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Jiricek S, Koudelka V, Lacik J, Vejmola C, Kuratko D, Wójcik DK, Raida Z, Hlinka J, Palenicek T. Electrical Source Imaging in Freely Moving Rats: Evaluation of a 12-Electrode Cortical Electroencephalography System. Front Neuroinform 2021; 14:589228. [PMID: 33568980 PMCID: PMC7868391 DOI: 10.3389/fninf.2020.589228] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Accepted: 12/28/2020] [Indexed: 11/23/2022] Open
Abstract
This work presents and evaluates a 12-electrode intracranial electroencephalography system developed at the National Institute of Mental Health (Klecany, Czech Republic) in terms of an electrical source imaging (ESI) technique in rats. The electrode system was originally designed for translational research purposes. This study demonstrates that it is also possible to use this well-established system for ESI, and estimates its precision, accuracy, and limitations. Furthermore, this paper sets a methodological basis for future implants. Source localization quality is evaluated using three approaches based on surrogate data, physical phantom measurements, and in vivo experiments. The forward model for source localization is obtained from the FieldTrip-SimBio pipeline using the finite-element method. Rat brain tissue extracted from a magnetic resonance imaging template is approximated by a single-compartment homogeneous tetrahedral head model. Four inverse solvers were tested: standardized low-resolution brain electromagnetic tomography, exact low-resolution brain electromagnetic tomography (eLORETA), linear constrained minimum variance (LCMV), and dynamic imaging of coherent sources. Based on surrogate data, this paper evaluates the accuracy and precision of all solvers within the brain volume using error distance and reliability maps. The mean error distance over the whole brain was found to be the lowest in the eLORETA solution through signal to noise ratios (SNRs) (0.2 mm for 25 dB SNR). The LCMV outperformed eLORETA under higher SNR conditions, and exhibiting higher spatial precision. Both of these inverse solvers provided accurate results in a phantom experiment (1.6 mm mean error distance across shallow and 2.6 mm across subcortical testing dipoles). Utilizing the developed technique in freely moving rats, an auditory steady-state response experiment provided results in line with previously reported findings. The obtained results support the idea of utilizing a 12-electrode system for ESI and using it as a solid basis for the development of future ESI dedicated implants.
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Affiliation(s)
- Stanislav Jiricek
- National Institute of Mental Health, Klecany, Czechia
- Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czechia
- Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Prague, Czechia
| | | | - Jaroslav Lacik
- Department of Radioengineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czechia
| | - Cestmir Vejmola
- National Institute of Mental Health, Klecany, Czechia
- Third Faculty of Medicine, Charles University, Prague, Czechia
| | - David Kuratko
- Department of Radioengineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czechia
| | - Daniel K. Wójcik
- Department of Radioengineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czechia
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of the Polish Academy of Sciences, Warsaw, Poland
| | - Zbynek Raida
- Department of Radioengineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Brno, Czechia
| | - Jaroslav Hlinka
- National Institute of Mental Health, Klecany, Czechia
- Department of Complex Systems, Institute of Computer Science of the Czech Academy of Sciences, Prague, Czechia
| | - Tomas Palenicek
- National Institute of Mental Health, Klecany, Czechia
- Third Faculty of Medicine, Charles University, Prague, Czechia
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Liu S, Moncion C, Zhang J, Balachandar L, Kwaku D, Riera JJ, Volakis JL, Chae J. Fully Passive Flexible Wireless Neural Recorder for the Acquisition of Neuropotentials from a Rat Model. ACS Sens 2019; 4:3175-3185. [PMID: 31670508 DOI: 10.1021/acssensors.9b01491] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Wireless implantable neural interfaces can record high-resolution neuropotentials without constraining patient movement. Existing wireless systems often require intracranial wires to connect implanted electrodes to an external head stage or/and deploy an application-specific integrated circuit (ASIC), which is battery-powered or externally power-transferred, raising safety concerns such as infection, electronics failure, or heat-induced tissue damage. This work presents a biocompatible, flexible, implantable neural recorder capable of wireless acquisition of neuropotentials without wires, batteries, energy harvesting units, or active electronics. The recorder, fabricated on a thin polyimide substrate, features a small footprint of 9 mm × 8 mm × 0.3 mm and is composed of passive electronic components. The absence of active electronics on the device leads to near zero power consumption, inherently avoiding the catastrophic failure of active electronics. We performed both in vitro validation in a tissue-simulating phantom and in vivo validation in an epileptic rat. The fully passive wireless recorder was implanted under rat scalp to measure neuropotentials from its contact electrodes. The implanted wireless recorder demonstrated its capability to capture low voltage neuropotentials, including somatosensory evoked potentials (SSEPs), and interictal epileptiform discharges (IEDs). Wirelessly recorded SSEP and IED signals were directly compared to those from wired electrodes to demonstrate the efficacy of the wireless data. In addition, a convoluted neural network-based machine learning algorithm successfully achieved IED signal recognition accuracy as high as 100 and 91% in wired and wireless IED data, respectively. These results strongly support the fully passive wireless neural recorder's capability to measure neuropotentials as low as tens of microvolts. With further improvement, the recorder system presented in this work may find wide applications in future brain machine interface systems.
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Affiliation(s)
- Shiyi Liu
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, United States
| | - Carolina Moncion
- NMD Laboratory, Department of Biomedical Engineering, Florida International University, Miami, Florida 33174, United States
| | - Jianwei Zhang
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, United States
| | - Lakshmini Balachandar
- NMD Laboratory, Department of Biomedical Engineering, Florida International University, Miami, Florida 33174, United States
| | - Dzifa Kwaku
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, United States
| | - Jorge J. Riera
- NMD Laboratory, Department of Biomedical Engineering, Florida International University, Miami, Florida 33174, United States
| | - John L. Volakis
- NMD Laboratory, Department of Biomedical Engineering, Florida International University, Miami, Florida 33174, United States
| | - Junseok Chae
- School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, Arizona 85287, United States
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Marinazzo D, Riera JJ, Marzetti L, Astolfi L, Yao D, Valdés Sosa PA. Controversies in EEG Source Imaging and Connectivity: Modeling, Validation, Benchmarking. Brain Topogr 2019; 32:527-529. [DOI: 10.1007/s10548-019-00709-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Accepted: 04/13/2019] [Indexed: 11/30/2022]
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Deshmukh A, Leichner J, Bae J, Song Y, Valdés-Hernández PA, Lin WC, Riera JJ. Histological Characterization of the Irritative Zones in Focal Cortical Dysplasia Using a Preclinical Rat Model. Front Cell Neurosci 2018; 12:52. [PMID: 29867355 PMCID: PMC5968101 DOI: 10.3389/fncel.2018.00052] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2017] [Accepted: 02/15/2018] [Indexed: 12/19/2022] Open
Abstract
Current clinical practice in focal epilepsy involves brain source imaging (BSI) to localize brain areas where from interictal epileptiform discharges (IEDs) emerge. These areas, named irritative zones, have been useful to define candidate seizures-onset zones during pre-surgical workup. Since human histological data are mostly available from final resected zones, systematic studies characterizing pathophysiological mechanisms and abnormal molecular/cellular substrates in irritative zones—independent of them being epileptogenic—are challenging. Combining BSI and histological analysis from all types of irritative zones is only possible through the use of preclinical animal models. Here, we recorded 32-channel spontaneous electroencephalographic data from rats that have focal cortical dysplasia (FCD) and chronic seizures. BSI for different IED subtypes was performed using the methodology presented in Bae et al. (2015). Post-mortem brain sections containing irritative zones were stained to quantify anatomical, functional, and inflammatory biomarkers specific for epileptogenesis, and the results were compared with those obtained using the contralateral healthy brain tissue. We found abnormal anatomical structures in all irritative zones (i.e., larger neuronal processes, glioreactivity, and vascular cuffing) and larger expressions for neurotransmission (i.e., NR2B) and inflammation (i.e., ILβ1, TNFα and HMGB1). We conclude that irritative zones in this rat preclinical model of FCD comprise abnormal tissues disregarding whether they are actually involved in icto-genesis or not. We hypothesize that seizure perpetuation happens gradually; hence, our results could support the use of IED-based BSI for the early diagnosis and preventive treatment of potential epileptic foci. Further verifications in humans are yet needed.
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Affiliation(s)
- Abhay Deshmukh
- Neuronal Mass Dynamics Laboratory, Department of Biomedical Engineering, Florida International University, Miami, FL, United States
| | - Jared Leichner
- Neuronal Mass Dynamics Laboratory, Department of Biomedical Engineering, Florida International University, Miami, FL, United States
| | - Jihye Bae
- Neuronal Mass Dynamics Laboratory, Department of Biomedical Engineering, Florida International University, Miami, FL, United States
| | - Yinchen Song
- Neuronal Mass Dynamics Laboratory, Department of Biomedical Engineering, Florida International University, Miami, FL, United States
| | - Pedro A Valdés-Hernández
- Neuronal Mass Dynamics Laboratory, Department of Biomedical Engineering, Florida International University, Miami, FL, United States
| | - Wei-Chiang Lin
- Neuronal Mass Dynamics Laboratory, Department of Biomedical Engineering, Florida International University, Miami, FL, United States
| | - Jorge J Riera
- Neuronal Mass Dynamics Laboratory, Department of Biomedical Engineering, Florida International University, Miami, FL, United States
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