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Chen FD, Sharma A, Roszko DA, Xue T, Mu X, Luo X, Chua H, Lo PGQ, Sacher WD, Poon JKS. Development of wafer-scale multifunctional nanophotonic neural probes for brain activity mapping. LAB ON A CHIP 2024; 24:2397-2417. [PMID: 38623840 DOI: 10.1039/d3lc00931a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
Optical techniques, such as optogenetic stimulation and functional fluorescence imaging, have been revolutionary for neuroscience by enabling neural circuit analysis with cell-type specificity. To probe deep brain regions, implantable light sources are crucial. Silicon photonics, commonly used for data communications, shows great promise in creating implantable devices with complex optical systems in a compact form factor compatible with high volume manufacturing practices. This article reviews recent developments of wafer-scale multifunctional nanophotonic neural probes. The probes can be realized on 200 or 300 mm wafers in commercial foundries and integrate light emitters for photostimulation, microelectrodes for electrophysiological recording, and microfluidic channels for chemical delivery and sampling. By integrating active optical devices to the probes, denser emitter arrays, enhanced on-chip biosensing, and increased ease of use may be realized. Silicon photonics technology makes possible highly versatile implantable neural probes that can transform neuroscience experiments.
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Affiliation(s)
- Fu Der Chen
- Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany.
- Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada
| | - Ankita Sharma
- Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany.
- Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada
| | - David A Roszko
- Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany.
- Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada
| | - Tianyuan Xue
- Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany.
- Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada
| | - Xin Mu
- Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany.
- Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada
| | - Xianshu Luo
- Advanced Micro Foundry Pte Ltd, 11 Science Park Road, Singapore Science Park II, 117685, Singapore
| | - Hongyao Chua
- Advanced Micro Foundry Pte Ltd, 11 Science Park Road, Singapore Science Park II, 117685, Singapore
| | - Patrick Guo-Qiang Lo
- Advanced Micro Foundry Pte Ltd, 11 Science Park Road, Singapore Science Park II, 117685, Singapore
| | - Wesley D Sacher
- Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany.
| | - Joyce K S Poon
- Max Planck Institute of Microstructure Physics, Weinberg 2, 06120 Halle, Germany.
- Department of Electrical and Computer Engineering, University of Toronto, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada
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Yan D, Ruiz JRL, Hsieh ML, Jeong D, Vöröslakos M, Lanzio V, Warner EV, Ko E, Tian Y, Patel PR, ElBidweihy H, Smith CS, Lee JH, Cheon J, Buzsáki G, Yoon E. Self-Assembled Origami Neural Probes for Scalable, Multifunctional, Three-Dimensional Neural Interface. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.25.591141. [PMID: 38712092 PMCID: PMC11071508 DOI: 10.1101/2024.04.25.591141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Flexible intracortical neural probes have drawn attention for their enhanced longevity in high-resolution neural recordings due to reduced tissue reaction. However, the conventional monolithic fabrication approach has met significant challenges in: (i) scaling the number of recording sites for electrophysiology; (ii) integrating of other physiological sensing and modulation; and (iii) configuring into three-dimensional (3D) shapes for multi-sided electrode arrays. We report an innovative self-assembly technology that allows for implementing flexible origami neural probes as an effective alternative to overcome these challenges. By using magnetic-field-assisted hybrid self-assembly, multiple probes with various modalities can be stacked on top of each other with precise alignment. Using this approach, we demonstrated a multifunctional device with scalable high-density recording sites, dopamine sensors and a temperature sensor integrated on a single flexible probe. Simultaneous large-scale, high-spatial-resolution electrophysiology was demonstrated along with local temperature sensing and dopamine concentration monitoring. A high-density 3D origami probe was assembled by wrapping planar probes around a thin fiber in a diameter of 80∼105 μm using optimal foldable design and capillary force. Directional optogenetic modulation could be achieved with illumination from the neuron-sized micro-LEDs (μLEDs) integrated on the surface of 3D origami probes. We could identify angular heterogeneous single-unit signals and neural connectivity 360° surrounding the probe. The probe longevity was validated by chronic recordings of 64-channel stacked probes in behaving mice for up to 140 days. With the modular, customizable assembly technologies presented, we demonstrated a novel and highly flexible solution to accommodate multifunctional integration, channel scaling, and 3D array configuration.
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Kopsick JD, Kilgore JA, Adam GC, Ascoli GA. Formation and Retrieval of Cell Assemblies in a Biologically Realistic Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.27.586909. [PMID: 38585941 PMCID: PMC10996657 DOI: 10.1101/2024.03.27.586909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
The hippocampal formation is critical for episodic memory, with area Cornu Ammonis 3 (CA3) a necessary substrate for auto-associative pattern completion. Recent theoretical and experimental evidence suggests that the formation and retrieval of cell assemblies enable these functions. Yet, how cell assemblies are formed and retrieved in a full-scale spiking neural network (SNN) of CA3 that incorporates the observed diversity of neurons and connections within this circuit is not well understood. Here, we demonstrate that a data-driven SNN model quantitatively reflecting the neuron type-specific population sizes, intrinsic electrophysiology, connectivity statistics, synaptic signaling, and long-term plasticity of the mouse CA3 is capable of robust auto-association and pattern completion via cell assemblies. Our results show that a broad range of assembly sizes could successfully and systematically retrieve patterns from heavily incomplete or corrupted cues after a limited number of presentations. Furthermore, performance was robust with respect to partial overlap of assemblies through shared cells, substantially enhancing memory capacity. These novel findings provide computational evidence that the specific biological properties of the CA3 circuit produce an effective neural substrate for associative learning in the mammalian brain.
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Affiliation(s)
- Jeffrey D. Kopsick
- Center for Neural Informatics, Structures, & Plasticity, College of Engineering and Computing, George Mason University, Fairfax, VA, United States
- Interdisciplinary Program in Neuroscience, College of Science, George Mason University, Fairfax, VA, United States
| | - Joseph A. Kilgore
- Department of Electrical and Computer Engineering, George Washington University, Washington, D.C., United States
| | - Gina C. Adam
- Department of Electrical and Computer Engineering, George Washington University, Washington, D.C., United States
| | - Giorgio A. Ascoli
- Center for Neural Informatics, Structures, & Plasticity, College of Engineering and Computing, George Mason University, Fairfax, VA, United States
- Interdisciplinary Program in Neuroscience, College of Science, George Mason University, Fairfax, VA, United States
- Bioengineering Department, College of Engineering and Computing, George Mason University, Fairfax, VA, United States
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4
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Farahani F, Khadka N, Parra LC, Bikson M, Vöröslakos M. Transcranial electric stimulation modulates firing rate at clinically relevant intensities. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.24.568618. [PMID: 38045400 PMCID: PMC10690262 DOI: 10.1101/2023.11.24.568618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Notwithstanding advances with low-intensity transcranial electrical stimulation (TES), there remain questions about the efficacy of clinically realistic electric fields on neuronal function. We used Neuropixels 2.0 probe with 384 channels in an in-vivo rat model of TES to detect effects of weak fields on neuronal firing rate. High-density field mapping and computational models verified field intensity (1 V/m in hippocampus per 50 μA of applied skull currents). We demonstrate that electric fields below 0.5 V/m acutely modulate firing rate in 5% of neurons recorded in the hippocampus. At these intensities, average firing rate effects increased monotonically with electric field intensity at a rate of 7 % per V/m. For the majority of excitatory neurons, firing increased for cathodal stimulation and diminished for anodal stimulation. While more diverse, the response of inhibitory neurons followed a similar pattern on average, likely as a result of excitatory drive. Our results indicate that responses to TES at clinically relevant intensities are driven by a fraction of high-responder excitatory neurons, with polarity-specific effects. We conclude that transcranial electric stimulation is an effective neuromodulator at clinically realistic intensities.
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Affiliation(s)
- Forouzan Farahani
- Department of Biomedical Engineering, The City College of New York, New York, NY, USA
| | - Niranjan Khadka
- Department of Biomedical Engineering, The City College of New York, New York, NY, USA
| | - Lucas C. Parra
- Department of Biomedical Engineering, The City College of New York, New York, NY, USA
| | - Marom Bikson
- Department of Biomedical Engineering, The City College of New York, New York, NY, USA
| | - Mihály Vöröslakos
- Neuroscience Institute and Department of Neurology, NYU Grossman School of Medicine, New York University, New York, NY, USA
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Wu Y, Li BZ, Wang L, Fan S, Chen C, Li A, Lin Q, Wang P. An unsupervised real-time spike sorting system based on optimized OSort. J Neural Eng 2023; 20:066015. [PMID: 37972395 DOI: 10.1088/1741-2552/ad0d15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 11/15/2023] [Indexed: 11/19/2023]
Abstract
Objective. The OSort algorithm, a pivotal unsupervised spike sorting method, has been implemented in dedicated hardware devices for real-time spike sorting. However, due to the inherent complexity of neural recording environments, OSort still grapples with numerous transient cluster occurrences during the practical sorting process. This leads to substantial memory usage, heavy computational load, and complex hardware architectures, especially in noisy recordings and multi-channel systems.Approach. This study introduces an optimized OSort algorithm (opt-OSort) which utilizes correlation coefficient (CC), instead of Euclidean distance as classification criterion. TheCCmethod not only bolsters the robustness of spike classification amidst the diverse and ever-changing conditions of physiological and recording noise environments, but also can finish the entire sorting procedure within a fixed number of cluster slots, thus preventing a large number of transient clusters. Moreover, the opt-OSort incorporates two configurable validation loops to efficiently reject cluster outliers and track recording variations caused by electrode drifting in real-time.Main results. The opt-OSort significantly reduces transient cluster occurrences by two orders of magnitude and decreases memory usage by 2.5-80 times in the number of pre-allocated transient clusters compared with other hardware implementations of OSort. The opt-OSort maintains an accuracy comparable to offline OSort and other commonly-used algorithms, with a sorting time of 0.68µs as measured by the hardware-implemented system in both simulated datasets and experimental data. The opt-OSort's ability to handle variations in neural activity caused by electrode drifting is also demonstrated.Significance. These results present a rapid, precise, and robust spike sorting solution suitable for integration into low-power, portable, closed-loop neural control systems and brain-computer interfaces.
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Affiliation(s)
- Yingjiang Wu
- School of Biomedical Engineering, Guangdong Medical University, Dongguan, People's Republic of China
- Songshan Lake Innovation Center of Medicine and Engineering, Guangdong Medical University, Dongguan, People's Republic of China
- Dongguan Key Laboratory of Medical Electronics and Medical Imaging Equipment, Dongguan, People's Republic of China
| | - Ben-Zheng Li
- Department of Physiology and Biophysics, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
- Department of Electrical Engineering, University of Colorado Denver, Denver, CO, United States of America
| | - Liyang Wang
- State Key Laboratory of Analog and Mixed Signal VLSI, University of Macau, Macau, People's Republic of China
- Department of Electrical and Computer Engineering, University of Macau, Macau, People's Republic of China
| | - Shaocan Fan
- School of Electronics and Communication Engineering, Sun Yat-sen University-Shenzhen Campus, Shenzhen, People's Republic of China
| | - Changhao Chen
- Zhuhai Hokai Medical Instruments Co., Ltd, Zhuhai, People's Republic of China
| | - Anan Li
- Jiangsu Key Laboratory of Brain Disease and Bioinformation, Research Center for Biochemistry and Molecular Biology, Xuzhou Medical University, Xuzhou, People's Republic of China
| | - Qin Lin
- School of Biomedical Engineering, Guangdong Medical University, Dongguan, People's Republic of China
- Songshan Lake Innovation Center of Medicine and Engineering, Guangdong Medical University, Dongguan, People's Republic of China
- Dongguan Key Laboratory of Medical Electronics and Medical Imaging Equipment, Dongguan, People's Republic of China
| | - Panke Wang
- School of Biomedical Engineering, Guangdong Medical University, Dongguan, People's Republic of China
- Songshan Lake Innovation Center of Medicine and Engineering, Guangdong Medical University, Dongguan, People's Republic of China
- Dongguan Key Laboratory of Medical Electronics and Medical Imaging Equipment, Dongguan, People's Republic of China
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Junge S, Ricci Signorini ME, Al Masri M, Gülink J, Brüning H, Kasperek L, Szepes M, Bakar M, Gruh I, Heisterkamp A, Torres-Mapa ML. A micro-LED array based platform for spatio-temporal optogenetic control of various cardiac models. Sci Rep 2023; 13:19490. [PMID: 37945622 PMCID: PMC10636122 DOI: 10.1038/s41598-023-46149-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 10/27/2023] [Indexed: 11/12/2023] Open
Abstract
Optogenetics relies on dynamic spatial and temporal control of light to address emerging fundamental and therapeutic questions in cardiac research. In this work, a compact micro-LED array, consisting of 16 × 16 pixels, is incorporated in a widefield fluorescence microscope for controlled light stimulation. We describe the optical design of the system that allows the micro-LED array to fully cover the field of view regardless of the imaging objective used. Various multicellular cardiac models are used in the experiments such as channelrhodopsin-2 expressing aggregates of cardiomyocytes, termed cardiac bodies, and bioartificial cardiac tissues derived from human induced pluripotent stem cells. The pacing efficiencies of the cardiac bodies and bioartificial cardiac tissues were characterized as a function of illumination time, number of switched-on pixels and frequency of stimulation. To demonstrate dynamic stimulation, steering of calcium waves in HL-1 cell monolayer expressing channelrhodopsin-2 was performed by applying different configurations of patterned light. This work shows that micro-LED arrays are powerful light sources for optogenetic control of contraction and calcium waves in cardiac monolayers, multicellular bodies as well as three-dimensional artificial cardiac tissues.
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Affiliation(s)
- Sebastian Junge
- Institute of Quantum Optics, Gottfried Wilhelm Leibniz University, 30167, Hannover, Germany
- Lower Saxony Centre for Biomedical Engineering, Implant Research and Development (NIFE), 30625, Hannover, Germany
| | - Maria Elena Ricci Signorini
- Department of Cardiac, Thoracic-, Transplantation and Vascular Surgery, Leibniz Research Laboratories for Biotechnology and Artificial Organs (LEBAO), Hannover Medical School, 30625, Hannover, Germany
| | - Masa Al Masri
- Institute of Quantum Optics, Gottfried Wilhelm Leibniz University, 30167, Hannover, Germany
- Lower Saxony Centre for Biomedical Engineering, Implant Research and Development (NIFE), 30625, Hannover, Germany
| | - Jan Gülink
- QubeDot GmbH, Wilhelmsgarten 3, 38100, Brunswick, Germany
| | - Heiko Brüning
- QubeDot GmbH, Wilhelmsgarten 3, 38100, Brunswick, Germany
| | - Leon Kasperek
- Institute of Quantum Optics, Gottfried Wilhelm Leibniz University, 30167, Hannover, Germany
- Lower Saxony Centre for Biomedical Engineering, Implant Research and Development (NIFE), 30625, Hannover, Germany
| | - Monika Szepes
- Department of Cardiac, Thoracic-, Transplantation and Vascular Surgery, Leibniz Research Laboratories for Biotechnology and Artificial Organs (LEBAO), Hannover Medical School, 30625, Hannover, Germany
| | - Mine Bakar
- Department of Cardiac, Thoracic-, Transplantation and Vascular Surgery, Leibniz Research Laboratories for Biotechnology and Artificial Organs (LEBAO), Hannover Medical School, 30625, Hannover, Germany
| | - Ina Gruh
- Department of Cardiac, Thoracic-, Transplantation and Vascular Surgery, Leibniz Research Laboratories for Biotechnology and Artificial Organs (LEBAO), Hannover Medical School, 30625, Hannover, Germany
| | - Alexander Heisterkamp
- Institute of Quantum Optics, Gottfried Wilhelm Leibniz University, 30167, Hannover, Germany
- Lower Saxony Centre for Biomedical Engineering, Implant Research and Development (NIFE), 30625, Hannover, Germany
| | - Maria Leilani Torres-Mapa
- Institute of Quantum Optics, Gottfried Wilhelm Leibniz University, 30167, Hannover, Germany.
- Lower Saxony Centre for Biomedical Engineering, Implant Research and Development (NIFE), 30625, Hannover, Germany.
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7
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Meyer LM, Samann F, Schanze T. DualSort: online spike sorting with a running neural network. J Neural Eng 2023; 20:056031. [PMID: 37795548 DOI: 10.1088/1741-2552/acfb3a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 09/19/2023] [Indexed: 10/06/2023]
Abstract
Objective.Spike sorting, i.e. the detection and separation of measured action potentials from different extracellularly recorded neurons, remains one of the bottlenecks in deciphering the brain. In recent years, the application of neural networks (NNs) for spike sorting has garnered significant attention. Most methods focus on specific sub-problems within the conventional spike sorting pipeline, such as spike detection or feature extraction, and attempt to solve them with complex network architectures. This paper presents DualSort, a simple NN that gets combined with downstream post-processing for real-time spike sorting. It shows high efficiency, low complexity, and requires a comparatively small amount of human interaction.Approach.Synthetic and experimentally obtained extracellular single-channel recordings were utilized to train and evaluate the proposed NN. For training, spike waveforms were labeled with respect to their associated neuron and position in the signal, allowing the detection and categorization of spikes in unison. DualSort classifies a single spike multiple times in succession, as it runs over the signal in a step-by-step manner and uses a post-processing algorithm that transmits the network output into spike trains. Main results.With the used datasets, DualSort was able to detect and distinguish different spike waveforms and separate them from background activity. The post-processing algorithm significantly strengthened the overall performance of the model, making the system more robust as a whole. Although DualSort is an end-to-end solution that efficiently transforms filtered signals into spike trains, it competes with contemporary state-of-the-art technologies that exclusively target single sub-problems in the conventional spike sorting pipeline.Significance.This work demonstrates that even under high noise levels, complex NNs are not necessary by any means to achieve high performance in spike detection and sorting. The utilization of data augmentation on a limited quantity of spikes could substantially decrease hand-labeling compared to other studies. Furthermore, the proposed framework can be utilized without human interaction when combined with an unsupervised technique that provides pseudo labels for DualSort. Due to the low complexity of our network, it works efficiently and enables real-time processing on basic hardware. The proposed approach is not limited to spike sorting, as it may also be used to process different signals, such as electroencephalogram (EEG), which needs to be investigated in future research.
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Affiliation(s)
- L M Meyer
- Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany
| | - F Samann
- Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany
- Department of Biomedical Engineering, University of Duhok, Kurdistan Region, Iraq
| | - T Schanze
- Technische Hochschule Mittelhessen - University of Applied Sciences, Giessen, Germany
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8
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Zheng N, Jiang Y, Jiang S, Kim J, Chen G, Li Y, Cheng JX, Jia X, Yang C. Multifunctional Fiber-Based Optoacoustic Emitter as a Bidirectional Brain Interface. Adv Healthc Mater 2023; 12:e2300430. [PMID: 37451259 PMCID: PMC10592200 DOI: 10.1002/adhm.202300430] [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: 02/09/2023] [Revised: 07/11/2023] [Accepted: 07/12/2023] [Indexed: 07/18/2023]
Abstract
A bidirectional brain interface with both "write" and "read" functions can be an important tool for fundamental studies and potential clinical treatments for neurological diseases. Herein, a miniaturized multifunctional fiber-based optoacoustic emitter (mFOE) is reported thatintegrates simultaneous optoacoustic stimulation for "write" and electrophysiology recording of neural circuits for "read". Because of the intrinsic ability of neurons to respond to acoustic wave, there is no requirement of the viral transfection. The orthogonality between optoacoustic waves and electrical field provides a solution to avoid the interference between electrical stimulation and recording. The stimulation function of the mFOE is first validated in cultured ratcortical neurons using calcium imaging. In vivo application of mFOE for successful simultaneous optoacoustic stimulation and electrical recording of brain activities is confirmed in mouse hippocampus in both acute and chronical applications up to 1 month. Minor brain tissue damage is confirmed after these applications. The capability of simultaneous neural stimulation and recording enabled by mFOE opens up new possibilities for the investigation of neural circuits and brings new insights into the study of ultrasound neurostimulation.
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Affiliation(s)
- Nan Zheng
- Division of Materials Science and Engineering, Boston University, Boston, MA, USA
| | - Ying Jiang
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Shan Jiang
- Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA
| | - Jongwoon Kim
- Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA
| | - Guo Chen
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Yueming Li
- Department of Mechanical Engineering, Boston University, Boston, MA, USA
| | - Ji-Xin Cheng
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | - Xiaoting Jia
- Bradley Department of Electrical and Computer Engineering, Virginia Tech, Blacksburg, VA, USA
| | - Chen Yang
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
- Department of Chemistry, Boston University, Boston, MA, USA
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9
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Sahasrabudhe A, Rupprecht LE, Orguc S, Khudiyev T, Tanaka T, Sands J, Zhu W, Tabet A, Manthey M, Allen H, Loke G, Antonini MJ, Rosenfeld D, Park J, Garwood IC, Yan W, Niroui F, Fink Y, Chandrakasan A, Bohórquez DV, Anikeeva P. Multifunctional microelectronic fibers enable wireless modulation of gut and brain neural circuits. Nat Biotechnol 2023:10.1038/s41587-023-01833-5. [PMID: 37349522 DOI: 10.1038/s41587-023-01833-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 05/23/2023] [Indexed: 06/24/2023]
Abstract
Progress in understanding brain-viscera interoceptive signaling is hindered by a dearth of implantable devices suitable for probing both brain and peripheral organ neurophysiology during behavior. Here we describe multifunctional neural interfaces that combine the scalability and mechanical versatility of thermally drawn polymer-based fibers with the sophistication of microelectronic chips for organs as diverse as the brain and the gut. Our approach uses meters-long continuous fibers that can integrate light sources, electrodes, thermal sensors and microfluidic channels in a miniature footprint. Paired with custom-fabricated control modules, the fibers wirelessly deliver light for optogenetics and transfer data for physiological recording. We validate this technology by modulating the mesolimbic reward pathway in the mouse brain. We then apply the fibers in the anatomically challenging intestinal lumen and demonstrate wireless control of sensory epithelial cells that guide feeding behaviors. Finally, we show that optogenetic stimulation of vagal afferents from the intestinal lumen is sufficient to evoke a reward phenotype in untethered mice.
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Affiliation(s)
- Atharva Sahasrabudhe
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Laura E Rupprecht
- Laboratory of Gut Brain Neurobiology, Duke University, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
| | - Sirma Orguc
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tural Khudiyev
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tomo Tanaka
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
- Secure System Platform Research Laboratories, NEC Corporation, Kawasaki, Japan
| | - Joanna Sands
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Weikun Zhu
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Anthony Tabet
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Marie Manthey
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Harrison Allen
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Gabriel Loke
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Marc-Joseph Antonini
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Harvard/MIT Health Sciences and Technology Graduate Program, Cambridge, MA, USA
| | - Dekel Rosenfeld
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jimin Park
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Indie C Garwood
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Harvard/MIT Health Sciences and Technology Graduate Program, Cambridge, MA, USA
| | - Wei Yan
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Farnaz Niroui
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yoel Fink
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Institute for Soldier Nanotechnologies, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Anantha Chandrakasan
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Diego V Bohórquez
- Laboratory of Gut Brain Neurobiology, Duke University, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
- Department of Neurobiology, Duke University, Durham, NC, USA
- Duke Institute for Brain Sciences, Duke University, Durham, NC, USA
| | - Polina Anikeeva
- Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA.
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
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10
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Qi Y, Kang SK, Fang H. Advanced materials for implantable neuroelectronics. MRS BULLETIN 2023; 48:475-483. [PMID: 37485070 PMCID: PMC10361212 DOI: 10.1557/s43577-023-00540-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/12/2023] [Indexed: 07/25/2023]
Abstract
Materials innovation has arguably played one of the most important roles in the development of implantable neuroelectronics. Such technologies explore biocompatible working systems for reading, triggering, and manipulating neural signals for neuroscience research and provide the additional potential to develop devices for medical diagnostics and/or treatment. The past decade has witnessed a golden era in neuroelectronic materials research. For example, R&D on soft material-based devices have exploded and taken center stage for many applications, including both central and peripheral nerve interfaces. Recent developments have also witnessed the emergence of biodegradable and multifunctional devices. In this article, we aim to overview recent advances in implantable neuroelectronics with an emphasis on chronic biocompatibility, biodegradability, and multifunctionality. In addition to highlighting fundamental materials innovations, we also discuss important challenges and future opportunities.
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Affiliation(s)
- Yongli Qi
- Thayer School of Engineering, Dartmouth College, Hanover, USA
| | - Seung-Kyun Kang
- Department of Materials Science and Engineering, Seoul National University, Seoul, Republic of Korea
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Erofeev A, Antifeev I, Bolshakova A, Bezprozvanny I, Vlasova O. In Vivo Penetrating Microelectrodes for Brain Electrophysiology. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22239085. [PMID: 36501805 PMCID: PMC9735502 DOI: 10.3390/s22239085] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/14/2022] [Accepted: 11/22/2022] [Indexed: 05/13/2023]
Abstract
In recent decades, microelectrodes have been widely used in neuroscience to understand the mechanisms behind brain functions, as well as the relationship between neural activity and behavior, perception and cognition. However, the recording of neuronal activity over a long period of time is limited for various reasons. In this review, we briefly consider the types of penetrating chronic microelectrodes, as well as the conductive and insulating materials for microelectrode manufacturing. Additionally, we consider the effects of penetrating microelectrode implantation on brain tissue. In conclusion, we review recent advances in the field of in vivo microelectrodes.
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Affiliation(s)
- Alexander Erofeev
- Laboratory of Molecular Neurodegeneration, Graduate School of Biomedical Systems and Technologies, Institute of Biomedical Systems and Biotechnology, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia
- Correspondence: (A.E.); (O.V.)
| | - Ivan Antifeev
- Laboratory of Methods and Instruments for Genetic and Immunoassay Analysis, Institute for Analytical Instrumentation of the Russian Academy of Sciences, 198095 Saint Petersburg, Russia
| | - Anastasia Bolshakova
- Laboratory of Molecular Neurodegeneration, Graduate School of Biomedical Systems and Technologies, Institute of Biomedical Systems and Biotechnology, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia
| | - Ilya Bezprozvanny
- Laboratory of Molecular Neurodegeneration, Graduate School of Biomedical Systems and Technologies, Institute of Biomedical Systems and Biotechnology, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia
- Department of Physiology, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390, USA
| | - Olga Vlasova
- Laboratory of Molecular Neurodegeneration, Graduate School of Biomedical Systems and Technologies, Institute of Biomedical Systems and Biotechnology, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia
- Correspondence: (A.E.); (O.V.)
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