105 Participants Needed

Sleep for Enhanced Learning

AC
RK
Overseen ByRishi Krishnamurthy, BA
No Placebo GroupAll trial participants will receive the active study treatment (no placebo)

What You Need to Know Before You Apply

What is the purpose of this trial?

Acting adaptively requires quickly picking up on structure in the environment and storing the acquired knowledge for effective future use. Dominant theories of the hippocampus have focused on its ability to encode individual snapshots of experience, but the investigators and others have found evidence that it is also crucial for finding structure across experiences. The mechanisms of this essential form of learning have not been established. The investigators have developed a neural network model of the hippocampus instantiating the theory that one of its subfields can quickly encode structure using distributed representations, a powerful form of representation in which populations of neurons become responsive to multiple related features of the environment.The first aim of this project is to test predictions of this model using high resolution functional magnetic resonance imaging (fMRI) in paradigms requiring integration of information across experiences. The results will clarify fundamental mechanisms of how humans learn novel structure, adjudicating between existing models of this process, and informing further model development. There are also competing theories as to the eventual fate of new hippocampal representations. One view posits that during sleep, the hippocampus replays recent information to build longer-term distributed representations in neocortex. Another view claims that memories are directly and independently formed and consolidated within the hippocampus and neocortex.The second aim of this project is to test between these theories. The investigators will assess changes in hippocampal and cortical representations over time by re-scanning participants and tracking changes in memory at a one-week delay. Any observed changes in the brain and behavior across time, however, may be due to generic effects of time or to active processing during sleep.The third aim is thus to assess the specific causal contributions of sleep to the consolidation of structured information. The investigators will use real-time sleep electroencephalography to play sound cues to bias memory reactivation. The investigators expect that this work will clarify the anatomical substrates and, critically, the nature of the representations that support encoding and consolidation of novel structure in the environment.

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.

How does the Sleep for Enhanced Learning treatment differ from other treatments for learning enhancement?

This treatment is unique because it leverages sleep to enhance learning by reinforcing memory consolidation and forming new associations, unlike other methods that focus on active learning during wakefulness. Sleep's recurring brain oscillations help process information hierarchically, improving generalization and adaptive behavior.12345

What data supports the effectiveness of the treatment Associative inference, Category learning, Sleep for enhanced learning?

Research shows that sleep helps consolidate memories and improve learning by replaying experiences and generating new insights. Studies indicate that sleep can enhance the ability to learn new vocabulary and improve memory performance, suggesting that sleep is crucial for associative learning and category formation.46789

Who Is on the Research Team?

AC

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.
See 3 more

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?
3Treatment groups
Experimental Treatment
Group I: Manipulating replay during sleep using real-time EEGExperimental Treatment2 Interventions
Group II: Learning and consolidation in category learningExperimental Treatment1 Intervention
Group III: Learning and consolidation in Associative InferenceExperimental Treatment1 Intervention

Find a Clinic Near You

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

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.
The "wake-sleep" algorithm for unsupervised neural networks.Hinton, GE., Dayan, P., Frey, BJ., et al.[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.
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.
Learning New Vocabulary Implicitly During Sleep Transfers With Cross-Modal Generalization Into Wakefulness.Koroma, M., Elbaz, M., Lรฉger, D., et al.[2023]

Citations

The "wake-sleep" algorithm for unsupervised neural networks. [2019]
Learning cortical representations through perturbed and adversarial dreaming. [2022]
Learning New Vocabulary Implicitly During Sleep Transfers With Cross-Modal Generalization Into Wakefulness. [2023]
Thalamo-cortical spiking model of incremental learning combining perception, context and NREM-sleep. [2021]
Oscillations, neural computations and learning during wake and sleep. [2018]
Neural Dynamics of Associative Learning during Human Sleep. [2021]
Dreaming neural networks: Forgetting spurious memories and reinforcing pure ones. [2019]
Factor analysis using delta-rule wake-sleep learning. [2019]
Sleep enhances category learning. [2022]
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