1
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Phil Brooks F, Davis HC, Wong-Campos JD, Cohen AE. Optical constraints on two-photon voltage imaging. NEUROPHOTONICS 2024; 11:035007. [PMID: 39139631 PMCID: PMC11321468 DOI: 10.1117/1.nph.11.3.035007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 07/23/2024] [Accepted: 07/25/2024] [Indexed: 08/15/2024]
Abstract
Significance Genetically encoded voltage indicators (GEVIs) are a valuable tool for studying neural circuits in vivo, but the relative merits and limitations of one-photon (1P) versus two-photon (2P) voltage imaging are not well characterized. Aim We consider the optical and biophysical constraints particular to 1P and 2P voltage imaging and compare the imaging properties of commonly used GEVIs under 1P and 2P excitation. Approach We measure the brightness and voltage sensitivity of voltage indicators from commonly used classes under 1P and 2P illumination. We also measure the decrease in fluorescence as a function of depth in the mouse brain. We develop a simple model of the number of measurable cells as a function of reporter properties, imaging parameters, and desired signal-to-noise ratio (SNR). We then discuss how the performance of voltage imaging would be affected by sensor improvements and by recently introduced advanced imaging modalities. Results Compared with 1P excitation, 2P excitation requires ∼ 10 4 -fold more illumination power per cell to produce similar photon count rates. For voltage imaging with JEDI-2P in the mouse cortex with a target SNR of 10 (spike height to baseline shot noise), a measurement bandwidth of 1 kHz, a thermally limited laser power of 200 mW, and an imaging depth of > 300 μ m , 2P voltage imaging using an 80-MHz source can record from no more than ∼ 12 neurons simultaneously. Conclusions Due to the stringent photon-count requirements of voltage imaging and the modest voltage sensitivity of existing reporters, 2P voltage imaging in vivo faces a stringent tradeoff between shot noise and tissue photodamage. 2P imaging of hundreds of neurons with high SNR at a depth of > 300 μ m will require either major improvements in 2P GEVIs or qualitatively new approaches to imaging.
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Affiliation(s)
- F. Phil Brooks
- Harvard University, Department of Chemistry and Chemical Biology, Cambridge, Massachusetts, United States
| | - Hunter C. Davis
- Harvard University, Department of Chemistry and Chemical Biology, Cambridge, Massachusetts, United States
| | - J. David Wong-Campos
- Harvard University, Department of Chemistry and Chemical Biology, Cambridge, Massachusetts, United States
| | - Adam E. Cohen
- Harvard University, Department of Chemistry and Chemical Biology, Cambridge, Massachusetts, United States
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2
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Brooks FP, Davis HC, Wong-Campos JD, Cohen AE. Optical constraints on two-photon voltage imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.18.567441. [PMID: 38014011 PMCID: PMC10680948 DOI: 10.1101/2023.11.18.567441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Significance Genetically encoded voltage indicators (GEVIs) are a valuable tool for studying neural circuits in vivo, but the relative merits and limitations of one-photon (1P) vs. two-photon (2P) voltage imaging are not well characterized. Aim We consider the optical and biophysical constraints particular to 1P and 2P voltage imaging and compare the imaging properties of commonly used GEVIs under 1P and 2P excitation. Approach We measure brightness and voltage sensitivity of voltage indicators from commonly used classes under 1P and 2P illumination. We also measure the decrease in fluorescence as a function of depth in mouse brain. We develop a simple model of the number of measurable cells as a function of reporter properties, imaging parameters, and desired signal-to-noise ratio (SNR). We then discuss how the performance of voltage imaging would be affected by sensor improvements and by recently introduced advanced imaging modalities. Results Compared to 1P excitation, 2P excitation requires ~104-fold more illumination power per cell to produce similar photon count rates. For voltage imaging with JEDI-2P in mouse cortex with a target SNR of 10 (spike height:baseline shot noise), a measurement bandwidth of 1 kHz, a thermally limited laser power of 200 mW, and an imaging depth of > 300 μm, 2P voltage imaging using an 80 MHz source can record from no more 12 cells simultaneously. Conclusions Due to the stringent photon-count requirements of voltage imaging and the modest voltage sensitivity of existing reporters, 2P voltage imaging in vivo faces a stringent tradeoff between shot noise and tissue photodamage. 2P imaging of hundreds of neurons with high SNR at depth > 300 μm will require either major improvements in 2P GEVIs or qualitatively new approaches to imaging.
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Affiliation(s)
- F Phil Brooks
- Department of Chemistry and Chemical Biology, Harvard University
| | - Hunter C Davis
- Department of Chemistry and Chemical Biology, Harvard University
| | | | - Adam E Cohen
- Department of Chemistry and Chemical Biology, Harvard University
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3
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Chettih SN, Mackevicius EL, Hale S, Aronov D. Barcoding of episodic memories in the hippocampus of a food-caching bird. Cell 2024; 187:1922-1935.e20. [PMID: 38554707 PMCID: PMC11015962 DOI: 10.1016/j.cell.2024.02.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 11/28/2023] [Accepted: 02/23/2024] [Indexed: 04/02/2024]
Abstract
The hippocampus is critical for episodic memory. Although hippocampal activity represents place and other behaviorally relevant variables, it is unclear how it encodes numerous memories of specific events in life. To study episodic coding, we leveraged the specialized behavior of chickadees-food-caching birds that form memories at well-defined moments in time whenever they cache food for subsequent retrieval. Our recordings during caching revealed very sparse, transient barcode-like patterns of firing across hippocampal neurons. Each "barcode" uniquely represented a caching event and transiently reactivated during the retrieval of that specific cache. Barcodes co-occurred with the conventional activity of place cells but were uncorrelated even for nearby cache locations that had similar place codes. We propose that animals recall episodic memories by reactivating hippocampal barcodes. Similarly to computer hash codes, these patterns assign unique identifiers to different events and could be a mechanism for rapid formation and storage of many non-interfering memories.
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Affiliation(s)
- Selmaan N Chettih
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Emily L Mackevicius
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Basis Research Institute, New York, NY 10027, USA
| | - Stephanie Hale
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Dmitriy Aronov
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA.
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4
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Pasarkar A, Kinsella I, Zhou P, Wu M, Pan D, Fan JL, Wang Z, Abdeladim L, Peterka DS, Adesnik H, Ji N, Paninski L. maskNMF: A denoise-sparsen-detect approach for extracting neural signals from dense imaging data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.14.557777. [PMID: 37745388 PMCID: PMC10515957 DOI: 10.1101/2023.09.14.557777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
A number of calcium imaging methods have been developed to monitor the activity of large populations of neurons. One particularly promising approach, Bessel imaging, captures neural activity from a volume by projecting within the imaged volume onto a single imaging plane, therefore effectively mixing signals and increasing the number of neurons imaged per pixel. These signals must then be computationally demixed to recover the desired neural activity. Unfortunately, currently-available demixing methods can perform poorly in the regime of high imaging density (i.e., many neurons per pixel). In this work we introduce a new pipeline (maskNMF) for demixing dense calcium imaging data. The main idea is to first denoise and temporally sparsen the observed video; this enhances signal strength and reduces spatial overlap significantly. Next we detect neurons in the sparsened video using a neural network trained on a library of neural shapes. These shapes are derived from segmented electron microscopy images input into a Bessel imaging model; therefore no manual selection of "good" neural shapes from the functional data is required here. After cells are detected, we use a constrained non-negative matrix factorization approach to demix the activity, using the detected cells' shapes to initialize the factorization. We test the resulting pipeline on both simulated and real datasets and find that it is able to achieve accurate demixing on denser data than was previously feasible, therefore enabling faithful imaging of larger neural populations. The method also provides good results on more "standard" two-photon imaging data. Finally, because much of the pipeline operates on a significantly compressed version of the raw data and is highly parallelizable, the algorithm is fast, processing large datasets faster than real time.
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Affiliation(s)
- Amol Pasarkar
- Center for Theoretical Neuroscience and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
- Department of Computer Science, Columbia University, New York, NY, 10027, USA
| | - Ian Kinsella
- Center for Theoretical Neuroscience and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
- Department of Statistics, Columbia University, New York, NY, 10027, USA
| | - Pengcheng Zhou
- Shenzhen Institute of Advanced Technology, Shenzhen, 518055, China
| | - Melissa Wu
- Department of Biomedical Engineering, Duke University, Durham, NC 27708
| | - Daisong Pan
- Department of Physics, University of California, Berkeley, California 94720, USA
| | - Jiang Lan Fan
- Joint Bioengineering Graduate Program, University of California, Berkeley, CA 94720
| | - Zhen Wang
- Department of Electrical and Computer Engineering, UCLA, Los Angeles, CA, 90095, USA
| | - Lamiae Abdeladim
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Darcy S Peterka
- Center for Theoretical Neuroscience and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Hillel Adesnik
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
- The Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Na Ji
- Department of Physics, University of California, Berkeley, California 94720, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA 94720, USA
- The Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Liam Paninski
- Center for Theoretical Neuroscience and Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
- Department of Statistics, Columbia University, New York, NY, 10027, USA
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5
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Chettih SN, Mackevicius EL, Hale S, Aronov D. Barcoding of episodic memories in the hippocampus of a food-caching bird. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.27.542597. [PMID: 37461442 PMCID: PMC10349996 DOI: 10.1101/2023.05.27.542597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Abstract
Episodic memory, or memory of experienced events, is a critical function of the hippocampus1-3. It is therefore important to understand how hippocampal activity represents specific events in an animal's life. We addressed this question in chickadees - specialist food-caching birds that hide food at scattered locations and use memory to find their caches later in time4,5. We performed high-density neural recordings in the hippocampus of chickadees as they cached and retrieved seeds in a laboratory arena. We found that each caching event was represented by a burst of firing in a unique set of hippocampal neurons. These 'barcode-like' patterns of activity were sparse (<10% of neurons active), uncorrelated even for immediately adjacent caches, and different even for separate caches at the same location. The barcode representing a specific caching event was transiently reactivated whenever a bird later interacted with the same cache - for example, to retrieve food. Barcodes co-occurred with conventional place cell activity6,7, as well as location-independent responses to cached seeds. We propose that barcodes are signatures of episodic memories evoked during memory recall. These patterns assign a unique identifier to each event and may be a mechanism for rapid formation and storage of many non-interfering memories.
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Affiliation(s)
| | | | - Stephanie Hale
- Zuckerman Mind Brain Behavior Institute, Columbia University
| | - Dmitriy Aronov
- Zuckerman Mind Brain Behavior Institute, Columbia University
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6
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Applegate MC, Gutnichenko KS, Mackevicius EL, Aronov D. An entorhinal-like region in food-caching birds. Curr Biol 2023; 33:2465-2477.e7. [PMID: 37295426 PMCID: PMC10329498 DOI: 10.1016/j.cub.2023.05.031] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 04/14/2023] [Accepted: 05/15/2023] [Indexed: 06/12/2023]
Abstract
The mammalian entorhinal cortex routes inputs from diverse sources into the hippocampus. This information is mixed and expressed in the activity of many specialized entorhinal cell types, which are considered indispensable for hippocampal function. However, functionally similar hippocampi exist even in non-mammals that lack an obvious entorhinal cortex or, generally, any layered cortex. To address this dilemma, we mapped extrinsic hippocampal connections in chickadees, whose hippocampi are used for remembering numerous food caches. We found a well-delineated structure in these birds that is topologically similar to the entorhinal cortex and interfaces between the hippocampus and other pallial regions. Recordings of this structure revealed entorhinal-like activity, including border and multi-field grid-like cells. These cells were localized to the subregion predicted by anatomical mapping to match the dorsomedial entorhinal cortex. Our findings uncover an anatomical and physiological equivalence of vastly different brains, suggesting a fundamental nature of entorhinal-like computations for hippocampal function.
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Affiliation(s)
- Marissa C Applegate
- Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, 3227 Broadway, New York, NY 10027, USA
| | - Konstantin S Gutnichenko
- Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, 3227 Broadway, New York, NY 10027, USA
| | - Emily L Mackevicius
- Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, 3227 Broadway, New York, NY 10027, USA
| | - Dmitriy Aronov
- Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, 3227 Broadway, New York, NY 10027, USA.
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7
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Curry RN, Aiba I, Meyer J, Lozzi B, Ko Y, McDonald MF, Rosenbaum A, Cervantes A, Huang-Hobbs E, Cocito C, Greenfield JP, Jalali A, Gavvala J, Mohila C, Serin Harmanci A, Noebels J, Rao G, Deneen B. Glioma epileptiform activity and progression are driven by IGSF3-mediated potassium dysregulation. Neuron 2023; 111:682-695.e9. [PMID: 36787748 PMCID: PMC9991983 DOI: 10.1016/j.neuron.2023.01.013] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 12/11/2022] [Accepted: 01/17/2023] [Indexed: 02/15/2023]
Abstract
Seizures are a frequent pathophysiological feature of malignant glioma. Recent studies implicate peritumoral synaptic dysregulation as a driver of brain hyperactivity and tumor progression; however, the molecular mechanisms that govern these phenomena remain elusive. Using scRNA-seq and intraoperative patient ECoG recordings, we show that tumors from seizure patients are enriched for gene signatures regulating synapse formation. Employing a human-to-mouse in vivo functionalization pipeline to screen these genes, we identify IGSF3 as a mediator of glioma progression and dysregulated neural circuitry that manifests as spreading depolarization (SD). Mechanistically, we discover that IGSF3 interacts with Kir4.1 to suppress potassium buffering and found that seizure patients exhibit reduced expression of potassium handlers in proliferating tumor cells. In vivo imaging reveals that dysregulated synaptic activity emanates from the tumor-neuron interface, which we confirm in patients. Our studies reveal that tumor progression and seizures are enabled by ion dyshomeostasis and identify SD as a driver of disease.
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Affiliation(s)
- Rachel Naomi Curry
- The Integrative Molecular and Biomedical Sciences Graduate Program, Baylor College of Medicine, Houston, TX 77030, USA; Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX 77030, USA; Center for Cancer Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Isamu Aiba
- Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jochen Meyer
- Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Brittney Lozzi
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX 77030, USA; Center for Cancer Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Program in Genetics and Genomics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yeunjung Ko
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX 77030, USA; Center for Cancer Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Program in Immunology and Microbiology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Malcolm Ford McDonald
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX 77030, USA; Center for Cancer Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Program in Development, Disease, Models, and Therapeutics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Anna Rosenbaum
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX 77030, USA; Center for Cancer Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Alexis Cervantes
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX 77030, USA; Center for Cancer Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Emmet Huang-Hobbs
- The Integrative Molecular and Biomedical Sciences Graduate Program, Baylor College of Medicine, Houston, TX 77030, USA; Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX 77030, USA; Center for Cancer Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA
| | - Carolina Cocito
- Department of Pediatric Neurological Surgery, Weill Cornell Medicine, New York, NY 10065, USA
| | | | - Ali Jalali
- Center for Cancer Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Department of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jay Gavvala
- Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Carrie Mohila
- Department of Pathology, Texas Children's Hospital, Houston, TX 77030, USA
| | - Akdes Serin Harmanci
- Center for Cancer Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Department of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jeffrey Noebels
- Center for Cancer Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Ganesh Rao
- Center for Cancer Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Department of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Benjamin Deneen
- The Integrative Molecular and Biomedical Sciences Graduate Program, Baylor College of Medicine, Houston, TX 77030, USA; Center for Cell and Gene Therapy, Baylor College of Medicine, Houston, TX 77030, USA; Center for Cancer Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Program in Development, Disease, Models, and Therapeutics, Baylor College of Medicine, Houston, TX 77030, USA; Department of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA.
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8
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Applegate MC, Gutnichenko KS, Mackevicius EL, Aronov D. An entorhinal-like region in food-caching birds. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.05.522940. [PMID: 36711539 PMCID: PMC9881956 DOI: 10.1101/2023.01.05.522940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
The mammalian entorhinal cortex routes inputs from diverse sources into the hippocampus. This information is mixed and expressed in the activity of many specialized entorhinal cell types, which are considered indispensable for hippocampal function. However, functionally similar hippocampi exist even in non-mammals that lack an obvious entorhinal cortex, or generally any layered cortex. To address this dilemma, we mapped extrinsic hippocampal connections in chickadees, whose hippocampi are used for remembering numerous food caches. We found a well-delineated structure in these birds that is topologically similar to the entorhinal cortex and interfaces between the hippocampus and other pallial regions. Recordings of this structure revealed entorhinal-like activity, including border and multi-field grid-like cells. These cells were localized to the subregion predicted by anatomical mapping to match the dorsomedial entorhinal cortex. Our findings uncover an anatomical and physiological equivalence of vastly different brains, suggesting a fundamental nature of entorhinal-like computations for hippocampal function.
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Affiliation(s)
| | | | | | - Dmitriy Aronov
- Zuckerman Mind Brain Behavior Institute, Columbia University,Corresponding author:
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9
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Benisty H, Song A, Mishne G, Charles AS. Review of data processing of functional optical microscopy for neuroscience. NEUROPHOTONICS 2022; 9:041402. [PMID: 35937186 PMCID: PMC9351186 DOI: 10.1117/1.nph.9.4.041402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 07/15/2022] [Indexed: 05/04/2023]
Abstract
Functional optical imaging in neuroscience is rapidly growing with the development of optical systems and fluorescence indicators. To realize the potential of these massive spatiotemporal datasets for relating neuronal activity to behavior and stimuli and uncovering local circuits in the brain, accurate automated processing is increasingly essential. We cover recent computational developments in the full data processing pipeline of functional optical microscopy for neuroscience data and discuss ongoing and emerging challenges.
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Affiliation(s)
- Hadas Benisty
- Yale Neuroscience, New Haven, Connecticut, United States
| | - Alexander Song
- Max Planck Institute for Intelligent Systems, Stuttgart, Germany
| | - Gal Mishne
- UC San Diego, Halıcığlu Data Science Institute, Department of Electrical and Computer Engineering and the Neurosciences Graduate Program, La Jolla, California, United States
| | - Adam S. Charles
- Johns Hopkins University, Kavli Neuroscience Discovery Institute, Center for Imaging Science, Department of Biomedical Engineering, Department of Neuroscience, and Mathematical Institute for Data Science, Baltimore, Maryland, United States
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10
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Bayat FK, Alp Mİ, Bostan S, Gülçür HÖ, Öztürk G, Güveniş A. An improved platform for cultured neuronal network electrophysiology: multichannel optogenetics integrated with MEAs. EUROPEAN BIOPHYSICS JOURNAL : EBJ 2022; 51:503-514. [PMID: 35930029 DOI: 10.1007/s00249-022-01613-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/11/2022] [Accepted: 07/24/2022] [Indexed: 06/15/2023]
Abstract
Cultured neuronal networks (CNNs) are powerful tools for studying how neuronal representation and adaptation emerge in networks of controlled populations of neurons. To ensure the interaction of a CNN and an artificial setting, reliable operation in both open and closed loops should be provided. In this study, we integrated optogenetic stimulation with microelectrode array (MEA) recordings using a digital micromirror device and developed an improved research tool with a 64-channel interface for neuronal network control and data acquisition. We determined the ideal stimulation parameters including light intensity, frequency, and duty cycle for our configuration. This resulted in robust and reproducible neuronal responses. We also demonstrated both open and closed loop configurations in the new platform involving multiple bidirectional channels. Unlike previous approaches that combined optogenetic stimulation and MEA recordings, we did not use binary grid patterns, but assigned an adjustable-size, non-binary optical spot to each electrode. This approach allowed simultaneous use of multiple input-output channels and facilitated adaptation of the stimulation parameters. Hence, we advanced a 64-channel interface in that each channel can be controlled individually in both directions simultaneously without any interference or interrupts. The presented setup meets the requirements of research in neuronal plasticity, network encoding and representation, closed-loop control of firing rate and synchronization. Researchers who develop closed-loop control techniques and adaptive stimulation strategies for network activity will benefit much from this novel setup.
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Affiliation(s)
- F Kemal Bayat
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey.
| | - M İkbal Alp
- Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Turkey
| | - Sevginur Bostan
- Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Turkey
| | - H Özcan Gülçür
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey
| | - Gürkan Öztürk
- Regenerative and Restorative Medicine Research Center (REMER), Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Turkey
| | - Albert Güveniş
- Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey
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11
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Moore JJ, Robert V, Rashid SK, Basu J. Assessing Local and Branch-specific Activity in Dendrites. Neuroscience 2022; 489:143-164. [PMID: 34756987 PMCID: PMC9125998 DOI: 10.1016/j.neuroscience.2021.10.022] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 10/09/2021] [Accepted: 10/21/2021] [Indexed: 01/12/2023]
Abstract
Dendrites are elaborate neural processes which integrate inputs from various sources in space and time. While decades of work have suggested an independent role for dendrites in driving nonlinear computations for the cell, only recently have technological advances enabled us to capture the variety of activity in dendrites and their coupling dynamics with the soma. Under certain circumstances, activity generated in a given dendritic branch remains isolated, such that the soma or even sister dendrites are not privy to these localized signals. Such branch-specific activity could radically increase the capacity and flexibility of coding for the cell as a whole. Here, we discuss these forms of localized and branch-specific activity, their functional relevance in plasticity and behavior, and their supporting biophysical and circuit-level mechanisms. We conclude by showcasing electrical and optical approaches in hippocampal area CA3, using original experimental data to discuss experimental and analytical methodology and key considerations to take when investigating the functional relevance of independent dendritic activity.
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Affiliation(s)
- Jason J Moore
- Neuroscience Institute, New York University Langone Health, New York, NY 10016, USA
| | - Vincent Robert
- Neuroscience Institute, New York University Langone Health, New York, NY 10016, USA
| | - Shannon K Rashid
- Neuroscience Institute, New York University Langone Health, New York, NY 10016, USA
| | - Jayeeta Basu
- Neuroscience Institute, New York University Langone Health, New York, NY 10016, USA; Department of Neuroscience and Physiology, New York University Grossman School of Medicine, New York, NY 10016, USA; Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA.
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12
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Xie ME, Adam Y, Fan LZ, Böhm UL, Kinsella I, Zhou D, Rozsa M, Singh A, Svoboda K, Paninski L, Cohen AE. High-fidelity estimates of spikes and subthreshold waveforms from 1-photon voltage imaging in vivo. Cell Rep 2021; 35:108954. [PMID: 33826882 PMCID: PMC8095336 DOI: 10.1016/j.celrep.2021.108954] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 02/24/2021] [Accepted: 03/15/2021] [Indexed: 11/24/2022] Open
Abstract
The ability to probe the membrane potential of multiple genetically defined neurons simultaneously would have a profound impact on neuroscience research. Genetically encoded voltage indicators are a promising tool for this purpose, and recent developments have achieved a high signal-to-noise ratio in vivo with 1-photon fluorescence imaging. However, these recordings exhibit several sources of noise and signal extraction remains a challenge. We present an improved signal extraction pipeline, spike-guided penalized matrix decomposition-nonnegative matrix factorization (SGPMD-NMF), which resolves supra- and subthreshold voltages in vivo. The method incorporates biophysical and optical constraints. We validate the pipeline with simultaneous patch-clamp and optical recordings from mouse layer 1 in vivo and with simulated and composite datasets with realistic noise. We demonstrate applications to mouse hippocampus expressing paQuasAr3-s or SomArchon1, mouse cortex expressing SomArchon1 or Voltron, and zebrafish spines expressing zArchon1.
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Affiliation(s)
- Michael E Xie
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
| | - Yoav Adam
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
| | - Linlin Z Fan
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
| | - Urs L Böhm
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
| | - Ian Kinsella
- Department of Statistics, Columbia University, New York, NY 10027, USA
| | - Ding Zhou
- Department of Statistics, Columbia University, New York, NY 10027, USA
| | - Marton Rozsa
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Amrita Singh
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA; Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Karel Svoboda
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Liam Paninski
- Department of Statistics, Columbia University, New York, NY 10027, USA.
| | - Adam E Cohen
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA.
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13
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Go MA, Rogers J, Gava GP, Davey CE, Prado S, Liu Y, Schultz SR. Place Cells in Head-Fixed Mice Navigating a Floating Real-World Environment. Front Cell Neurosci 2021; 15:618658. [PMID: 33642996 PMCID: PMC7906988 DOI: 10.3389/fncel.2021.618658] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Accepted: 01/25/2020] [Indexed: 12/27/2022] Open
Abstract
The hippocampal place cell system in rodents has provided a major paradigm for the scientific investigation of memory function and dysfunction. Place cells have been observed in area CA1 of the hippocampus of both freely moving animals, and of head-fixed animals navigating in virtual reality environments. However, spatial coding in virtual reality preparations has been observed to be impaired. Here we show that the use of a real-world environment system for head-fixed mice, consisting of an air-floating track with proximal cues, provides some advantages over virtual reality systems for the study of spatial memory. We imaged the hippocampus of head-fixed mice injected with the genetically encoded calcium indicator GCaMP6s while they navigated circularly constrained or open environments on the floating platform. We observed consistent place tuning in a substantial fraction of cells despite the absence of distal visual cues. Place fields remapped when animals entered a different environment. When animals re-entered the same environment, place fields typically remapped over a time period of multiple days, faster than in freely moving preparations, but comparable with virtual reality. Spatial information rates were within the range observed in freely moving mice. Manifold analysis indicated that spatial information could be extracted from a low-dimensional subspace of the neural population dynamics. This is the first demonstration of place cells in head-fixed mice navigating on an air-lifted real-world platform, validating its use for the study of brain circuits involved in memory and affected by neurodegenerative disorders.
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Affiliation(s)
- Mary Ann Go
- Department of Bioengineering and Centre for Neurotechnology, Imperial College London, London, United Kingdom
| | - Jake Rogers
- Department of Bioengineering and Centre for Neurotechnology, Imperial College London, London, United Kingdom
| | - Giuseppe P. Gava
- Department of Bioengineering and Centre for Neurotechnology, Imperial College London, London, United Kingdom
| | - Catherine E. Davey
- Department of Bioengineering and Centre for Neurotechnology, Imperial College London, London, United Kingdom
- Department of Biomedical Engineering, University of Melbourne, Melbourne, VIC, Australia
| | - Seigfred Prado
- Department of Bioengineering and Centre for Neurotechnology, Imperial College London, London, United Kingdom
| | - Yu Liu
- Department of Bioengineering and Centre for Neurotechnology, Imperial College London, London, United Kingdom
| | - Simon R. Schultz
- Department of Bioengineering and Centre for Neurotechnology, Imperial College London, London, United Kingdom
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14
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Friedrich J, Giovannucci A, Pnevmatikakis EA. Online analysis of microendoscopic 1-photon calcium imaging data streams. PLoS Comput Biol 2021; 17:e1008565. [PMID: 33507937 PMCID: PMC7842953 DOI: 10.1371/journal.pcbi.1008565] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 11/27/2020] [Indexed: 11/18/2022] Open
Abstract
In vivo calcium imaging through microendoscopic lenses enables imaging of neuronal populations deep within the brains of freely moving animals. Previously, a constrained matrix factorization approach (CNMF-E) has been suggested to extract single-neuronal activity from microendoscopic data. However, this approach relies on offline batch processing of the entire video data and is demanding both in terms of computing and memory requirements. These drawbacks prevent its applicability to the analysis of large datasets and closed-loop experimental settings. Here we address both issues by introducing two different online algorithms for extracting neuronal activity from streaming microendoscopic data. Our first algorithm, OnACID-E, presents an online adaptation of the CNMF-E algorithm, which dramatically reduces its memory and computation requirements. Our second algorithm proposes a convolution-based background model for microendoscopic data that enables even faster (real time) processing. Our approach is modular and can be combined with existing online motion artifact correction and activity deconvolution methods to provide a highly scalable pipeline for microendoscopic data analysis. We apply our algorithms on four previously published typical experimental datasets and show that they yield similar high-quality results as the popular offline approach, but outperform it with regard to computing time and memory requirements. They can be used instead of CNMF-E to process pre-recorded data with boosted speeds and dramatically reduced memory requirements. Further, they newly enable online analysis of live-streaming data even on a laptop. Calcium imaging methods enable researchers to measure the activity of genetically-targeted large-scale neuronal subpopulations. Whereas previous methods required the specimen to be stable, e.g. anesthetized or head-fixed, new brain imaging techniques using microendoscopic lenses and miniaturized microscopes have enabled deep brain imaging in freely moving mice. However, the very large background fluctuations, the inevitable movements and distortions of imaging field, and the extensive spatial overlaps of fluorescent signals complicate the goal of efficiently extracting accurate estimates of neural activity from the observed video data. Further, current activity extraction methods are computationally expensive due to the complex background model and are typically applied to imaging data long after the experiment is complete. Moreover, in some scenarios it is necessary to perform experiments in real-time and closed-loop—analyzing data on-the-fly to guide the next experimental steps or to control feedback –, and this calls for new methods for accurate real-time processing. Here we address both issues by adapting a popular extraction method to operate online and extend it to utilize GPU hardware that enables real time processing. Our algorithms yield similar high-quality results as the original offline approach, but outperform it with regard to computing time and memory requirements. Our results enable faster and scalable analysis, and open the door to new closed-loop experiments in deep brain areas and on freely-moving preparations. Our algorithms can be used for newly enabled real-time analysis of streaming data, as well as swapped in directly to replace the computationally costly offline approach.
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Affiliation(s)
- Johannes Friedrich
- Flatiron Institute, Simons Foundation, New York, New York, United States of America
- * E-mail:
| | - Andrea Giovannucci
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University; and UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
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15
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Saxena S, Kinsella I, Musall S, Kim SH, Meszaros J, Thibodeaux DN, Kim C, Cunningham J, Hillman EMC, Churchland A, Paninski L. Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data. PLoS Comput Biol 2020; 16:e1007791. [PMID: 32282806 PMCID: PMC7179949 DOI: 10.1371/journal.pcbi.1007791] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 04/23/2020] [Accepted: 03/17/2020] [Indexed: 12/12/2022] Open
Abstract
Widefield calcium imaging enables recording of large-scale neural activity across the mouse dorsal cortex. In order to examine the relationship of these neural signals to the resulting behavior, it is critical to demix the recordings into meaningful spatial and temporal components that can be mapped onto well-defined brain regions. However, no current tools satisfactorily extract the activity of the different brain regions in individual mice in a data-driven manner, while taking into account mouse-specific and preparation-specific differences. Here, we introduce Localized semi-Nonnegative Matrix Factorization (LocaNMF), a method that efficiently decomposes widefield video data and allows us to directly compare activity across multiple mice by outputting mouse-specific localized functional regions that are significantly more interpretable than more traditional decomposition techniques. Moreover, it provides a natural subspace to directly compare correlation maps and neural dynamics across different behaviors, mice, and experimental conditions, and enables identification of task- and movement-related brain regions.
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Affiliation(s)
- Shreya Saxena
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
| | - Ian Kinsella
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
| | - Simon Musall
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Sharon H Kim
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Laboratory for Functional Optical Imaging, Department of Biomedical Engineering, Columbia University, New York, New York, United States of America
| | - Jozsef Meszaros
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Laboratory for Functional Optical Imaging, Department of Biomedical Engineering, Columbia University, New York, New York, United States of America
| | - David N Thibodeaux
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Laboratory for Functional Optical Imaging, Department of Biomedical Engineering, Columbia University, New York, New York, United States of America
| | - Carla Kim
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Laboratory for Functional Optical Imaging, Department of Biomedical Engineering, Columbia University, New York, New York, United States of America
| | - John Cunningham
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
| | - Elizabeth M C Hillman
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Laboratory for Functional Optical Imaging, Department of Biomedical Engineering, Columbia University, New York, New York, United States of America
| | - Anne Churchland
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, United States of America
| | - Liam Paninski
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
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16
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Calarco JA, Samuel ADT. Imaging whole nervous systems: insights into behavior from worms to fish. Nat Methods 2019; 16:14-15. [PMID: 30573822 DOI: 10.1038/s41592-018-0276-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- John A Calarco
- Department of Cell and Systems Biology, Toronto, ON, Canada
| | - Aravinthan D T Samuel
- Center for Brain Science and Department of Physics, Harvard University, Cambridge, MA, USA.
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17
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Soltanian-Zadeh S, Sahingur K, Blau S, Gong Y, Farsiu S. Fast and robust active neuron segmentation in two-photon calcium imaging using spatiotemporal deep learning. Proc Natl Acad Sci U S A 2019; 116:8554-8563. [PMID: 30975747 PMCID: PMC6486774 DOI: 10.1073/pnas.1812995116] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Calcium imaging records large-scale neuronal activity with cellular resolution in vivo. Automated, fast, and reliable active neuron segmentation is a critical step in the analysis workflow of utilizing neuronal signals in real-time behavioral studies for discovery of neuronal coding properties. Here, to exploit the full spatiotemporal information in two-photon calcium imaging movies, we propose a 3D convolutional neural network to identify and segment active neurons. By utilizing a variety of two-photon microscopy datasets, we show that our method outperforms state-of-the-art techniques and is on a par with manual segmentation. Furthermore, we demonstrate that the network trained on data recorded at a specific cortical layer can be used to accurately segment active neurons from another layer with different neuron density. Finally, our work documents significant tabulation flaws in one of the most cited and active online scientific challenges in neuron segmentation. As our computationally fast method is an invaluable tool for a large spectrum of real-time optogenetic experiments, we have made our open-source software and carefully annotated dataset freely available online.
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Affiliation(s)
| | - Kaan Sahingur
- Department of Biomedical Engineering, Duke University, Durham, NC 27708
| | - Sarah Blau
- Department of Biomedical Engineering, Duke University, Durham, NC 27708
| | - Yiyang Gong
- Department of Biomedical Engineering, Duke University, Durham, NC 27708;
- Department of Neurobiology, Duke University, Durham, NC 27708
| | - Sina Farsiu
- Department of Biomedical Engineering, Duke University, Durham, NC 27708;
- Department of Ophthalmology, Duke University Medical Center, Durham, NC 27710
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18
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Chettih SN, Harvey CD. Single-neuron perturbations reveal feature-specific competition in V1. Nature 2019; 567:334-340. [PMID: 30842660 PMCID: PMC6682407 DOI: 10.1038/s41586-019-0997-6] [Citation(s) in RCA: 111] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Accepted: 02/07/2019] [Indexed: 12/22/2022]
Abstract
The computations performed by local neural populations, such as a cortical layer, are typically inferred from anatomical connectivity and observations of neural activity. Here we describe a method-influence mapping-that uses single-neuron perturbations to directly measure how cortical neurons reshape sensory representations. In layer 2/3 of the primary visual cortex (V1), we use two-photon optogenetics to trigger action potentials in a targeted neuron and calcium imaging to measure the effect on spiking in neighbouring neurons in awake mice viewing visual stimuli. Excitatory neurons on average suppressed other neurons and had a centre-surround influence profile over anatomical space. A neuron's influence on its neighbour depended on their similarity in activity. Notably, neurons suppressed activity in similarly tuned neurons more than in dissimilarly tuned neurons. In addition, photostimulation reduced the population response, specifically to the targeted neuron's preferred stimulus, by around 2%. Therefore, V1 layer 2/3 performed feature competition, in which a like-suppresses-like motif reduces redundancy in population activity and may assist with inference of the features that underlie sensory input. We anticipate that influence mapping can be extended to investigate computations in other neural populations.
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19
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Pnevmatikakis EA. Analysis pipelines for calcium imaging data. Curr Opin Neurobiol 2019; 55:15-21. [PMID: 30529147 DOI: 10.1016/j.conb.2018.11.004] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Revised: 09/11/2018] [Accepted: 11/19/2018] [Indexed: 11/26/2022]
Abstract
Calcium imaging is a popular tool among neuroscientists because of its capability to monitor in vivo large neural populations across weeks with single neuron and single spike resolution. Before any downstream analysis, the data needs to be pre-processed to extract the location and activity of the neurons and processes in the observed field of view. The ever increasing size of calcium imaging datasets necessitates scalable analysis pipelines that are reproducible and fully automated. This review focuses on recent methods for addressing the pre-processing problems that arise in calcium imaging data analysis, and available software tools for high throughput analysis pipelines.
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20
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Paninski L, Cunningham JP. Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience. Curr Opin Neurobiol 2019; 50:232-241. [PMID: 29738986 DOI: 10.1016/j.conb.2018.04.007] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2017] [Revised: 03/12/2018] [Accepted: 04/06/2018] [Indexed: 01/01/2023]
Abstract
Modern large-scale multineuronal recording methodologies, including multielectrode arrays, calcium imaging, and optogenetic techniques, produce single-neuron resolution data of a magnitude and precision that were the realm of science fiction twenty years ago. The major bottlenecks in systems and circuit neuroscience no longer lie in simply collecting data from large neural populations, but also in understanding this data: developing novel scientific questions, with corresponding analysis techniques and experimental designs to fully harness these new capabilities and meaningfully interrogate these questions. Advances in methods for signal processing, network analysis, dimensionality reduction, and optimal control-developed in lockstep with advances in experimental neurotechnology-promise major breakthroughs in multiple fundamental neuroscience problems. These trends are clear in a broad array of subfields of modern neuroscience; this review focuses on recent advances in methods for analyzing neural time-series data with single-neuronal precision.
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Affiliation(s)
- L Paninski
- Department of Statistics, Grossman Center for the Statistics of Mind, Zuckerman Mind Brain Behavior Institute, Center for Theoretical Neuroscience, Columbia University, United States; Department of Neuroscience, Grossman Center for the Statistics of Mind, Zuckerman Mind Brain Behavior Institute, Center for Theoretical Neuroscience, Columbia University, United States.
| | - J P Cunningham
- Department of Statistics, Grossman Center for the Statistics of Mind, Zuckerman Mind Brain Behavior Institute, Center for Theoretical Neuroscience, Columbia University, United States
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21
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Giovannucci A, Friedrich J, Gunn P, Kalfon J, Brown BL, Koay SA, Taxidis J, Najafi F, Gauthier JL, Zhou P, Khakh BS, Tank DW, Chklovskii DB, Pnevmatikakis EA. CaImAn an open source tool for scalable calcium imaging data analysis. eLife 2019; 8:e38173. [PMID: 30652683 PMCID: PMC6342523 DOI: 10.7554/elife.38173] [Citation(s) in RCA: 390] [Impact Index Per Article: 78.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Accepted: 11/23/2018] [Indexed: 12/11/2022] Open
Abstract
Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. We present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good scalability on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data. To benchmark the performance of CaImAn we collected and combined a corpus of manual annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.
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Affiliation(s)
- Andrea Giovannucci
- Center for Computational BiologyFlatiron Institute, Simons FoundationNew YorkUnited States
| | - Johannes Friedrich
- Center for Computational BiologyFlatiron Institute, Simons FoundationNew YorkUnited States
- Department of StatisticsColumbia UniversityNew YorkUnited States
- Center for Theoretical NeuroscienceColumbia UniversityNew YorkUnited States
| | - Pat Gunn
- Center for Computational BiologyFlatiron Institute, Simons FoundationNew YorkUnited States
| | | | - Brandon L Brown
- Department of PhysiologyUniversity of California, Los AngelesLos AngelesUnited States
| | - Sue Ann Koay
- Princeton Neuroscience InstitutePrinceton UniversityPrincetonUnited States
| | - Jiannis Taxidis
- Department of NeurologyUniversity of California, Los AngelesLos AngelesUnited States
| | | | - Jeffrey L Gauthier
- Princeton Neuroscience InstitutePrinceton UniversityPrincetonUnited States
| | - Pengcheng Zhou
- Department of StatisticsColumbia UniversityNew YorkUnited States
- Center for Theoretical NeuroscienceColumbia UniversityNew YorkUnited States
| | - Baljit S Khakh
- Department of PhysiologyUniversity of California, Los AngelesLos AngelesUnited States
- Department of NeurobiologyUniversity of California, Los AngelesLos AngelesUnited States
| | - David W Tank
- Princeton Neuroscience InstitutePrinceton UniversityPrincetonUnited States
| | - Dmitri B Chklovskii
- Center for Computational BiologyFlatiron Institute, Simons FoundationNew YorkUnited States
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22
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Weisenburger S, Vaziri A. A Guide to Emerging Technologies for Large-Scale and Whole-Brain Optical Imaging of Neuronal Activity. Annu Rev Neurosci 2018; 41:431-452. [PMID: 29709208 PMCID: PMC6037565 DOI: 10.1146/annurev-neuro-072116-031458] [Citation(s) in RCA: 66] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The mammalian brain is a densely interconnected network that consists of millions to billions of neurons. Decoding how information is represented and processed by this neural circuitry requires the ability to capture and manipulate the dynamics of large populations at high speed and high resolution over a large area of the brain. Although the use of optical approaches by the neuroscience community has rapidly increased over the past two decades, most microscopy approaches are unable to record the activity of all neurons comprising a functional network across the mammalian brain at relevant temporal and spatial resolutions. In this review, we survey the recent development in optical technologies for Ca2+ imaging in this regard and provide an overview of the strengths and limitations of each modality and its potential for scalability. We provide guidance from the perspective of a biological user driven by the typical biological applications and sample conditions. We also discuss the potential for future advances and synergies that could be obtained through hybrid approaches or other modalities.
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Affiliation(s)
- Siegfried Weisenburger
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, New York 10065, USA
| | - Alipasha Vaziri
- Laboratory of Neurotechnology and Biophysics, The Rockefeller University, New York, New York 10065, USA
- Kavli Neural Systems Institute, The Rockefeller University, New York, New York 10065, USA
- Research Institute of Molecular Pathology, 1030 Vienna, Austria;
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23
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Zeldenrust F, Wadman WJ, Englitz B. Neural Coding With Bursts-Current State and Future Perspectives. Front Comput Neurosci 2018; 12:48. [PMID: 30034330 PMCID: PMC6043860 DOI: 10.3389/fncom.2018.00048] [Citation(s) in RCA: 75] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Accepted: 06/06/2018] [Indexed: 12/11/2022] Open
Abstract
Neuronal action potentials or spikes provide a long-range, noise-resistant means of communication between neurons. As point processes single spikes contain little information in themselves, i.e., outside the context of spikes from other neurons. Moreover, they may fail to cross a synapse. A burst, which consists of a short, high frequency train of spikes, will more reliably cross a synapse, increasing the likelihood of eliciting a postsynaptic spike, depending on the specific short-term plasticity at that synapse. Both the number and the temporal pattern of spikes in a burst provide a coding space that lies within the temporal integration realm of single neurons. Bursts have been observed in many species, including the non-mammalian, and in brain regions that range from subcortical to cortical. Despite their widespread presence and potential relevance, the uncertainties of how to classify bursts seems to have limited the research into the coding possibilities for bursts. The present series of research articles provides new insights into the relevance and interpretation of bursts across different neural circuits, and new methods for their analysis. Here, we provide a succinct introduction to the history of burst coding and an overview of recent work on this topic.
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Affiliation(s)
- Fleur Zeldenrust
- Department of Neurophysiology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Wytse J Wadman
- Cellular and Systems Neurobiology Lab, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands
| | - Bernhard Englitz
- Department of Neurophysiology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, Netherlands
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24
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Zhou P, Resendez SL, Rodriguez-Romaguera J, Jimenez JC, Neufeld SQ, Giovannucci A, Friedrich J, Pnevmatikakis EA, Stuber GD, Hen R, Kheirbek MA, Sabatini BL, Kass RE, Paninski L. Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data. eLife 2018; 7:e28728. [PMID: 29469809 PMCID: PMC5871355 DOI: 10.7554/elife.28728] [Citation(s) in RCA: 348] [Impact Index Per Article: 58.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Accepted: 02/20/2018] [Indexed: 12/12/2022] Open
Abstract
In vivo calcium imaging through microendoscopic lenses enables imaging of previously inaccessible neuronal populations deep within the brains of freely moving animals. However, it is computationally challenging to extract single-neuronal activity from microendoscopic data, because of the very large background fluctuations and high spatial overlaps intrinsic to this recording modality. Here, we describe a new constrained matrix factorization approach to accurately separate the background and then demix and denoise the neuronal signals of interest. We compared the proposed method against previous independent components analysis and constrained nonnegative matrix factorization approaches. On both simulated and experimental data recorded from mice, our method substantially improved the quality of extracted cellular signals and detected more well-isolated neural signals, especially in noisy data regimes. These advances can in turn significantly enhance the statistical power of downstream analyses, and ultimately improve scientific conclusions derived from microendoscopic data.
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Affiliation(s)
- Pengcheng Zhou
- Center for the Neural Basis of CognitionCarnegie Mellon UniversityPittsburghUnited States
- Department of StatisticsColumbia UniversityNew YorkUnited States
- Machine Learning DepartmentCarnegie Mellon UniversityPittsburghUnited States
- Grossman Center for the Statistics of MindColumbia UniversityNew YorkUnited States
- Center for Theoretical NeuroscienceColumbia UniversityNew YorkUnited States
| | - Shanna L Resendez
- Department of PsychiatryUniversity of North Carolina at Chapel HillChapel HillUnited States
| | | | - Jessica C Jimenez
- Department of NeuroscienceColumbia UniversityNew YorkUnited States
- Division of Integrative Neuroscience, Department of PsychiatryNew York State Psychiatric InstituteNew YorkUnited States
- Department of Psychiatry & PharmacologyColumbia UniversityNew YorkUnited States
| | - Shay Q Neufeld
- Department of NeurobiologyHarvard Medical School, Howard Hughes Medical InstituteBostonUnited States
| | - Andrea Giovannucci
- Center for Computational BiologyFlatiron Institute, Simons FoundationNew YorkUnited States
| | - Johannes Friedrich
- Center for Computational BiologyFlatiron Institute, Simons FoundationNew YorkUnited States
| | | | - Garret D Stuber
- Department of PsychiatryUniversity of North Carolina at Chapel HillChapel HillUnited States
- Department of Cell Biology and PhysiologyUniversity of North Carolina at Chapel HillChapel HillUnited States
- Neuroscience CenterUniversity of North Carolina at Chapel HillChapel HillUnited States
| | - Rene Hen
- Department of NeuroscienceColumbia UniversityNew YorkUnited States
- Division of Integrative Neuroscience, Department of PsychiatryNew York State Psychiatric InstituteNew YorkUnited States
- Department of Psychiatry & PharmacologyColumbia UniversityNew YorkUnited States
| | - Mazen A Kheirbek
- Weill Institute for NeurosciencesUniversity of California, San FranciscoSan FranciscoUnited States
- Neuroscience Graduate ProgramUniversity of CaliforniaSan FranciscoUnited States
- Kavli Institute for Fundamental NeuroscienceUniversity of California, San FranciscoSan FranciscoUnited States
- Department of PsychiatryUniversity of California, San FranciscoSan FranciscoUnited States
| | - Bernardo L Sabatini
- Department of NeurobiologyHarvard Medical School, Howard Hughes Medical InstituteBostonUnited States
| | - Robert E Kass
- Center for the Neural Basis of CognitionCarnegie Mellon UniversityPittsburghUnited States
- Machine Learning DepartmentCarnegie Mellon UniversityPittsburghUnited States
- Department of StatisticsCarnegie Mellon UniversityPittsburghUnited States
| | - Liam Paninski
- Department of StatisticsColumbia UniversityNew YorkUnited States
- Grossman Center for the Statistics of MindColumbia UniversityNew YorkUnited States
- Center for Theoretical NeuroscienceColumbia UniversityNew YorkUnited States
- Department of NeuroscienceColumbia UniversityNew YorkUnited States
- Kavli Institute for Brain ScienceColumbia UniversityNew YorkUnited States
- Neurotechnology CenterColumbia UniversityNew YorkUnited States
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Keemink SW, Lowe SC, Pakan JMP, Dylda E, van Rossum MCW, Rochefort NL. FISSA: A neuropil decontamination toolbox for calcium imaging signals. Sci Rep 2018; 8:3493. [PMID: 29472547 PMCID: PMC5823956 DOI: 10.1038/s41598-018-21640-2] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Accepted: 02/06/2018] [Indexed: 11/11/2022] Open
Abstract
In vivo calcium imaging has become a method of choice to image neuronal population activity throughout the nervous system. These experiments generate large sequences of images. Their analysis is computationally intensive and typically involves motion correction, image segmentation into regions of interest (ROIs), and extraction of fluorescence traces from each ROI. Out of focus fluorescence from surrounding neuropil and other cells can strongly contaminate the signal assigned to a given ROI. In this study, we introduce the FISSA toolbox (Fast Image Signal Separation Analysis) for neuropil decontamination. Given pre-defined ROIs, the FISSA toolbox automatically extracts the surrounding local neuropil and performs blind-source separation with non-negative matrix factorization. Using both simulated and in vivo data, we show that this toolbox performs similarly or better than existing published methods. FISSA requires only little RAM, and allows for fast processing of large datasets even on a standard laptop. The FISSA toolbox is available in Python, with an option for MATLAB format outputs, and can easily be integrated into existing workflows. It is available from Github and the standard Python repositories.
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Affiliation(s)
- Sander W Keemink
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, UK. .,Bernstein Center Freiburg, Faculty of Biology, University of Freiburg, 79104, Freiburg, Germany.
| | - Scott C Lowe
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, UK.,Centre for Discovery Brain Sciences, Biomedical Sciences, University of Edinburgh, Edinburgh, EH8 9XD, UK
| | - Janelle M P Pakan
- Centre for Discovery Brain Sciences, Biomedical Sciences, University of Edinburgh, Edinburgh, EH8 9XD, UK
| | - Evelyn Dylda
- Centre for Discovery Brain Sciences, Biomedical Sciences, University of Edinburgh, Edinburgh, EH8 9XD, UK
| | - Mark C W van Rossum
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, UK
| | - Nathalie L Rochefort
- Centre for Discovery Brain Sciences, Biomedical Sciences, University of Edinburgh, Edinburgh, EH8 9XD, UK.
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Yang W, Yuste R. In vivo imaging of neural activity. Nat Methods 2017; 14:349-359. [PMID: 28362436 DOI: 10.1038/nmeth.4230] [Citation(s) in RCA: 239] [Impact Index Per Article: 34.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Accepted: 02/13/2017] [Indexed: 12/18/2022]
Abstract
Since the introduction of calcium imaging to monitor neuronal activity with single-cell resolution, optical imaging methods have revolutionized neuroscience by enabling systematic recordings of neuronal circuits in living animals. The plethora of methods for functional neural imaging can be daunting to the nonexpert to navigate. Here we review advanced microscopy techniques for in vivo functional imaging and offer guidelines for which technologies are best suited for particular applications.
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Affiliation(s)
- Weijian Yang
- Department of Biological Sciences, Neurotechnology Center, Columbia University, New York, New York, USA
| | - Rafael Yuste
- Department of Biological Sciences, Neurotechnology Center, Columbia University, New York, New York, USA
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