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Vareberg AD, Bok I, Eizadi J, Ren X, Hai A. Inference of network connectivity from temporally binned spike trains. J Neurosci Methods 2024; 404:110073. [PMID: 38309313 PMCID: PMC10949361 DOI: 10.1016/j.jneumeth.2024.110073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 01/19/2024] [Accepted: 01/30/2024] [Indexed: 02/05/2024]
Abstract
BACKGROUND Processing neural activity to reconstruct network connectivity is a central focus of neuroscience, yet the spatiotemporal requisites of biological nervous systems are challenging for current neuronal sensing modalities. Consequently, methods that leverage limited data to successfully infer synaptic connections, predict activity at single unit resolution, and decipher their effect on whole systems, can uncover critical information about neural processing. Despite the emergence of powerful methods for inferring connectivity, network reconstruction based on temporally subsampled data remains insufficiently unexplored. NEW METHOD We infer synaptic weights by processing firing rates within variable time bins for a heterogeneous feed-forward network of excitatory, inhibitory, and unconnected units. We assess classification and optimize model parameters for postsynaptic spike train reconstruction. We test our method on a physiological network of leaky integrate-and-fire neurons displaying bursting patterns and assess prediction of postsynaptic activity from microelectrode array data. RESULTS Results reveal parameters for improved prediction and performance and suggest that lower resolution data and limited access to neurons can be preferred. COMPARISON WITH EXISTING METHOD(S) Recent computational methods demonstrate highly improved reconstruction of connectivity from networks of parallel spike trains by considering spike lag, time-varying firing rates, and other underlying dynamics. However, these methods insufficiently explore temporal subsampling representative of novel data types. CONCLUSIONS We provide a framework for reverse engineering neural networks from data with limited temporal quality, describing optimal parameters for each bin size, which can be further improved using non-linear methods and applied to more complicated readouts and connectivity distributions in multiple brain circuits.
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Affiliation(s)
- Adam D Vareberg
- Department of Biomedical Engineering, University of Wisconsin-Madison, United States; Wisconsin Institute for Translational Neuroengineering (WITNe), University of Wisconsin-Madison, United States
| | - Ilhan Bok
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, United States; Wisconsin Institute for Translational Neuroengineering (WITNe), University of Wisconsin-Madison, United States
| | - Jenna Eizadi
- Department of Biomedical Engineering, University of Wisconsin-Madison, United States; Wisconsin Institute for Translational Neuroengineering (WITNe), University of Wisconsin-Madison, United States
| | - Xiaoxuan Ren
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, United States
| | - Aviad Hai
- Department of Biomedical Engineering, University of Wisconsin-Madison, United States; Department of Electrical and Computer Engineering, University of Wisconsin-Madison, United States; Wisconsin Institute for Translational Neuroengineering (WITNe), University of Wisconsin-Madison, United States.
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Functional Two-Dimensional Materials for Bioelectronic Neural Interfacing. J Funct Biomater 2023; 14:jfb14010035. [PMID: 36662082 PMCID: PMC9863167 DOI: 10.3390/jfb14010035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 12/26/2022] [Accepted: 01/03/2023] [Indexed: 01/11/2023] Open
Abstract
Realizing the neurological information processing by analyzing the complex data transferring behavior of populations and individual neurons is one of the fast-growing fields of neuroscience and bioelectronic technologies. This field is anticipated to cover a wide range of advanced applications, including neural dynamic monitoring, understanding the neurological disorders, human brain-machine communications and even ambitious mind-controlled prosthetic implant systems. To fulfill the requirements of high spatial and temporal resolution recording of neural activities, electrical, optical and biosensing technologies are combined to develop multifunctional bioelectronic and neuro-signal probes. Advanced two-dimensional (2D) layered materials such as graphene, graphene oxide, transition metal dichalcogenides and MXenes with their atomic-layer thickness and multifunctional capabilities show bio-stimulation and multiple sensing properties. These characteristics are beneficial factors for development of ultrathin-film electrodes for flexible neural interfacing with minimum invasive chronic interfaces to the brain cells and cortex. The combination of incredible properties of 2D nanostructure places them in a unique position, as the main materials of choice, for multifunctional reception of neural activities. The current review highlights the recent achievements in 2D-based bioelectronic systems for monitoring of biophysiological indicators and biosignals at neural interfaces.
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Ke XY, Hou W, Huang Q, Hou X, Bao XY, Kong WX, Li CX, Qiu YQ, Hu SY, Dong LH. Advances in electrical impedance tomography-based brain imaging. Mil Med Res 2022; 9:10. [PMID: 35227324 PMCID: PMC8883715 DOI: 10.1186/s40779-022-00370-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 02/08/2022] [Indexed: 11/10/2022] Open
Abstract
Novel advances in the field of brain imaging have enabled the unprecedented clinical application of various imaging modalities to facilitate disease diagnosis and treatment. Electrical impedance tomography (EIT) is a functional imaging technique that measures the transfer impedances between electrodes on the body surface to estimate the spatial distribution of electrical properties of tissues. EIT offers many advantages over other neuroimaging technologies, which has led to its potential clinical use. This qualitative review provides an overview of the basic principles, algorithms, and system composition of EIT. Recent advances in the field of EIT are discussed in the context of epilepsy, stroke, brain injuries and edema, and other brain diseases. Further, we summarize factors limiting the development of brain EIT and highlight prospects for the field. In epilepsy imaging, there have been advances in EIT imaging depth, from cortical to subcortical regions. In stroke research, a bedside EIT stroke monitoring system has been developed for clinical practice, and data support the role of EIT in multi-modal imaging for diagnosing stroke. Additionally, EIT has been applied to monitor the changes in brain water content associated with cerebral edema, enabling the early identification of brain edema and the evaluation of mannitol dehydration. However, anatomically realistic geometry, inhomogeneity, cranium completeness, anisotropy and skull type, etc., must be considered to improve the accuracy of EIT modeling. Thus, the further establishment of EIT as a mature and routine diagnostic technique will necessitate the accumulation of more supporting evidence.
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Affiliation(s)
- Xi-Yang Ke
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China.,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, 130021, China.,NHC Key Laboratory of Radiobiology, School of Public Health, Jilin University, Changchun, 130021, China
| | - Wei Hou
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China.,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, 130021, China.,NHC Key Laboratory of Radiobiology, School of Public Health, Jilin University, Changchun, 130021, China
| | - Qi Huang
- CAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, Jiangsu, China
| | - Xue Hou
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China.,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, 130021, China.,NHC Key Laboratory of Radiobiology, School of Public Health, Jilin University, Changchun, 130021, China
| | - Xue-Ying Bao
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China.,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, 130021, China.,NHC Key Laboratory of Radiobiology, School of Public Health, Jilin University, Changchun, 130021, China
| | - Wei-Xuan Kong
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China.,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, 130021, China
| | - Cheng-Xiang Li
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China.,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, 130021, China.,NHC Key Laboratory of Radiobiology, School of Public Health, Jilin University, Changchun, 130021, China
| | - Yu-Qi Qiu
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China.,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, 130021, China.,NHC Key Laboratory of Radiobiology, School of Public Health, Jilin University, Changchun, 130021, China
| | - Si-Yi Hu
- CAS Key Laboratory of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, Jiangsu, China.
| | - Li-Hua Dong
- Department of Radiation Oncology and Therapy, The First Hospital of Jilin University, 130021, Changchun, China. .,Jilin Provincial Key Laboratory of Radiation Oncology and Therapy, The First Hospital of Jilin University, Changchun, 130021, China. .,NHC Key Laboratory of Radiobiology, School of Public Health, Jilin University, Changchun, 130021, China.
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Vėbraitė I, Hanein Y. Soft Devices for High-Resolution Neuro-Stimulation: The Interplay Between Low-Rigidity and Resolution. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 3:675744. [PMID: 35047928 PMCID: PMC8757739 DOI: 10.3389/fmedt.2021.675744] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 05/14/2021] [Indexed: 12/27/2022] Open
Abstract
The field of neurostimulation has evolved over the last few decades from a crude, low-resolution approach to a highly sophisticated methodology entailing the use of state-of-the-art technologies. Neurostimulation has been tested for a growing number of neurological applications, demonstrating great promise and attracting growing attention in both academia and industry. Despite tremendous progress, long-term stability of the implants, their large dimensions, their rigidity and the methods of their introduction and anchoring to sensitive neural tissue remain challenging. The purpose of this review is to provide a concise introduction to the field of high-resolution neurostimulation from a technological perspective and to focus on opportunities stemming from developments in materials sciences and engineering to reduce device rigidity while optimizing electrode small dimensions. We discuss how these factors may contribute to smaller, lighter, softer and higher electrode density devices.
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Affiliation(s)
- Ieva Vėbraitė
- School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Yael Hanein
- School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel
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Proctor CM, Chan CY, Porcarelli L, Udabe E, Sanchez-Sanchez A, del Agua I, Mecerreyes D, Malliaras GG. Ionic Hydrogel for Accelerated Dopamine Delivery via Retrodialysis. CHEMISTRY OF MATERIALS : A PUBLICATION OF THE AMERICAN CHEMICAL SOCIETY 2019; 31:7080-7084. [PMID: 32063677 PMCID: PMC7011752 DOI: 10.1021/acs.chemmater.9b02135] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 07/26/2019] [Indexed: 05/26/2023]
Abstract
Local drug delivery directly to the source of a given pathology using retrodialysis is a promising approach to treating otherwise untreatable diseases. As the primary material component in retrodialysis, the semipermeable membrane represents a critical point for innovation. This work presents a new ionic hydrogel based on polyethylene glycol and acrylate with dopamine counterions. The ionic hydrogel membrane is shown to be a promising material for controlled diffusive delivery of dopamine. The ionic nature of the membrane accelerates uptake of cationic species compared to a nonionic membrane of otherwise similar composition. It is demonstrated that the increased uptake of cations can be exploited to confer an accelerated transport of cationic species between reservoirs as is desired in retrodialysis applications. This effect is shown to enable nearly 10-fold increases in drug delivery rates from low concentration solutions. The processability of the membrane is found to allow for integration with microfabricated devices which will in turn accelerate adaptation into both existing and emerging device modalities. It is anticipated that a similar materials design approach may be broadly applied to a variety of cationic and anionic compounds for drug delivery applications ranging from neurological disorders to cancer.
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Affiliation(s)
- Christopher M. Proctor
- Electrical Engineering
Division, Department of Engineering, University
of Cambridge, Cambridge CB3 0FA, United Kingdom
| | - Chung Yuen Chan
- Electrical Engineering
Division, Department of Engineering, University
of Cambridge, Cambridge CB3 0FA, United Kingdom
| | - Luca Porcarelli
- POLYMAT University of the Basque Country UPV/EHU, Joxe Mari Korta Center, Avda. Tolosa
72, 20018 Donostia-San
Sebastian, Spain
| | - Esther Udabe
- POLYMAT University of the Basque Country UPV/EHU, Joxe Mari Korta Center, Avda. Tolosa
72, 20018 Donostia-San
Sebastian, Spain
| | - Ana Sanchez-Sanchez
- Electrical Engineering
Division, Department of Engineering, University
of Cambridge, Cambridge CB3 0FA, United Kingdom
- POLYMAT University of the Basque Country UPV/EHU, Joxe Mari Korta Center, Avda. Tolosa
72, 20018 Donostia-San
Sebastian, Spain
| | - Isabel del Agua
- POLYMAT University of the Basque Country UPV/EHU, Joxe Mari Korta Center, Avda. Tolosa
72, 20018 Donostia-San
Sebastian, Spain
| | - David Mecerreyes
- POLYMAT University of the Basque Country UPV/EHU, Joxe Mari Korta Center, Avda. Tolosa
72, 20018 Donostia-San
Sebastian, Spain
| | - George G. Malliaras
- Electrical Engineering
Division, Department of Engineering, University
of Cambridge, Cambridge CB3 0FA, United Kingdom
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