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Wang B, Torok Z, Duffy A, Bell DG, Wongso S, Velho TAF, Fairhall AL, Lois C. Unsupervised restoration of a complex learned behavior after large-scale neuronal perturbation. Nat Neurosci 2024:10.1038/s41593-024-01630-6. [PMID: 38684893 DOI: 10.1038/s41593-024-01630-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 03/26/2024] [Indexed: 05/02/2024]
Abstract
Reliable execution of precise behaviors requires that brain circuits are resilient to variations in neuronal dynamics. Genetic perturbation of the majority of excitatory neurons in HVC, a brain region involved in song production, in adult songbirds with stereotypical songs triggered severe degradation of the song. The song fully recovered within 2 weeks, and substantial improvement occurred even when animals were prevented from singing during the recovery period, indicating that offline mechanisms enable recovery in an unsupervised manner. Song restoration was accompanied by increased excitatory synaptic input to neighboring, unmanipulated neurons in the same brain region. A model inspired by the behavioral and electrophysiological findings suggests that unsupervised single-cell and population-level homeostatic plasticity rules can support the functional restoration after large-scale disruption of networks that implement sequential dynamics. These observations suggest the existence of cellular and systems-level restorative mechanisms that ensure behavioral resilience.
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Affiliation(s)
- Bo Wang
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
| | - Zsofia Torok
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Alison Duffy
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
- Computational Neuroscience Center, University of Washington, Seattle, WA, USA
| | - David G Bell
- Computational Neuroscience Center, University of Washington, Seattle, WA, USA
- Department of Physics, University of Washington, Seattle, WA, USA
| | - Shelyn Wongso
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Tarciso A F Velho
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Adrienne L Fairhall
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
- Computational Neuroscience Center, University of Washington, Seattle, WA, USA
- Department of Physics, University of Washington, Seattle, WA, USA
| | - Carlos Lois
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
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2
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Boergens KM, Wildenberg G, Li R, Lambert L, Moradi A, Stam G, Tromp R, van der Molen SJ, King SB, Kasthuri N. Photoemission electron microscopy for connectomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.05.556423. [PMID: 37771915 PMCID: PMC10525389 DOI: 10.1101/2023.09.05.556423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
Detailing the physical basis of neural circuits with large-volume serial electron microscopy (EM), 'connectomics', has emerged as an invaluable tool in the neuroscience armamentarium. However, imaging synaptic resolution connectomes is currently limited to either transmission electron microscopy (TEM) or scanning electron microscopy (SEM). Here, we describe a third way, using photoemission electron microscopy (PEEM) which illuminates ultra-thin brain slices collected on solid substrates with UV light and images the photoelectron emission pattern with a wide-field electron microscope. PEEM works with existing sample preparations for EM and routinely provides sufficient resolution and contrast to reveal myelinated axons, somata, dendrites, and sub-cellular organelles. Under optimized conditions, PEEM provides synaptic resolution; and simulation and experiments show that PEEM can be transformatively fast, at Gigahertz pixel rates. We conclude that PEEM imaging leverages attractive aspects of SEM and TEM, namely reliable sample collection on robust substrates combined with fast wide-field imaging, and could enable faster data acquisition for next-generation circuit mapping.
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3
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Shvedov NR, Analoui S, Dafalias T, Bedell BL, Gardner TJ, Scott BB. In vivo imaging in transgenic songbirds reveals superdiffusive neuron migration in the adult brain. Cell Rep 2024; 43:113759. [PMID: 38345898 DOI: 10.1016/j.celrep.2024.113759] [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: 08/09/2023] [Revised: 12/01/2023] [Accepted: 01/23/2024] [Indexed: 03/02/2024] Open
Abstract
Neuron migration is a key phase of neurogenesis, critical for the assembly and function of neuronal circuits. In songbirds, this process continues throughout life, but how these newborn neurons disperse through the adult brain is unclear. We address this question using in vivo two-photon imaging in transgenic zebra finches that express GFP in young neurons and other cell types. In juvenile and adult birds, migratory cells are present at a high density, travel in all directions, and make frequent course changes. Notably, these dynamic migration patterns are well fit by a superdiffusive model. Simulations reveal that these superdiffusive dynamics are sufficient to disperse new neurons throughout the song nucleus HVC. These results suggest that superdiffusive migration may underlie the formation and maintenance of nuclear brain structures in the postnatal brain and indicate that transgenic songbirds are a useful resource for future studies into the mechanisms of adult neurogenesis.
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Affiliation(s)
- Naomi R Shvedov
- Graduate Program for Neuroscience, Boston University, Boston, MA 02215, USA
| | - Sina Analoui
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215, USA
| | - Theresia Dafalias
- Graduate Program for Neuroscience, Boston University, Boston, MA 02215, USA
| | - Brooke L Bedell
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215, USA
| | - Timothy J Gardner
- Phil and Penny Knight Campus for Accelerating Scientific Impact, University of Oregon, Eugene, OR 97403, USA
| | - Benjamin B Scott
- Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215, USA; Neurophotonics Center, Photonics Center, and Center for Systems Neuroscience, Boston University, Boston, MA 02215, USA.
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4
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Zemel BM, Nevue AA, Tavares LES, Dagostin A, Lovell PV, Jin DZ, Mello CV, von Gersdorff H. Motor cortex analogue neurons in songbirds utilize Kv3 channels to generate ultranarrow spikes. eLife 2023; 12:e81992. [PMID: 37158590 PMCID: PMC10241522 DOI: 10.7554/elife.81992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 05/08/2023] [Indexed: 05/10/2023] Open
Abstract
Complex motor skills in vertebrates require specialized upper motor neurons with precise action potential (AP) firing. To examine how diverse populations of upper motor neurons subserve distinct functions and the specific repertoire of ion channels involved, we conducted a thorough study of the excitability of upper motor neurons controlling somatic motor function in the zebra finch. We found that robustus arcopallialis projection neurons (RAPNs), key command neurons for song production, exhibit ultranarrow spikes and higher firing rates compared to neurons controlling non-vocal somatic motor functions (dorsal intermediate arcopallium [AId] neurons). Pharmacological and molecular data indicate that this striking difference is associated with the higher expression in RAPNs of high threshold, fast-activating voltage-gated Kv3 channels, that likely contain Kv3.1 (KCNC1) subunits. The spike waveform and Kv3.1 expression in RAPNs mirror properties of Betz cells, specialized upper motor neurons involved in fine digit control in humans and other primates but absent in rodents. Our study thus provides evidence that songbirds and primates have convergently evolved the use of Kv3.1 to ensure precise, rapid AP firing in upper motor neurons controlling fast and complex motor skills.
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Affiliation(s)
- Benjamin M Zemel
- Vollum Institute, Oregon Health and Science UniversityPortlandUnited States
| | - Alexander A Nevue
- Department of Behavioral Neuroscience, Oregon Health and Science UniversityPortlandUnited States
| | - Leonardo ES Tavares
- Vollum Institute, Oregon Health and Science UniversityPortlandUnited States
- Department of Physics, Pennsylvania State UniversityUniversity ParkUnited States
| | - Andre Dagostin
- Vollum Institute, Oregon Health and Science UniversityPortlandUnited States
| | - Peter V Lovell
- Department of Behavioral Neuroscience, Oregon Health and Science UniversityPortlandUnited States
| | - Dezhe Z Jin
- Department of Physics, Pennsylvania State UniversityUniversity ParkUnited States
| | - Claudio V Mello
- Department of Behavioral Neuroscience, Oregon Health and Science UniversityPortlandUnited States
| | - Henrique von Gersdorff
- Vollum Institute, Oregon Health and Science UniversityPortlandUnited States
- Oregon Hearing Research Center, Oregon Health and Science UniversityPortlandUnited States
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5
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Keijser J, Sprekeler H. Cortical interneurons: fit for function and fit to function? Evidence from development and evolution. Front Neural Circuits 2023; 17:1172464. [PMID: 37215503 PMCID: PMC10192557 DOI: 10.3389/fncir.2023.1172464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 03/30/2023] [Indexed: 05/24/2023] Open
Abstract
Cortical inhibitory interneurons form a broad spectrum of subtypes. This diversity suggests a division of labor, in which each cell type supports a distinct function. In the present era of optimisation-based algorithms, it is tempting to speculate that these functions were the evolutionary or developmental driving force for the spectrum of interneurons we see in the mature mammalian brain. In this study, we evaluated this hypothesis using the two most common interneuron types, parvalbumin (PV) and somatostatin (SST) expressing cells, as examples. PV and SST interneurons control the activity in the cell bodies and the apical dendrites of excitatory pyramidal cells, respectively, due to a combination of anatomical and synaptic properties. But was this compartment-specific inhibition indeed the function for which PV and SST cells originally evolved? Does the compartmental structure of pyramidal cells shape the diversification of PV and SST interneurons over development? To address these questions, we reviewed and reanalyzed publicly available data on the development and evolution of PV and SST interneurons on one hand, and pyramidal cell morphology on the other. These data speak against the idea that the compartment structure of pyramidal cells drove the diversification into PV and SST interneurons. In particular, pyramidal cells mature late, while interneurons are likely committed to a particular fate (PV vs. SST) during early development. Moreover, comparative anatomy and single cell RNA-sequencing data indicate that PV and SST cells, but not the compartment structure of pyramidal cells, existed in the last common ancestor of mammals and reptiles. Specifically, turtle and songbird SST cells also express the Elfn1 and Cbln4 genes that are thought to play a role in compartment-specific inhibition in mammals. PV and SST cells therefore evolved and developed the properties that allow them to provide compartment-specific inhibition before there was selective pressure for this function. This suggest that interneuron diversity originally resulted from a different evolutionary driving force and was only later co-opted for the compartment-specific inhibition it seems to serve in mammals today. Future experiments could further test this idea using our computational reconstruction of ancestral Elfn1 protein sequences.
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Affiliation(s)
- Joram Keijser
- Modelling of Cognitive Processes, Technical University of Berlin, Berlin, Germany
- Einstein Center for Neurosciences, Charité University Medicine Berlin, Berlin, Germany
| | - Henning Sprekeler
- Modelling of Cognitive Processes, Technical University of Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Humboldt University of Berlin, Berlin, Germany
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6
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Moll FW, Kranz D, Corredera Asensio A, Elmaleh M, Ackert-Smith LA, Long MA. Thalamus drives vocal onsets in the zebra finch courtship song. Nature 2023; 616:132-136. [PMID: 36949189 DOI: 10.1038/s41586-023-05818-x] [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: 06/04/2022] [Accepted: 02/09/2023] [Indexed: 03/24/2023]
Abstract
While motor cortical circuits contain information related to specific movement parameters1, long-range inputs also have a critical role in action execution2,3. Thalamic projections can shape premotor activity2-6 and have been suggested7 to mediate the selection of short, stereotyped actions comprising more complex behaviours8. However, the mechanisms by which thalamus interacts with motor cortical circuits to execute such movement sequences remain unknown. Here we find that thalamic drive engages a specific subpopulation of premotor neurons within the zebra finch song nucleus HVC (proper name) and that these inputs are critical for the progression between vocal motor elements (that is, 'syllables'). In vivo two-photon imaging of thalamic axons in HVC showed robust song-related activity, and online perturbations of thalamic function caused song to be truncated at syllable boundaries. We used thalamic stimulation to identify a sparse set of thalamically driven neurons within HVC, representing ~15% of the premotor neurons within that network. Unexpectedly, this population of putative thalamorecipient neurons is robustly active immediately preceding syllable onset, leading to the possibility that thalamic input can initiate individual song components through selectively targeting these 'starter cells'. Our findings highlight the motor thalamus as a director of cortical dynamics in the context of an ethologically relevant behavioural sequence.
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Affiliation(s)
- Felix W Moll
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY, USA
- Center for Neural Science, New York University, New York, NY, USA
- Animal Physiology, Institute of Neurobiology, University of Tübingen, Tübingen, Germany
| | - Devorah Kranz
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY, USA
- Center for Neural Science, New York University, New York, NY, USA
| | - Ariadna Corredera Asensio
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY, USA
- Center for Neural Science, New York University, New York, NY, USA
| | - Margot Elmaleh
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY, USA
- Center for Neural Science, New York University, New York, NY, USA
| | - Lyn A Ackert-Smith
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY, USA
- Center for Neural Science, New York University, New York, NY, USA
| | - Michael A Long
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY, USA.
- Center for Neural Science, New York University, New York, NY, USA.
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7
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Wilson A, Babadi M. SynapseCLR: Uncovering features of synapses in primary visual cortex through contrastive representation learning. PATTERNS 2023; 4:100693. [PMID: 37123442 PMCID: PMC10140600 DOI: 10.1016/j.patter.2023.100693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 11/23/2022] [Accepted: 01/25/2023] [Indexed: 03/09/2023]
Abstract
3D electron microscopy (EM) connectomics image volumes are surpassing 1 mm3, providing information-dense, multi-scale visualizations of brain circuitry and necessitating scalable analysis techniques. We present SynapseCLR, a self-supervised contrastive learning method for 3D EM data, and use it to extract features of synapses from mouse visual cortex. SynapseCLR feature representations separate synapses by appearance and functionally important structural annotations. We demonstrate SynapseCLR's utility for valuable downstream tasks, including one-shot identification of defective synapse segmentations, dataset-wide similarity-based querying, and accurate imputation of annotations for unlabeled synapses, using manual annotation of only 0.2% of the dataset's synapses. In particular, excitatory versus inhibitory neuronal types can be assigned with >99.8% accuracy to individual synapses and highly truncated neurites, enabling neurite-enhanced connectomics analysis. Finally, we present a data-driven, unsupervised study of synaptic structural variation on the representation manifold, revealing its intrinsic axes of variation and showing that representations contain inhibitory subtype information.
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8
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Fetterman GC, Margoliash D. Rhythmically bursting songbird vocomotor neurons are organized into multiple sequences, suggesting a network/intrinsic properties model encoding song and error, not time. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.23.525213. [PMID: 36747673 PMCID: PMC9900798 DOI: 10.1101/2023.01.23.525213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
In zebra finch, basal ganglia projecting "HVC X " neurons emit one or more spike bursts during each song motif (canonical sequence of syllables), which are thought to be driven in part by a process of spike rebound excitation. Zebra finch songs are highly stereotyped and recent results indicate that the intrinsic properties of HVC X neurons are similar within each bird, vary among birds depending on similarity of the songs, and vary with song errors. We tested the hypothesis that the timing of spike bursts during singing also evince individual-specific distributions. Examining previously published data, we demonstrated that the intervals between bursts of multibursting HVC X are similar for neurons within each bird, in many cases highly clustered at distinct peaks, with the patterns varying among birds. The fixed delay between bursts and different times when neurons are first recruited in the song yields precisely timed multiple sequences of bursts throughout the song, not the previously envisioned single sequence of bursts treated as events having statistically independent timing. A given moment in time engages multiple sequences and both single bursting and multibursting HVC X simultaneously. This suggests a model where a population of HVC X sharing common intrinsic properties driving spike rebound excitation influence the timing of a given HVC X burst through lateral inhibitory interactions. Perturbations in burst timing, representing error, could propagate in time. Our results extend the concept of central pattern generators to complex vertebrate vocal learning and suggest that network activity (timing of inhibition) and HVC X intrinsic properties become coordinated during developmental birdsong learning.
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9
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Sheridan A, Nguyen TM, Deb D, Lee WCA, Saalfeld S, Turaga SC, Manor U, Funke J. Local shape descriptors for neuron segmentation. Nat Methods 2023; 20:295-303. [PMID: 36585455 PMCID: PMC9911350 DOI: 10.1038/s41592-022-01711-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 11/01/2022] [Indexed: 12/31/2022]
Abstract
We present an auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a study comparing several existing methods across various specimen, imaging techniques, and resolutions, auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (flood-filling networks), while being two orders of magnitudes more efficient-a critical requirement for the processing of future petabyte-sized datasets.
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Affiliation(s)
- Arlo Sheridan
- grid.443970.dHHMI Janelia, Ashburn, VA USA ,grid.250671.70000 0001 0662 7144Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA USA
| | - Tri M. Nguyen
- grid.38142.3c000000041936754XDepartment of Neurobiology, Harvard Medical School, Boston, MA USA
| | | | - Wei-Chung Allen Lee
- grid.38142.3c000000041936754XF.M. Kirby Neurobiology Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA USA
| | | | | | - Uri Manor
- grid.250671.70000 0001 0662 7144Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA USA
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10
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Chequer Charan D, Hua Y, Wang H, Huang W, Wang F, Elgoyhen AB, Boergens KM, Di Guilmi MN. Volume electron microscopy reveals age-related circuit remodeling in the auditory brainstem. Front Cell Neurosci 2022; 16:1070438. [PMID: 36589288 PMCID: PMC9799098 DOI: 10.3389/fncel.2022.1070438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 11/21/2022] [Indexed: 12/23/2022] Open
Abstract
The medial nucleus of the trapezoid body (MNTB) is an integral component of the auditory brainstem circuitry involved in sound localization. The giant presynaptic nerve terminal with multiple active zones, the calyx of Held (CH), is a hallmark of this nucleus, which mediates fast and synchronized glutamatergic synaptic transmission. To delineate how these synaptic structures adapt to reduced auditory afferents due to aging, we acquired and reconstructed circuitry-level volumes of mouse MNTB at different ages (3 weeks, 6, 18, and 24 months) using serial block-face electron microscopy. We used C57BL/6J, the most widely inbred mouse strain used for transgenic lines, which displays a type of age-related hearing loss. We found that MNTB neurons reduce in density with age. Surprisingly we observed an average of approximately 10% of poly-innervated MNTB neurons along the mouse lifespan, with prevalence in the low frequency region. Moreover, a tonotopy-dependent heterogeneity in CH morphology was observed in young but not in older mice. In conclusion, our data support the notion that age-related hearing impairments can be in part a direct consequence of several structural alterations and circuit remodeling in the brainstem.
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Affiliation(s)
- Daniela Chequer Charan
- Instituto de Investigaciones en Ingeniería Genética y Biología Molecular, Dr. Héctor N. Torres, INGEBI-CONICET, Buenos Aires, Argentina
| | - Yunfeng Hua
- Shanghai Institute of Precision Medicine, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Haoyu Wang
- Shanghai Institute of Precision Medicine, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenqing Huang
- Shanghai Institute of Precision Medicine, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Fangfang Wang
- Shanghai Institute of Precision Medicine, Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ana Belén Elgoyhen
- Instituto de Investigaciones en Ingeniería Genética y Biología Molecular, Dr. Héctor N. Torres, INGEBI-CONICET, Buenos Aires, Argentina
| | - Kevin M. Boergens
- Department of Physics, The University of Illinois at Chicago, Chicago, IL, United States,*Correspondence: Kevin M. Boergens Mariano N. Di Guilmi
| | - Mariano N. Di Guilmi
- Instituto de Investigaciones en Ingeniería Genética y Biología Molecular, Dr. Héctor N. Torres, INGEBI-CONICET, Buenos Aires, Argentina,*Correspondence: Kevin M. Boergens Mariano N. Di Guilmi
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11
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Connectomic analysis of thalamus-driven disinhibition in cortical layer 4. Cell Rep 2022; 41:111476. [DOI: 10.1016/j.celrep.2022.111476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 07/29/2022] [Accepted: 09/19/2022] [Indexed: 11/18/2022] Open
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12
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Kadmon Harpaz N, Hardcastle K, Ölveczky BP. Learning-induced changes in the neural circuits underlying motor sequence execution. Curr Opin Neurobiol 2022; 76:102624. [PMID: 36030613 PMCID: PMC11125547 DOI: 10.1016/j.conb.2022.102624] [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: 03/21/2022] [Revised: 06/02/2022] [Accepted: 07/19/2022] [Indexed: 11/03/2022]
Abstract
As the old adage goes: practice makes perfect. Yet, the neural mechanisms by which rote repetition transforms a halting behavior into a fluid, effortless, and "automatic" action are not well understood. Here we consider the possibility that well-practiced motor sequences, which initially rely on higher-level decision-making circuits, become wholly specified in lower-level control circuits. We review studies informing this idea, discuss the constraints on such shift in control, and suggest approaches to pinpoint circuit-level changes associated with motor sequence learning.
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Affiliation(s)
- Naama Kadmon Harpaz
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University. https://twitter.com/@NKadmonHarpaz
| | - Kiah Hardcastle
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University. https://twitter.com/@kiahhardcastle
| | - Bence P Ölveczky
- Department of Organismic and Evolutionary Biology and Center for Brain Science, Harvard University.
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13
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Matejek B, Franzmeyer T, Wei D, Wang X, Zhao J, Palagyi K, Lichtman JW, Pfister H. Scalable Biologically-Aware Skeleton Generation for Connectomic Volumes. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2360-2370. [PMID: 35377840 DOI: 10.1109/tmi.2022.3164343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
As connectomic datasets exceed hundreds of terabytes in size, accurate and efficient skeleton generation of the label volumes has evolved into a critical component of the computation pipeline used for analysis, evaluation, visualization, and error correction. We propose a novel topological thinning strategy that uses biological-constraints to produce accurate centerlines from segmented neuronal volumes while still maintaining biologically relevant properties. Current methods are either agnostic to the underlying biology, have non-linear running times as a function of the number of input voxels, or both. First, we eliminate from the input segmentation biologically-infeasible bubbles, pockets of voxels incorrectly labeled within a neuron, to improve segmentation accuracy, allow for more accurate centerlines, and increase processing speed. Next, a Convolutional Neural Network (CNN) detects cell bodies from the input segmentation, allowing us to anchor our skeletons to the somata. Lastly, a synapse-aware topological thinning approach produces expressive skeletons for each neuron with a nearly one-to-one correspondence between endpoints and synapses. We simultaneously estimate geometric properties of neurite width and geodesic distance between synapse and cell body, improving accuracy by 47.5% and 62.8% over baseline methods. We separate the skeletonization process into a series of computation steps, leveraging data-parallel strategies to increase throughput significantly. We demonstrate our results on over 1250 neurons and neuron fragments from three different species, processing over one million voxels per second per CPU with linear scalability.
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14
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Matejek B, Wei D, Chen T, Tsourakakis CE, Mitzenmacher M, Pfister H. Edge-colored directed subgraph enumeration on the connectome. Sci Rep 2022; 12:11349. [PMID: 35790766 PMCID: PMC9256670 DOI: 10.1038/s41598-022-15027-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 06/16/2022] [Indexed: 11/24/2022] Open
Abstract
Following significant advances in image acquisition, synapse detection, and neuronal segmentation in connectomics, researchers have extracted an increasingly diverse set of wiring diagrams from brain tissue. Neuroscientists frequently represent these wiring diagrams as graphs with nodes corresponding to a single neuron and edges indicating synaptic connectivity. The edges can contain "colors" or "labels", indicating excitatory versus inhibitory connections, among other things. By representing the wiring diagram as a graph, we can begin to identify motifs, the frequently occurring subgraphs that correspond to specific biological functions. Most analyses on these wiring diagrams have focused on hypothesized motifs-those we expect to find. However, one of the goals of connectomics is to identify biologically-significant motifs that we did not previously hypothesize. To identify these structures, we need large-scale subgraph enumeration to find the frequencies of all unique motifs. Exact subgraph enumeration is a computationally expensive task, particularly in the edge-dense wiring diagrams. Furthermore, most existing methods do not differentiate between types of edges which can significantly affect the function of a motif. We propose a parallel, general-purpose subgraph enumeration strategy to count motifs in the connectome. Next, we introduce a divide-and-conquer community-based subgraph enumeration strategy that allows for enumeration per brain region. Lastly, we allow for differentiation of edges by types to better reflect the underlying biological properties of the graph. We demonstrate our results on eleven connectomes and publish for future analyses extensive overviews for the 26 trillion subgraphs enumerated that required approximately 9.25 years of computation time.
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Affiliation(s)
- Brian Matejek
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
- Computer Science Laboratory, SRI International, Washington, DC, USA.
| | - Donglai Wei
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Department of Computer Science, Boston College, Chestnut Hill, MA, USA
| | - Tianyi Chen
- Department of Computer Science, Boston University, Boston, MA, USA
| | - Charalampos E Tsourakakis
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Department of Computer Science, Boston University, Boston, MA, USA
- ISI Foundation, Turin, Italy
| | - Michael Mitzenmacher
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Hanspeter Pfister
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
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15
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Loomba S, Straehle J, Gangadharan V, Heike N, Khalifa A, Motta A, Ju N, Sievers M, Gempt J, Meyer HS, Helmstaedter M. Connectomic comparison of mouse and human cortex. Science 2022; 377:eabo0924. [PMID: 35737810 DOI: 10.1126/science.abo0924] [Citation(s) in RCA: 56] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The human cerebral cortex houses 1,000 times more neurons than the cerebral cortex of a mouse, but the possible differences in synaptic circuits between these species are still poorly understood. We used 3-dimensional electron microscopy of mouse, macaque and human cortical samples to study their cell type composition and synaptic circuit architecture. The 2.5-fold increase in interneurons in humans compared to mouse was compensated by a change in axonal connection probabilities and therefore did not yield a commensurate increase in inhibitory-vs-excitatory synaptic input balance on human pyramidal cells. Rather, increased inhibition created an expanded interneuron-to-interneuron network, driven by an expansion of interneuron-targeting interneuron types and an increase in their synaptic selectivity for interneuron innervation. These constitute key neuronal network alterations in human cortex.
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Affiliation(s)
- Sahil Loomba
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany.,Faculty of Science, Radboud University, Nijmegen, Netherlands
| | - Jakob Straehle
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Vijayan Gangadharan
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Natalie Heike
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Abdelrahman Khalifa
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Alessandro Motta
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Niansheng Ju
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Meike Sievers
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany.,Faculty of Science, Radboud University, Nijmegen, Netherlands
| | - Jens Gempt
- Department of Neurosurgery, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Germany
| | - Hanno S Meyer
- Department of Neurosurgery, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Germany
| | - Moritz Helmstaedter
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
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16
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A feedforward inhibitory premotor circuit for auditory-vocal interactions in zebra finches. Proc Natl Acad Sci U S A 2022; 119:e2118448119. [PMID: 35658073 PMCID: PMC9191632 DOI: 10.1073/pnas.2118448119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Significance During conversations, we frequently alternate between listening and speaking. This involves withholding responses while the other person is vocalizing and rapidly initiating a reply once they stop. Similar exchanges also occur in other animals, such as songbirds, yet little is known about how brain areas responsible for vocal production are influenced by areas dedicated to listening. Here, we combined neural recordings and mathematical modeling of a sensorimotor circuit to show that input-dependent inhibition can both suppress vocal responses and regulate the onset latencies of vocalizations. Our resulting model provides a simple generalizable circuit mechanism by which inhibition precisely times vocal output and integrates auditory input within a premotor nucleus.
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17
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Biswas T, Fitzgerald JE. Geometric framework to predict structure from function in neural networks. PHYSICAL REVIEW RESEARCH 2022; 4:023255. [PMID: 37635906 PMCID: PMC10456994 DOI: 10.1103/physrevresearch.4.023255] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
Neural computation in biological and artificial networks relies on the nonlinear summation of many inputs. The structural connectivity matrix of synaptic weights between neurons is a critical determinant of overall network function, but quantitative links between neural network structure and function are complex and subtle. For example, many networks can give rise to similar functional responses, and the same network can function differently depending on context. Whether certain patterns of synaptic connectivity are required to generate specific network-level computations is largely unknown. Here we introduce a geometric framework for identifying synaptic connections required by steady-state responses in recurrent networks of threshold-linear neurons. Assuming that the number of specified response patterns does not exceed the number of input synapses, we analytically calculate the solution space of all feedforward and recurrent connectivity matrices that can generate the specified responses from the network inputs. A generalization accounting for noise further reveals that the solution space geometry can undergo topological transitions as the allowed error increases, which could provide insight into both neuroscience and machine learning. We ultimately use this geometric characterization to derive certainty conditions guaranteeing a nonzero synapse between neurons. Our theoretical framework could thus be applied to neural activity data to make rigorous anatomical predictions that follow generally from the model architecture.
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Affiliation(s)
- Tirthabir Biswas
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia 20147, USA
- Department of Physics, Loyola University, New Orleans, Louisiana 70118, USA
| | - James E. Fitzgerald
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia 20147, USA
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18
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Elmaleh M, Kranz D, Asensio AC, Moll FW, Long MA. Sleep replay reveals premotor circuit structure for a skilled behavior. Neuron 2021; 109:3851-3861.e4. [PMID: 34626537 DOI: 10.1016/j.neuron.2021.09.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Revised: 08/12/2021] [Accepted: 09/13/2021] [Indexed: 10/20/2022]
Abstract
Neural circuits often exhibit sequences of activity, but the contribution of local networks to their generation remains unclear. In the zebra finch, song-related premotor sequences within HVC may result from some combination of local connectivity and long-range thalamic inputs from nucleus uvaeformis (Uva). Because lesions to either structure abolish song, we examine "sleep replay" using high-density recording methods to reconstruct precise song-related events. Replay activity persists after the upstream nucleus interfacialis of the nidopallium is lesioned and slows when HVC is cooled, demonstrating that HVC provides temporal structure for these events. To further gauge the importance of intra-HVC connectivity for shaping network dynamics, we lesion Uva during sleep and find that residual replay sequences could span syllable boundaries, supporting a model in which HVC can propagate sequences throughout the duration of the song. Our results highlight the power of studying offline activity to investigate behaviorally relevant circuit organization.
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Affiliation(s)
- Margot Elmaleh
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA
| | - Devorah Kranz
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA
| | - Ariadna Corredera Asensio
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA
| | - Felix W Moll
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA
| | - Michael A Long
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA.
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19
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Egger R, Tupikov Y, Elmaleh M, Katlowitz KA, Benezra SE, Picardo MA, Moll F, Kornfeld J, Jin DZ, Long MA. Local Axonal Conduction Shapes the Spatiotemporal Properties of Neural Sequences. Cell 2021; 183:537-548.e12. [PMID: 33064989 DOI: 10.1016/j.cell.2020.09.019] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 08/07/2020] [Accepted: 09/04/2020] [Indexed: 12/30/2022]
Abstract
Sequential activation of neurons has been observed during various behavioral and cognitive processes, but the underlying circuit mechanisms remain poorly understood. Here, we investigate premotor sequences in HVC (proper name) of the adult zebra finch forebrain that are central to the performance of the temporally precise courtship song. We use high-density silicon probes to measure song-related population activity, and we compare these observations with predictions from a range of network models. Our results support a circuit architecture in which heterogeneous delays between sequentially active neurons shape the spatiotemporal patterns of HVC premotor neuron activity. We gauge the impact of several delay sources, and we find the primary contributor to be slow conduction through axonal collaterals within HVC, which typically adds between 1 and 7.5 ms for each link within the sequence. Thus, local axonal "delay lines" can play an important role in determining the dynamical repertoire of neural circuits.
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Affiliation(s)
- Robert Egger
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA
| | - Yevhen Tupikov
- Department of Physics and Center for Neural Engineering, Pennsylvania State University, University Park, PA 16802, USA
| | - Margot Elmaleh
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA
| | - Kalman A Katlowitz
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA
| | - Sam E Benezra
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA
| | - Michel A Picardo
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA
| | - Felix Moll
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA
| | - Jörgen Kornfeld
- Max Planck Institute of Neurobiology, 82152 Martinsried, Germany
| | - Dezhe Z Jin
- Department of Physics and Center for Neural Engineering, Pennsylvania State University, University Park, PA 16802, USA
| | - Michael A Long
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York, NY 10016, USA; Center for Neural Science, New York University, New York, NY 10003, USA.
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20
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Friedrich RW, Wanner AA. Dense Circuit Reconstruction to Understand Neuronal Computation: Focus on Zebrafish. Annu Rev Neurosci 2021; 44:275-293. [PMID: 33730512 DOI: 10.1146/annurev-neuro-110220-013050] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The dense reconstruction of neuronal wiring diagrams from volumetric electron microscopy data has the potential to generate fundamentally new insights into mechanisms of information processing and storage in neuronal circuits. Zebrafish provide unique opportunities for dynamical connectomics approaches that combine reconstructions of wiring diagrams with measurements of neuronal population activity and behavior. Such approaches have the power to reveal higher-order structure in wiring diagrams that cannot be detected by sparse sampling of connectivity and that is essential for neuronal computations. In the brain stem, recurrently connected neuronal modules were identified that can account for slow, low-dimensional dynamics in an integrator circuit. In the spinal cord, connectivity specifies functional differences between premotor interneurons. In the olfactory bulb, tuning-dependent connectivity implements a whitening transformation that is based on the selective suppression of responses to overrepresented stimulus features. These findings illustrate the potential of dynamical connectomics in zebrafish to analyze the circuit mechanisms underlying higher-order neuronal computations.
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Affiliation(s)
- Rainer W Friedrich
- Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland; .,Faculty of Natural Sciences, University of Basel, 4003 Basel, Switzerland
| | - Adrian A Wanner
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544, USA;
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21
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Tupikov Y, Jin DZ. Addition of new neurons and the emergence of a local neural circuit for precise timing. PLoS Comput Biol 2021; 17:e1008824. [PMID: 33730085 PMCID: PMC8007041 DOI: 10.1371/journal.pcbi.1008824] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 03/29/2021] [Accepted: 02/19/2021] [Indexed: 11/28/2022] Open
Abstract
During development, neurons arrive at local brain areas in an extended period of time, but how they form local neural circuits is unknown. Here we computationally model the emergence of a network for precise timing in the premotor nucleus HVC in songbird. We show that new projection neurons, added to HVC post hatch at early stages of song development, are recruited to the end of a growing feedforward network. High spontaneous activity of the new neurons makes them the prime targets for recruitment in a self-organized process via synaptic plasticity. Once recruited, the new neurons fire readily at precise times, and they become mature. Neurons that are not recruited become silent and replaced by new immature neurons. Our model incorporates realistic HVC features such as interneurons, spatial distributions of neurons, and distributed axonal delays. The model predicts that the birth order of the projection neurons correlates with their burst timing during the song. Functions of local neural circuits depend on their specific network structures, but how the networks are wired is unknown. We show that such structures can emerge during development through a self-organized process, during which the network is wired by neuron-by-neuron recruitment. This growth is facilitated by steady supply of immature neurons, which are highly excitable and plastic. We suggest that neuron maturation dynamics is an integral part of constructing local neural circuits.
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Affiliation(s)
- Yevhen Tupikov
- Departments of Physics and Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Dezhe Z. Jin
- Departments of Physics and Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, Pennsylvania, United States of America
- * E-mail:
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22
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Daou A, Margoliash D. Intrinsic plasticity and birdsong learning. Neurobiol Learn Mem 2021; 180:107407. [PMID: 33631346 DOI: 10.1016/j.nlm.2021.107407] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 10/28/2020] [Accepted: 02/11/2021] [Indexed: 10/22/2022]
Abstract
Although information processing and storage in the brain is thought to be primarily orchestrated by synaptic plasticity, other neural mechanisms such as intrinsic plasticity are available. While a number of recent studies have described the plasticity of intrinsic excitability in several types of neurons, the significance of non-synaptic mechanisms in memory and learning remains elusive. After reviewing plasticity of intrinsic excitation in relation to learning and homeostatic mechanisms, we focus on the intrinsic properties of a class of basal-ganglia projecting song system neurons in zebra finch, how these related to each bird's unique learned song, how these properties change over development, and how they are maintained dynamically to rapidly change in response to auditory feedback perturbations. We place these results in the broader theme of learning and changes in intrinsic properties, emphasizing the computational implications of this form of plasticity, which are distinct from synaptic plasticity. The results suggest that exploring reciprocal interactions between intrinsic and network properties will be a fruitful avenue for understanding mechanisms of birdsong learning.
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Affiliation(s)
- Arij Daou
- University of Chicago, United States
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23
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Phelps JS, Hildebrand DGC, Graham BJ, Kuan AT, Thomas LA, Nguyen TM, Buhmann J, Azevedo AW, Sustar A, Agrawal S, Liu M, Shanny BL, Funke J, Tuthill JC, Lee WCA. Reconstruction of motor control circuits in adult Drosophila using automated transmission electron microscopy. Cell 2021; 184:759-774.e18. [PMID: 33400916 PMCID: PMC8312698 DOI: 10.1016/j.cell.2020.12.013] [Citation(s) in RCA: 74] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 09/17/2020] [Accepted: 12/09/2020] [Indexed: 02/08/2023]
Abstract
To investigate circuit mechanisms underlying locomotor behavior, we used serial-section electron microscopy (EM) to acquire a synapse-resolution dataset containing the ventral nerve cord (VNC) of an adult female Drosophila melanogaster. To generate this dataset, we developed GridTape, a technology that combines automated serial-section collection with automated high-throughput transmission EM. Using this dataset, we studied neuronal networks that control leg and wing movements by reconstructing all 507 motor neurons that control the limbs. We show that a specific class of leg sensory neurons synapses directly onto motor neurons with the largest-caliber axons on both sides of the body, representing a unique pathway for fast limb control. We provide open access to the dataset and reconstructions registered to a standard atlas to permit matching of cells between EM and light microscopy data. We also provide GridTape instrumentation designs and software to make large-scale EM more accessible and affordable to the scientific community.
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Affiliation(s)
- Jasper S Phelps
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA; Program in Neuroscience, Division of Medical Sciences, Graduate School of Arts and Sciences, Harvard University, Cambridge, MA 02138, USA
| | - David Grant Colburn Hildebrand
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA; Program in Neuroscience, Division of Medical Sciences, Graduate School of Arts and Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Brett J Graham
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Aaron T Kuan
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Logan A Thomas
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Tri M Nguyen
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Julia Buhmann
- HHMI Janelia Research Campus, Ashburn, VA 20147, USA
| | - Anthony W Azevedo
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA
| | - Anne Sustar
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA
| | - Sweta Agrawal
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA
| | - Mingguan Liu
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Brendan L Shanny
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
| | - Jan Funke
- HHMI Janelia Research Campus, Ashburn, VA 20147, USA
| | - John C Tuthill
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA
| | - Wei-Chung Allen Lee
- F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.
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24
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Düring DN, Dittrich F, Rocha MD, Tachibana RO, Mori C, Okanoya K, Boehringer R, Ehret B, Grewe BF, Gerber S, Ma S, Rauch M, Paterna JC, Kasper R, Gahr M, Hahnloser RHR. Fast Retrograde Access to Projection Neuron Circuits Underlying Vocal Learning in Songbirds. Cell Rep 2020; 33:108364. [PMID: 33176132 PMCID: PMC8236207 DOI: 10.1016/j.celrep.2020.108364] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 07/29/2020] [Accepted: 10/19/2020] [Indexed: 02/06/2023] Open
Abstract
Understanding the structure and function of neural circuits underlying speech and language is a vital step toward better treatments for diseases of these systems. Songbirds, among the few animal orders that share with humans the ability to learn vocalizations from a conspecific, have provided many insights into the neural mechanisms of vocal development. However, research into vocal learning circuits has been hindered by a lack of tools for rapid genetic targeting of specific neuron populations to meet the quick pace of developmental learning. Here, we present a viral tool that enables fast and efficient retrograde access to projection neuron populations. In zebra finches, Bengalese finches, canaries, and mice, we demonstrate fast retrograde labeling of cortical or dopaminergic neurons. We further demonstrate the suitability of our construct for detailed morphological analysis, for in vivo imaging of calcium activity, and for multi-color brainbow labeling. Düring et al. describe a fast and efficient viral vector to dissect structure and function of neural circuits underlying learned vocalizations in songbirds. The AAV variant provides retrograde access to projection neuron circuits, including dopaminergic pathways in songbirds and additionally in mice, and allows for retrograde calcium imaging and multispectral brainbow labeling.
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Affiliation(s)
- Daniel N Düring
- Institute of Neuroinformatics, University of Zurich/ETH Zurich, Zurich, Switzerland; Neuroscience Center Zurich, Zurich, Switzerland; Department of Behavioural Neurobiology, Max Planck Institute for Ornithology, Seewiesen, Germany.
| | - Falk Dittrich
- Department of Behavioural Neurobiology, Max Planck Institute for Ornithology, Seewiesen, Germany
| | - Mariana D Rocha
- Department of Behavioural Neurobiology, Max Planck Institute for Ornithology, Seewiesen, Germany
| | | | - Chihiro Mori
- Department of Life Sciences, The University of Tokyo, Tokyo, Japan
| | - Kazuo Okanoya
- Department of Life Sciences, The University of Tokyo, Tokyo, Japan
| | - Roman Boehringer
- Institute of Neuroinformatics, University of Zurich/ETH Zurich, Zurich, Switzerland; Neuroscience Center Zurich, Zurich, Switzerland
| | - Benjamin Ehret
- Institute of Neuroinformatics, University of Zurich/ETH Zurich, Zurich, Switzerland; Neuroscience Center Zurich, Zurich, Switzerland
| | - Benjamin F Grewe
- Institute of Neuroinformatics, University of Zurich/ETH Zurich, Zurich, Switzerland; Neuroscience Center Zurich, Zurich, Switzerland
| | - Stefan Gerber
- Institute of Neuroinformatics, University of Zurich/ETH Zurich, Zurich, Switzerland
| | - Shouwen Ma
- Department of Behavioural Neurobiology, Max Planck Institute for Ornithology, Seewiesen, Germany
| | - Melanie Rauch
- Viral Vector Facility, Neuroscience Center Zurich, Zurich, Switzerland
| | | | - Robert Kasper
- Imaging Facility at the Max Planck Institute of Neurobiology, Munich, Germany
| | - Manfred Gahr
- Department of Behavioural Neurobiology, Max Planck Institute for Ornithology, Seewiesen, Germany
| | - Richard H R Hahnloser
- Institute of Neuroinformatics, University of Zurich/ETH Zurich, Zurich, Switzerland; Neuroscience Center Zurich, Zurich, Switzerland
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25
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Yin W, Brittain D, Borseth J, Scott ME, Williams D, Perkins J, Own CS, Murfitt M, Torres RM, Kapner D, Mahalingam G, Bleckert A, Castelli D, Reid D, Lee WCA, Graham BJ, Takeno M, Bumbarger DJ, Farrell C, Reid RC, da Costa NM. A petascale automated imaging pipeline for mapping neuronal circuits with high-throughput transmission electron microscopy. Nat Commun 2020; 11:4949. [PMID: 33009388 PMCID: PMC7532165 DOI: 10.1038/s41467-020-18659-3] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 08/28/2020] [Indexed: 11/18/2022] Open
Abstract
Electron microscopy (EM) is widely used for studying cellular structure and network connectivity in the brain. We have built a parallel imaging pipeline using transmission electron microscopes that scales this technology, implements 24/7 continuous autonomous imaging, and enables the acquisition of petascale datasets. The suitability of this architecture for large-scale imaging was demonstrated by acquiring a volume of more than 1 mm3 of mouse neocortex, spanning four different visual areas at synaptic resolution, in less than 6 months. Over 26,500 ultrathin tissue sections from the same block were imaged, yielding a dataset of more than 2 petabytes. The combined burst acquisition rate of the pipeline is 3 Gpixel per sec and the net rate is 600 Mpixel per sec with six microscopes running in parallel. This work demonstrates the feasibility of acquiring EM datasets at the scale of cortical microcircuits in multiple brain regions and species.
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26
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Ma S, Ter Maat A, Gahr M. Neurotelemetry Reveals Putative Predictive Activity in HVC during Call-Based Vocal Communications in Zebra Finches. J Neurosci 2020; 40:6219-6227. [PMID: 32661023 PMCID: PMC7406282 DOI: 10.1523/jneurosci.2664-19.2020] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 05/22/2020] [Accepted: 06/11/2020] [Indexed: 01/19/2023] Open
Abstract
Premotor predictions facilitate vocal interactions. Here, we study such mechanisms in the forebrain nucleus HVC (proper name), a cortex-like sensorimotor area of songbirds, otherwise known for being essential for singing in zebra finches. To study the role of the HVC in calling interactions between male and female mates, we used wireless telemetric systems for simultaneous measurement of neuronal activity of male zebra finches and vocalizations of males and females that freely interact with each other. In a non-social context, male HVC neurons displayed stereotypic premotor activity in relation to active calling and showed auditory-evoked activity to hearing of played-back female calls. In a social context, HVC neurons displayed auditory-evoked activity to hearing of female calls only if that neuron showed activity preceding the upcoming female calls. We hypothesize that this activity preceding the auditory-evoked activity in the male HVC represents a neural correlate of behavioral anticipation, predictive activity that helps to coordinate vocal communication between social partners.SIGNIFICANCE STATEMENT Most social-living vertebrates produce large numbers of calls per day, and the calls have prominent roles in social interactions. Here, we show neuronal mechanisms that are active during call-based vocal communication of zebra finches, a highly social songbird species. HVC, a forebrain nucleus known for its importance in control of learned vocalizations of songbirds, displays predictive activity that may enable the male to adjust his own calling pattern to produce very fast sequences of male female call exchanges.
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Affiliation(s)
- Shouwen Ma
- Department of Behavioural Neurobiology, Max Planck Institute for Ornithology, 82319, Seewiesen, Germany
| | - Andries Ter Maat
- Department of Behavioural Neurobiology, Max Planck Institute for Ornithology, 82319, Seewiesen, Germany
| | - Manfred Gahr
- Department of Behavioural Neurobiology, Max Planck Institute for Ornithology, 82319, Seewiesen, Germany
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27
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Network dynamics underlie learning and performance of birdsong. Curr Opin Neurobiol 2020; 64:119-126. [PMID: 32480313 DOI: 10.1016/j.conb.2020.04.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 04/10/2020] [Accepted: 04/13/2020] [Indexed: 01/01/2023]
Abstract
Understanding the sensorimotor control of the endless variety of human speech patterns stands as one of the apex problems in neuroscience. The capacity to learn - through imitation - to rapidly sequence vocal sounds in meaningful patterns is clearly one of the most derived of human behavioral traits. Selection pressure produced an analogous capacity in numerous species of vocal-learning birds, and due to an increasing appreciation for the cognitive and computational flexibility of avian cortex and basal ganglia, a general understanding of the forebrain network that supports the learning and production of birdsong is beginning to emerge. Here, we review recent advances in experimental studies of the zebra finch (Taeniopygia guttata), which offer new insights into the network dynamics that support this surprising analogue of human speech learning and production.
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28
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Zhang N, Guo L, Simpson JH. Spatial Comparisons of Mechanosensory Information Govern the Grooming Sequence in Drosophila. Curr Biol 2020; 30:988-1001.e4. [PMID: 32142695 PMCID: PMC7184881 DOI: 10.1016/j.cub.2020.01.045] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2019] [Revised: 12/17/2019] [Accepted: 01/14/2020] [Indexed: 01/28/2023]
Abstract
Animals integrate information from different sensory modalities, body parts, and time points to inform behavioral choice, but the relevant sensory comparisons and the underlying neural circuits are still largely unknown. We use the grooming behavior of Drosophila melanogaster as a model to investigate the sensory comparisons that govern a motor sequence. Flies perform grooming movements spontaneously, but when covered with dust, they clean their bodies following an anterior-to-posterior sequence. After investigating different sensory modalities that could detect dust, we focus on mechanosensory bristle neurons, whose optogenetic activation induces a similar sequence. Computational modeling predicts that higher sensory input strength to the head will cause anterior grooming to occur first. We test this prediction using an optogenetic competition assay whereby two targeted light beams independently activate mechanosensory bristle neurons on different body parts. We find that the initial choice of grooming movement is determined by the ratio of sensory inputs to different body parts. In dust-covered flies, sensory inputs change as a result of successful cleaning movements. Simulations from our model suggest that this change results in sequence progression. One possibility is that flies perform frequent comparisons between anterior and posterior sensory inputs, and the changing ratios drive different behavior choices. Alternatively, flies may track the temporal change in sensory input to a given body part to measure cleaning effectiveness. The first hypothesis is supported by our optogenetic competition experiments: iterative spatial comparisons of sensory inputs between body parts is essential for organizing grooming movements in sequence. Zhang et al. find that Drosophila covered with dust compare sensory inputs from mechanosensory bristles on different body parts during grooming. The ratio of anterior:posterior sensory input and its dynamics, rather than the rate of dust removal from the anterior, drives the anterior-to-posterior grooming sequence.
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Affiliation(s)
- Neil Zhang
- Department of Molecular, Cellular, and Developmental Biology and Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Li Guo
- Department of Molecular, Cellular, and Developmental Biology and Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, CA 93106, USA
| | - Julie H Simpson
- Department of Molecular, Cellular, and Developmental Biology and Neuroscience Research Institute, University of California, Santa Barbara, Santa Barbara, CA 93106, USA.
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29
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Isola GR, Vochin A, Sakata JT. Manipulations of inhibition in cortical circuitry differentially affect spectral and temporal features of Bengalese finch song. J Neurophysiol 2020; 123:815-830. [PMID: 31967928 DOI: 10.1152/jn.00142.2019] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
The interplay between inhibition and excitation can regulate behavioral expression and control, including the expression of communicative behaviors like birdsong. Computational models postulate varying degrees to which inhibition within vocal motor circuitry influences birdsong, but few studies have tested these models by manipulating inhibition. Here we enhanced and attenuated inhibition in the cortical nucleus HVC (used as proper name) of Bengalese finches (Lonchura striata var. domestica). Enhancement of inhibition (with muscimol) in HVC dose-dependently reduced the amount of song produced. Infusions of higher concentrations of muscimol caused some birds to produce spectrally degraded songs, whereas infusions of lower doses of muscimol led to the production of relatively normal (nondegraded) songs. However, the spectral and temporal structures of these nondegraded songs were significantly different from songs produced under control conditions. In particular, muscimol infusions decreased the frequency and amplitude of syllables, increased various measures of acoustic entropy, and increased the variability of syllable structure. Muscimol also increased sequence durations and the variability of syllable timing and syllable sequencing. Attenuation of inhibition (with bicuculline) in HVC led to changes to song distinct from and often opposite to enhancing inhibition. For example, in contrast to muscimol, bicuculline infusions increased syllable amplitude, frequency, and duration and decreased the variability of acoustic features. However, like muscimol, bicuculline increased the variability of syllable sequencing. These data highlight the importance of inhibition to the production of stereotyped vocalizations and demonstrate that changes to neural dynamics within cortical circuitry can differentially affect spectral and temporal features of song.NEW & NOTEWORTHY We reveal that manipulations of inhibition in the cortical nucleus HVC affect the structure, timing, and sequencing of syllables in Bengalese finch song. Enhancing and blocking inhibition led to opposite changes to the acoustic structure and timing of vocalizations, but both caused similar changes to vocal sequencing. These data provide support for computational models of song control but also motivate refinement of existing models to account for differential effects on syllable structure, timing, and sequencing.
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Affiliation(s)
- Gaurav R Isola
- Department of Biology, McGill University, Montreal, Quebec, Canada
| | - Anca Vochin
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Jon T Sakata
- Department of Biology, McGill University, Montreal, Quebec, Canada.,Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada.,Centre for Research on Brain, Language, and Music, Montreal, Quebec, Canada.,Center for Studies in Behavioral Neurobiology, Montreal, Quebec, Canada
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30
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Benichov JI, Vallentin D. Inhibition within a premotor circuit controls the timing of vocal turn-taking in zebra finches. Nat Commun 2020; 11:221. [PMID: 31924758 PMCID: PMC6954284 DOI: 10.1038/s41467-019-13938-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 12/10/2019] [Indexed: 12/19/2022] Open
Abstract
Vocal turn-taking is a fundamental organizing principle of human conversation but the neural circuit mechanisms that structure coordinated vocal interactions are unknown. The ability to exchange vocalizations in an alternating fashion is also exhibited by other species, including zebra finches. With a combination of behavioral testing, electrophysiological recordings, and pharmacological manipulations we demonstrate that activity within a cortical premotor nucleus orchestrates the timing of calls in socially interacting zebra finches. Within this circuit, local inhibition precedes premotor neuron activation associated with calling. Blocking inhibition results in faster vocal responses as well as an impaired ability to flexibly avoid overlapping with a partner. These results support a working model in which premotor inhibition regulates context-dependent timing of vocalizations and enables the precise interleaving of vocal signals during turn-taking. Control over when to initiate or withhold vocalizations is essential for vocal turn-taking. Here the authors investigate vocal interactions in zebra finches and show that inhibition within the premotor nucleus HVC plays an important role in the precise timing of vocal motor responses.
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Affiliation(s)
- Jonathan I Benichov
- Institute of Animal Behavior, Freie Universität Berlin, Takustraße 6, 14195, Berlin, Germany.,Neural Circuits for Vocal Communication, Max Planck Institute for Ornithology, Eberhard-Gwinner-Straße, 82319, Seewiesen, Germany
| | - Daniela Vallentin
- Institute of Animal Behavior, Freie Universität Berlin, Takustraße 6, 14195, Berlin, Germany. .,Neural Circuits for Vocal Communication, Max Planck Institute for Ornithology, Eberhard-Gwinner-Straße, 82319, Seewiesen, Germany.
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31
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32
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Fluorescence-Based Quantitative Synapse Analysis for Cell Type-Specific Connectomics. eNeuro 2019; 6:ENEURO.0193-19.2019. [PMID: 31548370 PMCID: PMC6873163 DOI: 10.1523/eneuro.0193-19.2019] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Revised: 08/08/2019] [Accepted: 09/11/2019] [Indexed: 12/20/2022] Open
Abstract
Anatomical methods for determining cell type-specific connectivity are essential to inspire and constrain our understanding of neural circuit function. We developed genetically-encoded reagents for fluorescence-synapse labeling and connectivity analysis in brain tissue, using a fluorogen-activating protein (FAP)-coupled or YFP-coupled, postsynaptically-localized neuroligin-1 (NL-1) targeting sequence (FAP/YFPpost). FAPpost expression did not alter mEPSC or mIPSC properties. Sparse AAV-mediated expression of FAP/YFPpost with the cell-filling, red fluorophore dTomato (dTom) enabled high-throughput, compartment-specific detection of putative synapses across diverse neuron types in mouse somatosensory cortex. We took advantage of the bright, far-red emission of FAPpost puncta for multichannel fluorescence alignment of dendrites, FAPpost puncta, and presynaptic neurites in transgenic mice with saturated labeling of parvalbumin (PV), somatostatin (SST), or vasoactive intestinal peptide (VIP)-expressing neurons using Cre-reporter driven expression of YFP. Subtype-specific inhibitory connectivity onto layer 2/3 (L2/3) neocortical pyramidal (Pyr) neurons was assessed using automated puncta detection and neurite apposition. Quantitative and compartment-specific comparisons show that PV inputs are the predominant source of inhibition at both the soma and the dendrites and were particularly concentrated at the primary apical dendrite. SST inputs were interleaved with PV inputs at all secondary-order and higher-order dendritic branches. These fluorescence-based synapse labeling reagents can facilitate large-scale and cell-type specific quantitation of changes in synaptic connectivity across development, learning, and disease states.
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33
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Motta A, Berning M, Boergens KM, Staffler B, Beining M, Loomba S, Hennig P, Wissler H, Helmstaedter M. Dense connectomic reconstruction in layer 4 of the somatosensory cortex. Science 2019; 366:science.aay3134. [PMID: 31649140 DOI: 10.1126/science.aay3134] [Citation(s) in RCA: 130] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 10/11/2019] [Indexed: 12/11/2022]
Abstract
The dense circuit structure of mammalian cerebral cortex is still unknown. With developments in three-dimensional electron microscopy, the imaging of sizable volumes of neuropil has become possible, but dense reconstruction of connectomes is the limiting step. We reconstructed a volume of ~500,000 cubic micrometers from layer 4 of mouse barrel cortex, ~300 times larger than previous dense reconstructions from the mammalian cerebral cortex. The connectomic data allowed the extraction of inhibitory and excitatory neuron subtypes that were not predictable from geometric information. We quantified connectomic imprints consistent with Hebbian synaptic weight adaptation, which yielded upper bounds for the fraction of the circuit consistent with saturated long-term potentiation. These data establish an approach for the locally dense connectomic phenotyping of neuronal circuitry in the mammalian cortex.
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Affiliation(s)
- Alessandro Motta
- Department of Connectomics, Max Planck Institute for Brain Research, D-60438 Frankfurt, Germany
| | - Manuel Berning
- Department of Connectomics, Max Planck Institute for Brain Research, D-60438 Frankfurt, Germany
| | - Kevin M Boergens
- Department of Connectomics, Max Planck Institute for Brain Research, D-60438 Frankfurt, Germany
| | - Benedikt Staffler
- Department of Connectomics, Max Planck Institute for Brain Research, D-60438 Frankfurt, Germany
| | - Marcel Beining
- Department of Connectomics, Max Planck Institute for Brain Research, D-60438 Frankfurt, Germany
| | - Sahil Loomba
- Department of Connectomics, Max Planck Institute for Brain Research, D-60438 Frankfurt, Germany
| | - Philipp Hennig
- Probabilistic Numerics Group, Max Planck Institute for Intelligent Systems, D-72076 Tübingen, Germany
| | - Heiko Wissler
- Department of Connectomics, Max Planck Institute for Brain Research, D-60438 Frankfurt, Germany
| | - Moritz Helmstaedter
- Department of Connectomics, Max Planck Institute for Brain Research, D-60438 Frankfurt, Germany.
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34
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Schubert PJ, Dorkenwald S, Januszewski M, Jain V, Kornfeld J. Learning cellular morphology with neural networks. Nat Commun 2019; 10:2736. [PMID: 31227718 PMCID: PMC6588634 DOI: 10.1038/s41467-019-10836-3] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2018] [Accepted: 05/30/2019] [Indexed: 01/10/2023] Open
Abstract
Reconstruction and annotation of volume electron microscopy data sets of brain tissue is challenging but can reveal invaluable information about neuronal circuits. Significant progress has recently been made in automated neuron reconstruction as well as automated detection of synapses. However, methods for automating the morphological analysis of nanometer-resolution reconstructions are less established, despite the diversity of possible applications. Here, we introduce cellular morphology neural networks (CMNs), based on multi-view projections sampled from automatically reconstructed cellular fragments of arbitrary size and shape. Using unsupervised training, we infer morphology embeddings (Neuron2vec) of neuron reconstructions and train CMNs to identify glia cells in a supervised classification paradigm, which are then used to resolve neuron reconstruction errors. Finally, we demonstrate that CMNs can be used to identify subcellular compartments and the cell types of neuron reconstructions.
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Affiliation(s)
- Philipp J Schubert
- Max Planck Institute of Neurobiology, Electrons - Photons - Neurons, 82152, Planegg-Martinsried, Germany.
| | - Sven Dorkenwald
- Max Planck Institute of Neurobiology, Electrons - Photons - Neurons, 82152, Planegg-Martinsried, Germany
| | | | - Viren Jain
- Google AI, Mountain View, 94043, CA, USA
| | - Joergen Kornfeld
- Max Planck Institute of Neurobiology, Electrons - Photons - Neurons, 82152, Planegg-Martinsried, Germany.
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35
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Vellema M, Diales Rocha M, Bascones S, Zsebők S, Dreier J, Leitner S, Van der Linden A, Brewer J, Gahr M. Accelerated redevelopment of vocal skills is preceded by lasting reorganization of the song motor circuitry. eLife 2019; 8:43194. [PMID: 31099755 PMCID: PMC6570526 DOI: 10.7554/elife.43194] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Accepted: 05/16/2019] [Indexed: 01/16/2023] Open
Abstract
Complex motor skills take considerable time and practice to learn. Without continued practice the level of skill performance quickly degrades, posing a problem for the timely utilization of skilled motor behaviors. Here we quantified the recurring development of vocal motor skills and the accompanying changes in synaptic connectivity in the brain of a songbird, while manipulating skill performance by consecutively administrating and withdrawing testosterone. We demonstrate that a songbird with prior singing experience can significantly accelerate the re-acquisition of vocal performance. We further demonstrate that an increase in vocal performance is accompanied by a pronounced synaptic pruning in the forebrain vocal motor area HVC, a reduction that is not reversed when birds stop singing. These results provide evidence that lasting synaptic changes in the motor circuitry are associated with the savings of motor skills, enabling a rapid recovery of motor performance under environmental time constraints.
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Affiliation(s)
- Michiel Vellema
- Department of Behavioural Neurobiology, Max Planck Institute for Ornithology, Seewiesen, Germany.,Bio Imaging Lab, University of Antwerp, Antwerp, Belgium
| | - Mariana Diales Rocha
- Department of Behavioural Neurobiology, Max Planck Institute for Ornithology, Seewiesen, Germany
| | - Sabrina Bascones
- Program for Inflammatory and Cardiovascular Disorders, Institut Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain
| | - Sándor Zsebők
- Behavioural Ecology Group, Department of Systematic Zoology and Ecology, Eötvös Loránd University, Budapest, Hungary
| | - Jes Dreier
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
| | - Stefan Leitner
- Department of Behavioural Neurobiology, Max Planck Institute for Ornithology, Seewiesen, Germany
| | | | - Jonathan Brewer
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
| | - Manfred Gahr
- Department of Behavioural Neurobiology, Max Planck Institute for Ornithology, Seewiesen, Germany
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36
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Big data in nanoscale connectomics, and the greed for training labels. Curr Opin Neurobiol 2019; 55:180-187. [PMID: 31055238 DOI: 10.1016/j.conb.2019.03.012] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Revised: 03/26/2019] [Accepted: 03/30/2019] [Indexed: 01/08/2023]
Abstract
The neurosciences have developed methods that outpace most other biomedical fields in terms of acquired bytes. We review how the information content and analysis challenge of such data indicates that electron microscopy (EM)-based connectomics is an especially hard problem. Here, as in many other current machine learning applications, the need for excessive amounts of labelled data while utilizing only a small fraction of available raw image data for algorithm training illustrates the still fundamental gap between artificial and biological intelligence. Substantial improvements of label and energy efficiency in machine learning may be required to address the formidable challenge of acquiring the nanoscale connectome of a human brain.
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37
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Rocha MD, Düring DN, Bethge P, Voigt FF, Hildebrand S, Helmchen F, Pfeifer A, Hahnloser RHR, Gahr M. Tissue Clearing and Light Sheet Microscopy: Imaging the Unsectioned Adult Zebra Finch Brain at Cellular Resolution. Front Neuroanat 2019; 13:13. [PMID: 30837847 PMCID: PMC6382697 DOI: 10.3389/fnana.2019.00013] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Accepted: 01/28/2019] [Indexed: 12/28/2022] Open
Abstract
The inherent complexity of brain tissue, with brain cells intertwining locally and projecting to distant regions, has made three-dimensional visualization of intact brains a highly desirable but challenging task in neuroscience. The natural opaqueness of tissue has traditionally limited researchers to techniques short of single cell resolution such as computer tomography or magnetic resonance imaging. By contrast, techniques with single-cell resolution required mechanical slicing into thin sections, which entails tissue distortions that severely hinder accurate reconstruction of large volumes. Recent developments in tissue clearing and light sheet microscopy have made it possible to investigate large volumes at micrometer resolution. The value of tissue clearing has been shown in a variety of tissue types and animal models. However, its potential for examining the songbird brain remains unexplored. Songbirds are an established model system for the study of vocal learning and sensorimotor control. They share with humans the capacity to adapt vocalizations based on auditory input. Song learning and production are controlled in songbirds by the song system, which forms a network of interconnected discrete brain nuclei. Here, we use the CUBIC and iDISCO+ protocols for clearing adult songbird brain tissue. Combined with light sheet imaging, we show the potential of tissue clearing for the investigation of connectivity between song nuclei, as well as for neuroanatomy and brain vasculature studies.
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Affiliation(s)
- Mariana Diales Rocha
- Department of Behavioral Neurobiology, Max Planck Institute for Ornithology, Seewiesen, Germany
| | - Daniel Normen Düring
- Department of Behavioral Neurobiology, Max Planck Institute for Ornithology, Seewiesen, Germany.,Institute of Neuroinformatics, University of Zurich/ETH Zurich, Zurich, Switzerland.,Neuroscience Center Zurich (ZNZ), Zurich, Switzerland
| | - Philipp Bethge
- Neuroscience Center Zurich (ZNZ), Zurich, Switzerland.,Brain Research Institute, University of Zurich, Zurich, Switzerland
| | - Fabian F Voigt
- Neuroscience Center Zurich (ZNZ), Zurich, Switzerland.,Brain Research Institute, University of Zurich, Zurich, Switzerland
| | - Staffan Hildebrand
- Institute of Pharmacology and Toxicology, University Hospital Bonn, University of Bonn, Bonn, Germany
| | - Fritjof Helmchen
- Neuroscience Center Zurich (ZNZ), Zurich, Switzerland.,Brain Research Institute, University of Zurich, Zurich, Switzerland
| | - Alexander Pfeifer
- Institute of Pharmacology and Toxicology, University Hospital Bonn, University of Bonn, Bonn, Germany
| | - Richard Hans Robert Hahnloser
- Institute of Neuroinformatics, University of Zurich/ETH Zurich, Zurich, Switzerland.,Neuroscience Center Zurich (ZNZ), Zurich, Switzerland
| | - Manfred Gahr
- Department of Behavioral Neurobiology, Max Planck Institute for Ornithology, Seewiesen, Germany
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38
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Miller CT, Hale ME, Okano H, Okabe S, Mitra P. Comparative Principles for Next-Generation Neuroscience. Front Behav Neurosci 2019; 13:12. [PMID: 30787871 PMCID: PMC6373779 DOI: 10.3389/fnbeh.2019.00012] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Accepted: 01/15/2019] [Indexed: 01/10/2023] Open
Abstract
Neuroscience is enjoying a renaissance of discovery due in large part to the implementation of next-generation molecular technologies. The advent of genetically encoded tools has complemented existing methods and provided researchers the opportunity to examine the nervous system with unprecedented precision and to reveal facets of neural function at multiple scales. The weight of these discoveries, however, has been technique-driven from a small number of species amenable to the most advanced gene-editing technologies. To deepen interpretation and build on these breakthroughs, an understanding of nervous system evolution and diversity are critical. Evolutionary change integrates advantageous variants of features into lineages, but is also constrained by pre-existing organization and function. Ultimately, each species’ neural architecture comprises both properties that are species-specific and those that are retained and shared. Understanding the evolutionary history of a nervous system provides interpretive power when examining relationships between brain structure and function. The exceptional diversity of nervous systems and their unique or unusual features can also be leveraged to advance research by providing opportunities to ask new questions and interpret findings that are not accessible in individual species. As new genetic and molecular technologies are added to the experimental toolkits utilized in diverse taxa, the field is at a key juncture to revisit the significance of evolutionary and comparative approaches for next-generation neuroscience as a foundational framework for understanding fundamental principles of neural function.
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Affiliation(s)
- Cory T Miller
- Cortical Systems and Behavior Laboratory, Neurosciences Graduate Program, University of California, San Diego, San Diego, CA, United States
| | - Melina E Hale
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, United States
| | - Hideyuki Okano
- Department of Physiology, Keio University School of Medicine, Tokyo, Japan.,Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science (CBS), Wako, Japan
| | - Shigeo Okabe
- Department of Cellular Neurobiology, Graduate School of Medicine and Faculty of Medicine, University of Tokyo, Tokyo, Japan
| | - Partha Mitra
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, United States
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39
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Düring DN, Rocha MD, Dittrich F, Gahr M, Hahnloser RHR. Expansion Light Sheet Microscopy Resolves Subcellular Structures in Large Portions of the Songbird Brain. Front Neuroanat 2019; 13:2. [PMID: 30766480 PMCID: PMC6365838 DOI: 10.3389/fnana.2019.00002] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Accepted: 01/11/2019] [Indexed: 12/24/2022] Open
Abstract
Expansion microscopy and light sheet imaging (ExLSM) provide a viable alternative to existing tissue clearing and large volume imaging approaches. The analysis of intact volumes of brain tissue presents a distinct challenge in neuroscience. Recent advances in tissue clearing and light sheet microscopy have re-addressed this challenge and blossomed into a plethora of protocols with diverse advantages and disadvantages. While refractive index matching achieves near perfect transparency and allows for imaging at large depths, the resolution of cleared brains is usually limited to the micrometer range. Moreover, the often long and harsh tissue clearing protocols hinder preservation of native fluorescence and antigenicity. Here we image large expanded brain volumes of zebra finch brain tissue in commercially available light sheet microscopes. Our expansion light sheet microscopy (ExLSM) approach presents a viable alternative to many clearing and imaging methods because it improves on tissue processing times, fluorophore compatibility, and image resolution.
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Affiliation(s)
- Daniel Normen Düring
- Institute of Neuroinformatics, University of Zürich/ETH Zürich, Zurich, Switzerland
- Neuroscience Center Zurich (ZNZ), Zurich, Switzerland
- Department of Behavioral Neurobiology, Max Planck Institute for Ornithology, Seewiesen, Germany
| | - Mariana Diales Rocha
- Department of Behavioral Neurobiology, Max Planck Institute for Ornithology, Seewiesen, Germany
| | - Falk Dittrich
- Department of Behavioral Neurobiology, Max Planck Institute for Ornithology, Seewiesen, Germany
| | - Manfred Gahr
- Department of Behavioral Neurobiology, Max Planck Institute for Ornithology, Seewiesen, Germany
| | - Richard Hans Robert Hahnloser
- Institute of Neuroinformatics, University of Zürich/ETH Zürich, Zurich, Switzerland
- Neuroscience Center Zurich (ZNZ), Zurich, Switzerland
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40
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41
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Huang Z, Khaled HG, Kirschmann M, Gobes SM, Hahnloser RH. Excitatory and inhibitory synapse reorganization immediately after critical sensory experience in a vocal learner. eLife 2018; 7:37571. [PMID: 30355450 PMCID: PMC6255392 DOI: 10.7554/elife.37571] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Accepted: 10/24/2018] [Indexed: 11/24/2022] Open
Abstract
Excitatory and inhibitory synapses are the brain’s most abundant synapse types. However, little is known about their formation during critical periods of motor skill learning, when sensory experience defines a motor target that animals strive to imitate. In songbirds, we find that exposure to tutor song leads to elimination of excitatory synapses in HVC (used here as a proper name), a key song generating brain area. A similar pruning is associated with song maturation, because juvenile birds have fewer excitatory synapses, the better their song imitations. In contrast, tutoring is associated with rapid insertion of inhibitory synapses, but the tutoring-induced structural imbalance between excitation and inhibition is eliminated during subsequent song maturation. Our work suggests that sensory exposure triggers the developmental onset of goal-specific motor circuits by increasing the relative strength of inhibition and it suggests a synapse-elimination model of song memorization. A wide range of species use complex sounds to communicate, including humans and songbirds like zebra finches. During a critical period of learning, infants and young animals learn how to remember and discriminate this ‘language’ from other sounds. However, the changes that happen in the brain during this learning period are not well understood. The process of learning forms new connections between neurons in the brain and prunes away old connections. These connections, known as synapses, come in different types. Signals sent across excitatory synapses increase the activity of the receiving neuron, while signals sent across inhibitory synapses reduce neuron activity. What happens to the synapses in the brain during the critical period? To find out, Huang et al. used electron microscopy to examine the brains of young zebra finches that either had never heard birdsong, or had just heard birdsong for the first time. A single day of hearing song dramatically shifted the balance of excitatory and inhibitory synapses in the main vocal control area of the young birds’ brains. The number of excitatory synapses decreased, and the number of inhibitory synapses increased. The balance between excitation and inhibition is important for the brain to work correctly. Therefore, as well as helping us to understand how infants learn their first language, the results presented by Huang et al. could also help us to improve treatments for conditions where this balance goes wrong, such as mood disorders. For example, tailoring the time point of medication intake in combination with sensory exposure therapies could improve how effectively either one works.
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Affiliation(s)
- Ziqiang Huang
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.,Neuroscience Center Zurich, Zurich, Switzerland
| | - Houda G Khaled
- Neuroscience Program, Wellesley College, Wellesley, United States
| | - Moritz Kirschmann
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.,Neuroscience Center Zurich, Zurich, Switzerland.,Center for Microscopy and Image Analysis, University of Zurich, Zurich, Switzerland
| | - Sharon Mh Gobes
- Neuroscience Program, Wellesley College, Wellesley, United States
| | - Richard Hr Hahnloser
- Institute of Neuroinformatics, University of Zurich and ETH Zurich, Zurich, Switzerland.,Neuroscience Center Zurich, Zurich, Switzerland
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42
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Januszewski M, Kornfeld J, Li PH, Pope A, Blakely T, Lindsey L, Maitin-Shepard J, Tyka M, Denk W, Jain V. High-precision automated reconstruction of neurons with flood-filling networks. Nat Methods 2018; 15:605-610. [PMID: 30013046 DOI: 10.1038/s41592-018-0049-4] [Citation(s) in RCA: 135] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Accepted: 05/03/2018] [Indexed: 11/09/2022]
Abstract
Reconstruction of neural circuits from volume electron microscopy data requires the tracing of cells in their entirety, including all their neurites. Automated approaches have been developed for tracing, but their error rates are too high to generate reliable circuit diagrams without extensive human proofreading. We present flood-filling networks, a method for automated segmentation that, similar to most previous efforts, uses convolutional neural networks, but contains in addition a recurrent pathway that allows the iterative optimization and extension of individual neuronal processes. We used flood-filling networks to trace neurons in a dataset obtained by serial block-face electron microscopy of a zebra finch brain. Using our method, we achieved a mean error-free neurite path length of 1.1 mm, and we observed only four mergers in a test set with a path length of 97 mm. The performance of flood-filling networks was an order of magnitude better than that of previous approaches applied to this dataset, although with substantially increased computational costs.
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Affiliation(s)
| | - Jörgen Kornfeld
- Max Planck Institute of Neurobiology, Planegg, Martinsried, Germany
| | | | - Art Pope
- Google AI, Mountain View, CA, USA
| | | | | | | | | | - Winfried Denk
- Max Planck Institute of Neurobiology, Planegg, Martinsried, Germany
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Kornfeld J, Denk W. Progress and remaining challenges in high-throughput volume electron microscopy. Curr Opin Neurobiol 2018; 50:261-267. [PMID: 29758457 DOI: 10.1016/j.conb.2018.04.030] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Revised: 04/27/2018] [Accepted: 04/30/2018] [Indexed: 01/04/2023]
Abstract
Recent advances in the effectiveness of the automatic extraction of neural circuits from volume electron microscopy data have made us more optimistic that the goal of reconstructing the nervous system of an entire adult mammal (or bird) brain can be achieved in the next decade. The progress on the data analysis side-based mostly on variants of convolutional neural networks-has been particularly impressive, but improvements in the quality and spatial extent of published VEM datasets are substantial. Methodologically, the combination of hot-knife sample partitioning and ion milling stands out as a conceptual advance while the multi-beam scanning electron microscope promises to remove the data-acquisition bottleneck.
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Affiliation(s)
- Jörgen Kornfeld
- Max Planck Institute of Neurobiology, Am Klopferspitz 18, 82152 Planegg, Germany.
| | - Winfried Denk
- Max Planck Institute of Neurobiology, Am Klopferspitz 18, 82152 Planegg, Germany
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44
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Benezra SE, Narayanan RT, Egger R, Oberlaender M, Long MA. Morphological characterization of HVC projection neurons in the zebra finch (Taeniopygia guttata). J Comp Neurol 2018; 526:1673-1689. [PMID: 29577283 DOI: 10.1002/cne.24437] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Revised: 02/17/2018] [Accepted: 02/26/2018] [Indexed: 02/03/2023]
Abstract
Singing behavior in the adult male zebra finch is dependent upon the activity of a cortical region known as HVC (proper name). The vast majority of HVC projection neurons send primary axons to either the downstream premotor nucleus RA (robust nucleus of the arcopallium, or primary motor cortex) or Area X (basal ganglia), which play important roles in song production or song learning, respectively. In addition to these long-range outputs, HVC neurons also send local axon collaterals throughout that nucleus. Despite their implications for a range of circuit models, these local processes have never been completely reconstructed. Here, we use in vivo single-neuron Neurobiotin fills to examine 40 projection neurons across 31 birds with somatic positions distributed across HVC. We show that HVC(RA) and HVC(X) neurons have categorically distinct dendritic fields. Additionally, these cell classes send axon collaterals that are either restricted to a small portion of HVC ("local neurons") or broadly distributed throughout the entire nucleus ("broadcast neurons"). Overall, these processes within HVC offer a structural basis for significant local processing underlying behaviorally relevant population activity.
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Affiliation(s)
- Sam E Benezra
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York City, New York
- Center for Neural Science, New York University, New York City, New York
| | - Rajeevan T Narayanan
- Max Planck Group: In Silico Brain Sciences, Center of Advanced European Studies and Research, Bonn, Germany
- Computational Neuroanatomy Group, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Robert Egger
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York City, New York
- Center for Neural Science, New York University, New York City, New York
- Computational Neuroanatomy Group, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Marcel Oberlaender
- Max Planck Group: In Silico Brain Sciences, Center of Advanced European Studies and Research, Bonn, Germany
- Computational Neuroanatomy Group, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Michael A Long
- NYU Neuroscience Institute and Department of Otolaryngology, New York University Langone Medical Center, New York City, New York
- Center for Neural Science, New York University, New York City, New York
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45
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Dima GC, Goldin MA, Mindlin GB. Modeling temperature manipulations in a circular model of birdsong production. PAPERS IN PHYSICS 2018. [DOI: 10.4279/pip.100002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
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46
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Heinrich L, Funke J, Pape C, Nunez-Iglesias J, Saalfeld S. Synaptic Cleft Segmentation in Non-isotropic Volume Electron Microscopy of the Complete Drosophila Brain. MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION – MICCAI 2018 2018. [DOI: 10.1007/978-3-030-00934-2_36] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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47
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Schlegel P, Costa M, Jefferis GS. Learning from connectomics on the fly. CURRENT OPINION IN INSECT SCIENCE 2017; 24:96-105. [PMID: 29208230 DOI: 10.1016/j.cois.2017.09.011] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Revised: 09/18/2017] [Accepted: 09/19/2017] [Indexed: 06/07/2023]
Abstract
Parallels between invertebrates and vertebrates in nervous system development, organisation and circuits are powerful reasons to use insects to study the mechanistic basis of behaviour. The last few years have seen the generation in Drosophila melanogaster of very large light microscopy data sets, genetic driver lines and tools to report or manipulate neural activity. These resources in conjunction with computational tools are enabling large scale characterisation of neuronal types and their functional properties. These are complemented by 3D electron microscopy, providing synaptic resolution data. A whole brain connectome of the fly larva is approaching completion based on manual reconstruction of electron-microscopy data. An adult whole brain dataset is already publicly available and focussed reconstruction is under way, but its 40× greater volume would require ∼500-5000 person-years of manual labour. Nevertheless rapid technical improvements in imaging and especially automated segmentation will likely deliver a complete adult connectome in the next 5 years. To enhance our understanding of the circuit basis of behaviour, light and electron microscopy outputs must be integrated with functional and physiological information into comprehensive databases. We review presently available data, tools and opportunities in Drosophila. We then consider the limits and potential of future progress and how this may impact neuroscience in rich model systems provided by larger insects and vertebrates.
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Affiliation(s)
- Philipp Schlegel
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK.
| | - Gregory Sxe Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge CB2 0QH, UK; Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK.
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48
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Neuronal Intrinsic Physiology Changes During Development of a Learned Behavior. eNeuro 2017; 4:eN-NWR-0297-17. [PMID: 29062887 PMCID: PMC5649544 DOI: 10.1523/eneuro.0297-17.2017] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2017] [Accepted: 09/07/2017] [Indexed: 01/14/2023] Open
Abstract
Juvenile male zebra finches learn their songs over distinct auditory and sensorimotor stages, the former requiring exposure to an adult tutor song pattern. The cortical premotor nucleus HVC (acronym is name) plays a necessary role in both learning stages, as well as the production of adult song. Consistent with neural network models where synaptic plasticity mediates developmental forms of learning, exposure to tutor song drives changes in the turnover, density, and morphology of HVC synapses during vocal development. A network's output, however, is also influenced by the intrinsic properties (e.g., ion channels) of the component neurons, which could change over development. Here, we use patch clamp recordings to show cell-type-specific changes in the intrinsic physiology of HVC projection neurons as a function of vocal development. Developmental changes in HVC neurons that project to the basal ganglia include an increased voltage sag response to hyperpolarizing currents and an increased rebound depolarization following hyperpolarization. Developmental changes in HVC neurons that project to vocal-motor cortex include a decreased resting membrane potential and an increased spike amplitude. HVC interneurons, however, show a relatively stable range of intrinsic features across vocal development. We used mathematical models to deduce possible changes in ionic currents that underlie the physiological changes and to show that the magnitude of the observed changes could alter HVC circuit function. The results demonstrate developmental plasticity in the intrinsic physiology of HVC projection neurons and suggest that intrinsic plasticity may have a role in the process of song learning.
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Shigemoto R, Joesch M. The genetic encoded toolbox for electron microscopy and connectomics. WILEY INTERDISCIPLINARY REVIEWS-DEVELOPMENTAL BIOLOGY 2017; 6. [DOI: 10.1002/wdev.288] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Revised: 07/02/2017] [Accepted: 07/05/2017] [Indexed: 11/08/2022]
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