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Rudoler JH, Bruska JP, Chang W, Dougherty MR, Katerman BS, Halpern DJ, Diamond NB, Kahana MJ. Decoding EEG for optimizing naturalistic memory. J Neurosci Methods 2024; 410:110220. [PMID: 39033965 DOI: 10.1016/j.jneumeth.2024.110220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 06/26/2024] [Accepted: 07/17/2024] [Indexed: 07/23/2024]
Abstract
BACKGROUND Spectral features of human electroencephalographic (EEG) recordings during learning predict subsequent recall variability. NEW METHOD Capitalizing on these fluctuating neural features, we develop a non-invasive closed-loop (NICL) system for real-time optimization of human learning. Participants play a virtual navigation-and-memory game; recording multi-session data across days allowed us to build participant-specific classification models of recall success. In subsequent closed-loop sessions, our platform manipulated the timing of memory encoding, selectively presenting items during periods of predicted good or poor memory function based on EEG features decoded in real time. RESULTS The induced memory effect (the difference between recall rates when presenting items during predicted good vs. poor learning periods) increased with the accuracy of neural decoding. COMPARISON WITH EXISTING METHODS This study demonstrates greater-than-chance memory decoding from EEG recordings in a naturalistic virtual navigation task with greater real-world validity than basic word-list recall paradigms. Here we modulate memory by timing stimulus presentation based on noninvasive scalp EEG recordings, whereas prior closed-loop studies for memory improvement involved intracranial recordings and direct electrical stimulation. Other noninvasive studies have investigated the use of neurofeedback or remedial study for memory improvement. CONCLUSIONS These findings present a proof-of-concept for using non-invasive closed-loop technology to optimize human learning and memory through principled stimulus timing, but only in those participants for whom classifiers reliably predict out-of-sample memory function.
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Crombie KM, Azar A, Botsford C, Heilicher M, Jaeb M, Gruichich TS, Schomaker CM, Williams R, Stowe ZN, Dunsmoor JE, Cisler JM. Decoding context memories for threat in large-scale neural networks. Cereb Cortex 2024; 34:bhae018. [PMID: 38300181 PMCID: PMC10839849 DOI: 10.1093/cercor/bhae018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 01/08/2024] [Accepted: 01/10/2024] [Indexed: 02/02/2024] Open
Abstract
Humans are often tasked with determining the degree to which a given situation poses threat. Salient cues present during prior events help bring online memories for context, which plays an informative role in this process. However, it is relatively unknown whether and how individuals use features of the environment to retrieve context memories for threat, enabling accurate inferences about the current level of danger/threat (i.e. retrieve appropriate memory) when there is a degree of ambiguity surrounding the present context. We leveraged computational neuroscience approaches (i.e. independent component analysis and multivariate pattern analyses) to decode large-scale neural network activity patterns engaged during learning and inferring threat context during a novel functional magnetic resonance imaging task. Here, we report that individuals accurately infer threat contexts under ambiguous conditions through neural reinstatement of large-scale network activity patterns (specifically striatum, salience, and frontoparietal networks) that track the signal value of environmental cues, which, in turn, allows reinstatement of a mental representation, primarily within a ventral visual network, of the previously learned threat context. These results provide novel insight into distinct, but overlapping, neural mechanisms by which individuals may utilize prior learning to effectively make decisions about ambiguous threat-related contexts as they navigate the environment.
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Affiliation(s)
- Kevin M Crombie
- Department of Psychiatry and Behavioral Sciences, The University of Texas at Austin, 1601 Trinity Street, Building B, Austin, TX 78712, United States
- Department of Kinesiology, The University of Alabama, 620 Judy Bonner Drive, Box 870312, Tuscaloosa, AL 35487, United States
| | - Ameera Azar
- Department of Psychiatry and Behavioral Sciences, The University of Texas at Austin, 1601 Trinity Street, Building B, Austin, TX 78712, United States
| | - Chloe Botsford
- Department of Psychiatry, University of Wisconsin—Madison, 6001 Research Park Boulevard, Madison, WI 53719, United States
| | - Mickela Heilicher
- Department of Psychiatry, University of Wisconsin—Madison, 6001 Research Park Boulevard, Madison, WI 53719, United States
| | - Michael Jaeb
- Department of Psychiatry, University of Wisconsin—Madison, 6001 Research Park Boulevard, Madison, WI 53719, United States
| | - Tijana Sagorac Gruichich
- Department of Psychiatry, University of Wisconsin—Madison, 6001 Research Park Boulevard, Madison, WI 53719, United States
| | - Chloe M Schomaker
- Department of Psychiatry and Behavioral Sciences, The University of Texas at Austin, 1601 Trinity Street, Building B, Austin, TX 78712, United States
| | - Rachel Williams
- Department of Psychiatry, University of Wisconsin—Madison, 6001 Research Park Boulevard, Madison, WI 53719, United States
| | - Zachary N Stowe
- Department of Psychiatry, University of Wisconsin—Madison, 6001 Research Park Boulevard, Madison, WI 53719, United States
| | - Joseph E Dunsmoor
- Department of Psychiatry and Behavioral Sciences, The University of Texas at Austin, 1601 Trinity Street, Building B, Austin, TX 78712, United States
- Institute for Neuroscience, The University of Texas at Austin, Austin, TX 78712, United States
- Department of Neuroscience, The University of Texas at Austin, 1 University Station, Stop C7000, Austin, TX 78712, United States
| | - Josh M Cisler
- Department of Psychiatry and Behavioral Sciences, The University of Texas at Austin, 1601 Trinity Street, Building B, Austin, TX 78712, United States
- Institute for Early Life Adversity Research, The University of Texas at Austin Dell Medical School, 1601 Trinity Street, Building B, Austin, TX 78712, United States
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3
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Fagerland SM, Berntsen HR, Fredriksen M, Endestad T, Skouras S, Rootwelt-Revheim ME, Undseth RM. Exploring protocol development: Implementing systematic contextual memory to enhance real-time fMRI neurofeedback. JOURNAL OF ELECTRICAL BIOIMPEDANCE 2024; 15:41-62. [PMID: 38827812 PMCID: PMC11141335 DOI: 10.2478/joeb-2024-0006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Indexed: 06/05/2024]
Abstract
Objective The goal of this study was to explore the development and implementation of a protocol for real-time fMRI neurofeedback (rtfMRI-nf) and to assess the potential for enhancing the selective brain activation using stimuli from Virtual Reality (VR). In this study we focused on two specific brain regions, supplementary motor area (SMA) and right inferior frontal gyrus (rIFG). Publications by other study groups have suggested impaired function in these specific brain regions in patients with the diagnoses Attention Deficit Hyperactivity Disorder (ADHD) and Tourette's Syndrome (TS). This study explored the development of a protocol to investigate if attention and contextual memory may be used to systematically strengthen the procedure of rtfMRI-nf. Methods We used open-science software and platforms for rtfMRI-nf and for developing a simulated repetition of the rtfMRI-nf brain training in VR. We conducted seven exploratory tests in which we updated the protocol at each step. During rtfMRI-nf, MRI images are analyzed live while a person is undergoing an MRI scan, and the results are simultaneously shown to the person in the MRI-scanner. By focusing the analysis on specific regions of the brain, this procedure can be used to help the person strengthen conscious control of these regions. The VR simulation of the same experience involved a walk through the hospital toward the MRI scanner where the training sessions were conducted, as well as a subsequent simulated repetition of the MRI training. The VR simulation was a 2D projection of the experience.The seven exploratory tests involved 19 volunteers. Through this exploration, methods for aiming within the brain (e.g. masks/algorithms for coordinate-system control) and calculations for the analyses (e.g. calculations based on connectivity versus activity) were updated by the project team throughout the project. The final procedure involved three initial rounds of rtfMRI-nf for learning brain strategies. Then, the volunteers were provided with VR headsets and given instructions for one week of use. Afterward, a new session with three rounds of rtfMRI-nf was conducted. Results Through our exploration of the indirect effect parameters - brain region activity (directed oxygenated blood flow), connectivity (degree of correlated activity in different regions), and neurofeedback score - the volunteers tended to increase activity in the reinforced brain regions through our seven tests. Updates of procedures and analyses were always conducted between pilots, and never within. The VR simulated repetition was tested in pilot 7, but the role of the VR contribution in this setting is unclear due to underpowered testing. Conclusion This proof-of-concept protocol implies how rtfMRI-nf may be used to selectively train two brain regions (SMA and rIFG). The method may likely be adapted to train any given region in the brain, but readers are advised to update and adapt the procedure to experimental needs.
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Affiliation(s)
- Steffen Maude Fagerland
- The Intervention Centre, Division of Technology and Innovation, Oslo University Hospital, Oslo, Norway
- Department of Cognitive and Neuropsychology, Department of Psychology, University of Oslo, Oslo, Norway
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, Department of Psychology, University of Oslo, Norway
| | - Henrik Røsholm Berntsen
- The Intervention Centre, Division of Technology and Innovation, Oslo University Hospital, Oslo, Norway
| | - Mats Fredriksen
- Neuropsychatric Outpatient Clinic, Vestfold Hospital Trust, Tønsberg, Norway
| | - Tor Endestad
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, Department of Psychology, University of Oslo, Norway
- Department of Neuropsychology, Helgeland Hospital, Norway
| | - Stavros Skouras
- Department of Fundamental Neurosciences, Faculty of Medicine, University of Geneva, Geneva, CH-1202, Switzerland
- Department of Biological and Medical Psychology, University of Bergen, Bergen, NO-5020, Norway
- Department of Neurology, Inselspital University Hospital Bern, Bern, CH-3010, Switzerland
| | - Mona Elisabeth Rootwelt-Revheim
- The Intervention Centre, Division of Technology and Innovation, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
| | - Ragnhild Marie Undseth
- The Intervention Centre, Division of Technology and Innovation, Oslo University Hospital, Oslo, Norway
- Department of Cognitive and Neuropsychology, Department of Psychology, University of Oslo, Oslo, Norway
- Division of Radiology Research, The Intervention Centre, Oslo University Hospital, Oslo, Norway
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Peng K, Wammes JD, Nguyen A, Cătălin Iordan M, Norman KA, Turk-Browne NB. INDUCING REPRESENTATIONAL CHANGE IN THE HIPPOCAMPUS THROUGH REAL-TIME NEUROFEEDBACK. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.01.569487. [PMID: 38106228 PMCID: PMC10723264 DOI: 10.1101/2023.12.01.569487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
When you perceive or remember one thing, other related things come to mind. This competition has consequences for how these items are later perceived, attended, or remembered. Such behavioral consequences result from changes in how much the neural representations of the items overlap, especially in the hippocampus. These changes can reflect increased (integration) or decreased (differentiation) overlap; previous studies have posited that the amount of coactivation between competing representations in cortex determines which will occur: high coactivation leads to hippocampal integration, medium coactivation leads to differentiation, and low coactivation is inert. However, those studies used indirect proxies for coactivation, by manipulating stimulus similarity or task demands. Here we induce coactivation of competing memories in visual cortex more directly using closed-loop neurofeedback from real-time fMRI. While viewing one object, participants were rewarded for implicitly activating the representation of another object as strongly as possible. Across multiple real-time fMRI training sessions, they succeeded in using the neurofeedback to induce coactivation. Compared with untrained objects, this coactivation led to behavioral and neural integration: The trained objects became harder for participants to discriminate in a categorical perception task and harder to decode from patterns of fMRI activity in the hippocampus.
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Affiliation(s)
- Kailong Peng
- Department of Psychology, Interdepartmental Neuroscience Program, Yale University
| | - Jeffrey D Wammes
- Department of Psychology, Centre for Neuroscience Studies, Queen's University
| | - Alex Nguyen
- Department of Psychology, Princeton Neuroscience Institute, Princeton University
| | - Marius Cătălin Iordan
- Department of Brain and Cognitive Sciences, Department of Neuroscience, University of Rochester
| | - Kenneth A Norman
- Department of Psychology, Princeton Neuroscience Institute, Princeton University
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Deng Y, Song D, Ni J, Qing H, Quan Z. Reward prediction error in learning-related behaviors. Front Neurosci 2023; 17:1171612. [PMID: 37662112 PMCID: PMC10471312 DOI: 10.3389/fnins.2023.1171612] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 07/31/2023] [Indexed: 09/05/2023] Open
Abstract
Learning is a complex process, during which our opinions and decisions are easily changed due to unexpected information. But the neural mechanism underlying revision and correction during the learning process remains unclear. For decades, prediction error has been regarded as the core of changes to perception in learning, even driving the learning progress. In this article, we reviewed the concept of reward prediction error, and the encoding mechanism of dopaminergic neurons and the related neural circuities. We also discussed the relationship between reward prediction error and learning-related behaviors, including reversal learning. We then demonstrated the evidence of reward prediction error signals in several neurological diseases, including Parkinson's disease and addiction. These observations may help to better understand the regulatory mechanism of reward prediction error in learning-related behaviors.
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Affiliation(s)
- Yujun Deng
- Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Da Song
- Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Junjun Ni
- Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Hong Qing
- Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, Beijing, China
- Department of Biology, Shenzhen MSU-BIT University, Shenzhen, China
| | - Zhenzhen Quan
- Key Laboratory of Molecular Medicine and Biotherapy, School of Life Science, Beijing Institute of Technology, Beijing, China
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6
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Shah-Basak P, Boukrina O, Li XR, Jebahi F, Kielar A. Targeted neurorehabilitation strategies in post-stroke aphasia. Restor Neurol Neurosci 2023; 41:129-191. [PMID: 37980575 PMCID: PMC10741339 DOI: 10.3233/rnn-231344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2023]
Abstract
BACKGROUND Aphasia is a debilitating language impairment, affecting millions of people worldwide. About 40% of stroke survivors develop chronic aphasia, resulting in life-long disability. OBJECTIVE This review examines extrinsic and intrinsic neuromodulation techniques, aimed at enhancing the effects of speech and language therapies in stroke survivors with aphasia. METHODS We discuss the available evidence supporting the use of transcranial direct current stimulation (tDCS), repetitive transcranial magnetic stimulation, and functional MRI (fMRI) real-time neurofeedback in aphasia rehabilitation. RESULTS This review systematically evaluates studies focusing on efficacy and implementation of specialized methods for post-treatment outcome optimization and transfer to functional skills. It considers stimulation target determination and various targeting approaches. The translation of neuromodulation interventions to clinical practice is explored, emphasizing generalization and functional communication. The review also covers real-time fMRI neurofeedback, discussing current evidence for efficacy and essential implementation parameters. Finally, we address future directions for neuromodulation research in aphasia. CONCLUSIONS This comprehensive review aims to serve as a resource for a broad audience of researchers and clinicians interested in incorporating neuromodulation for advancing aphasia care.
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Affiliation(s)
| | - Olga Boukrina
- Kessler Foundation, Center for Stroke Rehabilitation Research, West Orange, NJ, USA
| | - Xin Ran Li
- School of Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Fatima Jebahi
- Department of Speech, Languageand Hearing Sciences, University of Arizona, Tucson, AZ, USA
| | - Aneta Kielar
- Department of Speech, Languageand Hearing Sciences, University of Arizona, Tucson, AZ, USA
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7
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Essoe JKY, Reggente N, Ohno AA, Baek YH, Dell'Italia J, Rissman J. Enhancing learning and retention with distinctive virtual reality environments and mental context reinstatement. NPJ SCIENCE OF LEARNING 2022; 7:31. [PMID: 36481776 PMCID: PMC9732332 DOI: 10.1038/s41539-022-00147-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 10/21/2022] [Indexed: 06/17/2023]
Abstract
Memory is inherently context-dependent: internal and environmental cues become bound to learnt information, and the later absence of these cues can impair recall. Here, we developed an approach to leverage context-dependence to optimise learning of challenging, interference-prone material. While navigating through desktop virtual reality (VR) contexts, participants learnt 80 foreign words in two phonetically similar languages. Those participants who learnt each language in its own unique context showed reduced interference and improved one-week retention (92%), relative to those who learnt the languages in the same context (76%)-however, this advantage was only apparent if participants subjectively experienced VR-based contexts as "real" environments. A follow-up fMRI experiment confirmed that reinstatement of brain activity patterns associated with the original encoding context during word retrieval was associated with improved recall performance. These findings establish that context-dependence can be harnessed with VR to optimise learning and showcase the important role of mental context reinstatement.
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Affiliation(s)
- Joey Ka-Yee Essoe
- Center for OCD, Anxiety, and Related Disorders for Children, Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- Department of Psychology, University of California, Los Angeles, CA, 90095, USA
| | - Nicco Reggente
- Department of Psychology, University of California, Los Angeles, CA, 90095, USA
- Institute for Advanced Consciousness Studies, Santa Monica, CA, 90403, USA
| | - Ai Aileen Ohno
- Department of Psychology, University of California, Los Angeles, CA, 90095, USA
- School of Medicine, California University of Science and Medicine, Colton, CA, 92324, USA
| | - Younji Hera Baek
- Department of Psychology, University of California, Los Angeles, CA, 90095, USA
- Division of Psychology, Communication, and Human Neuroscience, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PL, UK
| | - John Dell'Italia
- Department of Psychology, University of California, Los Angeles, CA, 90095, USA
- Birmingham Veterans Affairs, Birmingham, AL, 35233, USA
| | - Jesse Rissman
- Department of Psychology, University of California, Los Angeles, CA, 90095, USA.
- Department of Psychiatry & Biobehavioral Sciences, University of California, Los Angeles, CA, 90095, USA.
- Brain Research Institute, University of California, Los Angeles, CA, 90095, USA.
- Integrative Center for Learning and Memory, University of California, Los Angeles, CA, 90095, USA.
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8
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Wallace G, Polcyn S, Brooks PP, Mennen AC, Zhao K, Scotti PS, Michelmann S, Li K, Turk-Browne NB, Cohen JD, Norman KA. RT-Cloud: A cloud-based software framework to simplify and standardize real-time fMRI. Neuroimage 2022; 257:119295. [PMID: 35580808 PMCID: PMC9494277 DOI: 10.1016/j.neuroimage.2022.119295] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Accepted: 05/09/2022] [Indexed: 11/21/2022] Open
Abstract
Real-time fMRI (RT-fMRI) neurofeedback has been shown to be effective in treating neuropsychiatric disorders and holds tremendous promise for future breakthroughs, both with regard to basic science and clinical applications. However, the prevalence of its use has been hampered by computing hardware requirements, the complexity of setting up and running an experiment, and a lack of standards that would foster collaboration. To address these issues, we have developed RT-Cloud (https://github.com/brainiak/rt-cloud), a flexible, cloud-based, open-source Python software package for the execution of RT-fMRI experiments. RT-Cloud uses standardized data formats and adaptable processing streams to support and expand open science in RT-fMRI research and applications. Cloud computing is a key enabling technology for advancing RT-fMRI because it eliminates the need for on-premise technical expertise and high-performance computing; this allows installation, configuration, and maintenance to be automated and done remotely. Furthermore, the scalability of cloud computing makes it easier to deploy computationally-demanding multivariate analyses in real time. In this paper, we describe how RT-Cloud has been integrated with open standards, including the Brain Imaging Data Structure (BIDS) standard and the OpenNeuro database, how it has been applied thus far, and our plans for further development and deployment of RT-Cloud in the coming years.
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Affiliation(s)
- Grant Wallace
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - Stephen Polcyn
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - Paula P Brooks
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - Anne C Mennen
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - Ke Zhao
- Cognitive Science Program, University of Pennsylvania, Philadelphia, PA, United States
| | - Paul S Scotti
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - Sebastian Michelmann
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - Kai Li
- Department of Computer Science, Princeton University, Princeton, NJ, United States
| | | | - Jonathan D Cohen
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States; Department of Psychology, Princeton University, Princeton, NJ, United States
| | - Kenneth A Norman
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States; Department of Psychology, Princeton University, Princeton, NJ, United States.
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9
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Kang J, Kang W, Lee SH. Stronger memory representation after memory reinstatement during retrieval in the human hippocampus. Neuroimage 2022; 260:119493. [PMID: 35868616 DOI: 10.1016/j.neuroimage.2022.119493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 07/15/2022] [Accepted: 07/18/2022] [Indexed: 11/19/2022] Open
Abstract
Memory retrieval allows us to reinstate previously encoded information but is also considered to contribute to memory enhancement. Retrieval-induced enhancement may involve processing to strengthen memory traces, but neural processing beyond reinstatement during retrieval remains elusive. Here, we show that hippocampal processing, different from memory reinstatement, exists during retrieval in the human brain. By tracking changes in the response patterns in the selected hippocampal and cortical regions over time during retrieval based on functional MRI, we found that the representation of associative memory in CA3/DG became stronger even after cortical memory reinstatement, while CA1 showed significant memory representation at retrieval onset with the cortical reinstatement, but not afterwards. This tendency was not observed in the condition without active retrieval. Moreover, subsequent long-term memory performance depended on the delayed CA3/DG representation during retrieval. These findings suggest that CA3/DG contributes to neural processing beyond memory reinstatement during retrieval, which may lead to memory enhancement.
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Affiliation(s)
- Joonyoung Kang
- Department of Bio and Brain Engineering, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST); Program of Brain and Cognitive Engineering, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-Ro, Yuseong-Gu, Daejeon 34141 Republic of Korea
| | - Wonjun Kang
- Department of Bio and Brain Engineering, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST)
| | - Sue-Hyun Lee
- Department of Bio and Brain Engineering, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST); Program of Brain and Cognitive Engineering, College of Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-Ro, Yuseong-Gu, Daejeon 34141 Republic of Korea.
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10
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Jiang Y, Jessee W, Hoyng S, Borhani S, Liu Z, Zhao X, Price LK, High W, Suhl J, Cerel-Suhl S. Sharpening Working Memory With Real-Time Electrophysiological Brain Signals: Which Neurofeedback Paradigms Work? Front Aging Neurosci 2022; 14:780817. [PMID: 35418848 PMCID: PMC8995767 DOI: 10.3389/fnagi.2022.780817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 02/08/2022] [Indexed: 09/19/2023] Open
Abstract
Growing evidence supports the idea that the ultimate biofeedback is to reward sensory pleasure (e.g., enhanced visual clarity) in real-time to neural circuits that are associated with a desired performance, such as excellent memory retrieval. Neurofeedback is biofeedback that uses real-time sensory reward to brain activity associated with a certain performance (e.g., accurate and fast recall). Working memory is a key component of human intelligence. The challenges are in our current limited understanding of neurocognitive dysfunctions as well as in technical difficulties for closed-loop feedback in true real-time. Here we review recent advancements of real time neurofeedback to improve memory training in healthy young and older adults. With new advancements in neuromarkers of specific neurophysiological functions, neurofeedback training should be better targeted beyond a single frequency approach to include frequency interactions and event-related potentials. Our review confirms the positive trend that neurofeedback training mostly works to improve memory and cognition to some extent in most studies. Yet, the training typically takes multiple weeks with 2-3 sessions per week. We review various neurofeedback reward strategies and outcome measures. A well-known issue in such training is that some people simply do not respond to neurofeedback. Thus, we also review the literature of individual differences in psychological factors e.g., placebo effects and so-called "BCI illiteracy" (Brain Computer Interface illiteracy). We recommend the use of Neural modulation sensitivity or BCI insensitivity in the neurofeedback literature. Future directions include much needed research in mild cognitive impairment, in non-Alzheimer's dementia populations, and neurofeedback using EEG features during resting and sleep for memory enhancement and as sensitive outcome measures.
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Affiliation(s)
- Yang Jiang
- Lexington Veteran Affairs Medical Center, Lexington, KY, United States
- College of Medicine, University of Kentucky, Lexington, KY, United States
| | - William Jessee
- College of Medicine, University of Kentucky, Lexington, KY, United States
| | - Stevie Hoyng
- College of Medicine, University of Kentucky, Lexington, KY, United States
| | - Soheil Borhani
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN, United States
| | - Ziming Liu
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN, United States
| | - Xiaopeng Zhao
- Department of Mechanical, Aerospace, and Biomedical Engineering, University of Tennessee, Knoxville, Knoxville, TN, United States
| | - Lacey K. Price
- Lexington Veteran Affairs Medical Center, Lexington, KY, United States
| | - Walter High
- New Mexico Veteran Affairs Medical Center, Albuquerque, NM, United States
| | - Jeremiah Suhl
- Lexington Veteran Affairs Medical Center, Lexington, KY, United States
| | - Sylvia Cerel-Suhl
- Lexington Veteran Affairs Medical Center, Lexington, KY, United States
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11
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Ramot M, Martin A. Closed-loop neuromodulation for studying spontaneous activity and causality. Trends Cogn Sci 2022; 26:290-299. [PMID: 35210175 PMCID: PMC9396631 DOI: 10.1016/j.tics.2022.01.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 01/30/2022] [Accepted: 01/31/2022] [Indexed: 01/01/2023]
Abstract
Having established that spontaneous brain activity follows meaningful coactivation patterns and correlates with behavior, researchers have turned their attention to understanding its function and behavioral significance. We suggest closed-loop neuromodulation as a neural perturbation tool uniquely well suited for this task. Closed-loop neuromodulation has primarily been viewed as an interventionist tool to teach subjects to directly control their own brain activity. We examine an alternative operant conditioning model of closed-loop neuromodulation which, through implicit feedback, can manipulate spontaneous activity at the network level, without violating the spontaneous or endogenous nature of the signal, thereby providing a direct test of network causality.
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12
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Tuckute G, Hansen ST, Kjaer TW, Hansen LK. Real-Time Decoding of Attentional States Using Closed-Loop EEG Neurofeedback. Neural Comput 2021; 33:967-1004. [PMID: 33513324 DOI: 10.1162/neco_a_01363] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 10/16/2020] [Indexed: 11/04/2022]
Abstract
Sustained attention is a cognitive ability to maintain task focus over extended periods of time (Mackworth, 1948; Chun, Golomb, & Turk-Browne, 2011). In this study, scalp electroencephalography (EEG) signals were processed in real time using a 32 dry-electrode system during a sustained visual attention task. An attention training paradigm was implemented, as designed in DeBettencourt, Cohen, Lee, Norman, and Turk-Browne (2015) in which the composition of a sequence of blended images is updated based on the participant's decoded attentional level to a primed image category. It was hypothesized that a single neurofeedback training session would improve sustained attention abilities. Twenty-two participants were trained on a single neurofeedback session with behavioral pretraining and posttraining sessions within three consecutive days. Half of the participants functioned as controls in a double-blinded design and received sham neurofeedback. During the neurofeedback session, attentional states to primed categories were decoded in real time and used to provide a continuous feedback signal customized to each participant in a closed-loop approach. We report a mean classifier decoding error rate of 34.3% (chance = 50%). Within the neurofeedback group, there was a greater level of task-relevant attentional information decoded in the participant's brain before making a correct behavioral response than before an incorrect response. This effect was not visible in the control group (interaction p=7.23e-4), which strongly indicates that we were able to achieve a meaningful measure of subjective attentional state in real time and control participants' behavior during the neurofeedback session. We do not provide conclusive evidence whether the single neurofeedback session per se provided lasting effects in sustained attention abilities. We developed a portable EEG neurofeedback system capable of decoding attentional states and predicting behavioral choices in the attention task at hand. The neurofeedback code framework is Python based and open source, and it allows users to actively engage in the development of neurofeedback tools for scientific and translational use.
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Affiliation(s)
- Greta Tuckute
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark, and Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, 02139, U.S.A.,
| | - Sofie Therese Hansen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark,
| | - Troels Wesenberg Kjaer
- Department of Neurology, Zealand University Hospital, 4000 Roskilde, Denmark, and Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark,
| | - Lars Kai Hansen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark,
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13
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Coutanche MN, Koch GE, Paulus JP. Influences on memory for naturalistic visual episodes: sleep, familiarity, and traits differentially affect forms of recall. Learn Mem 2020; 27:284-291. [PMID: 32540918 PMCID: PMC7301751 DOI: 10.1101/lm.051300.119] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2020] [Accepted: 05/19/2020] [Indexed: 11/24/2022]
Abstract
The memories we form are composed of information that we extract from multifaceted episodes. Static stimuli and paired associations have proven invaluable stimuli for understanding memory, but real-life events feature spatial and temporal dimensions that help form new retrieval paths. We ask how the ability to recall semantic, temporal, and spatial aspects (the "what, when, and where") of naturalistic episodes is affected by three influences-prior familiarity, postencoding sleep, and individual differences-by testing their influence on three forms of recall: cued recall, free recall, and the extent that recalled details are recombined for a novel prompt. Naturalistic videos of events with rare animals were presented to 115 participants, randomly assigned to receive a 12- or 24-h delay with sleep and/or wakefulness. Participants' immediate and delayed recall was tested and coded by its spatial, temporal, and semantic content. We find that prior familiarity with items featured in events improved cued recall, but not free recall, particularly for temporal and spatial details. In contrast, postencoding sleep, relative to wakefulness, improved free recall, but not cued recall, of all forms of content. Finally, individuals with higher trait scores in the Survey of Autobiographical Memory spontaneously incorporated more spatial details during free recall, and more event details (at a trend level) in a novel recombination recall task. These findings show that prior familiarity, postencoding sleep, and memory traits can each enhance a different form of recall. More broadly, this work highlights that recall is heterogeneous in response to different influences on memory.
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Affiliation(s)
- Marc N Coutanche
- Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
- Learning Research and Development Center, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
- Brain Institute, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | - Griffin E Koch
- Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
- Learning Research and Development Center, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
| | - John P Paulus
- Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
- Learning Research and Development Center, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA
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14
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Affiliation(s)
- Michelle Hampson
- Department of Radiology and Biomedical Imaging, Department of Psychiatry, and the Child Study Center, Yale University School of Medicine, New Haven, CT, USA.
| | - Sergio Ruiz
- Department of Psychiatry, Medicine School, and Laboratory for Brain-Machine Interfaces and Neuromodulation, Pontificia Universidad Católica de Chile, Santiago, Chile.
| | - Junichi Ushiba
- Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University, Japan.
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15
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Sinclair AH, Barense MD. Prediction Error and Memory Reactivation: How Incomplete Reminders Drive Reconsolidation. Trends Neurosci 2019; 42:727-739. [DOI: 10.1016/j.tins.2019.08.007] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 07/26/2019] [Accepted: 08/12/2019] [Indexed: 01/10/2023]
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16
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Ritvo VJH, Turk-Browne NB, Norman KA. Nonmonotonic Plasticity: How Memory Retrieval Drives Learning. Trends Cogn Sci 2019; 23:726-742. [PMID: 31358438 PMCID: PMC6698209 DOI: 10.1016/j.tics.2019.06.007] [Citation(s) in RCA: 73] [Impact Index Per Article: 14.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 06/19/2019] [Accepted: 06/24/2019] [Indexed: 12/15/2022]
Abstract
What are the principles that govern whether neural representations move apart (differentiate) or together (integrate) as a function of learning? According to supervised learning models that are trained to predict outcomes in the world, integration should occur when two stimuli predict the same outcome. Numerous findings support this, but - paradoxically - some recent fMRI studies have found that pairing different stimuli with the same associate causes differentiation, not integration. To explain these and related findings, we argue that supervised learning needs to be supplemented with unsupervised learning that is driven by spreading activation in a U-shaped way, such that inactive memories are not modified, moderate activation of memories causes weakening (leading to differentiation), and higher activation causes strengthening (leading to integration).
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Affiliation(s)
- Victoria J H Ritvo
- Department of Psychology, Princeton University, Princeton, NJ 08540, USA
| | | | - Kenneth A Norman
- Department of Psychology, Princeton University, Princeton, NJ 08540, USA; Princeton Neuroscience Institute, Princeton University, Washington Road, Princeton, NJ 08544, USA.
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