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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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2
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Ezzyat Y, Kragel JE, Solomon EA, Lega BC, Aronson JP, Jobst BC, Gross RE, Sperling MR, Worrell GA, Sheth SA, Wanda PA, Rizzuto DS, Kahana MJ. Functional and anatomical connectivity predict brain stimulation's mnemonic effects. Cereb Cortex 2024; 34:bhad427. [PMID: 38041253 PMCID: PMC10793570 DOI: 10.1093/cercor/bhad427] [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: 08/27/2023] [Revised: 10/05/2023] [Accepted: 10/06/2023] [Indexed: 12/03/2023] Open
Abstract
Closed-loop direct brain stimulation is a promising tool for modulating neural activity and behavior. However, it remains unclear how to optimally target stimulation to modulate brain activity in particular brain networks that underlie particular cognitive functions. Here, we test the hypothesis that stimulation's behavioral and physiological effects depend on the stimulation target's anatomical and functional network properties. We delivered closed-loop stimulation as 47 neurosurgical patients studied and recalled word lists. Multivariate classifiers, trained to predict momentary lapses in memory function, triggered the stimulation of the lateral temporal cortex (LTC) during the study phase of the task. We found that LTC stimulation specifically improved memory when delivered to targets near white matter pathways. Memory improvement was largest for targets near white matter that also showed high functional connectivity to the brain's memory network. These targets also reduced low-frequency activity in this network, an established marker of successful memory encoding. These data reveal how anatomical and functional networks mediate stimulation's behavioral and physiological effects, provide further evidence that closed-loop LTC stimulation can improve episodic memory, and suggest a method for optimizing neuromodulation through improved stimulation targeting.
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Affiliation(s)
- Youssef Ezzyat
- Dept. of Psychology, Wesleyan University, Middletown, CT 06459, USA
| | - James E Kragel
- Dept. of Neurology, University of Chicago, Chicago, IL 60637, USA
| | - Ethan A Solomon
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bradley C Lega
- Dept. of Neurosurgery, University of Texas Southwestern, Dallas, TX 75390, USA
| | - Joshua P Aronson
- Dept. of Neurosurgery, Beth Israel Deaconess Medical Center, Boston, MA 02215, USA
| | - Barbara C Jobst
- Dept. of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756, USA
| | - Robert E Gross
- Dept. of Neurosurgery, Emory University Hospital, Atlanta, GA 30322, USA
| | - Michael R Sperling
- Dept. of Neurology, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA 19107, USA
| | | | - Sameer A Sheth
- Dept. of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Paul A Wanda
- Dept. of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Daniel S Rizzuto
- Dept. of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael J Kahana
- Dept. of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
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3
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Ezzyat Y, Kragel JE, Solomon EA, Lega BC, Aronson JP, Jobst BC, Gross RE, Sperling MR, Worrell GA, Sheth SA, Wanda PA, Rizzuto DS, Kahana MJ. Functional and anatomical connectivity predict brain stimulation's mnemonic effects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.27.550851. [PMID: 37609181 PMCID: PMC10441352 DOI: 10.1101/2023.07.27.550851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/24/2023]
Abstract
Closed-loop direct brain stimulation is a promising tool for modulating neural activity and behavior. However, it remains unclear how to optimally target stimulation to modulate brain activity in particular brain networks that underlie particular cognitive functions. Here, we test the hypothesis that stimulation's behavioral and physiological effects depend on the stimulation target's anatomical and functional network properties. We delivered closed-loop stimulation as 47 neurosurgical patients studied and recalled word lists. Multivariate classifiers, trained to predict momentary lapses in memory function, triggered stimulation of the lateral temporal cortex (LTC) during the study phase of the task. We found that LTC stimulation specifically improved memory when delivered to targets near white matter pathways. Memory improvement was largest for targets near white matter that also showed high functional connectivity to the brain's memory network. These targets also reduced low-frequency activity in this network, an established marker of successful memory encoding. These data reveal how anatomical and functional networks mediate stimulation's behavioral and physiological effects, provide further evidence that closed-loop LTC stimulation can improve episodic memory, and suggest a method for optimizing neuromodulation through improved stimulation targeting.
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Affiliation(s)
- Youssef Ezzyat
- Dept. of Psychology, Wesleyan University, Middletown CT 06459
| | | | - Ethan A. Solomon
- Perelman School of Medicine, University of Pennsylvania, Philadelphia PA 19104
| | - Bradley C. Lega
- Dept. of Neurosurgery, University of Texas Southwestern, Dallas TX 75390
| | - Joshua P. Aronson
- Dept. of Neurosurgery, Dartmouth-Hitchcock Medical Center, Lebanon NH 03756
| | - Barbara C. Jobst
- Dept. of Neurology, Dartmouth-Hitchcock Medical Center, Lebanon NH 03756
| | - Robert E. Gross
- Dept. of Neurosurgery, Emory University Hospital, Atlanta GA 30322
| | - Michael R. Sperling
- Dept. of Neurology, Thomas Jefferson University Hospital, Philadelphia PA 19107
| | | | - Sameer A. Sheth
- Dept. of Neurosurgery, Columbia University Medical Center, New York, NY 10032
| | - Paul A. Wanda
- Dept. of Psychology, University of Pennsylvania, Philadelphia PA 19104
| | - Daniel S. Rizzuto
- Dept. of Psychology, University of Pennsylvania, Philadelphia PA 19104
| | - Michael J. Kahana
- Dept. of Psychology, University of Pennsylvania, Philadelphia PA 19104
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4
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Jangwan NS, Ashraf GM, Ram V, Singh V, Alghamdi BS, Abuzenadah AM, Singh MF. Brain augmentation and neuroscience technologies: current applications, challenges, ethics and future prospects. Front Syst Neurosci 2022; 16:1000495. [PMID: 36211589 PMCID: PMC9538357 DOI: 10.3389/fnsys.2022.1000495] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 08/31/2022] [Indexed: 12/02/2022] Open
Abstract
Ever since the dawn of antiquity, people have strived to improve their cognitive abilities. From the advent of the wheel to the development of artificial intelligence, technology has had a profound leverage on civilization. Cognitive enhancement or augmentation of brain functions has become a trending topic both in academic and public debates in improving physical and mental abilities. The last years have seen a plethora of suggestions for boosting cognitive functions and biochemical, physical, and behavioral strategies are being explored in the field of cognitive enhancement. Despite expansion of behavioral and biochemical approaches, various physical strategies are known to boost mental abilities in diseased and healthy individuals. Clinical applications of neuroscience technologies offer alternatives to pharmaceutical approaches and devices for diseases that have been fatal, so far. Importantly, the distinctive aspect of these technologies, which shapes their existing and anticipated participation in brain augmentations, is used to compare and contrast them. As a preview of the next two decades of progress in brain augmentation, this article presents a plausible estimation of the many neuroscience technologies, their virtues, demerits, and applications. The review also focuses on the ethical implications and challenges linked to modern neuroscientific technology. There are times when it looks as if ethics discussions are more concerned with the hypothetical than with the factual. We conclude by providing recommendations for potential future studies and development areas, taking into account future advancements in neuroscience innovation for brain enhancement, analyzing historical patterns, considering neuroethics and looking at other related forecasts.
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Affiliation(s)
- Nitish Singh Jangwan
- Department of Pharmacology, School of Pharmaceutical Sciences and Technology, Sardar Bhagwan Singh University, Balawala, India
| | - Ghulam Md Ashraf
- Pre-Clinical Research Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Veerma Ram
- Department of Pharmacology, School of Pharmaceutical Sciences and Technology, Sardar Bhagwan Singh University, Balawala, India
| | - Vinod Singh
- Prabha Harji Lal College of Pharmacy and Paraclinical Sciences, University of Jammu, Jammu, India
| | - Badrah S. Alghamdi
- Pre-Clinical Research Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Physiology, Neuroscience Unit, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Adel Mohammad Abuzenadah
- Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
- King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Mamta F. Singh
- Department of Pharmacology, School of Pharmaceutical Sciences and Technology, Sardar Bhagwan Singh University, Balawala, India
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5
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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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Venugopalan J, Tong L, Hassanzadeh HR, Wang MD. Multimodal deep learning models for early detection of Alzheimer's disease stage. Sci Rep 2021; 11:3254. [PMID: 33547343 PMCID: PMC7864942 DOI: 10.1038/s41598-020-74399-w] [Citation(s) in RCA: 106] [Impact Index Per Article: 35.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 01/22/2020] [Indexed: 02/06/2023] Open
Abstract
Most current Alzheimer's disease (AD) and mild cognitive disorders (MCI) studies use single data modality to make predictions such as AD stages. The fusion of multiple data modalities can provide a holistic view of AD staging analysis. Thus, we use deep learning (DL) to integrally analyze imaging (magnetic resonance imaging (MRI)), genetic (single nucleotide polymorphisms (SNPs)), and clinical test data to classify patients into AD, MCI, and controls (CN). We use stacked denoising auto-encoders to extract features from clinical and genetic data, and use 3D-convolutional neural networks (CNNs) for imaging data. We also develop a novel data interpretation method to identify top-performing features learned by the deep-models with clustering and perturbation analysis. Using Alzheimer's disease neuroimaging initiative (ADNI) dataset, we demonstrate that deep models outperform shallow models, including support vector machines, decision trees, random forests, and k-nearest neighbors. In addition, we demonstrate that integrating multi-modality data outperforms single modality models in terms of accuracy, precision, recall, and meanF1 scores. Our models have identified hippocampus, amygdala brain areas, and the Rey Auditory Verbal Learning Test (RAVLT) as top distinguished features, which are consistent with the known AD literature.
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Affiliation(s)
- Janani Venugopalan
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Li Tong
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Hamid Reza Hassanzadeh
- School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - May D Wang
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
- Winship Cancer Institute, Parker H. Petit Institute for Bioengineering and Biosciences, Institute of People and Technology, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
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7
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Wicks RT, Witcher MR, Couture DE, Laxton AW, Popli G, Whitlow CT, Fetterhoff D, Dakos AS, Roeder BM, Deadwyler SA, Hampson RE. Hippocampal CA1 and CA3 neural recording in the human brain: validation of depth electrode placement through high-resolution imaging and electrophysiology. Neurosurg Focus 2020; 49:E5. [PMID: 32610296 DOI: 10.3171/2020.4.focus20164] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 04/16/2020] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Intracranial human brain recordings typically utilize recording systems that do not distinguish individual neuron action potentials. In such cases, individual neurons are not identified by location within functional circuits. In this paper, verified localization of singly recorded hippocampal neurons within the CA3 and CA1 cell fields is demonstrated. METHODS Macro-micro depth electrodes were implanted in 23 human patients undergoing invasive monitoring for identification of epileptic seizure foci. Individual neurons were isolated and identified via extracellular action potential waveforms recorded via macro-micro depth electrodes localized within the hippocampus. A morphometric survey was performed using 3T MRI scans of hippocampi from the 23 implanted patients, as well as 46 normal (i.e., nonepileptic) patients and 26 patients with a history of epilepsy but no history of depth electrode placement, which provided average dimensions of the hippocampus along typical implantation tracks. Localization within CA3 and CA1 cell fields was tentatively assigned on the basis of recording electrode site, stereotactic positioning of the depth electrode in comparison with the morphometric survey, and postsurgical MRI. Cells were selected as candidate CA3 and CA1 principal neurons on the basis of waveform and firing rate characteristics and confirmed within the CA3-to-CA1 neural projection pathways via measures of functional connectivity. RESULTS Cross-correlation analysis confirmed that nearly 80% of putative CA3-to-CA1 cell pairs exhibited positive correlations compatible with feed-forward connection between the cells, while only 2.6% exhibited feedback (inverse) connectivity. Even though synchronous and long-latency correlations were excluded, feed-forward correlation between CA3-CA1 pairs was identified in 1071 (26%) of 4070 total pairs, which favorably compares to reports of 20%-25% feed-forward CA3-CA1 correlation noted in published animal studies. CONCLUSIONS This study demonstrates the ability to record neurons in vivo from specified regions and subfields of the human brain. As brain-machine interface and neural prosthetic research continues to expand, it is necessary to be able to identify recording and stimulation sites within neural circuits of interest.
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Affiliation(s)
| | | | | | | | | | | | - Dustin Fetterhoff
- 6Program in Neuroscience, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina
| | - Alexander S Dakos
- 6Program in Neuroscience, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina
| | - Brent M Roeder
- 6Program in Neuroscience, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina
| | - Sam A Deadwyler
- 5Physiology and Pharmacology, and.,6Program in Neuroscience, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina
| | - Robert E Hampson
- 2Neurology.,5Physiology and Pharmacology, and.,6Program in Neuroscience, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina
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8
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Schneider F, Horowitz A, Lesch KP, Dandekar T. Delaying memory decline: different options and emerging solutions. Transl Psychiatry 2020; 10:13. [PMID: 32066684 PMCID: PMC7026464 DOI: 10.1038/s41398-020-0697-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Revised: 11/28/2019] [Accepted: 12/08/2019] [Indexed: 12/13/2022] Open
Abstract
Memory decline can be a devastating disease and increases in aging Western populations. Memory enhancement technologies hold promise for this and other conditions. Approaches include stem cell transplantation, which improved memory in several animal studies as well as vaccination against Alzheimer´s disease (AD) by β-amyloid antibodies. For a positive clinical effect, the vaccine should probably be administered over a long period of time and before amyloid pathologies manifest in the brain. Different drugs, such as erythropoietin or antiplatelet therapy, improve memory in neuropsychiatric diseases or AD or at least in animal studies. Omega-3 polyunsaturated fatty acid-rich diets improve memory through the gut-brain axis by altering the gut flora through probiotics. Sports, dancing, and memory techniques (e.g., Method of Loci) utilize behavioral approaches for memory enhancement, and were effective in several studies. Augmented reality (AR) is an auspicious way for enhancing memory in real time. Future approaches may include memory prosthesis for head-injured patients and light therapy for restoring memory in AD. Memory enhancement in humans in health and disease holds big promises for the future. Memory training helps only in mild or no impairment. Clinical application requires further investigation.
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Affiliation(s)
- Felicitas Schneider
- grid.8379.50000 0001 1958 8658Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Alan Horowitz
- grid.8379.50000 0001 1958 8658Department of Bioinformatics, Biocenter, University of Würzburg, 97074 Würzburg, Germany
| | - Klaus-Peter Lesch
- grid.8379.50000 0001 1958 8658Division of Molecular Psychiatry, Laboratory of Translational Neuroscience, Center of Mental Health, University of Würzburg, Würzburg, Germany ,grid.448878.f0000 0001 2288 8774Laboratory of Psychiatric Neurobiology, Institute of Molecular Medicine, I.M. Sechenov First Moscow State Medical University, Moscow, Russia ,grid.5012.60000 0001 0481 6099Department of Psychiatry and Psychology, School for Mental Health and Neuroscience (MHeNS), Maastricht University, Maastricht, The Netherlands
| | - Thomas Dandekar
- Department of Bioinformatics, Biocenter, University of Würzburg, 97074, Würzburg, Germany. .,EMBL, Computational Biology and Structures Program, 69117, Heidelberg, Germany.
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9
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Elyahoodayan S, Jiang W, Xu H, Song D. A Multi-Channel Asynchronous Neurostimulator With Artifact Suppression for Neural Code-Based Stimulations. Front Neurosci 2019; 13:1011. [PMID: 31611764 PMCID: PMC6776638 DOI: 10.3389/fnins.2019.01011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2019] [Accepted: 09/05/2019] [Indexed: 11/18/2022] Open
Abstract
A novel neurostimulator for generating neural code-based, precise, asynchronous electrical stimulation pulses is designed, fabricated, and characterized. Through multiplexing, this system can deliver constant current biphasic pulses, with arbitrary temporal patterns, and pulse parameters to 32 electrodes using one pulse generator. The design also features a stimulus artifact suppression (SAS) technique that can be integrated with commercial amplifiers. Using an array of CMOS switches, electrodes are disconnected from recording amplifiers during stimulation, while the input of the recording system is shorted to ground through another CMOS switch to suppress ringing in the recording system. The timing of the switches used to block and suppress the stimulus artifact are crucial and are determined by the electrochemical properties of the electrode. This system allows stimulation and recording from the same electrodes to monitor local field potentials with short latencies from the region of stimulation for achieving feedback control of neural stimulation. In this way, timing between each pulse is controlled by inputs from an external source and stimulus magnitude is controlled by feed-back from neural response from the stimulated tissue. The system was implemented with low-power and compact packaged microchips to constitute an effective, cost-efficient, and miniaturized neurostimulator. The device has been first evaluated in phantom preparations and then tested in hippocampi of behaving rats. Benchtop results demonstrate the capability of the stimulator to generate arbitrary spatio-temporal pattern of stimulation pulses dictated by random number generators (RNGs) to control magnitude and timing between each individual biphasic pulse. In vivo results show that evoked potentials elicited by the neurostimulator can be recorded ∼2 ms after the termination of stimulus pulses from the same electrodes where stimulation pulses are delivered, whereas commercial amplifiers without such an artifact suppression typically result in tens to hundreds of milliseconds recovery period. This neurostimulator design is desirable in a variety of neural interface applications, particularly hippocampal memory prosthesis aiming to restore cognitive functions by reinstating neural code transmissions in the brain.
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Affiliation(s)
- Sahar Elyahoodayan
- Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA, United States
| | - Wenxuan Jiang
- Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA, United States
| | - Huijing Xu
- Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA, United States
| | - Dong Song
- Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA, United States.,Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, United States
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10
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Cutsuridis V. Memory Prosthesis: Is It Time for a Deep Neuromimetic Computing Approach? Front Neurosci 2019; 13:667. [PMID: 31333399 PMCID: PMC6624412 DOI: 10.3389/fnins.2019.00667] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Accepted: 06/11/2019] [Indexed: 11/13/2022] Open
Abstract
Memory loss, one of the most dreaded afflictions of the human condition, presents considerable burden on the world's health care system and it is recognized as a major challenge in the elderly. There are only a few neuromodulation treatments for memory dysfunctions. Open loop deep brain stimulation is such a treatment for memory improvement, but with limited success and conflicting results. In recent years closed-loop neuroprosthesis systems able to simultaneously record signals during behavioral tasks and generate with the use of internal neural factors the precise timing of stimulation patterns are presented as attractive alternatives and show promise in memory enhancement and restoration. A few such strides have already been made in both animals and humans, but with limited insights into their mechanisms of action. Here, I discuss why a deep neuromimetic computing approach linking multiple levels of description, mimicking the dynamics of brain circuits, interfaced with recording and stimulating electrodes could enhance the performance of current memory prosthesis systems, shed light into the neurobiology of learning and memory and accelerate the progress of memory prosthesis research. I propose what the necessary components (nodes, structure, connectivity, learning rules, and physiological responses) of such a deep neuromimetic model should be and what type of data are required to train/test its performance, so it can be used as a true substitute of damaged brain areas capable of restoring/enhancing their missing memory formation capabilities. Considerations to neural circuit targeting, tissue interfacing, electrode placement/implantation, and multi-network interactions in complex cognition are also provided.
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11
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Cinel C, Valeriani D, Poli R. Neurotechnologies for Human Cognitive Augmentation: Current State of the Art and Future Prospects. Front Hum Neurosci 2019; 13:13. [PMID: 30766483 PMCID: PMC6365771 DOI: 10.3389/fnhum.2019.00013] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 01/10/2019] [Indexed: 01/10/2023] Open
Abstract
Recent advances in neuroscience have paved the way to innovative applications that cognitively augment and enhance humans in a variety of contexts. This paper aims at providing a snapshot of the current state of the art and a motivated forecast of the most likely developments in the next two decades. Firstly, we survey the main neuroscience technologies for both observing and influencing brain activity, which are necessary ingredients for human cognitive augmentation. We also compare and contrast such technologies, as their individual characteristics (e.g., spatio-temporal resolution, invasiveness, portability, energy requirements, and cost) influence their current and future role in human cognitive augmentation. Secondly, we chart the state of the art on neurotechnologies for human cognitive augmentation, keeping an eye both on the applications that already exist and those that are emerging or are likely to emerge in the next two decades. Particularly, we consider applications in the areas of communication, cognitive enhancement, memory, attention monitoring/enhancement, situation awareness and complex problem solving, and we look at what fraction of the population might benefit from such technologies and at the demands they impose in terms of user training. Thirdly, we briefly review the ethical issues associated with current neuroscience technologies. These are important because they may differentially influence both present and future research on (and adoption of) neurotechnologies for human cognitive augmentation: an inferior technology with no significant ethical issues may thrive while a superior technology causing widespread ethical concerns may end up being outlawed. Finally, based on the lessons learned in our analysis, using past trends and considering other related forecasts, we attempt to forecast the most likely future developments of neuroscience technology for human cognitive augmentation and provide informed recommendations for promising future research and exploitation avenues.
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Affiliation(s)
- Caterina Cinel
- Brain Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom
| | - Davide Valeriani
- Brain Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom
- Department of Otolaryngology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, United States
| | - Riccardo Poli
- Brain Computer Interfaces and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom
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12
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13
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Hampson RE, Song D, Robinson BS, Fetterhoff D, Dakos AS, Roeder BM, She X, Wicks RT, Witcher MR, Couture DE, Laxton AW, Munger-Clary H, Popli G, Sollman MJ, Whitlow CT, Marmarelis VZ, Berger TW, Deadwyler SA. Developing a hippocampal neural prosthetic to facilitate human memory encoding and recall. J Neural Eng 2018; 15:036014. [PMID: 29589592 DOI: 10.1088/1741-2552/aaaed7] [Citation(s) in RCA: 79] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE We demonstrate here the first successful implementation in humans of a proof-of-concept system for restoring and improving memory function via facilitation of memory encoding using the patient's own hippocampal spatiotemporal neural codes for memory. Memory in humans is subject to disruption by drugs, disease and brain injury, yet previous attempts to restore or rescue memory function in humans typically involved only nonspecific, modulation of brain areas and neural systems related to memory retrieval. APPROACH We have constructed a model of processes by which the hippocampus encodes memory items via spatiotemporal firing of neural ensembles that underlie the successful encoding of short-term memory. A nonlinear multi-input, multi-output (MIMO) model of hippocampal CA3 and CA1 neural firing is computed that predicts activation patterns of CA1 neurons during the encoding (sample) phase of a delayed match-to-sample (DMS) human short-term memory task. MAIN RESULTS MIMO model-derived electrical stimulation delivered to the same CA1 locations during the sample phase of DMS trials facilitated short-term/working memory by 37% during the task. Longer term memory retention was also tested in the same human subjects with a delayed recognition (DR) task that utilized images from the DMS task, along with images that were not from the task. Across the subjects, the stimulated trials exhibited significant improvement (35%) in both short-term and long-term retention of visual information. SIGNIFICANCE These results demonstrate the facilitation of memory encoding which is an important feature for the construction of an implantable neural prosthetic to improve human memory.
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Affiliation(s)
- Robert E Hampson
- Wake Forest Baptist Medical Center, Winston-Salem, NC, United States of America
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Bishop JM. Is Anyone Home? A Way to Find Out If AI Has Become Self-Aware. Front Robot AI 2018; 5:17. [PMID: 33500904 PMCID: PMC7805864 DOI: 10.3389/frobt.2018.00017] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Accepted: 02/05/2018] [Indexed: 11/17/2022] Open
Affiliation(s)
- John Mark Bishop
- TCIDA, Goldsmiths, University of London, London, United Kingdom
- *Correspondence: John Mark Bishop,
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15
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Ramirez-Zamora A, Giordano JJ, Gunduz A, Brown P, Sanchez JC, Foote KD, Almeida L, Starr PA, Bronte-Stewart HM, Hu W, McIntyre C, Goodman W, Kumsa D, Grill WM, Walker HC, Johnson MD, Vitek JL, Greene D, Rizzuto DS, Song D, Berger TW, Hampson RE, Deadwyler SA, Hochberg LR, Schiff ND, Stypulkowski P, Worrell G, Tiruvadi V, Mayberg HS, Jimenez-Shahed J, Nanda P, Sheth SA, Gross RE, Lempka SF, Li L, Deeb W, Okun MS. Evolving Applications, Technological Challenges and Future Opportunities in Neuromodulation: Proceedings of the Fifth Annual Deep Brain Stimulation Think Tank. Front Neurosci 2018; 11:734. [PMID: 29416498 PMCID: PMC5787550 DOI: 10.3389/fnins.2017.00734] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2017] [Accepted: 12/15/2017] [Indexed: 12/21/2022] Open
Abstract
The annual Deep Brain Stimulation (DBS) Think Tank provides a focal opportunity for a multidisciplinary ensemble of experts in the field of neuromodulation to discuss advancements and forthcoming opportunities and challenges in the field. The proceedings of the fifth Think Tank summarize progress in neuromodulation neurotechnology and techniques for the treatment of a range of neuropsychiatric conditions including Parkinson's disease, dystonia, essential tremor, Tourette syndrome, obsessive compulsive disorder, epilepsy and cognitive, and motor disorders. Each section of this overview of the meeting provides insight to the critical elements of discussion, current challenges, and identified future directions of scientific and technological development and application. The report addresses key issues in developing, and emphasizes major innovations that have occurred during the past year. Specifically, this year's meeting focused on technical developments in DBS, design considerations for DBS electrodes, improved sensors, neuronal signal processing, advancements in development and uses of responsive DBS (closed-loop systems), updates on National Institutes of Health and DARPA DBS programs of the BRAIN initiative, and neuroethical and policy issues arising in and from DBS research and applications in practice.
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Affiliation(s)
- Adolfo Ramirez-Zamora
- Department of Neurology, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States,*Correspondence: Adolfo Ramirez-Zamora
| | - James J. Giordano
- Department of Neurology, Pellegrino Center for Clinical Bioethics, Georgetown University Medical Center, Washington, DC, United States
| | - Aysegul Gunduz
- J. Crayton Pruitt Family Department of Biomedical Engineering, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Peter Brown
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Justin C. Sanchez
- Biological Technologies Office, Defense Advanced Research Projects Agency, Arlington, VA, United States
| | - Kelly D. Foote
- Department of Neurosurgery, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Leonardo Almeida
- Department of Neurology, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Philip A. Starr
- Department of Neurological Surgery, Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, United States
| | - Helen M. Bronte-Stewart
- Departments of Neurology and Neurological Sciences and Neurosurgery, Stanford University, Stanford, CA, United States
| | - Wei Hu
- Department of Neurology, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Cameron McIntyre
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Wayne Goodman
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States,Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Doe Kumsa
- Division of Biomedical Physics, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, United States Food and Drug Administration, White Oak Federal Research Center, Silver Spring, MD, United States
| | - Warren M. Grill
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Harrison C. Walker
- Division of Movement Disorders, Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, United States,Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Matthew D. Johnson
- Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN, United States
| | - Jerrold L. Vitek
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
| | - David Greene
- NeuroPace, Inc., Mountain View, CA, United States
| | - Daniel S. Rizzuto
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, United States
| | - Dong Song
- 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
| | - Robert E. Hampson
- Physiology and Pharmacology, Wake Forest University School of Medicine, Wake Forest University, Winston-Salem, NC, United States
| | - Sam A. Deadwyler
- Physiology and Pharmacology, Wake Forest University School of Medicine, Wake Forest University, Winston-Salem, NC, United States
| | - Leigh R. Hochberg
- Department of Neurology, Center for Neurotechnology and Neurorecovery, Massachusetts General Hospital, Harvard Medical School, Harvard University, Boston, MA, United States,Center for Neurorestoration and Neurotechnology, Rehabilitation R and D Service, Veterans Affairs Medical Center, Providence, RI, United States,School of Engineering and Brown Institute for Brain Science, Brown University, Providence, RI, United States
| | - Nicholas D. Schiff
- Laboratory of Cognitive Neuromodulation, Feil Family Brain Mind Research Institute, Weill Cornell Medicine, New York, NY, United States
| | | | - Greg Worrell
- Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Vineet Tiruvadi
- Department of Biomedical Engineering, Georgia Institute of Technology, Emory University School of Medicine, Emory University, Atlanta, GA, United States
| | - Helen S. Mayberg
- Departments of Psychiatry, Neurology, and Radiology, Emory University School of Medicine, Emory University, Atlanta, GA, United States
| | - Joohi Jimenez-Shahed
- Parkinson's Disease Center and Movement Disorders Clinic, Department of Neurology, Baylor College of Medicine, Houston, TX, United States
| | - Pranav Nanda
- Department of Neurological Surgery, The Neurological Institute, Columbia University Herbert and Florence Irving Medical Center, Colombia University, New York, NY, United States
| | - Sameer A. Sheth
- Department of Neurological Surgery, The Neurological Institute, Columbia University Herbert and Florence Irving Medical Center, Colombia University, New York, NY, United States
| | - Robert E. Gross
- Department of Neurosurgery, Emory University, Atlanta, GA, United States
| | - Scott F. Lempka
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Luming Li
- National Engineering Laboratory for Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China,Precision Medicine and Healthcare Research Center, Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Beijing, China,Center of Epilepsy, Beijing Institute for Brain Disorders, Beijing, China
| | - Wissam Deeb
- Department of Neurology, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
| | - Michael S. Okun
- Department of Neurology, Center for Movement Disorders and Neurorestoration, University of Florida, Gainesville, FL, United States
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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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17
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Geng K, Shin DC, Song D, Hampson RE, Deadwyler SA, Berger TW, Marmarelis VZ. Mechanism-Based and Input-Output Modeling of the Key Neuronal Connections and Signal Transformations in the CA3-CA1 Regions of the Hippocampus. Neural Comput 2017; 30:149-183. [PMID: 29064783 DOI: 10.1162/neco_a_01031] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
This letter examines the results of input-output (nonparametric) modeling based on the analysis of data generated by a mechanism-based (parametric) model of CA3-CA1 neuronal connections in the hippocampus. The motivation is to obtain biological insight into the interpretation of such input-output (Volterra-equivalent) models estimated from synthetic data. The insights obtained may be subsequently used to interpretat input-output models extracted from actual experimental data. Specifically, we found that a simplified parametric model may serve as a useful tool to study the signal transformations in the hippocampal CA3-CA1 regions. Input-output modeling of model-based synthetic data show that GABAergic interneurons are responsible for regulating neuronal excitation, controlling the precision of spike timing, and maintaining network oscillations, in a manner consistent with previous studies. The input-output model obtained from real data exhibits intriguing similarities with its synthetic-data counterpart, demonstrating the importance of a dynamic resonance in the system/model response around 2 Hz to 3 Hz. Using the input-output model from real data as a guide, we may be able to amend the parametric model by incorporating more mechanisms in order to yield better-matching input-output model. The approach we present can also be applied to the study of other neural systems and pathways.
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Affiliation(s)
- Kunling Geng
- Department of Biomedical Engineering and the Biomedical Simulations Resource Center at the University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Dae C Shin
- Department of Biomedical Engineering and the Biomedical Simulations Resource Center at the University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Dong Song
- Department of Biomedical Engineering and the Biomedical Simulations Resource Center at the University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Robert E Hampson
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, U.S.A.
| | - Samuel A Deadwyler
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, U.S.A.
| | - Theodore W Berger
- Department of Biomedical Engineering and the Biomedical Simulations Resource Center at the University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Vasilis Z Marmarelis
- Department of Biomedical Engineering and the Biomedical Simulations Resource Center at the University of Southern California, Los Angeles, CA, 90089, U.S.A.
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18
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Affiliation(s)
- Walter Glannon
- Philosophy/Arts, University of Calgary, Calgary, AB, Canada
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19
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Hampson RE, Robinson BS, Marmarelis VZ, Deadwyler SA, Berger TW. Decoding memory features from hippocampal spiking activities using sparse classification models. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2016:1620-1623. [PMID: 28268639 DOI: 10.1109/embc.2016.7591023] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
To understand how memory information is encoded in the hippocampus, we build classification models to decode memory features from hippocampal CA3 and CA1 spatio-temporal patterns of spikes recorded from epilepsy patients performing a memory-dependent delayed match-to-sample task. The classification model consists of a set of B-spline basis functions for extracting memory features from the spike patterns, and a sparse logistic regression classifier for generating binary categorical output of memory features. Results show that classification models can extract significant amount of memory information with respects to types of memory tasks and categories of sample images used in the task, despite the high level of variability in prediction accuracy due to the small sample size. These results support the hypothesis that memories are encoded in the hippocampal activities and have important implication to the development of hippocampal memory prostheses.
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20
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Song D, Robinson BS, Hampson RE, Marmarelis VZ, Deadwyler SA, Berger TW. Sparse Large-Scale Nonlinear Dynamical Modeling of Human Hippocampus for Memory Prostheses. IEEE Trans Neural Syst Rehabil Eng 2016; 26:272-280. [PMID: 28113595 DOI: 10.1109/tnsre.2016.2604423] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In order to build hippocampal prostheses for restoring memory functions, we build sparse multi-input, multi-output (MIMO) nonlinear dynamical models of the human hippocampus. Spike trains are recorded from hippocampal CA3 and CA1 regions of epileptic patients performing a variety of memory-dependent delayed match-to-sample (DMS) tasks. Using CA3 and CA1 spike trains as inputs and outputs respectively, sparse generalized Laguerre-Volterra models are estimated with group lasso and local coordinate descent methods to capture the nonlinear dynamics underlying the CA3-CA1 spike train transformations. These models can accurately predict the CA1 spike trains based on the ongoing CA3 spike trains during multiple memory events, e.g., sample presentation, sample response, match presentation and match response, of the DMS task, and thus will serve as the computational basis of human hippocampal memory prostheses.
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Kohno T, Sekikawa M, Li J, Nanami T, Aihara K. Qualitative-Modeling-Based Silicon Neurons and Their Networks. Front Neurosci 2016; 10:273. [PMID: 27378842 PMCID: PMC4908299 DOI: 10.3389/fnins.2016.00273] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Accepted: 05/31/2016] [Indexed: 11/13/2022] Open
Abstract
The ionic conductance models of neuronal cells can finely reproduce a wide variety of complex neuronal activities. However, the complexity of these models has prompted the development of qualitative neuron models. They are described by differential equations with a reduced number of variables and their low-dimensional polynomials, which retain the core mathematical structures. Such simple models form the foundation of a bottom-up approach in computational and theoretical neuroscience. We proposed a qualitative-modeling-based approach for designing silicon neuron circuits, in which the mathematical structures in the polynomial-based qualitative models are reproduced by differential equations with silicon-native expressions. This approach can realize low-power-consuming circuits that can be configured to realize various classes of neuronal cells. In this article, our qualitative-modeling-based silicon neuron circuits for analog and digital implementations are quickly reviewed. One of our CMOS analog silicon neuron circuits can realize a variety of neuronal activities with a power consumption less than 72 nW. The square-wave bursting mode of this circuit is explained. Another circuit can realize Class I and II neuronal activities with about 3 nW. Our digital silicon neuron circuit can also realize these classes. An auto-associative memory realized on an all-to-all connected network of these silicon neurons is also reviewed, in which the neuron class plays important roles in its performance.
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Affiliation(s)
- Takashi Kohno
- Institute of Industrial Science, University of Tokyo Tokyo, Japan
| | - Munehisa Sekikawa
- Department of Mechanical and Intelligent Engineering, Utsunomiya University Utsunomiya, Japan
| | - Jing Li
- College of Electronic Engineering, Xi'an Shiyou University Xi'an, China
| | - Takuya Nanami
- Department of Electrical Engineering and Information Systems, University of Tokyo Tokyo, Japan
| | - Kazuyuki Aihara
- Institute of Industrial Science, University of Tokyo Tokyo, Japan
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Deadwyler SA, Hampson RE, Song D, Opris I, Gerhardt GA, Marmarelis VZ, Berger TW. A cognitive prosthesis for memory facilitation by closed-loop functional ensemble stimulation of hippocampal neurons in primate brain. Exp Neurol 2016; 287:452-460. [PMID: 27233622 DOI: 10.1016/j.expneurol.2016.05.031] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2015] [Revised: 05/21/2016] [Accepted: 05/23/2016] [Indexed: 11/25/2022]
Abstract
Very productive collaborative investigations characterized how multineuron hippocampal ensembles recorded in nonhuman primates (NHPs) encode short-term memory necessary for successful performance in a delayed match to sample (DMS) task and utilized that information to devise a unique nonlinear multi-input multi-output (MIMO) memory prosthesis device to enhance short-term memory in real-time during task performance. Investigations have characterized how the hippocampus in primate brain encodes information in a multi-item, rule-controlled, delayed match to sample (DMS) task. The MIMO model was applied via closed loop feedback micro-current stimulation during the task via conformal electrode arrays and enhanced performance of the complex memory requirements. These findings clearly indicate detection of a means by which the hippocampus encodes information and transmits this information to other brain regions involved in memory processing. By employing the nonlinear dynamic multi-input/multi-output (MIMO) model, developed and adapted to hippocampal neural ensemble firing patterns derived from simultaneous recorded multi-neuron CA1 and CA3 activity, it was possible to extract information encoded in the Sample phase of DMS trials that was necessary for successful performance in the subsequent Match phase of the task. The extension of this MIMO model to online delivery of electrical stimulation patterns to the same recording loci that exhibited successful CA1 firing in the DMS Sample Phase provided the means to increase task performance on a trial-by-trial basis. Increased utility of the MIMO model as a memory prosthesis was exhibited by the demonstration of cumulative increases in DMS task performance with repeated MIMO stimulation over many sessions. These results, reported below in this article, provide the necessary demonstrations to further the feasibility of the MIMO model as a memory prosthesis to recover and/or enhance encoding of cognitive information in humans with memory disruptions resulting from brain injury, disease or aging.
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Dura-Bernal S, Li K, Neymotin SA, Francis JT, Principe JC, Lytton WW. Restoring Behavior via Inverse Neurocontroller in a Lesioned Cortical Spiking Model Driving a Virtual Arm. Front Neurosci 2016; 10:28. [PMID: 26903796 PMCID: PMC4746359 DOI: 10.3389/fnins.2016.00028] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Accepted: 01/25/2016] [Indexed: 01/08/2023] Open
Abstract
Neural stimulation can be used as a tool to elicit natural sensations or behaviors by modulating neural activity. This can be potentially used to mitigate the damage of brain lesions or neural disorders. However, in order to obtain the optimal stimulation sequences, it is necessary to develop neural control methods, for example by constructing an inverse model of the target system. For real brains, this can be very challenging, and often unfeasible, as it requires repeatedly stimulating the neural system to obtain enough probing data, and depends on an unwarranted assumption of stationarity. By contrast, detailed brain simulations may provide an alternative testbed for understanding the interactions between ongoing neural activity and external stimulation. Unlike real brains, the artificial system can be probed extensively and precisely, and detailed output information is readily available. Here we employed a spiking network model of sensorimotor cortex trained to drive a realistic virtual musculoskeletal arm to reach a target. The network was then perturbed, in order to simulate a lesion, by either silencing neurons or removing synaptic connections. All lesions led to significant behvaioral impairments during the reaching task. The remaining cells were then systematically probed with a set of single and multiple-cell stimulations, and results were used to build an inverse model of the neural system. The inverse model was constructed using a kernel adaptive filtering method, and was used to predict the neural stimulation pattern required to recover the pre-lesion neural activity. Applying the derived neurostimulation to the lesioned network improved the reaching behavior performance. This work proposes a novel neurocontrol method, and provides theoretical groundwork on the use biomimetic brain models to develop and evaluate neurocontrollers that restore the function of damaged brain regions and the corresponding motor behaviors.
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Affiliation(s)
- Salvador Dura-Bernal
- Department of Physiology and Pharmacology, State University of New York Downstate Medical Center Brooklyn, NY, USA
| | - Kan Li
- Department of Electrical and Computer Engineering, University of Florida Gainesville, FL, USA
| | - Samuel A Neymotin
- Department of Physiology and Pharmacology, State University of New York Downstate Medical Center Brooklyn, NY, USA
| | - Joseph T Francis
- Department of Physiology and Pharmacology, State University of New York Downstate Medical CenterBrooklyn, NY, USA; BME Cullen College of Engineering, University of HoustonHouston, TX, USA
| | - Jose C Principe
- Department of Electrical and Computer Engineering, University of Florida Gainesville, FL, USA
| | - William W Lytton
- Department of Physiology and Pharmacology, State University of New York Downstate Medical CenterBrooklyn, NY, USA; Department of Neurology, State University of New York Downstate Medical CenterBrooklyn, NY, USA; Department of Neurology, Kings County Hospital CenterBrooklyn, NY, USA
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Greenwald E, Masters MR, Thakor NV. Implantable neurotechnologies: bidirectional neural interfaces--applications and VLSI circuit implementations. Med Biol Eng Comput 2016; 54:1-17. [PMID: 26753776 PMCID: PMC4839984 DOI: 10.1007/s11517-015-1429-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2015] [Accepted: 12/10/2015] [Indexed: 12/20/2022]
Abstract
A bidirectional neural interface is a device that transfers information into and out of the nervous system. This class of devices has potential to improve treatment and therapy in several patient populations. Progress in very large-scale integration has advanced the design of complex integrated circuits. System-on-chip devices are capable of recording neural electrical activity and altering natural activity with electrical stimulation. Often, these devices include wireless powering and telemetry functions. This review presents the state of the art of bidirectional circuits as applied to neuroprosthetic, neurorepair, and neurotherapeutic systems.
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Affiliation(s)
- Elliot Greenwald
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
| | - Matthew R Masters
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Nitish V Thakor
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Singapore Institute for Neurotechnology (SINAPSE), National University of Singapore, Singapore, Singapore.
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Adewole DO, Serruya MD, Harris JP, Burrell JC, Petrov D, Chen HI, Wolf JA, Cullen DK. The Evolution of Neuroprosthetic Interfaces. Crit Rev Biomed Eng 2016; 44:123-52. [PMID: 27652455 PMCID: PMC5541680 DOI: 10.1615/critrevbiomedeng.2016017198] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The ideal neuroprosthetic interface permits high-quality neural recording and stimulation of the nervous system while reliably providing clinical benefits over chronic periods. Although current technologies have made notable strides in this direction, significant improvements must be made to better achieve these design goals and satisfy clinical needs. This article provides an overview of the state of neuroprosthetic interfaces, starting with the design and placement of these interfaces before exploring the stimulation and recording platforms yielded from contemporary research. Finally, we outline emerging research trends in an effort to explore the potential next generation of neuroprosthetic interfaces.
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Affiliation(s)
- Dayo O. Adewole
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - Mijail D. Serruya
- Department of Neurology, Jefferson University, Philadelphia, PA, USA
| | - James P. Harris
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
| | - Justin C. Burrell
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
| | - Dmitriy Petrov
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
| | - H. Isaac Chen
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
| | - John A. Wolf
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
| | - D. Kacy Cullen
- Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA
- Penn Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
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26
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Harrington IA, Grisham W, Brasier DJ, Gallagher SP, Gizerian SS, Gordon RG, Hagenauer MH, Linden ML, Lom B, Olivo R, Sandstrom NJ, Stough S, Vilinsky I, Wiest MC. An Instructor's Guide to (Some of) the Most Amazing Papers in Neuroscience. JOURNAL OF UNDERGRADUATE NEUROSCIENCE EDUCATION : JUNE : A PUBLICATION OF FUN, FACULTY FOR UNDERGRADUATE NEUROSCIENCE 2015; 14:R3-R14. [PMID: 26557803 PMCID: PMC4640495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 08/13/2015] [Accepted: 08/22/2015] [Indexed: 06/05/2023]
Abstract
Although textbooks are still assigned in many undergraduate science courses, it is now not uncommon, even in some of the earliest courses in the curriculum, to supplement texts with primary source readings from the scientific literature. Not only does reading these articles help students develop an understanding of specific course content, it also helps foster an ability to engage with the discipline the way its practitioners do. One challenge with this approach, however, is that it can be difficult for instructors to select appropriate readings on topics outside of their areas of expertise as would be required in a survey course, for example. Here we present a subset of the papers that were offered in response to a request for the "most amazing papers in neuroscience" that appeared on the listserv of the Faculty for Undergraduate Neuroscience (FUN). Each contributor was subsequently asked to describe briefly the content of their recommended papers, their pedagogical value, and the audiences for which these papers are best suited. Our goal is to provide readers with sufficient information to decide whether such articles might be useful in their own classes. It is not our intention that any article within this collection will provide the final word on an area of investigation, nor that this collection will provide the final word for the discipline as a whole. Rather, this article is a collection of papers that have proven themselves valuable in the hands of these particular educators. Indeed, it is our hope that this collection represents the inaugural offering of what will become a regular feature in this journal, so that we can continue to benefit from the diverse expertise of the FUN community.
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Opris I, Santos LM, Gerhardt GA, Song D, Berger TW, Hampson RE, Deadwyler SA. Distributed encoding of spatial and object categories in primate hippocampal microcircuits. Front Neurosci 2015; 9:317. [PMID: 26500473 PMCID: PMC4594006 DOI: 10.3389/fnins.2015.00317] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2015] [Accepted: 08/24/2015] [Indexed: 11/16/2022] Open
Abstract
The primate hippocampus plays critical roles in the encoding, representation, categorization and retrieval of cognitive information. Such cognitive abilities may use the transformational input-output properties of hippocampal laminar microcircuitry to generate spatial representations and to categorize features of objects, images, and their numeric characteristics. Four nonhuman primates were trained in a delayed-match-to-sample (DMS) task while multi-neuron activity was simultaneously recorded from the CA1 and CA3 hippocampal cell fields. The results show differential encoding of spatial location and categorization of images presented as relevant stimuli in the task. Individual hippocampal cells encoded visual stimuli only on specific types of trials in which retention of either, the Sample image, or the spatial position of the Sample image indicated at the beginning of the trial, was required. Consistent with such encoding, it was shown that patterned microstimulation applied during Sample image presentation facilitated selection of either Sample image spatial locations or types of images, during the Match phase of the task. These findings support the existence of specific codes for spatial and numeric object representations in primate hippocampus which can be applied on differentially signaled trials. Moreover, the transformational properties of hippocampal microcircuitry, together with the patterned microstimulation are supporting the practical importance of this approach for cognitive enhancement and rehabilitation, needed for memory neuroprosthetics.
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Affiliation(s)
- Ioan Opris
- Department of Physiology and Pharmacology, Wake Forest University School of MedicineWinston-Salem, NC, USA
| | - Lucas M. Santos
- Department of Physiology and Pharmacology, Wake Forest University School of MedicineWinston-Salem, NC, USA
| | - Greg A. Gerhardt
- Department of Anatomy and Neurobiology, University of KentuckyLexington, KY, USA
| | - Dong Song
- Department of Biomedical Engineering, University of Southern CaliforniaLos Angeles, CA, USA
| | - Theodore W. Berger
- Department of Biomedical Engineering, University of Southern CaliforniaLos Angeles, CA, USA
| | - Robert E. Hampson
- Department of Physiology and Pharmacology, Wake Forest University School of MedicineWinston-Salem, NC, USA
| | - Sam A. Deadwyler
- Department of Physiology and Pharmacology, Wake Forest University School of MedicineWinston-Salem, NC, USA
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Lewis PM, Rosenfeld JV. Electrical stimulation of the brain and the development of cortical visual prostheses: An historical perspective. Brain Res 2015; 1630:208-24. [PMID: 26348986 DOI: 10.1016/j.brainres.2015.08.038] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2015] [Accepted: 08/14/2015] [Indexed: 10/23/2022]
Abstract
Rapid advances are occurring in neural engineering, bionics and the brain-computer interface. These milestones have been underpinned by staggering advances in micro-electronics, computing, and wireless technology in the last three decades. Several cortically-based visual prosthetic devices are currently being developed, but pioneering advances with early implants were achieved by Brindley followed by Dobelle in the 1960s and 1970s. We have reviewed these discoveries within the historical context of the medical uses of electricity including attempts to cure blindness, the discovery of the visual cortex, and opportunities for cortex stimulation experiments during neurosurgery. Further advances were made possible with improvements in electrode design, greater understanding of cortical electrophysiology and miniaturisation of electronic components. Human trials of a new generation of prototype cortical visual prostheses for the blind are imminent. This article is part of a Special Issue entitled Hold Item.
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Affiliation(s)
- Philip M Lewis
- Monash Vision Group, Department of Electrical and Computer Systems Engineering, Monash University, Wellington Road, Clayton, VIC 3800, Australia; Department of Neurosurgery, Level 1 Old Baker Building, Alfred Hospital, 55 Commercial Road, Melbourne, VIC 3004, Australia; Department of Surgery, Monash University Central Clinical School, Level 6 Alfred Centre, 99 Commercial Road, Melbourne, VIC 3004, Australia; Monash Institute of Medical Engineering, Monash University, Wellington Road, Clayton, VIC 3800, Australia.
| | - Jeffrey V Rosenfeld
- Monash Vision Group, Department of Electrical and Computer Systems Engineering, Monash University, Wellington Road, Clayton, VIC 3800, Australia; Department of Neurosurgery, Level 1 Old Baker Building, Alfred Hospital, 55 Commercial Road, Melbourne, VIC 3004, Australia; Department of Surgery, Monash University Central Clinical School, Level 6 Alfred Centre, 99 Commercial Road, Melbourne, VIC 3004, Australia; Monash Institute of Medical Engineering, Monash University, Wellington Road, Clayton, VIC 3800, Australia; F. Edward Hébert School of Medicine, Uniformed Services University of the Health Sciences, 4301 Jones Bridge Rd., Bethesda, MD 20814, United States.
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30
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A neural network model of reliably optimized spike transmission. Cogn Neurodyn 2015; 9:265-77. [PMID: 25972976 DOI: 10.1007/s11571-015-9329-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2014] [Revised: 12/17/2014] [Accepted: 01/07/2015] [Indexed: 10/24/2022] Open
Abstract
We studied the detailed structure of a neuronal network model in which the spontaneous spike activity is correctly optimized to match the experimental data and discuss the reliability of the optimized spike transmission. Two stochastic properties of the spontaneous activity were calculated: the spike-count rate and synchrony size. The synchrony size, expected to be an important factor for optimization of spike transmission in the network, represents a percentage of observed coactive neurons within a time bin, whose probability approximately follows a power-law. We systematically investigated how these stochastic properties could matched to those calculated from the experimental data in terms of the log-normally distributed synaptic weights between excitatory and inhibitory neurons and synaptic background activity induced by the input current noise in the network model. To ensure reliably optimized spike transmission, the synchrony size as well as spike-count rate were simultaneously optimized. This required changeably balanced log-normal distributions of synaptic weights between excitatory and inhibitory neurons and appropriately amplified synaptic background activity. Our results suggested that the inhibitory neurons with a hub-like structure driven by intensive feedback from excitatory neurons were a key factor in the simultaneous optimization of the spike-count rate and synchrony size, regardless of different spiking types between excitatory and inhibitory neurons.
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31
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Opris I, Fuqua JL, Gerhardt GA, Hampson RE, Deadwyler SA. Prefrontal cortical recordings with biomorphic MEAs reveal complex columnar-laminar microcircuits for BCI/BMI implementation. J Neurosci Methods 2015; 244:104-13. [PMID: 24954713 PMCID: PMC4595476 DOI: 10.1016/j.jneumeth.2014.05.029] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2014] [Revised: 05/22/2014] [Accepted: 05/24/2014] [Indexed: 01/25/2023]
Abstract
The mammalian prefrontal cortex known as the seat of high brain functions uses a six layer distribution of minicolumnar neurons to coordinate the integration of sensory information and the selection of relevant signals for goal driven behavior. To reveal the complex functionality of these columnar microcircuits we employed simultaneous recordings with several configurations of biomorphic microelectrode arrays (MEAs) within cortical layers in adjacent minicolumns, in four nohuman primates (NHPs) performing a delayed match-to-sample (DMS) visual discrimination task. We examined: (1) the functionality of inter-laminar, and inter-columnar interactions between pairs of cells in the same or different minicolumns by use of normalized cross-correlation histograms (CCH), (2) the modulation of glutamate concentration in layer 2/3, and (3) the potential interactions within these microcircuits. The results demonstrate that neurons in both infra-granular and supra-granular layers interact through inter-laminar loops, as well as through intra-laminar to produce behavioral response signals. These results provide new insights into the manner in which prefrontal cortical microcircuitry integrates sensory stimuli used to provide behaviorally relevant signals that may be implemented in brain computer/machine interfaces (BCI/BMIs) during performance of the task.
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Affiliation(s)
- Ioan Opris
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
| | - Joshua L Fuqua
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Greg A Gerhardt
- Department of Anatomy and Neurobiology, University of Kentucky, Lexington, KY, USA
| | - Robert E Hampson
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Samuel A Deadwyler
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
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33
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Miranda RA, Casebeer WD, Hein AM, Judy JW, Krotkov EP, Laabs TL, Manzo JE, Pankratz KG, Pratt GA, Sanchez JC, Weber DJ, Wheeler TL, Ling GS. DARPA-funded efforts in the development of novel brain–computer interface technologies. J Neurosci Methods 2015; 244:52-67. [PMID: 25107852 DOI: 10.1016/j.jneumeth.2014.07.019] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2014] [Revised: 07/08/2014] [Accepted: 07/24/2014] [Indexed: 02/01/2023]
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Hanbury DB, Robbins ME, Bourland JD, Wheeler KT, Peiffer AM, Mitchell EL, Daunais JB, Deadwyler SA, Cline JM. Pathology of fractionated whole-brain irradiation in rhesus monkeys ( Macaca mulatta ). Radiat Res 2015; 183:367-74. [PMID: 25688996 PMCID: PMC4467778 DOI: 10.1667/rr13898.1] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Fractionated whole-brain irradiation (fWBI), used to treat brain metastases, often leads to neurologic injury and cognitive impairment. The cognitive effects of irradiation in nonhuman primates (NHP) have been previously published; this report focuses on corresponding neuropathologic changes that could have served as the basis for those effects in the same study. Four rhesus monkeys were exposed to 40 Gy of fWBI [5 Gy × 8 fraction (fx), 2 fx/week for four weeks] and received anatomical MRI prior to, and 14 months after fWBI. Neurologic and histologic sequelae were studied posthumously. Three of the NHPs underwent cognitive assessments, and each exhibited radiation-induced impairment associated with various degrees of vascular and inflammatory neuropathology. Two NHPs had severe multifocal necrosis of the forebrain, midbrain and brainstem. Histologic and MRI findings were in agreement, and the severity of cognitive decrement previously reported corresponded to the degree of observed pathology in two of the animals. In response to fWBI, the NHPs showed pathology similar to humans exposed to radiation and show comparable cognitive decline. These results provide a basis for implementing NHPs to examine and treat adverse cognitive and neurophysiologic sequelae of radiation exposure in humans.
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Affiliation(s)
- David B. Hanbury
- Department of Pathology/Comparative Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - Mike E. Robbins
- Department of Pathology/Comparative Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina
- Department of Radiation Oncology, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - J. Daniel Bourland
- Department of Radiation Oncology, Wake Forest University School of Medicine, Winston-Salem, North Carolina
- Brain Tumor Center of Excellence, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - Kenneth T. Wheeler
- Brain Tumor Center of Excellence, Wake Forest University School of Medicine, Winston-Salem, North Carolina
- Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - Ann M. Peiffer
- Department of Radiation Oncology, Wake Forest University School of Medicine, Winston-Salem, North Carolina
- Brain Tumor Center of Excellence, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - Erin L. Mitchell
- Animal Resources Program, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - James B. Daunais
- Department of Physiology & Pharmacology, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - Samuel A. Deadwyler
- Department of Physiology & Pharmacology, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - J. Mark Cline
- Department of Pathology/Comparative Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina
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Song D, Robinson BS, Hampson RE, Marmarelis VZ, Deadwyler SA, Berger TW. Sparse generalized volterra model of human hippocampal spike train transformation for memory prostheses. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2015:3961-3964. [PMID: 26737161 DOI: 10.1109/embc.2015.7319261] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
In order to build hippocampal prostheses for restoring memory functions, we build multi-input, multi-output (MIMO) nonlinear dynamical models of the human hippocampus. Spike trains are recorded from the hippocampal CA3 and CA1 regions of epileptic patients performing a memory-dependent delayed match-to-sample task. Using CA3 and CA1 spike trains as inputs and outputs respectively, second-order sparse generalized Laguerre-Volterra models are estimated with group lasso and local coordinate descent methods to capture the nonlinear dynamics underlying the spike train transformations. These models can accurately predict the CA1 spike trains based on the ongoing CA3 spike trains and thus will serve as the computational basis of the hippocampal memory prosthesis.
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Deadwyler SA, Berger TW, Opris I, Song D, Hampson RE. Neurons and networks organizing and sequencing memories. Brain Res 2014; 1621:335-44. [PMID: 25553617 DOI: 10.1016/j.brainres.2014.12.037] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2014] [Revised: 12/16/2014] [Accepted: 12/17/2014] [Indexed: 01/23/2023]
Abstract
Hippocampal CA1 and CA3 neurons sampled randomly in large numbers in primate brain show conclusive examples of hierarchical encoding of task specific information. Hierarchical encoding allows multi-task utilization of the same hippocampal neural networks via distributed firing between neurons that respond to subsets, attributes or "categories" of stimulus features which can be applied in events in different contexts. In addition, such networks are uniquely adaptable to neural systems unrestricted by rigid synaptic architecture (i.e. columns, layers or "patches") which physically limits the number of possible task-specific interactions between neurons. Also hierarchical encoding is not random; it requires multiple exposures to the same types of relevant events to elevate synaptic connectivity between neurons for different stimulus features that occur in different task-dependent contexts. The large number of cells within associated hierarchical circuits in structures such as hippocampus provides efficient processing of information relevant to common memory-dependent behavioral decisions within different contextual circumstances. This article is part of a Special Issue entitled SI: Brain and Memory.
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Affiliation(s)
- Sam A Deadwyler
- Department of Physiology & Pharmacology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157-1083, USA.
| | - Theodore W Berger
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, 1042 Downey Way (DRB140), Los Angeles, CA 90089-1111, USA
| | - Ioan Opris
- Department of Physiology & Pharmacology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157-1083, USA
| | - Dong Song
- Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, 1042 Downey Way (DRB140), Los Angeles, CA 90089-1111, USA
| | - Robert E Hampson
- Department of Physiology & Pharmacology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157-1083, USA
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Serruya MD. Bottlenecks to clinical translation of direct brain-computer interfaces. Front Syst Neurosci 2014; 8:226. [PMID: 25520632 PMCID: PMC4251316 DOI: 10.3389/fnsys.2014.00226] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2014] [Accepted: 11/10/2014] [Indexed: 12/17/2022] Open
Abstract
Despite several decades of research into novel brain-implantable devices to treat a range of diseases, only two—cochlear implants for sensorineural hearing loss and deep brain stimulation for movement disorders—have yielded any appreciable clinical benefit. Obstacles to translation include technical factors (e.g., signal loss due to gliosis or micromotion), lack of awareness of current clinical options for patients that the new therapy must outperform, traversing between federal and corporate funding needed to support clinical trials, and insufficient management expertise. This commentary reviews these obstacles preventing the translation of promising new neurotechnologies into clinical application and suggests some principles that interdisciplinary teams in academia and industry could adopt to enhance their chances of success.
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Affiliation(s)
- Mijail D Serruya
- Department of Neurology, Thomas Jefferson University Philadelphia, PA, USA
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38
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Deca D, Koene RA. Experimental enhancement of neurphysiological function. Front Syst Neurosci 2014; 8:189. [PMID: 25339871 PMCID: PMC4189435 DOI: 10.3389/fnsys.2014.00189] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2014] [Accepted: 09/17/2014] [Indexed: 01/27/2023] Open
Affiliation(s)
- Diana Deca
- Center for Integrated Protein Science and SyNergy Cluster, Institute of Neuroscience, Technical University Munich Munich, Germany
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Song D, Chan RHM, Robinson BS, Marmarelis VZ, Opris I, Hampson RE, Deadwyler SA, Berger TW. Identification of functional synaptic plasticity from spiking activities using nonlinear dynamical modeling. J Neurosci Methods 2014; 244:123-35. [PMID: 25280984 DOI: 10.1016/j.jneumeth.2014.09.023] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2014] [Revised: 09/23/2014] [Accepted: 09/23/2014] [Indexed: 11/30/2022]
Abstract
This paper presents a systems identification approach for studying the long-term synaptic plasticity using natural spiking activities. This approach consists of three modeling steps. First, a multi-input, single-output (MISO), nonlinear dynamical spiking neuron model is formulated to estimate and represent the synaptic strength in means of functional connectivity between input and output neurons. Second, this MISO model is extended to a nonstationary form to track the time-varying properties of the synaptic strength. Finally, a Volterra modeling method is used to extract the synaptic learning rule, e.g., spike-timing-dependent plasticity, for the explanation of the input-output nonstationarity as the consequence of the past input-output spiking patterns. This framework is developed to study the underlying mechanisms of learning and memory formation in behaving animals, and may serve as the computational basis for building the next-generation adaptive cortical prostheses.
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Affiliation(s)
- Dong Song
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA.
| | - Rosa H M Chan
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China.
| | - Brian S Robinson
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA.
| | - Vasilis Z Marmarelis
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA.
| | - Ioan Opris
- Department of Physiology & Pharmacology, Wake Forest University, School of Medicine, Winston-Salem, NC 27157, USA.
| | - Robert E Hampson
- Department of Physiology & Pharmacology, Wake Forest University, School of Medicine, Winston-Salem, NC 27157, USA.
| | - Sam A Deadwyler
- Department of Physiology & Pharmacology, Wake Forest University, School of Medicine, Winston-Salem, NC 27157, USA.
| | - Theodore W Berger
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA.
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Opris I, Ferrera VP. Modifying cognition and behavior with electrical microstimulation: implications for cognitive prostheses. Neurosci Biobehav Rev 2014; 47:321-35. [PMID: 25242103 DOI: 10.1016/j.neubiorev.2014.09.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2014] [Accepted: 09/09/2014] [Indexed: 11/18/2022]
Abstract
A fundamental goal of cognitive neuroscience is to understand how brain activity generates complex mental states and behaviors. While neuronal activity may predict or correlate with behavioral responses in a cognitive task, the use of electrical microstimulation presents the possibility to augment such correlational findings with direct evidence for causal relationships. Although microstimulation has been used for many years as a tool for mapping sensory and motor function, its role in learning, memory and decision-making has emerged only recently. Focal microstimulation of higher cortical areas can produce complex mental states and sequences of action. However, the relationship between the locus of stimulation and the percepts or actions evoked is often stereotyped and inflexible. The challenge is to develop stimulation systems that do not have fixed output but can flexibly contribute to complex cognitive and behavioral tasks. We discuss how microstimulation has been instrumental in manipulating a wide spectrum of cognitive functions including working memory, perceptual decisions and executive control by enhancing attention, re-ordering temporal sequence of saccades, improving associative learning or cognitive performance. For example, stimulation in prefrontal, parietal and sensory cortices may establish causal effects on decision-making, while microstimulation of inferotemporal cortex or caudate nucleus enhances associative learning. Building cognitive prosthetics based on the insights gleaned from such studies may depend on the development of multiple-input, multiple-output (MIMO) devices that allow subjects to control stimulation with their own thoughts in a closed-loop system.
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Affiliation(s)
- Ioan Opris
- Department of Physiology & Pharmacology, Wake Forest University School of Medicine, Winston Salem, NC 27157, USA.
| | - Vincent P Ferrera
- Departments of Neuroscience and Psychiatry, Columbia University, New York, NY 10032, USA
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Abstract
Brain–computer interface (BCI) has proven to be a useful tool for providing alternative communication and mobility to patients suffering from nervous system injury. BCI has been and will continue to be implemented into rehabilitation practices for more interactive and speedy neurological recovery. The most exciting BCI technology is evolving to provide therapeutic benefits by inducing cortical reorganization via neuronal plasticity. This article presents a state-of-the-art review of BCI technology used after nervous system injuries, specifically: amyotrophic lateral sclerosis, Parkinson’s disease, spinal cord injury, stroke, and disorders of consciousness. Also presented is transcending, innovative research involving new treatment of neurological disorders.
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Affiliation(s)
- Alexis Burns
- Biomedical Engineering Graduate Program, The Ohio State University, Columbus, OH, USA
| | - Hojjat Adeli
- Departments of Biomedical Engineering, Biomedical Informatics, Civil and Environmental Engineering and Geodetic Science, Electrical and Computer Engineering, and Neuroscience, and the Biophysics Graduate Program, The Ohio State University, Columbus, OH, USA
| | - John A. Buford
- Physical Therapy Division, School of Health and Rehabilitation Sciences, The Ohio State University, Columbus, OH, USA
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Multifractal analysis of information processing in hippocampal neural ensembles during working memory under Δ⁹-tetrahydrocannabinol administration. J Neurosci Methods 2014; 244:136-53. [PMID: 25086297 DOI: 10.1016/j.jneumeth.2014.07.013] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2014] [Revised: 06/06/2014] [Accepted: 07/16/2014] [Indexed: 12/22/2022]
Abstract
BACKGROUND Multifractal analysis quantifies the time-scale-invariant properties in data by describing the structure of variability over time. By applying this analysis to hippocampal interspike interval sequences recorded during performance of a working memory task, a measure of long-range temporal correlations and multifractal dynamics can reveal single neuron correlates of information processing. NEW METHOD Wavelet leaders-based multifractal analysis (WLMA) was applied to hippocampal interspike intervals recorded during a working memory task. WLMA can be used to identify neurons likely to exhibit information processing relevant to operation of brain-computer interfaces and nonlinear neuronal models. RESULTS Neurons involved in memory processing ("Functional Cell Types" or FCTs) showed a greater degree of multifractal firing properties than neurons without task-relevant firing characteristics. In addition, previously unidentified FCTs were revealed because multifractal analysis suggested further functional classification. The cannabinoid type-1 receptor (CB1R) partial agonist, tetrahydrocannabinol (THC), selectively reduced multifractal dynamics in FCT neurons compared to non-FCT neurons. COMPARISON WITH EXISTING METHODS WLMA is an objective tool for quantifying the memory-correlated complexity represented by FCTs that reveals additional information compared to classification of FCTs using traditional z-scores to identify neuronal correlates of behavioral events. CONCLUSION z-Score-based FCT classification provides limited information about the dynamical range of neuronal activity characterized by WLMA. Increased complexity, as measured with multifractal analysis, may be a marker of functional involvement in memory processing. The level of multifractal attributes can be used to differentially emphasize neural signals to improve computational models and algorithms underlying brain-computer interfaces.
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Song D, Harway M, Marmarelis VZ, Hampson RE, Deadwyler SA, Berger TW. Extraction and restoration of hippocampal spatial memories with non-linear dynamical modeling. Front Syst Neurosci 2014; 8:97. [PMID: 24904318 PMCID: PMC4036140 DOI: 10.3389/fnsys.2014.00097] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2014] [Accepted: 05/06/2014] [Indexed: 11/17/2022] Open
Abstract
To build a cognitive prosthesis that can replace the memory function of the hippocampus, it is essential to model the input-output function of the damaged hippocampal region, so the prosthetic device can stimulate the downstream hippocampal region, e.g., CA1, with the output signal, e.g., CA1 spike trains, predicted from the ongoing input signal, e.g., CA3 spike trains, and the identified input-output function, e.g., CA3-CA1 model. In order for the downstream region to form appropriate long-term memories based on the restored output signal, furthermore, the output signal should contain sufficient information about the memories that the animal has formed. In this study, we verify this premise by applying regression and classification modelings of the spatio-temporal patterns of spike trains to the hippocampal CA3 and CA1 data recorded from rats performing a memory-dependent delayed non-match-to-sample (DNMS) task. The regression model is essentially the multiple-input, multiple-output (MIMO) non-linear dynamical model of spike train transformation. It predicts the output spike trains based on the input spike trains and thus restores the output signal. In addition, the classification model interprets the signal by relating the spatio-temporal patterns to the memory events. We have found that: (1) both hippocampal CA3 and CA1 spike trains contain sufficient information for predicting the locations of the sample responses (i.e., left and right memories) during the DNMS task; and more importantly (2) the CA1 spike trains predicted from the CA3 spike trains by the MIMO model also are sufficient for predicting the locations on a single-trial basis. These results show quantitatively that, with a moderate number of unitary recordings from the hippocampus, the MIMO non-linear dynamical model is able to extract and restore spatial memory information for the formation of long-term memories and thus can serve as the computational basis of the hippocampal memory prosthesis.
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Affiliation(s)
- Dong Song
- Department of Biomedical Engineering, University of Southern CaliforniaLos Angeles, CA, USA
| | - Madhuri Harway
- Department of Biomedical Engineering, University of Southern CaliforniaLos Angeles, CA, USA
| | - Vasilis Z. Marmarelis
- Department of Biomedical Engineering, University of Southern CaliforniaLos Angeles, CA, USA
| | - Robert E. Hampson
- Department of Physiology and Pharmacology, School of Medicine, Wake Forest UniversityWinston-Salem, NC, USA
| | - Sam A. Deadwyler
- Department of Physiology and Pharmacology, School of Medicine, Wake Forest UniversityWinston-Salem, NC, USA
| | - Theodore W. Berger
- Department of Biomedical Engineering, University of Southern CaliforniaLos Angeles, CA, USA
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Lynch G, Cox CD, Gall CM. Pharmacological enhancement of memory or cognition in normal subjects. Front Syst Neurosci 2014; 8:90. [PMID: 24904313 PMCID: PMC4033242 DOI: 10.3389/fnsys.2014.00090] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2014] [Accepted: 04/30/2014] [Indexed: 12/14/2022] Open
Abstract
The possibility of expanding memory or cognitive capabilities above the levels in high functioning individuals is a topic of intense discussion among scientists and in society at large. The majority of animal studies use behavioral endpoint measures; this has produced valuable information but limited predictability for human outcomes. Accordingly, several groups are pursuing a complementary strategy with treatments targeting synaptic events associated with memory encoding or forebrain network operations. Transcription and translation figure prominently in substrate work directed at enhancement. Notably, the question of why new proteins would be needed for a now-forming memory given that learning-driven synthesis presumably occurred throughout the immediate past has been largely ignored. Despite this conceptual problem, and some controversy, recent studies have reinvigorated the idea that selective gene manipulation is a plausible route to enhancement. Efforts to improve memory by facilitating synaptic encoding of information have also progressed, in part due of breakthroughs on mechanisms that stabilize learning-related, long-term potentiation (LTP). These advances point to a reductionistic hypothesis for a diversity of experimental results on enhancement, and identify under-explored possibilities. Cognitive enhancement remains an elusive goal, in part due to the difficulty of defining the target. The popular view of cognition as a collection of definable computations seems to miss the fluid, integrative process experienced by high functioning individuals. The neurobiological approach obviates these psychological issues to directly test the consequences of improving throughput in networks underlying higher order behaviors. The few relevant studies testing drugs that selectively promote excitatory transmission indicate that it is possible to expand cortical networks engaged by complex tasks and that this is accompanied by capabilities not found in normal animals.
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Affiliation(s)
- Gary Lynch
- Department of Psychiatry and Human Behavior, University of California Irvine, CA, USA ; Department of Anatomy and Neurobiology, University of California Irvine, CA, USA
| | - Conor D Cox
- Department of Anatomy and Neurobiology, University of California Irvine, CA, USA
| | - Christine M Gall
- Department of Anatomy and Neurobiology, University of California Irvine, CA, USA
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Affiliation(s)
- Walter Glannon
- Department of Philosophy/Arts, University of Calgary Calgary, AB, Canada
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46
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Madan CR. Augmented memory: a survey of the approaches to remembering more. Front Syst Neurosci 2014; 8:30. [PMID: 24624063 PMCID: PMC3939671 DOI: 10.3389/fnsys.2014.00030] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2014] [Accepted: 02/10/2014] [Indexed: 11/13/2022] Open
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Deadwyler SA, Berger TW, Sweatt AJ, Song D, Chan RHM, Opris I, Gerhardt GA, Marmarelis VZ, Hampson RE. Donor/recipient enhancement of memory in rat hippocampus. Front Syst Neurosci 2013; 7:120. [PMID: 24421759 PMCID: PMC3872745 DOI: 10.3389/fnsys.2013.00120] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2013] [Accepted: 12/06/2013] [Indexed: 11/13/2022] Open
Abstract
The critical role of the mammalian hippocampus in the formation, translation and retrieval of memory has been documented over many decades. There are many theories of how the hippocampus operates to encode events and a precise mechanism was recently identified in rats performing a short-term memory task which demonstrated that successful information encoding was promoted via specific patterns of activity generated within ensembles of hippocampal neurons. In the study presented here, these “representations” were extracted via a customized non-linear multi-input multi-output (MIMO) mathematical model which allowed prediction of successful performance on specific trials within the testing session. A unique feature of this characterization was demonstrated when successful information encoding patterns were derived online from well-trained “donor” animals during difficult long-delay trials and delivered via online electrical stimulation to synchronously tested naïve “recipient” animals never before exposed to the delay feature of the task. By transferring such model-derived trained (donor) animal hippocampal firing patterns via stimulation to coupled naïve recipient animals, their task performance was facilitated in a direct “donor-recipient” manner. This provides the basis for utilizing extracted appropriate neural information from one brain to induce, recover, or enhance memory related processing in the brain of another subject.
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Affiliation(s)
- Sam A Deadwyler
- Department of Physiology and Pharmacology, Wake Forest School of Medicine Winston-Salem, NC, USA
| | - Theodore W Berger
- Department of Biomedical Engineering, University of Southern California Los Angeles, CA, USA
| | - Andrew J Sweatt
- Department of Physiology and Pharmacology, Wake Forest School of Medicine Winston-Salem, NC, USA
| | - Dong Song
- Department of Biomedical Engineering, University of Southern California Los Angeles, CA, USA
| | - Rosa H M Chan
- Department of Biomedical Engineering, University of Southern California Los Angeles, CA, USA
| | - Ioan Opris
- Department of Physiology and Pharmacology, Wake Forest School of Medicine Winston-Salem, NC, USA
| | - Greg A Gerhardt
- Department of Neurobiology, Chandler Medical School, University of Kentucky Lexington, KY, USA
| | - Vasilis Z Marmarelis
- Department of Biomedical Engineering, University of Southern California Los Angeles, CA, USA
| | - Robert E Hampson
- Department of Physiology and Pharmacology, Wake Forest School of Medicine Winston-Salem, NC, USA
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