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de Toledo GRA, Reissig GN, Senko LGS, Pereira DR, da Silva AF, Souza GM. Common bean under different water availability reveals classifiable stimuli-specific signatures in plant electrome. PLANT SIGNALING & BEHAVIOR 2024; 19:2333144. [PMID: 38545860 PMCID: PMC10984121 DOI: 10.1080/15592324.2024.2333144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Accepted: 01/30/2024] [Indexed: 04/04/2024]
Abstract
Plant electrophysiology has unveiled the involvement of electrical signals in the physiology and behavior of plants. Spontaneously generated bioelectric activity can be altered in response to changes in environmental conditions, suggesting that a plant's electrome may possess a distinct signature associated with various stimuli. Analyzing electrical signals, particularly the electrome, in conjunction with Machine Learning (ML) techniques has emerged as a promising approach to classify characteristic electrical signals corresponding to each stimulus. This study aimed to characterize the electrome of common bean (Phaseolus vulgaris L.) cv. BRS-Expedito, subjected to different water availabilities, seeking patterns linked to these stimuli. For this purpose, bean plants in the vegetative stage were subjected to the following treatments: (I) distilled water; (II) half-strength Hoagland's nutrient solution; (III) -2 MPa PEG solution; and (IV) -2 MPa NaCl solution. Electrical signals were recorded within a Faraday's cage using the MP36 electronic system for data acquisition. Concurrently, plant water status was assessed by monitoring leaf turgor variation. Leaf temperature was additionally measured. Various analyses were conducted on the electrical time series data, including arithmetic average of voltage variation, skewness, kurtosis, Probability Density Function (PDF), autocorrelation, Power Spectral Density (PSD), Approximate Entropy (ApEn), Fast Fourier Transform (FFT), and Multiscale Approximate Entropy (ApEn(s)). Statistical analyses were performed on leaf temperature, voltage variation, skewness, kurtosis, PDF µ exponent, autocorrelation, PSD β exponent, and approximate entropy data. Machine Learning analyses were applied to identify classifiable patterns in the electrical time series. Characterization of the electrome of BRS-Expedito beans revealed stimulus-dependent profiles, even when alterations in water availability stimuli were similar in terms of quality and intensity. Additionally, it was observed that the bean electrome exhibits high levels of complexity, which are altered by different stimuli, with more intense and aversive stimuli leading to drastic reductions in complexity levels. Notably, one of the significant findings was the 100% accuracy of Small Vector Machine in detecting salt stress using electrome data. Furthermore, the study highlighted alterations in the plant electrome under low water potential before observable leaf turgor changes. This work demonstrates the potential use of the electrome as a physiological indicator of the water status in bean plants.
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Affiliation(s)
- Gabriel R. A. de Toledo
- Laboratory of Plant Cognition and Electrophysiology, Department of Botany, Institute of Biology, Federal University of Pelotas, Pelotas, Brazil
| | - Gabriela N. Reissig
- Laboratory of Plant Cognition and Electrophysiology, Department of Botany, Institute of Biology, Federal University of Pelotas, Pelotas, Brazil
| | - Luiz G. S. Senko
- Laboratory of Plant Cognition and Electrophysiology, Department of Botany, Institute of Biology, Federal University of Pelotas, Pelotas, Brazil
| | | | - Arlan F. da Silva
- Department of Physics, Federal University of Pelotas, Pelotas, Brazil
| | - Gustavo M. Souza
- Laboratory of Plant Cognition and Electrophysiology, Department of Botany, Institute of Biology, Federal University of Pelotas, Pelotas, Brazil
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2
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Emmons SW. FUNCTIONS OF C. ELEGANS NEURONS FROM SYNAPTIC CONNECTIVITY. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.08.584145. [PMID: 38562755 PMCID: PMC10983851 DOI: 10.1101/2024.03.08.584145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Despite decades of research on the C. elegans nervous system based on an anatomical description of synaptic connectivity, the circuits underlying behavior remain incompletely described and the functions of many neurons are still unknown. Updated and more complete chemical and gap junction connectomes of both adult sexes covering the entire animal including the muscle end organ have become available recently. Here these are analyzed to gain insight into the overall structure of the connectivity network and to suggest functions of individual neuron classes. Modularity analysis divides the connectome graph into ten communities that can be correlated with broad categories of behavior. A significant role of the body wall musculature end organ is emphasized as both a site of significant information convergence and as a source of sensory input in a feedback loop. Convergence of pathways for multisensory integration occurs throughout the network - most interneurons have similar indegrees and outdegrees and hence disperse information as much as they aggregate it. New insights include description of a set of high degree interneurons connected by many gap junctions running through the ventral cord that may represent a previously unrecognized locus of information processing. There is an apparent mechanosensory and proprioceptive field covering the entire body formed by connectivity of the many mechanosensory neurons of multiple types to two interneurons with output connections across the nervous system. Several additional significant, previously unrecognized circuits and pathways are uncovered, some involving unstudied neurons. The insights are valuable for guiding theoretical investigation of network properties as well as experimental studies of the functions of individual neurons, groups of neurons, and circuits.
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Affiliation(s)
- Scott W Emmons
- Department of Genetics and Dominic P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 10461, USA
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3
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Donner C, Bartram J, Hornauer P, Kim T, Roqueiro D, Hierlemann A, Obozinski G, Schröter M. Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains. PLoS Comput Biol 2024; 20:e1011964. [PMID: 38683881 PMCID: PMC11081509 DOI: 10.1371/journal.pcbi.1011964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 05/09/2024] [Accepted: 03/02/2024] [Indexed: 05/02/2024] Open
Abstract
Probing the architecture of neuronal circuits and the principles that underlie their functional organization remains an important challenge of modern neurosciences. This holds true, in particular, for the inference of neuronal connectivity from large-scale extracellular recordings. Despite the popularity of this approach and a number of elaborate methods to reconstruct networks, the degree to which synaptic connections can be reconstructed from spike-train recordings alone remains controversial. Here, we provide a framework to probe and compare connectivity inference algorithms, using a combination of synthetic ground-truth and in vitro data sets, where the connectivity labels were obtained from simultaneous high-density microelectrode array (HD-MEA) and patch-clamp recordings. We find that reconstruction performance critically depends on the regularity of the recorded spontaneous activity, i.e., their dynamical regime, the type of connectivity, and the amount of available spike-train data. We therefore introduce an ensemble artificial neural network (eANN) to improve connectivity inference. We train the eANN on the validated outputs of six established inference algorithms and show how it improves network reconstruction accuracy and robustness. Overall, the eANN demonstrated strong performance across different dynamical regimes, worked well on smaller datasets, and improved the detection of synaptic connectivity, especially inhibitory connections. Results indicated that the eANN also improved the topological characterization of neuronal networks. The presented methodology contributes to advancing the performance of inference algorithms and facilitates our understanding of how neuronal activity relates to synaptic connectivity.
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Affiliation(s)
- Christian Donner
- Swiss Data Science Center, ETH Zürich & EPFL, Zürich & Lausanne, Switzerland
| | - Julian Bartram
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Philipp Hornauer
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Taehoon Kim
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Damian Roqueiro
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Andreas Hierlemann
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Guillaume Obozinski
- Swiss Data Science Center, ETH Zürich & EPFL, Zürich & Lausanne, Switzerland
| | - Manuel Schröter
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
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4
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Boccato T, Ferrante M, Duggento A, Toschi N. Beyond multilayer perceptrons: Investigating complex topologies in neural networks. Neural Netw 2024; 171:215-228. [PMID: 38096650 DOI: 10.1016/j.neunet.2023.12.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 12/07/2023] [Accepted: 12/07/2023] [Indexed: 01/29/2024]
Abstract
This study delves into the crucial aspect of network topology in artificial neural networks (NNs) and its impact on model performance. Addressing the need to comprehend how network structures influence learning capabilities, the research contrasts traditional multilayer perceptrons (MLPs) with models built on various complex topologies using novel network generation techniques. Drawing insights from synthetic datasets, the study reveals the remarkable accuracy of complex NNs, particularly in high-difficulty scenarios, outperforming MLPs. Our exploration extends to real-world datasets, highlighting the task-specific nature of optimal network topologies and unveiling trade-offs, including increased computational demands and reduced robustness to graph damage in complex NNs compared to MLPs. This research underscores the pivotal role of complex topologies in addressing challenging learning tasks. However, it also signals the necessity for deeper insights into the complex interplay among topological attributes influencing NN performance. By shedding light on the advantages and limitations of complex topologies, this study provides valuable guidance for practitioners and paves the way for future endeavors to design more efficient and adaptable neural architectures across various applications.
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Affiliation(s)
- Tommaso Boccato
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy.
| | - Matteo Ferrante
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy.
| | - Andrea Duggento
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy.
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy; A.A. Martinos Center for Biomedical Imaging and Harvard Medical School, Boston, USA.
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5
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Lin A, Yang R, Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, Yu SC, McKellar CE, Costa M, Eichler K, Bates AS, Eckstein N, Funke J, Jefferis GSXE, Murthy M. Network Statistics of the Whole-Brain Connectome of Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.29.551086. [PMID: 37547019 PMCID: PMC10402125 DOI: 10.1101/2023.07.29.551086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Brains comprise complex networks of neurons and connections. Network analysis applied to the wiring diagrams of brains can offer insights into how brains support computations and regulate information flow. The completion of the first whole-brain connectome of an adult Drosophila, the largest connectome to date, containing 130,000 neurons and millions of connections, offers an unprecedented opportunity to analyze its network properties and topological features. To gain insights into local connectivity, we computed the prevalence of two- and three-node network motifs, examined their strengths and neurotransmitter compositions, and compared these topological metrics with wiring diagrams of other animals. We discovered that the network of the fly brain displays rich club organization, with a large population (30% percent of the connectome) of highly connected neurons. We identified subsets of rich club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex and will serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.
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Affiliation(s)
- Albert Lin
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Center for the Physics of Biological Function, Princeton University, Princeton, NJ, USA
| | - Runzhe Yang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Amy R Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Claire E McKellar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Alexander Shakeel Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
| | - Nils Eckstein
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Jan Funke
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Gregory S X E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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6
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Bazinet V, Hansen JY, Misic B. Towards a biologically annotated brain connectome. Nat Rev Neurosci 2023; 24:747-760. [PMID: 37848663 DOI: 10.1038/s41583-023-00752-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/20/2023] [Indexed: 10/19/2023]
Abstract
The brain is a network of interleaved neural circuits. In modern connectomics, brain connectivity is typically encoded as a network of nodes and edges, abstracting away the rich biological detail of local neuronal populations. Yet biological annotations for network nodes - such as gene expression, cytoarchitecture, neurotransmitter receptors or intrinsic dynamics - can be readily measured and overlaid on network models. Here we review how connectomes can be represented and analysed as annotated networks. Annotated connectomes allow us to reconceptualize architectural features of networks and to relate the connection patterns of brain regions to their underlying biology. Emerging work demonstrates that annotated connectomes help to make more veridical models of brain network formation, neural dynamics and disease propagation. Finally, annotations can be used to infer entirely new inter-regional relationships and to construct new types of network that complement existing connectome representations. In summary, biologically annotated connectomes offer a compelling way to study neural wiring in concert with local biological features.
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Affiliation(s)
- Vincent Bazinet
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Justine Y Hansen
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada.
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7
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Ripoll-Sánchez L, Watteyne J, Sun H, Fernandez R, Taylor SR, Weinreb A, Bentley BL, Hammarlund M, Miller DM, Hobert O, Beets I, Vértes PE, Schafer WR. The neuropeptidergic connectome of C. elegans. Neuron 2023; 111:3570-3589.e5. [PMID: 37935195 PMCID: PMC7615469 DOI: 10.1016/j.neuron.2023.09.043] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 08/02/2023] [Accepted: 09/29/2023] [Indexed: 11/09/2023]
Abstract
Efforts are ongoing to map synaptic wiring diagrams, or connectomes, to understand the neural basis of brain function. However, chemical synapses represent only one type of functionally important neuronal connection; in particular, extrasynaptic, "wireless" signaling by neuropeptides is widespread and plays essential roles in all nervous systems. By integrating single-cell anatomical and gene-expression datasets with biochemical analysis of receptor-ligand interactions, we have generated a draft connectome of neuropeptide signaling in the C. elegans nervous system. This network is characterized by high connection density, extended signaling cascades, autocrine foci, and a decentralized topology, with a large, highly interconnected core containing three constituent communities sharing similar patterns of input connectivity. Intriguingly, several key network hubs are little-studied neurons that appear specialized for peptidergic neuromodulation. We anticipate that the C. elegans neuropeptidergic connectome will serve as a prototype to understand how networks of neuromodulatory signaling are organized.
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Affiliation(s)
- Lidia Ripoll-Sánchez
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK; Department of Psychiatry, Cambridge University, Cambridge, UK
| | - Jan Watteyne
- Department of Biology, KU Leuven, Leuven, Belgium
| | - HaoSheng Sun
- Department of Biological Sciences/HHMI, Columbia University, New York, NY, USA; Department of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Robert Fernandez
- Department of Biological Sciences/HHMI, Columbia University, New York, NY, USA
| | - Seth R Taylor
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Alexis Weinreb
- Departments of Genetics and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - Barry L Bentley
- Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, UK
| | - Marc Hammarlund
- Departments of Genetics and Neuroscience, Yale University School of Medicine, New Haven, CT, USA
| | - David M Miller
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Oliver Hobert
- Department of Biological Sciences/HHMI, Columbia University, New York, NY, USA
| | - Isabel Beets
- Department of Biology, KU Leuven, Leuven, Belgium
| | - Petra E Vértes
- Department of Psychiatry, Cambridge University, Cambridge, UK
| | - William R Schafer
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK; Department of Biology, KU Leuven, Leuven, Belgium.
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8
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Pan W, Zhao F, Zeng Y, Han B. Adaptive structure evolution and biologically plausible synaptic plasticity for recurrent spiking neural networks. Sci Rep 2023; 13:16924. [PMID: 37805632 PMCID: PMC10560283 DOI: 10.1038/s41598-023-43488-x] [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: 02/07/2023] [Accepted: 09/25/2023] [Indexed: 10/09/2023] Open
Abstract
The architecture design and multi-scale learning principles of the human brain that evolved over hundreds of millions of years are crucial to realizing human-like intelligence. Spiking neural network based Liquid State Machine (LSM) serves as a suitable architecture to study brain-inspired intelligence because of its brain-inspired structure and the potential for integrating multiple biological principles. Existing researches on LSM focus on different certain perspectives, including high-dimensional encoding or optimization of the liquid layer, network architecture search, and application to hardware devices. There is still a lack of in-depth inspiration from the learning and structural evolution mechanism of the brain. Considering these limitations, this paper presents a novel LSM learning model that integrates adaptive structural evolution and multi-scale biological learning rules. For structural evolution, an adaptive evolvable LSM model is developed to optimize the neural architecture design of liquid layer with separation property. For brain-inspired learning of LSM, we propose a dopamine-modulated Bienenstock-Cooper-Munros (DA-BCM) method that incorporates global long-term dopamine regulation and local trace-based BCM synaptic plasticity. Comparative experimental results on different decision-making tasks show that introducing structural evolution of the liquid layer, and the DA-BCM regulation of the liquid layer and the readout layer could improve the decision-making ability of LSM and flexibly adapt to rule reversal. This work is committed to exploring how evolution can help to design more appropriate network architectures and how multi-scale neuroplasticity principles coordinated to enable the optimization and learning of LSMs for relatively complex decision-making tasks.
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Affiliation(s)
- Wenxuan Pan
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Feifei Zhao
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yi Zeng
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, China.
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
| | - Bing Han
- Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
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9
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Zhao H, Shao C, Shi Z, He S, Gong Z. The Intrinsic Similarity of Topological Structure in Biological Neural Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:3292-3305. [PMID: 37224366 DOI: 10.1109/tcbb.2023.3279443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Most previous studies mainly have focused on the analysis of structural properties of individual neuronal networks from C. elegans. In recent years, an increasing number of synapse-level neural maps, also known as biological neural networks, have been reconstructed. However, it is not clear whether there are intrinsic similarities of structural properties of biological neural networks from different brain compartments or species. To explore this issue, we collected nine connectomes at synaptic resolution including C. elegans, and analyzed their structural properties. We found that these biological neural networks possess small-world properties and modules. Excluding the Drosophila larval visual system, these networks have rich clubs. The distributions of synaptic connection strength for these networks can be fitted by the truncated pow-law distributions. Additionally, compared with the power-law model, a log-normal distribution is a better model to fit the complementary cumulative distribution function (CCDF) of degree for these neuronal networks. Moreover, we also observed that these neural networks belong to the same superfamily based on the significance profile (SP) of small subgraphs in the network. Taken together, these findings suggest that biological neural networks share intrinsic similarities in their topological structure, revealing some principles underlying the formation of biological neural networks within and across species.
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10
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Barabási DL, Bianconi G, Bullmore E, Burgess M, Chung S, Eliassi-Rad T, George D, Kovács IA, Makse H, Nichols TE, Papadimitriou C, Sporns O, Stachenfeld K, Toroczkai Z, Towlson EK, Zador AM, Zeng H, Barabási AL, Bernard A, Buzsáki G. Neuroscience Needs Network Science. J Neurosci 2023; 43:5989-5995. [PMID: 37612141 PMCID: PMC10451115 DOI: 10.1523/jneurosci.1014-23.2023] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/10/2023] [Accepted: 07/14/2023] [Indexed: 08/25/2023] Open
Abstract
The brain is a complex system comprising a myriad of interacting neurons, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such interconnected systems, offering a framework for integrating multiscale data and complexity. To date, network methods have significantly advanced functional imaging studies of the human brain and have facilitated the development of control theory-based applications for directing brain activity. Here, we discuss emerging frontiers for network neuroscience in the brain atlas era, addressing the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease. We underscore the importance of fostering interdisciplinary opportunities through workshops, conferences, and funding initiatives, such as supporting students and postdoctoral fellows with interests in both disciplines. By bringing together the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way toward a deeper understanding of the brain and its functions, as well as offering new challenges for network science.
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Affiliation(s)
- Dániel L Barabási
- Biophysics Program, Harvard University, Cambridge, 02138, Massachusetts
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, 02138, Massachusetts
| | - Ginestra Bianconi
- School of Mathematical Sciences, Queen Mary University of London, London, E1 4NS, United Kingdom
- Alan Turing Institute, The British Library, London, NW1 2DB, United Kingdom
| | - Ed Bullmore
- Department of Psychiatry and Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, CB2 0QQ, United Kingdom
| | | | - SueYeon Chung
- Center for Neural Science, New York University, New York, New York 10003
- Center for Computational Neuroscience, Flatiron Institute, Simons Foundation, New York, New York 10010
| | - Tina Eliassi-Rad
- Network Science Institute, Northeastern University, Boston, 02115, Massachusetts
- Khoury College of Computer Sciences, Northeastern University, Boston, 02115, Massachusetts
- Santa Fe Institute, Santa Fe, New Mexico 87501
| | | | - István A Kovács
- Department of Physics and Astronomy, Northwestern University, Evanston, Illinois 60208
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, Illinois 60208
| | - Hernán Makse
- Levich Institute and Physics Department, City College of New York, New York, New York 10031
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, United Kingdom
| | | | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana 47405
| | - Kim Stachenfeld
- DeepMind, London, EC4A 3TW, United Kingdom
- Columbia University, New York, New York 10027
| | - Zoltán Toroczkai
- Department of Physics, University of Notre Dame, Notre Dame, Indiana 46556
| | - Emma K Towlson
- Department of Computer Science, University of Calgary, Calgary, Alberta, AB T2N 1N4, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, AB T2N 1N4, Canada
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, AB T2N 1N4, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, AB T2N 1N4, Canada
| | - Anthony M Zador
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, 98109, Washington
| | - Albert-László Barabási
- Network Science Institute, Northeastern University, Boston, 02115, Massachusetts
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
- Department of Network and Data Science, Central European University, Budapest, H-1051, Hungary
| | - Amy Bernard
- The Kavli Foundation, Los Angeles, 90230, California
| | - György Buzsáki
- Center for Neural Science, New York University, New York, New York 10003
- Neuroscience Institute and Department of Neurology, NYU Grossman School of Medicine, New York University, New York, New York 10016
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11
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Hadjiabadi D, Soltesz I. From single-neuron dynamics to higher-order circuit motifs in control and pathological brain networks. J Physiol 2023; 601:3011-3024. [PMID: 35815823 PMCID: PMC10655857 DOI: 10.1113/jp282749] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 06/27/2022] [Indexed: 11/08/2022] Open
Abstract
The convergence of advanced single-cell in vivo functional imaging techniques, computational modelling tools and graph-based network analytics has heralded new opportunities to study single-cell dynamics across large-scale networks, providing novel insights into principles of brain communication and pointing towards potential new strategies for treating neurological disorders. A major recent finding has been the identification of unusually richly connected hub cells that have capacity to synchronize networks and may also be critical in network dysfunction. While hub neurons are traditionally defined by measures that consider solely the number and strength of connections, novel higher-order graph analytics now enables the mining of massive networks for repeating subgraph patterns called motifs. As an illustration of the power offered by higher-order analysis of neuronal networks, we highlight how recent methodological advances uncovered a new functional cell type, the superhub, that is predicted to play a major role in regulating network dynamics. Finally, we discuss open questions that will be critical for assessing the importance of higher-order cellular-scale network analytics in understanding brain function in health and disease.
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Affiliation(s)
- Darian Hadjiabadi
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
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12
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Dag U, Nwabudike I, Kang D, Gomes MA, Kim J, Atanas AA, Bueno E, Estrem C, Pugliese S, Wang Z, Towlson E, Flavell SW. Dissecting the functional organization of the C. elegans serotonergic system at whole-brain scale. Cell 2023; 186:2574-2592.e20. [PMID: 37192620 PMCID: PMC10484565 DOI: 10.1016/j.cell.2023.04.023] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 03/07/2023] [Accepted: 04/17/2023] [Indexed: 05/18/2023]
Abstract
Serotonin influences many aspects of animal behavior. But how serotonin acts on its diverse receptors across the brain to modulate global activity and behavior is unknown. Here, we examine how serotonin release in C. elegans alters brain-wide activity to induce foraging behaviors, like slow locomotion and increased feeding. Comprehensive genetic analyses identify three core serotonin receptors (MOD-1, SER-4, and LGC-50) that induce slow locomotion upon serotonin release and others (SER-1, SER-5, and SER-7) that interact with them to modulate this behavior. SER-4 induces behavioral responses to sudden increases in serotonin release, whereas MOD-1 induces responses to persistent release. Whole-brain imaging reveals widespread serotonin-associated brain dynamics, spanning many behavioral networks. We map all sites of serotonin receptor expression in the connectome, which, together with synaptic connectivity, helps predict which neurons show serotonin-associated activity. These results reveal how serotonin acts at defined sites across a connectome to modulate brain-wide activity and behavior.
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Affiliation(s)
- Ugur Dag
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ijeoma Nwabudike
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Di Kang
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Matthew A Gomes
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jungsoo Kim
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Adam A Atanas
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA; Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Eric Bueno
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Cassi Estrem
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sarah Pugliese
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ziyu Wang
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Emma Towlson
- Department of Computer Science, Department of Physics and Astronomy, Hotchkiss Brain Institute, Alberta Children's Research Hospital, University of Calgary, Calgary, AB, Canada
| | - Steven W Flavell
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
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13
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Starr LA, McKay LE, Peter KN, Seyfarth LM, Berkowitz LA, Caldwell KA, Caldwell GA. Attenuation of Dopaminergic Neurodegeneration in a C. elegans Parkinson's Model through Regulation of Xanthine Dehydrogenase (XDH-1) Expression by the RNA Editase, ADR-2. J Dev Biol 2023; 11:jdb11020020. [PMID: 37218814 DOI: 10.3390/jdb11020020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Revised: 05/16/2023] [Accepted: 05/18/2023] [Indexed: 05/24/2023] Open
Abstract
Differential RNA editing by adenosine deaminases that act on RNA (ADARs) has been implicated in several neurological disorders, including Parkinson's disease (PD). Here, we report results of a RNAi screen of genes differentially regulated in adr-2 mutants, normally encoding the only catalytically active ADAR in Caenorhabditis elegans, ADR-2. Subsequent analysis of candidate genes that alter the misfolding of human α-synuclein (α-syn) and dopaminergic neurodegeneration, two PD pathologies, reveal that reduced expression of xdh-1, the ortholog of human xanthine dehydrogenase (XDH), is protective against α-synuclein-induced dopaminergic neurodegeneration. Further, RNAi experiments show that WHT-2, the worm ortholog of the human ABCG2 transporter and a predicted interactor of XDH-1, is the rate-limiting factor in the ADR-2, XDH-1, WHT-2 system for dopaminergic neuroprotection. In silico structural modeling of WHT-2 indicates that the editing of one nucleotide in the wht-2 mRNA leads to the substitution of threonine with alanine at residue 124 in the WHT-2 protein, changing hydrogen bonds in this region. Thus, we propose a model where wht-2 is edited by ADR-2, which promotes optimal export of uric acid, a known substrate of WHT-2 and a product of XDH-1 activity. In the absence of editing, uric acid export is limited, provoking a reduction in xdh-1 transcription to limit uric acid production and maintain cellular homeostasis. As a result, elevation of uric acid is protective against dopaminergic neuronal cell death. In turn, increased levels of uric acid are associated with a decrease in ROS production. Further, downregulation of xdh-1 is protective against PD pathologies because decreased levels of XDH-1 correlate to a concomitant reduction in xanthine oxidase (XO), the form of the protein whose by-product is superoxide anion. These data indicate that modifying specific targets of RNA editing may represent a promising therapeutic strategy for PD.
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Affiliation(s)
- Lindsey A Starr
- Department of Biological Sciences, Center for Convergent Biomedicine, Alabama Life Research Institute, The University of Alabama, Tuscaloosa, AL 35487, USA
| | - Luke E McKay
- Department of Biological Sciences, Center for Convergent Biomedicine, Alabama Life Research Institute, The University of Alabama, Tuscaloosa, AL 35487, USA
| | - Kylie N Peter
- Department of Biological Sciences, Center for Convergent Biomedicine, Alabama Life Research Institute, The University of Alabama, Tuscaloosa, AL 35487, USA
| | - Lena M Seyfarth
- Department of Biological Sciences, Center for Convergent Biomedicine, Alabama Life Research Institute, The University of Alabama, Tuscaloosa, AL 35487, USA
| | - Laura A Berkowitz
- Department of Biological Sciences, Center for Convergent Biomedicine, Alabama Life Research Institute, The University of Alabama, Tuscaloosa, AL 35487, USA
| | - Kim A Caldwell
- Department of Biological Sciences, Center for Convergent Biomedicine, Alabama Life Research Institute, The University of Alabama, Tuscaloosa, AL 35487, USA
- Department of Neurology, Center for Neurodegeneration and Experimental Therapeutics, Nathan Shock Center of Excellence for the Basic Biology of Aging, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Guy A Caldwell
- Department of Biological Sciences, Center for Convergent Biomedicine, Alabama Life Research Institute, The University of Alabama, Tuscaloosa, AL 35487, USA
- Department of Neurology, Center for Neurodegeneration and Experimental Therapeutics, Nathan Shock Center of Excellence for the Basic Biology of Aging, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
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14
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Barabási DL, Bianconi G, Bullmore E, Burgess M, Chung S, Eliassi-Rad T, George D, Kovács IA, Makse H, Papadimitriou C, Nichols TE, Sporns O, Stachenfeld K, Toroczkai Z, Towlson EK, Zador AM, Zeng H, Barabási AL, Bernard A, Buzsáki G. Neuroscience needs Network Science. ARXIV 2023:arXiv:2305.06160v2. [PMID: 37214134 PMCID: PMC10197734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
The brain is a complex system comprising a myriad of interacting elements, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such intricate systems, offering a framework for integrating multiscale data and complexity. Here, we discuss the application of network science in the study of the brain, addressing topics such as network models and metrics, the connectome, and the role of dynamics in neural networks. We explore the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease, and discuss the potential for collaboration between network science and neuroscience communities. We underscore the importance of fostering interdisciplinary opportunities through funding initiatives, workshops, and conferences, as well as supporting students and postdoctoral fellows with interests in both disciplines. By uniting the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way towards a deeper understanding of the brain and its functions.
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Affiliation(s)
- Dániel L Barabási
- Biophysics Program, Harvard University, Cambridge, MA, USA
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA
| | - Ginestra Bianconi
- School of Mathematical Sciences, Queen Mary University of London, London, E1 4NS, UK
- The Alan Turing Institute, The British Library, London, NW1 2DB, UK
| | - Ed Bullmore
- Department of Psychiatry and Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, United Kingdom
| | | | - SueYeon Chung
- Center for Neural Science, New York University, New York, NY, USA
- Center for Computational Neuroscience, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Tina Eliassi-Rad
- Network Science Institute, Northeastern University, Boston, MA, USA
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | | | - István A. Kovács
- Department of Physics and Astronomy, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA
- Northwestern Institute on Complex Systems, Chambers Hall, 600 Foster St, Northwestern University, Evanston, IL 60208
| | - Hernán Makse
- Levich Institute and Physics Department, City College of New York, New York, NY 10031 US
| | | | - Thomas E. Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47405
| | | | - Zoltán Toroczkai
- Department of Physics, University of Notre Dame, 225 Nieuwland Science Hall, Notre Dame IN 46556, USA
| | - Emma K. Towlson
- Department of Computer Science, Department of Physics and Astronomy, Hotchkiss Brain Institute, Children’s Research Hospital, University of Calgary, Calgary, Alberta, Canada 22
| | - Anthony M Zador
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Albert-László Barabási
- Network Science Institute, Northeastern University, Boston, MA, USA
- Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, 02115, USA
- Department of Network and Data Science, Central European University, Budapest, H-1051, Hungary
| | | | - György Buzsáki
- Neuroscience Institute and Department of Neurology, NYU Grossman School of Medicine, New York University, New York, NY, USA
- Center for Neural Science, New York University, New York, NY, USA
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15
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Prasad K, Rubin J, Iyengar S, Cape J. Global network disorganization underlying psychosis high risk states. Schizophr Res 2023; 255:67-68. [PMID: 36965361 DOI: 10.1016/j.schres.2023.03.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 03/18/2023] [Indexed: 03/27/2023]
Affiliation(s)
- Konasale Prasad
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States of America; VA Pittsburgh Healthcare System, Pittsburgh, PA, United States of America; Department of Bioengineering, University of Pittsburgh Swanson School of Engineering, Pittsburgh, PA, United States of America.
| | - Jonathan Rubin
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Satish Iyengar
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Joshua Cape
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA, United States of America; Department of Statistics, University of Wisconsin-Madison, Madison, WI, United States of America
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16
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Winding M, Pedigo BD, Barnes CL, Patsolic HG, Park Y, Kazimiers T, Fushiki A, Andrade IV, Khandelwal A, Valdes-Aleman J, Li F, Randel N, Barsotti E, Correia A, Fetter RD, Hartenstein V, Priebe CE, Vogelstein JT, Cardona A, Zlatic M. The connectome of an insect brain. Science 2023; 379:eadd9330. [PMID: 36893230 PMCID: PMC7614541 DOI: 10.1126/science.add9330] [Citation(s) in RCA: 76] [Impact Index Per Article: 76.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 02/07/2023] [Indexed: 03/11/2023]
Abstract
Brains contain networks of interconnected neurons and so knowing the network architecture is essential for understanding brain function. We therefore mapped the synaptic-resolution connectome of an entire insect brain (Drosophila larva) with rich behavior, including learning, value computation, and action selection, comprising 3016 neurons and 548,000 synapses. We characterized neuron types, hubs, feedforward and feedback pathways, as well as cross-hemisphere and brain-nerve cord interactions. We found pervasive multisensory and interhemispheric integration, highly recurrent architecture, abundant feedback from descending neurons, and multiple novel circuit motifs. The brain's most recurrent circuits comprised the input and output neurons of the learning center. Some structural features, including multilayer shortcuts and nested recurrent loops, resembled state-of-the-art deep learning architectures. The identified brain architecture provides a basis for future experimental and theoretical studies of neural circuits.
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Affiliation(s)
- Michael Winding
- University of Cambridge, Department of Zoology, Cambridge, UK
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Benjamin D. Pedigo
- Johns Hopkins University, Department of Biomedical Engineering, Baltimore, MD, USA
| | - Christopher L. Barnes
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK
| | - Heather G. Patsolic
- Johns Hopkins University, Department of Applied Mathematics and Statistics, Baltimore, MD, USA
- Accenture, Arlington, VA, USA
| | - Youngser Park
- Johns Hopkins University, Center for Imaging Science, Baltimore, MD, USA
| | - Tom Kazimiers
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- kazmos GmbH, Dresden, Germany
| | - Akira Fushiki
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
| | - Ingrid V. Andrade
- University of California Los Angeles, Department of Molecular, Cell and Developmental Biology, Los Angeles, CA, USA
| | - Avinash Khandelwal
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Javier Valdes-Aleman
- University of Cambridge, Department of Zoology, Cambridge, UK
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Feng Li
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Nadine Randel
- University of Cambridge, Department of Zoology, Cambridge, UK
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
| | - Elizabeth Barsotti
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK
| | - Ana Correia
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK
| | - Richard D. Fetter
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Stanford University, Stanford, CA, USA
| | - Volker Hartenstein
- University of California Los Angeles, Department of Molecular, Cell and Developmental Biology, Los Angeles, CA, USA
| | - Carey E. Priebe
- Johns Hopkins University, Department of Applied Mathematics and Statistics, Baltimore, MD, USA
- Johns Hopkins University, Center for Imaging Science, Baltimore, MD, USA
| | - Joshua T. Vogelstein
- Johns Hopkins University, Department of Biomedical Engineering, Baltimore, MD, USA
- Johns Hopkins University, Center for Imaging Science, Baltimore, MD, USA
| | - Albert Cardona
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- University of Cambridge, Department of Physiology, Development, and Neuroscience, Cambridge, UK
| | - Marta Zlatic
- University of Cambridge, Department of Zoology, Cambridge, UK
- MRC Laboratory of Molecular Biology, Neurobiology Division, Cambridge, UK
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
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17
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Dag U, Nwabudike I, Kang D, Gomes MA, Kim J, Atanas AA, Bueno E, Estrem C, Pugliese S, Wang Z, Towlson E, Flavell SW. Dissecting the Functional Organization of the C. elegans Serotonergic System at Whole-Brain Scale. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.15.524132. [PMID: 36711891 PMCID: PMC9882198 DOI: 10.1101/2023.01.15.524132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Serotonin controls many aspects of animal behavior and cognition. But how serotonin acts on its diverse receptor types in neurons across the brain to modulate global activity and behavior is unknown. Here, we examine how serotonin release from a feeding-responsive neuron in C. elegans alters brain-wide activity to induce foraging behaviors, like slow locomotion and increased feeding. A comprehensive genetic analysis identifies three core serotonin receptors that collectively induce slow locomotion upon serotonin release and three others that interact with them to further modulate this behavior. The core receptors have different functional roles: some induce behavioral responses to sudden increases in serotonin release, whereas others induce responses to persistent release. Whole-brain calcium imaging reveals widespread serotonin-associated brain dynamics, impacting different behavioral networks in different ways. We map out all sites of serotonin receptor expression in the connectome, which, together with synaptic connectivity, helps predict serotonin-associated brain-wide activity changes. These results provide a global view of how serotonin acts at defined sites across a connectome to modulate brain-wide activity and behavior.
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Affiliation(s)
- Ugur Dag
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA,These authors contributed equally to this work
| | - Ijeoma Nwabudike
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA,These authors contributed equally to this work
| | - Di Kang
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA,These authors contributed equally to this work
| | - Matthew A. Gomes
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jungsoo Kim
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Adam A. Atanas
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA,Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Eric Bueno
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Cassi Estrem
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sarah Pugliese
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ziyu Wang
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Emma Towlson
- Department of Computer Science, Department of Physics and Astronomy, Hotchkiss Brain Institute, Alberta Children’s Research Hospital, University of Calgary, Calgary, Alberta, Canada
| | - Steven W. Flavell
- Picower Institute for Learning & Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA,Corresponding Author:
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18
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Ruach R, Ratner N, Emmons SW, Zaslaver A. The synaptic organization in the Caenorhabditis elegans neural network suggests significant local compartmentalized computations. Proc Natl Acad Sci U S A 2023; 120:e2201699120. [PMID: 36630454 PMCID: PMC9934027 DOI: 10.1073/pnas.2201699120] [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] [Indexed: 01/12/2023] Open
Abstract
Neurons are characterized by elaborate tree-like dendritic structures that support local computations by integrating multiple inputs from upstream presynaptic neurons. It is less clear whether simple neurons, consisting of a few or even a single neurite, may perform local computations as well. To address this question, we focused on the compact neural network of Caenorhabditis elegans animals for which the full wiring diagram is available, including the coordinates of individual synapses. We find that the positions of the chemical synapses along the neurites are not randomly distributed nor can they be explained by anatomical constraints. Instead, synapses tend to form clusters, an organization that supports local compartmentalized computations. In mutually synapsing neurons, connections of opposite polarity cluster separately, suggesting that positive and negative feedback dynamics may be implemented in discrete compartmentalized regions along neurites. In triple-neuron circuits, the nonrandom synaptic organization may facilitate local functional roles, such as signal integration and coordinated activation of functionally related downstream neurons. These clustered synaptic topologies emerge as a guiding principle in the network, presumably to facilitate distinct parallel functions along a single neurite, which effectively increase the computational capacity of the neural network.
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Affiliation(s)
- Rotem Ruach
- aDepartment of Genetics, Silberman Institute of Life Science, The Hebrew University, Jerusalem9190401, Israel
| | - Nir Ratner
- aDepartment of Genetics, Silberman Institute of Life Science, The Hebrew University, Jerusalem9190401, Israel
| | - Scott W. Emmons
- bDominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, New York10461, NY
- cDepartment of Genetics, Albert Einstein College of Medicine, New York10461, NY
| | - Alon Zaslaver
- aDepartment of Genetics, Silberman Institute of Life Science, The Hebrew University, Jerusalem9190401, Israel
- 1To whom correspondence may be addressed.
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19
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Valencia Urbina CE, Cannas SA, Gleiser PM. Emergent dynamics in a robotic model based on the Caenorhabditis elegans connectome. Front Neurorobot 2023; 16:1041410. [PMID: 36699947 PMCID: PMC9868850 DOI: 10.3389/fnbot.2022.1041410] [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: 09/16/2022] [Accepted: 12/08/2022] [Indexed: 01/12/2023] Open
Abstract
We analyze the neural dynamics and their relation with the emergent actions of a robotic vehicle that is controlled by a neural network numerical simulation based on the nervous system of the nematode Caenorhabditis elegans. The robot interacts with the environment through a sensor that transmits the information to sensory neurons, while motor neurons outputs are connected to wheels. This is enough to allow emergent robot actions in complex environments, such as avoiding collisions with obstacles. Working with robotic models makes it possible to simultaneously keep track of the dynamics of all the neurons and also register the actions of the robot in the environment in real time, while avoiding the complex technicalities of simulating a real environment. This allowed us to identify several relevant features of the neural dynamics associated with the emergent actions of the robot, some of which have already been observed in biological worms. These results suggest that some basic aspects of behaviors observed in living beings are determined by the underlying structure of the associated neural network.
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Affiliation(s)
- Carlos E Valencia Urbina
- Medical Physics Department, Centro Atómico Bariloche, Instituto Balseiro, Universidad Nacional de Cuyo, Río Negro, Argentina
| | - Sergio A Cannas
- Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba, Instituto de Física Enrique Gaviola (IFEG-CONICET), Ciudad Universitaria, Córdoba, Argentina
| | - Pablo M Gleiser
- Medical Physics Department, Centro Atómico Bariloche, Instituto Balseiro, Universidad Nacional de Cuyo, Río Negro, Argentina.,Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba, Instituto de Física Enrique Gaviola (IFEG-CONICET), Ciudad Universitaria, Córdoba, Argentina.,Laboratorio de Neurociencia de Sistemas Complejos, Departamento de Ciencias de la Vida, Instituto Tecnològico de Buenos Aires (ITBA), Buenos Aires, Argentina
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20
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Comparing models of information transfer in the structural brain network and their relationship to functional connectivity: diffusion versus shortest path routing. Brain Struct Funct 2023; 228:651-662. [PMID: 36723674 PMCID: PMC9944050 DOI: 10.1007/s00429-023-02613-2] [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: 07/06/2022] [Accepted: 01/16/2023] [Indexed: 02/02/2023]
Abstract
The relationship between structural and functional connectivity in the human brain is a core question in network neuroscience, and a topic of paramount importance to our ability to meaningfully describe and predict functional outcomes. Graph theory has been used to produce measures based on the structural connectivity network that are related to functional connectivity. These measures are commonly based on either the shortest path routing model or the diffusion model, which carry distinct assumptions about how information is transferred through the network. Unlike shortest path routing, which assumes the most efficient path is always known, the diffusion model makes no such assumption, and lets information diffuse in parallel based on the number of connections to other regions. Past research has also developed hybrid measures that use concepts from both models, which have better predicted functional connectivity from structural connectivity than the shortest path length alone. We examined the extent to which each of these models can account for the structure-function relationship of interest using graph theory measures that are exclusively based on each model. This analysis was performed on multiple parcellations of the Human Connectome Project using multiple approaches, which all converged on the same finding. We found that the diffusion model accounts for much more variance in functional connectivity than the shortest path routing model, suggesting that the diffusion model is better suited to describing the structure-function relationship in the human brain at the macroscale.
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21
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Yan Y, Fan G, Liao X, Zhao X. Research trends and hotspots on connectomes from 2005 to 2021: A bibliometric and latent Dirichlet allocation application study. Front Neurosci 2022; 16:1046562. [PMID: 36620450 PMCID: PMC9814013 DOI: 10.3389/fnins.2022.1046562] [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: 09/16/2022] [Accepted: 12/06/2022] [Indexed: 12/24/2022] Open
Abstract
Background This study aimed to conduct a bibliometric analysis of publications on connectomes and illustrate its trends and hotspots using a machine-learning-based text mining algorithm. Methods Documents were retrieved from the Web of Science Core Collection (WoSCC) and Scopus databases and analyzed in Rstudio 1.3.1. Through quantitative and qualitative methods, the most productive and impactful academic journals in the field of connectomes were compared in terms of the total number of publications and h-index over time. Meanwhile, the countries/regions and institutions involved in connectome research were compared, as well as their scientific collaboration. The study analyzed topics and research trends by R package "bibliometrix." The major topics of connectomes were classified by Latent Dirichlet allocation (LDA). Results A total of 14,140 publications were included in the study. NEUROIMAGE ranked first in terms of publication volume (1,427 articles) and impact factor (h-index:122) among all the relevant journals. The majority of articles were published by developed countries, with the United States having the most. Harvard Medical School and the University of Pennsylvania were the two most productive institutions. Neuroimaging analysis technology and brain functions and diseases were the two major topics of connectome research. The application of machine learning, deep learning, and graph theory analysis in connectome research has become the current trend, while an increasing number of studies were concentrating on dynamic functional connectivity. Meanwhile, researchers have begun investigating alcohol use disorders and migraine in terms of brain connectivity in the past 2 years. Conclusion This study illustrates a comprehensive overview of connectome research and provides researchers with critical information for understanding the recent trends and hotspots of connectomes.
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Affiliation(s)
- Yangye Yan
- Tongji University School of Medicine, Shanghai Eastern Hospital Affiliated to Tongji University, Shanghai, China
| | - Guoxin Fan
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China,School of Biomedical Engineering, School of Medicine, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, China,Department of Spine Surgery, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiang Liao
- Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China,School of Biomedical Engineering, School of Medicine, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen University, Shenzhen, China,*Correspondence: Xiang Liao,
| | - Xudong Zhao
- Clinical Research Center for Mental Disorders, Chinese-German Institute of Mental Health, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China,Xudong Zhao,
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22
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Suarez LE, Yovel Y, van den Heuvel MP, Sporns O, Assaf Y, Lajoie G, Misic B. A connectomics-based taxonomy of mammals. eLife 2022; 11:e78635. [PMID: 36342363 PMCID: PMC9681214 DOI: 10.7554/elife.78635] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022] Open
Abstract
Mammalian taxonomies are conventionally defined by morphological traits and genetics. How species differ in terms of neural circuits and whether inter-species differences in neural circuit organization conform to these taxonomies is unknown. The main obstacle to the comparison of neural architectures has been differences in network reconstruction techniques, yielding species-specific connectomes that are not directly comparable to one another. Here, we comprehensively chart connectome organization across the mammalian phylogenetic spectrum using a common reconstruction protocol. We analyse the mammalian MRI (MaMI) data set, a database that encompasses high-resolution ex vivo structural and diffusion MRI scans of 124 species across 12 taxonomic orders and 5 superorders, collected using a unified MRI protocol. We assess similarity between species connectomes using two methods: similarity of Laplacian eigenspectra and similarity of multiscale topological features. We find greater inter-species similarities among species within the same taxonomic order, suggesting that connectome organization reflects established taxonomic relationships defined by morphology and genetics. While all connectomes retain hallmark global features and relative proportions of connection classes, inter-species variation is driven by local regional connectivity profiles. By encoding connectomes into a common frame of reference, these findings establish a foundation for investigating how neural circuits change over phylogeny, forging a link from genes to circuits to behaviour.
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Affiliation(s)
- Laura E Suarez
- Montréal Neurological Institute, McGill UniversityMontrealCanada
- Mila - Quebec Artificial Intelligence InstituteMontrealCanada
| | - Yossi Yovel
- School of Neurobiology, Biochemistry and Biophysics, Tel Aviv UniversityTel AvivIsrael
| | - Martijn P van den Heuvel
- Center for Neurogenomics and Cognitive Research, Vrije Universiteit AmsterdamAmsterdamNetherlands
| | - Olaf Sporns
- Psychological and Brain Sciences, Indiana UniversityBloomingtonUnited States
| | - Yaniv Assaf
- School of Neurobiology, Biochemistry and Biophysics, Tel Aviv UniversityTel AvivIsrael
| | | | - Bratislav Misic
- Montréal Neurological Institute, McGill UniversityMontrealCanada
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23
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Yuan L, Ma X, Li D, Ouyang L, Fan L, Li C, He Y, Chen X. Alteration of a brain network with stable and strong functional connections in subjects with schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2022; 8:91. [PMID: 36333328 PMCID: PMC9636375 DOI: 10.1038/s41537-022-00305-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
It is widely accepted that there are some common network patterns in the human brain. However, the existence of stable and strong functional connections in the human brain and whether they change in schizophrenia is still a question. By setting 1% connections with the smallest coefficient of variation, we found a widespread brain functional network (frame network) in healthy people(n = 380, two datasets from public databases). We then explored the alterations in a medicated group (60 subjects with schizophrenia vs 71 matched controls) and a drug-naive first-episode group (68 subjects with schizophrenia vs 45 matched controls). A linear support vector classifier (SVC) was constructed to distinguish patients and controls using the medicated patients' frame network. We found most frame connections of healthy people had high strength, which were symmetrical and connected the left and right hemispheres. Conversely, significant differences in frame connections were observed in both patient groups, which were positively correlated with negative symptoms (mainly language dysfunction). Additionally, patients' frame network were more left-lateralized, concentrating on the left frontal lobe, and was quite accurate at distinguishing medicated patients from controls (classifier accuracy was 78.63%, sensitivity was 86.67%, specificity was 76.06%, and the area under the curve (AUC) was 0.83). Furthermore, the results were repeated in the drug-naive set (accuracy was 84.96%, sensitivity was 85.29%, specificity was 88.89%, and AUC was 0.93). These findings indicate that the abnormal pattern of frame network in subjects with schizophrenia might provide new insights into the dysconnectivity in schizophrenia.
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Affiliation(s)
- Liu Yuan
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan, China
| | - Xiaoqian Ma
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan, China
| | - David Li
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan, China
| | - Lijun Ouyang
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan, China
| | - Lejia Fan
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan, China
| | - Chunwang Li
- Department of Radiology, Hunan Children's Hospital, Changsha, China
| | - Ying He
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan, China.
| | - Xiaogang Chen
- Department of Psychiatry, and National Clinical Research Center for Mental Disorders, The Second Xiangya Hospital of Central South University, Changsha, China.
- Mental Health Institute of Central South University, China National Technology Institute on Mental Disorders, Hunan Technology Institute of Psychiatry, Hunan Key Laboratory of Psychiatry and Mental Health, Changsha, Hunan, China.
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24
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Change of motifs in C. elegans reveals developmental principle of neural network. Biochem Biophys Res Commun 2022; 624:112-119. [PMID: 35940123 DOI: 10.1016/j.bbrc.2022.07.108] [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: 07/24/2022] [Accepted: 07/28/2022] [Indexed: 11/23/2022]
Abstract
Revealing the organizing principles of developing neural networks is a difficult but significant task in neuroscience. As a creature with a rather compact and well-studied neural network, C. elegans is an ideal subject for neuroscience study. However, the researches on its developing neural network remain challenging. The changes in specific properties of neural network across development may uncover part of its principles. Motif is a typical structure property that can be well applied to various complex networks. Here, we study the motif changes in C. elegans neural network across development. By counting the occurrence number of all three-node subgraph motif structures in its neural network at different stages of C. elegans development, along with those in corresponding random networks, we determine which of these structures are motifs for C. elegans, finding out the regular changes of motifs during its development. Combined with the potential function of these subgraph motifs and synaptic information, we gain insight into the organizing principle of neural network during development, which may increase our understanding of neuroscience and inspire the construction of artificial neural network.
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25
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Wajnerman Paz A. The global neuronal workspace as a broadcasting network. Netw Neurosci 2022; 6:1186-1204. [PMID: 38800460 PMCID: PMC11117084 DOI: 10.1162/netn_a_00261] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 06/13/2022] [Indexed: 05/29/2024] Open
Abstract
A new strategy for moving forward in the characterization of the global neuronal workspace (GNW) is proposed. According to Dehaene, Changeux, and colleagues (Dehaene, 2014, pp. 304, 312; Dehaene & Changeux, 2004, 2005), broadcasting is the main function of the GNW. However, the dynamic network properties described by recent graph theoretic GNW models are consistent with many large-scale communication processes that are different from broadcasting. We propose to apply a different graph theoretic approach, originally developed for optimizing information dissemination in communication networks, which can be used to identify the pattern of frequency and phase-specific directed functional connections that the GNW would exhibit only if it were a broadcasting network.
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Affiliation(s)
- Abel Wajnerman Paz
- Department of Philosophy, Universidad Alberto Hurtado, Santiago, Chile
- Neuroethics Buenos Aires, Buenos Aires, Argentina
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26
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Pechuk V, Goldman G, Salzberg Y, Chaubey AH, Bola RA, Hoffman JR, Endreson ML, Miller RM, Reger NJ, Portman DS, Ferkey DM, Schneidman E, Oren-Suissa M. Reprogramming the topology of the nociceptive circuit in C. elegans reshapes sexual behavior. Curr Biol 2022; 32:4372-4385.e7. [PMID: 36075218 DOI: 10.1016/j.cub.2022.08.038] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 06/28/2022] [Accepted: 08/15/2022] [Indexed: 10/14/2022]
Abstract
The effect of the detailed connectivity of a neural circuit on its function and the resulting behavior of the organism is a key question in many neural systems. Here, we study the circuit for nociception in C. elegans, which is composed of the same neurons in the two sexes that are wired differently. We show that the nociceptive sensory neurons respond similarly in the two sexes, yet the animals display sexually dimorphic behaviors to the same aversive stimuli. To uncover the role of the downstream network topology in shaping behavior, we learn and simulate network models that replicate the observed dimorphic behaviors and use them to predict simple network rewirings that would switch behavior between the sexes. We then show experimentally that these subtle synaptic rewirings indeed flip behavior. Interestingly, when presented with aversive cues, rewired males were compromised in finding mating partners, suggesting that network topologies that enable efficient avoidance of noxious cues have a reproductive "cost." Our results present a deconstruction of the design of a neural circuit that controls sexual behavior and how to reprogram it.
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Affiliation(s)
- Vladyslava Pechuk
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Gal Goldman
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Yehuda Salzberg
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Aditi H Chaubey
- Department of Biological Sciences, University at Buffalo, The State University of New York, Buffalo, NY 14260, USA
| | - R Aaron Bola
- Department of Biological Sciences, University at Buffalo, The State University of New York, Buffalo, NY 14260, USA
| | - Jonathon R Hoffman
- Department of Biological Sciences, University at Buffalo, The State University of New York, Buffalo, NY 14260, USA
| | - Morgan L Endreson
- Department of Biological Sciences, University at Buffalo, The State University of New York, Buffalo, NY 14260, USA
| | - Renee M Miller
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14627, USA
| | - Noah J Reger
- Department of Biomedical Genetics, University of Rochester, Rochester, NY 14642, USA
| | - Douglas S Portman
- Department of Biomedical Genetics, University of Rochester, Rochester, NY 14642, USA
| | - Denise M Ferkey
- Department of Biological Sciences, University at Buffalo, The State University of New York, Buffalo, NY 14260, USA
| | - Elad Schneidman
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel.
| | - Meital Oren-Suissa
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel.
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27
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Dvoretskii S, Gong Z, Gupta A, Parent J, Alicea B. Braitenberg Vehicles as Developmental Neurosimulation. ARTIFICIAL LIFE 2022; 28:369-395. [PMID: 35881679 DOI: 10.1162/artl_a_00384] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Connecting brain and behavior is a longstanding issue in the areas of behavioral science, artificial intelligence, and neurobiology. As is standard among models of artificial and biological neural networks, an analogue of the fully mature brain is presented as a blank slate. However, this does not consider the realities of biological development and developmental learning. Our purpose is to model the development of an artificial organism that exhibits complex behaviors. We introduce three alternate approaches to demonstrate how developmental embodied agents can be implemented. The resulting developmental Braitenberg vehicles (dBVs) will generate behaviors ranging from stimulus responses to group behavior that resembles collective motion. We will situate this work in the domain of artificial brain networks along with broader themes such as embodied cognition, feedback, and emergence. Our perspective is exemplified by three software instantiations that demonstrate how a BV-genetic algorithm hybrid model, a multisensory Hebbian learning model, and multi-agent approaches can be used to approach BV development. We introduce use cases such as optimized spatial cognition (vehicle-genetic algorithm hybrid model), hinges connecting behavioral and neural models (multisensory Hebbian learning model), and cumulative classification (multi-agent approaches). In conclusion, we consider future applications of the developmental neurosimulation approach.
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Affiliation(s)
| | | | | | | | - Bradly Alicea
- Orthogonal Research and Education Laboratory
- OpenWorm Foundation.
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28
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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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29
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Abstract
The nematode worm Caenorhabditis elegans has a relatively simple neural system for analysis of information transmission from sensory organ to muscle fiber. Consequently, this study includes an example of a neural circuit from the nematode worm, and a procedure is shown for measuring its information optimality by use of a logic gate model. This approach is useful where the assumptions are applicable for a neural circuit, and also for choosing between competing mathematical hypotheses that explain the function of a neural circuit. In this latter case, the logic gate model can estimate computational complexity and distinguish which of the mathematical models require fewer computations. In addition, the concept of information optimality is generalized to other biological systems, along with an extended discussion of its role in genetic-based pathways of organisms.
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30
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Krendl AC, Betzel RF. Social cognitive network neuroscience. Soc Cogn Affect Neurosci 2022; 17:510-529. [PMID: 35352125 PMCID: PMC9071476 DOI: 10.1093/scan/nsac020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 01/27/2022] [Accepted: 03/10/2022] [Indexed: 12/31/2022] Open
Abstract
Over the past three decades, research from the field of social neuroscience has identified a constellation of brain regions that relate to social cognition. Although these studies have provided important insights into the specific neural regions underlying social behavior, they may overlook the broader neural context in which those regions and the interactions between them are embedded. Network neuroscience is an emerging discipline that focuses on modeling and analyzing brain networks-collections of interacting neural elements. Because human cognition requires integrating information across multiple brain regions and systems, we argue that a novel social cognitive network neuroscience approach-which leverages methods from the field of network neuroscience and graph theory-can advance our understanding of how brain systems give rise to social behavior. This review provides an overview of the field of network neuroscience, discusses studies that have leveraged this approach to advance social neuroscience research, highlights the potential contributions of social cognitive network neuroscience to understanding social behavior and provides suggested tools and resources for conducting network neuroscience research.
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Affiliation(s)
- Anne C Krendl
- Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Richard F Betzel
- Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN 47405, USA
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31
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Flavell SW, Gordus A. Dynamic functional connectivity in the static connectome of Caenorhabditis elegans. Curr Opin Neurobiol 2022; 73:102515. [PMID: 35183877 PMCID: PMC9621599 DOI: 10.1016/j.conb.2021.12.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 12/16/2021] [Accepted: 12/22/2021] [Indexed: 01/01/2023]
Abstract
A hallmark of adaptive behavior is the ability to flexibly respond to sensory cues. To understand how neural circuits implement this flexibility, it is critical to resolve how a static anatomical connectome can be modulated such that functional connectivity in the network can be dynamically regulated. Here, we review recent work in the roundworm Caenorhabditis elegans on this topic. EM studies have mapped anatomical connectomes of many C. elegans animals, highlighting the level of stereotypy in the anatomical network. Brain-wide calcium imaging and studies of specified neural circuits have uncovered striking flexibility in the functional coupling of neurons. The coupling between neurons is controlled by neuromodulators that act over long timescales. This gives rise to persistent behavioral states that animals switch between, allowing them to generate adaptive behavioral responses across environmental conditions. Thus, the dynamic coupling of neurons enables multiple behavioral states to be encoded in a physically stereotyped connectome.
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Affiliation(s)
- Steven W Flavell
- Picower Institute for Learning and Memory, Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Andrew Gordus
- Department of Biology, Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA.
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32
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Xu N, LaGrow TJ, Anumba N, Lee A, Zhang X, Yousefi B, Bassil Y, Clavijo GP, Khalilzad Sharghi V, Maltbie E, Meyer-Baese L, Nezafati M, Pan WJ, Keilholz S. Functional Connectivity of the Brain Across Rodents and Humans. Front Neurosci 2022; 16:816331. [PMID: 35350561 PMCID: PMC8957796 DOI: 10.3389/fnins.2022.816331] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 02/14/2022] [Indexed: 12/15/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI), which measures the spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal, is increasingly utilized for the investigation of the brain's physiological and pathological functional activity. Rodents, as a typical animal model in neuroscience, play an important role in the studies that examine the neuronal processes that underpin the spontaneous fluctuations in the BOLD signal and the functional connectivity that results. Translating this knowledge from rodents to humans requires a basic knowledge of the similarities and differences across species in terms of both the BOLD signal fluctuations and the resulting functional connectivity. This review begins by examining similarities and differences in anatomical features, acquisition parameters, and preprocessing techniques, as factors that contribute to functional connectivity. Homologous functional networks are compared across species, and aspects of the BOLD fluctuations such as the topography of the global signal and the relationship between structural and functional connectivity are examined. Time-varying features of functional connectivity, obtained by sliding windowed approaches, quasi-periodic patterns, and coactivation patterns, are compared across species. Applications demonstrating the use of rs-fMRI as a translational tool for cross-species analysis are discussed, with an emphasis on neurological and psychiatric disorders. Finally, open questions are presented to encapsulate the future direction of the field.
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Affiliation(s)
- Nan Xu
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Theodore J. LaGrow
- Electrical and Computer Engineering, Georgia Tech, Atlanta, GA, United States
| | - Nmachi Anumba
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Azalea Lee
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
- Emory University School of Medicine, Atlanta, GA, United States
| | - Xiaodi Zhang
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Behnaz Yousefi
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Yasmine Bassil
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
| | - Gloria P. Clavijo
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | | | - Eric Maltbie
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Lisa Meyer-Baese
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Maysam Nezafati
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Wen-Ju Pan
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
| | - Shella Keilholz
- Biomedical Engineering, Emory University and Georgia Tech, Atlanta, GA, United States
- Neuroscience Graduate Program, Emory University, Atlanta, GA, United States
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33
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Networks behind the morphology and structural design of living systems. Phys Life Rev 2022; 41:1-21. [DOI: 10.1016/j.plrev.2022.03.001] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 03/04/2022] [Indexed: 01/06/2023]
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34
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Haber SN, Liu H, Seidlitz J, Bullmore E. Prefrontal connectomics: from anatomy to human imaging. Neuropsychopharmacology 2022; 47:20-40. [PMID: 34584210 PMCID: PMC8617085 DOI: 10.1038/s41386-021-01156-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 07/23/2021] [Accepted: 08/02/2021] [Indexed: 12/22/2022]
Abstract
The fundamental importance of prefrontal cortical connectivity to information processing and, therefore, disorders of cognition, emotion, and behavior has been recognized for decades. Anatomic tracing studies in animals have formed the basis for delineating the direct monosynaptic connectivity, from cells of origin, through axon trajectories, to synaptic terminals. Advances in neuroimaging combined with network science have taken the lead in developing complex wiring diagrams or connectomes of the human brain. A key question is how well these magnetic resonance imaging (MRI)-derived networks and hubs reflect the anatomic "hard wiring" first proposed to underlie the distribution of information for large-scale network interactions. In this review, we address this challenge by focusing on what is known about monosynaptic prefrontal cortical connections in non-human primates and how this compares to MRI-derived measurements of network organization in humans. First, we outline the anatomic cortical connections and pathways for each prefrontal cortex (PFC) region. We then review the available MRI-based techniques for indirectly measuring structural and functional connectivity, and introduce graph theoretical methods for analysis of hubs, modules, and topologically integrative features of the connectome. Finally, we bring these two approaches together, using specific examples, to demonstrate how monosynaptic connections, demonstrated by tract-tracing studies, can directly inform understanding of the composition of PFC nodes and hubs, and the edges or pathways that connect PFC to cortical and subcortical areas.
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Affiliation(s)
- Suzanne N. Haber
- grid.412750.50000 0004 1936 9166Department of Pharmacology and Physiology, University of Rochester School of Medicine & Dentistry, Rochester, NY 14642 USA ,grid.38142.3c000000041936754XDepartment of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, MA 02478 USA
| | - Hesheng Liu
- grid.259828.c0000 0001 2189 3475Department of Neuroscience, Medical University of South Carolina, Charleston, SC USA ,grid.38142.3c000000041936754XDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
| | - Jakob Seidlitz
- grid.25879.310000 0004 1936 8972Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Ed Bullmore
- grid.5335.00000000121885934Department of Psychiatry, University of Cambridge, Herchel Smith Building for Brain and Mind Sciences, Cambridge Biomedical Campus, Cambridge, CB2 0SZ UK
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35
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Searching for the principles of a less artificial A.I. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.101018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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36
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Sakelaris BG, Li Z, Sun J, Banerjee S, Booth V, Gourgou E. Modelling learning in C. elegans chemosensory and locomotive circuitry for T-maze navigation. Eur J Neurosci 2021; 55:354-376. [PMID: 34894022 PMCID: PMC9269982 DOI: 10.1111/ejn.15560] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 11/11/2021] [Accepted: 11/21/2021] [Indexed: 11/30/2022]
Abstract
Recently, a new type of Caenorhabditis elegans associative learning was reported, where nematodes learn to reach a target arm in an empty T‐maze, after they have successfully located reward (food) in the same side arm of a similar, baited, training maze. Here, we present a simplified mathematical model of C. elegans chemosensory and locomotive circuitry that replicates C. elegans navigation in a T‐maze and predicts the underlying mechanisms generating maze learning. Based on known neural circuitry, the model circuit responds to food‐released chemical cues by modulating motor neuron activity that drives simulated locomotion. We show that, through modulation of interneuron activity, such a circuit can mediate maze learning by acquiring a turning bias, even after a single training session. Simulated nematode maze navigation during training conditions in food‐baited mazes and during testing conditions in empty mazes is validated by comparing simulated behaviour with new experimental video data, extracted through the implementation of a custom‐made maze tracking algorithm. Our work provides a mathematical framework for investigating the neural mechanisms underlying this novel learning behaviour in C. elegans. Model results predict neuronal components involved in maze and spatial learning and identify target neurons and potential neural mechanisms for future experimental investigations into this learning behaviour.
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Affiliation(s)
| | - Zongyu Li
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor
| | - Jiawei Sun
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor
| | - Shurjo Banerjee
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor
| | - Victoria Booth
- Department of Mathematics, University of Michigan, Ann Arbor.,Department of Anesthesiology, University of Michigan, Ann Arbor
| | - Eleni Gourgou
- Department of Mechanical Engineering, University of Michigan, Ann Arbor.,Institute of Gerontology, Medical School, University of Michigan, Ann Arbor
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37
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Where the genome meets the connectome: Understanding how genes shape human brain connectivity. Neuroimage 2021; 244:118570. [PMID: 34508898 DOI: 10.1016/j.neuroimage.2021.118570] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 08/10/2021] [Accepted: 09/07/2021] [Indexed: 02/07/2023] Open
Abstract
The integration of modern neuroimaging methods with genetically informative designs and data can shed light on the molecular mechanisms underlying the structural and functional organization of the human connectome. Here, we review studies that have investigated the genetic basis of human brain network structure and function through three complementary frameworks: (1) the quantification of phenotypic heritability through classical twin designs; (2) the identification of specific DNA variants linked to phenotypic variation through association and related studies; and (3) the analysis of correlations between spatial variations in imaging phenotypes and gene expression profiles through the integration of neuroimaging and transcriptional atlas data. We consider the basic foundations, strengths, limitations, and discoveries associated with each approach. We present converging evidence to indicate that anatomical connectivity is under stronger genetic influence than functional connectivity and that genetic influences are not uniformly distributed throughout the brain, with phenotypic variation in certain regions and connections being under stronger genetic control than others. We also consider how the combination of imaging and genetics can be used to understand the ways in which genes may drive brain dysfunction in different clinical disorders.
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38
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Wu Y, Han X, Su Y, Glidewell M, Daniels JS, Liu J, Sengupta T, Rey-Suarez I, Fischer R, Patel A, Combs C, Sun J, Wu X, Christensen R, Smith C, Bao L, Sun Y, Duncan LH, Chen J, Pommier Y, Shi YB, Murphy E, Roy S, Upadhyaya A, Colón-Ramos D, La Riviere P, Shroff H. Multiview confocal super-resolution microscopy. Nature 2021; 600:279-284. [PMID: 34837071 PMCID: PMC8686173 DOI: 10.1038/s41586-021-04110-0] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 10/07/2021] [Indexed: 12/31/2022]
Abstract
Confocal microscopy1 remains a major workhorse in biomedical optical microscopy owing to its reliability and flexibility in imaging various samples, but suffers from substantial point spread function anisotropy, diffraction-limited resolution, depth-dependent degradation in scattering samples and volumetric bleaching2. Here we address these problems, enhancing confocal microscopy performance from the sub-micrometre to millimetre spatial scale and the millisecond to hour temporal scale, improving both lateral and axial resolution more than twofold while simultaneously reducing phototoxicity. We achieve these gains using an integrated, four-pronged approach: (1) developing compact line scanners that enable sensitive, rapid, diffraction-limited imaging over large areas; (2) combining line-scanning with multiview imaging, developing reconstruction algorithms that improve resolution isotropy and recover signal otherwise lost to scattering; (3) adapting techniques from structured illumination microscopy, achieving super-resolution imaging in densely labelled, thick samples; (4) synergizing deep learning with these advances, further improving imaging speed, resolution and duration. We demonstrate these capabilities on more than 20 distinct fixed and live samples, including protein distributions in single cells; nuclei and developing neurons in Caenorhabditis elegans embryos, larvae and adults; myoblasts in imaginal disks of Drosophila wings; and mouse renal, oesophageal, cardiac and brain tissues.
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Affiliation(s)
- Yicong Wu
- Laboratory of High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA.
| | - Xiaofei Han
- Laboratory of High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
- Department of Automation, Tsinghua University, Beijing, China
| | - Yijun Su
- Laboratory of High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
- Leica Microsystems, Buffalo Grove, IL, USA
- SVision, Bellevue, WA, USA
| | | | | | - Jiamin Liu
- Advanced Imaging and Microscopy Resource, National Institutes of Health, Bethesda, MD, USA
| | - Titas Sengupta
- Department of Neuroscience and Department of Cell Biology, Yale University School of Medicine, New Haven, CT, USA
| | - Ivan Rey-Suarez
- Institute for Physical Science and Technology, University of Maryland, College Park, MD, USA
| | - Robert Fischer
- Cell and Developmental Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Akshay Patel
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Christian Combs
- NHLBI Light Microscopy Facility, National Institutes of Health, Bethesda, MD, USA
| | - Junhui Sun
- Laboratory of Cardiac Physiology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Xufeng Wu
- NHLBI Light Microscopy Facility, National Institutes of Health, Bethesda, MD, USA
| | - Ryan Christensen
- Laboratory of High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
| | - Corey Smith
- Department of Radiology, University of Chicago, Chicago, IL, USA
| | - Lingyu Bao
- Section on Molecular Morphogenesis, Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Yilun Sun
- Laboratory of Molecular Pharmacology, Developmental Therapeutics Branch, Center for Cancer Research, National Institutes of Health, Bethesda, MD, USA
| | - Leighton H Duncan
- Department of Neuroscience and Department of Cell Biology, Yale University School of Medicine, New Haven, CT, USA
| | - Jiji Chen
- Advanced Imaging and Microscopy Resource, National Institutes of Health, Bethesda, MD, USA
| | - Yves Pommier
- Laboratory of Molecular Pharmacology, Developmental Therapeutics Branch, Center for Cancer Research, National Institutes of Health, Bethesda, MD, USA
| | - Yun-Bo Shi
- Section on Molecular Morphogenesis, Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), National Institutes of Health (NIH), Bethesda, MD, USA
| | - Elizabeth Murphy
- Laboratory of Cardiac Physiology, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Sougata Roy
- Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, USA
| | - Arpita Upadhyaya
- Institute for Physical Science and Technology, University of Maryland, College Park, MD, USA
- Department of Physics, University of Maryland, College Park, MD, USA
| | - Daniel Colón-Ramos
- Department of Neuroscience and Department of Cell Biology, Yale University School of Medicine, New Haven, CT, USA
- Marine Biological Laboratory, Woods Hole, MA, USA
- Instituto de Neurobiología, Recinto de Ciencias Médicas, Universidad de Puerto Rico, San Juan, Puerto Rico
| | - Patrick La Riviere
- Department of Radiology, University of Chicago, Chicago, IL, USA
- Marine Biological Laboratory, Woods Hole, MA, USA
| | - Hari Shroff
- Laboratory of High Resolution Optical Imaging, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
- Advanced Imaging and Microscopy Resource, National Institutes of Health, Bethesda, MD, USA
- Marine Biological Laboratory, Woods Hole, MA, USA
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39
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Sengupta T, Koonce NL, Vázquez-Martínez N, Moyle MW, Duncan LH, Emerson SE, Han X, Shao L, Wu Y, Santella A, Fan L, Bao Z, Mohler W, Shroff H, Colón-Ramos DA. Differential adhesion regulates neurite placement via a retrograde zippering mechanism. eLife 2021; 10:71171. [PMID: 34783657 PMCID: PMC8843091 DOI: 10.7554/elife.71171] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 11/15/2021] [Indexed: 11/13/2022] Open
Abstract
During development, neurites and synapses segregate into specific neighborhoods or layers within nerve bundles. The developmental programs guiding placement of neurites in specific layers, and hence their incorporation into specific circuits, are not well understood. We implement novel imaging methods and quantitative models to document the embryonic development of the C. elegans brain neuropil, and discover that differential adhesion mechanisms control precise placement of single neurites onto specific layers. Differential adhesion is orchestrated via developmentally-regulated expression of the IgCAM SYG-1, and its partner ligand SYG-2. Changes in SYG-1 expression across neuropil layers result in changes in adhesive forces, which sort SYG-2-expressing neurons. Sorting to layers occurs, not via outgrowth from the neurite tip, but via an alternate mechanism of retrograde zippering, involving interactions between neurite shafts. Our study indicates that biophysical principles from differential adhesion govern neurite placement and synaptic specificity in vivo in developing neuropil bundles.
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Affiliation(s)
- Titas Sengupta
- Yale University School of Medicine, New Haven, United States
| | - Noelle L Koonce
- Yale University School of Medicine, New Haven, United States
| | | | - Mark W Moyle
- Yale University School of Medicine, New Haven, United States
| | | | - Sarah E Emerson
- Yale University School of Medicine, New Haven, United States
| | - Xiaofei Han
- National Institutes of Health, Bethesda, United States
| | - Lin Shao
- Yale University School of Medicine, New Haven, United States
| | - Yicong Wu
- National Institutes of Health, Bethesda, United States
| | - Anthony Santella
- Developmental Biology Program, Molecular Cytology Core, Sloan-Kettering Institute, New York, United States
| | - Li Fan
- Helen and Robert Appel Alzheimer's Disease Institute, Weill Cornell Medicine, New York, United States
| | - Zhirong Bao
- Developmental Biology Program, Sloan-Kettering Institute, New York, United States
| | - William Mohler
- Department of Genetics and Developmental Biology, University of Connecticut Health Center, Farmington, United States
| | - Hari Shroff
- National Institutes of Health, Bethesda, United States
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40
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Oldham S, Ball G, Fornito A. Early and late development of hub connectivity in the human brain. Curr Opin Psychol 2021; 44:321-329. [PMID: 34896927 DOI: 10.1016/j.copsyc.2021.10.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 10/14/2021] [Accepted: 10/28/2021] [Indexed: 12/28/2022]
Abstract
Human brain networks undergo pronounced changes during development. The emergence of highly connected hub regions that can support integrated brain function is central to this maturational process, with these areas undergoing a particularly protracted period of development that extends into adulthood. The location of cortical network hubs emerges early but connections to and from hubs continue to strengthen throughout childhood and adolescence. Patterns of functional coupling in cortical association hubs are immature and incomplete at birth, but gradually strengthen during development. Early establishment of hub connectivity may provide a stable substrate that is refined by changes in tissue organization and microstructure, resulting in the emergence of complex functional dynamics by adulthood.
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Affiliation(s)
- Stuart Oldham
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia; Developmental Imaging, Murdoch Children's Research Institute, Victoria, Australia.
| | - Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, Victoria, Australia; Department of Paediatrics, University of Melbourne, Victoria, Australia
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
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41
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Hadjiabadi D, Lovett-Barron M, Raikov IG, Sparks FT, Liao Z, Baraban SC, Leskovec J, Losonczy A, Deisseroth K, Soltesz I. Maximally selective single-cell target for circuit control in epilepsy models. Neuron 2021; 109:2556-2572.e6. [PMID: 34197732 PMCID: PMC8448204 DOI: 10.1016/j.neuron.2021.06.007] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 04/19/2021] [Accepted: 06/04/2021] [Indexed: 12/21/2022]
Abstract
Neurological and psychiatric disorders are associated with pathological neural dynamics. The fundamental connectivity patterns of cell-cell communication networks that enable pathological dynamics to emerge remain unknown. Here, we studied epileptic circuits using a newly developed computational pipeline that leveraged single-cell calcium imaging of larval zebrafish and chronically epileptic mice, biologically constrained effective connectivity modeling, and higher-order motif-focused network analysis. We uncovered a novel functional cell type that preferentially emerged in the preseizure state, the superhub, that was unusually richly connected to the rest of the network through feedforward motifs, critically enhancing downstream excitation. Perturbation simulations indicated that disconnecting superhubs was significantly more effective in stabilizing epileptic circuits than disconnecting hub cells that were defined traditionally by connection count. In the dentate gyrus of chronically epileptic mice, superhubs were predominately modeled adult-born granule cells. Collectively, these results predict a new maximally selective and minimally invasive cellular target for seizure control.
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Affiliation(s)
- Darian Hadjiabadi
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA.
| | - Matthew Lovett-Barron
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | | | - Fraser T Sparks
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; The Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA
| | - Zhenrui Liao
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; The Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA
| | - Scott C Baraban
- Department of Neurological Surgery and Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Jure Leskovec
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Attila Losonczy
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; The Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA
| | - Karl Deisseroth
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA
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42
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Suárez LE, Richards BA, Lajoie G, Misic B. Learning function from structure in neuromorphic networks. NAT MACH INTELL 2021. [DOI: 10.1038/s42256-021-00376-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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43
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Arnatkeviciute A, Fulcher BD, Oldham S, Tiego J, Paquola C, Gerring Z, Aquino K, Hawi Z, Johnson B, Ball G, Klein M, Deco G, Franke B, Bellgrove MA, Fornito A. Genetic influences on hub connectivity of the human connectome. Nat Commun 2021; 12:4237. [PMID: 34244483 PMCID: PMC8271018 DOI: 10.1038/s41467-021-24306-2] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 06/03/2021] [Indexed: 02/06/2023] Open
Abstract
Brain network hubs are both highly connected and highly inter-connected, forming a critical communication backbone for coherent neural dynamics. The mechanisms driving this organization are poorly understood. Using diffusion-weighted magnetic resonance imaging in twins, we identify a major role for genes, showing that they preferentially influence connectivity strength between network hubs of the human connectome. Using transcriptomic atlas data, we show that connected hubs demonstrate tight coupling of transcriptional activity related to metabolic and cytoarchitectonic similarity. Finally, comparing over thirteen generative models of network growth, we show that purely stochastic processes cannot explain the precise wiring patterns of hubs, and that model performance can be improved by incorporating genetic constraints. Our findings indicate that genes play a strong and preferential role in shaping the functionally valuable, metabolically costly connections between connectome hubs.
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Affiliation(s)
- Aurina Arnatkeviciute
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia.
| | - Ben D Fulcher
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
| | - Stuart Oldham
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Jeggan Tiego
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
| | - Zachary Gerring
- Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Kevin Aquino
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
| | - Ziarih Hawi
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Beth Johnson
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, VIC, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia
| | - Marieke Klein
- Department of Human Genetics, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Gustavo Deco
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Barbara Franke
- Department of Human Genetics, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Mark A Bellgrove
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
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44
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Betzel R, Vértes P. Organizing principles of the C. elegans contactome. Cell Syst 2021; 12:689-691. [PMID: 34293322 DOI: 10.1016/j.cels.2021.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Two recent studies published in Nature generate and analyze, for the first time, the network of ∼100,000 membrane contacts between neurons in the C. elegans nerve ring. These novel data, extracted from legacy electron microscographs, represent a shift in our understanding of C. elegans neural networks and their similarity to mammalian brains.
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Affiliation(s)
- Richard Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Cognitive Science Program, Indiana University, Bloomington, IN 47405, USA; Program in Neuroscience, Indiana University, Bloomington, IN 47405, USA; Network Science Institute, Indiana University, Bloomington, IN 47405, USA.
| | - Petra Vértes
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK; The Alan Turing Institute, London SE5 8AF, UK.
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45
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Emmons SW, Yemini E, Zimmer M. Methods for analyzing neuronal structure and activity in Caenorhabditis elegans. Genetics 2021; 218:6303616. [PMID: 34151952 DOI: 10.1093/genetics/iyab072] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 04/20/2021] [Indexed: 11/12/2022] Open
Abstract
The model research animal Caenorhabditis elegans has unique properties making it particularly advantageous for studies of the nervous system. The nervous system is composed of a stereotyped complement of neurons connected in a consistent manner. Here, we describe methods for studying nervous system structure and function. The transparency of the animal makes it possible to visualize and identify neurons in living animals with fluorescent probes. These methods have been recently enhanced for the efficient use of neuron-specific reporter genes. Because of its simple structure, for a number of years, C. elegans has been at the forefront of connectomic studies defining synaptic connectivity by electron microscopy. This field is burgeoning with new, more powerful techniques, and recommended up-to-date methods are here described that encourage the possibility of new work in C. elegans. Fluorescent probes for single synapses and synaptic connections have allowed verification of the EM reconstructions and for experimental approaches to synapse formation. Advances in microscopy and in fluorescent reporters sensitive to Ca2+ levels have opened the way to observing activity within single neurons across the entire nervous system.
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Affiliation(s)
- Scott W Emmons
- Department of Genetics and Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY 1041, USA
| | - Eviatar Yemini
- Department of Biological Sciences, Howard Hughes Medical Institute, Columbia University, New York, NY 10027, USA
| | - Manuel Zimmer
- Department of Neuroscience and Developmental Biology, University of Vienna, Vienna 1090, Austria and.,Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), Vienna 1030, Austria
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46
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Wystrach A. Movements, embodiment and the emergence of decisions. Insights from insect navigation. Biochem Biophys Res Commun 2021; 564:70-77. [PMID: 34023071 DOI: 10.1016/j.bbrc.2021.04.114] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 04/06/2021] [Accepted: 04/27/2021] [Indexed: 02/07/2023]
Abstract
We readily infer that animals make decisions, but what this implies is usually not clearly defined. The notion of 'decision-making' ultimately stems from human introspection, and is thus loaded with anthropomorphic assumptions. Notably, the decision is made internally, is based on information, and precedes the goal directed behaviour. Also, making a decision implies that 'something' did it, thus hints at the presence of a cognitive mind, whose existence is independent of the decision itself. This view may convey some truth, but here I take the opposite stance. Using examples from research in insect navigation, this essay highlights how apparent decisions can emerge without a brain, how actions can precede information or how sophisticated goal directed behaviours can be implemented without neural decisions. This perspective requires us to shake off the idea that behaviour is a consequence of the brain; and embrace the concept that movements arise from - as much as participate in - distributed interactions between various computational centres - including the body - that reverberate in closed-loop with the environment. From this perspective we may start to picture how a cognitive mind can be the consequence, rather than the cause, of such neural and body movements.
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Affiliation(s)
- Antoine Wystrach
- Research Centre on Animal Cognition, Centre for Integrative Biology, CNRS, University of Toulouse, 118 route deNarbonne, F-31062, Toulouse, France.
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47
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Gourgou E, Adiga K, Goettemoeller A, Chen C, Hsu AL. Caenorhabditis elegans learning in a structured maze is a multisensory behavior. iScience 2021; 24:102284. [PMID: 33889812 PMCID: PMC8050377 DOI: 10.1016/j.isci.2021.102284] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 11/23/2020] [Accepted: 03/04/2021] [Indexed: 11/05/2022] Open
Abstract
We show that C. elegans nematodes learn to associate food with a combination of proprioceptive cues and information on the structure of their surroundings (maze), perceived through mechanosensation. By using the custom-made Worm-Maze platform, we demonstrate that C. elegans young adults can locate food in T-shaped mazes and, following that experience, learn to reach a specific maze arm. C. elegans learning inside the maze is possible after a single training session, it resembles working memory, and it prevails over conflicting environmental cues. We provide evidence that the observed learning is a food-triggered multisensory behavior, which requires mechanosensory and proprioceptive input, and utilizes cues about the structural features of nematodes' environment and their body actions. The CREB-like transcription factor and dopamine signaling are also involved in maze performance. Lastly, we show that the observed aging-driven decline of C. elegans learning ability in the maze can be reversed by starvation. C. elegans can be trained to reach a target arm in a T-shaped maze Learning requires the contribution of tactile and proprioceptive cues C. elegans follow a kind of response learning strategy in the maze environment Learning is short-term and sensitive to distraction
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Affiliation(s)
- Eleni Gourgou
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.,Institute of Gerontology, University of Michigan Medical School, Ann Arbor, MI 41809, USA
| | - Kavya Adiga
- Department of Internal Medicine, Division of Geriatrics & Palliative Medicine, University of Michigan Medical School, Ann Arbor, MI 41809, USA
| | - Anne Goettemoeller
- Neuroscience Program, College of Literature, Science and the Arts, University of Michigan, Ann Arbor, MI 41809, USA
| | - Chieh Chen
- Institute of Biochemistry and Molecular Biology, National Yang Ming University, Taipei, 112 Taiwan
| | - Ao-Lin Hsu
- Department of Internal Medicine, Division of Geriatrics & Palliative Medicine, University of Michigan Medical School, Ann Arbor, MI 41809, USA.,Institute of Biochemistry and Molecular Biology, National Yang Ming University, Taipei, 112 Taiwan.,Research Center for Healthy Aging and Institute of New Drug Development, China Medical University, Taichung, 404, Taiwan
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48
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George VK, Puppo F, Silva GA. Computing Temporal Sequences Associated With Dynamic Patterns on the C. elegans Connectome. Front Syst Neurosci 2021; 15:564124. [PMID: 33767613 PMCID: PMC7985353 DOI: 10.3389/fnsys.2021.564124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 02/04/2021] [Indexed: 12/03/2022] Open
Abstract
Understanding how the structural connectivity and spatial geometry of a network constrains the dynamics it is able to support is an active and open area of research. We simulated the plausible dynamics resulting from the known C. elegans connectome using a recent model and theoretical analysis that computes the dynamics of neurobiological networks by focusing on how local interactions among connected neurons give rise to the global dynamics in an emergent way. We studied the dynamics which resulted from stimulating a chemosensory neuron (ASEL) in a known feeding circuit, both in isolation and embedded in the full connectome. We show that contralateral motorneuron activations in ventral (VB) and dorsal (DB) classes of motorneurons emerged from the simulations, which are qualitatively similar to rhythmic motorneuron firing pattern associated with locomotion of the worm. One interpretation of these results is that there is an inherent—and we propose—purposeful structural wiring to the C. elegans connectome that has evolved to serve specific behavioral functions. To study network signaling pathways responsible for the dynamics we developed an analytic framework that constructs Temporal Sequences (TSeq), time-ordered walks of signals on graphs. We found that only 5% of TSeq are preserved between the isolated feeding network relative to its embedded counterpart. The remaining 95% of signaling pathways computed in the isolated network are not present in the embedded network. This suggests a cautionary note for computational studies of isolated neurobiological circuits and networks.
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Affiliation(s)
- Vivek Kurien George
- Department of Bioengineering, University of California, San Diego, San Diego, CA, United States.,Center for Engineered Natural Intelligence, University of California, San Diego, San Diego, CA, United States
| | - Francesca Puppo
- Department of Bioengineering, University of California, San Diego, San Diego, CA, United States.,Center for Engineered Natural Intelligence, University of California, San Diego, San Diego, CA, United States.,BioCircuits Institute, University of California, San Diego, San Diego, CA, United States
| | - Gabriel A Silva
- Department of Bioengineering, University of California, San Diego, San Diego, CA, United States.,Center for Engineered Natural Intelligence, University of California, San Diego, San Diego, CA, United States.,BioCircuits Institute, University of California, San Diego, San Diego, CA, United States.,Department of Neurosciences, University of California, San Diego, San Diego, CA, United States
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Structural and developmental principles of neuropil assembly in C. elegans. Nature 2021; 591:99-104. [PMID: 33627875 PMCID: PMC8385650 DOI: 10.1038/s41586-020-03169-5] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 11/12/2020] [Indexed: 01/31/2023]
Abstract
Neuropil is a fundamental form of tissue organization within the brain1, in which densely packed neurons synaptically interconnect into precise circuit architecture2,3. However, the structural and developmental principles that govern this nanoscale precision remain largely unknown4,5. Here we use an iterative data coarse-graining algorithm termed 'diffusion condensation'6 to identify nested circuit structures within the Caenorhabditis elegans neuropil, which is known as the nerve ring. We show that the nerve ring neuropil is largely organized into four strata that are composed of related behavioural circuits. The stratified architecture of the neuropil is a geometrical representation of the functional segregation of sensory information and motor outputs, with specific sensory organs and muscle quadrants mapping onto particular neuropil strata. We identify groups of neurons with unique morphologies that integrate information across strata and that create neural structures that cage the strata within the nerve ring. We use high resolution light-sheet microscopy7,8 coupled with lineage-tracing and cell-tracking algorithms9,10 to resolve the developmental sequence and reveal principles of cell position, migration and outgrowth that guide stratified neuropil organization. Our results uncover conserved structural design principles that underlie the architecture and function of the nerve ring neuropil, and reveal a temporal progression of outgrowth-based on pioneer neurons-that guides the hierarchical development of the layered neuropil. Our findings provide a systematic blueprint for using structural and developmental approaches to understand neuropil organization within the brain.
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Brittin CA, Cook SJ, Hall DH, Emmons SW, Cohen N. A multi-scale brain map derived from whole-brain volumetric reconstructions. Nature 2021; 591:105-110. [DOI: 10.1038/s41586-021-03284-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 01/25/2021] [Indexed: 12/16/2022]
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