1
|
Hoang H, Tsutsumi S, Matsuzaki M, Kano M, Kawato M, Kitamura K, Toyama K. Dynamic organization of cerebellar climbing fiber response and synchrony in multiple functional components reduces dimensions for reinforcement learning. eLife 2023; 12:e86340. [PMID: 37712651 PMCID: PMC10531405 DOI: 10.7554/elife.86340] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 09/13/2023] [Indexed: 09/16/2023] Open
Abstract
Cerebellar climbing fibers convey diverse signals, but how they are organized in the compartmental structure of the cerebellar cortex during learning remains largely unclear. We analyzed a large amount of coordinate-localized two-photon imaging data from cerebellar Crus II in mice undergoing 'Go/No-go' reinforcement learning. Tensor component analysis revealed that a majority of climbing fiber inputs to Purkinje cells were reduced to only four functional components, corresponding to accurate timing control of motor initiation related to a Go cue, cognitive error-based learning, reward processing, and inhibition of erroneous behaviors after a No-go cue. Changes in neural activities during learning of the first two components were correlated with corresponding changes in timing control and error learning across animals, indirectly suggesting causal relationships. Spatial distribution of these components coincided well with boundaries of Aldolase-C/zebrin II expression in Purkinje cells, whereas several components are mixed in single neurons. Synchronization within individual components was bidirectionally regulated according to specific task contexts and learning stages. These findings suggest that, in close collaborations with other brain regions including the inferior olive nucleus, the cerebellum, based on anatomical compartments, reduces dimensions of the learning space by dynamically organizing multiple functional components, a feature that may inspire new-generation AI designs.
Collapse
Affiliation(s)
- Huu Hoang
- ATR Neural Information Analysis LaboratoriesKyotoJapan
| | | | | | - Masanobu Kano
- Department of Neurophysiology, The University of TokyoTokyoJapan
- International Research Center for Neurointelligence (WPI-IRCN), The University of TokyoTokyoJapan
| | - Mitsuo Kawato
- ATR Brain Information Communication Research Laboratory GroupKyotoJapan
| | - Kazuo Kitamura
- Department of Neurophysiology, University of YamanashiKofuJapan
| | | |
Collapse
|
2
|
Zhou Z, Yip HM, Tsimring K, Sur M, Ip JPK, Tin C. Effective and efficient neural networks for spike inference from in vivo calcium imaging. CELL REPORTS METHODS 2023; 3:100462. [PMID: 37323579 PMCID: PMC10261900 DOI: 10.1016/j.crmeth.2023.100462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 02/21/2023] [Accepted: 03/31/2023] [Indexed: 06/17/2023]
Abstract
Calcium imaging provides advantages in monitoring large populations of neuronal activities simultaneously. However, it lacks the signal quality provided by neural spike recording in traditional electrophysiology. To address this issue, we developed a supervised data-driven approach to extract spike information from calcium signals. We propose the ENS2 (effective and efficient neural networks for spike inference from calcium signals) system for spike-rate and spike-event predictions using ΔF/F0 calcium inputs based on a U-Net deep neural network. When testing on a large, ground-truth public database, it consistently outperformed state-of-the-art algorithms in both spike-rate and spike-event predictions with reduced computational load. We further demonstrated that ENS2 can be applied to analyses of orientation selectivity in primary visual cortex neurons. We conclude that it would be a versatile inference system that may benefit diverse neuroscience studies.
Collapse
Affiliation(s)
- Zhanhong Zhou
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong SAR, China
| | - Hei Matthew Yip
- School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Katya Tsimring
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Mriganka Sur
- Department of Brain and Cognitive Sciences, Picower Institute for Learning and Memory, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Jacque Pak Kan Ip
- School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Chung Tin
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong SAR, China
| |
Collapse
|
3
|
Zhu F, Grier HA, Tandon R, Cai C, Agarwal A, Giovannucci A, Kaufman MT, Pandarinath C. A deep learning framework for inference of single-trial neural population dynamics from calcium imaging with subframe temporal resolution. Nat Neurosci 2022; 25:1724-1734. [PMID: 36424431 PMCID: PMC9825112 DOI: 10.1038/s41593-022-01189-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 09/23/2022] [Indexed: 11/26/2022]
Abstract
In many areas of the brain, neural populations act as a coordinated network whose state is tied to behavior on a millisecond timescale. Two-photon (2p) calcium imaging is a powerful tool to probe such network-scale phenomena. However, estimating the network state and dynamics from 2p measurements has proven challenging because of noise, inherent nonlinearities and limitations on temporal resolution. Here we describe Recurrent Autoencoder for Discovering Imaged Calcium Latents (RADICaL), a deep learning method to overcome these limitations at the population level. RADICaL extends methods that exploit dynamics in spiking activity for application to deconvolved calcium signals, whose statistics and temporal dynamics are quite distinct from electrophysiologically recorded spikes. It incorporates a new network training strategy that capitalizes on the timing of 2p sampling to recover network dynamics with high temporal precision. In synthetic tests, RADICaL infers the network state more accurately than previous methods, particularly for high-frequency components. In 2p recordings from sensorimotor areas in mice performing a forelimb reach task, RADICaL infers network state with close correspondence to single-trial variations in behavior and maintains high-quality inference even when neuronal populations are substantially reduced.
Collapse
Affiliation(s)
- Feng Zhu
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
- Neuroscience Graduate Program, Graduate Division of Biological and Biomedical Sciences, Emory University, Atlanta, GA, USA
| | - Harrison A Grier
- Committee on Computational Neuroscience, The University of Chicago, Chicago, IL, USA
| | - Raghav Tandon
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA
| | - Changjia Cai
- Joint Biomedical Engineering Department, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC, USA
| | | | - Andrea Giovannucci
- Joint Biomedical Engineering Department, University of North Carolina at Chapel Hill and North Carolina State University, 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.
| | - Matthew T Kaufman
- Department of Organismal Biology and Anatomy, The University of Chicago, Chicago, IL, USA.
- Neuroscience Institute, The University of Chicago, Chicago, IL, USA.
| | - Chethan Pandarinath
- Wallace H. Coulter Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA, USA.
- Department of Neurosurgery, Emory University, Atlanta, GA, USA.
- Center for Machine Learning, Georgia Institute of Technology, Atlanta, GA, USA.
| |
Collapse
|
4
|
Wide-Field Calcium Imaging of Neuronal Network Dynamics In Vivo. BIOLOGY 2022; 11:biology11111601. [PMID: 36358302 PMCID: PMC9687960 DOI: 10.3390/biology11111601] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/28/2022] [Accepted: 10/31/2022] [Indexed: 11/06/2022]
Abstract
A central tenet of neuroscience is that sensory, motor, and cognitive behaviors are generated by the communications and interactions among neurons, distributed within and across anatomically and functionally distinct brain regions. Therefore, to decipher how the brain plans, learns, and executes behaviors requires characterizing neuronal activity at multiple spatial and temporal scales. This includes simultaneously recording neuronal dynamics at the mesoscale level to understand the interactions among brain regions during different behavioral and brain states. Wide-field Ca2+ imaging, which uses single photon excitation and improved genetically encoded Ca2+ indicators, allows for simultaneous recordings of large brain areas and is proving to be a powerful tool to study neuronal activity at the mesoscopic scale in behaving animals. This review details the techniques used for wide-field Ca2+ imaging and the various approaches employed for the analyses of the rich neuronal-behavioral data sets obtained. Also discussed is how wide-field Ca2+ imaging is providing novel insights into both normal and altered neural processing in disease. Finally, we examine the limitations of the approach and new developments in wide-field Ca2+ imaging that are bringing new capabilities to this important technique for investigating large-scale neuronal dynamics.
Collapse
|
5
|
D'Angelo L, Canale A, Yu Z, Guindani M. Bayesian nonparametric analysis for the detection of spikes in noisy calcium imaging data. Biometrics 2022. [PMID: 35191539 DOI: 10.1111/biom.13626] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 01/13/2022] [Indexed: 11/30/2022]
Abstract
Recent advancements in miniaturized fluorescence microscopy have made it possible to investigate neuronal responses to external stimuli in awake behaving animals through the analysis of intra-cellular calcium signals. An on-going challenge is deconvolving the temporal signals to extract the spike trains from the noisy calcium signals' time-series. In this manuscript, we propose a nested Bayesian finite mixture specification that allows the estimation of spiking activity and, simultaneously, reconstructing the distributions of the calcium transient spikes' amplitudes under different experimental conditions. The proposed model leverages two nested layers of random discrete mixture priors to borrow information between experiments and discover similarities in the distributional patterns of neuronal responses to different stimuli. Furthermore, the spikes' intensity values are also clustered within and between experimental conditions to determine the existence of common (recurring) response amplitudes. Simulation studies and the analysis of a data set from the Allen Brain Observatory show the effectiveness of the method in clustering and detecting neuronal activities. This article is protected by copyright. All rights reserved.
Collapse
Affiliation(s)
- Laura D'Angelo
- Department of Economics, Management and Statistics, University of Milano-Bicocca, Milan, Italy
| | - Antonio Canale
- Department of Statistical Sciences, University of Padova, Padova, Italy
| | - Zhaoxia Yu
- Department of Statistics, University of California, Irvine Irvine, U.S.A
| | - Michele Guindani
- Department of Statistics, University of California, Irvine Irvine, U.S.A
| |
Collapse
|
6
|
Nishimura M, Song WJ. Region-dependent Millisecond Time-scale Sensitivity in Spectrotemporal Integrations in Guinea Pig Primary Auditory Cortex. Neuroscience 2022; 480:229-245. [PMID: 34762984 DOI: 10.1016/j.neuroscience.2021.10.030] [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: 07/19/2021] [Revised: 10/28/2021] [Accepted: 10/29/2021] [Indexed: 11/18/2022]
Abstract
Spectrotemporal integration is a key function of our auditory system for discriminating spectrotemporally complex sounds, such as words. Response latency in the auditory cortex is known to change with the millisecond time-scale depending on acoustic parameters, such as sound frequency and intensity. The functional significance of the millisecond-range latency difference in the integration remains unclear. Actually, whether the auditory cortex has a sensitivity to the millisecond-range difference has not been systematically examined. Herein, we examined the sensitivity in the primary auditory cortex (A1) using voltage-sensitive dye imaging techniques in guinea pigs. Bandpass noise bursts in two different bands (band-noises), centered at 1 and 16 kHz, respectively, were used for the examination. Onset times of individual band-noises (spectral onset-times) were varied to virtually cancel or magnify the latency difference observed with the band-noises. Conventionally defined nonlinear effects in integration were analyzed at A1 with varying sound intensities (or response latencies) and/or spectral onset-times of the two band-noises. The nonlinear effect measured in the high-frequency region of the A1 linearly changed depending on the millisecond difference of the response onset-times, which were estimated from the spatially-local response latencies and spectral onset-times. In contrast, the low-frequency region of the A1 had no significant sensitivity to the millisecond difference. The millisecond-range latency difference may have functional significance in the spectrotemporal integration with the millisecond time-scale sensitivity at the high-frequency region of A1 but not at the low-frequency region.
Collapse
Affiliation(s)
- Masataka Nishimura
- Department of Sensory and Cognitive Physiology, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjo, Kumamoto 8608556, Japan.
| | - Wen-Jie Song
- Department of Sensory and Cognitive Physiology, Faculty of Life Sciences, Kumamoto University, 1-1-1 Honjo, Kumamoto 8608556, Japan; Program for Leading Graduate Schools HIGO Program, Kumamoto University, Kumamoto, Japan
| |
Collapse
|
7
|
Rupprecht P, Carta S, Hoffmann A, Echizen M, Blot A, Kwan AC, Dan Y, Hofer SB, Kitamura K, Helmchen F, Friedrich RW. A database and deep learning toolbox for noise-optimized, generalized spike inference from calcium imaging. Nat Neurosci 2021; 24:1324-1337. [PMID: 34341584 PMCID: PMC7611618 DOI: 10.1038/s41593-021-00895-5] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 06/23/2021] [Indexed: 02/06/2023]
Abstract
Inference of action potentials ('spikes') from neuronal calcium signals is complicated by the scarcity of simultaneous measurements of action potentials and calcium signals ('ground truth'). In this study, we compiled a large, diverse ground truth database from publicly available and newly performed recordings in zebrafish and mice covering a broad range of calcium indicators, cell types and signal-to-noise ratios, comprising a total of more than 35 recording hours from 298 neurons. We developed an algorithm for spike inference (termed CASCADE) that is based on supervised deep networks, takes advantage of the ground truth database, infers absolute spike rates and outperforms existing model-based algorithms. To optimize performance for unseen imaging data, CASCADE retrains itself by resampling ground truth data to match the respective sampling rate and noise level; therefore, no parameters need to be adjusted by the user. In addition, we developed systematic performance assessments for unseen data, openly released a resource toolbox and provide a user-friendly cloud-based implementation.
Collapse
Affiliation(s)
- Peter Rupprecht
- Brain Research Institute, University of Zürich, Zurich, Switzerland.
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.
| | - Stefano Carta
- Brain Research Institute, University of Zürich, Zurich, Switzerland
| | - Adrian Hoffmann
- Brain Research Institute, University of Zürich, Zurich, Switzerland
| | - Mayumi Echizen
- Department of Neurophysiology, University of Tokyo, Tokyo, Japan
- Department of Anesthesiology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan
| | - Antonin Blot
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, United Kingdom
- Biozentrum, University of Basel, Basel, Switzerland
| | - Alex C Kwan
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Yang Dan
- Division of Neurobiology, Department of Molecular and Cell Biology, Helen Wills Neuroscience Institute, Howard Hughes Medical Institute, University of California, Berkeley, Berkeley CA, USA
| | - Sonja B Hofer
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, United Kingdom
- Biozentrum, University of Basel, Basel, Switzerland
| | - Kazuo Kitamura
- Department of Neurophysiology, University of Tokyo, Tokyo, Japan
- Department of Neurophysiology, University of Yamanashi, Yamanashi, Japan
| | - Fritjof Helmchen
- Brain Research Institute, University of Zürich, Zurich, Switzerland.
| | - Rainer W Friedrich
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland.
- University of Basel, Basel, Switzerland.
| |
Collapse
|
8
|
Bodea SV, Westmeyer GG. Photoacoustic Neuroimaging - Perspectives on a Maturing Imaging Technique and its Applications in Neuroscience. Front Neurosci 2021; 15:655247. [PMID: 34220420 PMCID: PMC8253050 DOI: 10.3389/fnins.2021.655247] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 03/08/2021] [Indexed: 11/13/2022] Open
Abstract
A prominent goal of neuroscience is to improve our understanding of how brain structure and activity interact to produce perception, emotion, behavior, and cognition. The brain's network activity is inherently organized in distinct spatiotemporal patterns that span scales from nanometer-sized synapses to meter-long nerve fibers and millisecond intervals between electrical signals to decades of memory storage. There is currently no single imaging method that alone can provide all the relevant information, but intelligent combinations of complementary techniques can be effective. Here, we thus present the latest advances in biomedical and biological engineering on photoacoustic neuroimaging in the context of complementary imaging techniques. A particular focus is placed on recent advances in whole-brain photoacoustic imaging in rodent models and its influential role in bridging the gap between fluorescence microscopy and more non-invasive techniques such as magnetic resonance imaging (MRI). We consider current strategies to address persistent challenges, particularly in developing molecular contrast agents, and conclude with an overview of potential future directions for photoacoustic neuroimaging to provide deeper insights into healthy and pathological brain processes.
Collapse
Affiliation(s)
- Silviu-Vasile Bodea
- Department of Chemistry and School of Medicine, Technical University of Munich (TUM), Munich, Germany
- Institute for Synthetic Biomedicine, Helmholtz Center Munich, Munich, Germany
| | - Gil Gregor Westmeyer
- Department of Chemistry and School of Medicine, Technical University of Munich (TUM), Munich, Germany
- Institute for Synthetic Biomedicine, Helmholtz Center Munich, Munich, Germany
| |
Collapse
|