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Pramotton FM, Spitz S, Kamm RD. Challenges and Future Perspectives in Modeling Neurodegenerative Diseases Using Organ-on-a-Chip Technology. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2403892. [PMID: 38922799 PMCID: PMC11348103 DOI: 10.1002/advs.202403892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 06/01/2024] [Indexed: 06/28/2024]
Abstract
Neurodegenerative diseases (NDDs) affect more than 50 million people worldwide, posing a significant global health challenge as well as a high socioeconomic burden. With aging constituting one of the main risk factors for some NDDs such as Alzheimer's disease (AD) and Parkinson's disease (PD), this societal toll is expected to rise considering the predicted increase in the aging population as well as the limited progress in the development of effective therapeutics. To address the high failure rates in clinical trials, legislative changes permitting the use of alternatives to traditional pre-clinical in vivo models are implemented. In this regard, microphysiological systems (MPS) such as organ-on-a-chip (OoC) platforms constitute a promising tool, due to their ability to mimic complex and human-specific tissue niches in vitro. This review summarizes the current progress in modeling NDDs using OoC technology and discusses five critical aspects still insufficiently addressed in OoC models to date. Taking these aspects into consideration in the future MPS will advance the modeling of NDDs in vitro and increase their translational value in the clinical setting.
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Affiliation(s)
- Francesca Michela Pramotton
- Department of Mechanical Engineering and Biological EngineeringMassachusetts Institute of TechnologyCambridgeMA02139USA
| | - Sarah Spitz
- Department of Mechanical Engineering and Biological EngineeringMassachusetts Institute of TechnologyCambridgeMA02139USA
| | - Roger D. Kamm
- Department of Mechanical Engineering and Biological EngineeringMassachusetts Institute of TechnologyCambridgeMA02139USA
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2
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Chauhan K, Neiman AB, Tass PA. Synaptic reorganization of synchronized neuronal networks with synaptic weight and structural plasticity. PLoS Comput Biol 2024; 20:e1012261. [PMID: 38980898 PMCID: PMC11259284 DOI: 10.1371/journal.pcbi.1012261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 07/19/2024] [Accepted: 06/20/2024] [Indexed: 07/11/2024] Open
Abstract
Abnormally strong neural synchronization may impair brain function, as observed in several brain disorders. We computationally study how neuronal dynamics, synaptic weights, and network structure co-emerge, in particular, during (de)synchronization processes and how they are affected by external perturbation. To investigate the impact of different types of plasticity mechanisms, we combine a network of excitatory integrate-and-fire neurons with different synaptic weight and/or structural plasticity mechanisms: (i) only spike-timing-dependent plasticity (STDP), (ii) only homeostatic structural plasticity (hSP), i.e., without weight-dependent pruning and without STDP, (iii) a combination of STDP and hSP, i.e., without weight-dependent pruning, and (iv) a combination of STDP and structural plasticity (SP) that includes hSP and weight-dependent pruning. To accommodate the diverse time scales of neuronal firing, STDP, and SP, we introduce a simple stochastic SP model, enabling detailed numerical analyses. With tools from network theory, we reveal that structural reorganization may remarkably enhance the network's level of synchrony. When weaker contacts are preferentially eliminated by weight-dependent pruning, synchrony is achieved with significantly sparser connections than in randomly structured networks in the STDP-only model. In particular, the strengthening of contacts from neurons with higher natural firing rates to those with lower rates and the weakening of contacts in the opposite direction, followed by selective removal of weak contacts, allows for strong synchrony with fewer connections. This activity-led network reorganization results in the emergence of degree-frequency, degree-degree correlations, and a mixture of degree assortativity. We compare the stimulation-induced desynchronization of synchronized states in the STDP-only model (i) with the desynchronization of models (iii) and (iv). The latter require stimuli of significantly higher intensity to achieve long-term desynchronization. These findings may inform future pre-clinical and clinical studies with invasive or non-invasive stimulus modalities aiming at inducing long-lasting relief of symptoms, e.g., in Parkinson's disease.
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Affiliation(s)
- Kanishk Chauhan
- Department of Physics and Astronomy, Ohio University, Athens, Ohio, United States of America
- Neuroscience Program, Ohio University, Athens, Ohio, United States of America
| | - Alexander B. Neiman
- Department of Physics and Astronomy, Ohio University, Athens, Ohio, United States of America
- Neuroscience Program, Ohio University, Athens, Ohio, United States of America
| | - Peter A. Tass
- Department of Neurosurgery, Stanford University, Stanford, California, United States of America
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3
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Wang HE, Triebkorn P, Breyton M, Dollomaja B, Lemarechal JD, Petkoski S, Sorrentino P, Depannemaecker D, Hashemi M, Jirsa VK. Virtual brain twins: from basic neuroscience to clinical use. Natl Sci Rev 2024; 11:nwae079. [PMID: 38698901 PMCID: PMC11065363 DOI: 10.1093/nsr/nwae079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 02/05/2024] [Accepted: 02/20/2024] [Indexed: 05/05/2024] Open
Abstract
Virtual brain twins are personalized, generative and adaptive brain models based on data from an individual's brain for scientific and clinical use. After a description of the key elements of virtual brain twins, we present the standard model for personalized whole-brain network models. The personalization is accomplished using a subject's brain imaging data by three means: (1) assemble cortical and subcortical areas in the subject-specific brain space; (2) directly map connectivity into the brain models, which can be generalized to other parameters; and (3) estimate relevant parameters through model inversion, typically using probabilistic machine learning. We present the use of personalized whole-brain network models in healthy ageing and five clinical diseases: epilepsy, Alzheimer's disease, multiple sclerosis, Parkinson's disease and psychiatric disorders. Specifically, we introduce spatial masks for relevant parameters and demonstrate their use based on the physiological and pathophysiological hypotheses. Finally, we pinpoint the key challenges and future directions.
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Affiliation(s)
- Huifang E Wang
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Paul Triebkorn
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Martin Breyton
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
- Service de Pharmacologie Clinique et Pharmacosurveillance, AP–HM, Marseille, 13005, France
| | - Borana Dollomaja
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Jean-Didier Lemarechal
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Spase Petkoski
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Pierpaolo Sorrentino
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Damien Depannemaecker
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Meysam Hashemi
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Viktor K Jirsa
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
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Ng PR, Bush A, Vissani M, McIntyre CC, Richardson RM. Biophysical Principles and Computational Modeling of Deep Brain Stimulation. Neuromodulation 2024; 27:422-439. [PMID: 37204360 DOI: 10.1016/j.neurom.2023.04.471] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 04/02/2023] [Accepted: 04/24/2023] [Indexed: 05/20/2023]
Abstract
BACKGROUND Deep brain stimulation (DBS) has revolutionized the treatment of neurological disorders, yet the mechanisms of DBS are still under investigation. Computational models are important in silico tools for elucidating these underlying principles and potentially for personalizing DBS therapy to individual patients. The basic principles underlying neurostimulation computational models, however, are not well known in the clinical neuromodulation community. OBJECTIVE In this study, we present a tutorial on the derivation of computational models of DBS and outline the biophysical contributions of electrodes, stimulation parameters, and tissue substrates to the effects of DBS. RESULTS Given that many aspects of DBS are difficult to characterize experimentally, computational models have played an important role in understanding how material, size, shape, and contact segmentation influence device biocompatibility, energy efficiency, the spatial spread of the electric field, and the specificity of neural activation. Neural activation is dictated by stimulation parameters including frequency, current vs voltage control, amplitude, pulse width, polarity configurations, and waveform. These parameters also affect the potential for tissue damage, energy efficiency, the spatial spread of the electric field, and the specificity of neural activation. Activation of the neural substrate also is influenced by the encapsulation layer surrounding the electrode, the conductivity of the surrounding tissue, and the size and orientation of white matter fibers. These properties modulate the effects of the electric field and determine the ultimate therapeutic response. CONCLUSION This article describes biophysical principles that are useful for understanding the mechanisms of neurostimulation.
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Affiliation(s)
| | - Alan Bush
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Matteo Vissani
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
| | - Cameron C McIntyre
- Department of Biomedical Engineering, Duke University, Durham, NC, USA; Department of Neurosurgery, Duke University, Durham, NC, USA
| | - Robert Mark Richardson
- Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital, Boston, MA, USA
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Yang H, Yang X, Yan S. A dynamic computational model of the parallel circuit on the basal ganglia-cortex associated with Parkinson's disease dementia. BIOLOGICAL CYBERNETICS 2024; 118:127-143. [PMID: 38644417 DOI: 10.1007/s00422-024-00988-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 03/26/2024] [Indexed: 04/23/2024]
Abstract
The cognitive impairment will gradually appear over time in Parkinson's patients, which is closely related to the basal ganglia-cortex network. This network contains two parallel circuits mediated by putamen and caudate nucleus, respectively. Based on the biophysical mean-field model, we construct a dynamic computational model of the parallel circuit in the basal ganglia-cortex network associated with Parkinson's disease dementia. The simulated results show that the decrease of power ratio in the prefrontal cortex is mainly caused by dopamine depletion in the caudate nucleus and is less related to that in the putamen, which indicates Parkinson's disease dementia may be caused by a lesion of the caudate nucleus rather than putamen. Furthermore, the underlying dynamic mechanism behind the decrease of power ratio is investigated by bifurcation analysis, which demonstrates that the decrease of power ratio is due to the change of brain discharge pattern from the limit cycle mode to the point attractor mode. More importantly, the spatiotemporal course of dopamine depletion in Parkinson's disease patients is well simulated, which states that with the loss of dopaminergic neurons projecting to the striatum, motor dysfunction of Parkinson's disease is first observed, whereas cognitive impairment occurs after a period of onset of motor dysfunction. These results are helpful to understand the pathogenesis of cognitive impairment and provide insights into the treatment of Parkinson's disease dementia.
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Affiliation(s)
- Hao Yang
- School of Mathematics and Statistics, Shaanxi Normal University, Xi'an, 710062, People's Republic of China
| | - XiaoLi Yang
- School of Mathematics and Statistics, Shaanxi Normal University, Xi'an, 710062, People's Republic of China.
| | - SiLu Yan
- School of Mathematics and Statistics, Shaanxi Normal University, Xi'an, 710062, People's Republic of China
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6
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Wang J, Wang T, Liu H, Wang K, Moses K, Feng Z, Li P, Huang W. Flexible Electrodes for Brain-Computer Interface System. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2211012. [PMID: 37143288 DOI: 10.1002/adma.202211012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 04/27/2023] [Indexed: 05/06/2023]
Abstract
Brain-computer interface (BCI) has been the subject of extensive research recently. Governments and companies have substantially invested in relevant research and applications. The restoration of communication and motor function, the treatment of psychological disorders, gaming, and other daily and therapeutic applications all benefit from BCI. The electrodes hold the key to the essential, fundamental BCI precondition of electrical brain activity detection and delivery. However, the traditional rigid electrodes are limited due to their mismatch in Young's modulus, potential damages to the human body, and a decline in signal quality with time. These factors make the development of flexible electrodes vital and urgent. Flexible electrodes made of soft materials have grown in popularity in recent years as an alternative to conventional rigid electrodes because they offer greater conformance, the potential for higher signal-to-noise ratio (SNR) signals, and a wider range of applications. Therefore, the latest classifications and future developmental directions of fabricating these flexible electrodes are explored in this paper to further encourage the speedy advent of flexible electrodes for BCI. In summary, the perspectives and future outlook for this developing discipline are provided.
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Affiliation(s)
- Junjie Wang
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics (IFE) & Xi'an Institute of Biomedical Materials and Engineering (IBME), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, Shaanxi, 710072, P. R. China
| | - Tengjiao Wang
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics (IFE) & Xi'an Institute of Biomedical Materials and Engineering (IBME), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, Shaanxi, 710072, P. R. China
| | - Haoyan Liu
- Department of Computer Science & Computer Engineering (CSCE), University of Arkansas, Fayetteville, AR, 72701, USA
| | - Kun Wang
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics (IFE) & Xi'an Institute of Biomedical Materials and Engineering (IBME), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, Shaanxi, 710072, P. R. China
| | - Kumi Moses
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics (IFE) & Xi'an Institute of Biomedical Materials and Engineering (IBME), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, Shaanxi, 710072, P. R. China
| | - Zhuoya Feng
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics (IFE) & Xi'an Institute of Biomedical Materials and Engineering (IBME), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, Shaanxi, 710072, P. R. China
| | - Peng Li
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics (IFE) & Xi'an Institute of Biomedical Materials and Engineering (IBME), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, Shaanxi, 710072, P. R. China
| | - Wei Huang
- Frontiers Science Center for Flexible Electronics (FSCFE), Xi'an Institute of Flexible Electronics (IFE) & Xi'an Institute of Biomedical Materials and Engineering (IBME), Northwestern Polytechnical University (NPU), 127 West Youyi Road, Xi'an, Shaanxi, 710072, P. R. China
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Senthilvelmurugan NN, Subbian S. Active fault tolerant deep brain stimulator for epilepsy using deep neural network. BIOMED ENG-BIOMED TE 2023:bmt-2021-0302. [PMID: 36920096 DOI: 10.1515/bmt-2021-0302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 02/27/2023] [Indexed: 03/16/2023]
Abstract
Millions of people around the world are affected by different kinds of epileptic seizures. A deep brain stimulator is now claimed to be one of the most promising tools to control severe epileptic seizures. The present study proposes Hodgkin-Huxley (HH) model-based Active Fault Tolerant Deep Brain Stimulator (AFTDBS) for brain neurons to suppress epileptic seizures against ion channel conductance variations using a Deep Neural Network (DNN). The AFTDBS contains the following three modules: (i) Detection of epileptic seizures using black box classifiers such as Support Vector Machine (SVM) and K-Nearest Neighbor (KNN), (ii) Prediction of ion channels conductance variations using Long Short-Term Memory (LSTM), and (iii) Development of Reconfigurable Deep Brain Stimulator (RDBS) to control epileptic spikes using Proportional Integral (PI) Controller and Model Predictive Controller (MPC). Initially, the synthetic data were collected from the HH model by varying ion channel conductance. Then, the seizure was classified into four groups namely, normal and epileptic due to variations in sodium ion-channel conductance, potassium ion-channel conductance, and both sodium and potassium ion-channel conductance. In the present work, current controlled deep brain stimulators were designed for epileptic suppression. Finally, the closed-loop performances and stability of the proposed control schemes were analyzed. The simulation results demonstrated the efficacy of the proposed DNN-based AFTDBS.
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Affiliation(s)
| | - Sutha Subbian
- Department of Instrumentation Engineering, MIT Campus, Anna University, Tamilnadu, Chennai, India
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Sun Z, Liu Y, Yang X, Xu W. Control of epileptic activities in a cortex network of multiple coupled neural populations under electromagnetic induction. APPLIED MATHEMATICS AND MECHANICS 2023; 44:499-514. [PMID: 36880095 PMCID: PMC9976671 DOI: 10.1007/s10483-023-2969-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 12/30/2022] [Indexed: 06/18/2023]
Abstract
Epilepsy is believed to be associated with the abnormal synchronous neuronal activity in the brain, which results from large groups or circuits of neurons. In this paper, we choose to focus on the temporal lobe epilepsy, and establish a cortex network of multiple coupled neural populations to explore the epileptic activities under electromagnetic induction. We demonstrate that the epileptic activities can be controlled and modulated by electromagnetic induction and coupling among regions. In certain regions, these two types of control are observed to show exactly reverse effects. The results show that the strong electromagnetic induction is conducive to eliminating the epileptic seizures. The coupling among regions has a conduction effect that the previous normal background activity of the region gives way to the epileptic discharge, owing to coupling with spike wave discharge regions. Overall, these results highlight the role of electromagnetic induction and coupling among the regions in controlling and modulating epileptic activities, and might provide novel insights into the treatments of epilepsy.
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Affiliation(s)
- Zhongkui Sun
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an, 710129 China
| | - Yuanyuan Liu
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an, 710129 China
| | - Xiaoli Yang
- School of Mathematics and Statistics, Shaanxi Normal University, Xi’an, 710062 China
| | - Wei Xu
- School of Mathematics and Statistics, Northwestern Polytechnical University, Xi’an, 710129 China
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9
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Loeb GE. Remembrance of things perceived: Adding thalamocortical function to artificial neural networks. Front Integr Neurosci 2023; 17:1108271. [PMID: 36959924 PMCID: PMC10027940 DOI: 10.3389/fnint.2023.1108271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 02/13/2023] [Indexed: 03/09/2023] Open
Abstract
Recent research has illuminated the complexity and importance of the thalamocortical system but it has been difficult to identify what computational functions it performs. Meanwhile, deep-learning artificial neural networks (ANNs) based on bio-inspired models of purely cortical circuits have achieved surprising success solving sophisticated cognitive problems associated historically with human intelligence. Nevertheless, the limitations and shortcomings of artificial intelligence (AI) based on such ANNs are becoming increasingly clear. This review considers how the addition of thalamocortical connectivity and its putative functions related to cortical attention might address some of those shortcomings. Such bio-inspired models are now providing both testable theories of biological cognition and improved AI technology, much of which is happening outside the usual academic venues.
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10
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Hou S, Fan D, Wang Q. Regulating absence seizures by tri-phase delay stimulation applied to globus pallidus internal. APPLIED MATHEMATICS AND MECHANICS 2022; 43:1399-1414. [PMID: 36092985 PMCID: PMC9438882 DOI: 10.1007/s10483-022-2896-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 06/15/2022] [Indexed: 06/15/2023]
Abstract
In this paper, a reduced globus pallidus internal (GPI)-corticothalamic (GCT) model is developed, and a tri-phase delay stimulation (TPDS) with sequentially applying three pulses on the GPI representing the inputs from the striatal D 1 neurons, subthalamic nucleus (STN), and globus pallidus external (GPE), respectively, is proposed. The GPI is evidenced to control absence seizures characterized by 2 Hz-4 Hz spike and wave discharge (SWD). Hence, based on the basal ganglia-thalamocortical (BGCT) model, we firstly explore the triple effects of D l-GPI, GPE-GPI, and STN-GPI pathways on seizure patterns. Then, using the GCT model, we apply the TPDS on the GPI to potentially investigate the alternative and improved approach if these pathways to the GPI are blocked. The results show that the striatum D 1, GPE, and STN can indeed jointly and significantly affect seizure patterns. In particular, the TPDS can effectively reproduce the seizure pattern if the D 1-GPI, GPE-GPI, and STN-GPI pathways are cut off. In addition, the seizure abatement can be obtained by well tuning the TPDS stimulation parameters. This implies that the TPDS can play the surrogate role similar to the modulation of basal ganglia, which hopefully can be helpful for the development of the brain-computer interface in the clinical application of epilepsy.
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Affiliation(s)
- Songan Hou
- Department of Dynamics and Control, Beihang University, Beijing, 100191 China
| | - Denggui Fan
- School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, 100083 China
| | - Qingyun Wang
- Department of Dynamics and Control, Beihang University, Beijing, 100191 China
- Beijing Institute of Brain Disorders, Capital Medical University, Beijing, 100069 China
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11
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Wang K, Wang J, Zhu Y, Li H, Liu C, Fietkiewicz C, Loparo KA. Adaptive closed-loop control strategy inhibiting pathological basal ganglia oscillations. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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12
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Yu Y, Han F, Wang Q. Exploring phase–amplitude coupling from primary motor cortex-basal ganglia-thalamus network model. Neural Netw 2022; 153:130-141. [DOI: 10.1016/j.neunet.2022.05.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 04/11/2022] [Accepted: 05/27/2022] [Indexed: 10/18/2022]
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13
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Mori R, Mino H, Durand DM. Pulse-frequency-dependent resonance in a population of pyramidal neuron models. BIOLOGICAL CYBERNETICS 2022; 116:363-375. [PMID: 35303154 DOI: 10.1007/s00422-022-00925-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 02/18/2022] [Indexed: 05/07/2023]
Abstract
Stochastic resonance is known as a phenomenon whereby information transmission of weak signal or subthreshold stimuli can be enhanced by additive random noise with a suitable intensity. Another phenomenon induced by applying deterministic pulsatile electric stimuli with a pulse frequency, commonly used for deep brain stimulation (DBS), was also shown to improve signal-to-noise ratio in neuron models. The objective of this study was to test the hypothesis that pulsatile high-frequency stimulation could improve the detection of both sub- and suprathreshold synaptic stimuli by tuning the frequency of the stimulation in a population of pyramidal neuron models. Computer simulations showed that mutual information estimated from a population of neural spike trains displayed a typical resonance curve with a peak value of the pulse frequency at 80-120 Hz, similar to those utilized for DBS in clinical situations. It is concluded that a "pulse-frequency-dependent resonance" (PFDR) can enhance information transmission over a broad range of synaptically connected networks. Since the resonance frequency matches that used clinically, PFDR could contribute to the mechanism of the therapeutic effect of DBS.
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Affiliation(s)
- Ryosuke Mori
- Department of Engineering, Graduate School of Engineering, Kanto Gakuin University, 1-50-1 Mutsuura E., Kanazawa-ku, Yokohama, 236-8501, Japan
| | - Hiroyuki Mino
- Department of Engineering, Graduate School of Engineering, Kanto Gakuin University, 1-50-1 Mutsuura E., Kanazawa-ku, Yokohama, 236-8501, Japan.
| | - Dominique M Durand
- Department of Biomedical Engineering, Neural Engineering Center, Case Western Reserve University, Cleveland, OH, 44106, USA
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14
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Yang X, Zhang R, Sun Z, Kurths J. Controlling Alzheimer's Disease Through the Deep Brain Stimulation to Thalamic Relay Cells. Front Comput Neurosci 2021; 15:636770. [PMID: 34819845 PMCID: PMC8606419 DOI: 10.3389/fncom.2021.636770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 10/11/2021] [Indexed: 11/23/2022] Open
Abstract
Experimental and clinical studies have shown that the technique of deep brain stimulation (DBS) plays a potential role in the regulation of Alzheimer’s disease (AD), yet it still desires for ongoing studies including clinical trials, theoretical approach and action mechanism. In this work, we develop a modified thalamo-cortico-thalamic (TCT) model associated with AD to explore the therapeutic effects of DBS on AD from the perspective of neurocomputation. First, the neuropathological state of AD resulting from synapse loss is mimicked by decreasing the synaptic connectivity strength from the Inter-Neurons (IN) neuron population to the Thalamic Relay Cells (TRC) neuron population. Under such AD condition, a specific deep brain stimulation voltage is then implanted into the neural nucleus of TRC in this TCT model. The symptom of AD is found significantly relieved by means of power spectrum analysis and nonlinear dynamical analysis. Furthermore, the therapeutic effects of DBS on AD are systematically examined in different parameter space of DBS. The results demonstrate that the controlling effect of DBS on AD can be efficient by appropriately tuning the key parameters of DBS including amplitude A, period P and duration D. This work highlights the critical role of thalamus stimulation for brain disease, and provides a theoretical basis for future experimental and clinical studies in treating AD.
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Affiliation(s)
- XiaoLi Yang
- School of Mathematics and Statistics, Shaanxi Normal University, Xi'an, China
| | - RuiXi Zhang
- School of Mathematics and Statistics, Shaanxi Normal University, Xi'an, China
| | - ZhongKui Sun
- Department of Applied Mathematics, Northwestern Polytechnical University, Xi'an, China
| | - Jürgen Kurths
- Potsdam Institute for Climate Impact Research, Potsdam, Germany.,Department of Physics, Humboldt University of Berlin, Berlin, Germany.,Centre for Analysis of Complex Systems, World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia
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Yu Y, Han F, Wang Q, Wang Q. Model-based optogenetic stimulation to regulate beta oscillations in Parkinsonian neural networks. Cogn Neurodyn 2021; 16:667-681. [DOI: 10.1007/s11571-021-09729-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/16/2021] [Accepted: 10/02/2021] [Indexed: 12/27/2022] Open
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Berry JK, Cox D. Increased oscillatory power in a computational model of the olfactory bulb due to synaptic degeneration. Phys Rev E 2021; 104:024405. [PMID: 34525666 DOI: 10.1103/physreve.104.024405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 06/30/2021] [Indexed: 11/07/2022]
Abstract
Several neurodegenerative diseases impact the olfactory system, and in particular the olfactory bulb, early in disease progression. One mechanism by which damage occurs is via synaptic dysfunction. Here, we implement a computational model of the olfactory bulb and investigate the effect of weakened connection weights on network oscillatory behavior. Olfactory bulb network activity can be modeled by a system of equations that describes a set of coupled nonlinear oscillators. In this modeling framework, we propagate damage to synaptic weights using several strategies, varying from localized to global. Damage propagated in a dispersed or spreading manner leads to greater oscillatory power at moderate levels of damage. This increase arises from a higher average level of mitral cell activity due to a shift in the balance between excitation and inhibition. That this shift leads to greater oscillations depends critically on the nonlinearity of the activation function. Linearized analysis of the network dynamics predicts when this shift leads to loss of oscillatory activity. We thus demonstrate one potential mechanism involved in the increased gamma oscillations seen in some animal models of Alzheimer's disease, and we highlight the potential that pathological olfactory bulb behavior presents as an early biomarker of disease.
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Affiliation(s)
- J Kendall Berry
- University of California, Davis, Davis, California 95616, USA
| | - Daniel Cox
- University of California, Davis, Davis, California 95616, USA
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Gandolfi D, Boiani GM, Bigiani A, Mapelli J. Modeling Neurotransmission: Computational Tools to Investigate Neurological Disorders. Int J Mol Sci 2021; 22:4565. [PMID: 33925434 PMCID: PMC8123833 DOI: 10.3390/ijms22094565] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 04/22/2021] [Accepted: 04/25/2021] [Indexed: 02/06/2023] Open
Abstract
The investigation of synaptic functions remains one of the most fascinating challenges in the field of neuroscience and a large number of experimental methods have been tuned to dissect the mechanisms taking part in the neurotransmission process. Furthermore, the understanding of the insights of neurological disorders originating from alterations in neurotransmission often requires the development of (i) animal models of pathologies, (ii) invasive tools and (iii) targeted pharmacological approaches. In the last decades, additional tools to explore neurological diseases have been provided to the scientific community. A wide range of computational models in fact have been developed to explore the alterations of the mechanisms involved in neurotransmission following the emergence of neurological pathologies. Here, we review some of the advancements in the development of computational methods employed to investigate neuronal circuits with a particular focus on the application to the most diffuse neurological disorders.
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Affiliation(s)
- Daniela Gandolfi
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (G.M.B.); (A.B.)
| | - Giulia Maria Boiani
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (G.M.B.); (A.B.)
| | - Albertino Bigiani
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (G.M.B.); (A.B.)
- Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy
| | - Jonathan Mapelli
- Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy; (D.G.); (G.M.B.); (A.B.)
- Center for Neuroscience and Neurotechnology, University of Modena and Reggio Emilia, Via Campi 287, 41125 Modena, Italy
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