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Yu D, Li T, Ding Q, Wu Y, Fu Z, Zhan X, Yang L, Jia Y. Maintenance of delay-period activity in working memory task is modulated by local network structure. PLoS Comput Biol 2024; 20:e1012415. [PMID: 39226309 DOI: 10.1371/journal.pcbi.1012415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 08/14/2024] [Indexed: 09/05/2024] Open
Abstract
Revealing the relationship between neural network structure and function is one central theme of neuroscience. In the context of working memory (WM), anatomical data suggested that the topological structure of microcircuits within WM gradient network may differ, and the impact of such structural heterogeneity on WM activity remains unknown. Here, we proposed a spiking neural network model that can replicate the fundamental characteristics of WM: delay-period neural activity involves association cortex but not sensory cortex. First, experimentally observed receptor expression gradient along the WM gradient network is reproduced by our network model. Second, by analyzing the correlation between different local structures and duration of WM activity, we demonstrated that small-worldness, excitation-inhibition balance, and cycle structures play crucial roles in sustaining WM-related activity. To elucidate the relationship between the structure and functionality of neural networks, structural circuit gradients in brain should also be subject to further measurement. Finally, combining anatomical data, we simulated the duration of WM activity across different brain regions, its maintenance relies on the interaction between local and distributed networks. Overall, network structural gradient and interaction between local and distributed networks are of great significance for WM.
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Affiliation(s)
- Dong Yu
- Institute of Biophysics, Central China Normal University, Wuhan, China
- College of Physical Science and Technology, Central China Normal University, Wuhan, China
| | - Tianyu Li
- Institute of Biophysics, Central China Normal University, Wuhan, China
- College of Physical Science and Technology, Central China Normal University, Wuhan, China
| | - Qianming Ding
- Institute of Biophysics, Central China Normal University, Wuhan, China
- College of Physical Science and Technology, Central China Normal University, Wuhan, China
| | - Yong Wu
- Institute of Biophysics, Central China Normal University, Wuhan, China
- College of Physical Science and Technology, Central China Normal University, Wuhan, China
| | - Ziying Fu
- Institute of Biophysics, Central China Normal University, Wuhan, China
- School of Life Sciences, Central China Normal University, Wuhan, China
| | - Xuan Zhan
- Institute of Biophysics, Central China Normal University, Wuhan, China
- College of Physical Science and Technology, Central China Normal University, Wuhan, China
| | - Lijian Yang
- Institute of Biophysics, Central China Normal University, Wuhan, China
- College of Physical Science and Technology, Central China Normal University, Wuhan, China
| | - Ya Jia
- Institute of Biophysics, Central China Normal University, Wuhan, China
- College of Physical Science and Technology, Central China Normal University, Wuhan, China
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2
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Clusella P, Manubens-Gil L, Garcia-Ojalvo J, Dierssen M. Modeling the impact of neuromorphological alterations in Down syndrome on fast neural oscillations. PLoS Comput Biol 2024; 20:e1012259. [PMID: 38968294 PMCID: PMC11253980 DOI: 10.1371/journal.pcbi.1012259] [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: 01/13/2024] [Revised: 07/17/2024] [Accepted: 06/18/2024] [Indexed: 07/07/2024] Open
Abstract
Cognitive disorders, including Down syndrome (DS), present significant morphological alterations in neuron architectural complexity. However, the relationship between neuromorphological alterations and impaired brain function is not fully understood. To address this gap, we propose a novel computational model that accounts for the observed cell deformations in DS. The model consists of a cross-sectional layer of the mouse motor cortex, composed of 3000 neurons. The network connectivity is obtained by accounting explicitly for two single-neuron morphological parameters: the mean dendritic tree radius and the spine density in excitatory pyramidal cells. We obtained these values by fitting reconstructed neuron data corresponding to three mouse models: wild-type (WT), transgenic (TgDyrk1A), and trisomic (Ts65Dn). Our findings reveal a dynamic interplay between pyramidal and fast-spiking interneurons leading to the emergence of gamma activity (∼40 Hz). In the DS models this gamma activity is diminished, corroborating experimental observations and validating our computational methodology. We further explore the impact of disrupted excitation-inhibition balance by mimicking the reduction recurrent inhibition present in DS. In this case, gamma power exhibits variable responses as a function of the external input to the network. Finally, we perform a numerical exploration of the morphological parameter space, unveiling the direct influence of each structural parameter on gamma frequency and power. Our research demonstrates a clear link between changes in morphology and the disruption of gamma oscillations in DS. This work underscores the potential of computational modeling to elucidate the relationship between neuron architecture and brain function, and ultimately improve our understanding of cognitive disorders.
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Affiliation(s)
- Pau Clusella
- Department of Mathematics, Universitat Politècnica de Catalunya, Manresa, Spain
| | - Linus Manubens-Gil
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu, China
| | - Jordi Garcia-Ojalvo
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Mara Dierssen
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology (BIST), Barcelona, Spain
- Systems Neurology and Neurotherapies, Hospital del Mar Research Institute, Barcelona, Spain
- Center for Biomedical Research in the Network of Rare Diseases (CIBERER), Spain
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3
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Mittal D, Narayanan R. Network motifs in cellular neurophysiology. Trends Neurosci 2024; 47:506-521. [PMID: 38806296 DOI: 10.1016/j.tins.2024.04.008] [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: 01/15/2024] [Revised: 04/08/2024] [Accepted: 04/29/2024] [Indexed: 05/30/2024]
Abstract
Concepts from network science and graph theory, including the framework of network motifs, have been frequently applied in studying neuronal networks and other biological complex systems. Network-based approaches can also be used to study the functions of individual neurons, where cellular elements such as ion channels and membrane voltage are conceptualized as nodes within a network, and their interactions are denoted by edges. Network motifs in this context provide functional building blocks that help to illuminate the principles of cellular neurophysiology. In this review we build a case that network motifs operating within neurons provide tools for defining the functional architecture of single-neuron physiology and neuronal adaptations. We highlight the presence of such computational motifs in the cellular mechanisms underlying action potential generation, neuronal oscillations, dendritic integration, and neuronal plasticity. Future work applying the network motifs perspective may help to decipher the functional complexities of neurons and their adaptation during health and disease.
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Affiliation(s)
- Divyansh Mittal
- Centre for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Rishikesh Narayanan
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India.
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4
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Celii B, Papadopoulos S, Ding Z, Fahey PG, Wang E, Papadopoulos C, Kunin AB, Patel S, Bae JA, Bodor AL, Brittain D, Buchanan J, Bumbarger DJ, Castro MA, Cobos E, Dorkenwald S, Elabbady L, Halageri A, Jia Z, Jordan C, Kapner D, Kemnitz N, Kinn S, Lee K, Li K, Lu R, Macrina T, Mahalingam G, Mitchell E, Mondal SS, Mu S, Nehoran B, Popovych S, Schneider-Mizell CM, Silversmith W, Takeno M, Torres R, Turner NL, Wong W, Wu J, Yu SC, Yin W, Xenes D, Kitchell LM, Rivlin PK, Rose VA, Bishop CA, Wester B, Froudarakis E, Walker EY, Sinz F, Seung HS, Collman F, da Costa NM, Reid RC, Pitkow X, Tolias AS, Reimer J. NEURD: automated proofreading and feature extraction for connectomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.03.14.532674. [PMID: 36993282 PMCID: PMC10055177 DOI: 10.1101/2023.03.14.532674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution (Shapson-Coe et al., 2021; Consortium et al., 2021). Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML) (Lee et al., 2017; Wu et al., 2021; Lu et al., 2021; Macrina et al., 2021). Automated segmentation methods can now yield exceptionally accurate reconstructions of cells, but despite this accuracy, laborious post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons produced by these segmentations contain detailed morphological information, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting information about these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes each meshed neuron into a compact and extensively-annotated graph representation. With these feature-rich graphs, we implement workflows to automate a variety of tasks that would otherwise require extensive manual effort, such as state of the art automated post-hoc proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and computation of other features. These features enable many downstream analyses of neural morphology and connectivity, making these new massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.
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5
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Kim R, Ananth MR, Desai NS, Role LW, Talmage DA. Distinct subpopulations of ventral pallidal cholinergic projection neurons encode valence of olfactory stimuli. Cell Rep 2024; 43:114009. [PMID: 38536818 PMCID: PMC11080946 DOI: 10.1016/j.celrep.2024.114009] [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: 10/18/2023] [Revised: 03/08/2024] [Accepted: 03/13/2024] [Indexed: 04/09/2024] Open
Abstract
To better understand the function of cholinergic projection neurons in the ventral pallidum (VP), we examined behavioral responses to appetitive (APP) and aversive (AV) odors that elicited approach or avoidance, respectively. Exposure to each odor increased cFos expression and calcium signaling in VP cholinergic neurons. Activity and Cre-dependent viral vectors selectively labeled VP cholinergic neurons that were activated and reactivated in response to either APP or AV odors, but not both, identifying two non-overlapping populations of VP cholinergic neurons differentially activated by the valence of olfactory stimuli. These two subpopulations showed differences in electrophysiological properties, morphology, and projections to the basolateral amygdala. Although VP neurons are engaged in both approach and avoidance behavioral responses, cholinergic signaling is only required for approach behavior. Thus, two distinct subpopulations of VP cholinergic neurons differentially encode valence of olfactory stimuli and play distinct roles in approach and avoidance behaviors.
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Affiliation(s)
- Ronald Kim
- Genetics of Neuronal Signaling Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Mala R Ananth
- Circuits, Synapses and Molecular Signaling Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Niraj S Desai
- Circuits, Synapses and Molecular Signaling Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA
| | - Lorna W Role
- Circuits, Synapses and Molecular Signaling Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA.
| | - David A Talmage
- Genetics of Neuronal Signaling Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA.
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6
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Bast A, Fruengel R, de Kock CPJ, Oberlaender M. Network-neuron interactions underlying sensory responses of layer 5 pyramidal tract neurons in barrel cortex. PLoS Comput Biol 2024; 20:e1011468. [PMID: 38626210 PMCID: PMC11051592 DOI: 10.1371/journal.pcbi.1011468] [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: 08/25/2023] [Revised: 04/26/2024] [Accepted: 03/14/2024] [Indexed: 04/18/2024] Open
Abstract
Neurons in the cerebral cortex receive thousands of synaptic inputs per second from thousands of presynaptic neurons. How the dendritic location of inputs, their timing, strength, and presynaptic origin, in conjunction with complex dendritic physiology, impact the transformation of synaptic input into action potential (AP) output remains generally unknown for in vivo conditions. Here, we introduce a computational approach to reveal which properties of the input causally underlie AP output, and how this neuronal input-output computation is influenced by the morphology and biophysical properties of the dendrites. We demonstrate that this approach allows dissecting of how different input populations drive in vivo observed APs. For this purpose, we focus on fast and broadly tuned responses that pyramidal tract neurons in layer 5 (L5PTs) of the rat barrel cortex elicit upon passive single whisker deflections. By reducing a multi-scale model that we reported previously, we show that three features are sufficient to predict with high accuracy the sensory responses and receptive fields of L5PTs under these specific in vivo conditions: the count of active excitatory versus inhibitory synapses preceding the response, their spatial distribution on the dendrites, and the AP history. Based on these three features, we derive an analytically tractable description of the input-output computation of L5PTs, which enabled us to dissect how synaptic input from thalamus and different cell types in barrel cortex contribute to these responses. We show that the input-output computation is preserved across L5PTs despite morphological and biophysical diversity of their dendrites. We found that trial-to-trial variability in L5PT responses, and cell-to-cell variability in their receptive fields, are sufficiently explained by variability in synaptic input from the network, whereas variability in biophysical and morphological properties have minor contributions. Our approach to derive analytically tractable models of input-output computations in L5PTs provides a roadmap to dissect network-neuron interactions underlying L5PT responses across different in vivo conditions and for other cell types.
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Affiliation(s)
- Arco Bast
- In Silico Brain Sciences Group, Max Planck Institute for Neurobiology of Behavior ˗ caesar, Bonn, Germany
- International Max Planck Research School (IMPRS) for Brain and Behavior, Bonn, Germany
| | - Rieke Fruengel
- In Silico Brain Sciences Group, Max Planck Institute for Neurobiology of Behavior ˗ caesar, Bonn, Germany
- International Max Planck Research School (IMPRS) for Brain and Behavior, Bonn, Germany
| | - Christiaan P. J. de Kock
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Marcel Oberlaender
- In Silico Brain Sciences Group, Max Planck Institute for Neurobiology of Behavior ˗ caesar, Bonn, Germany
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
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7
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Qian P, Manubens-Gil L, Jiang S, Peng H. Non-homogenous axonal bouton distribution in whole-brain single-cell neuronal networks. Cell Rep 2024; 43:113871. [PMID: 38451816 DOI: 10.1016/j.celrep.2024.113871] [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: 09/09/2023] [Revised: 01/08/2024] [Accepted: 02/09/2024] [Indexed: 03/09/2024] Open
Abstract
We examined the distribution of pre-synaptic contacts in axons of mouse neurons and constructed whole-brain single-cell neuronal networks using an extensive dataset of 1,891 fully reconstructed neurons. We found that bouton locations were not homogeneous throughout the axon and among brain regions. As our algorithm was able to generate whole-brain single-cell connectivity matrices from full morphology reconstruction datasets, we further found that non-homogeneous bouton locations have a significant impact on network wiring, including degree distribution, triad census, and community structure. By perturbing neuronal morphology, we further explored the link between anatomical details and network topology. In our in silico exploration, we found that dendritic and axonal tree span would have the greatest impact on network wiring, followed by synaptic contact deletion. Our results suggest that neuroanatomical details must be carefully addressed in studies of whole-brain networks at the single-cell level.
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Affiliation(s)
- Penghao Qian
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, State Key Laboratory of Digital Medical Engineering, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China; School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Linus Manubens-Gil
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, State Key Laboratory of Digital Medical Engineering, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China.
| | - Shengdian Jiang
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, State Key Laboratory of Digital Medical Engineering, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China; School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu 210096, China
| | - Hanchuan Peng
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, State Key Laboratory of Digital Medical Engineering, Institute for Brain and Intelligence, Southeast University, Nanjing, Jiangsu 210096, China.
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8
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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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9
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Caznok Silveira AC, Antunes ASLM, Athié MCP, da Silva BF, Ribeiro dos Santos JV, Canateli C, Fontoura MA, Pinto A, Pimentel-Silva LR, Avansini SH, de Carvalho M. Between neurons and networks: investigating mesoscale brain connectivity in neurological and psychiatric disorders. Front Neurosci 2024; 18:1340345. [PMID: 38445254 PMCID: PMC10912403 DOI: 10.3389/fnins.2024.1340345] [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: 11/17/2023] [Accepted: 01/29/2024] [Indexed: 03/07/2024] Open
Abstract
The study of brain connectivity has been a cornerstone in understanding the complexities of neurological and psychiatric disorders. It has provided invaluable insights into the functional architecture of the brain and how it is perturbed in disorders. However, a persistent challenge has been achieving the proper spatial resolution, and developing computational algorithms to address biological questions at the multi-cellular level, a scale often referred to as the mesoscale. Historically, neuroimaging studies of brain connectivity have predominantly focused on the macroscale, providing insights into inter-regional brain connections but often falling short of resolving the intricacies of neural circuitry at the cellular or mesoscale level. This limitation has hindered our ability to fully comprehend the underlying mechanisms of neurological and psychiatric disorders and to develop targeted interventions. In light of this issue, our review manuscript seeks to bridge this critical gap by delving into the domain of mesoscale neuroimaging. We aim to provide a comprehensive overview of conditions affected by aberrant neural connections, image acquisition techniques, feature extraction, and data analysis methods that are specifically tailored to the mesoscale. We further delineate the potential of brain connectivity research to elucidate complex biological questions, with a particular focus on schizophrenia and epilepsy. This review encompasses topics such as dendritic spine quantification, single neuron morphology, and brain region connectivity. We aim to showcase the applicability and significance of mesoscale neuroimaging techniques in the field of neuroscience, highlighting their potential for gaining insights into the complexities of neurological and psychiatric disorders.
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Affiliation(s)
- Ana Clara Caznok Silveira
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
- School of Electrical and Computer Engineering, University of Campinas, Campinas, Brazil
| | | | - Maria Carolina Pedro Athié
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Bárbara Filomena da Silva
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | | | - Camila Canateli
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Marina Alves Fontoura
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Allan Pinto
- Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | | | - Simoni Helena Avansini
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
| | - Murilo de Carvalho
- National Laboratory of Biosciences, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
- Brazilian Synchrotron Light Laboratory, Brazilian Center for Research in Energy and Materials, Campinas, Brazil
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10
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Schneider-Mizell CM, Bodor AL, Brittain D, Buchanan J, Bumbarger DJ, Elabbady L, Gamlin C, Kapner D, Kinn S, Mahalingam G, Seshamani S, Suckow S, Takeno M, Torres R, Yin W, Dorkenwald S, Bae JA, Castro MA, Halageri A, Jia Z, Jordan C, Kemnitz N, Lee K, Li K, Lu R, Macrina T, Mitchell E, Mondal SS, Mu S, Nehoran B, Popovych S, Silversmith W, Turner NL, Wong W, Wu J, Reimer J, Tolias AS, Seung HS, Reid RC, Collman F, Maçarico da Costa N. Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.01.23.525290. [PMID: 36747710 PMCID: PMC9900837 DOI: 10.1101/2023.01.23.525290] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity powerfully shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy1 to investigate the connectivity of all inhibitory neurons across a densely-segmented neuronal population of 1352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibitory connections with more than 70,000 synapses. Taking a data-driven approach inspired by classical neuroanatomy, we classified inhibitory neurons based on the relative targeting of dendritic compartments and other inhibitory cells and developed a novel classification of excitatory neurons based on the morphological and synaptic input properties. The synaptic connectivity between inhibitory cells revealed a novel class of disinhibitory specialist targeting basket cells, in addition to familiar subclasses. Analysis of the inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of certain subpopulations spatially intermingled with other potential targets. Inhibitory targeting was organized into "motif groups," diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Sam Kinn
- Allen Institute for Brain Science, Seattle, WA
| | | | | | | | - Marc Takeno
- Allen Institute for Brain Science, Seattle, WA
| | | | - Wenjing Yin
- Allen Institute for Brain Science, Seattle, WA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - J Alexander Bae
- Princeton Neuroscience Institute, Princeton University, NJ
- Electrical and Computer Engineering Department, Princeton University
| | | | | | - Zhen Jia
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - Chris Jordan
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Kisuk Lee
- Brain & Cognitive Sciences Department, Massachusetts Institute of Technology
| | - Kai Li
- Computer Science Department, Princeton University
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - Eric Mitchell
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Shanka Subhra Mondal
- Princeton Neuroscience Institute, Princeton University, NJ
- Electrical and Computer Engineering Department, Princeton University
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Barak Nehoran
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | | | - Nicholas L Turner
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - William Wong
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, NJ
| | - Jacob Reimer
- Department of Neuroscience, Baylor College of Medicine, Houston, TX
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, TX
- Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine
- Department of Electrical and Computer Engineering, Rice University
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, NJ
- Computer Science Department, Princeton University
| | - R Clay Reid
- Allen Institute for Brain Science, Seattle, WA
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11
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Timonidis N, Bakker R, Rubio-Teves M, Alonso-Martínez C, Garcia-Amado M, Clascá F, Tiesinga PHE. Translating single-neuron axonal reconstructions into meso-scale connectivity statistics in the mouse somatosensory thalamus. Front Neuroinform 2023; 17:1272243. [PMID: 38107469 PMCID: PMC10722239 DOI: 10.3389/fninf.2023.1272243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 11/13/2023] [Indexed: 12/19/2023] Open
Abstract
Characterizing the connectomic and morphological diversity of thalamic neurons is key for better understanding how the thalamus relays sensory inputs to the cortex. The recent public release of complete single-neuron morphological reconstructions enables the analysis of previously inaccessible connectivity patterns from individual neurons. Here we focus on the Ventral Posteromedial (VPM) nucleus and characterize the full diversity of 257 VPM neurons, obtained by combining data from the MouseLight and Braintell projects. Neurons were clustered according to their most dominantly targeted cortical area and further subdivided by their jointly targeted areas. We obtained a 2D embedding of morphological diversity using the dissimilarity between all pairs of axonal trees. The curved shape of the embedding allowed us to characterize neurons by a 1-dimensional coordinate. The coordinate values were aligned both with the progression of soma position along the dorsal-ventral and lateral-medial axes and with that of axonal terminals along the posterior-anterior and medial-lateral axes, as well as with an increase in the number of branching points, distance from soma and branching width. Taken together, we have developed a novel workflow for linking three challenging aspects of connectomics, namely the topography, higher order connectivity patterns and morphological diversity, with VPM as a test-case. The workflow is linked to a unified access portal that contains the morphologies and integrated with 2D cortical flatmap and subcortical visualization tools. The workflow and resulting processed data have been made available in Python, and can thus be used for modeling and experimentally validating new hypotheses on thalamocortical connectivity.
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Affiliation(s)
- Nestor Timonidis
- Neuroinformatics Department, Donders Centre for Neuroscience, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Rembrandt Bakker
- Neuroinformatics Department, Donders Centre for Neuroscience, Radboud University Nijmegen, Nijmegen, Netherlands
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany
| | - Mario Rubio-Teves
- Department of Anatomy and Neuroscience, School of Medicine, Autónoma de Madrid University, Madrid, Spain
| | - Carmen Alonso-Martínez
- Department of Anatomy and Neuroscience, School of Medicine, Autónoma de Madrid University, Madrid, Spain
| | - Maria Garcia-Amado
- Department of Anatomy and Neuroscience, School of Medicine, Autónoma de Madrid University, Madrid, Spain
| | - Francisco Clascá
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA BRAIN Institute I, Jülich Research Centre, Jülich, Germany
| | - Paul H. E. Tiesinga
- Neuroinformatics Department, Donders Centre for Neuroscience, Radboud University Nijmegen, Nijmegen, Netherlands
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12
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Arnaudon A, Reva M, Zbili M, Markram H, Van Geit W, Kanari L. Controlling morpho-electrophysiological variability of neurons with detailed biophysical models. iScience 2023; 26:108222. [PMID: 37953946 PMCID: PMC10638024 DOI: 10.1016/j.isci.2023.108222] [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: 05/08/2023] [Revised: 07/21/2023] [Accepted: 10/12/2023] [Indexed: 11/14/2023] Open
Abstract
Variability, which is known to be a universal feature among biological units such as neuronal cells, holds significant importance, as, for example, it enables a robust encoding of a high volume of information in neuronal circuits and prevents hypersynchronizations. While most computational studies on electrophysiological variability in neuronal circuits were done with single-compartment neuron models, we instead focus on the variability of detailed biophysical models of neuron multi-compartmental morphologies. We leverage a Markov chain Monte Carlo method to generate populations of electrical models reproducing the variability of experimental recordings while being compatible with a set of morphologies to faithfully represent specifi morpho-electrical type. We demonstrate our approach on layer 5 pyramidal cells and study the morpho-electrical variability and in particular, find that morphological variability alone is insufficient to reproduce electrical variability. Overall, this approach provides a strong statistical basis to create detailed models of neurons with controlled variability.
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Affiliation(s)
- Alexis Arnaudon
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Maria Reva
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Mickael Zbili
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Henry Markram
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Werner Van Geit
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Lida Kanari
- Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
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13
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Troidl J, Warchol S, Choi J, Matelsky J, Dhanyasi N, Wang X, Wester B, Wei D, Lichtman JW, Pfister H, Beyer J. Vimo - Visual Analysis of Neuronal Connectivity Motifs. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2023; PP:10.1109/TVCG.2023.3327388. [PMID: 37883279 PMCID: PMC11045665 DOI: 10.1109/tvcg.2023.3327388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Recent advances in high-resolution connectomics provide researchers with access to accurate petascale reconstructions of neuronal circuits and brain networks for the first time. Neuroscientists are analyzing these networks to better understand information processing in the brain. In particular, scientists are interested in identifying specific small network motifs, i.e., repeating subgraphs of the larger brain network that are believed to be neuronal building blocks. Although such motifs are typically small (e.g., 2 - 6 neurons), the vast data sizes and intricate data complexity present significant challenges to the search and analysis process. To analyze these motifs, it is crucial to review instances of a motif in the brain network and then map the graph structure to detailed 3D reconstructions of the involved neurons and synapses. We present Vimo, an interactive visual approach to analyze neuronal motifs and motif chains in large brain networks. Experts can sketch network motifs intuitively in a visual interface and specify structural properties of the involved neurons and synapses to query large connectomics datasets. Motif instances (MIs) can be explored in high-resolution 3D renderings. To simplify the analysis of MIs, we designed a continuous focus&context metaphor inspired by visual abstractions. This allows users to transition from a highly-detailed rendering of the anatomical structure to views that emphasize the underlying motif structure and synaptic connectivity. Furthermore, Vimo supports the identification of motif chains where a motif is used repeatedly (e.g., 2 - 4 times) to form a larger network structure. We evaluate Vimo in a user study and an in-depth case study with seven domain experts on motifs in a large connectome of the fruit fly, including more than 21,000 neurons and 20 million synapses. We find that Vimo enables hypothesis generation and confirmation through fast analysis iterations and connectivity highlighting.
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14
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Kim R, Ananth M, Desai NS, Role LW, Talmage DA. Distinct subpopulations of ventral pallidal cholinergic projection neurons encode valence of olfactory stimuli. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.06.561261. [PMID: 37986753 PMCID: PMC10659428 DOI: 10.1101/2023.10.06.561261] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
The ventral pallidum (VP) mediates motivated behaviors largely via the action of VP GABA and glutamatergic neurons. In addition to these neuronal subtypes, there is a population of cholinergic projection neurons in the VP, whose functional significance remains unclear. To understand the functional role of VP cholinergic neurons, we first examined behavioral responses to an appetitive (APP) odor that elicited approach, and an aversive (AV) odor that led to avoidance. To examine how VP cholinergic neurons were engaged in APP vs. AV responses, we used an immediate early gene marker and in-vivo fiber photometry, examining the activation profile of VP cholinergic neurons in response to each odor. Exposure to each odor led to an increase in the number of cFos counts and increased calcium signaling of VP cholinergic neurons. Activity and cre-dependent viral vectors were designed to label engaged VP cholinergic neurons in two distinct contexts: (1) exposure to the APP odor, (2) followed by subsequent exposure to the AV odor, and vice versa. These studies revealed two distinct, non-overlapping subpopulations of VP cholinergic neurons: one activated in response to the APP odor, and a second distinct population activated in response to the AV odor. These two subpopulations of VP cholinergic neurons are spatially intermingled within the VP, but show differences in electrophysiological properties, neuronal morphology, and projections to the basolateral amygdala. Although VP cholinergic neurons are engaged in behavioral responses to each odor, VP cholinergic signaling is only required for approach behavior. Indeed, inhibition of VP cholinergic neurons not only blocks approach to the APP odor, but reverses the behavior, leading to active avoidance. Our results highlight the functional heterogeneity of cholinergic projection neurons within the VP. These two subpopulations of VP cholinergic neurons differentially encode valence of olfactory stimuli and play unique roles in approach and avoidance behaviors.
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Affiliation(s)
- Ronald Kim
- Genetics of Neuronal Signaling Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Mala Ananth
- Circuits, Synapses and Molecular Signaling Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Niraj S. Desai
- Circuits, Synapses and Molecular Signaling Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Lorna W. Role
- Circuits, Synapses and Molecular Signaling Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
| | - David A. Talmage
- Genetics of Neuronal Signaling Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, 20892, USA
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15
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Toni M, Arena C, Cioni C, Tedeschi G. Temperature- and chemical-induced neurotoxicity in zebrafish. Front Physiol 2023; 14:1276941. [PMID: 37854466 PMCID: PMC10579595 DOI: 10.3389/fphys.2023.1276941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 09/22/2023] [Indexed: 10/20/2023] Open
Abstract
Throughout their lives, humans encounter a plethora of substances capable of inducing neurotoxic effects, including drugs, heavy metals and pesticides. Neurotoxicity manifests when exposure to these chemicals disrupts the normal functioning of the nervous system, and some neurotoxic agents have been linked to neurodegenerative pathologies such as Parkinson's and Alzheimer's disease. The growing concern surrounding the neurotoxic impacts of both naturally occurring and man-made toxic substances necessitates the identification of animal models for rapid testing across a wide spectrum of substances and concentrations, and the utilization of tools capable of detecting nervous system alterations spanning from the molecular level up to the behavioural one. Zebrafish (Danio rerio) is gaining prominence in the field of neuroscience due to its versatility. The possibility of analysing all developmental stages (embryo, larva and adult), applying the most common "omics" approaches (transcriptomics, proteomics, lipidomics, etc.) and conducting a wide range of behavioural tests makes zebrafish an excellent model for neurotoxicity studies. This review delves into the main experimental approaches adopted and the main markers analysed in neurotoxicity studies in zebrafish, showing that neurotoxic phenomena can be triggered not only by exposure to chemical substances but also by fluctuations in temperature. The findings presented here serve as a valuable resource for the study of neurotoxicity in zebrafish and define new scenarios in ecotoxicology suggesting that alterations in temperature can synergistically compound the neurotoxic effects of chemical substances, intensifying their detrimental impact on fish populations.
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Affiliation(s)
- Mattia Toni
- Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University, Rome, Italy
| | - Chiara Arena
- Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University, Rome, Italy
| | - Carla Cioni
- Department of Biology and Biotechnologies “Charles Darwin”, Sapienza University, Rome, Italy
| | - Gabriella Tedeschi
- Department of Veterinary Medicine and Animal Science (DIVAS), Università Degli Studi di Milano, Milano, Italy
- CRC “Innovation for Well-Being and Environment” (I-WE), Università Degli Studi di Milano, Milano, Italy
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16
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Boelts J, Harth P, Gao R, Udvary D, Yáñez F, Baum D, Hege HC, Oberlaender M, Macke JH. Simulation-based inference for efficient identification of generative models in computational connectomics. PLoS Comput Biol 2023; 19:e1011406. [PMID: 37738260 PMCID: PMC10550169 DOI: 10.1371/journal.pcbi.1011406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 10/04/2023] [Accepted: 08/01/2023] [Indexed: 09/24/2023] Open
Abstract
Recent advances in connectomics research enable the acquisition of increasing amounts of data about the connectivity patterns of neurons. How can we use this wealth of data to efficiently derive and test hypotheses about the principles underlying these patterns? A common approach is to simulate neuronal networks using a hypothesized wiring rule in a generative model and to compare the resulting synthetic data with empirical data. However, most wiring rules have at least some free parameters, and identifying parameters that reproduce empirical data can be challenging as it often requires manual parameter tuning. Here, we propose to use simulation-based Bayesian inference (SBI) to address this challenge. Rather than optimizing a fixed wiring rule to fit the empirical data, SBI considers many parametrizations of a rule and performs Bayesian inference to identify the parameters that are compatible with the data. It uses simulated data from multiple candidate wiring rule parameters and relies on machine learning methods to estimate a probability distribution (the 'posterior distribution over parameters conditioned on the data') that characterizes all data-compatible parameters. We demonstrate how to apply SBI in computational connectomics by inferring the parameters of wiring rules in an in silico model of the rat barrel cortex, given in vivo connectivity measurements. SBI identifies a wide range of wiring rule parameters that reproduce the measurements. We show how access to the posterior distribution over all data-compatible parameters allows us to analyze their relationship, revealing biologically plausible parameter interactions and enabling experimentally testable predictions. We further show how SBI can be applied to wiring rules at different spatial scales to quantitatively rule out invalid wiring hypotheses. Our approach is applicable to a wide range of generative models used in connectomics, providing a quantitative and efficient way to constrain model parameters with empirical connectivity data.
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Affiliation(s)
- Jan Boelts
- Machine Learning in Science, University of Tübingen, Tübingen, Germany
- Tübingen AI Center, University of Tübingen, Tübingen, Germany
| | - Philipp Harth
- Department of Visual and Data-centric Computing, Zuse Institute Berlin, Berlin, Germany
| | - Richard Gao
- Machine Learning in Science, University of Tübingen, Tübingen, Germany
- Tübingen AI Center, University of Tübingen, Tübingen, Germany
| | - Daniel Udvary
- In Silico Brain Sciences, Max Planck Institute for Neurobiology of Behavior – caesar, Bonn, Germany
| | - Felipe Yáñez
- In Silico Brain Sciences, Max Planck Institute for Neurobiology of Behavior – caesar, Bonn, Germany
| | - Daniel Baum
- Department of Visual and Data-centric Computing, Zuse Institute Berlin, Berlin, Germany
| | - Hans-Christian Hege
- Department of Visual and Data-centric Computing, Zuse Institute Berlin, Berlin, Germany
| | - Marcel Oberlaender
- In Silico Brain Sciences, Max Planck Institute for Neurobiology of Behavior – caesar, Bonn, Germany
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Free University Amsterdam, Amsterdam, Netherlands
| | - Jakob H. Macke
- Machine Learning in Science, University of Tübingen, Tübingen, Germany
- Tübingen AI Center, University of Tübingen, Tübingen, Germany
- Empirical Inference, Max Planck Institute for Intelligent Systems, Tübingen, Germany
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17
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Yang R, Vishwanathan A, Wu J, Kemnitz N, Ih D, Turner N, Lee K, Tartavull I, Silversmith WM, Jordan CS, David C, Bland D, Sterling A, Goldman MS, Aksay ERF, Seung HS. Cyclic structure with cellular precision in a vertebrate sensorimotor neural circuit. Curr Biol 2023; 33:2340-2349.e3. [PMID: 37236180 PMCID: PMC10419332 DOI: 10.1016/j.cub.2023.05.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/24/2023] [Accepted: 05/05/2023] [Indexed: 05/28/2023]
Abstract
Neuronal wiring diagrams reconstructed by electron microscopy1,2,3,4,5 pose new questions about the organization of nervous systems following the time-honored tradition of cross-species comparisons.6,7 The C. elegans connectome has been conceptualized as a sensorimotor circuit that is approximately feedforward,8,9,10,11 starting from sensory neurons proceeding to interneurons and ending with motor neurons. Overrepresentation of a 3-cell motif often known as the "feedforward loop" has provided further evidence for feedforwardness.10,12 Here, we contrast with another sensorimotor wiring diagram that was recently reconstructed from a larval zebrafish brainstem.13 We show that the 3-cycle, another 3-cell motif, is highly overrepresented in the oculomotor module of this wiring diagram. This is a first for any neuronal wiring diagram reconstructed by electron microscopy, whether invertebrate12,14 or mammalian.15,16,17 The 3-cycle of cells is "aligned" with a 3-cycle of neuronal groups in a stochastic block model (SBM)18 of the oculomotor module. However, the cellular cycles exhibit more specificity than can be explained by the group cycles-recurrence to the same neuron is surprisingly common. Cyclic structure could be relevant for theories of oculomotor function that depend on recurrent connectivity. The cyclic structure coexists with the classic vestibulo-ocular reflex arc for horizontal eye movements,19 and could be relevant for recurrent network models of temporal integration by the oculomotor system.20,21.
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Affiliation(s)
- Runzhe Yang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA; Computer Science Department, Princeton University, Princeton, NJ 08540, USA
| | - Ashwin Vishwanathan
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
| | - Dodam Ih
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
| | - Nicholas Turner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA; Computer Science Department, Princeton University, Princeton, NJ 08540, USA
| | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA; Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ignacio Tartavull
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
| | | | - Chris S Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
| | - Celia David
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
| | - Doug Bland
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
| | - Amy Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
| | - Mark S Goldman
- Center for Neuroscience, Department of Neurobiology, Physiology, and Behavior, and Department of Ophthalmology and Vision Science, University of California, Davis, Davis, CA 95616, USA
| | - Emre R F Aksay
- Institute for Computational Biomedicine and Department of Physiology and Biophysics, Weill Cornell Medical College, New York, NY 10021, USA
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA; Computer Science Department, Princeton University, Princeton, NJ 08540, USA.
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18
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Haggie L, Schmid L, Röhrle O, Besier T, McMorland A, Saini H. Linking cortex and contraction-Integrating models along the corticomuscular pathway. Front Physiol 2023; 14:1095260. [PMID: 37234419 PMCID: PMC10206006 DOI: 10.3389/fphys.2023.1095260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 04/21/2023] [Indexed: 05/28/2023] Open
Abstract
Computational models of the neuromusculoskeletal system provide a deterministic approach to investigate input-output relationships in the human motor system. Neuromusculoskeletal models are typically used to estimate muscle activations and forces that are consistent with observed motion under healthy and pathological conditions. However, many movement pathologies originate in the brain, including stroke, cerebral palsy, and Parkinson's disease, while most neuromusculoskeletal models deal exclusively with the peripheral nervous system and do not incorporate models of the motor cortex, cerebellum, or spinal cord. An integrated understanding of motor control is necessary to reveal underlying neural-input and motor-output relationships. To facilitate the development of integrated corticomuscular motor pathway models, we provide an overview of the neuromusculoskeletal modelling landscape with a focus on integrating computational models of the motor cortex, spinal cord circuitry, α-motoneurons and skeletal muscle in regard to their role in generating voluntary muscle contraction. Further, we highlight the challenges and opportunities associated with an integrated corticomuscular pathway model, such as challenges in defining neuron connectivities, modelling standardisation, and opportunities in applying models to study emergent behaviour. Integrated corticomuscular pathway models have applications in brain-machine-interaction, education, and our understanding of neurological disease.
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Affiliation(s)
- Lysea Haggie
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Laura Schmid
- Institute for Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Stuttgart, Germany
| | - Oliver Röhrle
- Institute for Modelling and Simulation of Biomechanical Systems, University of Stuttgart, Stuttgart, Germany
- Stuttgart Center for Simulation Sciences (SC SimTech), University of Stuttgart, Stuttgart, Germany
| | - Thor Besier
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Angus McMorland
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
- Department of Exercise Sciences, University of Auckland, Auckland, New Zealand
| | - Harnoor Saini
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
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19
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Renner J, Rasia-Filho AA. Morphological Features of Human Dendritic Spines. ADVANCES IN NEUROBIOLOGY 2023; 34:367-496. [PMID: 37962801 DOI: 10.1007/978-3-031-36159-3_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Dendritic spine features in human neurons follow the up-to-date knowledge presented in the previous chapters of this book. Human dendrites are notable for their heterogeneity in branching patterns and spatial distribution. These data relate to circuits and specialized functions. Spines enhance neuronal connectivity, modulate and integrate synaptic inputs, and provide additional plastic functions to microcircuits and large-scale networks. Spines present a continuum of shapes and sizes, whose number and distribution along the dendritic length are diverse in neurons and different areas. Indeed, human neurons vary from aspiny or "relatively aspiny" cells to neurons covered with a high density of intermingled pleomorphic spines on very long dendrites. In this chapter, we discuss the phylogenetic and ontogenetic development of human spines and describe the heterogeneous features of human spiny neurons along the spinal cord, brainstem, cerebellum, thalamus, basal ganglia, amygdala, hippocampal regions, and neocortical areas. Three-dimensional reconstructions of Golgi-impregnated dendritic spines and data from fluorescence microscopy are reviewed with ultrastructural findings to address the complex possibilities for synaptic processing and integration in humans. Pathological changes are also presented, for example, in Alzheimer's disease and schizophrenia. Basic morphological data can be linked to current techniques, and perspectives in this research field include the characterization of spines in human neurons with specific transcriptome features, molecular classification of cellular diversity, and electrophysiological identification of coexisting subpopulations of cells. These data would enlighten how cellular attributes determine neuron type-specific connectivity and brain wiring for our diverse aptitudes and behavior.
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Affiliation(s)
- Josué Renner
- Department of Basic Sciences/Physiology and Graduate Program in Biosciences, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, RS, Brazil
| | - Alberto A Rasia-Filho
- Department of Basic Sciences/Physiology and Graduate Program in Biosciences, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, RS, Brazil
- Graduate Program in Neuroscience, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
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20
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Rasia-Filho AA, Calcagnotto ME, von Bohlen Und Halbach O. Introduction: What Are Dendritic Spines? ADVANCES IN NEUROBIOLOGY 2023; 34:1-68. [PMID: 37962793 DOI: 10.1007/978-3-031-36159-3_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Dendritic spines are cellular specializations that greatly increase the connectivity of neurons and modulate the "weight" of most postsynaptic excitatory potentials. Spines are found in very diverse animal species providing neural networks with a high integrative and computational possibility and plasticity, enabling the perception of sensorial stimuli and the elaboration of a myriad of behavioral displays, including emotional processing, memory, and learning. Humans have trillions of spines in the cerebral cortex, and these spines in a continuum of shapes and sizes can integrate the features that differ our brain from other species. In this chapter, we describe (1) the discovery of these small neuronal protrusions and the search for the biological meaning of dendritic spines; (2) the heterogeneity of shapes and sizes of spines, whose structure and composition are associated with the fine-tuning of synaptic processing in each nervous area, as well as the findings that support the role of dendritic spines in increasing the wiring of neural circuits and their functions; and (3) within the intraspine microenvironment, the integration and activation of signaling biochemical pathways, the compartmentalization of molecules or their spreading outside the spine, and the biophysical properties that can affect parent dendrites. We also provide (4) examples of plasticity involving dendritic spines and neural circuits relevant to species survival and comment on (5) current research advancements and challenges in this exciting research field.
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Affiliation(s)
- Alberto A Rasia-Filho
- Department of Basic Sciences/Physiology and Graduate Program in Biosciences, Universidade Federal de Ciências da Saúde de Porto Alegre, Porto Alegre, RS, Brazil
- Graduate Program in Neuroscience, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
| | - Maria Elisa Calcagnotto
- Graduate Program in Neuroscience, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- Department of Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- Graduate Program in Biochemistry, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
- Graduate Program in Psychiatry and Behavioral Science, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS, Brazil
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21
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A realistic morpho-anatomical connection strategy for modelling full-scale point-neuron microcircuits. Sci Rep 2022; 12:13864. [PMID: 35974119 PMCID: PMC9381785 DOI: 10.1038/s41598-022-18024-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 08/03/2022] [Indexed: 01/03/2023] Open
Abstract
The modeling of extended microcircuits is emerging as an effective tool to simulate the neurophysiological correlates of brain activity and to investigate brain dysfunctions. However, for specific networks, a realistic modeling approach based on the combination of available physiological, morphological and anatomical data is still an open issue. One of the main problems in the generation of realistic networks lies in the strategy adopted to build network connectivity. Here we propose a method to implement a neuronal network at single cell resolution by using the geometrical probability volumes associated with pre- and postsynaptic neurites. This allows us to build a network with plausible connectivity properties without the explicit use of computationally intensive touch detection algorithms using full 3D neuron reconstructions. The method has been benchmarked for the mouse hippocampus CA1 area, and the results show that this approach is able to generate full-scale brain networks at single cell resolution that are in good agreement with experimental findings. This geometric reconstruction of axonal and dendritic occupancy, by effectively reflecting morphological and anatomical constraints, could be integrated into structured simulators generating entire circuits of different brain areas facilitating the simulation of different brain regions with realistic models.
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