used successfully without major problems. Overall, based on current and past studies, the procedures in this trial have proven safe for participants. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the Sleep for Enhanced Learning trial because it explores innovative ways to boost memory and learning through sleep and brain activity manipulation, which are not typical in current educational or cognitive enhancement methods. Unlike traditional learning techniques that focus solely on active study or repetition, this trial investigates how the brain consolidates information during sleep using real-time EEG to enhance memory retention. By presenting object names during optimal sleep phases, the trial aims to strengthen memory recall, potentially offering a new, non-invasive approach to learning. This method could revolutionize how we understand and improve learning processes, offering benefits beyond what conventional study techniques provide.

What evidence suggests that this trial's treatments could be effective for learning and memory consolidation?

This trial will explore different aspects of learning and memory. In one arm, participants will engage in associative inference tasks, learning about pairs of objects and inferring their relationships. This process aids in making simple guesses and generalizations, which are vital for learning new things.

Another arm will focus on category learning, where participants will learn to recognize new groups of objects. Research has found that the brain quickly adjusts to new categories, involving changes in brain activity as different things are distinguished. This enhances the ability to recognize and categorize new objects.

The third arm will investigate the role of sleep in memory consolidation. Studies have suggested that sleep plays a crucial role in strengthening memories, turning detailed memories into more general knowledge, and facilitating later recall and use of information. This process is especially active during REM (rapid eye movement) and deep sleep stages. Participants in this arm will have their brain activity monitored during a nap to assess how sleep impacts memory for learned objects. ^{6 7 8 9 10}

Who Is on the Research Team?

Anna C Schapiro, PhD

Principal Investigator

University of Pennsylvania

Are You a Good Fit for This Trial?

This trial is for healthy adults aged 18-35 with normal or corrected vision, normal hearing, fluent English skills, and no history of major psychiatric/neurological disorders. It excludes vulnerable populations, those on antidepressants/sedatives (for MRI), anyone with neurological disorders (for EEG), MR contraindications like metal implants, claustrophobia (MRI-specific), and pregnant women.

Inclusion Criteria

I am not currently taking any antidepressants or sedatives.

Not a member of a vulnerable population

My vision is normal or corrected to normal.

Exclusion Criteria

Claustrophobia (Aims 1 and 2; MRI-specific)

Individuals with MR contraindications such as non-removable biomedical devices or metal in or on the body (Aims 1 and 2; MRI-specific)

Pregnant women will also be excluded from neuroimaging, as the effects of MR on pregnancy are not fully understood (Aims 1 and 2; MRI-specific)

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

1-2 weeks

Initial Learning and fMRI Assessment

Participants undergo fMRI to assess neural representations during learning tasks involving object associations and category learning.

2-3 hours

1 visit (in-person)

Sleep EEG and Memory Reactivation

Participants' memory is tested before and after a nap, with EEG monitoring and auditory cues to encourage memory reactivation.

4-5 hours

1 visit (in-person)

Follow-up fMRI Assessment

Participants return for a second fMRI scan to evaluate changes in neural substrates and memory consolidation after one week.

1-2 hours

1 visit (in-person)

Follow-up

Participants are monitored for changes in memory and neural representations over time.

1 week

What Are the Treatments Tested in This Trial?

Interventions

• Associative inference
• Category learning
• Sleep

Trial Overview

The study investigates how humans learn structured information over time and the role of sleep in this process. Using high-resolution fMRI to track brain activity and real-time sleep EEG to influence memory reactivation, it aims to understand hippocampal functions in learning and memory consolidation.

How Is the Trial Designed?

Group I: Manipulating replay during sleep using real-time EEG Experimental Treatment 2 Interventions

In the proposed electroencephalography (EEG) study, all participants will undergo the same procedure. Participants will learn the visual features and spoken names associated with three categories of novel objects. Participants' memory for these objects and the objects' parts will be tested before and after a nap. The investigators will monitor brain activity during the nap in real time and, at optimal moments, quietly play the spoken names of the objects to encourage reactivation of particular objects in particular orders. The investigators will assess how this manipulation impacts memory for these objects.

Group II: Learning and consolidation in category learning Experimental Treatment 1 Intervention

The proposed functional magnetic resonance imaging study assesses the neural representations contributing to humans' ability to learn new categories of objects. All participants will undergo the same procedure. Participants will learn about novel objects, each with several colored parts. Some parts are unique to individual objects and others are shared among the members of the category. The investigators will assess how different regions of the brain contribute to learning and remembering these different kinds of parts, and how the resulting representations support category understanding. Participants will be brought back one week later for a second scan, to evaluate how the neural substrates of these processes change with consolidation.

Group III: Learning and consolidation in Associative Inference Experimental Treatment 1 Intervention

The proposed functional magnetic resonance imaging study assesses the neural representations contributing to humans' ability to associate objects in the support of simple inferences and generalization. All participants will undergo the same procedure. Participants will learn about pairs of objects and then be asked to make judgments and inferences about the relationships between the objects. The order of presentation of the objects will be manipulated within subjects, as different learning theories make different predictions about how learning will unfold under different orderings. Participants will be brought back one week later for a second scan, to evaluate how the neural substrates of these processes change with consolidation.

Who Is Running the Clinical Trial?

University of Pennsylvania

Lead Sponsor

Trials

2,118

Recruited

45,270,000+

National Institute of Mental Health (NIMH)

Collaborator

Trials

3,007

Recruited

2,852,000+

Published Research Related to This Trial

Healthy adults trained on a probabilistic category-learning task showed improved performance after a night of sleep, indicating that sleep enhances learning.

The study compared two training modes (feedback and observational) but found that both types benefited from sleep, suggesting a universal mechanism for sleep-related learning enhancement.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Sleep enhances category learning. Djonlagic, I., Rosenfeld, A., Shohamy, D., et al.[2022]

The study presents a linear network model for factor analysis that uses hidden variables to understand correlations between visible variables, which can be learned through a simple wake-sleep learning method.

This approach offers a straightforward alternative to Hebbian learning for modeling how the brain adapts and changes based on activity, suggesting it could effectively represent cortical plasticity.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Factor analysis using delta-rule wake-sleep learning. Neal, RM., Dayan, P.[2019]

Sleep plays a crucial role in enhancing learning and memory by facilitating the reactivation of neural patterns in the hippocampus, which is essential for long-term memory consolidation.

The study suggests that sleep's nested oscillations support hierarchical processing of information, allowing the brain to better infer causal relationships and improve predictive abilities, ultimately leading to more adaptive behaviors.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Oscillations, neural computations and learning during wake and sleep. Penagos, H., Varela, C., Wilson, MA.[2018]

Humans can learn new information during sleep, as shown by brain activity changes during auditory-olfactory associative learning.

Different brain wave patterns are involved at various stages of sleep-learning: delta and sigma waves are linked to the early formation of new associations, while theta waves are associated with later stages of the learning process.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Neural Dynamics of Associative Learning during Human Sleep. Canales-Johnson, A., Merlo, E., Bekinschtein, TA., et al.[2021]

The study presents an unsupervised learning algorithm for a multilayer network of stochastic neurons that uses both bottom-up recognition and top-down generative connections to process and reconstruct data representations.

The algorithm operates in two phases: a 'wake' phase where recognition connections help interpret input data, and a 'sleep' phase where generative connections refine the model, enhancing its ability to accurately reconstruct data across layers.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The "wake-sleep" algorithm for unsupervised neural networks. Hinton, GE., Dayan, P., Frey, BJ., et al.[2019]

The study developed a thalamo-cortical model that mimics brain states of wakefulness and deep sleep, demonstrating the brain's ability to learn quickly from limited and noisy examples.

The model showed that sleep enhances visual classification abilities by promoting synaptic homeostasis and helping to associate similar memories, highlighting the importance of sleep in cognitive functions.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Thalamo-cortical spiking model of incremental learning combining perception, context and NREM-sleep. Golosio, B., De Luca, C., Capone, C., et al.[2021]

The proposed model extends the standard Hopfield network by incorporating both on-line learning (for storing new information) and off-line unlearning (for removing errors), achieving the theoretical maximum storage capacity of α=1, which is a significant improvement over the traditional model's capacity of α∼0.14.

The model demonstrates robustness against thermal noise and provides explicit guidelines for synaptic plasticity and the necessary 'sleep rate' to ensure effective learning and pattern reinforcement, validated through extensive numerical simulations.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Dreaming neural networks: Forgetting spurious memories and reinforcing pure ones. Fachechi, A., Agliari, E., Barra, A.[2019]

The study proposes that different sleep states, particularly REM and NREM, play distinct roles in learning: REM sleep facilitates the generation of new sensory inputs, enhancing the extraction of semantic concepts, while NREM sleep focuses on replaying memories to strengthen learned representations.

Using a model inspired by generative adversarial networks (GANs), the research demonstrates that these offline states optimize learning processes, suggesting a novel computational framework for understanding how sleep contributes to memory and concept formation.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Learning cortical representations through perturbed and adversarial dreaming. Deperrois, N., Petrovici, MA., Senn, W., et al.[2022]

Participants who heard unknown Japanese words during sleep were able to identify images related to those words after waking, demonstrating that new associative memories can form during sleep (N = 22).

Electroencephalography revealed that specific brain responses during sleep predicted how well participants would remember the information upon waking, indicating that slow-wave activity plays a crucial role in memory formation.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Learning New Vocabulary Implicitly During Sleep Transfers With Cross-Modal Generalization Into Wakefulness. Koroma, M., Elbaz, M., Léger, D., et al.[2023]

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