LearningNCT05910762
Human Learning of New Structured Information Across Time and Sleep
A study testing Associative inference, Category learning and Sleep in people with Learning and Humans, run by University of Pennsylvania at 1 site in United States.
Trial summary
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… Read more about this study
- Who is it for?
- Ages 18 to 35 yearsHealthy volunteers can take part
- Conditions the study is for
- Learning, Humans, Consolidation
- What is being tested?
- Associative inference, Category learning and Sleep
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 119 participants
- Where
- 1 siteUnited States
- Study dates
- 5 Jun 2023to 31 Jul 2026
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 35 years
- All sexes
- Healthy volunteers accepted
- Adult
Who may be able to join
- Between 18 and 35 years of age (all aims)
- Not a member of a vulnerable population (all aims)
- Normal or corrected-to-normal vision (all aims)
- Normal hearing (all aims)
- Able to speak English fluently (all aims)
- No prior history of major psychiatric or neurological disorders (Aims 1 and 2; MRI-specific)
- Not currently taking any antidepressants or sedatives (Aims 1 and 2; MRI-specific)
- No known neurological disorders (Aim 3; EEG-specific)
Who cannot join
- The investigators will exclude individuals with MR contraindications such as non-removable biomedical devices or metal in or on the body (Aims 1 and 2; MRI-specific)
- Claustrophobia (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)
Trial locations and contacts
1 site in 1 country.
United States1 site
University of Pennsylvania
Philadelphia, Pennsylvania, 19104
About this study
Official title: Learning Novel Structure Across Time and Sleep
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.
Read more
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.
- Conditions
- Learning, Humans, Consolidation, Sleep
- Being tested
- Associative inference, Category learning, Sleep
- Keywords
- memory, sleep, learning
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Associative inferenceBehavioural
Participants will engage in an associative inference paradigm. Memory will be assessed behaviorally and neural representations will be assessed using functional magnetic resonance imaging.
Category learningBehavioural
Participants will engage in a category learning paradigm. Memory will be assessed behaviorally (Arms 2 and 3), and neural representations will be assessed using functional magnetic resonance imaging (Arm 2).
SleepBehavioural
Participants will sleep after engaging in a category learning paradigm while electroencephalography data are collected, and memory will be assessed behaviorally after sleep.
| Study group | Role | What they receive |
|---|---|---|
| Learning and consolidation in Associative Inference | Experimental | 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. Receives: Behavioral: Associative inference |
| Learning and consolidation in category learning | Experimental | 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. Receives: Behavioral: Category learning |
| Manipulating replay during sleep using real-time EEG | Experimental | 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. Receives: Behavioral: Category learning; Behavioral: Sleep |
What is being measured
The primary outcome is the main question the study is designed to answer.
Changes in multivariate representations
Changes in spatial correlations between the MRI BOLD pattern associated with related objects over the course of learning and across the one-week delay.
Measured: Within first session (spanning 2-3 hrs.) and at approximately one week delay in second session (spanning 1-2 hrs.)
Brain-behavior correlations
Correlations between BOLD signal in the brain and participant behavior during judgments about objects.
Measured: Within first session (spanning 2-3 hrs.) and at approximately one week delay in second session (spanning 1-2 hrs.)
Correlations between activity across brain regions
Relationships between BOLD activity across different regions of the brain as a function of trial type and delay.
Measured: Within first session (spanning 2-3 hrs.) and at approximately one week delay in second session (spanning 1-2 hrs.)
Memory accuracy
Change in generalization ability from before to after the nap as a function of the different conditions of object cueing during sleep.
Measured: Within single study session (spanning 4-5 hrs.)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Jun 2023
- Study start5 Jun 2023
- Primary completion31 Jul 2026
- Study completion31 Jul 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (56)Papers the sponsor has linked to this study.
- Other trials for LearningStudies of the same condition, open and under way.
- More trials run by University of PennsylvaniaThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Basic science
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 119 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Pennsylvania
- Sponsor type
- University, hospital or other
- Collaborators
- National Institute of Mental Health (NIMH)
- Responsible party
- Sponsor
- Investigators
- Anna C Schapiro, PhD, Principal investigator, University of Pennsylvania
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05910762
- Sponsor's protocol ID
- 833228B
- Other IDs
- R01MH129436
- Status last verified
- Aug 2026
- First submitted
- 10 May 2023
- Last updated
- 20 Aug 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: All IPD that underlie results in a publication.
Publications
Schapiro AC, Kustner LV, Turk-Browne NB. Shaping of object representations in the human medial temporal lobe based on temporal regularities. Curr Biol. 2012 Sep 11;22(17):1622-7. doi: 10.1016/j.cub.2012.06.056. Epub 2012 Aug 9.
PubMed 22885059Schapiro AC, Turk-Browne NB, Norman KA, Botvinick MM. Statistical learning of temporal community structure in the hippocampus. Hippocampus. 2016 Jan;26(1):3-8. doi: 10.1002/hipo.22523. Epub 2015 Oct 13.
PubMed 26332666Schapiro AC, Gregory E, Landau B, McCloskey M, Turk-Browne NB. The necessity of the medial temporal lobe for statistical learning. J Cogn Neurosci. 2014 Aug;26(8):1736-47. doi: 10.1162/jocn_a_00578. Epub 2014 Jan 23.
PubMed 24456393Covington NV, Brown-Schmidt S, Duff MC. The Necessity of the Hippocampus for Statistical Learning. J Cogn Neurosci. 2018 May;30(5):680-697. doi: 10.1162/jocn_a_01228. Epub 2018 Jan 8.
PubMed 29308986Schlichting ML, Preston AR. Memory integration: neural mechanisms and implications for behavior. Curr Opin Behav Sci. 2015 Feb;1:1-8. doi: 10.1016/j.cobeha.2014.07.005.
PubMed 25750931Hinton, GE. Distributed representations. Technical Report CMU-CS-84-157. 1984.
McClelland JL, McNaughton BL, O'Reilly RC. Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychol Rev. 1995 Jul;102(3):419-457. doi: 10.1037/0033-295X.102.3.419.
PubMed 7624455Schapiro AC, Turk-Browne NB, Botvinick MM, Norman KA. Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning. Philos Trans R Soc Lond B Biol Sci. 2017 Jan 5;372(1711):20160049. doi: 10.1098/rstb.2016.0049.
PubMed 27872368Norman KA, O'Reilly RC. Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach. Psychol Rev. 2003 Oct;110(4):611-46. doi: 10.1037/0033-295X.110.4.611.
PubMed 14599236Zhou Z, Singh D, Tandoc MC, Schapiro AC. Building integrated representations through interleaved learning. J Exp Psychol Gen. 2023 Sep;152(9):2666-2684. doi: 10.1037/xge0001415. Epub 2023 May 25.
PubMed 37227843Kumaran D, McClelland JL. Generalization through the recurrent interaction of episodic memories: a model of the hippocampal system. Psychol Rev. 2012 Jul;119(3):573-616. doi: 10.1037/a0028681.
PubMed 22775499Yonelinas AP, Ranganath C, Ekstrom AD, Wiltgen BJ. A contextual binding theory of episodic memory: systems consolidation reconsidered. Nat Rev Neurosci. 2019 Jun;20(6):364-375. doi: 10.1038/s41583-019-0150-4.
PubMed 30872808Schapiro AC, McDevitt EA, Chen L, Norman KA, Mednick SC, Rogers TT. Sleep Benefits Memory for Semantic Category Structure While Preserving Exemplar-Specific Information. Sci Rep. 2017 Nov 1;7(1):14869. doi: 10.1038/s41598-017-12884-5.
PubMed 29093451Schapiro AC, McDevitt EA, Rogers TT, Mednick SC, Norman KA. Human hippocampal replay during rest prioritizes weakly learned information and predicts memory performance. Nat Commun. 2018 Sep 25;9(1):3920. doi: 10.1038/s41467-018-06213-1.
PubMed 30254219Singh D, Norman KA, Schapiro AC. A model of autonomous interactions between hippocampus and neocortex driving sleep-dependent memory consolidation. Proc Natl Acad Sci U S A. 2022 Nov;119(44):e2123432119. doi: 10.1073/pnas.2123432119. Epub 2022 Oct 24.
PubMed 36279437
Study at a glance
From the registry record, last updated by the sponsor on 20 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Learning Novel Structure Across Time and Sleep
- ClinicalTrials.gov ID
- NCT05910762
- Acronym / sponsor ID
- 833228B
- Conditions
- Learning, Humans, Consolidation, Sleep
- Keywords
- memory, sleep, learning
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 119 participants (actual)
- Lead sponsor
- University of PennsylvaniaUniversity, hospital or other
- Collaborators
- National Institute of Mental Health (NIMH)
- Study start
- 5 Jun 2023 (actual)
- Primary completion
- 31 Jul 2026 (actual)
- Study completion
- 31 Jul 2026 (actual)
- First posted
- 20 Jun 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Basic science
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (participant)
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Learning and consolidation in Associative Inference | Experimental | Behavioral: Associative inferenceThe 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. |
| Learning and consolidation in category learning | Experimental | Behavioral: Category learningThe 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. |
| Manipulating replay during sleep using real-time EEG | Experimental | Behavioral: Category learning, Behavioral: SleepIn 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. |
Who may qualify
- Ages
- 18 to 35 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult
Key inclusion criteria (8 listed)
- Between 18 and 35 years of age (all aims)
- Not a member of a vulnerable population (all aims)
- Normal or corrected-to-normal vision (all aims)
- Normal hearing (all aims)
- Able to speak English fluently (all aims)
Key exclusion criteria (3 listed)
- The investigators will exclude individuals with MR contraindications such as non-removable biomedical devices or metal in or on the body (Aims 1 and 2; MRI-specific)
- Claustrophobia (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)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Associative inferenceParticipants will engage in an associative inference paradigm. Memory will be assessed behaviorally and neural representations will be assessed using functional magnetic resonance imaging. | Behavioural | Learning and consolidation in Associative Inference |
| Category learningParticipants will engage in a category learning paradigm. Memory will be assessed behaviorally (Arms 2 and 3), and neural representations will be assessed using functional magnetic resonance imaging (Arm 2). | Behavioural | Learning and consolidation in category learning, Manipulating replay during sleep using real-time EEG |
| SleepParticipants will sleep after engaging in a category learning paradigm while electroencephalography data are collected, and memory will be assessed behaviorally after sleep. | Behavioural | Manipulating replay during sleep using real-time EEG |
Primary outcome
Changes in multivariate representations
Time frame: Within first session (spanning 2-3 hrs.) and at approximately one week delay in second session (spanning 1-2 hrs.)
Changes in spatial correlations between the MRI BOLD pattern associated with related objects over the course of learning and across the one-week delay.
Brain-behavior correlations
Time frame: Within first session (spanning 2-3 hrs.) and at approximately one week delay in second session (spanning 1-2 hrs.)
Correlations between BOLD signal in the brain and participant behavior during judgments about objects.
Correlations between activity across brain regions
Time frame: Within first session (spanning 2-3 hrs.) and at approximately one week delay in second session (spanning 1-2 hrs.)
Relationships between BOLD activity across different regions of the brain as a function of trial type and delay.
Memory accuracy
Time frame: Within single study session (spanning 4-5 hrs.)
Change in generalization ability from before to after the nap as a function of the different conditions of object cueing during sleep.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Aug 2026.
- Background: PubMed 22885059Schapiro AC, Kustner LV, Turk-Browne NB. Shaping of object representations in the human medial temporal lobe based on temporal regularities. Curr Biol. 2012 Sep 11;22(17):1622-7. doi: 10.1016/j.cub.2012.06.056. Epub 2012 Aug 9.
- Background: PubMed 26332666Schapiro AC, Turk-Browne NB, Norman KA, Botvinick MM. Statistical learning of temporal community structure in the hippocampus. Hippocampus. 2016 Jan;26(1):3-8. doi: 10.1002/hipo.22523. Epub 2015 Oct 13.
- Background: PubMed 24456393Schapiro AC, Gregory E, Landau B, McCloskey M, Turk-Browne NB. The necessity of the medial temporal lobe for statistical learning. J Cogn Neurosci. 2014 Aug;26(8):1736-47. doi: 10.1162/jocn_a_00578. Epub 2014 Jan 23.
- Background: PubMed 29308986Covington NV, Brown-Schmidt S, Duff MC. The Necessity of the Hippocampus for Statistical Learning. J Cogn Neurosci. 2018 May;30(5):680-697. doi: 10.1162/jocn_a_01228. Epub 2018 Jan 8.
- Background: PubMed 25750931Schlichting ML, Preston AR. Memory integration: neural mechanisms and implications for behavior. Curr Opin Behav Sci. 2015 Feb;1:1-8. doi: 10.1016/j.cobeha.2014.07.005.
- BackgroundHinton, GE. Distributed representations. Technical Report CMU-CS-84-157. 1984.
- Background: PubMed 7624455McClelland JL, McNaughton BL, O'Reilly RC. Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychol Rev. 1995 Jul;102(3):419-457. doi: 10.1037/0033-295X.102.3.419.
- Background: PubMed 27872368Schapiro AC, Turk-Browne NB, Botvinick MM, Norman KA. Complementary learning systems within the hippocampus: a neural network modelling approach to reconciling episodic memory with statistical learning. Philos Trans R Soc Lond B Biol Sci. 2017 Jan 5;372(1711):20160049. doi: 10.1098/rstb.2016.0049.
- Background: PubMed 14599236Norman KA, O'Reilly RC. Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach. Psychol Rev. 2003 Oct;110(4):611-46. doi: 10.1037/0033-295X.110.4.611.
- Background: PubMed 37227843Zhou Z, Singh D, Tandoc MC, Schapiro AC. Building integrated representations through interleaved learning. J Exp Psychol Gen. 2023 Sep;152(9):2666-2684. doi: 10.1037/xge0001415. Epub 2023 May 25.
- Background: PubMed 22775499Kumaran D, McClelland JL. Generalization through the recurrent interaction of episodic memories: a model of the hippocampal system. Psychol Rev. 2012 Jul;119(3):573-616. doi: 10.1037/a0028681.
- Background: PubMed 30872808Yonelinas AP, Ranganath C, Ekstrom AD, Wiltgen BJ. A contextual binding theory of episodic memory: systems consolidation reconsidered. Nat Rev Neurosci. 2019 Jun;20(6):364-375. doi: 10.1038/s41583-019-0150-4.
- Individual participant dataSharing planned: Yes. All IPD that underlie results in a publication.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Anna C Schapiro, PhD principal investigator
University of Pennsylvania
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Associative inference, Category learning and Sleep in people with Learning and Humans, run by University of Pennsylvania at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 8 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 20 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Changes in multivariate representations (time frame: Within first session (spanning 2-3 hrs.) and at approximately one week delay in second session (spanning 1-2 hrs.)). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05910762, which also lists 56 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Aug 2026.