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Agyeman KA, Lee DJ, Russin J, Kreydin EI, Choi W, Abedi A, Lo YT, Cavaleri J, Wu K, Edgerton VR, Liu C, Christopoulos VN. Functional ultrasound imaging of the human spinal cord. Neuron 2024; 112:1710-1722.e3. [PMID: 38458198 DOI: 10.1016/j.neuron.2024.02.012] [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: 06/08/2023] [Revised: 11/03/2023] [Accepted: 02/15/2024] [Indexed: 03/10/2024]
Abstract
Utilizing the first in-human functional ultrasound imaging (fUSI) of the spinal cord, we demonstrate the integration of spinal functional responses to electrical stimulation. We record and characterize the hemodynamic responses of the spinal cord to a neuromodulatory intervention commonly used for treating pain and increasingly used for the restoration of sensorimotor and autonomic function. We found that the hemodynamic response to stimulation reflects a spatiotemporal modulation of the spinal cord circuitry not previously recognized. Our analytical capability offers a mechanism to assess blood flow changes with a new level of spatial and temporal precision in vivo and demonstrates that fUSI can decode the functional state of spinal networks in a single trial, which is of fundamental importance for developing real-time closed-loop neuromodulation systems. This work is a critical step toward developing a vital technique to study spinal cord function and effects of clinical neuromodulation.
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Affiliation(s)
- K A Agyeman
- Department of Bioengineering, University of California Riverside, Riverside, CA, USA
| | - D J Lee
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Neurorestoration Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Rancho Los Amigos National Rehabilitation Center, Downey, CA, USA; Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - J Russin
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Neurorestoration Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Rancho Los Amigos National Rehabilitation Center, Downey, CA, USA; Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - E I Kreydin
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Neurorestoration Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Rancho Los Amigos National Rehabilitation Center, Downey, CA, USA; Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - W Choi
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Neurorestoration Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - A Abedi
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Neurorestoration Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Y T Lo
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Neurorestoration Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore
| | - J Cavaleri
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - K Wu
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Neurorestoration Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - V R Edgerton
- Neurorestoration Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Rancho Los Amigos National Rehabilitation Center, Downey, CA, USA.
| | - C Liu
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Neurorestoration Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Rancho Los Amigos National Rehabilitation Center, Downey, CA, USA; Institute of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
| | - V N Christopoulos
- Department of Bioengineering, University of California Riverside, Riverside, CA, USA; Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Neurorestoration Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Neuroscience Graduate Program, University of California Riverside, Riverside, CA, USA.
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2
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Crown LM, Agyeman KA, Choi W, Zepeda N, Iseri E, Pahlavan P, Siegel SJ, Liu C, Christopoulos V, Lee DJ. Theta-frequency medial septal nucleus deep brain stimulation increases neurovascular activity in MK-801-treated mice. Front Neurosci 2024; 18:1372315. [PMID: 38560047 PMCID: PMC10978728 DOI: 10.3389/fnins.2024.1372315] [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: 01/17/2024] [Accepted: 02/29/2024] [Indexed: 04/04/2024] Open
Abstract
Introduction Deep brain stimulation (DBS) has shown remarkable success treating neurological and psychiatric disorders including Parkinson's disease, essential tremor, dystonia, epilepsy, and obsessive-compulsive disorder. DBS is now being explored to improve cognitive and functional outcomes in other psychiatric conditions, such as those characterized by reduced N-methyl-D-aspartate (NMDA) function (i.e., schizophrenia). While DBS for movement disorders generally involves high-frequency (>100 Hz) stimulation, there is evidence that low-frequency stimulation may have beneficial and persisting effects when applied to cognitive brain networks. Methods In this study, we utilize a novel technology, functional ultrasound imaging (fUSI), to characterize the cerebrovascular impact of medial septal nucleus (MSN) DBS under conditions of NMDA antagonism (pharmacologically using Dizocilpine [MK-801]) in anesthetized male mice. Results Imaging from a sagittal plane across a variety of brain regions within and outside of the septohippocampal circuit, we find that MSN theta-frequency (7.7 Hz) DBS increases hippocampal cerebral blood volume (CBV) during and after stimulation. This effect was not present using standard high-frequency stimulation parameters [i.e., gamma (100 Hz)]. Discussion These results indicate the MSN DBS increases circuit-specific hippocampal neurovascular activity in a frequency-dependent manner and does so in a way that continues beyond the period of electrical stimulation.
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Affiliation(s)
- Lindsey M Crown
- Department of Psychiatry and Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Kofi A Agyeman
- Department of Bioengineering, University of California Riverside, Riverside, CA, United States
| | - Wooseong Choi
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Nancy Zepeda
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Ege Iseri
- Department of Bioengineering, University of California Riverside, Riverside, CA, United States
| | - Pooyan Pahlavan
- Department of Bioengineering, University of California Riverside, Riverside, CA, United States
| | - Steven J Siegel
- Department of Psychiatry and Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Charles Liu
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Neurorestoration Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
- Rancho Los Amigos National Rehabilitation Center, Downey, CA, United States
| | - Vasileios Christopoulos
- Department of Bioengineering, University of California Riverside, Riverside, CA, United States
- Neurorestoration Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Neuroscience Graduate Program, University of California Riverside, Riverside, CA, United States
| | - Darrin J Lee
- Department of Psychiatry and Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Department of Bioengineering, University of California Riverside, Riverside, CA, United States
- Department of Neurological Surgery, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Neurorestoration Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
- Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States
- Rancho Los Amigos National Rehabilitation Center, Downey, CA, United States
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3
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Sullere S, Kunczt A, McGehee DS. A cholinergic circuit that relieves pain despite opioid tolerance. Neuron 2023; 111:3414-3434.e15. [PMID: 37734381 PMCID: PMC10843525 DOI: 10.1016/j.neuron.2023.08.017] [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: 01/18/2023] [Revised: 04/19/2023] [Accepted: 08/16/2023] [Indexed: 09/23/2023]
Abstract
Chronic pain is a tremendous burden for afflicted individuals and society. Although opioids effectively relieve pain, significant adverse outcomes limit their utility and efficacy. To investigate alternate pain control mechanisms, we explored cholinergic signaling in the ventrolateral periaqueductal gray (vlPAG), a critical nexus for descending pain modulation. Biosensor assays revealed that pain states decreased acetylcholine release in vlPAG. Activation of cholinergic projections from the pedunculopontine tegmentum to vlPAG relieved pain, even in opioid-tolerant conditions, through ⍺7 nicotinic acetylcholine receptors (nAChRs). Activating ⍺7 nAChRs with agonists or stimulating endogenous acetylcholine inhibited vlPAG neuronal activity through Ca2+ and peroxisome proliferator-activated receptor α (PPAR⍺)-dependent signaling. In vivo 2-photon imaging revealed that chronic pain induces aberrant excitability of vlPAG neuronal ensembles and that ⍺7 nAChR-mediated inhibition of these cells relieves pain, even after opioid tolerance. Finally, pain relief through these cholinergic mechanisms was not associated with tolerance, reward, or withdrawal symptoms, highlighting its potential clinical relevance.
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Affiliation(s)
- Shivang Sullere
- Committee on Neurobiology, University of Chicago, Chicago, IL 60637, USA
| | - Alissa Kunczt
- Department of Anesthesia and Critical Care, University of Chicago, Chicago, IL 60637, USA
| | - Daniel S McGehee
- Committee on Neurobiology, University of Chicago, Chicago, IL 60637, USA; Department of Anesthesia and Critical Care, University of Chicago, Chicago, IL 60637, USA.
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4
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Cai C, Dong C, Friedrich J, Rozsa M, Pnevmatikakis EA, Giovannucci A. FIOLA: an accelerated pipeline for fluorescence imaging online analysis. Nat Methods 2023; 20:1417-1425. [PMID: 37679524 DOI: 10.1038/s41592-023-01964-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 06/19/2023] [Indexed: 09/09/2023]
Abstract
Optical microscopy methods such as calcium and voltage imaging enable fast activity readout of large neuronal populations using light. However, the lack of corresponding advances in online algorithms has slowed progress in retrieving information about neural activity during or shortly after an experiment. This gap not only prevents the execution of real-time closed-loop experiments, but also hampers fast experiment-analysis-theory turnover for high-throughput imaging modalities. Reliable extraction of neural activity from fluorescence imaging frames at speeds compatible with indicator dynamics and imaging modalities poses a challenge. We therefore developed FIOLA, a framework for fluorescence imaging online analysis that extracts neuronal activity from calcium and voltage imaging movies at speeds one order of magnitude faster than state-of-the-art methods. FIOLA exploits algorithms optimized for parallel processing on GPUs and CPUs. We demonstrate reliable and scalable performance of FIOLA on both simulated and real calcium and voltage imaging datasets. Finally, we present an online experimental scenario to provide guidance in setting FIOLA parameters and to highlight the trade-offs of our approach.
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Affiliation(s)
- Changjia Cai
- Joint Department of Biomedical Engineering UNC/NCSU, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Cynthia Dong
- Joint Department of Biomedical Engineering UNC/NCSU, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Marton Rozsa
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | | | - Andrea Giovannucci
- Joint Department of Biomedical Engineering UNC/NCSU, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Closed-Loop Engineering for Advanced Rehabilitation (CLEAR), North Carolina State University, Raleigh, NC, USA.
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5
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Zhang Y, Zhang G, Han X, Wu J, Li Z, Li X, Xiao G, Xie H, Fang L, Dai Q. Rapid detection of neurons in widefield calcium imaging datasets after training with synthetic data. Nat Methods 2023; 20:747-754. [PMID: 37002377 PMCID: PMC10172132 DOI: 10.1038/s41592-023-01838-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 03/07/2023] [Indexed: 04/03/2023]
Abstract
Widefield microscopy can provide optical access to multi-millimeter fields of view and thousands of neurons in mammalian brains at video rate. However, tissue scattering and background contamination results in signal deterioration, making the extraction of neuronal activity challenging, laborious and time consuming. Here we present our deep-learning-based widefield neuron finder (DeepWonder), which is trained by simulated functional recordings and effectively works on experimental data to achieve high-fidelity neuronal extraction. Equipped with systematic background contribution priors, DeepWonder conducts neuronal inference with an order-of-magnitude-faster speed and improved accuracy compared with alternative approaches. DeepWonder removes background contaminations and is computationally efficient. Specifically, DeepWonder accomplishes 50-fold signal-to-background ratio enhancement when processing terabytes-scale cortex-wide functional recordings, with over 14,000 neurons extracted in 17 h.
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Affiliation(s)
- Yuanlong Zhang
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China
| | - Guoxun Zhang
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China
| | - Xiaofei Han
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China
| | - Jiamin Wu
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Ziwei Li
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Xinyang Li
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China
| | - Guihua Xiao
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China
| | - Hao Xie
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China
| | - Lu Fang
- Department of Electronic Engineering, Tsinghua University, Beijing, China.
| | - Qionghai Dai
- Department of Automation, Tsinghua University, Beijing, China.
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China.
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China.
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China.
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6
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Chen Z, Blair GJ, Cao C, Zhou J, Aharoni D, Golshani P, Blair HT, Cong J. FPGA-Based In-Vivo Calcium Image Decoding for Closed-Loop Feedback Applications. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2023; 17:169-179. [PMID: 37071510 PMCID: PMC10414190 DOI: 10.1109/tbcas.2023.3268130] [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] [Indexed: 05/11/2023]
Abstract
Miniaturized calcium imaging is an emerging neural recording technique that has been widely used for monitoring neural activity on a large scale at a specific brain region of rats or mice. Most existing calcium-image analysis pipelines operate offline. This results in long processing latency, making it difficult to realize closed-loop feedback stimulation for brain research. In recent work, we have proposed an FPGA-based real-time calcium image processing pipeline for closed-loop feedback applications. It can perform real-time calcium image motion correction, enhancement, fast trace extraction, and real-time decoding from extracted traces. Here, we extend this work by proposing a variety of neural network based methods for real-time decoding and evaluate the tradeoff among these decoding methods and accelerator designs. We introduce the implementation of the neural network based decoders on the FPGA, and show their speedup against the implementation on the ARM processor. Our FPGA implementation enables the real-time calcium image decoding with sub-ms processing latency for closed-loop feedback applications.
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7
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Guo C, Wang A, Cheng H, Chen L. New imaging instrument in animal models: Two-photon miniature microscope and large field of view miniature microscope for freely behaving animals. J Neurochem 2023; 164:270-283. [PMID: 36281555 DOI: 10.1111/jnc.15711] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2022] [Revised: 09/19/2022] [Accepted: 10/12/2022] [Indexed: 11/30/2022]
Abstract
Over the past decade, novel optical imaging tools have been developed for imaging neuronal activities along with the evolution of fluorescence indicators with brighter expression and higher sensitivity. Miniature microscopes, as revolutionary approaches, enable the imaging of large populations of neuron ensembles in freely behaving rodents and mammals, which allows exploring the neural basis of behaviors. Recent progress in two-photon miniature microscopes and mesoscale single-photon miniature microscopes further expand those affordable methods to navigate neural activities during naturalistic behaviors. In this review article, two-photon miniature microscopy techniques are summarized historically from the first documented attempt to the latest ones, and comparisons are made. The driving force behind and their potential for neuroscientific inquiries are also discussed. Current progress in terms of the mesoscale, i.e., the large field-of-view miniature microscopy technique, is addressed as well. Then, pipelines for registering single cells from the data of two-photon and large field-of-view miniature microscopes are discussed. Finally, we present the potential evolution of the techniques.
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Affiliation(s)
- Changliang Guo
- Beijing Institute of Collaborative Innovation, Beijing, China.,State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, College of Future Technology, Peking University, Beijing, China
| | - Aimin Wang
- School of Electronics, Peking University, Beijing, China.,State Key Laboratory of Advanced Optical Communication System and Networks, Peking University, Beijing, China
| | - Heping Cheng
- State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, College of Future Technology, Peking University, Beijing, China.,Research Unit of Mitochondria in Brain Diseases, Chinese Academy of Medical Sciences, PKU-Nanjing Institute of Translational Medicine, Nanjing, China
| | - Liangyi Chen
- State Key Laboratory of Membrane Biology, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, College of Future Technology, Peking University, Beijing, China.,PKU-IDG/McGovern Institute for Brain Research, Beijing, China.,Beijing Academy of Artificial Intelligence, Beijing, China
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8
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Chen Z, Blair GJ, Guo C, Zhou J, Romero-Sosa JL, Izquierdo A, Golshani P, Cong J, Aharoni D, Blair HT. A hardware system for real-time decoding of in vivo calcium imaging data. eLife 2023; 12:e78344. [PMID: 36692269 PMCID: PMC9908073 DOI: 10.7554/elife.78344] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 01/23/2023] [Indexed: 01/25/2023] Open
Abstract
Epifluorescence miniature microscopes ('miniscopes') are widely used for in vivo calcium imaging of neural population activity. Imaging data are typically collected during a behavioral task and stored for later offline analysis, but emerging techniques for online imaging can support novel closed-loop experiments in which neural population activity is decoded in real time to trigger neurostimulation or sensory feedback. To achieve short feedback latencies, online imaging systems must be optimally designed to maximize computational speed and efficiency while minimizing errors in population decoding. Here we introduce DeCalciOn, an open-source device for real-time imaging and population decoding of in vivo calcium signals that is hardware compatible with all miniscopes that use the UCLA Data Acquisition (DAQ) interface. DeCalciOn performs online motion stabilization, neural enhancement, calcium trace extraction, and decoding of up to 1024 traces per frame at latencies of <50 ms after fluorescence photons arrive at the miniscope image sensor. We show that DeCalciOn can accurately decode the position of rats (n = 12) running on a linear track from calcium fluorescence in the hippocampal CA1 layer, and can categorically classify behaviors performed by rats (n = 2) during an instrumental task from calcium fluorescence in orbitofrontal cortex. DeCalciOn achieves high decoding accuracy at short latencies using innovations such as field-programmable gate array hardware for real-time image processing and contour-free methods to efficiently extract calcium traces from sensor images. In summary, our system offers an affordable plug-and-play solution for real-time calcium imaging experiments in behaving animals.
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Affiliation(s)
- Zhe Chen
- Department of Electrical and Computer Engineering, University of California, Los AngelesLos AngelesUnited States
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
| | - Garrett J Blair
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
| | - Changliang Guo
- David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
- Department of Neurology, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Jim Zhou
- Department of Electrical and Computer Engineering, University of California, Los AngelesLos AngelesUnited States
| | - Juan-Luis Romero-Sosa
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
| | - Alicia Izquierdo
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
- Integrative Center for Learning and Memory, University of California, Los AngelesLos AngelesUnited States
| | - Peyman Golshani
- David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
- Department of Neurology, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
- Integrative Center for Learning and Memory, University of California, Los AngelesLos AngelesUnited States
| | - Jason Cong
- Department of Electrical and Computer Engineering, University of California, Los AngelesLos AngelesUnited States
| | - Daniel Aharoni
- David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
- Department of Neurology, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
- Integrative Center for Learning and Memory, University of California, Los AngelesLos AngelesUnited States
| | - Hugh T Blair
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
- Integrative Center for Learning and Memory, University of California, Los AngelesLos AngelesUnited States
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9
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Machado TA, Kauvar IV, Deisseroth K. Multiregion neuronal activity: the forest and the trees. Nat Rev Neurosci 2022; 23:683-704. [PMID: 36192596 PMCID: PMC10327445 DOI: 10.1038/s41583-022-00634-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/25/2022] [Indexed: 12/12/2022]
Abstract
The past decade has witnessed remarkable advances in the simultaneous measurement of neuronal activity across many brain regions, enabling fundamentally new explorations of the brain-spanning cellular dynamics that underlie sensation, cognition and action. These recently developed multiregion recording techniques have provided many experimental opportunities, but thoughtful consideration of methodological trade-offs is necessary, especially regarding field of view, temporal acquisition rate and ability to guarantee cellular resolution. When applied in concert with modern optogenetic and computational tools, multiregion recording has already made possible fundamental biological discoveries - in part via the unprecedented ability to perform unbiased neural activity screens for principles of brain function, spanning dozens of brain areas and from local to global scales.
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Affiliation(s)
- Timothy A Machado
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Isaac V Kauvar
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Karl Deisseroth
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
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10
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Hirrlinger J, Nimmerjahn A. A perspective on astrocyte regulation of neural circuit function and animal behavior. Glia 2022; 70:1554-1580. [PMID: 35297525 PMCID: PMC9291267 DOI: 10.1002/glia.24168] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 01/19/2022] [Accepted: 02/27/2022] [Indexed: 12/16/2022]
Abstract
Studies over the past two decades have demonstrated that astrocytes are tightly associated with neurons and play pivotal roles in neural circuit development, operation, and adaptation in health and disease. Nevertheless, precisely how astrocytes integrate diverse neuronal signals, modulate neural circuit structure and function at multiple temporal and spatial scales, and influence animal behavior or disease through aberrant excitation and molecular output remains unclear. This Perspective discusses how new and state-of-the-art approaches, including fluorescence indicators, opto- and chemogenetic actuators, genetic targeting tools, quantitative behavioral assays, and computational methods, might help resolve these longstanding questions. It also addresses complicating factors in interpreting astrocytes' role in neural circuit regulation and animal behavior, such as their heterogeneity, metabolism, and inter-glial communication. Research on these questions should provide a deeper mechanistic understanding of astrocyte-neuron assemblies' role in neural circuit function, complex behaviors, and disease.
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Affiliation(s)
- Johannes Hirrlinger
- Carl-Ludwig-Institute for Physiology, Medical Faculty,
University of Leipzig, Leipzig, Germany
- Department of Neurogenetics, Max-Planck-Institute for
Multidisciplinary Sciences, Göttingen, Germany
| | - Axel Nimmerjahn
- Waitt Advanced Biophotonics Center, The Salk Institute for
Biological Studies, La Jolla, California
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11
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Li M, Liu C, Cui X, Jung H, You H, Feng L, Zhang S. An Open-Source Real-Time Motion Correction Plug-In for Single-Photon Calcium Imaging of Head-Mounted Microscopy. Front Neural Circuits 2022; 16:891825. [PMID: 35814484 PMCID: PMC9265215 DOI: 10.3389/fncir.2022.891825] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 06/01/2022] [Indexed: 11/29/2022] Open
Abstract
Single-photon-based head-mounted microscopy is widely used to record the brain activities of freely-moving animals. However, during data acquisition, the free movement of animals will cause shaking in the field of view, which deteriorates subsequent neural signal analyses. Existing motion correction methods applied to calcium imaging data either focus on offline analyses or lack sufficient accuracy in real-time processing for single-photon data. In this study, we proposed an open-source real-time motion correction (RTMC) plug-in for single-photon calcium imaging data acquisition. The RTMC plug-in is a real-time subpixel registration algorithm that can run GPUs in UCLA Miniscope data acquisition software. When used with the UCLA Miniscope, the RTMC algorithm satisfies real-time processing requirements in terms of speed, memory, and accuracy. We tested the RTMC algorithm by extending a manual neuron labeling function to extract calcium signals in a real experimental setting. The results demonstrated that the neural calcium dynamics and calcium events can be restored with high accuracy from the calcium data that were collected by the UCLA Miniscope system embedded with our RTMC plug-in. Our method could become an essential component in brain science research, where real-time brain activity is needed for closed-loop experiments.
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Affiliation(s)
- Mingkang Li
- Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
| | - Changhao Liu
- Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
| | - Xin Cui
- Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, South Korea
| | - Hayoung Jung
- Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, South Korea
| | - Heecheon You
- Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, South Korea
| | - Linqing Feng
- Research Institute of Artificial Intelligence, Zhejiang Lab, Hangzhou, China
- *Correspondence: Linqing Feng
| | - Shaomin Zhang
- Key Laboratory of Biomedical Engineering of Education Ministry, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Department of Biomedical Engineering, School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
- Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China
- Shaomin Zhang
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12
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Dong Z, Mau W, Feng Y, Pennington ZT, Chen L, Zaki Y, Rajan K, Shuman T, Aharoni D, Cai DJ. Minian an open-source miniscope analysis pipeline. eLife 2022; 11:70661. [PMID: 35642786 PMCID: PMC9205633 DOI: 10.7554/elife.70661] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 05/31/2022] [Indexed: 11/13/2022] Open
Abstract
Miniature microscopes have gained considerable traction for in vivo calcium imaging in freely behaving animals. However, extracting calcium signals from raw videos is a computationally complex problem and remains a bottleneck for many researchers utilizing single-photon in vivo calcium imaging. Despite the existence of many powerful analysis packages designed to detect and extract calcium dynamics, most have either key parameters that are hard-coded or insufficient step-by-step guidance and validations to help the users choose the best parameters. This makes it difficult to know whether the output is reliable and meets the assumptions necessary for proper analysis. Moreover, large memory demand is often a constraint for setting up these pipelines since it limits the choice of hardware to specialized computers. Given these difficulties, there is a need for a low memory demand, user-friendly tool offering interactive visualizations of how altering parameters at each step of the analysis affects data output. Our open-source analysis pipeline, Minian (Miniscope Analysis), facilitates the transparency and accessibility of single-photon calcium imaging analysis, permitting users with little computational experience to extract the location of cells and their corresponding calcium traces and deconvolved neural activities. Minian contains interactive visualization tools for every step of the analysis, as well as detailed documentation and tips on parameter exploration. Furthermore, Minian has relatively small memory demands and can be run on a laptop, making it available to labs that do not have access to specialized computational hardware. Minian has been validated to reliably and robustly extract calcium events across different brain regions and from different cell types. In practice, Minian provides an open-source calcium imaging analysis pipeline with user-friendly interactive visualizations to explore parameters and validate results.
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Affiliation(s)
- Zhe Dong
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, United States
| | - William Mau
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Yu Feng
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Zachary T Pennington
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Lingxuan Chen
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Yosif Zaki
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Kanaka Rajan
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Tristan Shuman
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Daniel Aharoni
- Department of Neurology, University of California, Los Angeles, Los Angeles, United States
| | - Denise J Cai
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, United States
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13
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Development of an Ergonomic User Interface Design of Calcium Imaging Processing System. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12041877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
An optical brain-machine interface (O-BMI) system using calcium imaging has various advantages such as high resolution, a comprehensive view of large neural populations, abilities such as long-term stable recording, and applicability to freely behaving animals in neuroscience research. The present study developed an ergonomic user interface (UI) design, based on a use scenario for an O-BMI system that can be used for the acquisition and processing of calcium imaging in freely behaving rodents. The UI design was developed in three steps: (1) identification of design and function requirements of users, (2) establishment of a use scenario, and (3) development of a UI prototype. The UI design requirements were identified by a literature review, a benchmark of existing systems, and a focus group interview with five neuroscience researchers. Then, the use scenario was developed for tasks of data acquisition, feature extraction, and neural decoding for offline and online processing by considering the sequences of operations and needs of users. Lastly, a digital prototype incorporating an information architecture, graphic user interfaces, and simulated functions was fabricated. A usability test was conducted with five neuroscientists (work experience = 3.4 ± 1.1 years) and five ergonomic experts (work experience = 3.6 ± 2.7 years) to compare the digital prototypes with four existing systems (Miniscope, nVista, Mosaic, and Suite2p). The usability testing results showed that the ergonomic UI design was significantly preferred to the UI designs of the existing systems by reducing the task completion time by 10.1% to 70.2% on average, the scan path length by 14.4% to 88.7%, and perceived workload by 12.2% to 37.9%, increasing satisfaction by 11.3% to 74.3% in data acquisition and signal-extraction tasks. The present study demonstrates the significance of the user-centered design approach in the development of a system for neuroscience research. Further research is needed to validate the usability test results of the UI prototype as a corresponding real system is implemented.
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14
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Bao Y, Redington E, Agarwal A, Gong Y. Decontaminate Traces From Fluorescence Calcium Imaging Videos Using Targeted Non-negative Matrix Factorization. Front Neurosci 2022; 15:797421. [PMID: 35126042 PMCID: PMC8815790 DOI: 10.3389/fnins.2021.797421] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 12/06/2021] [Indexed: 01/26/2023] Open
Abstract
Fluorescence microscopy and genetically encoded calcium indicators help understand brain function by recording large-scale in vivo videos in assorted animal models. Extracting the fluorescent transients that represent active periods of individual neurons is a key step when analyzing imaging videos. Non-specific calcium sources and background adjacent to segmented neurons contaminate the neurons' temporal traces with false transients. We developed and characterized a novel method, temporal unmixing of calcium traces (TUnCaT), to quickly and accurately unmix the calcium signals of neighboring neurons and background. Our algorithm used background subtraction to remove the false transients caused by background fluctuations, and then applied targeted non-negative matrix factorization to remove the false transients caused by neighboring calcium sources. TUnCaT was more accurate than existing algorithms when processing multiple experimental and simulated datasets. TUnCaT's speed was faster than or comparable to existing algorithms.
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Affiliation(s)
- Yijun Bao
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Emily Redington
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
| | - Agnim Agarwal
- North Carolina School of Science and Mathematics, Durham, NC, United States
| | - Yiyang Gong
- Department of Biomedical Engineering, Duke University, Durham, NC, United States
- Department of Neurobiology, Duke University, Durham, NC, United States
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15
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Taniguchi M, Tezuka T, Vergara P, Srinivasan S, Hosokawa T, Cherasse Y, Naoi T, Sakurai T, Sakaguchi M. Open-Source Software for Real-time Calcium Imaging and Synchronized Neuron Firing Detection. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2997-3003. [PMID: 34891875 DOI: 10.1109/embc46164.2021.9629611] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
We developed Carignan, a real-time calcium imaging software that can automatically detect activity patterns of neurons. Carignan can activate an external device when synchronized neural activity is detected in calcium imaging obtained by a one-photon (1p) miniscope. Combined with optogenetics, our software enables closed-loop experiments for investigating functions of specific types of neurons in the brain. In addition to making existing pattern detection algorithms run in real-time seamlessly, we developed a new classification module that distinguishes neurons from false-positives using deep learning. We used a combination of convolutional and recurrent neural networks to incorporate both spatial and temporal features in activity patterns. Our method performed better than existing neuron detection methods for false-positive neuron detection in terms of the F1 score. Using Carignan, experimenters can activate or suppress a group of neurons when specific neural activity is observed. Because the system uses a 1p miniscope, it can be used on the brain of a freely-moving animal, making it applicable to a wide range of experimental paradigms.
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16
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Macpherson T, Churchland A, Sejnowski T, DiCarlo J, Kamitani Y, Takahashi H, Hikida T. Natural and Artificial Intelligence: A brief introduction to the interplay between AI and neuroscience research. Neural Netw 2021; 144:603-613. [PMID: 34649035 DOI: 10.1016/j.neunet.2021.09.018] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 09/15/2021] [Accepted: 09/21/2021] [Indexed: 10/20/2022]
Abstract
Neuroscience and artificial intelligence (AI) share a long history of collaboration. Advances in neuroscience, alongside huge leaps in computer processing power over the last few decades, have given rise to a new generation of in silico neural networks inspired by the architecture of the brain. These AI systems are now capable of many of the advanced perceptual and cognitive abilities of biological systems, including object recognition and decision making. Moreover, AI is now increasingly being employed as a tool for neuroscience research and is transforming our understanding of brain functions. In particular, deep learning has been used to model how convolutional layers and recurrent connections in the brain's cerebral cortex control important functions, including visual processing, memory, and motor control. Excitingly, the use of neuroscience-inspired AI also holds great promise for understanding how changes in brain networks result in psychopathologies, and could even be utilized in treatment regimes. Here we discuss recent advancements in four areas in which the relationship between neuroscience and AI has led to major advancements in the field; (1) AI models of working memory, (2) AI visual processing, (3) AI analysis of big neuroscience datasets, and (4) computational psychiatry.
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Affiliation(s)
- Tom Macpherson
- Laboratory for Advanced Brain Functions, Institute for Protein Research, Osaka University, Osaka, Japan
| | - Anne Churchland
- Cold Spring Harbor Laboratory, Neuroscience, Cold Spring Harbor, NY, USA
| | - Terry Sejnowski
- Computational Neurobiology Laboratory, Salk Institute for Biological Studies, CA, USA; Division of Biological Sciences, University of California San Diego, CA, USA
| | - James DiCarlo
- Brain and Cognitive Sciences, Massachusetts Institute of Technology, MA, USA
| | - Yukiyasu Kamitani
- Department of Neuroinformatics, ATR Computational Neuroscience Laboratories, Kyoto, Japan; Graduate School of Informatics, Kyoto University, Kyoto, Japan
| | - Hidehiko Takahashi
- Department of Psychiatry and Behavioral Sciences, Tokyo Medical and Dental University Graduate School, Tokyo, Japan
| | - Takatoshi Hikida
- Laboratory for Advanced Brain Functions, Institute for Protein Research, Osaka University, Osaka, Japan.
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