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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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52
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A set of hub neurons and non-local connectivity features support global brain dynamics in C. elegans. Curr Biol 2022; 32:3443-3459.e8. [PMID: 35809568 DOI: 10.1016/j.cub.2022.06.039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 05/17/2022] [Accepted: 06/13/2022] [Indexed: 11/20/2022]
Abstract
The wiring architecture of neuronal networks is assumed to be a strong determinant of their dynamical computations. An ongoing effort in neuroscience is therefore to generate comprehensive synapse-resolution connectomes alongside brain-wide activity maps. However, the structure-function relationship, i.e., how the anatomical connectome and neuronal dynamics relate to each other on a global scale, remains unsolved. Systematically, comparing graph features in the C. elegans connectome with correlations in nervous system-wide neuronal dynamics, we found that few local connectivity motifs and mostly other non-local features such as triplet motifs and input similarities can predict functional relationships between neurons. Surprisingly, quantities such as connection strength and amount of common inputs do not improve these predictions, suggesting that the network's topology is sufficient. We demonstrate that hub neurons in the connectome are key to these relevant graph features. Consistently, inhibition of multiple hub neurons specifically disrupts brain-wide correlations. Thus, we propose that a set of hub neurons and non-local connectivity features provide an anatomical substrate for global brain dynamics.
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53
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Vafidis P, Owald D, D'Albis T, Kempter R. Learning accurate path integration in ring attractor models of the head direction system. eLife 2022; 11:e69841. [PMID: 35723252 PMCID: PMC9286743 DOI: 10.7554/elife.69841] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 06/17/2022] [Indexed: 11/13/2022] Open
Abstract
Ring attractor models for angular path integration have received strong experimental support. To function as integrators, head direction circuits require precisely tuned connectivity, but it is currently unknown how such tuning could be achieved. Here, we propose a network model in which a local, biologically plausible learning rule adjusts synaptic efficacies during development, guided by supervisory allothetic cues. Applied to the Drosophila head direction system, the model learns to path-integrate accurately and develops a connectivity strikingly similar to the one reported in experiments. The mature network is a quasi-continuous attractor and reproduces key experiments in which optogenetic stimulation controls the internal representation of heading in flies, and where the network remaps to integrate with different gains in rodents. Our model predicts that path integration requires self-supervised learning during a developmental phase, and proposes a general framework to learn to path-integrate with gain-1 even in architectures that lack the physical topography of a ring.
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Affiliation(s)
- Pantelis Vafidis
- Computation and Neural Systems, California Institute of TechnologyPasadenaUnited States
- Bernstein Center for Computational NeuroscienceBerlinGermany
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu BerlinBerlinGermany
| | - David Owald
- Institute of Neurophysiology, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, and Berlin Institute of HealthBerlinGermany
- NeuroCure, Charité - Universitätsmedizin BerlinBerlinGermany
- Einstein Center for NeurosciencesBerlinGermany
| | - Tiziano D'Albis
- Bernstein Center for Computational NeuroscienceBerlinGermany
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu BerlinBerlinGermany
| | - Richard Kempter
- Bernstein Center for Computational NeuroscienceBerlinGermany
- Institute for Theoretical Biology, Department of Biology, Humboldt-Universität zu BerlinBerlinGermany
- Einstein Center for NeurosciencesBerlinGermany
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54
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Andreas J, Beguš G, Bronstein MM, Diamant R, Delaney D, Gero S, Goldwasser S, Gruber DF, de Haas S, Malkin P, Pavlov N, Payne R, Petri G, Rus D, Sharma P, Tchernov D, Tønnesen P, Torralba A, Vogt D, Wood RJ. Toward understanding the communication in sperm whales. iScience 2022; 25:104393. [PMID: 35663036 PMCID: PMC9160774 DOI: 10.1016/j.isci.2022.104393] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022] Open
Abstract
Machine learning has been advancing dramatically over the past decade. Most strides are human-based applications due to the availability of large-scale datasets; however, opportunities are ripe to apply this technology to more deeply understand non-human communication. We detail a scientific roadmap for advancing the understanding of communication of whales that can be built further upon as a template to decipher other forms of animal and non-human communication. Sperm whales, with their highly developed neuroanatomical features, cognitive abilities, social structures, and discrete click-based encoding make for an excellent model for advanced tools that can be applied to other animals in the future. We outline the key elements required for the collection and processing of massive datasets, detecting basic communication units and language-like higher-level structures, and validating models through interactive playback experiments. The technological capabilities developed by such an undertaking hold potential for cross-applications in broader communities investigating non-human communication and behavioral research.
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Affiliation(s)
- Jacob Andreas
- MIT CSAIL, Cambridge, MA, USA
- Project CETI, New York, NY, USA
| | - Gašper Beguš
- Department of Linguistics, University of California, Berkeley, CA, USA
- Project CETI, New York, NY, USA
| | - Michael M. Bronstein
- Department of Computer Science, University of Oxford, Oxford, UK
- IDSIA, University of Lugano, Lugano, Switzerland
- Twitter, London, UK
- Project CETI, New York, NY, USA
| | - Roee Diamant
- Leon H. Charney School of Marine Sciences, University of Haifa, Haifa, Israel
- Project CETI, New York, NY, USA
| | - Denley Delaney
- Exploration Technology Lab, National Geographic Society, Washington DC, USA
- Project CETI, New York, NY, USA
| | - Shane Gero
- Dominica Sperm Whale Project, Roseau, Commonwealth of Dominica
- Department of Biology, Carleton University, Ottawa, ON, Canada
- Project CETI, New York, NY, USA
| | - Shafi Goldwasser
- Simons Institute for the Theory of Computing, University of California, Berkeley, CA, USA
| | - David F. Gruber
- Department of Natural Sciences, Baruch College and The Graduate Center, PhD Program in Biology, City University of New York, New York, NY, USA
- Project CETI, New York, NY, USA
| | - Sarah de Haas
- Google Research, Mountain View, CA USA
- Project CETI, New York, NY, USA
| | - Peter Malkin
- Google Research, Mountain View, CA USA
- Project CETI, New York, NY, USA
| | | | | | - Giovanni Petri
- ISI Foundation, Turin, Italy
- Project CETI, New York, NY, USA
| | - Daniela Rus
- MIT CSAIL, Cambridge, MA, USA
- Project CETI, New York, NY, USA
| | | | - Dan Tchernov
- Leon H. Charney School of Marine Sciences, University of Haifa, Haifa, Israel
- Project CETI, New York, NY, USA
| | - Pernille Tønnesen
- Marine Bioacoustics Lab, Zoophysiology, Department of Biology, Aarhus University, Aarhus, Denmark
- Project CETI, New York, NY, USA
| | | | - Daniel Vogt
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Project CETI, New York, NY, USA
| | - Robert J. Wood
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Project CETI, New York, NY, USA
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55
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Bosch C, Ackels T, Pacureanu A, Zhang Y, Peddie CJ, Berning M, Rzepka N, Zdora MC, Whiteley I, Storm M, Bonnin A, Rau C, Margrie T, Collinson L, Schaefer AT. Functional and multiscale 3D structural investigation of brain tissue through correlative in vivo physiology, synchrotron microtomography and volume electron microscopy. Nat Commun 2022; 13:2923. [PMID: 35614048 PMCID: PMC9132960 DOI: 10.1038/s41467-022-30199-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 04/19/2022] [Indexed: 12/16/2022] Open
Abstract
Understanding the function of biological tissues requires a coordinated study of physiology and structure, exploring volumes that contain complete functional units at a detail that resolves the relevant features. Here, we introduce an approach to address this challenge: Mouse brain tissue sections containing a region where function was recorded using in vivo 2-photon calcium imaging were stained, dehydrated, resin-embedded and imaged with synchrotron X-ray computed tomography with propagation-based phase contrast (SXRT). SXRT provided context at subcellular detail, and could be followed by targeted acquisition of multiple volumes using serial block-face electron microscopy (SBEM). In the olfactory bulb, combining SXRT and SBEM enabled disambiguation of in vivo-assigned regions of interest. In the hippocampus, we found that superficial pyramidal neurons in CA1a displayed a larger density of spine apparati than deeper ones. Altogether, this approach can enable a functional and structural investigation of subcellular features in the context of cells and tissues.
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Affiliation(s)
- Carles Bosch
- Sensory Circuits and Neurotechnology Lab., The Francis Crick Institute, London, UK.
| | - Tobias Ackels
- Sensory Circuits and Neurotechnology Lab., The Francis Crick Institute, London, UK
- Department of Neuroscience, Physiology and Pharmacology, University College, London, UK
| | - Alexandra Pacureanu
- Sensory Circuits and Neurotechnology Lab., The Francis Crick Institute, London, UK
- Department of Neuroscience, Physiology and Pharmacology, University College, London, UK
- ESRF, The European Synchrotron, Grenoble, France
| | - Yuxin Zhang
- Sensory Circuits and Neurotechnology Lab., The Francis Crick Institute, London, UK
- Department of Neuroscience, Physiology and Pharmacology, University College, London, UK
| | | | - Manuel Berning
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt am Main, Germany
- Scalable minds GmbH, Potsdam, Germany
| | | | - Marie-Christine Zdora
- Department of Physics and Astronomy, University College London, London, UK
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot, UK
- School of Physics and Astronomy, University of Southampton, Highfield Campus, Southampton, UK
| | - Isabell Whiteley
- Sensory Circuits and Neurotechnology Lab., The Francis Crick Institute, London, UK
- Department of Neuroscience, Physiology and Pharmacology, University College, London, UK
| | - Malte Storm
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot, UK
- Institute of Materials Physics, Helmholtz-Zentrum Hereon, Geesthacht, Germany
| | - Anne Bonnin
- Paul Scherrer Institut, Villigen, Switzerland
| | - Christoph Rau
- Diamond Light Source, Harwell Science and Innovation Campus, Didcot, UK
| | - Troy Margrie
- Sainsbury Wellcome Centre, University College London, London, UK
| | - Lucy Collinson
- Electron Microscopy STP, The Francis Crick Institute, London, UK
| | - Andreas T Schaefer
- Sensory Circuits and Neurotechnology Lab., The Francis Crick Institute, London, UK.
- Department of Neuroscience, Physiology and Pharmacology, University College, London, UK.
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56
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Lin A, Witvliet D, Hernandez-Nunez L, Linderman SW, Samuel ADT, Venkatachalam V. Imaging whole-brain activity to understand behavior. NATURE REVIEWS. PHYSICS 2022; 4:292-305. [PMID: 37409001 PMCID: PMC10320740 DOI: 10.1038/s42254-022-00430-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/25/2022] [Indexed: 07/07/2023]
Abstract
The brain evolved to produce behaviors that help an animal inhabit the natural world. During natural behaviors, the brain is engaged in many levels of activity from the detection of sensory inputs to decision-making to motor planning and execution. To date, most brain studies have focused on small numbers of neurons that interact in limited circuits. This allows analyzing individual computations or steps of neural processing. During behavior, however, brain activity must integrate multiple circuits in different brain regions. The activities of different brain regions are not isolated, but may be contingent on one another. Coordinated and concurrent activity within and across brain areas is organized by (1) sensory information from the environment, (2) the animal's internal behavioral state, and (3) recurrent networks of synaptic and non-synaptic connectivity. Whole-brain recording with cellular resolution provides a new opportunity to dissect the neural basis of behavior, but whole-brain activity is also mutually contingent on behavior itself. This is especially true for natural behaviors like navigation, mating, or hunting, which require dynamic interaction between the animal, its environment, and other animals. In such behaviors, the sensory experience of an unrestrained animal is actively shaped by its movements and decisions. Many of the signaling and feedback pathways that an animal uses to guide behavior only occur in freely moving animals. Recent technological advances have enabled whole-brain recording in small behaving animals including nematodes, flies, and zebrafish. These whole-brain experiments capture neural activity with cellular resolution spanning sensory, decision-making, and motor circuits, and thereby demand new theoretical approaches that integrate brain dynamics with behavioral dynamics. Here, we review the experimental and theoretical methods that are being employed to understand animal behavior and whole-brain activity, and the opportunities for physics to contribute to this emerging field of systems neuroscience.
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Affiliation(s)
- Albert Lin
- Department of Physics, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Center for the Physics of Biological Function, Princeton University, Princeton, NJ, USA
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Daniel Witvliet
- Department of Physics, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Luis Hernandez-Nunez
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Scott W Linderman
- Department of Statistics, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Aravinthan D T Samuel
- Department of Physics, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Vivek Venkatachalam
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Physics, Northeastern University, Boston, MA, USA
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57
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Kempermann G. What Is Adult Hippocampal Neurogenesis Good for? Front Neurosci 2022; 16:852680. [PMID: 35495058 PMCID: PMC9051245 DOI: 10.3389/fnins.2022.852680] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 03/17/2022] [Indexed: 12/29/2022] Open
Abstract
Adult hippocampal neurogenesis is a unique and exceptional process in the mammalian brain that in a lifelong and activity-dependent way generates new excitatory principal neurons. A comprehensive view on their function in greater contexts has now emerged, revealing to which extent the hippocampus (and hence brain and mind) depend on these neurons. Due to a postmitotic period of heightened synaptic plasticity they bias incoming excitation to the dentate gyrus to non-overlapping subnetworks, resulting in pattern separation and the avoidance of catastrophic interference. Temporally, this promotes the flexible integration of novel information into familiar contexts and contributes to episodic memory, which in humans would be critical for autobiographic memory. Together these local effects represent a unique strategy to solve the plasticity-stability dilemma that all learning neuronal networks are facing. Neurogenesis-dependent plasticity also improves memory consolidation. This relates to the surprising involvement of adult neurogenesis in forgetting, which is also hypothesized to be critically relevant for negative plasticity, for example in post-traumatic stress disorder. In addition, adult-born neurons also directly mediate stress-resilience and take part in affective behaviors. Finally, the activity- and experience-dependent plasticity that is contributed by adult neurogenesis is associated with an individualization of the hippocampal circuitry. While a solid and largely consensual understanding of how new neurons contribute to hippocampal function has been reached, an overarching unifying theory that embeds neurogenesis-dependent functionality and effects on connectomics is still missing. More sophisticated multi-electrode electrophysiology, advanced ethologically relevant behavioral tests, and next-generation computational modeling will let us take the next steps.
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Affiliation(s)
- Gerd Kempermann
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
- Center for Regenerative Therapies Dresden (CRTD), Technische Universität Dresden, Dresden, Germany
- *Correspondence: Gerd Kempermann, ;
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58
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Chen S, Loper J, Zhou P, Paninski L. Blind demixing methods for recovering dense neuronal morphology from barcode imaging data. PLoS Comput Biol 2022; 18:e1009991. [PMID: 35395020 PMCID: PMC9020678 DOI: 10.1371/journal.pcbi.1009991] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 04/20/2022] [Accepted: 03/07/2022] [Indexed: 11/19/2022] Open
Abstract
Cellular barcoding methods offer the exciting possibility of 'infinite-pseudocolor' anatomical reconstruction-i.e., assigning each neuron its own random unique barcoded 'pseudocolor,' and then using these pseudocolors to trace the microanatomy of each neuron. Here we use simulations, based on densely-reconstructed electron microscopy microanatomy, with signal structure matched to real barcoding data, to quantify the feasibility of this procedure. We develop a new blind demixing approach to recover the barcodes that label each neuron, and validate this method on real data with known barcodes. We also develop a neural network which uses the recovered barcodes to reconstruct the neuronal morphology from the observed fluorescence imaging data, 'connecting the dots' between discontiguous barcode amplicon signals. We find that accurate recovery should be feasible, provided that the barcode signal density is sufficiently high. This study suggests the possibility of mapping the morphology and projection pattern of many individual neurons simultaneously, at high resolution and at large scale, via conventional light microscopy.
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Affiliation(s)
- Shuonan Chen
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
- Department of Systems Biology, Columbia University, New York, New York, United States of America
| | - Jackson Loper
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
- Data Science Institute, Columbia University, New York, New York, United States of America
| | - Pengcheng Zhou
- Faculty of Life and Health Sciences, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Liam Paninski
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, New York, United States of America
- Department of Statistics, Columbia University, New York, New York, United States of America
- Center for Theoretical Neuroscience, Columbia University, New York, New York, United States of America
- Grossman Center for the Statistics of Mind, Columbia University, New York, New York, United States of America
- Department of Neuroscience, Columbia University, New York, New York, United States of America
- Data Science Institute, Columbia University, New York, New York, United States of America
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59
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Turner NL, Macrina T, Bae JA, Yang R, Wilson AM, Schneider-Mizell C, Lee K, Lu R, Wu J, Bodor AL, Bleckert AA, Brittain D, Froudarakis E, Dorkenwald S, Collman F, Kemnitz N, Ih D, Silversmith WM, Zung J, Zlateski A, Tartavull I, Yu SC, Popovych S, Mu S, Wong W, Jordan CS, Castro M, Buchanan J, Bumbarger DJ, Takeno M, Torres R, Mahalingam G, Elabbady L, Li Y, Cobos E, Zhou P, Suckow S, Becker L, Paninski L, Polleux F, Reimer J, Tolias AS, Reid RC, da Costa NM, Seung HS. Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity. Cell 2022; 185:1082-1100.e24. [PMID: 35216674 PMCID: PMC9337909 DOI: 10.1016/j.cell.2022.01.023] [Citation(s) in RCA: 75] [Impact Index Per Article: 37.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 07/26/2021] [Accepted: 01/27/2022] [Indexed: 12/31/2022]
Abstract
We assembled a semi-automated reconstruction of L2/3 mouse primary visual cortex from ∼250 × 140 × 90 μm3 of electron microscopic images, including pyramidal and non-pyramidal neurons, astrocytes, microglia, oligodendrocytes and precursors, pericytes, vasculature, nuclei, mitochondria, and synapses. Visual responses of a subset of pyramidal cells are included. The data are publicly available, along with tools for programmatic and three-dimensional interactive access. Brief vignettes illustrate the breadth of potential applications relating structure to function in cortical circuits and neuronal cell biology. Mitochondria and synapse organization are characterized as a function of path length from the soma. Pyramidal connectivity motif frequencies are predicted accurately using a configuration model of random graphs. Pyramidal cells receiving more connections from nearby cells exhibit stronger and more reliable visual responses. Sample code shows data access and analysis.
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Affiliation(s)
- Nicholas L Turner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - J Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Electrical and Computer Engineering Department, Princeton University, Princeton, NJ 08544, USA
| | - Runzhe Yang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Alyssa M Wilson
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | | | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Agnes L Bodor
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Emmanouil Froudarakis
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | | | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Dodam Ih
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | | | - Jonathan Zung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Aleksandar Zlateski
- Electrical Engineering and Computer Science Department, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ignacio Tartavull
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - William Wong
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Chris S Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - JoAnn Buchanan
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Marc Takeno
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Russel Torres
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Leila Elabbady
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Yang Li
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Erick Cobos
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Pengcheng Zhou
- Department of Statistics, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA
| | - Shelby Suckow
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lynne Becker
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Liam Paninski
- Department of Statistics, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science at Columbia University, New York, NY 10027, USA
| | - Franck Polleux
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science at Columbia University, New York, NY 10027, USA
| | - Jacob Reimer
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA; Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - R Clay Reid
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA.
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60
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Park C, Gim J, Lee S, Lee KJ, Kim JS. Automated Synapse Detection Method for Cerebellar Connectomics. Front Neuroanat 2022; 16:760279. [PMID: 35360651 PMCID: PMC8963724 DOI: 10.3389/fnana.2022.760279] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 02/14/2022] [Indexed: 11/17/2022] Open
Abstract
The connectomic analyses of large-scale volumetric electron microscope (EM) images enable the discovery of hidden neural connectivity. While the technologies for neuronal reconstruction of EM images are under rapid progress, the technologies for synapse detection are lagging behind. Here, we propose a method that automatically detects the synapses in the 3D EM images, specifically for the mouse cerebellar molecular layer (CML). The method aims to accurately detect the synapses between the reconstructed neuronal fragments whose types can be identified. It extracts the contacts between the reconstructed neuronal fragments and classifies them as synaptic or non-synaptic with the help of type information and two deep learning artificial intelligences (AIs). The method can also assign the pre- and postsynaptic sides of a synapse and determine excitatory and inhibitory synapse types. The accuracy of this method is estimated to be 0.955 in F1-score for a test volume of CML containing 508 synapses. To demonstrate the usability, we measured the size and number of the synapses in the volume and investigated the subcellular connectivity between the CML neuronal fragments. The basic idea of the method to exploit tissue-specific properties can be extended to other brain regions.
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Affiliation(s)
- Changjoo Park
- Department of Biological Sciences, Sungkyunkwan University, Suwon-si, South Korea
- Laboratory of Computational Neuroscience, Korea Brain Research Institute, Daegu, South Korea
- Department of Brain and Cognitive Sciences, Daegu Gyeongbuk Institute of Science and Technology, Daegu, South Korea
| | - Jawon Gim
- Laboratory of Computational Neuroscience, Korea Brain Research Institute, Daegu, South Korea
- Laboratory of Synaptic Circuit Plasticity in Neural Circuits Research Group, Korea Brain Research Institute, Daegu, South Korea
| | - Sungjin Lee
- Department of Electrical Engineering and Computer Science, Daegu Gyeongbuk Institute of Science and Technology, Daegu, South Korea
| | - Kea Joo Lee
- Laboratory of Synaptic Circuit Plasticity in Neural Circuits Research Group, Korea Brain Research Institute, Daegu, South Korea
| | - Jinseop S. Kim
- Department of Biological Sciences, Sungkyunkwan University, Suwon-si, South Korea
- Laboratory of Computational Neuroscience, Korea Brain Research Institute, Daegu, South Korea
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61
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Amunts K, DeFelipe J, Pennartz C, Destexhe A, Migliore M, Ryvlin P, Furber S, Knoll A, Bitsch L, Bjaalie JG, Ioannidis Y, Lippert T, Sanchez-Vives MV, Goebel R, Jirsa V. Linking Brain Structure, Activity, and Cognitive Function through Computation. eNeuro 2022; 9:ENEURO.0316-21.2022. [PMID: 35217544 PMCID: PMC8925650 DOI: 10.1523/eneuro.0316-21.2022] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 01/11/2022] [Accepted: 01/17/2022] [Indexed: 01/19/2023] Open
Abstract
Understanding the human brain is a "Grand Challenge" for 21st century research. Computational approaches enable large and complex datasets to be addressed efficiently, supported by artificial neural networks, modeling and simulation. Dynamic generative multiscale models, which enable the investigation of causation across scales and are guided by principles and theories of brain function, are instrumental for linking brain structure and function. An example of a resource enabling such an integrated approach to neuroscientific discovery is the BigBrain, which spatially anchors tissue models and data across different scales and ensures that multiscale models are supported by the data, making the bridge to both basic neuroscience and medicine. Research at the intersection of neuroscience, computing and robotics has the potential to advance neuro-inspired technologies by taking advantage of a growing body of insights into perception, plasticity and learning. To render data, tools and methods, theories, basic principles and concepts interoperable, the Human Brain Project (HBP) has launched EBRAINS, a digital neuroscience research infrastructure, which brings together a transdisciplinary community of researchers united by the quest to understand the brain, with fascinating insights and perspectives for societal benefits.
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Affiliation(s)
- Katrin Amunts
- Institute of Neurosciences and Medicine (INM-1), Research Centre Jülich, Jülich 52425, Germany
- C. & O. Vogt Institute for Brain Research, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf 40225, Germany
| | - Javier DeFelipe
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Madrid 28223, Spain
- Instituto Cajal, Consejo Superior de Investigaciones Científicas (CSIC), Madrid 28002, Spain
| | - Cyriel Pennartz
- Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, 1098 XH, The Netherlands
| | - Alain Destexhe
- Centre National de la Recherche Scientifique, Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Gif sur Yvette 91400, France
| | - Michele Migliore
- Institute of Biophysics, National Research Council, Palermo 90146, Italy
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, Lausanne CH-1011, Switzerland
| | - Steve Furber
- Department of Computer Science, The University of Manchester, Manchester M13 9PL, United Kingdom
| | - Alois Knoll
- Department of Informatics, Technical University of Munich, Garching 385748, Germany
| | - Lise Bitsch
- The Danish Board of Technology Foundation, Copenhagen, 2650 Hvidovre, Denmark
| | - Jan G Bjaalie
- Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Yannis Ioannidis
- ATHENA Research & Innovation Center, Athena 12125, Greece
- Department of Informatics & Telecom, Nat'l and Kapodistrian University of Athens, 157 84 Athens, Greece
| | - Thomas Lippert
- Institute for Advanced Simulation (IAS), Jülich Supercomputing Centre (JSC), Research Centre Jülich, Jülich 52425, Germany
| | - Maria V Sanchez-Vives
- ICREA and Systems Neuroscience, Institute of Biomedical Investigations August Pi i Sunyer, Barcelona 08036, Spain
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht 6229 EV, The Netherlands
| | - Viktor Jirsa
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106, Marseille 13005, France
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62
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Abstract
Synaptic wiring diagrams, or connectomes, promise constraints for highly detailed neural circuit models, but relating the connectivity information they provide to physiological properties is challenging. A new study describes this relationship for a fruit fly neural pathway, suggesting a path forward for future models.
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Affiliation(s)
- Ishani Ganguly
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA
| | - Ashok Litwin-Kumar
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Department of Neuroscience, Columbia University, New York, NY 10027, USA.
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63
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Barsotti E, Correia A, Cardona A. Neural architectures in the light of comparative connectomics. Curr Opin Neurobiol 2021; 71:139-149. [PMID: 34837731 PMCID: PMC8694100 DOI: 10.1016/j.conb.2021.10.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 10/20/2021] [Accepted: 10/26/2021] [Indexed: 01/06/2023]
Abstract
Since the Cambrian, animals diversified from a few body forms or bauplans, into many extinct and all extant species. A characteristic neural architecture serves each bauplan. How the connectome of each animal differs from that of closely related species or whether it converged into an optimal architecture shared with more distant ones is unknown. Recent technological innovations in molecular biology, microscopy, digital data storage and processing, and computational neuroscience have lowered the barriers for whole-brain connectomics. Comparative connectomics of suitable, relatively small, representative species across the phylogenetic tree can infer the archetypal neural architecture of each bauplan and identify any circuits that possibly converged onto a shared and potentially optimal, structure.
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Affiliation(s)
- Elizabeth Barsotti
- MRC Laboratory of Molecular Biology, Cambridge, UK; Department of Physiology, Development and Neuroscience, University of Cambridge, UK
| | - Ana Correia
- MRC Laboratory of Molecular Biology, Cambridge, UK; Department of Physiology, Development and Neuroscience, University of Cambridge, UK
| | - Albert Cardona
- MRC Laboratory of Molecular Biology, Cambridge, UK; Department of Physiology, Development and Neuroscience, University of Cambridge, UK.
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64
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Elam JS, Glasser MF, Harms MP, Sotiropoulos SN, Andersson JLR, Burgess GC, Curtiss SW, Oostenveld R, Larson-Prior LJ, Schoffelen JM, Hodge MR, Cler EA, Marcus DM, Barch DM, Yacoub E, Smith SM, Ugurbil K, Van Essen DC. The Human Connectome Project: A retrospective. Neuroimage 2021; 244:118543. [PMID: 34508893 PMCID: PMC9387634 DOI: 10.1016/j.neuroimage.2021.118543] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 08/13/2021] [Accepted: 08/30/2021] [Indexed: 01/21/2023] Open
Abstract
The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the "WU-Minn-Ox" HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The "HCP-style" neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium.
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Affiliation(s)
| | | | - Michael P Harms
- Washington University School of Medicine, St. Louis, MO, USA
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre & NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, School of Medicine, University of Nottingham, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
| | | | | | | | - Robert Oostenveld
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, the Netherlands
| | | | - Jan-Mathijs Schoffelen
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, the Netherlands
| | - Michael R Hodge
- Washington University School of Medicine, St. Louis, MO, USA
| | - Eileen A Cler
- Washington University School of Medicine, St. Louis, MO, USA
| | - Daniel M Marcus
- Washington University School of Medicine, St. Louis, MO, USA
| | - Deanna M Barch
- Washington University School of Medicine, St. Louis, MO, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
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65
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Hulse BK, Haberkern H, Franconville R, Turner-Evans D, Takemura SY, Wolff T, Noorman M, Dreher M, Dan C, Parekh R, Hermundstad AM, Rubin GM, Jayaraman V. A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection. eLife 2021; 10:e66039. [PMID: 34696823 PMCID: PMC9477501 DOI: 10.7554/elife.66039] [Citation(s) in RCA: 122] [Impact Index Per Article: 40.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 09/07/2021] [Indexed: 11/13/2022] Open
Abstract
Flexible behaviors over long timescales are thought to engage recurrent neural networks in deep brain regions, which are experimentally challenging to study. In insects, recurrent circuit dynamics in a brain region called the central complex (CX) enable directed locomotion, sleep, and context- and experience-dependent spatial navigation. We describe the first complete electron microscopy-based connectome of the Drosophila CX, including all its neurons and circuits at synaptic resolution. We identified new CX neuron types, novel sensory and motor pathways, and network motifs that likely enable the CX to extract the fly's head direction, maintain it with attractor dynamics, and combine it with other sensorimotor information to perform vector-based navigational computations. We also identified numerous pathways that may facilitate the selection of CX-driven behavioral patterns by context and internal state. The CX connectome provides a comprehensive blueprint necessary for a detailed understanding of network dynamics underlying sleep, flexible navigation, and state-dependent action selection.
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Affiliation(s)
- Brad K Hulse
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Hannah Haberkern
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Romain Franconville
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Daniel Turner-Evans
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Shin-ya Takemura
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Tanya Wolff
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Marcella Noorman
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Marisa Dreher
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Chuntao Dan
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Ruchi Parekh
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Ann M Hermundstad
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Gerald M Rubin
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Vivek Jayaraman
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
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66
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Makarova AA, Polilov AA, Chklovskii DB. Small brains for big science. Curr Opin Neurobiol 2021; 71:77-83. [PMID: 34656052 DOI: 10.1016/j.conb.2021.09.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 08/30/2021] [Accepted: 09/06/2021] [Indexed: 01/03/2023]
Abstract
As the study of the human brain is complicated by its sheer scale, complexity, and impracticality of invasive experiments, neuroscience research has long relied on model organisms. The brains of macaque, mouse, zebrafish, fruit fly, nematode, and others have yielded many secrets that advanced our understanding of the human brain. Here, we propose that adding miniature insects to this collection would reduce the costs and accelerate brain research. The smallest insects occupy a special place among miniature animals: despite their body sizes, comparable to unicellular organisms, they retain complex brains that include thousands of neurons. Their brains possess the advantages of those in insects, such as neuronal identifiability and the connectome stereotypy, yet are smaller and hence easier to map and understand. Finally, the brains of miniature insects offer insights into the evolution of brain design.
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Affiliation(s)
| | - Alexey A Polilov
- Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia
| | - Dmitri B Chklovskii
- CCN, Flatiron Institute and Neuroscience Institute, NYUMC, New York, United States.
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67
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Gal E, Amsalem O, Schindel A, London M, Schürmann F, Markram H, Segev I. The Role of Hub Neurons in Modulating Cortical Dynamics. Front Neural Circuits 2021; 15:718270. [PMID: 34630046 PMCID: PMC8500625 DOI: 10.3389/fncir.2021.718270] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 08/24/2021] [Indexed: 12/03/2022] Open
Abstract
Many neurodegenerative diseases are associated with the death of specific neuron types in particular brain regions. What makes the death of specific neuron types particularly harmful for the integrity and dynamics of the respective network is not well understood. To start addressing this question we used the most up-to-date biologically realistic dense neocortical microcircuit (NMC) of the rodent, which has reconstructed a volume of 0.3 mm3 and containing 31,000 neurons, ∼37 million synapses, and 55 morphological cell types arranged in six cortical layers. Using modern network science tools, we identified hub neurons in the NMC, that are connected synaptically to a large number of their neighbors and systematically examined the impact of abolishing these cells. In general, the structural integrity of the network is robust to cells’ attack; yet, attacking hub neurons strongly impacted the small-world topology of the network, whereas similar attacks on random neurons have a negligible effect. Such hub-specific attacks are also impactful on the network dynamics, both when the network is at its spontaneous synchronous state and when it was presented with synchronized thalamo-cortical visual-like input. We found that attacking layer 5 hub neurons is most harmful to the structural and functional integrity of the NMC. The significance of our results for understanding the role of specific neuron types and cortical layers for disease manifestation is discussed.
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Affiliation(s)
- Eyal Gal
- The Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.,Department of Neurobiology, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Oren Amsalem
- Department of Neurobiology, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Alon Schindel
- Department of Neurobiology, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Michael London
- The Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.,Department of Neurobiology, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Felix Schürmann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Idan Segev
- The Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.,Department of Neurobiology, The Hebrew University of Jerusalem, Jerusalem, Israel
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68
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Abstract
[Figure: see text].
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Affiliation(s)
- Liqun Luo
- Department of Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
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69
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Mehta K, Goldin RF, Marchette D, Vogelstein JT, Priebe CE, Ascoli GA. Neuronal classification from network connectivity via adjacency spectral embedding. Netw Neurosci 2021; 5:689-710. [PMID: 34746623 PMCID: PMC8567830 DOI: 10.1162/netn_a_00195] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 04/02/2021] [Indexed: 02/02/2023] Open
Abstract
This work presents a novel strategy for classifying neurons, represented by nodes of a directed graph, based on their circuitry (edge connectivity). We assume a stochastic block model (SBM) in which neurons belong together if they connect to neurons of other groups according to the same probability distributions. Following adjacency spectral embedding of the SBM graph, we derive the number of classes and assign each neuron to a class with a Gaussian mixture model-based expectation maximization (EM) clustering algorithm. To improve accuracy, we introduce a simple variation using random hierarchical agglomerative clustering to initialize the EM algorithm and picking the best solution over multiple EM restarts. We test this procedure on a large (≈212-215 neurons), sparse, biologically inspired connectome with eight neuron classes. The simulation results demonstrate that the proposed approach is broadly stable to the choice of embedding dimension, and scales extremely well as the number of neurons in the network increases. Clustering accuracy is robust to variations in model parameters and highly tolerant to simulated experimental noise, achieving perfect classifications with up to 40% of swapped edges. Thus, this approach may be useful to analyze and interpret large-scale brain connectomics data in terms of underlying cellular components.
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Affiliation(s)
- Ketan Mehta
- Department of Bioengineering and Center for Neural Informatics, Structures, and Plasticity, George Mason University, Fairfax, VA, USA
| | - Rebecca F. Goldin
- Department of Mathematical Sciences and Center for Neural Informatics, Structures, and Plasticity, George Mason University, Fairfax, VA, USA
| | | | - Joshua T. Vogelstein
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
| | - Carey E. Priebe
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
| | - Giorgio A. Ascoli
- Department of Bioengineering and Center for Neural Informatics, Structures, and Plasticity, George Mason University, Fairfax, VA, USA
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70
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Lyamzin DR, Aoki R, Abdolrahmani M, Benucci A. Probabilistic discrimination of relative stimulus features in mice. Proc Natl Acad Sci U S A 2021; 118:e2103952118. [PMID: 34301903 PMCID: PMC8325293 DOI: 10.1073/pnas.2103952118] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
During perceptual decision-making, the brain encodes the upcoming decision and the stimulus information in a mixed representation. Paradigms suitable for studying decision computations in isolation rely on stimulus comparisons, with choices depending on relative rather than absolute properties of the stimuli. The adoption of tasks requiring relative perceptual judgments in mice would be advantageous in view of the powerful tools available for the dissection of brain circuits. However, whether and how mice can perform a relative visual discrimination task has not yet been fully established. Here, we show that mice can solve a complex orientation discrimination task in which the choices are decoupled from the orientation of individual stimuli. Moreover, we demonstrate a typical discrimination acuity of 9°, challenging the common belief that mice are poor visual discriminators. We reached these conclusions by introducing a probabilistic choice model that explained behavioral strategies in 40 mice and demonstrated that the circularity of the stimulus space is an additional source of choice variability for trials with fixed difficulty. Furthermore, history biases in the model changed with task engagement, demonstrating behavioral sensitivity to the availability of cognitive resources. In conclusion, our results reveal that mice adopt a diverse set of strategies in a task that decouples decision-relevant information from stimulus-specific information, thus demonstrating their usefulness as an animal model for studying neural representations of relative categories in perceptual decision-making research.
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Affiliation(s)
- Dmitry R Lyamzin
- RIKEN Center for Brain Science, RIKEN, Wako-shi 351-0198, Japan;
| | - Ryo Aoki
- RIKEN Center for Brain Science, RIKEN, Wako-shi 351-0198, Japan
| | | | - Andrea Benucci
- RIKEN Center for Brain Science, RIKEN, Wako-shi 351-0198, Japan;
- Department of Mathematical Informatics, Graduate School of Information Science and Technology, University of Tokyo, Bunkyo City 113-0032, Japan
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71
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Bertero A, Garcia C, Apicella AJ. Corticofugal VIP Gabaergic Projection Neurons in the Mouse Auditory and Motor Cortex. Front Neural Circuits 2021; 15:714780. [PMID: 34366798 PMCID: PMC8343102 DOI: 10.3389/fncir.2021.714780] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 07/05/2021] [Indexed: 11/21/2022] Open
Abstract
Anatomical and physiological studies have described the cortex as a six-layer structure that receives, elaborates, and sends out information exclusively as excitatory output to cortical and subcortical regions. This concept has increasingly been challenged by several anatomical and functional studies that showed that direct inhibitory cortical outputs are also a common feature of the sensory and motor cortices. Similar to their excitatory counterparts, subsets of Somatostatin- and Parvalbumin-expressing neurons have been shown to innervate distal targets like the sensory and motor striatum and the contralateral cortex. However, no evidence of long-range VIP-expressing neurons, the third major class of GABAergic cortical inhibitory neurons, has been shown in such cortical regions. Here, using anatomical anterograde and retrograde viral tracing, we tested the hypothesis that VIP-expressing neurons of the mouse auditory and motor cortices can also send long-range projections to cortical and subcortical areas. We were able to demonstrate, for the first time, that VIP-expressing neurons of the auditory cortex can reach not only the contralateral auditory cortex and the ipsilateral striatum and amygdala, as shown for Somatostatin- and Parvalbumin-expressing long-range neurons, but also the medial geniculate body and both superior and inferior colliculus. We also demonstrate that VIP-expressing neurons of the motor cortex send long-range GABAergic projections to the dorsal striatum and contralateral cortex. Because of its presence in two such disparate cortical areas, this would suggest that the long-range VIP projection is likely a general feature of the cortex’s network.
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Affiliation(s)
- Alice Bertero
- Department of Biology, Neurosciences Institute, University of Texas at San Antonio, San Antonio, TX, United States
| | - Charles Garcia
- Department of Biology, Neurosciences Institute, University of Texas at San Antonio, San Antonio, TX, United States
| | - Alfonso Junior Apicella
- Department of Biology, Neurosciences Institute, University of Texas at San Antonio, San Antonio, TX, United States
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72
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73
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Turner MH, Mann K, Clandinin TR. The connectome predicts resting-state functional connectivity across the Drosophila brain. Curr Biol 2021; 31:2386-2394.e3. [PMID: 33770490 PMCID: PMC8519013 DOI: 10.1016/j.cub.2021.03.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 02/08/2021] [Accepted: 03/01/2021] [Indexed: 12/26/2022]
Abstract
Anatomical connectivity can constrain both a neural circuit's function and its underlying computation. This principle has been demonstrated for many small, defined neural circuits. For example, connectome reconstructions have informed models for direction selectivity in the vertebrate retina1,2 as well as the Drosophila visual system.3 In these cases, the circuit in question is relatively compact, well-defined, and has known functions. However, how the connectome constrains global properties of large-scale networks, across multiple brain regions or the entire brain, is incompletely understood. As the availability of partial or complete connectomes expands to more systems and species4-8 it becomes critical to understand how this detailed anatomical information can inform our understanding of large-scale circuit function.9,10 Here, we use data from the Drosophila connectome4 in conjunction with whole-brain in vivo imaging11 to relate structural and functional connectivity in the central brain. We find a strong relationship between resting-state functional correlations and direct region-to-region structural connectivity. We find that the relationship between structure and function varies across the brain, with some regions displaying a tight correspondence between structural and functional connectivity whereas others, including the mushroom body, are more strongly dependent on indirect connections. Throughout this work, we observe features of structural and functional networks in Drosophila that are strikingly similar to those seen in mammalian cortex, including in the human brain. Given the vast anatomical and functional differences between Drosophila and mammalian nervous systems, these observations suggest general principles that govern brain structure, function, and the relationship between the two.
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Affiliation(s)
- Maxwell H Turner
- Department of Neurobiology, Stanford University, Stanford, CA 94103, USA
| | - Kevin Mann
- Department of Neurobiology, Stanford University, Stanford, CA 94103, USA
| | - Thomas R Clandinin
- Department of Neurobiology, Stanford University, Stanford, CA 94103, USA.
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Schlegel P, Bates AS, Stürner T, Jagannathan SR, Drummond N, Hsu J, Serratosa Capdevila L, Javier A, Marin EC, Barth-Maron A, Tamimi IFM, Li F, Rubin GM, Plaza SM, Costa M, Jefferis GSXE. Information flow, cell types and stereotypy in a full olfactory connectome. eLife 2021; 10:e66018. [PMID: 34032214 PMCID: PMC8298098 DOI: 10.7554/elife.66018] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 05/24/2021] [Indexed: 12/19/2022] Open
Abstract
The hemibrain connectome provides large-scale connectivity and morphology information for the majority of the central brain of Drosophila melanogaster. Using this data set, we provide a complete description of the Drosophila olfactory system, covering all first, second and lateral horn-associated third-order neurons. We develop a generally applicable strategy to extract information flow and layered organisation from connectome graphs, mapping olfactory input to descending interneurons. This identifies a range of motifs including highly lateralised circuits in the antennal lobe and patterns of convergence downstream of the mushroom body and lateral horn. Leveraging a second data set we provide a first quantitative assessment of inter- versus intra-individual stereotypy. Comparing neurons across two brains (three hemispheres) reveals striking similarity in neuronal morphology across brains. Connectivity correlates with morphology and neurons of the same morphological type show similar connection variability within the same brain as across two brains.
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Affiliation(s)
- Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | | | - Tomke Stürner
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | | | - Nikolas Drummond
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Joseph Hsu
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | | | - Alexandre Javier
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Elizabeth C Marin
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Asa Barth-Maron
- Department of Neurobiology, Harvard Medical SchoolBostonUnited States
| | - Imaan FM Tamimi
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Feng Li
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Gerald M Rubin
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Stephen M Plaza
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Marta Costa
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom
| | - Gregory S X E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular BiologyCambridgeUnited Kingdom
- Department of Zoology, University of CambridgeCambridgeUnited Kingdom
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75
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Neurodevelopment: Comparative connectomics and the study of circuit assembly. Curr Biol 2021; 31:R452-R454. [PMID: 33974875 DOI: 10.1016/j.cub.2021.03.053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
A new study reveals the effects of specific single cell type perturbations on the assembly and function of a mechanosensory circuit in the Drosophila melanogaster larva with synaptic resolution. This work paves the way for the use of comparative connectomics to understand general principles governing the development of wiring specificity.
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77
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Du M, Di Z(W, Gürsoy D, Xian RP, Kozorovitskiy Y, Jacobsen C. Upscaling X-ray nanoimaging to macroscopic specimens. J Appl Crystallogr 2021; 54:386-401. [PMID: 33953650 PMCID: PMC8056767 DOI: 10.1107/s1600576721000194] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 01/06/2021] [Indexed: 11/10/2022] Open
Abstract
Upscaling X-ray nanoimaging to macroscopic specimens has the potential for providing insights across multiple length scales, but its feasibility has long been an open question. By combining the imaging requirements and existing proof-of-principle examples in large-specimen preparation, data acquisition and reconstruction algorithms, the authors provide imaging time estimates for howX-ray nanoimaging can be scaled to macroscopic specimens. To arrive at this estimate, a phase contrast imaging model that includes plural scattering effects is used to calculate the required exposure and corresponding radiation dose. The coherent X-ray flux anticipated from upcoming diffraction-limited light sources is then considered. This imaging time estimation is in particular applied to the case of the connectomes of whole mouse brains. To image the connectome of the whole mouse brain, electron microscopy connectomics might require years, whereas optimized X-ray microscopy connectomics could reduce this to one week. Furthermore, this analysis points to challenges that need to be overcome (such as increased X-ray detector frame rate) and opportunities that advances in artificial-intelligence-based 'smart' scanning might provide. While the technical advances required are daunting, it is shown that X-ray microscopy is indeed potentially applicable to nanoimaging of millimetre- or even centimetre-size specimens.
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Affiliation(s)
- Ming Du
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Zichao (Wendy) Di
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA
- Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL 60439, USA
| | - Doǧa Gürsoy
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA
- Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, IL 60208, USA
| | - R. Patrick Xian
- Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
| | - Yevgenia Kozorovitskiy
- Department of Neurobiology, Northwestern University, Evanston, IL 60208, USA
- Chemistry of Life Processes Institute, Northwestern University, Evanston, IL 60208, USA
| | - Chris Jacobsen
- Advanced Photon Source, Argonne National Laboratory, Argonne, IL 60439, USA
- Chemistry of Life Processes Institute, Northwestern University, Evanston, IL 60208, USA
- Department of Physics and Astronomy, Northwestern University, Evanston, IL 60208, USA
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78
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Gomez-Marin A. Promisomics and the Short-Circuiting of Mind. eNeuro 2021; 8:ENEURO.0521-20.2021. [PMID: 33648976 PMCID: PMC8114901 DOI: 10.1523/eneuro.0521-20.2021] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 01/03/2021] [Accepted: 01/11/2021] [Indexed: 02/08/2023] Open
Affiliation(s)
- Alex Gomez-Marin
- Instituto de Neurociencias de Alicante, CSIC-UMH, 03550 San Juan de Alicante, Spain
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79
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Affiliation(s)
- Adrienne L Fairhall
- Department of Physiology and Biophysics, Computational Neuroscience Center, University of Washington, Seattle, WA 98195
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80
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Minehart JA, Speer CM. A Picture Worth a Thousand Molecules-Integrative Technologies for Mapping Subcellular Molecular Organization and Plasticity in Developing Circuits. Front Synaptic Neurosci 2021; 12:615059. [PMID: 33469427 PMCID: PMC7813761 DOI: 10.3389/fnsyn.2020.615059] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 12/07/2020] [Indexed: 12/23/2022] Open
Abstract
A key challenge in developmental neuroscience is identifying the local regulatory mechanisms that control neurite and synaptic refinement over large brain volumes. Innovative molecular techniques and high-resolution imaging tools are beginning to reshape our view of how local protein translation in subcellular compartments drives axonal, dendritic, and synaptic development and plasticity. Here we review recent progress in three areas of neurite and synaptic study in situ-compartment-specific transcriptomics/translatomics, targeted proteomics, and super-resolution imaging analysis of synaptic organization and development. We discuss synergies between sequencing and imaging techniques for the discovery and validation of local molecular signaling mechanisms regulating synaptic development, plasticity, and maintenance in circuits.
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Affiliation(s)
| | - Colenso M. Speer
- Department of Biology, University of Maryland, College Park, MD, United States
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81
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Wang XJ, Pereira U, Rosa MG, Kennedy H. Brain connectomes come of age. Curr Opin Neurobiol 2020; 65:152-161. [PMID: 33276230 PMCID: PMC7770070 DOI: 10.1016/j.conb.2020.11.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 11/07/2020] [Accepted: 11/08/2020] [Indexed: 01/06/2023]
Abstract
Databases of consistent, directed- and weighted inter-areal connectivity for mouse, macaque and marmoset monkeys have recently become available and begun to be used to build structural and dynamical models. A structural hierarchy can be defined based by laminar patterns of cortical connections. A large-scale dynamical model of the macaque cortex endowed with a laminar structure accounts for empirically observed frequency-modulated interplay between bottom-up and top-down processes. Signal propagation in the model with spiking neurons displays a threshold of stimulus amplitude for the activity to gain access to the prefrontal cortex, reminiscent of the ignition phenomenon associated with conscious perception. These two examples illustrate how connectomics inform structurally based dynamic models of multi-regional brain systems. Theory raises novel questions for future anatomical and physiological empirical research, in a back-and-forth collaboration between experimentalists and theorists. Directed- and weighted inter-areal cortical connectivity matrices of macaque, marmoset and mouse exhibit similarities as well as marked differences. The new connectomic data provide quantitative information for structural and dynamical modeling of multi-regional cortical circuit providing insight to the global cortical function. Quantification of cortical hierarchy guides investigations of interplay between bottom-up and top-down information processes.
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Affiliation(s)
- Xiao-Jing Wang
- Center for Neural Science, New York University, 4 Washington Place, New York, NY 10003, USA.
| | - Ulises Pereira
- Center for Neural Science, New York University, 4 Washington Place, New York, NY 10003, USA
| | - Marcello Gp Rosa
- Biomedicine Discovery Institute and Australian Research Council Centre of Excellence for Integrative Brain Function, Monash University, Clayton, VIC 3800, Australia
| | - Henry Kennedy
- Stem Cell and Brain Research Institute, INSERM U846, 69500 Bron, France; Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences (CAS) Key Laboratory of Primate Neurobiology, CAS, Shanghai 200031, China
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82
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Perich MG, Rajan K. Rethinking brain-wide interactions through multi-region 'network of networks' models. Curr Opin Neurobiol 2020; 65:146-151. [PMID: 33254073 DOI: 10.1016/j.conb.2020.11.003] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 10/17/2020] [Accepted: 11/08/2020] [Indexed: 12/20/2022]
Abstract
The neural control of behavior is distributed across many functionally and anatomically distinct brain regions even in small nervous systems. While classical neuroscience models treated these regions as a set of hierarchically isolated nodes, the brain comprises a recurrently interconnected network in which each region is intimately modulated by many others. Uncovering these interactions is now possible through experimental techniques that access large neural populations from many brain regions simultaneously. Harnessing these large-scale datasets, however, requires new theoretical approaches. Here, we review recent work to understand brain-wide interactions using multi-region 'network of networks' models and discuss how they can guide future experiments. We also emphasize the importance of multi-region recordings, and posit that studying individual components in isolation will be insufficient to understand the neural basis of behavior.
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Affiliation(s)
- Matthew G Perich
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Kanaka Rajan
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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83
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Winnubst J, Spruston N, Harris JA. Linking axon morphology to gene expression: a strategy for neuronal cell-type classification. Curr Opin Neurobiol 2020; 65:70-76. [PMID: 33181399 DOI: 10.1016/j.conb.2020.10.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 10/04/2020] [Accepted: 10/05/2020] [Indexed: 12/28/2022]
Abstract
To study how the brain drives cognition and behavior we need to understand its cellular composition. Advances in single-cell transcriptomics have revolutionized our ability to characterize neuronal diversity. To arrive at meaningful descriptions of cell types, however, gene expression must be linked to structural and functional properties. Axonal projection patterns are an appropriate measure, as they are diverse, change only gradually over time, and they influence and constrain circuit function. Here, we consider how efforts to map transcriptional and morphological diversity in the mouse brain could be linked to generate a modern taxonomy of the mouse brain.
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Affiliation(s)
- Johan Winnubst
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Nelson Spruston
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Julie A Harris
- Allen Institute for Brain Science, Seattle, WA, 98109, USA
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