Human Learning of New Structured Information Across Time and Sleep

Sleep Clinical Trial

Official title:

Learning Novel Structure Across Time and Sleep

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05910762
• Other study ID #: 833228B
• Secondary ID: R01MH129436
• Status: Recruiting
• Phase: N/A
• Start date: June 5, 2023
• Est. completion date: March 2028

Study information

• Verified date: July 2023
• Source: University of Pennsylvania
• Contact: Anna C Schapiro, PhD
• Phone: 6177974555
• Email: aschapir[@]sas.upenn.edu
• Is FDA regulated: No
• Study type: Interventional

Clinical 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 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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 105
• Est. completion date: March 2028
• Est. primary completion date: March 2028
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years to 35 Years

Eligibility

Inclusion Criteria:

• 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)

Exclusion Criteria:

• 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)

Related Conditions & MeSH terms

• Consolidation
• Humans
• Learning
• Sleep

Intervention

Behavioral:
• Associative inference
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 learning
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).
• Sleep
Participants will sleep after engaging in a category learning paradigm while electroencephalography data are collected, and memory will be assessed behaviorally after sleep.

Locations

Country	Name	City	State
United States	University of Pennsylvania	Philadelphia	Pennsylvania

Sponsors (2)

Lead Sponsor	Collaborator
University of Pennsylvania	National Institute of Mental Health (NIMH)

Country where clinical trial is conducted

United States, 

References & Publications (56)

Antony JW, Schapiro AC. Active and effective replay: systems consolidation reconsidered again. Nat Rev Neurosci. 2019 Aug;20(8):506-507. doi: 10.1038/s41583-019-0191-8. No abstract available. — View Citation

Armstrong K, Kose S, Williams L, Woolard A, Heckers S. Impaired associative inference in patients with schizophrenia. Schizophr Bull. 2012 May;38(3):622-9. doi: 10.1093/schbul/sbq145. Epub 2010 Dec 6. — View Citation

Ashby FG, Maddox WT. Human category learning 2.0. Ann N Y Acad Sci. 2011 Apr;1224:147-161. doi: 10.1111/j.1749-6632.2010.05874.x. Epub 2010 Dec 23. — View Citation

Barker GR, Banks PJ, Scott H, Ralph GS, Mitrophanous KA, Wong LF, Bashir ZI, Uney JB, Warburton EC. Separate elements of episodic memory subserved by distinct hippocampal-prefrontal connections. Nat Neurosci. 2017 Feb;20(2):242-250. doi: 10.1038/nn.4472. Epub 2017 Jan 9. — View Citation

Bowman CR, Iwashita T, Zeithamova D. Tracking prototype and exemplar representations in the brain across learning. Elife. 2020 Nov 26;9:e59360. doi: 10.7554/eLife.59360. — View Citation

Cairney SA, Guttesen AAV, El Marj N, Staresina BP. Memory Consolidation Is Linked to Spindle-Mediated Information Processing during Sleep. Curr Biol. 2018 Mar 19;28(6):948-954.e4. doi: 10.1016/j.cub.2018.01.087. Epub 2018 Mar 8. — View Citation

Carr VA, Rissman J, Wagner AD. Imaging the human medial temporal lobe with high-resolution fMRI. Neuron. 2010 Feb 11;65(3):298-308. doi: 10.1016/j.neuron.2009.12.022. — View Citation

Chanales AJH, Tremblay-McGaw AG, Drascher ML, Kuhl BA. Adaptive Repulsion of Long-Term Memory Representations Is Triggered by Event Similarity. Psychol Sci. 2021 May;32(5):705-720. doi: 10.1177/0956797620972490. Epub 2021 Apr 21. — View Citation

Covington 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. — View Citation

Daw ND, Niv Y, Dayan P. Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control. Nat Neurosci. 2005 Dec;8(12):1704-11. doi: 10.1038/nn1560. Epub 2005 Nov 6. — View Citation

Dimsdale-Zucker HR, Ritchey M, Ekstrom AD, Yonelinas AP, Ranganath C. CA1 and CA3 differentially support spontaneous retrieval of episodic contexts within human hippocampal subfields. Nat Commun. 2018 Jan 18;9(1):294. doi: 10.1038/s41467-017-02752-1. — View Citation

Eichenbaum H. Prefrontal-hippocampal interactions in episodic memory. Nat Rev Neurosci. 2017 Sep;18(9):547-558. doi: 10.1038/nrn.2017.74. Epub 2017 Jun 29. — View Citation

Goldi M, van Poppel EAM, Rasch B, Schreiner T. Increased neuronal signatures of targeted memory reactivation during slow-wave up states. Sci Rep. 2019 Feb 25;9(1):2715. doi: 10.1038/s41598-019-39178-2. — View Citation

Guise KG, Shapiro ML. Medial Prefrontal Cortex Reduces Memory Interference by Modifying Hippocampal Encoding. Neuron. 2017 Apr 5;94(1):183-192.e8. doi: 10.1016/j.neuron.2017.03.011. Epub 2017 Mar 23. — View Citation

Hassabis D, Kumaran D, Summerfield C, Botvinick M. Neuroscience-Inspired Artificial Intelligence. Neuron. 2017 Jul 19;95(2):245-258. doi: 10.1016/j.neuron.2017.06.011. — View Citation

Hinton, GE. Distributed representations. Technical Report CMU-CS-84-157. 1984.

Hu X, Cheng LY, Chiu MH, Paller KA. Promoting memory consolidation during sleep: A meta-analysis of targeted memory reactivation. Psychol Bull. 2020 Mar;146(3):218-244. doi: 10.1037/bul0000223. — View Citation

Klinzing JG, Niethard N, Born J. Mechanisms of systems memory consolidation during sleep. Nat Neurosci. 2019 Oct;22(10):1598-1610. doi: 10.1038/s41593-019-0467-3. Epub 2019 Aug 26. Erratum In: Nat Neurosci. 2019 Sep 11;: — View Citation

Kriegeskorte N, Mur M, Bandettini P. Representational similarity analysis - connecting the branches of systems neuroscience. Front Syst Neurosci. 2008 Nov 24;2:4. doi: 10.3389/neuro.06.004.2008. eCollection 2008. — View Citation

Kumaran 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. — View Citation

Landmann N, Kuhn M, Piosczyk H, Feige B, Baglioni C, Spiegelhalder K, Frase L, Riemann D, Sterr A, Nissen C. The reorganisation of memory during sleep. Sleep Med Rev. 2014 Dec;18(6):531-41. doi: 10.1016/j.smrv.2014.03.005. Epub 2014 Mar 18. — View Citation

Leutgeb JK, Leutgeb S, Moser MB, Moser EI. Pattern separation in the dentate gyrus and CA3 of the hippocampus. Science. 2007 Feb 16;315(5814):961-6. doi: 10.1126/science.1135801. — View Citation

Leutgeb S, Leutgeb JK, Treves A, Moser MB, Moser EI. Distinct ensemble codes in hippocampal areas CA3 and CA1. Science. 2004 Aug 27;305(5688):1295-8. doi: 10.1126/science.1100265. Epub 2004 Jul 22. — View Citation

Mack ML, Love BC, Preston AR. Building concepts one episode at a time: The hippocampus and concept formation. Neurosci Lett. 2018 Jul 27;680:31-38. doi: 10.1016/j.neulet.2017.07.061. Epub 2017 Aug 8. — View Citation

Margalit E, Biederman I, Tjan BS, Shah MP. What Is Actually Affected by the Scrambling of Objects When Localizing the Lateral Occipital Complex? J Cogn Neurosci. 2017 Sep;29(9):1595-1604. doi: 10.1162/jocn_a_01144. Epub 2017 May 11. — View Citation

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. — View Citation

McCloskey M, Cohen NJ. Catastrophic interference in connectionist networks: The sequential learning problem. Psychology of Learning and Motivation. 1989; 24: 109-165.

Miner AE, Schurgin MW, Brady TF. Is working memory inherently more "precise" than long-term memory? Extremely high fidelity visual long-term memories for frequently encountered objects. J Exp Psychol Hum Percept Perform. 2020 Aug;46(8):813-830. doi: 10.1037/xhp0000748. Epub 2020 Apr 23. — View Citation

Molitor RJ, Sherrill KR, Morton NW, Miller AA, Preston AR. Memory Reactivation during Learning Simultaneously Promotes Dentate Gyrus/CA2,3 Pattern Differentiation and CA1 Memory Integration. J Neurosci. 2021 Jan 27;41(4):726-738. doi: 10.1523/JNEUROSCI.0394-20.2020. Epub 2020 Nov 25. — View Citation

Nakashiba T, Young JZ, McHugh TJ, Buhl DL, Tonegawa S. Transgenic inhibition of synaptic transmission reveals role of CA3 output in hippocampal learning. Science. 2008 Feb 29;319(5867):1260-4. doi: 10.1126/science.1151120. Epub 2008 Jan 24. — View Citation

Norman KA, Newman EL, Perotte AJ. Methods for reducing interference in the Complementary Learning Systems model: oscillating inhibition and autonomous memory rehearsal. Neural Netw. 2005 Nov;18(9):1212-28. doi: 10.1016/j.neunet.2005.08.010. Epub 2005 Nov 2. — View Citation

Norman 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. — View Citation

Paller KA. Sleeping in a Brave New World: Opportunities for Improving Learning and Clinical Outcomes through Targeted Memory Reactivation. Curr Dir Psychol Sci. 2017 Dec;26(6):532-537. doi: 10.1177/0963721417716928. Epub 2017 Nov 1. — View Citation

Poppenk J, Evensmoen HR, Moscovitch M, Nadel L. Long-axis specialization of the human hippocampus. Trends Cogn Sci. 2013 May;17(5):230-40. doi: 10.1016/j.tics.2013.03.005. Epub 2013 Apr 16. — View Citation

Rogers TT, Hocking J, Noppeney U, Mechelli A, Gorno-Tempini ML, Patterson K, Price CJ. Anterior temporal cortex and semantic memory: reconciling findings from neuropsychology and functional imaging. Cogn Affect Behav Neurosci. 2006 Sep;6(3):201-13. doi: 10.3758/cabn.6.3.201. — View Citation

Schapiro 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. — View Citation

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. — View Citation

Schapiro 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. — View Citation

Schapiro 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. — View Citation

Schapiro AC, Rogers TT, Cordova NI, Turk-Browne NB, Botvinick MM. Neural representations of events arise from temporal community structure. Nat Neurosci. 2013 Apr;16(4):486-92. doi: 10.1038/nn.3331. Epub 2013 Feb 17. — View Citation

Schapiro 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. — View Citation

Schapiro 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. — View Citation

Schlichting ML, Mumford JA, Preston AR. Learning-related representational changes reveal dissociable integration and separation signatures in the hippocampus and prefrontal cortex. Nat Commun. 2015 Aug 25;6:8151. doi: 10.1038/ncomms9151. — View Citation

Schlichting ML, Preston AR. Hippocampal-medial prefrontal circuit supports memory updating during learning and post-encoding rest. Neurobiol Learn Mem. 2016 Oct;134 Pt A(Pt A):91-106. doi: 10.1016/j.nlm.2015.11.005. Epub 2015 Nov 25. — View Citation

Schlichting 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. — View Citation

Schlichting ML, Zeithamova D, Preston AR. CA1 subfield contributions to memory integration and inference. Hippocampus. 2014 Oct;24(10):1248-60. doi: 10.1002/hipo.22310. Epub 2014 Jun 11. — View Citation

Shohamy D, Wagner AD. Integrating memories in the human brain: hippocampal-midbrain encoding of overlapping events. Neuron. 2008 Oct 23;60(2):378-89. doi: 10.1016/j.neuron.2008.09.023. — View Citation

Singh 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. — View Citation

Tompary A, Al-Aidroos N, Turk-Browne NB. Attending to What and Where: Background Connectivity Integrates Categorical and Spatial Attention. J Cogn Neurosci. 2018 Sep;30(9):1281-1297. doi: 10.1162/jocn_a_01284. Epub 2018 May 23. — View Citation

Tompary A, Davachi L. Consolidation Promotes the Emergence of Representational Overlap in the Hippocampus and Medial Prefrontal Cortex. Neuron. 2020 Jan 8;105(1):199-200. doi: 10.1016/j.neuron.2019.12.020. No abstract available. — View Citation

Whitmore NW, Bassard AM, Paller KA. Targeted memory reactivation of face-name learning depends on ample and undisturbed slow-wave sleep. NPJ Sci Learn. 2022 Jan 12;7(1):1. doi: 10.1038/s41539-021-00119-2. — View Citation

Wimmer GE, Daw ND, Shohamy D. Generalization of value in reinforcement learning by humans. Eur J Neurosci. 2012 Apr;35(7):1092-104. doi: 10.1111/j.1460-9568.2012.08017.x. — View Citation

Yonelinas 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. — View Citation

Zeithamova D, Schlichting ML, Preston AR. The hippocampus and inferential reasoning: building memories to navigate future decisions. Front Hum Neurosci. 2012 Mar 26;6:70. doi: 10.3389/fnhum.2012.00070. eCollection 2012. — View Citation

Zhao Y, Chanales AJH, Kuhl BA. Adaptive Memory Distortions Are Predicted by Feature Representations in Parietal Cortex. J Neurosci. 2021 Mar 31;41(13):3014-3024. doi: 10.1523/JNEUROSCI.2875-20.2021. Epub 2021 Feb 22. — View Citation

Zhou Z, Singh D, Tandoc MC, Schapiro AC. Building integrated representations through interleaved learning. J Exp Psychol Gen. 2023 May 25. doi: 10.1037/xge0001415. Online ahead of print. — View Citation

* Note: There are 56 references in all — Click here to view all references

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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.	Within first session (spanning 2-3 hrs.) and at approximately one week delay in second session (spanning 1-2 hrs.)
Primary	Brain-behavior correlations	Correlations between BOLD signal in the brain and participant behavior during judgments about objects.	Within first session (spanning 2-3 hrs.) and at approximately one week delay in second session (spanning 1-2 hrs.)
Primary	Correlations between activity across brain regions	Relationships between BOLD activity across different regions of the brain as a function of trial type and delay.	Within first session (spanning 2-3 hrs.) and at approximately one week delay in second session (spanning 1-2 hrs.)
Primary	Memory accuracy	Change in generalization ability from before to after the nap as a function of the different conditions of object cueing during sleep.	Within single study session (spanning 4-5 hrs.)