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Kostoglou K, Bello-Robles F, Brassard P, Chacon M, Claassen JA, Czosnyka M, Elting JW, Hu K, Labrecque L, Liu J, Marmarelis VZ, Payne SJ, Shin DC, Simpson D, Smirl J, Panerai RB, Mitsis GD. Time-domain methods for quantifying dynamic cerebral blood flow autoregulation: Review and recommendations. A white paper from the Cerebrovascular Research Network (CARNet). J Cereb Blood Flow Metab 2024:271678X241249276. [PMID: 38688529 DOI: 10.1177/0271678x241249276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
Cerebral Autoregulation (CA) is an important physiological mechanism stabilizing cerebral blood flow (CBF) in response to changes in cerebral perfusion pressure (CPP). By maintaining an adequate, relatively constant supply of blood flow, CA plays a critical role in brain function. Quantifying CA under different physiological and pathological states is crucial for understanding its implications. This knowledge may serve as a foundation for informed clinical decision-making, particularly in cases where CA may become impaired. The quantification of CA functionality typically involves constructing models that capture the relationship between CPP (or arterial blood pressure) and experimental measures of CBF. Besides describing normal CA function, these models provide a means to detect possible deviations from the latter. In this context, a recent white paper from the Cerebrovascular Research Network focused on Transfer Function Analysis (TFA), which obtains frequency domain estimates of dynamic CA. In the present paper, we consider the use of time-domain techniques as an alternative approach. Due to their increased flexibility, time-domain methods enable the mitigation of measurement/physiological noise and the incorporation of nonlinearities and time variations in CA dynamics. Here, we provide practical recommendations and guidelines to support researchers and clinicians in effectively utilizing these techniques to study CA.
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Affiliation(s)
- Kyriaki Kostoglou
- Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Felipe Bello-Robles
- Departamento de Ingeniería Informática, Universidad de Santiago de Chile, Santiago, Chile
| | - Patrice Brassard
- Department of Kinesiology, Faculty of Medicine, Université Laval, Quebec, QC, Canada
- Research Center of the Institut universitaire de cardiologie et de pneumologie de Québec, Quebec, QC, Canada
| | - Max Chacon
- Departamento de Ingeniería Informática, Universidad de Santiago de Chile, Santiago, Chile
| | - Jurgen Ahr Claassen
- Department of Geriatrics, Radboud University Medical Center, Research Institute for Medical Innovation and Donders Institute, Nijmegen, The Netherlands
- Cerebral Haemodynamics in Ageing and Stroke Medicine (CHiASM), Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Marek Czosnyka
- Department of Clinical Neurosciences, Neurosurgery Department, University of Cambridge, Cambridge, UK
| | - Jan-Willem Elting
- Department of Neurology and Clinical Neurophysiology, University Medical Center Groningen, Groningen, The Netherlands
| | - Kun Hu
- Medical Biodynamics Program, Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA
| | - Lawrence Labrecque
- Department of Kinesiology, Faculty of Medicine, Université Laval, Quebec, QC, Canada
- Research Center of the Institut universitaire de cardiologie et de pneumologie de Québec, Quebec, QC, Canada
| | - Jia Liu
- Laboratory for Engineering and Scientific Computing, Institute of Advanced Computing and Digital Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Vasilis Z Marmarelis
- Department Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Stephen J Payne
- Institute of Applied Mechanics, National Taiwan University, Taipei, Taiwan
| | - Dae Cheol Shin
- Department Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - David Simpson
- Institute of Sound and Vibration Research, University of Southampton, Southampton, UK
| | - Jonathan Smirl
- Cerebrovascular Concussion Laboratory, Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada
- Sport Injury Prevention Research Centre, Faculty of Kinesiology, University of Calgary, Calgary, Alberta, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada
| | - Ronney B Panerai
- Cerebral Haemodynamics in Ageing and Stroke Medicine (CHiASM), Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
- NIHR Leicester Biomedical Research Centre, British Heart Foundation, Glenfield Hospital, Leicester, UK
| | - Georgios D Mitsis
- Department of Bioengineering, McGill University, Montreal, QC, Canada
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2
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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3
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Yoo HJ, Nashiro K, Min J, Cho C, Mercer N, Bachman SL, Nasseri P, Dutt S, Porat S, Choi P, Zhang Y, Grigoryan V, Feng T, Thayer JF, Lehrer P, Chang C, Stanley JA, Head E, Rouanet J, Marmarelis VZ, Narayanan S, Wisnowski J, Nation DA, Mather M. Multimodal neuroimaging data from a 5-week heart rate variability biofeedback randomized clinical trial. Sci Data 2023; 10:503. [PMID: 37516756 PMCID: PMC10387077 DOI: 10.1038/s41597-023-02396-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 07/17/2023] [Indexed: 07/31/2023] Open
Abstract
We present data from the Heart Rate Variability and Emotion Regulation (HRV-ER) randomized clinical trial testing effects of HRV biofeedback. Younger (N = 121) and older (N = 72) participants completed baseline magnetic resonance imaging (MRI) including T1-weighted, resting and emotion regulation task functional MRI (fMRI), pulsed continuous arterial spin labeling (PCASL), and proton magnetic resonance spectroscopy (1H MRS). During fMRI scans, physiological measures (blood pressure, pulse, respiration, and end-tidal CO2) were continuously acquired. Participants were randomized to either increase heart rate oscillations or decrease heart rate oscillations during daily sessions. After 5 weeks of HRV biofeedback, they repeated the baseline measurements in addition to new measures (ultimatum game fMRI, training mimicking during blood oxygen level dependent (BOLD) and PCASL fMRI). Participants also wore a wristband sensor to estimate sleep time. Psychological assessment comprised three cognitive tests and ten questionnaires related to emotional well-being. A subset (N = 104) provided plasma samples pre- and post-intervention that were assayed for amyloid and tau. Data is publicly available via the OpenNeuro data sharing platform.
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Affiliation(s)
- Hyun Joo Yoo
- University of Southern California, Los Angeles, CA, 90007, USA
| | - Kaoru Nashiro
- University of Southern California, Los Angeles, CA, 90007, USA
| | - Jungwon Min
- University of Southern California, Los Angeles, CA, 90007, USA
| | - Christine Cho
- University of Southern California, Los Angeles, CA, 90007, USA
| | - Noah Mercer
- University of Southern California, Los Angeles, CA, 90007, USA
| | | | - Padideh Nasseri
- University of Southern California, Los Angeles, CA, 90007, USA
| | - Shubir Dutt
- University of Southern California, Los Angeles, CA, 90007, USA
| | - Shai Porat
- University of Southern California, Los Angeles, CA, 90007, USA
| | - Paul Choi
- University of Southern California, Los Angeles, CA, 90007, USA
| | - Yong Zhang
- University of Southern California, Los Angeles, CA, 90007, USA
| | | | - Tiantian Feng
- University of Southern California, Los Angeles, CA, 90007, USA
| | | | - Paul Lehrer
- Rutgers University, New Brunswick-Piscataway, USA
| | | | | | | | | | | | | | | | | | - Mara Mather
- University of Southern California, Los Angeles, CA, 90007, USA.
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Nashiro K, Min J, Yoo HJ, Cho C, Bachman SL, Dutt S, Thayer JF, Lehrer PM, Feng T, Mercer N, Nasseri P, Wang D, Chang C, Marmarelis VZ, Narayanan S, Nation DA, Mather M. Increasing coordination and responsivity of emotion-related brain regions with a heart rate variability biofeedback randomized trial. Cogn Affect Behav Neurosci 2023; 23:66-83. [PMID: 36109422 PMCID: PMC9931635 DOI: 10.3758/s13415-022-01032-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/25/2022] [Indexed: 12/15/2022]
Abstract
Heart rate variability is a robust biomarker of emotional well-being, consistent with the shared brain networks regulating emotion regulation and heart rate. While high heart rate oscillatory activity clearly indicates healthy regulatory brain systems, can increasing this oscillatory activity also enhance brain function? To test this possibility, we randomly assigned 106 young adult participants to one of two 5-week interventions involving daily biofeedback that either increased heart rate oscillations (Osc+ condition) or had little effect on heart rate oscillations (Osc- condition) and examined effects on brain activity during rest and during regulating emotion. While there were no significant changes in the right amygdala-medial prefrontal cortex (MPFC) functional connectivity (our primary outcome), the Osc+ intervention increased left amygdala-MPFC functional connectivity and functional connectivity in emotion-related resting-state networks during rest. It also increased down-regulation of activity in somatosensory brain regions during an emotion regulation task. The Osc- intervention did not have these effects. In this healthy cohort, the two conditions did not differentially affect anxiety, depression, or mood. These findings indicate that modulating heart rate oscillatory activity changes emotion network coordination in the brain.
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Affiliation(s)
- Kaoru Nashiro
- University of Southern California, 3715 McClintock Avenue, Los Angeles, CA, 90089, USA
| | - Jungwon Min
- University of Southern California, 3715 McClintock Avenue, Los Angeles, CA, 90089, USA
| | - Hyun Joo Yoo
- University of Southern California, 3715 McClintock Avenue, Los Angeles, CA, 90089, USA
| | - Christine Cho
- University of Southern California, 3715 McClintock Avenue, Los Angeles, CA, 90089, USA
| | - Shelby L Bachman
- University of Southern California, 3715 McClintock Avenue, Los Angeles, CA, 90089, USA
| | - Shubir Dutt
- University of Southern California, 3715 McClintock Avenue, Los Angeles, CA, 90089, USA
| | | | | | - Tiantian Feng
- University of Southern California, 3715 McClintock Avenue, Los Angeles, CA, 90089, USA
| | - Noah Mercer
- University of Southern California, 3715 McClintock Avenue, Los Angeles, CA, 90089, USA
| | - Padideh Nasseri
- University of Southern California, 3715 McClintock Avenue, Los Angeles, CA, 90089, USA
| | - Diana Wang
- University of Southern California, 3715 McClintock Avenue, Los Angeles, CA, 90089, USA
| | | | - Vasilis Z Marmarelis
- University of Southern California, 3715 McClintock Avenue, Los Angeles, CA, 90089, USA
| | - Shri Narayanan
- University of Southern California, 3715 McClintock Avenue, Los Angeles, CA, 90089, USA
| | | | - Mara Mather
- University of Southern California, 3715 McClintock Avenue, Los Angeles, CA, 90089, USA.
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5
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Marmarelis VZ, Shin DC, Hamner JW, Tan CO. Dynamic effects of cholinergic blockade upon cerebral blood flow autoregulation in healthy adults. Front Physiol 2022; 13:1015544. [PMID: 36406984 PMCID: PMC9666788 DOI: 10.3389/fphys.2022.1015544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 10/05/2022] [Indexed: 01/25/2023] Open
Abstract
Background: Cerebral flow autoregulation (CFA) is a homeostatic mechanism critical for survival. The autonomic nervous system (ANS) plays a key role in maintaining proper CFA function. More quantitative studies of how the ANS influences CFA are desirable. Objective: To discover and quantify the dynamic effects of cholinergic blockade upon CFA in response to changes of arterial blood pressure and blood CO2 tension in healthy adults. Methods: We analyzed time-series data of spontaneous beat-to-beat mean arterial blood pressure (ABP) and cerebral blood flow velocity in the middle cerebral arteries (CFV), as well as breath-to-breath end-tidal CO2 (CO2), collected in 9 adults before and after cholinergic blockade, in order to obtain subject-specific predictive input-output models of the dynamic effects of changes in ABP and CO2 (inputs) upon CFV (output). These models are defined in convolutional form using "kernel" functions (or, equivalently, Transfer Functions in the frequency domain) that are estimated via the robust method of Laguerre expansions. Results: Cholinergic blockade caused statistically significant changes in the obtained kernel estimates (and the corresponding Transfer Functions) that define the linear dynamics of the ABP-to-CFV and CO2-to-CFV causal relations. The kernel changes due to cholinergic blockade reflect the effects of the cholinergic mechanism and exhibited, in the frequency domain, resonant peaks at 0.22 Hz and 0.06 Hz for the ABP-to-CFV and CO2-to-CFV dynamics, respectively. Conclusion: Quantitative estimates of the dynamics of the cholinergic component in CFA are found as average changes of the ABP-to-CFV and CO2-to-CFV kernels, and corresponding Transfer Functions, before and after cholinergic blockade.
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Affiliation(s)
- Vasilis Z. Marmarelis
- Biomedical Engineering, University of Southern CA, Los Angeles, MA, United States,*Correspondence: Vasilis Z. Marmarelis,
| | - Dae C. Shin
- Biomedical Engineering, University of Southern CA, Los Angeles, MA, United States
| | - Jason W. Hamner
- Cardiovascular Research Laboratory, Spaulding Rehabilitation Hospital, Boston, MA, United States
| | - Can Ozan Tan
- Electrical Engineering Math and Computer Science, University of Twente, Enschede, Netherlands
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6
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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. Corrigendum: Patterned hippocampal stimulation facilitates memory in patients with a history of head impact and/or brain injury. Front Hum Neurosci 2022; 16:1039221. [PMID: 36277045 PMCID: PMC9583523 DOI: 10.3389/fnhum.2022.1039221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 09/09/2022] [Indexed: 11/21/2022] Open
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
- *Correspondence: Robert E. Hampson
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7
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Marmarelis VZ, Shin DC, Zhang R. The Dynamic Relationship Between Cortical Oxygenation and End-Tidal CO 2 Transient Changes Is Impaired in Mild Cognitive Impairment Patients. Front Physiol 2021; 12:772456. [PMID: 34955886 PMCID: PMC8695976 DOI: 10.3389/fphys.2021.772456] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 11/19/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Recent studies have utilized data-based dynamic modeling to establish strong association between dysregulation of cerebral perfusion and Mild Cognitive Impairment (MCI), expressed in terms of impaired CO2 dynamic vasomotor reactivity in the cerebral vasculature. This raises the question of whether this is due to dysregulation of central mechanisms (baroreflex and chemoreflex) or mechanisms of cortical tissue oxygenation (CTO) in MCI patients. We seek to answer this question using data-based input-output predictive dynamic models. Objective: To use subject-specific data-based multivariate input-output dynamic models to quantify the effects of systemic hemodynamic and blood CO2 changes upon CTO and to examine possible differences in CTO regulation in MCI patients versus age-matched controls, after the dynamic effects of central regulatory mechanisms have been accounted for by using cerebral flow measurements as another input. Methods: The employed model-based approach utilized the general dynamic modeling methodology of Laguerre expansions of kernels to analyze spontaneous time-series data in order to quantify the dynamic effects upon CTO (an index of relative capillary hemoglobin saturation distribution measured via near-infrared spectroscopy) of contemporaneous changes in end-tidal CO2 (proxy for arterial CO2), arterial blood pressure and cerebral blood flow velocity in the middle cerebral arteries (measured via transcranial Doppler). Model-based indices (physio-markers) were computed for these distinct dynamic relationships. Results: The obtained model-based indices revealed significant statistical differences of CO2 dynamic vasomotor reactivity in cortical tissue, combined with "perfusivity" that quantifies the dynamic relationship between flow velocity in cerebral arteries and CTO in MCI patients versus age-matched controls (p = 0.006). Significant difference between MCI patients and age-matched controls was also found in the respective model-prediction accuracy (p = 0.0001). Combination of these model-based indices via the Fisher Discriminant achieved even smaller p-value (p = 5 × 10-5) when comparing MCI patients with controls. The differences in dynamics of CTO in MCI patients are in lower frequencies (<0.05 Hz), suggesting impairment in endocrine/metabolic (rather than neural) mechanisms. Conclusion: The presented model-based approach elucidates the multivariate dynamic connectivity in the regulation of cerebral perfusion and yields model-based indices that may serve as physio-markers of possible dysregulation of CTO during transient CO2 changes in MCI patients.
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Affiliation(s)
- Vasilis Z. Marmarelis
- Biomedical Engineering Department, University of Southern California, Los Angeles, CA, United States
| | - Dae C. Shin
- Biomedical Engineering Department, University of Southern California, Los Angeles, CA, United States
| | - Rong Zhang
- Institute for Exercise and Environmental Medicine, UT Southwestern Medical Center, Dallas, TX, United States
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9
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Abstract
There are currently intensified efforts by the scientific community world-wide to analyze the dynamics of the Covid-19 pandemic in order to predict key epidemiological effects and assist the proper planning for its clinical management, as well as guide sociopolitical decision-making regarding proper mitigation measures. Most efforts follow variants of the established SIR methodological framework that divides a population into "Susceptible", "Infectious" and "Recovered/Removed" fractions and defines their dynamic inter-relationships with first-order differential equations. Goal This paper proposes a novel approach based on data-guided detection and concatenation of infection waves - each of them described by a Riccati equation with adaptively estimated parameters. Methods This approach was applied to Covid-19 daily time-series data of US confirmed cases, resulting in the decomposition of the epidemic time-course into five "Riccati modules" representing major infection waves to date (June 18th). Results Four waves have passed the time-point of peak infection rate, with the fifth expected to peak on July 20th. The obtained parameter estimates indicate gradual reduction of infectivity rate, although the latest wave is expected to be the largest. Conclusions This analysis suggests that, if no new waves of infection emerge, the Covid-19 epidemic will be controlled in the US (<5000 new daily cases) by September 26th, and the maximum of confirmed cases will reach 4,160,000. Importantly, this approach can be used to detect (via rigorous statistical methods) the emergence of possible new waves of infections in the future. Analysis of data from individual states or countries may quantify the distinct effects of different mitigation measures.
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Affiliation(s)
- Vasilis Z Marmarelis
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089 USA
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10
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Marmarelis VZ, Shin DC, Zhang R. Closed-loop modeling of the heart-rate reflex for improved diagnosis and monitoring of Mild Cognitive Impairment. Annu Int Conf IEEE Eng Med Biol Soc 2020; 2019:1879-1882. [PMID: 31946264 DOI: 10.1109/embc.2019.8856837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Analysis of beat-to-beat spontaneous cerebral hemodynamic data has yielded predictive dynamic models of cerebral hemodynamics and has shown previously that patients with Mild Cognitive Impairment (MCI) exhibit significantly reduced cerebral vasomotor reactivity to CO2 relative to cognitively normal control subjects [1]. The present work examines the heart-rate reflex (HRR) dynamics of 46 MCI patients compared to 20 control subjects, using closed-loop modeling of HRR under resting conditions of spontaneous variations of arterial blood pressure (baroreflex) and end-tidal CO2 (chemoreflex). These subject-specific predictive dynamic models are obtained via the methodology of Principal Dynamic Modes [2] and allow the computation of model-based markers of baroreflex and chemoreflex function. We found that the chemoreflex gain is significantly weakened in MCI patients relative to controls (p=0.0086), while the baroreflex is not significantly affected. These findings offer another tool for diagnosis and monitoring of MCI (via model-based markers), when used in conjunction with current methods.
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11
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Elting JW, Sanders ML, Panerai RB, Aries M, Bor-Seng-Shu E, Caicedo A, Chacon M, Gommer ED, Van Huffel S, Jara JL, Kostoglou K, Mahdi A, Marmarelis VZ, Mitsis GD, Müller M, Nikolic D, Nogueira RC, Payne SJ, Puppo C, Shin DC, Simpson DM, Tarumi T, Yelicich B, Zhang R, Claassen JAHR. Assessment of dynamic cerebral autoregulation in humans: Is reproducibility dependent on blood pressure variability? PLoS One 2020; 15:e0227651. [PMID: 31923919 PMCID: PMC6954074 DOI: 10.1371/journal.pone.0227651] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Accepted: 12/22/2019] [Indexed: 01/02/2023] Open
Abstract
We tested the influence of blood pressure variability on the reproducibility of dynamic cerebral autoregulation (DCA) estimates. Data were analyzed from the 2nd CARNet bootstrap initiative, where mean arterial blood pressure (MABP), cerebral blood flow velocity (CBFV) and end tidal CO2 were measured twice in 75 healthy subjects. DCA was analyzed by 14 different centers with a variety of different analysis methods. Intraclass Correlation (ICC) values increased significantly when subjects with low power spectral density MABP (PSD-MABP) values were removed from the analysis for all gain, phase and autoregulation index (ARI) parameters. Gain in the low frequency band (LF) had the highest ICC, followed by phase LF and gain in the very low frequency band. No significant differences were found between analysis methods for gain parameters, but for phase and ARI parameters, significant differences between the analysis methods were found. Alternatively, the Spearman-Brown prediction formula indicated that prolongation of the measurement duration up to 35 minutes may be needed to achieve good reproducibility for some DCA parameters. We conclude that poor DCA reproducibility (ICC<0.4) can improve to good (ICC > 0.6) values when cases with low PSD-MABP are removed, and probably also when measurement duration is increased.
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Affiliation(s)
- Jan Willem Elting
- Department of Neurology, University Medical Center Groningen, Groningen, The Netherlands
- * E-mail:
| | - Marit L. Sanders
- Department of Geriatric Medicine, Radboudumc Alzheimer Centre and Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Ronney B. Panerai
- Department of Cardiovascular Sciences and Leicester Biomedical Research Centre in Cardiovascular Sciences, Glenfield Hospital, Leicester, United Kingdom
| | - Marcel Aries
- Department of Intensive Care, University of Maastricht, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Edson Bor-Seng-Shu
- Department of Neurology, Hospital das Clinicas University of Sao Paulo, Sao Paulo, Brazil
| | - Alexander Caicedo
- Mathematics and Computer Science, Faculty of Natural Sciences and Mathematics, Universidad del Rosario, Bogotá, Colombia
| | - Max Chacon
- Departemento de Ingeniería Informática, Universidad de Santiago de Chile, Santiago de Chile, Chile
| | - Erik D. Gommer
- Department of Clinical Neurophysiology, University of Maastricht, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Sabine Van Huffel
- Department of Electronic Engineering, Stadius Center for Dynamical Systems, Signal Processing and Data Analytics, Katholieke Universiteit Leuven, Leuven, Belgium
| | - José L. Jara
- Departemento de Ingeniería Informática, Universidad de Santiago de Chile, Santiago de Chile, Chile
| | - Kyriaki Kostoglou
- Department of Electrical, Computer and Software Engineering, McGill University, Montreal, Canada
| | - Adam Mahdi
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Vasilis Z. Marmarelis
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America
| | | | - Martin Müller
- Department of Neurology, Luzerner Kantonsspital, Luzern, Switzerland
| | - Dragana Nikolic
- Institute of Sound and Vibration Research, University of Southampton, Southampton, United Kingdom
| | - Ricardo C. Nogueira
- Department of Neurology, Hospital das Clinicas University of Sao Paulo, Sao Paulo, Brazil
| | - Stephen J. Payne
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Corina Puppo
- Departamento de Emergencia, Hospital de Clínicas, Universidad de la República, Montevideo, Uruguay
| | - Dae C. Shin
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America
| | - David M. Simpson
- Institute of Sound and Vibration Research, University of Southampton, Southampton, United Kingdom
| | - Takashi Tarumi
- The Institute for Exercise and Environmental Medicine, Presbyterian Hospital Dallas, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
| | - Bernardo Yelicich
- Departamento de Emergencia, Hospital de Clínicas, Universidad de la República, Montevideo, Uruguay
| | - Rong Zhang
- The Institute for Exercise and Environmental Medicine, Presbyterian Hospital Dallas, University of Texas Southwestern Medical Center, Dallas, Texas, United States of America
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Marmarelis VZ, Shin DC, Oesterreich M, Mueller M. Quantification of dynamic cerebral autoregulation and CO 2 dynamic vasomotor reactivity impairment in essential hypertension. J Appl Physiol (1985) 2020; 128:397-409. [PMID: 31917625 DOI: 10.1152/japplphysiol.00620.2019] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
The study of dynamic cerebral autoregulation (DCA) in essential hypertension has received considerable attention because of its clinical importance. Several studies have examined the dynamic relationship between spontaneous beat-to-beat arterial blood pressure data and contemporaneous cerebral blood flow velocity measurements (obtained via transcranial Doppler at the middle cerebral arteries) in the form of a linear input-output model using transfer function analysis. This analysis is more reliable when the contemporaneous effects of changes in blood CO2 tension are also taken into account, because of the significant effects of CO2 dynamic vasomotor reactivity (DVR) upon cerebral flow. In this article, we extract such input-output predictive models from spontaneous time series hemodynamic data of 24 patients with essential hypertension and 20 normotensive control subjects under resting conditions, using the novel methodology of principal dynamic modes (PDMs) that achieves improved estimation accuracy over previous methods for relatively short and noisy data. The obtained data-based models are subsequently used to compute indexes and markers that quantify DCA and DVR in each subject or patient and therefore can be used to assess the effects of essential hypertension. These model-based DCA and DVR indexes were properly defined to capture the observed effects of DCA and VR and found to be significantly different (P < 0.05) in the hypertensive patients. We also found significant differences between patients and control subjects in the relative contribution of three PDMs to the model output prediction, a finding that offers the prospect of identifying the physiological mechanisms affected by essential hypertension when the PDMs are interpreted in terms of specific physiological mechanisms.NEW & NOTEWORTHY This article presents novel model-based methodology for obtaining diagnostic indexes of dynamic cerebral autoregulation and dynamic vasomotor reactivity in hypertension.
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Affiliation(s)
- Vasilis Z Marmarelis
- Biomedical Simulations Resource Center, University of Southern California, Los Angeles, California
| | - Dae C Shin
- Biomedical Simulations Resource Center, University of Southern California, Los Angeles, California
| | | | - Martin Mueller
- Neurocenter, Luzerner Kantonsspital, Lucerne, Switzerland
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13
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Marmarelis VZ, Shin DC, Zhang R. Dysregulation of CO2-Driven Heart-Rate Chemoreflex Is Related Closely to Impaired CO2 Dynamic Vasomotor Reactivity in Mild Cognitive Impairment Patients. J Alzheimers Dis 2020; 75:855-870. [PMID: 32333588 PMCID: PMC7369119 DOI: 10.3233/jad-191238] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/18/2020] [Indexed: 11/15/2022]
Abstract
BACKGROUND Significant reduction of dynamic vasomotor reactivity (DVR) was recently reported in patients with amnestic mild cognitive impairment (MCI) relative to age-matched controls. These results were obtained via a novel approach that utilizes data-based predictive dynamic models to quantify DVR. OBJECTIVE Using the same methodological approach, we seek to quantify the dynamic effects of the CO2-driven chemoreflex and baroreflex upon heart-rate in order to examine their possible correlation with the observed DVR impairment in each MCI patient. METHODS The employed approach utilizes time-series data to obtain subject-specific predictive input-output models of the dynamic effects of changes in arterial blood pressure and end-tidal CO2 (putative "inputs") upon cerebral blood flow velocity in large cerebral arteries, cortical tissue oxygenation, and heart-rate (putative "outputs"). RESULTS There was significant dysregulation of CO2-driven heart-rate chemoreflex (p = 0.0031), but not of baroreflex (p = 0.5061), in MCI patients relative to age-matched controls. The model-based index of CO2-driven heart-rate chemoreflex gain (CRG) correlated significantly with the DVR index in large cerebral arteries (p = 0.0146), but not with the DVR index in small/micro-cortical vessels (p = 0.1066). This suggests that DVR impairment in small/micro-cortical vessels is not mainly due to CO2-driven heart-rate chemoreflex dysregulation, but to other factors (possibly dysfunction of neurovascular coupling). CONCLUSION Improved delineation between MCI patients and controls is achieved by combining the DVR index for small/micro-cortical vessels with the CRG index (p = 2×10-5). There is significant correlation (p < 0.01) between neuropsychological test scores and model-based DVR indices. Combining neuropsychological scores with DVR indices reduces the composite diagnostic index p-value (p∼10-10).
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Affiliation(s)
| | - Dae C. Shin
- Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Rong Zhang
- Internal Medicine, Neurology & Neurotherapeutics, UT Southwestern Medical Center, Dallas, TX, USA
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14
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Marmarelis VZ, Shin DC, Tarumi T, Zhang R. Comparing model-based cerebrovascular physiomarkers with DTI biomarkers in MCI patients. Brain Behav 2019; 9:e01356. [PMID: 31286695 PMCID: PMC6710205 DOI: 10.1002/brb3.1356] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Revised: 06/14/2019] [Accepted: 06/14/2019] [Indexed: 11/30/2022] Open
Abstract
OBJECTIVE To compare the novel model-based hemodynamic physiomarker of Dynamic Vasomotor Reactivity (DVR) with biomarkers based on Diffusion Tensor Imaging (DTI) and some widely used neurocognitive scores in terms of their ability to delineate patients with amnestic Mild Cognitive Impairment (MCI) from age-matched cognitively normal controls. MATERIALS & METHODS The model-based DVR and MRI-based DTI markers were obtained from 36 patients with amnestic MCI and 16 age-matched controls without cognitive impairment, for whom widely used neurocognitive scores were available. These markers and scores were subsequently compared in terms of statistical delineation between patients and controls. RESULTS It was found that statistically significant delineation between MCI patients and controls was comparable for DVR or DTI markers (p < 0.01). The performance of both types of markers was consistent with the scores of some (but not all) widely used neurocognitive tests. CONCLUSION Since DTI offers a measure of cerebral white matter integrity, the results suggest that the model-based hemodynamic marker of DVR may correlate with cognitive impairment due to white matter lesions. This finding is consistent with the hypothesis that dysregulation of cerebral microcirculation may be an early cause of cognitive impairment, which has been recently corroborated by several studies.
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Affiliation(s)
- Vasilis Z. Marmarelis
- Department of Biomedical EngineeringUniversity of Southern CaliforniaLos AngelesCalifornia
| | - Dae C. Shin
- Biomedical Simulations Resource CenterUniversity of Southern CaliforniaLos AngelesCalifornia
| | - Takashi Tarumi
- Neurology and NeurotherapeuticsUT Southwestern Medical CenterDallasTexas
- Institute for Exercise and Environmental MedicineTexas Health Presbyterian HospitalDallasTexas
- Present address:
Human Informatics Research InstituteNational Institute of Advanced Industrial Science and TechnologyTsukubaJapan
| | - Rong Zhang
- Neurology and NeurotherapeuticsUT Southwestern Medical CenterDallasTexas
- Institute for Exercise and Environmental MedicineTexas Health Presbyterian HospitalDallasTexas
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15
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Sanders ML, Elting JWJ, Panerai RB, Aries M, Bor-Seng-Shu E, Caicedo A, Chacon M, Gommer ED, Van Huffel S, Jara JL, Kostoglou K, Mahdi A, Marmarelis VZ, Mitsis GD, Müller M, Nikolic D, Nogueira RC, Payne SJ, Puppo C, Shin DC, Simpson DM, Tarumi T, Yelicich B, Zhang R, Claassen JAHR. Dynamic Cerebral Autoregulation Reproducibility Is Affected by Physiological Variability. Front Physiol 2019; 10:865. [PMID: 31354518 PMCID: PMC6634255 DOI: 10.3389/fphys.2019.00865] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 06/20/2019] [Indexed: 11/24/2022] Open
Abstract
Parameters describing dynamic cerebral autoregulation (DCA) have limited reproducibility. In an international, multi-center study, we evaluated the influence of multiple analytical methods on the reproducibility of DCA. Fourteen participating centers analyzed repeated measurements from 75 healthy subjects, consisting of 5 min of spontaneous fluctuations in blood pressure and cerebral blood flow velocity signals, based on their usual methods of analysis. DCA methods were grouped into three broad categories, depending on output types: (1) transfer function analysis (TFA); (2) autoregulation index (ARI); and (3) correlation coefficient. Only TFA gain in the low frequency (LF) band showed good reproducibility in approximately half of the estimates of gain, defined as an intraclass correlation coefficient (ICC) of >0.6. None of the other DCA metrics had good reproducibility. For TFA-like and ARI-like methods, ICCs were lower than values obtained with surrogate data (p < 0.05). For TFA-like methods, ICCs were lower for the very LF band (gain 0.38 ± 0.057, phase 0.17 ± 0.13) than for LF band (gain 0.59 ± 0.078, phase 0.39 ± 0.11, p ≤ 0.001 for both gain and phase). For ARI-like methods, the mean ICC was 0.30 ± 0.12 and for the correlation methods 0.24 ± 0.23. Based on comparisons with ICC estimates obtained from surrogate data, we conclude that physiological variability or non-stationarity is likely to be the main reason for the poor reproducibility of DCA parameters.
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Affiliation(s)
- Marit L Sanders
- Department of Geriatric Medicine, Radboudumc Alzheimer Center, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Jan Willem J Elting
- Department of Neurology, University Medical Center Groningen, Groningen, Netherlands
| | - Ronney B Panerai
- Department of Cardiovascular Sciences, NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Marcel Aries
- Department of Intensive Care, University of Maastricht, Maastricht University Medical Center, Maastricht, Netherlands
| | - Edson Bor-Seng-Shu
- Department of Neurology, Faculty of Medicine, Hospital das Clinicas University of São Paulo, São Paulo, Brazil
| | - Alexander Caicedo
- Department of Applied Mathematics and Computer Science, Faculty of Natural Sciences and Mathematics, Universidad del Rosario, Bogotá, Colombia
| | - Max Chacon
- Department of Engineering Informatics, Institute of Biomedical Engineering, University of Santiago, Santiago, Chile
| | - Erik D Gommer
- Department of Clinical Neurophysiology, Maastricht University Medical Centre, Maastricht, Netherlands
| | - Sabine Van Huffel
- Department of Electronic Engineering (ESAT), Stadius Center for Dynamical Systems, Signal Processing and Data Analytics, Katholieke Universiteit Leuven, Leuven, Belgium.,Interuniversity Microelectronics Centre, Leuven, Belgium
| | - José L Jara
- Department of Engineering Informatics, Institute of Biomedical Engineering, University of Santiago, Santiago, Chile
| | - Kyriaki Kostoglou
- Department of Electrical, Computer and Software Engineering, McGill University, Montreal, QC, Canada
| | - Adam Mahdi
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Vasilis Z Marmarelis
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
| | - Georgios D Mitsis
- Department of Bioengineering, McGill University, Montreal, QC, Canada
| | - Martin Müller
- Department of Neurology, Luzerner Kantonsspital, Luzern, Switzerland
| | - Dragana Nikolic
- Faculty of Engineering and the Environment, Institute of Sound and Vibration Research, University of Southampton, Southampton, United Kingdom
| | - Ricardo C Nogueira
- Department of Neurology, Faculty of Medicine, Hospital das Clinicas University of São Paulo, São Paulo, Brazil
| | - Stephen J Payne
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom
| | - Corina Puppo
- Departamento de Emergencia, Hospital de Clínicas, Universidad de la República, Montevideo, Uruguay
| | - Dae C Shin
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States
| | - David M Simpson
- Faculty of Engineering and the Environment, Institute of Sound and Vibration Research, University of Southampton, Southampton, United Kingdom
| | - Takashi Tarumi
- Institute for Exercise and Environmental Medicine, Presbyterian Hospital of Dallas, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Bernardo Yelicich
- Departamento de Emergencia, Hospital de Clínicas, Universidad de la República, Montevideo, Uruguay
| | - Rong Zhang
- Institute for Exercise and Environmental Medicine, Presbyterian Hospital of Dallas, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Jurgen A H R Claassen
- Department of Geriatric Medicine, Radboudumc Alzheimer Center, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
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Geng K, Shin DC, Song D, Hampson RE, Deadwyler SA, Berger TW, Marmarelis VZ. Multi-Input, Multi-Output Neuronal Mode Network Approach to Modeling the Encoding Dynamics and Functional Connectivity of Neural Systems. Neural Comput 2019; 31:1327-1355. [PMID: 31113305 DOI: 10.1162/neco_a_01204] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
This letter proposes a novel method, multi-input, multi-output neuronal mode network (MIMO-NMN), for modeling encoding dynamics and functional connectivity in neural ensembles such as the hippocampus. Compared with conventional approaches such as the Volterra-Wiener model, linear-nonlinear-cascade (LNC) model, and generalized linear model (GLM), the NMN has several advantages in terms of estimation accuracy, model interpretation, and functional connectivity analysis. We point out the limitations of current neural spike modeling methods, especially the estimation biases caused by the imbalanced class problem when the number of zeros is significantly larger than ones in the spike data. We use synthetic data to test the performance of NMN with a comparison of the traditional methods, and the results indicate the NMN approach could reduce the imbalanced class problem and achieve better predictions. Subsequently, we apply the MIMO-NMN method to analyze data from the human hippocampus. The results indicate that the MIMO-NMN method is a promising approach to modeling neural dynamics and analyzing functional connectivity of multi-neuronal data.
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Affiliation(s)
- Kunling Geng
- Department of Biomedical Engineering and Biomedical Simulations Resource Center, University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Dae C Shin
- Department of Biomedical Engineering and Biomedical Simulations Resource Center, University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Dong Song
- Department of Biomedical Engineering and Biomedical Simulations Resource Center, 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 Biomedical Simulations Resource Center, University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Vasilis Z Marmarelis
- Department of Biomedical Engineering and Biomedical Simulations Resource Center, University of Southern California, Los Angeles, CA, 90089, U.S.A.
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Sanders ML, Claassen JAHR, Aries M, Bor-Seng-Shu E, Caicedo A, Chacon M, Gommer ED, Van Huffel S, Jara JL, Kostoglou K, Mahdi A, Marmarelis VZ, Mitsis GD, Müller M, Nikolic D, Nogueira RC, Payne SJ, Puppo C, Shin DC, Simpson DM, Tarumi T, Yelicich B, Zhang R, Panerai RB, Elting JWJ. Reproducibility of dynamic cerebral autoregulation parameters: a multi-centre, multi-method study. Physiol Meas 2018; 39:125002. [PMID: 30523976 DOI: 10.1088/1361-6579/aae9fd] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
OBJECTIVE Different methods to calculate dynamic cerebral autoregulation (dCA) parameters are available. However, most of these methods demonstrate poor reproducibility that limit their reliability for clinical use. Inter-centre differences in study protocols, modelling approaches and default parameter settings have all led to a lack of standardisation and comparability between studies. We evaluated reproducibility of dCA parameters by assessing systematic errors in surrogate data resulting from different modelling techniques. APPROACH Fourteen centres analysed 22 datasets consisting of two repeated physiological blood pressure measurements with surrogate cerebral blood flow velocity signals, generated using Tiecks curves (autoregulation index, ARI 0-9) and added noise. For reproducibility, dCA methods were grouped in three broad categories: 1. Transfer function analysis (TFA)-like output; 2. ARI-like output; 3. Correlation coefficient-like output. For all methods, reproducibility was determined by one-way intraclass correlation coefficient analysis (ICC). MAIN RESULTS For TFA-like methods the mean (SD; [range]) ICC gain was 0.71 (0.10; [0.49-0.86]) and 0.80 (0.17; [0.36-0.94]) for VLF and LF (p = 0.003) respectively. For phase, ICC values were 0.53 (0.21; [0.09-0.80]) for VLF, and 0.92 (0.13; [0.44-1.00]) for LF (p < 0.001). Finally, ICC for ARI-like methods was equal to 0.84 (0.19; [0.41-0.94]), and for correlation-like methods, ICC was 0.21 (0.21; [0.056-0.35]). SIGNIFICANCE When applied to realistic surrogate data, free from the additional exogenous influences of physiological variability on cerebral blood flow, most methods of dCA modelling showed ICC values considerably higher than what has been reported for physiological data. This finding suggests that the poor reproducibility reported by previous studies may be mainly due to the inherent physiological variability of cerebral blood flow regulatory mechanisms rather than related to (stationary) random noise and the signal analysis methods.
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Affiliation(s)
- Marit L Sanders
- Department of Geriatric Medicine, Radboudumc Alzheimer Centre and Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Sandler RA, Geng K, Song D, Hampson RE, Witcher MR, Deadwyler SA, Berger TW, Marmarelis VZ. Designing Patient-Specific Optimal Neurostimulation Patterns for Seizure Suppression. Neural Comput 2018; 30:1180-1208. [PMID: 29566356 DOI: 10.1162/neco_a_01075] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Neurostimulation is a promising therapy for abating epileptic seizures. However, it is extremely difficult to identify optimal stimulation patterns experimentally. In this study, human recordings are used to develop a functional 24 neuron network statistical model of hippocampal connectivity and dynamics. Spontaneous seizure-like activity is induced in silico in this reconstructed neuronal network. The network is then used as a testbed to design and validate a wide range of neurostimulation patterns. Commonly used periodic trains were not able to permanently abate seizures at any frequency. A simulated annealing global optimization algorithm was then used to identify an optimal stimulation pattern, which successfully abated 92% of seizures. Finally, in a fully responsive, or closed-loop, neurostimulation paradigm, the optimal stimulation successfully prevented the network from entering the seizure state. We propose that the framework presented here for algorithmically identifying patient-specific neurostimulation patterns can greatly increase the efficacy of neurostimulation devices for seizures.
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Affiliation(s)
- Roman A Sandler
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, U.S.A.
| | - Kunling Geng
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, U.S.A.
| | - Dong Song
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, U.S.A.
| | - Robert E Hampson
- Department of Physiology and Pharmacology, Wake Forest University, Winston-Salem, NC 27109, U.S.A.
| | - Mark R Witcher
- Department of Neurosurgery, Wake Forest University, Winston-Salem, NC 27109, U.S.A.
| | - Sam A Deadwyler
- Department of Physiology and Pharmacology, Wake Forest University, Winston-Salem, NC 27109, U.S.A.
| | - Theodore W Berger
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, U.S.A.
| | - Vasilis Z Marmarelis
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, U.S.A.
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Lytton WW, Arle J, Bobashev G, Ji S, Klassen TL, Marmarelis VZ, Schwaber J, Sherif MA, Sanger TD. Multiscale modeling in the clinic: diseases of the brain and nervous system. Brain Inform 2017; 4:219-230. [PMID: 28488252 PMCID: PMC5709279 DOI: 10.1007/s40708-017-0067-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Accepted: 04/27/2017] [Indexed: 12/26/2022] Open
Abstract
Computational neuroscience is a field that traces its origins to the efforts of Hodgkin and Huxley, who pioneered quantitative analysis of electrical activity in the nervous system. While also continuing as an independent field, computational neuroscience has combined with computational systems biology, and neural multiscale modeling arose as one offshoot. This consolidation has added electrical, graphical, dynamical system, learning theory, artificial intelligence and neural network viewpoints with the microscale of cellular biology (neuronal and glial), mesoscales of vascular, immunological and neuronal networks, on up to macroscales of cognition and behavior. The complexity of linkages that produces pathophysiology in neurological, neurosurgical and psychiatric disease will require multiscale modeling to provide understanding that exceeds what is possible with statistical analysis or highly simplified models: how to bring together pharmacotherapeutics with neurostimulation, how to personalize therapies, how to combine novel therapies with neurorehabilitation, how to interlace periodic diagnostic updates with frequent reevaluation of therapy, how to understand a physical disease that manifests as a disease of the mind. Multiscale modeling will also help to extend the usefulness of animal models of human diseases in neuroscience, where the disconnects between clinical and animal phenomenology are particularly pronounced. Here we cover areas of particular interest for clinical application of these new modeling neurotechnologies, including epilepsy, traumatic brain injury, ischemic disease, neurorehabilitation, drug addiction, schizophrenia and neurostimulation.
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Affiliation(s)
- William W. Lytton
- Department of Physiology and Pharmacology and Neurology, SUNY Downstate, Kings County Hospital, Brooklyn, NY 11203 USA
| | | | | | - Songbai Ji
- Thayer School of Engineering, Department of Surgery and of Orthopaedic Surgery, Geisel School of Medicine, Dartmouth College, Hanover, NH 3755 USA
| | | | | | | | - Mohamed A. Sherif
- Yale U, New Haven, CT USA
- VA Connecticut Healthcare System, West Haven, CT USA
- Ain Shams U Institute of Psychiatry, Cairo, Egypt
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Geng K, Marmarelis VZ. Methodology of Recurrent Laguerre-Volterra Network for Modeling Nonlinear Dynamic Systems. IEEE Trans Neural Netw Learn Syst 2017; 28:2196-2208. [PMID: 27352401 PMCID: PMC5596897 DOI: 10.1109/tnnls.2016.2581141] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
In this paper, we have introduced a general modeling approach for dynamic nonlinear systems that utilizes a variant of the simulated annealing algorithm for training the Laguerre-Volterra network (LVN) to overcome the local minima and convergence problems and employs a pruning technique to achieve sparse LVN representations with l1 regularization. We tested this new approach with computer simulated systems and extended it to autoregressive sparse LVN (ASLVN) model structures that are suitable for input-output modeling of nonlinear systems that exhibit transitions in dynamic states, such as the Hodgkin-Huxley (H-H) equations of neuronal firing. Application of the proposed ASLVN to the H-H equations yields a more parsimonious input-output model with improved predictive capability that is amenable to more insightful physiological/biological interpretation.
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Sandler RA, Fetterhoff D, Hampson RE, Deadwyler SA, Marmarelis VZ. Cannabinoids disrupt memory encoding by functionally isolating hippocampal CA1 from CA3. PLoS Comput Biol 2017; 13:e1005624. [PMID: 28686594 PMCID: PMC5521875 DOI: 10.1371/journal.pcbi.1005624] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 07/21/2017] [Accepted: 06/13/2017] [Indexed: 01/02/2023] Open
Abstract
Much of the research on cannabinoids (CBs) has focused on their effects at the molecular and synaptic level. However, the effects of CBs on the dynamics of neural circuits remains poorly understood. This study aims to disentangle the effects of CBs on the functional dynamics of the hippocampal Schaffer collateral synapse by using data-driven nonparametric modeling. Multi-unit activity was recorded from rats doing an working memory task in control sessions and under the influence of exogenously administered tetrahydrocannabinol (THC), the primary CB found in marijuana. It was found that THC left firing rate unaltered and only slightly reduced theta oscillations. Multivariate autoregressive models, estimated from spontaneous spiking activity, were then used to describe the dynamical transformation from CA3 to CA1. They revealed that THC served to functionally isolate CA1 from CA3 by reducing feedforward excitation and theta information flow. The functional isolation was compensated by increased feedback excitation within CA1, thus leading to unaltered firing rates. Finally, both of these effects were shown to be correlated with memory impairments in the working memory task. By elucidating the circuit mechanisms of CBs, these results help close the gap in knowledge between the cellular and behavioral effects of CBs. Research into cannabinoids (CBs) over the last several decades has found that they induce a large variety of oftentimes opposing effects on various neuronal receptors and processes. Due to this plethora of effects, disentangling how CBs influence neuronal circuits has proven challenging. This paper contributes to our understanding of the circuit level effects of CBs by using data driven modeling to examine how THC affects the input-output relationship in the Schaffer collateral synapse in the hippocampus. It was found that THC functionally isolated CA1 from CA3 by reducing feedforward excitation and theta information flow while simultaneously increasing feedback excitation within CA1. By elucidating the circuit mechanisms of CBs, these results help close the gap in knowledge between the cellular and behavioral effects of CBs.
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Affiliation(s)
- Roman A. Sandler
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America
- * E-mail:
| | - Dustin Fetterhoff
- Department of Physiology & Pharmacology, Wake Forest University, Winston-Salem, North Carolina, United States of America
| | - Robert E. Hampson
- Department of Physiology & Pharmacology, Wake Forest University, Winston-Salem, North Carolina, United States of America
| | - Sam A. Deadwyler
- Department of Physiology & Pharmacology, Wake Forest University, Winston-Salem, North Carolina, United States of America
| | - Vasilis Z. Marmarelis
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California, United States of America
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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. Annu Int Conf IEEE Eng Med Biol Soc 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] [What about the content of this article? (0)] [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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Cole M, Eikenberry S, Kato T, Sandler RA, Yamashiro SM, Marmarelis VZ. Nonparametric Model of Smooth Muscle Force Production During Electrical Stimulation. J Comput Biol 2017; 24:229-237. [DOI: 10.1089/cmb.2016.0070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Marc Cole
- Department of Biomedical Engineering, University of Southern California Viterbi School of Engineering, Los Angeles, California
| | - Steffen Eikenberry
- Department of Biomedical Engineering, University of Southern California Viterbi School of Engineering, Los Angeles, California
- Biomedical Simulations Resource, Department of Biomedical Engineering, University of Southern California, Los Angeles, California
| | - Takahide Kato
- Department of General Education, National Institute of Technology, Toyota College, Toyota, Japan
| | - Roman A. Sandler
- Department of Biomedical Engineering, University of Southern California Viterbi School of Engineering, Los Angeles, California
- Biomedical Simulations Resource, Department of Biomedical Engineering, University of Southern California, Los Angeles, California
| | - Stanley M. Yamashiro
- Department of Biomedical Engineering, University of Southern California Viterbi School of Engineering, Los Angeles, California
| | - Vasilis Z. Marmarelis
- Department of Biomedical Engineering, University of Southern California Viterbi School of Engineering, Los Angeles, California
- Biomedical Simulations Resource, Department of Biomedical Engineering, University of Southern California, Los Angeles, California
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Marmarelis VZ, Shin DC, Tarumi T, Zhang R. Comparison of Model-Based Indices of Cerebral Autoregulation and Vasomotor Reactivity Using Transcranial Doppler versus Near-Infrared Spectroscopy in Patients with Amnestic Mild Cognitive Impairment. J Alzheimers Dis 2017; 56:89-105. [PMID: 27911329 PMCID: PMC5240580 DOI: 10.3233/jad-161004] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/17/2016] [Indexed: 01/24/2023]
Abstract
We recently introduced model-based "physiomarkers" of dynamic cerebral autoregulation and CO2 vasomotor reactivity as an aid for diagnosis of early-stage Alzheimer's disease (AD) [1], where significant impairment of dynamic vasomotor reactivity (DVR) was observed in early-stage AD patients relative to age-matched controls. Milder impairment of DVR was shown in patients with amnestic mild cognitive impairment (MCI) using the same approach in a subsequent study [2]. The advocated approach utilizes subject-specific data-based models of cerebral hemodynamics to quantify the dynamic effects of resting-state changes in arterial blood pressure and end-tidal CO2 (the putative inputs) upon cerebral blood flow velocity (the putative output) measured at the middle cerebral artery via transcranial Doppler (TCD). The obtained input-output models are then used to compute model-based indices of DCA and DVR from model-predicted responses to an input pressure pulse or an input CO2 pulse, respectively. In this paper, we compare these model-based indices of DVR and DCA in 46 amnestic MCI patients, relative to 20 age-matched controls, using TCD measurements with their counterparts using Near-Infrared Spectroscopy (NIRS) measurements of blood oxygenation at the lateral prefrontal cortex in 43 patients and 22 age-matched controls. The goal of the study is to assess whether NIRS measurements can be used instead of TCD measurements to obtain model-based physiomarkers with comparable diagnostic utility. The results corroborate this view in terms of the ability of either output to yield model-based physiomarkers that can differentiate the group of aMCI patients from age-matched healthy controls.
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Affiliation(s)
- Vasilis Z. Marmarelis
- Biomedical Simulations Resource Center, University of Southern California, Los Angeles, CA, USA
| | - Dae C. Shin
- Biomedical Simulations Resource Center, University of Southern California, Los Angeles, CA, USA
| | - Takashi Tarumi
- Exercise Physiology & Rehabilitation Center, UT Southwestern Medical Center, Dallas, TX, USA
| | - Rong Zhang
- Exercise Physiology & Rehabilitation Center, UT Southwestern Medical Center, Dallas, TX, USA
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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] [What about the content of this article? (0)] [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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Abstract
OBJECTIVE As an extension to our study comparing a putative compartmental and data-based model of linear dynamic cerebral autoregulation (CA) and CO2-vasomotor reactivity (VR), we study the CA-VR process in a nonlinear context. METHODS We use the concept of principal dynamic modes (PDM) in order to obtain a compact and more easily interpretable input-output model. This in silico study permits the use of input data with a dynamic range large enough to simulate the classic homeostatic CA and VR curves using a putative structural model of the regulatory control of the cerebral circulation. The PDM model obtained using theoretical and experimental data are compared. RESULTS It was found that the PDM model was able to reflect accurately both the simulated static CA and VR curves in the associated nonlinear functions (ANFs). Similar to experimental observations, the PDM model essentially separates the pressure-flow relationship into a linear component with fast dynamics and nonlinear components with slow dynamics. In addition, we found good qualitative agreement between the PDMs representing the dynamic theoretical and experimental CO2-flow relationship. CONCLUSION Under the modeling assumption and in light of other experimental findings, we hypothesize that PDMs obtained from experimental data correspond with passive fluid dynamical and active regulatory mechanisms. SIGNIFICANCE Both hypothesis-based and data-based modeling approaches can be combined to offer some insight into the physiological basis of PDM model obtained from human experimental data. The PDM modeling approach potentially offers a practical way to quantify the status of specific regulatory mechanisms in the CA-VR process.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Marmarelis VZ, Mitsis GD, Shin DC, Zhang R. Multiple-input nonlinear modelling of cerebral haemodynamics using spontaneous arterial blood pressure, end-tidal CO2 and heart rate measurements. Philos Trans A Math Phys Eng Sci 2016; 374:rsta.2015.0180. [PMID: 27044989 PMCID: PMC4822442 DOI: 10.1098/rsta.2015.0180] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/11/2016] [Indexed: 05/24/2023]
Abstract
In order to examine the effect of changes in heart rate (HR) upon cerebral perfusion and autoregulation, we include the HR signal recorded from 18 control subjects as a third input in a two-input model of cerebral haemodynamics that has been used previously to quantify the dynamic effects of changes in arterial blood pressure and end-tidal CO2upon cerebral blood flow velocity (CBFV) measured at the middle cerebral arteries via transcranial Doppler ultrasound. It is shown that the inclusion of HR as a third input reduces the output prediction error in a statistically significant manner, which implies that there is a functional connection between HR changes and CBFV. The inclusion of nonlinearities in the model causes further statistically significant reduction of the output prediction error. To achieve this task, we employ the concept of principal dynamic modes (PDMs) that yields dynamic nonlinear models of multi-input systems using relatively short data records. The obtained PDMs suggest model-driven quantitative hypotheses for the role of sympathetic and parasympathetic activity (corresponding to distinct PDMs) in the underlying physiological mechanisms by virtue of their relative contributions to the model output. These relative PDM contributions are subject-specific and, therefore, may be used to assess personalized characteristics for diagnostic purposes.
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Affiliation(s)
- V Z Marmarelis
- Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
| | - G D Mitsis
- Bioengineering, McGill University, Montreal, Quebec, Canada
| | - D C Shin
- Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
| | - R Zhang
- Institute for Exercise and Environmental Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
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Charalampidis AC, Pontikis K, Mitsis GD, Dimitriadis G, Lampadiari V, Marmarelis VZ, Armaganidis A, Papavassilopoulos GP. Calibration of a microdialysis sensor and recursive glucose level estimation in ICU patients using Kalman and particle filtering. Biomed Signal Process Control 2016. [DOI: 10.1016/j.bspc.2015.11.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Abstract
Receptive field identification is a vital problem in sensory neurophysiology and vision. Much research has been done in identifying the receptive fields of nonlinear neurons whose firing rate is determined by the nonlinear interactions of a small number of linear filters. Despite more advanced methods that have been proposed, spike-triggered covariance (STC) continues to be the most widely used method in such situations due to its simplicity and intuitiveness. Although the connection between STC and Wiener/Volterra kernels has often been mentioned in the literature, this relationship has never been explicitly derived. Here we derive this relationship and show that the STC matrix is actually a modified version of the second-order Wiener kernel, which incorporates the input autocorrelation and mixes first- and second-order dynamics. It is then shown how, with little modification of the STC method, the Wiener kernels may be obtained and, from them, the principal dynamic modes, a set of compact and efficient linear filters that essentially combine the spike-triggered average and STC matrix and generalize to systems with both continuous and point-process outputs. Finally, using Wiener theory, we show how these obtained filters may be corrected when they were estimated using correlated inputs. Our correction technique is shown to be superior to those commonly used in the literature for both correlated Gaussian images and natural images.
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Fetterhoff D, Kraft RA, Sandler RA, Opris I, Sexton CA, Marmarelis VZ, Hampson RE, Deadwyler SA. Distinguishing cognitive state with multifractal complexity of hippocampal interspike interval sequences. Front Syst Neurosci 2015; 9:130. [PMID: 26441562 PMCID: PMC4585000 DOI: 10.3389/fnsys.2015.00130] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2015] [Accepted: 09/03/2015] [Indexed: 11/15/2022] Open
Abstract
Fractality, represented as self-similar repeating patterns, is ubiquitous in nature and the brain. Dynamic patterns of hippocampal spike trains are known to exhibit multifractal properties during working memory processing; however, it is unclear whether the multifractal properties inherent to hippocampal spike trains reflect active cognitive processing. To examine this possibility, hippocampal neuronal ensembles were recorded from rats before, during and after a spatial working memory task following administration of tetrahydrocannabinol (THC), a memory-impairing component of cannabis. Multifractal detrended fluctuation analysis was performed on hippocampal interspike interval sequences to determine characteristics of monofractal long-range temporal correlations (LRTCs), quantified by the Hurst exponent, and the degree/magnitude of multifractal complexity, quantified by the width of the singularity spectrum. Our results demonstrate that multifractal firing patterns of hippocampal spike trains are a marker of functional memory processing, as they are more complex during the working memory task and significantly reduced following administration of memory impairing THC doses. Conversely, LRTCs are largest during resting state recordings, therefore reflecting different information compared to multifractality. In order to deepen conceptual understanding of multifractal complexity and LRTCs, these measures were compared to classical methods using hippocampal frequency content and firing variability measures. These results showed that LRTCs, multifractality, and theta rhythm represent independent processes, while delta rhythm correlated with multifractality. Taken together, these results provide a novel perspective on memory function by demonstrating that the multifractal nature of spike trains reflects hippocampal microcircuit activity that can be used to detect and quantify cognitive, physiological, and pathological states.
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Affiliation(s)
- Dustin Fetterhoff
- Neuroscience Program, Wake Forest School of Medicine Winston-Salem, NC, USA ; Department of Physiology and Pharmacology, Wake Forest School of Medicine Winston-Salem, NC, USA
| | - Robert A Kraft
- Department of Biomedical Engineering, Wake Forest School of Medicine Winston-Salem, NC, USA
| | - Roman A Sandler
- 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
| | - Cheryl A Sexton
- Department of Biomedical Engineering, Wake Forest School of Medicine Winston-Salem, NC, 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
| | - Sam A Deadwyler
- Department of Physiology and Pharmacology, Wake Forest School of Medicine Winston-Salem, NC, USA
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Sandler RA, Song D, Hampson RE, Deadwyler SA, Berger TW, Marmarelis VZ. Hippocampal closed-loop modeling and implications for seizure stimulation design. J Neural Eng 2015; 12:056017. [PMID: 26355815 DOI: 10.1088/1741-2560/12/5/056017] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Traditional hippocampal modeling has focused on the series of feedforward synapses known as the trisynaptic pathway. However, feedback connections from CA1 back to the hippocampus through the entorhinal cortex (EC) actually make the hippocampus a closed-loop system. By constructing a functional closed-loop model of the hippocampus, one may learn how both physiological and epileptic oscillations emerge and design efficient neurostimulation patterns to abate such oscillations. APPROACH Point process input-output models where estimated from recorded rodent hippocampal data to describe the nonlinear dynamical transformation from CA3 → CA1, via the schaffer-collateral synapse, and CA1 → CA3 via the EC. Each Volterra-like subsystem was composed of linear dynamics (principal dynamic modes) followed by static nonlinearities. The two subsystems were then wired together to produce the full closed-loop model of the hippocampus. MAIN RESULTS Closed-loop connectivity was found to be necessary for the emergence of theta resonances as seen in recorded data, thus validating the model. The model was then used to identify frequency parameters for the design of neurostimulation patterns to abate seizures. SIGNIFICANCE Deep-brain stimulation (DBS) is a new and promising therapy for intractable seizures. Currently, there is no efficient way to determine optimal frequency parameters for DBS, or even whether periodic or broadband stimuli are optimal. Data-based computational models have the potential to be used as a testbed for designing optimal DBS patterns for individual patients. However, in order for these models to be successful they must incorporate the complex closed-loop structure of the seizure focus. This study serves as a proof-of-concept of using such models to design efficient personalized DBS patterns for epilepsy.
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Affiliation(s)
- Roman A Sandler
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
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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. Annu Int Conf IEEE Eng Med Biol Soc 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] [What about the content of this article? (0)] [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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Sandler RA, Deadwyler SA, Hampson RE, Song D, Berger TW, Marmarelis VZ. System identification of point-process neural systems using probability based Volterra kernels. J Neurosci Methods 2014; 240:179-92. [PMID: 25479231 DOI: 10.1016/j.jneumeth.2014.11.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2014] [Revised: 11/19/2014] [Accepted: 11/20/2014] [Indexed: 11/30/2022]
Abstract
BACKGROUND Neural information processing involves a series of nonlinear dynamical input/output transformations between the spike trains of neurons/neuronal ensembles. Understanding and quantifying these transformations is critical both for understanding neural physiology such as short-term potentiation and for developing cognitive neural prosthetics. NEW METHOD A novel method for estimating Volterra kernels for systems with point-process inputs and outputs is developed based on elementary probability theory. These Probability Based Volterra (PBV) kernels essentially describe the probability of an output spike given q input spikes at various lags t1, t2, …, tq. RESULTS The PBV kernels are used to estimate both synthetic systems where ground truth is available and data from the CA3 and CA1 regions rodent hippocampus. The PBV kernels give excellent predictive results in both cases. Furthermore, they are shown to be quite robust to noise and to have good convergence and overfitting properties. Through a slight modification, the PBV kernels are shown to also deal well with correlated point-process inputs. COMPARISON WITH EXISTING METHODS The PBV kernels were compared with kernels estimated through least squares estimation (LSE) and through the Laguerre expansion technique (LET). The LSE kernels were shown to fair very poorly with real data due to the large amount of input noise. Although the LET kernels gave the best predictive results in all cases, they require prior parameter estimation. It was shown how the PBV and LET methods can be combined synergistically to maximize performance. CONCLUSIONS The PBV kernels provide a novel and intuitive method of characterizing point-process input-output nonlinear systems.
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Affiliation(s)
- Roman A Sandler
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA.
| | - Samuel A Deadwyler
- Department of Physiology & Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Robert E Hampson
- Department of Physiology & Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Dong Song
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Theodore W Berger
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
| | - Vasilis Z Marmarelis
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA
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Abstract
We develop an autoregressive model framework based on the concept of Principal Dynamic Modes (PDMs) for the process of action potential (AP) generation in the excitable neuronal membrane described by the Hodgkin-Huxley (H-H) equations. The model's exogenous input is injected current, and whenever the membrane potential output exceeds a specified threshold, it is fed back as a second input. The PDMs are estimated from the previously developed Nonlinear Autoregressive Volterra (NARV) model, and represent an efficient functional basis for Volterra kernel expansion. The PDM-based model admits a modular representation, consisting of the forward and feedback PDM bases as linear filterbanks for the exogenous and autoregressive inputs, respectively, whose outputs are then fed to a static nonlinearity composed of polynomials operating on the PDM outputs and cross-terms of pair-products of PDM outputs. A two-step procedure for model reduction is performed: first, influential subsets of the forward and feedback PDM bases are identified and selected as the reduced PDM bases. Second, the terms of the static nonlinearity are pruned. The first step reduces model complexity from a total of 65 coefficients to 27, while the second further reduces the model coefficients to only eight. It is demonstrated that the performance cost of model reduction in terms of out-of-sample prediction accuracy is minimal. Unlike the full model, the eight coefficient pruned model can be easily visualized to reveal the essential system components, and thus the data-derived PDM model can yield insight into the underlying system structure and function.
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Affiliation(s)
- Steffen E Eikenberry
- Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, Los Angeles, CA 90089, USA
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Sandler RA, Song D, Hampson RE, Deadwyler SA, Berger TW, Marmarelis VZ. Model-based asessment of an in-vivo predictive relationship from CA1 to CA3 in the rodent hippocampus. J Comput Neurosci 2014; 38:89-103. [PMID: 25260381 DOI: 10.1007/s10827-014-0530-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2014] [Revised: 09/02/2014] [Accepted: 09/05/2014] [Indexed: 01/02/2023]
Abstract
Although an anatomical connection from CA1 to CA3 via the Entorhinal Cortex (EC) and through backprojecting interneurons has long been known it exist, it has never been examined quantitatively on the single neuron level, in the in-vivo nonpatholgical, nonperturbed brain. Here, single spike activity was recorded using a multi-electrode array from the CA3 and CA1 areas of the rodent hippocampus (N = 7) during a behavioral task. The predictive power from CA3→CA1 and CA1→CA3 was examined by constructing Multivariate Autoregressive (MVAR) models from recorded neurons in both directions. All nonsignificant inputs and models were identified and removed by means of Monte Carlo simulation methods. It was found that 121/166 (73 %) CA3→CA1 models and 96/145 (66 %) CA1→CA3 models had significant predictive power, thus confirming a predictive 'Granger' causal relationship from CA1 to CA3. This relationship is thought to be caused by a combination of truly causal connections such as the CA1→EC→CA3 pathway and common inputs such as those from the Septum. All MVAR models were then examined in the frequency domain and it was found that CA3 kernels had significantly more power in the theta and beta range than those of CA1, confirming CA3's role as an endogenous hippocampal pacemaker.
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Affiliation(s)
- Roman A Sandler
- Department of Biomedical Engineering, University of Southern California, DRB 367, 1042 Downey Way Los Angeles, Los Angeles, CA, 90089, USA,
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Marmarelis VZ, Shin DC, Orme ME, Zhang R. Model-based physiomarkers of cerebral hemodynamics in patients with mild cognitive impairment. Med Eng Phys 2014; 36:628-37. [PMID: 24698010 DOI: 10.1016/j.medengphy.2014.02.025] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2013] [Revised: 02/17/2014] [Accepted: 02/26/2014] [Indexed: 02/02/2023]
Abstract
In our previous studies, we have introduced model-based "functional biomarkers" or "physiomarkers" of cerebral hemodynamics that hold promise for improved diagnosis of early-stage Alzheimer's disease (AD). The advocated methodology utilizes subject-specific data-based dynamic nonlinear models of cerebral hemodynamics to compute indices (serving as possible diagnostic physiomarkers) that quantify the state of cerebral blood flow autoregulation to pressure-changes (CFAP) and cerebral CO2 vasomotor reactivity (CVMR) in each subject. The model is estimated from beat-to-beat measurements of mean arterial blood pressure, mean cerebral blood flow velocity and end-tidal CO2, which can be made reliably and non-invasively under resting conditions. In a previous study, it was found that a CVMR index quantifying the impairment in CO2 vasomotor reactivity correlates with clinical indications of early AD, offering the prospect of a potentially useful diagnostic tool. In this paper, we explore the use of the same model-based indices for patients with amnestic Mild Cognitive Impairment (MCI), a preclinical stage of AD, relative to a control subjects and clinical cognitive assessments. It was found that the model-based CVMR values were lower for MCI patients relative to the control subjects.
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Affiliation(s)
- V Z Marmarelis
- Department of Biomedical Engineering & Biomedical Simulations Resource, University of Southern California, United States.
| | - D C Shin
- Department of Biomedical Engineering & Biomedical Simulations Resource, University of Southern California, United States
| | - M E Orme
- Sonovation Imaging & Diagnostics Inc., Los Angeles, CA, United States
| | - R Zhang
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, United States
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Hampson RE, Song D, Opris I, Santos LM, Shin DC, Gerhardt GA, Marmarelis VZ, Berger TW, Deadwyler SA. Facilitation of memory encoding in primate hippocampus by a neuroprosthesis that promotes task-specific neural firing. J Neural Eng 2013; 10:066013. [PMID: 24216292 PMCID: PMC3919468 DOI: 10.1088/1741-2560/10/6/066013] [Citation(s) in RCA: 74] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
OBJECTIVE Memory accuracy is a major problem in human disease and is the primary factor that defines Alzheimer's, ageing and dementia resulting from impaired hippocampal function in the medial temporal lobe. Development of a hippocampal memory neuroprosthesis that facilitates normal memory encoding in nonhuman primates (NHPs) could provide the basis for improving memory in human disease states. APPROACH NHPs trained to perform a short-term delayed match-to-sample (DMS) memory task were examined with multi-neuron recordings from synaptically connected hippocampal cell fields, CA1 and CA3. Recordings were analyzed utilizing a previously developed nonlinear multi-input multi-output (MIMO) neuroprosthetic model, capable of extracting CA3-to-CA1 spatiotemporal firing patterns during DMS performance. MAIN RESULTS The MIMO model verified that specific CA3-to-CA1 firing patterns were critical for the successful encoding of sample phase information on more difficult DMS trials. This was validated by the delivery of successful MIMO-derived encoding patterns via electrical stimulation to the same CA1 recording locations during the sample phase which facilitated task performance in the subsequent, delayed match phase, on difficult trials that required more precise encoding of sample information. SIGNIFICANCE These findings provide the first successful application of a neuroprosthesis designed to enhance and/or repair memory encoding in primate brain.
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Affiliation(s)
- Robert E. Hampson
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Dong Song
- Department of Biomedical Engineering, University of Southern California, LA, CA
| | - Ioan Opris
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Lucas M. Santos
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Dae C. Shin
- Department of Biomedical Engineering, University of Southern California, LA, CA
| | - Greg A. Gerhardt
- Department of Anatomy and Neurobiology, University of Kentucky, Lexington, KY
| | | | - Theodore W. Berger
- Department of Biomedical Engineering, University of Southern California, LA, CA
| | - Sam A. Deadwyler
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC
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Marmarelis VZ, Shin DC, Orme ME, Zhang R. Model-based quantification of cerebral hemodynamics as a physiomarker for Alzheimer's disease? Ann Biomed Eng 2013; 41:2296-317. [PMID: 23771298 PMCID: PMC3992829 DOI: 10.1007/s10439-013-0837-z] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2013] [Accepted: 05/29/2013] [Indexed: 01/27/2023]
Abstract
Previous studies have found that Alzheimer's disease (AD) impairs cerebral vascular function, even at early stages of the disease. This offers the prospect of a useful diagnostic method for AD, if cerebral vascular dysfunction can be quantified reliably within practical clinical constraints. We present a recently developed methodology that utilizes a data-based dynamic nonlinear closed-loop model of cerebral hemodynamics to compute "physiomarkers" quantifying the state of cerebral flow autoregulation to pressure-changes (CA) and cerebral CO2 vasomotor reactivity (CVMR) in each subject. This model is estimated from beat-to-beat measurements of mean arterial blood pressure, mean cerebral blood flow velocity and end-tidal CO2, which can be made reliably and non-invasively under resting conditions. This model may also take an open-loop form and comparisons are made with the closed-loop counterpart. The proposed model-based physiomarkers take the form of two indices that quantify the gain of the CA and CVMR processes in each subject. It was found in an initial set of clinical data that the CVMR index delineates AD patients from control subjects and, therefore, may prove useful in the improved diagnosis of early-stage AD.
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Affiliation(s)
- V Z Marmarelis
- University of Southern California, Los Angeles, CA, USA,
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Abstract
The scientific and clinical importance of cerebral hemodynamics has generated considerable interest in their quantitative understanding via computational modeling. In particular, two aspects of cerebral hemodynamics, cerebral flow autoregulation (CFA) and CO2 vasomotor reactivity (CVR), have attracted much attention because they are implicated in many important clinical conditions and pathologies (orthostatic intolerance, syncope, hypertension, stroke, vascular dementia, mild cognitive impairment, Alzheimer's disease, and other neurodegenerative diseases with cerebrovascular components). Both CFA and CVR are dynamic physiological processes by which cerebral blood flow is regulated in response to fluctuations in cerebral perfusion pressure and blood CO2 tension. Several modeling studies to date have analyzed beat-to-beat hemodynamic data in order to advance our quantitative understanding of CFA-CVR dynamics. A confounding factor in these studies is the fact that the dynamics of the CFA-CVR processes appear to vary with time (i.e., changes in cerebrovascular characteristics) due to neural, endocrine, and metabolic effects. This paper seeks to address this issue by tracking the changes in linear time-invariant models obtained from short successive segments of data from ten healthy human subjects. The results suggest that systemic variations exist but have stationary statistics and, therefore, the use of time-invariant modeling yields "time-averaged models" of physiological and clinical utility.
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Hampson RE, Fuqua JL, Huettl PF, Opris I, Song D, Shin D, Marmarelis VZ, Berger TW, Gerhardt GA, Deadwyler SA. Conformal ceramic electrodes that record glutamate release and corresponding neural activity in primate prefrontal cortex. Annu Int Conf IEEE Eng Med Biol Soc 2013; 2013:5954-7. [PMID: 24111095 DOI: 10.1109/embc.2013.6610908] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Conformal ceramic electrodes utilized in prior recordings of nonhuman primate prefrontal cortical layer 2/3 and layer 5 neurons were used in this study to record tonic glutamate concentration and transient release in layer 2/3 PFC. Tonic glutamate concentration increased in the Match (decision) phase of a visual delayed-match-to-sample (DMS) task, while increased transient glutamate release occurred in the Sample (encoding) phase of the task. Further, spatial vs. object-oriented DMS trials evoked differential changes in glutamate concentration. Thus the same conformal recording electrodes were capable of electrophysiological and electrochemical recording, and revealed similar evidence of neural processing in layers 2/3 and layer 5 during cognitive processing in a behavioral task.
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Marmarelis VZ, Shin DC, Song D, Hampson RE, Deadwyler SA, Berger TW. On parsing the neural code in the prefrontal cortex of primates using principal dynamic modes. J Comput Neurosci 2013; 36:321-37. [PMID: 23929124 DOI: 10.1007/s10827-013-0475-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2013] [Revised: 07/16/2013] [Accepted: 07/17/2013] [Indexed: 11/25/2022]
Abstract
Nonlinear modeling of multi-input multi-output (MIMO) neuronal systems using Principal Dynamic Modes (PDMs) provides a novel method for analyzing the functional connectivity between neuronal groups. This paper presents the PDM-based modeling methodology and initial results from actual multi-unit recordings in the prefrontal cortex of non-human primates. We used the PDMs to analyze the dynamic transformations of spike train activity from Layer 2 (input) to Layer 5 (output) of the prefrontal cortex in primates performing a Delayed-Match-to-Sample task. The PDM-based models reduce the complexity of representing large-scale neural MIMO systems that involve large numbers of neurons, and also offer the prospect of improved biological/physiological interpretation of the obtained models. PDM analysis of neuronal connectivity in this system revealed "input-output channels of communication" corresponding to specific bands of neural rhythms that quantify the relative importance of these frequency-specific PDMs across a variety of different tasks. We found that behavioral performance during the Delayed-Match-to-Sample task (correct vs. incorrect outcome) was associated with differential activation of frequency-specific PDMs in the prefrontal cortex.
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Affiliation(s)
- V Z Marmarelis
- Department of Biomedical Engineering and the Biomedical Simulations Resource (BMSR), University of Southern California, Los Angeles, CA, 90089, USA,
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Marmarelis VZ, Shin DC, Zhang Y, Kautzky-Willer A, Pacini G, D’Argenio DZ. Analysis of intravenous glucose tolerance test data using parametric and nonparametric modeling: application to a population at risk for diabetes. J Diabetes Sci Technol 2013; 7:952-62. [PMID: 23911176 PMCID: PMC3879759 DOI: 10.1177/193229681300700417] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Modeling studies of the insulin-glucose relationship have mainly utilized parametric models, most notably the minimal model (MM) of glucose disappearance. This article presents results from the comparative analysis of the parametric MM and a nonparametric Laguerre based Volterra Model (LVM) applied to the analysis of insulin modified (IM) intravenous glucose tolerance test (IVGTT) data from a clinical study of gestational diabetes mellitus (GDM). METHODS An IM IVGTT study was performed 8 to 10 weeks postpartum in 125 women who were diagnosed with GDM during their pregnancy [population at risk of developing diabetes (PRD)] and in 39 control women with normal pregnancies (control subjects). The measured plasma glucose and insulin from the IM IVGTT in each group were analyzed via a population analysis approach to estimate the insulin sensitivity parameter of the parametric MM. In the nonparametric LVM analysis, the glucose and insulin data were used to calculate the first-order kernel, from which a diagnostic scalar index representing the integrated effect of insulin on glucose was derived. RESULTS Both the parametric MM and nonparametric LVM describe the glucose concentration data in each group with good fidelity, with an improved measured versus predicted r² value for the LVM of 0.99 versus 0.97 for the MM analysis in the PRD. However, application of the respective diagnostic indices of the two methods does result in a different classification of 20% of the individuals in the PRD. CONCLUSIONS It was found that the data based nonparametric LVM revealed additional insights about the manner in which infused insulin affects blood glucose concentration.
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Affiliation(s)
- Vasilis Z. Marmarelis
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California
| | - Dae C. Shin
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California
| | - Yaping Zhang
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California
| | | | - Giovanni Pacini
- Metabolic Unit, Institute of Biomedical Engineering, Italian National Research Council, Padova, Italy
| | - David Z. D’Argenio
- Department of Biomedical Engineering, University of Southern California, Los Angeles, California
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Marmarelis VZ, Shin DC, Orme ME, Zhang R. Closed-loop dynamic modeling of cerebral hemodynamics. Ann Biomed Eng 2013; 41:1029-48. [PMID: 23292615 DOI: 10.1007/s10439-012-0736-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2012] [Accepted: 12/26/2012] [Indexed: 11/28/2022]
Abstract
The dynamics of cerebral hemodynamics have been studied extensively because of their fundamental physiological and clinical importance. In particular, the dynamic processes of cerebral flow autoregulation (CFA) and CO2 vasomotor reactivity have attracted broad attention because of their involvement in a host of pathologies and clinical conditions (e.g., hypertension, syncope, stroke, traumatic brain injury, vascular dementia, Alzheimer's disease, mild cognitive impairment etc.). This raises the prospect of useful diagnostic methods being developed on the basis of quantitative models of cerebral hemodynamics, if cerebral vascular dysfunction can be quantified reliably from data collected within practical clinical constraints. This paper presents a modeling method that utilizes beat-to-beat measurements of mean arterial blood pressure, cerebral blood flow velocity and end-tidal CO2 (collected non-invasively under resting conditions) to quantify the dynamics of CFA and cerebral vasomotor reactivity (CVMR). The unique and novel aspect of this dynamic model is that it is nonlinear and operates in a closed-loop configuration.
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Affiliation(s)
- V Z Marmarelis
- University of Southern California, Los Angeles, CA, USA.
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