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Roeder BM, She X, Dakos AS, Moore B, Wicks RT, Witcher MR, Couture DE, Laxton AW, Clary HM, Popli G, Liu C, Lee B, Heck C, Nune G, Gong H, Shaw S, Marmarelis VZ, Berger TW, Deadwyler SA, Song D, Hampson RE. Developing a hippocampal neural prosthetic to facilitate human memory encoding and recall of stimulus features and categories. Front Comput Neurosci 2024; 18:1263311. [PMID: 38390007 PMCID: PMC10881797 DOI: 10.3389/fncom.2024.1263311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 01/19/2024] [Indexed: 02/24/2024] Open
Abstract
Objective Here, we demonstrate the first successful use of static neural stimulation patterns for specific information content. These static patterns were derived by a model that was applied to a subject's own hippocampal spatiotemporal neural codes for memory. Approach We constructed a new model of processes by which the hippocampus encodes specific memory items via spatiotemporal firing of neural ensembles that underlie the successful encoding of targeted content into short-term memory. A memory decoding model (MDM) of hippocampal CA3 and CA1 neural firing was computed which derives a stimulation pattern for CA1 and CA3 neurons to be applied during the encoding (sample) phase of a delayed match-to-sample (DMS) human short-term memory task. Main results MDM electrical stimulation delivered to the CA1 and CA3 locations in the hippocampus during the sample phase of DMS trials facilitated memory of images from the DMS task during a delayed recognition (DR) task that also included control images that were not from the DMS task. Across all subjects, the stimulated trials exhibited significant changes in performance in 22.4% of patient and category combinations. Changes in performance were a combination of both increased memory performance and decreased memory performance, with increases in performance occurring at almost 2 to 1 relative to decreases in performance. Across patients with impaired memory that received bilateral stimulation, significant changes in over 37.9% of patient and category combinations was seen with the changes in memory performance show a ratio of increased to decreased performance of over 4 to 1. Modification of memory performance was dependent on whether memory function was intact or impaired, and if stimulation was applied bilaterally or unilaterally, with nearly all increase in performance seen in subjects with impaired memory receiving bilateral stimulation. Significance These results demonstrate that memory encoding in patients with impaired memory function can be facilitated for specific memory content, which offers a stimulation method for a future implantable neural prosthetic to improve human memory.
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Affiliation(s)
- Brent M Roeder
- Wake Forest Baptist Medical Center, Winston-Salem, NC, United States
| | - Xiwei She
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Alexander S Dakos
- Wake Forest Baptist Medical Center, Winston-Salem, NC, United States
| | - Bryan Moore
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Robert T Wicks
- Wake Forest Baptist Medical Center, Winston-Salem, NC, United States
- Johns Hopkins Children's Center, Baltimore, MD, United States
| | - Mark R Witcher
- Wake Forest Baptist Medical Center, Winston-Salem, NC, United States
- Virginia Tech Carilion School of Medicine and Research Institute, Roanoke, VA, United States
| | - Daniel E Couture
- Wake Forest Baptist Medical Center, Winston-Salem, NC, United States
| | - Adrian W Laxton
- Wake Forest Baptist Medical Center, Winston-Salem, NC, United States
| | | | - Gautam Popli
- Wake Forest Baptist Medical Center, Winston-Salem, NC, United States
| | - Charles Liu
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
- USC Keck Memorial Hospital, Los Angeles, CA, United States
| | - Brian Lee
- USC Keck Memorial Hospital, Los Angeles, CA, United States
| | - Christianne Heck
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
- USC Keck Memorial Hospital, Los Angeles, CA, United States
| | - George Nune
- USC Keck Memorial Hospital, Los Angeles, CA, United States
| | - Hui Gong
- Rancho Los Amigos National Rehabilitation Hospital, Los Angeles, CA, United States
| | - Susan Shaw
- Rancho Los Amigos National Rehabilitation Hospital, Los Angeles, CA, United States
| | - Vasilis Z Marmarelis
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Theodore W Berger
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Sam A Deadwyler
- Wake Forest Baptist Medical Center, Winston-Salem, NC, United States
| | - Dong Song
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Robert E Hampson
- Wake Forest Baptist Medical Center, Winston-Salem, NC, United States
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Roeder BM, Riley MR, She X, Dakos AS, Robinson BS, Moore BJ, Couture DE, Laxton AW, Popli G, Munger Clary HM, Sam M, Heck C, Nune G, Lee B, Liu C, Shaw S, Gong H, Marmarelis VZ, Berger TW, Deadwyler SA, Song D, Hampson RE. Patterned Hippocampal Stimulation Facilitates Memory in Patients With a History of Head Impact and/or Brain Injury. Front Hum Neurosci 2022; 16:933401. [PMID: 35959242 PMCID: PMC9358788 DOI: 10.3389/fnhum.2022.933401] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 06/13/2022] [Indexed: 11/24/2022] Open
Abstract
Rationale: Deep brain stimulation (DBS) of the hippocampus is proposed for enhancement of memory impaired by injury or disease. Many pre-clinical DBS paradigms can be addressed in epilepsy patients undergoing intracranial monitoring for seizure localization, since they already have electrodes implanted in brain areas of interest. Even though epilepsy is usually not a memory disorder targeted by DBS, the studies can nevertheless model other memory-impacting disorders, such as Traumatic Brain Injury (TBI). Methods: Human patients undergoing Phase II invasive monitoring for intractable epilepsy were implanted with depth electrodes capable of recording neurophysiological signals. Subjects performed a delayed-match-to-sample (DMS) memory task while hippocampal ensembles from CA1 and CA3 cell layers were recorded to estimate a multi-input, multi-output (MIMO) model of CA3-to-CA1 neural encoding and a memory decoding model (MDM) to decode memory information from CA3 and CA1 neuronal signals. After model estimation, subjects again performed the DMS task while either MIMO-based or MDM-based patterned stimulation was delivered to CA1 electrode sites during the encoding phase of the DMS trials. Each subject was sorted (post hoc) by prior experience of repeated and/or mild-to-moderate brain injury (RMBI), TBI, or no history (control) and scored for percentage successful delayed recognition (DR) recall on stimulated vs. non-stimulated DMS trials. The subject’s medical history was unknown to the experimenters until after individual subject memory retention results were scored. Results: When examined compared to control subjects, both TBI and RMBI subjects showed increased memory retention in response to both MIMO and MDM-based hippocampal stimulation. Furthermore, effects of stimulation were also greater in subjects who were evaluated as having pre-existing mild-to-moderate memory impairment. Conclusion: These results show that hippocampal stimulation for memory facilitation was more beneficial for subjects who had previously suffered a brain injury (other than epilepsy), compared to control (epilepsy) subjects who had not suffered a brain injury. This study demonstrates that the epilepsy/intracranial recording model can be extended to test the ability of DBS to restore memory function in subjects who previously suffered a brain injury other than epilepsy, and support further investigation into the beneficial effect of DBS in TBI patients.
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Affiliation(s)
- Brent M. Roeder
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Mitchell R. Riley
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Xiwei She
- Department Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
| | - Alexander S. Dakos
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Brian S. Robinson
- Department Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
| | - Bryan J. Moore
- Department Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
| | - Daniel E. Couture
- Department of Neurosurgery, Wake Forest School of Medicine/Atrium Health Wake Forest Baptist, Winston-Salem, NC, United States
| | - Adrian W. Laxton
- Department of Neurosurgery, Wake Forest School of Medicine/Atrium Health Wake Forest Baptist, Winston-Salem, NC, United States
| | - Gautam Popli
- Department of Neurology, Wake Forest School of Medicine/Atrium Health Wake Forest Baptist, Winston-Salem, NC, United States
| | - Heidi M. Munger Clary
- Department of Neurology, Wake Forest School of Medicine/Atrium Health Wake Forest Baptist, Winston-Salem, NC, United States
| | - Maria Sam
- Department of Neurology, Wake Forest School of Medicine/Atrium Health Wake Forest Baptist, Winston-Salem, NC, United States
| | - Christi Heck
- Department of Neurology, W. M. Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - George Nune
- Department of Neurology, W. M. Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Brian Lee
- Department of Neurosurgery, W. M. Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Charles Liu
- Department of Neurosurgery, W. M. Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Susan Shaw
- Department of Neurology, Rancho Los Amigos National Rehabilitation Hospital, Los Angeles, CA, United States
| | - Hui Gong
- Department of Neurology, Rancho Los Amigos National Rehabilitation Hospital, Los Angeles, CA, United States
| | - Vasilis Z. Marmarelis
- Department Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
| | - Theodore W. Berger
- Department Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
| | - Sam A. Deadwyler
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Dong Song
- Department Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
| | - Robert E. Hampson
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, United States
- Department of Neurology, Wake Forest School of Medicine/Atrium Health Wake Forest Baptist, Winston-Salem, NC, United States
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She X, Berger TW, Song D. A Double-Layer Multi-Resolution Classification Model for Decoding Spatiotemporal Patterns of Spikes With Small Sample Size. Neural Comput 2021; 34:219-254. [PMID: 34758485 DOI: 10.1162/neco_a_01459] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 08/19/2021] [Indexed: 11/04/2022]
Abstract
We build a double-layer, multiple temporal-resolution classification model for decoding single-trial spatiotemporal patterns of spikes. The model takes spiking activities as input signals and binary behavioral or cognitive variables as output signals and represents the input-output mapping with a double-layer ensemble classifier. In the first layer, to solve the underdetermined problem caused by the small sample size and the very high dimensionality of input signals, B-spline functional expansion and L1-regularized logistic classifiers are used to reduce dimensionality and yield sparse model estimations. A wide range of temporal resolutions of neural features is included by using a large number of classifiers with different numbers of B-spline knots. Each classifier serves as a base learner to classify spatiotemporal patterns into the probability of the output label with a single temporal resolution. A bootstrap aggregating strategy is used to reduce the estimation variances of these classifiers. In the second layer, another L1-regularized logistic classifier takes outputs of first-layer classifiers as inputs to generate the final output predictions. This classifier serves as a meta-learner that fuses multiple temporal resolutions to classify spatiotemporal patterns of spikes into binary output labels. We test this decoding model with both synthetic and experimental data recorded from rats and human subjects performing memory-dependent behavioral tasks. Results show that this method can effectively avoid overfitting and yield accurate prediction of output labels with small sample size. The double-layer, multi-resolution classifier consistently outperforms the best single-layer, single-resolution classifier by extracting and utilizing multi-resolution spatiotemporal features of spike patterns in the classification.
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Affiliation(s)
- Xiwei She
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Theodore W Berger
- Department of Biomedical Engineering and Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Dong Song
- Department of Biomedical Engineering and Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, 90089, U.S.A.
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Herff C, Krusienski DJ, Kubben P. The Potential of Stereotactic-EEG for Brain-Computer Interfaces: Current Progress and Future Directions. Front Neurosci 2020; 14:123. [PMID: 32174810 PMCID: PMC7056827 DOI: 10.3389/fnins.2020.00123] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 01/30/2020] [Indexed: 12/17/2022] Open
Abstract
Stereotactic electroencephalogaphy (sEEG) utilizes localized, penetrating depth electrodes to measure electrophysiological brain activity. It is most commonly used in the identification of epileptogenic zones in cases of refractory epilepsy. The implanted electrodes generally provide a sparse sampling of a unique set of brain regions including deeper brain structures such as hippocampus, amygdala and insula that cannot be captured by superficial measurement modalities such as electrocorticography (ECoG). Despite the overlapping clinical application and recent progress in decoding of ECoG for Brain-Computer Interfaces (BCIs), sEEG has thus far received comparatively little attention for BCI decoding. Additionally, the success of the related deep-brain stimulation (DBS) implants bodes well for the potential for chronic sEEG applications. This article provides an overview of sEEG technology, BCI-related research, and prospective future directions of sEEG for long-term BCI applications.
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Affiliation(s)
- Christian Herff
- Department of Neurosurgery, School of Mental Health and Neurosciences, Maastricht University, Maastricht, Netherlands
| | - Dean J Krusienski
- ASPEN Lab, Biomedical Engineering Department, Virginia Commonwealth University, Richmond, VA, United States
| | - Pieter Kubben
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, Netherlands
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Hampson RE, Deadwyler SA, Berger TW. Multi-resolution multi-trial sparse classification model for decoding visual memories from hippocampal spikes in human. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:1046-1049. [PMID: 29060053 DOI: 10.1109/embc.2017.8037006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
To understand how memories are encoded in the hippocampus, we build memory decoding models to classify visual memories based on hippocampal activities in human. Model inputs are spatio-temporal patterns of spikes recorded in the hippocampal CA3 and CA1 regions of epilepsy patients performing a delayed match-to-sample (DMS) task. Model outputs are binary labels indicating categories and features of sample images. To solve the super high-dimensional estimation problem with short data length, we develop a multi-trial, sparse model estimation method utilizing B-spline basis functions with a large range of temporal resolutions and a regularized logistic classifier. Results show that this model can effectively avoid overfitting and provide significant amount of prediction to memory categories and features using very limited number of data points. Stable estimation of sparse classification function matrices for each label can be obtained with this multi-resolution, multi-trial procedure. These classification models can be used not only to predict memory contents, but also to design optimal spatio-temporal patterns for eliciting specific memories in the hippocampus, and thus have important implications to the development of hippocampal memory prostheses.
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