51
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Ohtsuki G, Shishikura M, Ozaki A. Synergistic excitability plasticity in cerebellar functioning. FEBS J 2020; 287:4557-4593. [PMID: 32367676 DOI: 10.1111/febs.15355] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2020] [Revised: 04/22/2020] [Accepted: 04/30/2020] [Indexed: 12/27/2022]
Abstract
The cerebellum, a universal processor for sensory acquisition and internal models, and its association with synaptic and nonsynaptic plasticity have been envisioned as the biological correlates of learning, perception, and even thought. Indeed, the cerebellum is no longer considered merely as the locus of motor coordination and its learning. Here, we introduce the mechanisms underlying the induction of multiple types of plasticity in cerebellar circuit and give an overview focusing on the plasticity of nonsynaptic intrinsic excitability. The discovery of long-term potentiation of synaptic responsiveness in hippocampal neurons led investigations into changes of their intrinsic excitability. This activity-dependent potentiation of neuronal excitability is distinct from that of synaptic efficacy. Systematic examination of excitability plasticity has indicated that the modulation of various types of Ca2+ - and voltage-dependent K+ channels underlies the phenomenon, which is also triggered by immune activity. Intrinsic plasticity is expressed specifically on dendrites and modifies the integrative processing and filtering effect. In Purkinje cells, modulation of the discordance of synaptic current on soma and dendrite suggested a novel type of cellular learning mechanism. This property enables a plausible synergy between synaptic efficacy and intrinsic excitability, by amplifying electrical conductivity and influencing the polarity of bidirectional synaptic plasticity. Furthermore, the induction of intrinsic plasticity in the cerebellum correlates with motor performance and cognitive processes, through functional connections from the cerebellar nuclei to neocortex and associated regions: for example, thalamus and midbrain. Taken together, recent advances in neuroscience have begun to shed light on the complex functioning of nonsynaptic excitability and the synergy.
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Affiliation(s)
- Gen Ohtsuki
- The Hakubi Center for Advanced Research, Kyoto University, Japan.,Department of Biophysics, Kyoto University Graduate School of Science, Japan.,Department of Drug Discovery Medicine, Kyoto University Graduate School of Medicine, Japan
| | - Mari Shishikura
- Department of Biophysics, Kyoto University Graduate School of Science, Japan
| | - Akitoshi Ozaki
- Department of Biophysics, Kyoto University Graduate School of Science, Japan
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52
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Ecker A, Romani A, Sáray S, Káli S, Migliore M, Falck J, Lange S, Mercer A, Thomson AM, Muller E, Reimann MW, Ramaswamy S. Data-driven integration of hippocampal CA1 synaptic physiology in silico. Hippocampus 2020; 30:1129-1145. [PMID: 32520422 PMCID: PMC7687201 DOI: 10.1002/hipo.23220] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 04/20/2020] [Accepted: 05/07/2020] [Indexed: 12/31/2022]
Abstract
The anatomy and physiology of monosynaptic connections in rodent hippocampal CA1 have been extensively studied in recent decades. Yet, the resulting knowledge remains disparate and difficult to reconcile. Here, we present a data‐driven approach to integrate the current state‐of‐the‐art knowledge on the synaptic anatomy and physiology of rodent hippocampal CA1, including axo‐dendritic innervation patterns, number of synapses per connection, quantal conductances, neurotransmitter release probability, and short‐term plasticity into a single coherent resource. First, we undertook an extensive literature review of paired recordings of hippocampal neurons and compiled experimental data on their synaptic anatomy and physiology. The data collected in this manner is sparse and inhomogeneous due to the diversity of experimental techniques used by different groups, which necessitates the need for an integrative framework to unify these data. To this end, we extended a previously developed workflow for the neocortex to constrain a unifying in silico reconstruction of the synaptic physiology of CA1 connections. Our work identifies gaps in the existing knowledge and provides a complementary resource toward a more complete quantification of synaptic anatomy and physiology in the rodent hippocampal CA1 region.
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Affiliation(s)
- András Ecker
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Armando Romani
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Sára Sáray
- Institute of Experimental Medicine, Budapest, Hungary.,Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Szabolcs Káli
- Institute of Experimental Medicine, Budapest, Hungary.,Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Michele Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy
| | - Joanne Falck
- UCL School of Pharmacy, University College London, London, UK
| | - Sigrun Lange
- UCL School of Pharmacy, University College London, London, UK.,School of Life Sciences, University of Westminster, London, UK
| | - Audrey Mercer
- UCL School of Pharmacy, University College London, London, UK
| | - Alex M Thomson
- UCL School of Pharmacy, University College London, London, UK
| | - Eilif Muller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Michael W Reimann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Srikanth Ramaswamy
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
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53
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Nakahara S, Stark CE, Turner JA, Calhoun VD, Lim KO, Mueller B, Bustillo JR, O’Leary DS, McEwen S, Voyvodic J, Belger A, Mathalon DH, Ford JM, Macciardi F, Matsumoto M, Potkin SG, van Erp TG. Dentate gyrus volume deficit in schizophrenia. Psychol Med 2020; 50:1267-1277. [PMID: 31155012 PMCID: PMC7068799 DOI: 10.1017/s0033291719001144] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
BACKGROUND Schizophrenia is associated with robust hippocampal volume deficits but subregion volume deficits, their associations with cognition, and contributing genes remain to be determined. METHODS Hippocampal formation (HF) subregion volumes were obtained using FreeSurfer 6.0 from individuals with schizophrenia (n = 176, mean age ± s.d. = 39.0 ± 11.5, 132 males) and healthy volunteers (n = 173, mean age ± s.d. = 37.6 ± 11.3, 123 males) with similar mean age, gender, handedness, and race distributions. Relationships between the HF subregion volume with the largest between group difference, neuropsychological performance, and single-nucleotide polymorphisms were assessed. RESULTS This study found a significant group by region interaction on hippocampal subregion volumes. Compared to healthy volunteers, individuals with schizophrenia had significantly smaller dentate gyrus (DG) (Cohen's d = -0.57), Cornu Ammonis (CA) 4, molecular layer of the hippocampus, hippocampal tail, and CA 1 volumes, when statistically controlling for intracranial volume; DG (d = -0.43) and CA 4 volumes remained significantly smaller when statistically controlling for mean hippocampal volume. DG volume showed the largest between group difference and significant positive associations with visual memory and speed of processing in the overall sample. Genome-wide association analysis with DG volume as the quantitative phenotype identified rs56055643 (β = 10.8, p < 5 × 10-8, 95% CI 7.0-14.5) on chromosome 3 in high linkage disequilibrium with MOBP. Gene-based analyses identified associations between SLC25A38 and RPSA and DG volume. CONCLUSIONS This study suggests that DG dysfunction is fundamentally involved in schizophrenia pathophysiology, that it may contribute to cognitive abnormalities in schizophrenia, and that underlying biological mechanisms may involve contributions from MOBP, SLC25A38, and RPSA.
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Affiliation(s)
- Soichiro Nakahara
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, 92617, United States
- Unit 2, Candidate Discovery Science Labs, Drug Discovery Research, Astellas Pharma Inc, 21, Miyukigaoka, Tsukuba, Ibaraki 305-8585, Japan
| | - Craig E.L. Stark
- Department of Neurobiology and Behavior, University of California Irvine, Irvine, CA, 92697, United States
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA, 92697, United States
| | - Jessica A. Turner
- Departments of Psychology and Neuroscience, Georgia State University, Atlanta, GA, 30302, United States
- Mind Research Network, Albuquerque, NM, 87106, United States
| | - Vince D. Calhoun
- Mind Research Network, Albuquerque, NM, 87106, United States
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, 87131, United States
- Departments of Psychiatry & Neuroscience, University of New Mexico, Albuquerque, NM, 87131, United States
| | - Kelvin O. Lim
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, 55454, United States
| | - Bryon Mueller
- Department of Psychiatry, University of Minnesota, Minneapolis, MN, 55454, United States
| | - Juan R. Bustillo
- Departments of Psychiatry & Neuroscience, University of New Mexico, Albuquerque, NM, 87131, United States
| | - Daniel S. O’Leary
- Department of Psychiatry, University of Iowa, Iowa City, IA, 52242, United States
| | - Sarah McEwen
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, 92093, United States
| | - James Voyvodic
- Brain Imaging and Analysis Center, Duke University Medical Center, Durham, NC, 27710, United States
| | - Aysenil Belger
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, United States
| | - Daniel H. Mathalon
- Department of Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, 94143, United States
- Veterans Affairs San Francisco Healthcare System, San Francisco, CA, 94121, United States
| | - Judith M. Ford
- Department of Psychiatry, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, 94143, United States
- Veterans Affairs San Francisco Healthcare System, San Francisco, CA, 94121, United States
| | - Fabio Macciardi
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, 92617, United States
| | - Mitsuyuki Matsumoto
- Unit 2, Candidate Discovery Science Labs, Drug Discovery Research, Astellas Pharma Inc, 21, Miyukigaoka, Tsukuba, Ibaraki 305-8585, Japan
| | - Steven G. Potkin
- Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, 92617, United States
| | - Theo G.M. van Erp
- Clinical Translational Neuroscience Laboratory, Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, 92617, United States
- Center for the Neurobiology of Learning and Memory, University of California Irvine, Irvine, CA, 92697, United States
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54
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Attili SM, Mackesey ST, Ascoli GA. Operations Research Methods for Estimating the Population Size of Neuron Types. ANNALS OF OPERATIONS RESEARCH 2020; 289:33-50. [PMID: 33343053 PMCID: PMC7748248 DOI: 10.1007/s10479-020-03542-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Understanding brain computation requires assembling a complete catalog of its architectural components. Although the brain is organized into several anatomical and functional regions, it is ultimately the neurons in every region that are responsible for cognition and behavior. Thus, classifying neuron types throughout the brain and quantifying the population sizes of distinct classes in different regions is a key subject of research in the neuroscience community. The total number of neurons in the brain has been estimated for multiple species, but the definition and population size of each neuron type are still open questions even in common model organisms: the so called "cell census" problem. We propose a methodology that uses operations research principles to estimate the number of neurons in each type based on available information on their distinguishing properties. Thus, assuming a set of neuron type definitions, we provide a solution to the issue of assessing their relative proportions. Specifically, we present a three-step approach that includes literature search, equation generation, and numerical optimization. Solving computationally the set of equations generated by literature mining yields best estimates or most likely ranges for the number of neurons in each type. While this strategy can be applied towards any neural system, we illustrate its usage on the rodent hippocampus.
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55
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Kang J, Jung K, Eo J, Son J, Park HJ. Dynamic causal modeling of hippocampal activity measured via mesoscopic voltage-sensitive dye imaging. Neuroimage 2020; 213:116755. [DOI: 10.1016/j.neuroimage.2020.116755] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 03/10/2020] [Accepted: 03/14/2020] [Indexed: 10/24/2022] Open
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56
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Anatomically revealed morphological patterns of pyramidal neurons in layer 5 of the motor cortex. Sci Rep 2020; 10:7916. [PMID: 32405029 PMCID: PMC7220918 DOI: 10.1038/s41598-020-64665-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 04/17/2020] [Indexed: 11/24/2022] Open
Abstract
Neuronal cell types are essential to the comprehensive understanding of the neuronal function and neuron can be categorized by their anatomical property. However, complete morphology data for neurons with a whole brain projection, for example the pyramidal neurons in the cortex, are sparse because it is difficult to trace the neuronal fibers across the whole brain and acquire the neuron morphology at the single axon resolution. Thus the cell types of pyramidal neurons have yet to be studied at the single axon resolution thoroughly. In this work, we acquire images for a Thy1 H-line mouse brain using a fluorescence micro-optical sectioning tomography system. Then we sample 42 pyramidal neurons whose somata are in the layer 5 of the motor cortex and reconstruct their morphology across the whole brain. Based on the reconstructed neuronal anatomy, we analyze the axonal and dendritic fibers of the neurons in addition to the soma spatial distributions, and identify two axonal projection pattern of pyramidal tract neurons and two dendritic spreading patterns of intratelencephalic neurons. The raw image data are available upon request as an additional asset to the community. The morphological patterns identified in this work can be a typical representation of neuron subtypes and reveal the possible input-output function of a single pyramidal neuron.
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57
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Navas-Olive A, Valero M, Jurado-Parras T, de Salas-Quiroga A, Averkin RG, Gambino G, Cid E, de la Prida LM. Multimodal determinants of phase-locked dynamics across deep-superficial hippocampal sublayers during theta oscillations. Nat Commun 2020; 11:2217. [PMID: 32371879 PMCID: PMC7200700 DOI: 10.1038/s41467-020-15840-6] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 03/30/2020] [Indexed: 12/15/2022] Open
Abstract
Theta oscillations play a major role in temporarily defining the hippocampal rate code by translating behavioral sequences into neuronal representations. However, mechanisms constraining phase timing and cell-type-specific phase preference are unknown. Here, we employ computational models tuned with evolutionary algorithms to evaluate phase preference of individual CA1 pyramidal cells recorded in mice and rats not engaged in any particular memory task. We applied unbiased and hypothesis-free approaches to identify effects of intrinsic and synaptic factors, as well as cell morphology, in determining phase preference. We found that perisomatic inhibition delivered by complementary populations of basket cells interacts with input pathways to shape phase-locked specificity of deep and superficial pyramidal cells. Somatodendritic integration of fluctuating glutamatergic inputs defined cycle-by-cycle by unsupervised methods demonstrated that firing selection is tuneable across sublayers. Our data identify different mechanisms of phase-locking selectivity that are instrumental for flexible dynamical representations of theta sequences. Theta oscillations have been implicated in hippocampal processing but mechanisms constraining phase timing of specific cell types are unknown. Here, the authors combine single-cell and multisite recordings with evolutionary computational models to evaluate mechanisms of phase preference of deep and superficial CA1 pyramidal cells.
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Affiliation(s)
| | | | | | - Adan de Salas-Quiroga
- Instituto Cajal, CSIC, 28002, Madrid, Spain.,Department of Biochemistry and Molecular Biology, Instituto Ramón y Cajal de Investigación Sanitaria (IRYCIS) and Instituto Universitario de Investigación Neuroquímica (IUIN), Complutense University, 28040, Madrid, Spain
| | - Robert G Averkin
- MTA-SZTE Research Group for Cortical Microcircuits, University of Szeged, Szeged, Hungary
| | - Giuditta Gambino
- Instituto Cajal, CSIC, 28002, Madrid, Spain.,Department of Biomedicine, Neuroscience and Advanced Diagnostics, University of Palermo, Palermo, Italy
| | - Elena Cid
- Instituto Cajal, CSIC, 28002, Madrid, Spain
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58
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Moradi K, Ascoli GA. A comprehensive knowledge base of synaptic electrophysiology in the rodent hippocampal formation. Hippocampus 2020; 30:314-331. [PMID: 31472001 DOI: 10.1101/632760] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Revised: 07/16/2019] [Accepted: 08/06/2019] [Indexed: 05/25/2023]
Abstract
The cellular and synaptic architecture of the rodent hippocampus has been described in thousands of peer-reviewed publications. However, no human- or machine-readable public catalog of synaptic electrophysiology data exists for this or any other neural system. Harnessing state-of-the-art information technology, we have developed a cloud-based toolset for identifying empirical evidence from the scientific literature pertaining to synaptic electrophysiology, for extracting the experimental data of interest, and for linking each entry to relevant text or figure excerpts. Mining more than 1,200 published journal articles, we have identified eight different signal modalities quantified by 90 different methods to measure synaptic amplitude, kinetics, and plasticity in hippocampal neurons. We have designed a data structure that both reflects the differences and maintains the existing relations among experimental modalities. Moreover, we mapped every annotated experiment to identified potential connections, that is, specific pairs of presynaptic and postsynaptic neuron types. To this aim, we leveraged Hippocampome.org, an open-access knowledge base of morphologically, electrophysiologically, and molecularly characterized neuron types in the rodent hippocampal formation. Specifically, we have implemented a computational pipeline to systematically translate neuron type properties into formal queries in order to find all compatible potential connections. With this system, we have collected nearly 40,000 synaptic data entities covering 88% of the 3,120 potential connections in Hippocampome.org. Correcting membrane potentials with respect to liquid junction potentials significantly reduced the difference between theoretical and experimental reversal potentials, thereby enabling the accurate conversion of all synaptic amplitudes to conductance. This data set allows for large-scale hypothesis testing of the general rules governing synaptic signals. To illustrate these applications, we confirmed several expected correlations between synaptic measurements and their covariates while suggesting previously unreported ones. We release all data open-source at Hippocampome.org in order to further research across disciplines.
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Affiliation(s)
- Keivan Moradi
- Neuroscience Program, Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia
| | - Giorgio A Ascoli
- Neuroscience Program, Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia
- Bioengineering Department, Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia
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59
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Moradi K, Ascoli GA. A comprehensive knowledge base of synaptic electrophysiology in the rodent hippocampal formation. Hippocampus 2020; 30:314-331. [PMID: 31472001 PMCID: PMC7875289 DOI: 10.1002/hipo.23148] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Revised: 07/16/2019] [Accepted: 08/06/2019] [Indexed: 01/14/2023]
Abstract
The cellular and synaptic architecture of the rodent hippocampus has been described in thousands of peer-reviewed publications. However, no human- or machine-readable public catalog of synaptic electrophysiology data exists for this or any other neural system. Harnessing state-of-the-art information technology, we have developed a cloud-based toolset for identifying empirical evidence from the scientific literature pertaining to synaptic electrophysiology, for extracting the experimental data of interest, and for linking each entry to relevant text or figure excerpts. Mining more than 1,200 published journal articles, we have identified eight different signal modalities quantified by 90 different methods to measure synaptic amplitude, kinetics, and plasticity in hippocampal neurons. We have designed a data structure that both reflects the differences and maintains the existing relations among experimental modalities. Moreover, we mapped every annotated experiment to identified potential connections, that is, specific pairs of presynaptic and postsynaptic neuron types. To this aim, we leveraged Hippocampome.org, an open-access knowledge base of morphologically, electrophysiologically, and molecularly characterized neuron types in the rodent hippocampal formation. Specifically, we have implemented a computational pipeline to systematically translate neuron type properties into formal queries in order to find all compatible potential connections. With this system, we have collected nearly 40,000 synaptic data entities covering 88% of the 3,120 potential connections in Hippocampome.org. Correcting membrane potentials with respect to liquid junction potentials significantly reduced the difference between theoretical and experimental reversal potentials, thereby enabling the accurate conversion of all synaptic amplitudes to conductance. This data set allows for large-scale hypothesis testing of the general rules governing synaptic signals. To illustrate these applications, we confirmed several expected correlations between synaptic measurements and their covariates while suggesting previously unreported ones. We release all data open-source at Hippocampome.org in order to further research across disciplines.
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Affiliation(s)
- Keivan Moradi
- Neuroscience Program, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA (USA)
| | - Giorgio A. Ascoli
- Neuroscience Program, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA (USA)
- Bioengineering Department, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA (USA)
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60
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Schumm SN, Gabrieli D, Meaney DF. Neuronal Degeneration Impairs Rhythms Between Connected Microcircuits. Front Comput Neurosci 2020; 14:18. [PMID: 32194390 PMCID: PMC7063469 DOI: 10.3389/fncom.2020.00018] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Accepted: 02/11/2020] [Indexed: 11/23/2022] Open
Abstract
Synchronization of neural activity across brain regions is critical to processes that include perception, learning, and memory. After traumatic brain injury (TBI), neuronal degeneration is one possible effect and can alter communication between neural circuits. Consequently, synchronization between neurons may change and can contribute to both lasting changes in functional brain networks and cognitive impairment in patients. However, fundamental principles relating exactly how TBI at the cellular scale affects synchronization of mesoscale circuits are not well understood. In this work, we use computational networks of Izhikevich integrate-and-fire neurons to study synchronized, oscillatory activity between clusters of neurons, which also adapt according to spike-timing-dependent plasticity (STDP). We study how the connections within and between these neuronal clusters change as unidirectional connections form between the two neuronal populations. In turn, we examine how neuronal deletion, intended to mimic the temporary or permanent loss of neurons in the mesoscale circuit, affects these dynamics. We determine synchronization of two neuronal circuits requires very modest connectivity between these populations; approximately 10% of neurons projecting from one circuit to another circuit will result in high synchronization. In addition, we find that synchronization level inversely affects the strength of connection between neuronal microcircuits - moderately synchronized microcircuits develop stronger intercluster connections than do highly synchronized circuits. Finally, we find that highly synchronized circuits are largely protected against the effects of neuronal deletion but may display changes in frequency properties across circuits with targeted neuronal loss. Together, our results suggest that strongly and weakly connected regions differ in their inherent resilience to damage and may serve different roles in a larger network.
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Affiliation(s)
- Samantha N. Schumm
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, United States
| | - David Gabrieli
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, United States
| | - David F. Meaney
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, United States
- Penn Center for Brain Injury and Repair, Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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61
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Depannemaecker D, Canton Santos LE, Rodrigues AM, Scorza CA, Scorza FA, Almeida ACGD. Realistic spiking neural network: Non-synaptic mechanisms improve convergence in cell assembly. Neural Netw 2019; 122:420-433. [PMID: 31841876 DOI: 10.1016/j.neunet.2019.09.038] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Revised: 09/17/2019] [Accepted: 09/23/2019] [Indexed: 01/26/2023]
Abstract
Learning in neural networks inspired by brain tissue has been studied for machine learning applications. However, existing works primarily focused on the concept of synaptic weight modulation, and other aspects of neuronal interactions, such as non-synaptic mechanisms, have been neglected. Non-synaptic interaction mechanisms have been shown to play significant roles in the brain, and four classes of these mechanisms can be highlighted: (i) electrotonic coupling; (ii) ephaptic interactions; (iii) electric field effects; and iv) extracellular ionic fluctuations. In this work, we proposed simple rules for learning inspired by recent findings in machine learning adapted to a realistic spiking neural network. We show that the inclusion of non-synaptic interaction mechanisms improves cell assembly convergence. By including extracellular ionic fluctuation represented by the extracellular electrodiffusion in the network, we showed the importance of these mechanisms to improve cell assembly convergence. Additionally, we observed a variety of electrophysiological patterns of neuronal activity, particularly bursting and synchronism when the convergence is improved.
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Affiliation(s)
- Damien Depannemaecker
- Laboratório de Neurociência Experimental e Computacional, Departamento de Engenharia de Biossistemas, Universidade Federal de São João del-Rei (UFSJ), Brazil; Disciplina de Neurociência, Departamento de Neurologia e Neurocirurgia, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Luiz Eduardo Canton Santos
- Laboratório de Neurociência Experimental e Computacional, Departamento de Engenharia de Biossistemas, Universidade Federal de São João del-Rei (UFSJ), Brazil; Disciplina de Neurociência, Departamento de Neurologia e Neurocirurgia, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Antônio Márcio Rodrigues
- Laboratório de Neurociência Experimental e Computacional, Departamento de Engenharia de Biossistemas, Universidade Federal de São João del-Rei (UFSJ), Brazil
| | - Carla Alessandra Scorza
- Laboratório de Neurociência Experimental e Computacional, Departamento de Engenharia de Biossistemas, Universidade Federal de São João del-Rei (UFSJ), Brazil
| | - Fulvio Alexandre Scorza
- Disciplina de Neurociência, Departamento de Neurologia e Neurocirurgia, Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Antônio-Carlos Guimarães de Almeida
- Laboratório de Neurociência Experimental e Computacional, Departamento de Engenharia de Biossistemas, Universidade Federal de São João del-Rei (UFSJ), Brazil.
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62
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Komendantov AO, Venkadesh S, Rees CL, Wheeler DW, Hamilton DJ, Ascoli GA. Quantitative firing pattern phenotyping of hippocampal neuron types. Sci Rep 2019; 9:17915. [PMID: 31784578 PMCID: PMC6884469 DOI: 10.1038/s41598-019-52611-w] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 09/20/2019] [Indexed: 01/19/2023] Open
Abstract
Systematically organizing the anatomical, molecular, and physiological properties of cortical neurons is important for understanding their computational functions. Hippocampome.org defines 122 neuron types in the rodent hippocampal formation based on their somatic, axonal, and dendritic locations, putative excitatory/inhibitory outputs, molecular marker expression, and biophysical properties. We augmented the electrophysiological data of this knowledge base by collecting, quantifying, and analyzing the firing responses to depolarizing current injections for every hippocampal neuron type from published experiments. We designed and implemented objective protocols to classify firing patterns based on 5 transients (delay, adapting spiking, rapidly adapting spiking, transient stuttering, and transient slow-wave bursting) and 4 steady states (non-adapting spiking, persistent stuttering, persistent slow-wave bursting, and silence). This automated approach revealed 9 unique (plus one spurious) families of firing pattern phenotypes while distinguishing potential new neuronal subtypes. Novel statistical associations emerged between firing responses and other electrophysiological properties, morphological features, and molecular marker expression. The firing pattern parameters, experimental conditions, spike times, references to the original empirical evidences, and analysis scripts are released open-source through Hippocampome.org for all neuron types, greatly enhancing the existing search and browse capabilities. This information, collated online in human- and machine-accessible form, will help design and interpret both experiments and model simulations.
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Affiliation(s)
- Alexander O Komendantov
- Krasnow Institute for Advanced Study, George Mason University, 4400 University Drive, MS 2A1, Fairfax, Virginia, 2230, USA.
| | - Siva Venkadesh
- Krasnow Institute for Advanced Study, George Mason University, 4400 University Drive, MS 2A1, Fairfax, Virginia, 2230, USA
| | - Christopher L Rees
- Krasnow Institute for Advanced Study, George Mason University, 4400 University Drive, MS 2A1, Fairfax, Virginia, 2230, USA
| | - Diek W Wheeler
- Krasnow Institute for Advanced Study, George Mason University, 4400 University Drive, MS 2A1, Fairfax, Virginia, 2230, USA
| | - David J Hamilton
- Krasnow Institute for Advanced Study, George Mason University, 4400 University Drive, MS 2A1, Fairfax, Virginia, 2230, USA
| | - Giorgio A Ascoli
- Krasnow Institute for Advanced Study, George Mason University, 4400 University Drive, MS 2A1, Fairfax, Virginia, 2230, USA.
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White CM, Rees CL, Wheeler DW, Hamilton DJ, Ascoli GA. Molecular expression profiles of morphologically defined hippocampal neuron types: Empirical evidence and relational inferences. Hippocampus 2019; 30:472-487. [PMID: 31596053 PMCID: PMC7875254 DOI: 10.1002/hipo.23165] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 08/14/2019] [Accepted: 08/22/2019] [Indexed: 12/12/2022]
Abstract
Gene and protein expressions are key determinants of cellular function. Neurons are the building blocks of brain circuits, yet the relationship between their molecular identity and the spatial distribution of their dendritic inputs and axonal outputs remains incompletely understood. The open-source knowledge base Hippocampome.org amasses such transcriptomic data from the scientific literature for morphologically defined neuron types in the rodent hippocampal formation: dentate gyrus, CA3, CA2, CA1, subiculum, and entorhinal cortex. Positive, negative, or mixed expression reports were initially obtained from published articles directly connecting molecular evidence to neurons with known axonal and dendritic patterns across hippocampal layers. Here, we supplement this information by collating, formalizing, and leveraging relational expression inferences that link a gene or protein expression or lack thereof to that of another molecule or to an anatomical location. With these additional interpretations, we freely release online a comprehensive human- and machine-readable molecular profile for more than 100 neuron types in Hippocampome.org. Analysis of these data ascertains the ability to distinguish unequivocally most neuron types in each of the major subdivisions of the hippocampus based on currently known biochemical markers. Moreover, grouping neuron types by expression similarity reveals eight superfamilies characterized by a few defining molecules.
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Affiliation(s)
- Charise M White
- Center for Neural Informatics, Structure, & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia
| | - Christopher L Rees
- Center for Neural Informatics, Structure, & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia
| | - Diek W Wheeler
- Center for Neural Informatics, Structure, & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia
| | - David J Hamilton
- Center for Neural Informatics, Structure, & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structure, & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, Virginia
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64
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Venkadesh S, Komendantov AO, Wheeler DW, Hamilton DJ, Ascoli GA. Simple models of quantitative firing phenotypes in hippocampal neurons: Comprehensive coverage of intrinsic diversity. PLoS Comput Biol 2019; 15:e1007462. [PMID: 31658260 PMCID: PMC6837624 DOI: 10.1371/journal.pcbi.1007462] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 11/07/2019] [Accepted: 10/07/2019] [Indexed: 12/02/2022] Open
Abstract
Patterns of periodic voltage spikes elicited by a neuron help define its dynamical identity. Experimentally recorded spike trains from various neurons show qualitatively distinguishable features such as delayed spiking, spiking with or without frequency adaptation, and intrinsic bursting. Moreover, the input-dependent responses of a neuron not only show different quantitative features, such as higher spike frequency for a stronger input current injection, but can also exhibit qualitatively different responses, such as spiking and bursting under different input conditions, thus forming a complex phenotype of responses. In previous work, the comprehensive knowledge base of hippocampal neuron types Hippocampome.org systematically characterized various spike pattern phenotypes experimentally identified from 120 neuron types/subtypes. In this paper, we present a complete set of simple phenomenological models that quantitatively reproduce the diverse and complex phenotypes of hippocampal neurons. In addition to point-neuron models, we created compact multi-compartment models with up to four compartments, which will allow spatial segregation of synaptic integration in network simulations. Electrotonic compartmentalization observed in our compact multi-compartment models is qualitatively consistent with experimental observations. The models were created using an automated pipeline based on evolutionary algorithms. This work maps 120 neuron types/subtypes in the rodent hippocampus to a low-dimensional model space and adds another dimension to the knowledge accumulated in Hippocampome.org. Computationally efficient representations of intrinsic dynamics, along with other pieces of knowledge available in Hippocampome.org, provide a biologically realistic platform to explore the large-scale interactions of various neuron types at the mesoscopic level.
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Affiliation(s)
- Siva Venkadesh
- Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, United States of America
| | - Alexander O. Komendantov
- Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, United States of America
| | - Diek W. Wheeler
- Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, United States of America
| | - David J. Hamilton
- Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, United States of America
| | - Giorgio A. Ascoli
- Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, United States of America
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65
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Alternative classifications of neurons based on physiological properties and synaptic responses, a computational study. Sci Rep 2019; 9:13096. [PMID: 31511545 PMCID: PMC6739481 DOI: 10.1038/s41598-019-49197-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 08/16/2019] [Indexed: 01/25/2023] Open
Abstract
One of the central goals of today's neuroscience is to achieve the conceivably most accurate classification of neuron types in the mammalian brain. As part of this research effort, electrophysiologists commonly utilize current clamp techniques to gain a detailed characterization of the neurons' physiological properties. While this approach has been useful, it is not well understood whether neurons that share physiological properties of a particular phenotype would also operate consistently under the action of natural synaptic inputs. We approached this problem by simulating a biophysically diverse population of model neurons based on 3 generic phenotypes. We exposed the model neurons to two types of stimulation to investigate their voltage responses under conventional current step protocols and under simulated synaptic bombardment. We extracted standard physiological parameters from the voltage responses elicited by current step stimulation and spike arrival times descriptive of the model's firing behavior under synaptic inputs. The biophysical phenotypes could be reliably identified using classification based on the 'static' physiological properties, but not the interspike interval-based parameters. However, the model neurons associated with the biophysically different phenotypes retained cell type specific features in the fine structure of their spike responses that allowed their accurate classification.
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66
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Cid E, de la Prida LM. Methods for single-cell recording and labeling in vivo. J Neurosci Methods 2019; 325:108354. [PMID: 31302156 DOI: 10.1016/j.jneumeth.2019.108354] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2019] [Revised: 07/07/2019] [Accepted: 07/07/2019] [Indexed: 01/29/2023]
Abstract
Targeting individual neurons in vivo is a key method to study the role of single cell types within local and brain-wide microcircuits. While novel technological developments now permit assessing activity from large number of cells simultaneously, there is currently no better solution than glass micropipettes to relate the physiology and morphology of single-cells. Sharp intracellular, juxtacellular, loose-patch and whole-cell approaches are some of the configurations used to record and label individual neurons. Here, we review procedures to establish successful electrophysiological recordings in vivo followed by appropriate labeling for post hoc morphological analysis. We provide operational recommendations for optimizing each configuration and a generic framework for functional, neurochemical and morphological identification of the different cell-types in a given region. Finally, we highlight emerging approaches that are challenging our current paradigms for single-cell recording and labeling in the living brain.
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Affiliation(s)
- Elena Cid
- Instituto Cajal, CSIC, Ave Doctor Arce 37, Madrid, 28002, Spain
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67
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Temido-Ferreira M, Coelho JE, Pousinha PA, Lopes LV. Novel Players in the Aging Synapse: Impact on Cognition. J Caffeine Adenosine Res 2019; 9:104-127. [PMID: 31559391 PMCID: PMC6761599 DOI: 10.1089/caff.2019.0013] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
While neuronal loss has long been considered as the main contributor to age-related cognitive decline, these alterations are currently attributed to gradual synaptic dysfunction driven by calcium dyshomeostasis and alterations in ionotropic/metabotropic receptors. Given the key role of the hippocampus in encoding, storage, and retrieval of memory, the morpho- and electrophysiological alterations that occur in the major synapse of this network-the glutamatergic-deserve special attention. We guide you through the hippocampal anatomy, circuitry, and function in physiological context and focus on alterations in neuronal morphology, calcium dynamics, and plasticity induced by aging and Alzheimer's disease (AD). We provide state-of-the art knowledge on glutamatergic transmission and discuss implications of these novel players for intervention. A link between regular consumption of caffeine-an adenosine receptor blocker-to decreased risk of AD in humans is well established, while the mechanisms responsible have only now been uncovered. We review compelling evidence from humans and animal models that implicate adenosine A2A receptors (A2AR) upsurge as a crucial mediator of age-related synaptic dysfunction. The relevance of this mechanism in patients was very recently demonstrated in the form of a significant association of the A2AR-encoding gene with hippocampal volume (synaptic loss) in mild cognitive impairment and AD. Novel pathways implicate A2AR in the control of mGluR5-dependent NMDAR activation and subsequent Ca2+ dysfunction upon aging. The nature of this receptor makes it particularly suited for long-term therapies, as an alternative for regulating aberrant mGluR5/NMDAR signaling in aging and disease, without disrupting their crucial constitutive activity.
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Affiliation(s)
- Mariana Temido-Ferreira
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal
| | - Joana E. Coelho
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal
| | - Paula A. Pousinha
- Institut de Pharmacologie Moléculaire et Cellulaire (IPMC), CNRS UMR7275, Université Côte d'Azur, Valbonne, France
| | - Luísa V. Lopes
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal
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Cell numbers, distribution, shape, and regional variation throughout the murine hippocampal formation from the adult brain Allen Reference Atlas. Brain Struct Funct 2019; 224:2883-2897. [PMID: 31444616 DOI: 10.1007/s00429-019-01940-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Accepted: 08/13/2019] [Indexed: 01/07/2023]
Abstract
Quantifying the distribution of cells in every brain region is fundamental to attaining a comprehensive census of distinct neuronal and glial types. Until recently, estimating neuron numbers involved time-consuming procedures that were practically limited to stereological sampling. Progress in open-source image recognition software, growth in computing power, and unprecedented neuroinformatics developments now offer the potentially paradigm-shifting alternative of comprehensive cell-by-cell analysis in an entire brain region. The Allen Brain Atlas provides free digital access to complete series of raw Nissl-stained histological section images along with regional delineations. Automated cell segmentation of these data enables reliable and reproducible high-throughput quantification of regional variations in cell count, density, size, and shape at whole-system scale. While this strategy is directly applicable to any regions of the mouse brain, we first deploy it here on the closed-loop circuit of the hippocampal formation: the medial and lateral entorhinal cortices; dentate gyrus (DG); areas Cornu Ammonis 3 (CA3), CA2, and CA1; and dorsal and ventral subiculum. Using two independent image processing pipelines and the adult mouse reference atlas, we report the first cellular-level soma segmentation in every sub-region and non-principal layer of the left hippocampal formation through the full rostral-caudal extent. It is important to note that our techniques excluded the layers with the largest number of cells, DG granular and CA pyramidal, due to dense packing. The numerical estimates for the remaining layers are corroborated by traditional stereological sampling on a data subset and well match sparse published reports.
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69
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Schwanke S, Jenssen J, Eipert P, Schmitt O. Towards Differential Connectomics with NeuroVIISAS. Neuroinformatics 2019; 17:163-179. [PMID: 30014279 DOI: 10.1007/s12021-018-9389-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The comparison of connectomes is an essential step to identify changes in structural and functional neuronal networks. However, the connectomes themselves as well as the comparisons of connectomes could be manifold. In most applications, comparisons of connectomes are applied to specific sets of data. In many studies collections of scripts are applied optimized for certain species (non-generic approaches) or diseases (control versus disease group connectomes). These collections of scripts have a limited functionality which do not support functional and topographic mappings of connectomes (hemispherical asymmetries, peripheral nervous system). The platform-independent and generic neuroVIISAS framework is built to circumvent limitations that come with variants of nomenclatures, connectivity lists and connectional hierarchies as well as restrictions to structural connectome analyses. A new analytical module is introduced into the framework to compare different types of connectomes and different representations of the same connectome within a unique software environment. As an example a differential analysis of the partial connectome of the laboratory rat that is based on virus tract tracing with the same regions of non-virus tract tracing has been performed. A relatively large connectional coherence between the two different techniques was found. However, some detected connections are described by virus tract-tracing only.
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Affiliation(s)
- Sebastian Schwanke
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057, Rostock, Germany
| | - Jörg Jenssen
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057, Rostock, Germany
| | - Peter Eipert
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057, Rostock, Germany
| | - Oliver Schmitt
- Department of Anatomy, University of Rostock, Gertrudenstr. 9, 18057, Rostock, Germany.
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70
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Katz PS, Quinlan PD. The importance of identified neurons in gastropod molluscs to neuroscience. Curr Opin Neurobiol 2019; 56:1-7. [PMID: 30390485 DOI: 10.1016/j.conb.2018.10.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2018] [Accepted: 10/08/2018] [Indexed: 01/10/2023]
Abstract
Gastropod molluscs have large neurons that are uniquely identifiable across individuals and across species based on neuroanatomical and neurochemical criteria, facilitating research into neural signaling and neural circuits. Novel neuropeptides have been identified through RNA sequencing and mass spectroscopic analysis of single neurons. The roles of peptides and other signaling molecules including second messengers have been placed in the context of small circuits that control simple behaviors. Despite the stereotypy, neurons vary over time in their activity in large ensembles. Furthermore, there is both intra-species and inter-species variation in synaptic properties and gene expression. Research on gastropod identified neurons highlights the features that might be expected to be stable in more complex systems when trying to identify cell types.
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Affiliation(s)
- Paul S Katz
- Neuroscience and Behavior Graduate Program, Department of Biology, University of Massachusetts Amherst, 611 North Pleasant Street, 221 Morrill Science Center 3, Amherst, MA 01003, United States.
| | - Phoenix D Quinlan
- Neuroscience and Behavior Graduate Program, Department of Biology, University of Massachusetts Amherst, 611 North Pleasant Street, 221 Morrill Science Center 3, Amherst, MA 01003, United States
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71
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Shepherd GM, Marenco L, Hines ML, Migliore M, McDougal RA, Carnevale NT, Newton AJH, Surles-Zeigler M, Ascoli GA. Neuron Names: A Gene- and Property-Based Name Format, With Special Reference to Cortical Neurons. Front Neuroanat 2019; 13:25. [PMID: 30949034 DOI: 10.3389/fnana.2019.00025/full] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 02/07/2019] [Indexed: 05/25/2023] Open
Abstract
Precision in neuron names is increasingly needed. We are entering a new era in which classical anatomical criteria are only the beginning toward defining the identity of a neuron as carried in its name. New criteria include patterns of gene expression, membrane properties of channels and receptors, pharmacology of neurotransmitters and neuropeptides, physiological properties of impulse firing, and state-dependent variations in expression of characteristic genes and proteins. These gene and functional properties are increasingly defining neuron types and subtypes. Clarity will therefore be enhanced by conveying as much as possible the genes and properties in the neuron name. Using a tested format of parent-child relations for the region and subregion for naming a neuron, we show how the format can be extended so that these additional properties can become an explicit part of a neuron's identity and name, or archived in a linked properties database. Based on the mouse, examples are provided for neurons in several brain regions as proof of principle, with extension to the complexities of neuron names in the cerebral cortex. The format has dual advantages, of ensuring order in archiving the hundreds of neuron types across all brain regions, as well as facilitating investigation of a given neuron type or given gene or property in the context of all its properties. In particular, we show how the format is extensible to the variety of neuron types and subtypes being revealed by RNA-seq and optogenetics. As current research reveals increasingly complex properties, the proposed approach can facilitate a consensus that goes beyond traditional neuron types.
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Affiliation(s)
- Gordon M Shepherd
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, United States
- Yale Center for Medical Informatics, New Haven, CT, United States
| | - Luis Marenco
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, United States
- Yale Center for Medical Informatics, New Haven, CT, United States
| | - Michael L Hines
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, United States
| | - Michele Migliore
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, United States
- Institute of Biophysics, National Research Council, Palermo, Italy
| | - Robert A McDougal
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, United States
- Yale Center for Medical Informatics, New Haven, CT, United States
| | - Nicholas T Carnevale
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, United States
| | - Adam J H Newton
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, United States
- Department of Physiology and Pharmacology, SUNY Downstate Medical Center, Brooklyn, NY, United States
| | - Monique Surles-Zeigler
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, United States
- Yale Center for Medical Informatics, New Haven, CT, United States
| | - Giorgio A Ascoli
- Bioengineering Department and Center for Neural Informatics, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, United States
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72
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Shepherd GM, Marenco L, Hines ML, Migliore M, McDougal RA, Carnevale NT, Newton AJH, Surles-Zeigler M, Ascoli GA. Neuron Names: A Gene- and Property-Based Name Format, With Special Reference to Cortical Neurons. Front Neuroanat 2019; 13:25. [PMID: 30949034 PMCID: PMC6437103 DOI: 10.3389/fnana.2019.00025] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 02/07/2019] [Indexed: 12/15/2022] Open
Abstract
Precision in neuron names is increasingly needed. We are entering a new era in which classical anatomical criteria are only the beginning toward defining the identity of a neuron as carried in its name. New criteria include patterns of gene expression, membrane properties of channels and receptors, pharmacology of neurotransmitters and neuropeptides, physiological properties of impulse firing, and state-dependent variations in expression of characteristic genes and proteins. These gene and functional properties are increasingly defining neuron types and subtypes. Clarity will therefore be enhanced by conveying as much as possible the genes and properties in the neuron name. Using a tested format of parent-child relations for the region and subregion for naming a neuron, we show how the format can be extended so that these additional properties can become an explicit part of a neuron's identity and name, or archived in a linked properties database. Based on the mouse, examples are provided for neurons in several brain regions as proof of principle, with extension to the complexities of neuron names in the cerebral cortex. The format has dual advantages, of ensuring order in archiving the hundreds of neuron types across all brain regions, as well as facilitating investigation of a given neuron type or given gene or property in the context of all its properties. In particular, we show how the format is extensible to the variety of neuron types and subtypes being revealed by RNA-seq and optogenetics. As current research reveals increasingly complex properties, the proposed approach can facilitate a consensus that goes beyond traditional neuron types.
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Affiliation(s)
- Gordon M. Shepherd
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, United States
- Yale Center for Medical Informatics, New Haven, CT, United States
| | - Luis Marenco
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, United States
- Yale Center for Medical Informatics, New Haven, CT, United States
| | - Michael L. Hines
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, United States
| | - Michele Migliore
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, United States
- Institute of Biophysics, National Research Council, Palermo, Italy
| | - Robert A. McDougal
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, United States
- Yale Center for Medical Informatics, New Haven, CT, United States
| | | | - Adam J. H. Newton
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, United States
- Department of Physiology and Pharmacology, SUNY Downstate Medical Center, Brooklyn, NY, United States
| | - Monique Surles-Zeigler
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, United States
- Yale Center for Medical Informatics, New Haven, CT, United States
| | - Giorgio A. Ascoli
- Bioengineering Department and Center for Neural Informatics, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, United States
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73
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Maraver P, Armañanzas R, Gillette TA, Ascoli GA. PaperBot: open-source web-based search and metadata organization of scientific literature. BMC Bioinformatics 2019; 20:50. [PMID: 30678631 PMCID: PMC6345070 DOI: 10.1186/s12859-019-2613-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Accepted: 01/07/2019] [Indexed: 12/16/2022] Open
Abstract
Background The biomedical literature is expanding at ever-increasing rates, and it has become extremely challenging for researchers to keep abreast of new data and discoveries even in their own domains of expertise. We introduce PaperBot, a configurable, modular, open-source crawler to automatically find and efficiently index peer-reviewed publications based on periodic full-text searches across publisher web portals. Results PaperBot may operate stand-alone or it can be easily integrated with other software platforms and knowledge bases. Without user interactions, PaperBot retrieves and stores the bibliographic information (full reference, corresponding email contact, and full-text keyword hits) based on pre-set search logic from a wide range of sources including Elsevier, Wiley, Springer, PubMed/PubMedCentral, Nature, and Google Scholar. Although different publishing sites require different search configurations, the common interface of PaperBot unifies the process from the user perspective. Once saved, all information becomes web accessible allowing efficient triage of articles based on their actual relevance and seamless annotation of suitable metadata content. The platform allows the agile reconfiguration of all key details, such as the selection of search portals, keywords, and metadata dimensions. The tool also provides a one-click option for adding articles manually via digital object identifier or PubMed ID. The microservice architecture of PaperBot implements these capabilities as a loosely coupled collection of distinct modules devised to work separately, as a whole, or to be integrated with or replaced by additional software. All metadata is stored in a schema-less NoSQL database designed to scale efficiently in clusters by minimizing the impedance mismatch between relational model and in-memory data structures. Conclusions As a testbed, we deployed PaperBot to help identify and manage peer-reviewed articles pertaining to digital reconstructions of neuronal morphology in support of the NeuroMorpho.Org data repository. PaperBot enabled the custom definition of both general and neuroscience-specific metadata dimensions, such as animal species, brain region, neuron type, and digital tracing system. Since deployment, PaperBot helped NeuroMorpho.Org more than quintuple the yearly volume of processed information while maintaining a stable personnel workforce.
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Affiliation(s)
- Patricia Maraver
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, USA.,Bioengineering Department; George Mason University, Fairfax, USA
| | - Rubén Armañanzas
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, USA.,Bioengineering Department; George Mason University, Fairfax, USA
| | - Todd A Gillette
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, USA
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, USA. .,Bioengineering Department; George Mason University, Fairfax, USA.
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74
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Harris KD, Hochgerner H, Skene NG, Magno L, Katona L, Bengtsson Gonzales C, Somogyi P, Kessaris N, Linnarsson S, Hjerling-Leffler J. Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics. PLoS Biol 2018; 16:e2006387. [PMID: 29912866 PMCID: PMC6029811 DOI: 10.1371/journal.pbio.2006387] [Citation(s) in RCA: 168] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Revised: 07/03/2018] [Accepted: 05/22/2018] [Indexed: 01/19/2023] Open
Abstract
Understanding any brain circuit will require a categorization of its constituent neurons. In hippocampal area CA1, at least 23 classes of GABAergic neuron have been proposed to date. However, this list may be incomplete; additionally, it is unclear whether discrete classes are sufficient to describe the diversity of cortical inhibitory neurons or whether continuous modes of variability are also required. We studied the transcriptomes of 3,663 CA1 inhibitory cells, revealing 10 major GABAergic groups that divided into 49 fine-scale clusters. All previously described and several novel cell classes were identified, with three previously described classes unexpectedly found to be identical. A division into discrete classes, however, was not sufficient to describe the diversity of these cells, as continuous variation also occurred between and within classes. Latent factor analysis revealed that a single continuous variable could predict the expression levels of several genes, which correlated similarly with it across multiple cell types. Analysis of the genes correlating with this variable suggested it reflects a range from metabolically highly active faster-spiking cells that proximally target pyramidal cells to slower-spiking cells targeting distal dendrites or interneurons. These results elucidate the complexity of inhibitory neurons in one of the simplest cortical structures and show that characterizing these cells requires continuous modes of variation as well as discrete cell classes.
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Affiliation(s)
- Kenneth D. Harris
- University College London Institute of Neurology, London, United Kingdom
- University College London Department of Neuroscience, Physiology and Pharmacology, London, United Kingdom
| | - Hannah Hochgerner
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Nathan G. Skene
- University College London Institute of Neurology, London, United Kingdom
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Lorenza Magno
- University College London Wolfson Institute for Biomedical Research, London, United Kingdom
| | - Linda Katona
- Department of Pharmacology, University of Oxford, Oxford, United Kingdom
| | - Carolina Bengtsson Gonzales
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Peter Somogyi
- Department of Pharmacology, University of Oxford, Oxford, United Kingdom
| | - Nicoletta Kessaris
- University College London Wolfson Institute for Biomedical Research, London, United Kingdom
| | - Sten Linnarsson
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Jens Hjerling-Leffler
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
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Venkadesh S, Komendantov AO, Listopad S, Scott EO, De Jong K, Krichmar JL, Ascoli GA. Evolving Simple Models of Diverse Intrinsic Dynamics in Hippocampal Neuron Types. Front Neuroinform 2018; 12:8. [PMID: 29593519 PMCID: PMC5859109 DOI: 10.3389/fninf.2018.00008] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Accepted: 02/21/2018] [Indexed: 12/24/2022] Open
Abstract
The diversity of intrinsic dynamics observed in neurons may enhance the computations implemented in the circuit by enriching network-level emergent properties such as synchronization and phase locking. Large-scale spiking network models of entire brain regions offer a platform to test theories of neural computation and cognitive function, providing useful insights on information processing in the nervous system. However, a systematic in-depth investigation requires network simulations to capture the biological intrinsic diversity of individual neurons at a sufficient level of accuracy. The computationally efficient Izhikevich model can reproduce a wide range of neuronal behaviors qualitatively. Previous studies using optimization techniques, however, were less successful in quantitatively matching experimentally recorded voltage traces. In this article, we present an automated pipeline based on evolutionary algorithms to quantitatively reproduce features of various classes of neuronal spike patterns using the Izhikevich model. Employing experimental data from Hippocampome.org, a comprehensive knowledgebase of neuron types in the rodent hippocampus, we demonstrate that our approach reliably fit Izhikevich models to nine distinct classes of experimentally recorded spike patterns, including delayed spiking, spiking with adaptation, stuttering, and bursting. Importantly, by leveraging the parameter-exploration capabilities of evolutionary algorithms, and by representing qualitative spike pattern class definitions in the error landscape, our approach creates several suitable models for each neuron type, exhibiting appropriate feature variabilities among neurons. Moreover, we demonstrate the flexibility of our methodology by creating multi-compartment Izhikevich models for each neuron type in addition to single-point versions. Although the results presented here focus on hippocampal neuron types, the same strategy is broadly applicable to any neural systems.
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Affiliation(s)
- Siva Venkadesh
- Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, United States
| | - Alexander O Komendantov
- Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, United States
| | - Stanislav Listopad
- Cognitive Anteater Robotics Laboratory, Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States
| | - Eric O Scott
- Adaptive Systems Laboratory, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, United States
| | - Kenneth De Jong
- Adaptive Systems Laboratory, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, United States
| | - Jeffrey L Krichmar
- Cognitive Anteater Robotics Laboratory, Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures, and Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, United States
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76
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Morphine-potentiated cognitive deficits correlate to suppressed hippocampal iNOS RNA expression and an absent type 1 interferon response in LP-BM5 murine AIDS. J Neuroimmunol 2018. [PMID: 29526406 DOI: 10.1016/j.jneuroim.2018.02.017] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Opioid use accelerates neurocognitive impairment in HIV/AIDS patients. We assessed the effect of chronic morphine treatment and LP-BM5/murine AIDS (MAIDS) infection on cognition, cytokine production, and type 1 interferon (IFN) expression in the murine CNS. Morphine treatment decreased expression of pro-inflammatory factors (CCL5, iNOS) and reduced cognitive performance in LP-BM5-infected mice, correlating to increased hippocampal viral load and a blunted type 1 IFN response. In the striatum, morphine reduced viral load while increasing IFN-α RNA expression. Our results suggest that differentially regulated type 1 IFN responses may contribute to distinct regional outcomes in the hippocampus and striatum in LP-BM5/MAIDS.
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77
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Akram MA, Nanda S, Maraver P, Armañanzas R, Ascoli GA. An open repository for single-cell reconstructions of the brain forest. Sci Data 2018; 5:180006. [PMID: 29485626 PMCID: PMC5827689 DOI: 10.1038/sdata.2018.6] [Citation(s) in RCA: 42] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Accepted: 01/08/2018] [Indexed: 02/06/2023] Open
Abstract
NeuroMorpho.Org was launched in 2006 to provide unhindered access to any and all digital tracings of neuronal morphology that researchers were willing to share freely upon request. Today this database is the largest public inventory of cellular reconstructions in neuroscience with a content of over 80,000 neurons and glia from a representative diversity of animal species, anatomical regions, and experimental methods. Datasets continuously contributed by hundreds of laboratories worldwide are centrally curated, converted into a common non-proprietary format, morphometrically quantified, and annotated with comprehensive metadata. Users download digital reconstructions for a variety of scientific applications including visualization, classification, analysis, and simulations. With more than 1,000 peer-reviewed publications describing data stored in or utilizing data retrieved from NeuroMorpho.Org, this ever-growing repository can already be considered a mature resource for neuroscience.
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Affiliation(s)
- Masood A. Akram
- Center for Neural Informatics, Structures & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA
| | - Sumit Nanda
- Center for Neural Informatics, Structures & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA
| | - Patricia Maraver
- Center for Neural Informatics, Structures & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA
| | - Rubén Armañanzas
- Center for Neural Informatics, Structures & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA
| | - Giorgio A. Ascoli
- Center for Neural Informatics, Structures & Plasticity, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA
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78
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Nakano Y, Karube F, Hirai Y, Kobayashi K, Hioki H, Okamoto S, Kameda H, Fujiyama F. Parvalbumin-producing striatal interneurons receive excitatory inputs onto proximal dendrites from the motor thalamus in male mice. J Neurosci Res 2018; 96:1186-1207. [PMID: 29314192 DOI: 10.1002/jnr.24214] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2017] [Revised: 12/12/2017] [Accepted: 12/12/2017] [Indexed: 01/09/2023]
Abstract
In rodents, the dorsolateral striatum regulates voluntary movement by integrating excitatory inputs from the motor-related cerebral cortex and thalamus to produce contingent inhibitory output to other basal ganglia nuclei. Striatal parvalbumin (PV)-producing interneurons receiving this excitatory input then inhibit medium spiny neurons (MSNs) and modify their outputs. To understand basal ganglia function in motor control, it is important to reveal the precise synaptic organization of motor-related cortical and thalamic inputs to striatal PV interneurons. To examine which domains of the PV neurons receive these excitatory inputs, we used male bacterial artificial chromosome transgenic mice expressing somatodendritic membrane-targeted green fluorescent protein in PV neurons. An anterograde tracing study with the adeno-associated virus vector combined with immunodetection of pre- and postsynaptic markers visualized the distribution of the excitatory appositions on PV dendrites. Statistical analysis revealed that the density of thalamostriatal appositions along the dendrites was significantly higher on the proximal than distal dendrites. In contrast, there was no positional preference in the density of appositions from axons of the dorsofrontal cortex. Population observations of thalamostriatal and corticostriatal appositions by immunohistochemistry for pathway-specific vesicular glutamate transporters confirmed that thalamic inputs preferentially, and cortical ones less preferentially, made apposition on proximal dendrites of PV neurons. This axodendritic organization suggests that PV neurons produce fast and reliable inhibition of MSNs in response to thalamic inputs and process excitatory inputs from motor cortices locally and plastically, possibly together with other GABAergic and dopaminergic dendritic inputs, to modulate MSN inhibition.
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Affiliation(s)
- Yasutake Nakano
- Laboratory of Neural Circuitry, Graduate School of Brain Science, Doshisha University, Kyotanabe, Japan
| | - Fuyuki Karube
- Laboratory of Neural Circuitry, Graduate School of Brain Science, Doshisha University, Kyotanabe, Japan
| | - Yasuharu Hirai
- Laboratory of Neural Circuitry, Graduate School of Brain Science, Doshisha University, Kyotanabe, Japan
| | - Kenta Kobayashi
- Section of Viral Vector Development, National Institute for Physiological Sciences, Okazaki, Japan
| | - Hiroyuki Hioki
- Department of Morphological Brain Science, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Shinichiro Okamoto
- Department of Morphological Brain Science, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Hiroshi Kameda
- Department of Physiology, Teikyo University School of Medicine, Tokyo, Japan
| | - Fumino Fujiyama
- Laboratory of Neural Circuitry, Graduate School of Brain Science, Doshisha University, Kyotanabe, Japan
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79
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Faghihi F, Moustafa AA. Combined Computational Systems Biology and Computational Neuroscience Approaches Help Develop of Future "Cognitive Developmental Robotics". Front Neurorobot 2017; 11:63. [PMID: 29276486 PMCID: PMC5727420 DOI: 10.3389/fnbot.2017.00063] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Accepted: 10/24/2017] [Indexed: 11/13/2022] Open
Affiliation(s)
- Faramarz Faghihi
- Department for Cognitive Modeling, Institute for Cognitive and Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | - Ahmed A Moustafa
- School of Social Sciences and Psychology and Marcs Institute for Brain and Behavior, Western Sydney University, Sydney, NSW, Australia
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80
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Ceder MM, Lekholm E, Hellsten SV, Perland E, Fredriksson R. The Neuronal and Peripheral Expressed Membrane-Bound UNC93A Respond to Nutrient Availability in Mice. Front Mol Neurosci 2017; 10:351. [PMID: 29163028 PMCID: PMC5671512 DOI: 10.3389/fnmol.2017.00351] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Accepted: 10/13/2017] [Indexed: 12/31/2022] Open
Abstract
Many transporters such as the solute carriers belonging to the Major facilitator superfamily Pfam clan are orphans in that their tissue and cellular localization as well as substrate profile and function are still unknown. Here we have characterized the putative solute carrier UNC93A. We aimed to investigate the expression profile on both protein and mRNA level of UNC93A in mouse since it has not been clarified. UNC93A staining was found in cortex, hippocampus and cerebellum. It was found to be expressed in many neurons, but not all, with staining located in close proximity to the plasma membrane. Furthermore, we aimed to extend the starvation data available for Unc93a in hypothalamic cell cultures from mouse. We investigated the Unc93a alterations with focus on amino acid deprivation in embryonic cortex cells from mice as well as 24 h starvation in adult male mice and compared it to recently studied putative and known solute carriers. Unc93a expression was found both in the brain and peripheral organs, in low to moderate levels in the adult mice and was affected by amino acid deprivation in embryonic cortex cultures and starvation in in vivo samples. In conclusion, the membrane-bound UNC93A is expressed in both the brain and peripheral tissues and responds to nutrient availability in mice.
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Affiliation(s)
- Mikaela M Ceder
- Molecular Neuropharmacology, Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Emilia Lekholm
- Molecular Neuropharmacology, Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Sofie V Hellsten
- Molecular Neuropharmacology, Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Emelie Perland
- Molecular Neuropharmacology, Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
| | - Robert Fredriksson
- Molecular Neuropharmacology, Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
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81
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McKiernan EC, Marrone DF. CA1 pyramidal cells have diverse biophysical properties, affected by development, experience, and aging. PeerJ 2017; 5:e3836. [PMID: 28948109 PMCID: PMC5609525 DOI: 10.7717/peerj.3836] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Accepted: 08/31/2017] [Indexed: 12/04/2022] Open
Abstract
Neuron types (e.g., pyramidal cells) within one area of the brain are often considered homogeneous, despite variability in their biophysical properties. Here we review literature demonstrating variability in the electrical activity of CA1 hippocampal pyramidal cells (PCs), including responses to somatic current injection, synaptic stimulation, and spontaneous network-related activity. In addition, we describe how responses of CA1 PCs vary with development, experience, and aging, and some of the underlying ionic currents responsible. Finally, we suggest directions that may be the most impactful in expanding this knowledge, including the use of text and data mining to systematically study cellular heterogeneity in more depth; dynamical systems theory to understand and potentially classify neuron firing patterns; and mathematical modeling to study the interaction between cellular properties and network output. Our goals are to provide a synthesis of the literature for experimentalists studying CA1 PCs, to give theorists an idea of the rich diversity of behaviors models may need to reproduce to accurately represent these cells, and to provide suggestions for future research.
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Affiliation(s)
- Erin C McKiernan
- Departamento de Física, Facultad de Ciencias, Universidad Nacional Autónoma de México, Ciudad de México, México
| | - Diano F Marrone
- Department of Psychology, Wilfrid Laurier University, Waterloo, Ontario, Canada.,McKnight Brain Institute, University of Arizona, Tucson, AZ, United States of America
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82
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Noori HR, Schöttler J, Ercsey-Ravasz M, Cosa-Linan A, Varga M, Toroczkai Z, Spanagel R. A multiscale cerebral neurochemical connectome of the rat brain. PLoS Biol 2017; 15:e2002612. [PMID: 28671956 PMCID: PMC5507471 DOI: 10.1371/journal.pbio.2002612] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2017] [Revised: 07/11/2017] [Accepted: 06/08/2017] [Indexed: 12/05/2022] Open
Abstract
Understanding the rat neurochemical connectome is fundamental for exploring neuronal information processing. By using advanced data mining, supervised machine learning, and network analysis, this study integrates over 5 decades of neuroanatomical investigations into a multiscale, multilayer neurochemical connectome of the rat brain. This neurochemical connectivity database (ChemNetDB) is supported by comprehensive systematically-determined receptor distribution maps. The rat connectome has an onion-type structural organization and shares a number of structural features with mesoscale connectomes of mouse and macaque. Furthermore, we demonstrate that extremal values of graph theoretical measures (e.g., degree and betweenness) are associated with evolutionary-conserved deep brain structures such as amygdala, bed nucleus of the stria terminalis, dorsal raphe, and lateral hypothalamus, which regulate primitive, yet fundamental functions, such as circadian rhythms, reward, aggression, anxiety, and fear. The ChemNetDB is a freely available resource for systems analysis of motor, sensory, emotional, and cognitive information processing.
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Affiliation(s)
- Hamid R. Noori
- Institute of Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
- Neuronal Convergence Group, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Institut des Hautes Etudes Scientifiques, Bures-sur-Yvette, France
| | - Judith Schöttler
- Institute of Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Maria Ercsey-Ravasz
- Faculty of Physics, Babes-Bolyai University, Cluj-Napoca, Romania
- Romanian Institute of Science and Technology, Cluj-Napoca, Romania
| | - Alejandro Cosa-Linan
- Institute of Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Melinda Varga
- Physics Department and the Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Zoltan Toroczkai
- Physics Department and the Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Rainer Spanagel
- Institute of Psychopharmacology, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
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83
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Hamilton DJ, White CM, Rees CL, Wheeler DW, Ascoli GA. Molecular fingerprinting of principal neurons in the rodent hippocampus: A neuroinformatics approach. J Pharm Biomed Anal 2017; 144:269-278. [PMID: 28549853 DOI: 10.1016/j.jpba.2017.03.062] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2016] [Revised: 03/05/2017] [Accepted: 03/29/2017] [Indexed: 12/17/2022]
Abstract
Neurons are often classified by their morphological and molecular properties. The online knowledge base Hippocampome.org primarily defines neuron types from the rodent hippocampal formation based on their main neurotransmitter (glutamate or GABA) and the spatial distributions of their axons and dendrites. For each neuron type, this open-access resource reports any and all published information regarding the presence or absence of known molecular markers, including calcium-binding proteins, neuropeptides, receptors, channels, transcription factors, and other molecules of biomedical relevance. The resulting chemical profile is relatively sparse: even for the best studied neuron types, the expression or lack thereof of fewer than 70 molecules has been firmly established to date. The mouse genome-wide in situ hybridization mapping of the Allen Brain Atlas provides a wealth of data that, when appropriately analyzed, can substantially augment the molecular marker knowledge in Hippocampome.org. Here we focus on the principal cell layers of dentate gyrus (DG), CA3, CA2, and CA1, which together contain approximately 90% of hippocampal neurons. These four anatomical parcels are densely packed with somata of mostly excitatory projection neurons. Thus, gene expression data for those layers can be justifiably linked to the respective principal neuron types: granule cells in DG and pyramidal cells in CA3, CA2, and CA1. In order to enable consistent interpretation across genes and regions, we screened the whole-genome dataset against known molecular markers of those neuron types. The resulting threshold values allow over 6000 very-high confidence (>99.5%) expressed/not-expressed assignments, expanding the biochemical information content of Hippocampome.org more than five-fold. Many of these newly identified molecular markers are potential pharmacological targets for major neurological and psychiatric conditions. Furthermore, our approach yields reasonable expression/non-expression estimates for every single gene in each of these four neuron types with >90% average confidence, providing a considerably complete genetic characterization of hippocampal principal neurons.
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Affiliation(s)
- D J Hamilton
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, United States.
| | - C M White
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, United States
| | - C L Rees
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, United States
| | - D W Wheeler
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, United States
| | - G A Ascoli
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, United States.
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84
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Parvalbumin-expressing interneurons coordinate hippocampal network dynamics required for memory consolidation. Nat Commun 2017; 8:15039. [PMID: 28382952 PMCID: PMC5384212 DOI: 10.1038/ncomms15039] [Citation(s) in RCA: 116] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2016] [Accepted: 02/21/2017] [Indexed: 12/19/2022] Open
Abstract
Activity in hippocampal area CA1 is essential for consolidating episodic memories, but it is unclear how CA1 activity patterns drive memory formation. We find that in the hours following single-trial contextual fear conditioning (CFC), fast-spiking interneurons (which typically express parvalbumin (PV)) show greater firing coherence with CA1 network oscillations. Post-CFC inhibition of PV+ interneurons blocks fear memory consolidation. This effect is associated with loss of two network changes associated with normal consolidation: (1) augmented sleep-associated delta (0.5-4 Hz), theta (4-12 Hz) and ripple (150-250 Hz) oscillations; and (2) stabilization of CA1 neurons' functional connectivity patterns. Rhythmic activation of PV+ interneurons increases CA1 network coherence and leads to a sustained increase in the strength and stability of functional connections between neurons. Our results suggest that immediately following learning, PV+ interneurons drive CA1 oscillations and reactivation of CA1 ensembles, which directly promotes network plasticity and long-term memory formation.
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85
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Abstract
Despite substantial recent progress, our understanding of the principles and mechanisms underlying complex brain function and cognition remains incomplete. Network neuroscience proposes to tackle these enduring challenges. Approaching brain structure and function from an explicitly integrative perspective, network neuroscience pursues new ways to map, record, analyze and model the elements and interactions of neurobiological systems. Two parallel trends drive the approach: the availability of new empirical tools to create comprehensive maps and record dynamic patterns among molecules, neurons, brain areas and social systems; and the theoretical framework and computational tools of modern network science. The convergence of empirical and computational advances opens new frontiers of scientific inquiry, including network dynamics, manipulation and control of brain networks, and integration of network processes across spatiotemporal domains. We review emerging trends in network neuroscience and attempt to chart a path toward a better understanding of the brain as a multiscale networked system.
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Affiliation(s)
- Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Electrical &Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, USA
- Indiana University Network Science Institute, Indiana University, Bloomington, Indiana, USA
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86
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Rees CL, Moradi K, Ascoli GA. Weighing the Evidence in Peters' Rule: Does Neuronal Morphology Predict Connectivity? Trends Neurosci 2016; 40:63-71. [PMID: 28041634 DOI: 10.1016/j.tins.2016.11.007] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Revised: 11/23/2016] [Accepted: 11/29/2016] [Indexed: 10/20/2022]
Abstract
Although the importance of network connectivity is increasingly recognized, identifying synapses remains challenging relative to the routine characterization of neuronal morphology. Thus, researchers frequently employ axon-dendrite colocations as proxies of potential connections. This putative equivalence, commonly referred to as Peters' rule, has been recently studied at multiple levels and scales, fueling passionate debates regarding its validity. Our critical literature review identifies three conceptually distinct but often confused applications: inferring neuron type circuitry, predicting synaptic contacts among individual cells, and estimating synapse numbers within neuron pairs. Paradoxically, at the originally proposed cell-type level, Peters' rule remains largely untested. Leveraging Hippocampome.org, we validate and refine the relationship between axonal-dendritic colocations and synaptic circuits, clarifying the interpretation of existing and forthcoming data.
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Affiliation(s)
- Christopher L Rees
- Kransnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA
| | - Keivan Moradi
- Kransnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA
| | - Giorgio A Ascoli
- Kransnow Institute for Advanced Study, George Mason University, Fairfax, VA 22030, USA.
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87
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Bezaire MJ, Raikov I, Burk K, Vyas D, Soltesz I. Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit. eLife 2016; 5:e18566. [PMID: 28009257 DOI: 10.7554/elife.18566.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2016] [Accepted: 12/15/2016] [Indexed: 05/25/2023] Open
Abstract
The hippocampal theta rhythm plays important roles in information processing; however, the mechanisms of its generation are not well understood. We developed a data-driven, supercomputer-based, full-scale (1:1) model of the rodent CA1 area and studied its interneurons during theta oscillations. Theta rhythm with phase-locked gamma oscillations and phase-preferential discharges of distinct interneuronal types spontaneously emerged from the isolated CA1 circuit without rhythmic inputs. Perturbation experiments identified parvalbumin-expressing interneurons and neurogliaform cells, as well as interneuronal diversity itself, as important factors in theta generation. These simulations reveal new insights into the spatiotemporal organization of the CA1 circuit during theta oscillations.
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Affiliation(s)
- Marianne J Bezaire
- Department of Anatomy and Neurobiology, University of California, Irvine, Irvine, United States
| | - Ivan Raikov
- Department of Anatomy and Neurobiology, University of California, Irvine, Irvine, United States
- Department of Neurosurgery, Stanford University, Stanford, United States
| | - Kelly Burk
- Department of Anatomy and Neurobiology, University of California, Irvine, Irvine, United States
| | - Dhrumil Vyas
- Department of Anatomy and Neurobiology, University of California, Irvine, Irvine, United States
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford University, Stanford, United States
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88
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Bezaire MJ, Raikov I, Burk K, Vyas D, Soltesz I. Interneuronal mechanisms of hippocampal theta oscillations in a full-scale model of the rodent CA1 circuit. eLife 2016; 5. [PMID: 28009257 PMCID: PMC5313080 DOI: 10.7554/elife.18566] [Citation(s) in RCA: 115] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2016] [Accepted: 12/15/2016] [Indexed: 12/16/2022] Open
Abstract
The hippocampal theta rhythm plays important roles in information processing; however, the mechanisms of its generation are not well understood. We developed a data-driven, supercomputer-based, full-scale (1:1) model of the rodent CA1 area and studied its interneurons during theta oscillations. Theta rhythm with phase-locked gamma oscillations and phase-preferential discharges of distinct interneuronal types spontaneously emerged from the isolated CA1 circuit without rhythmic inputs. Perturbation experiments identified parvalbumin-expressing interneurons and neurogliaform cells, as well as interneuronal diversity itself, as important factors in theta generation. These simulations reveal new insights into the spatiotemporal organization of the CA1 circuit during theta oscillations. DOI:http://dx.doi.org/10.7554/eLife.18566.001
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Affiliation(s)
- Marianne J Bezaire
- Department of Anatomy and Neurobiology, University of California, Irvine, Irvine, United States
| | - Ivan Raikov
- Department of Anatomy and Neurobiology, University of California, Irvine, Irvine, United States.,Department of Neurosurgery, Stanford University, Stanford, United States
| | - Kelly Burk
- Department of Anatomy and Neurobiology, University of California, Irvine, Irvine, United States
| | - Dhrumil Vyas
- Department of Anatomy and Neurobiology, University of California, Irvine, Irvine, United States
| | - Ivan Soltesz
- Department of Neurosurgery, Stanford University, Stanford, United States
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89
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Oh YM, Karube F, Takahashi S, Kobayashi K, Takada M, Uchigashima M, Watanabe M, Nishizawa K, Kobayashi K, Fujiyama F. Using a novel PV-Cre rat model to characterize pallidonigral cells and their terminations. Brain Struct Funct 2016; 222:2359-2378. [PMID: 27995326 DOI: 10.1007/s00429-016-1346-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Accepted: 11/25/2016] [Indexed: 10/20/2022]
Abstract
In the present study, we generated a novel parvalbumin (PV)-Cre rat model and conducted detailed morphological and electrophysiological investigations of axons from PV neurons in globus pallidus (GP). The GP is considered as a relay nucleus in the indirect pathway of the basal ganglia (BG). Previous studies have used molecular profiling and projection patterns to demonstrate cellular heterogeneity in the GP; for example, PV-expressing neurons are known to comprise approximately 50% of GP neurons and represent majority of prototypic neurons that project to the subthalamic nucleus and/or output nuclei of BG, entopeduncular nucleus and substantia nigra (SN). The present study aimed to identify the characteristic projection patterns of PV neurons in the GP (PV-GP neurons) and determine whether these neurons target dopaminergic or GABAergic neurons in SN pars compacta (SNc) or reticulata (SNr), respectively. We initially found that (1) 57% of PV neurons co-expressed Lim-homeobox 6, (2) the PV-GP terminals were preferentially distributed in the ventral part of dorsal tier of SNc, (3) PV-GP neurons formed basket-like appositions with the somata of tyrosine hydroxylase, PV, calretinin and cholecystokinin immunoreactive neurons in the SN, and (4) in vitro whole-cell recording during optogenetic photo-stimulation of PV-GP terminals in SNc demonstrated that PV-GP neurons strongly inhibited dopamine neurons via GABAA receptors. These results suggest that dopamine neurons receive direct focal inputs from PV-GP prototypic neurons. The identification of high-contrast inhibitory systems on dopamine neurons might represent a key step toward understanding the BG function.
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Affiliation(s)
- Yoon-Mi Oh
- Laboratory of Neural Circuitry, Graduate School of Brain Science, Doshisha University, Kyotanabe, 610-0394, Japan
| | - Fuyuki Karube
- Laboratory of Neural Circuitry, Graduate School of Brain Science, Doshisha University, Kyotanabe, 610-0394, Japan
| | - Susumu Takahashi
- Laboratory of Neural Circuitry, Graduate School of Brain Science, Doshisha University, Kyotanabe, 610-0394, Japan
| | - Kenta Kobayashi
- Section of Viral Vector Development, National Institute for Physiological Sciences, Okazaki, 444-8585, Japan
| | - Masahiko Takada
- Systems Neuroscience Section, Primate Research Institute, Kyoto University, Inuyama, 484-8506, Japan
| | - Motokazu Uchigashima
- Department of Anatomy, Hokkaido University Graduate School of Medicine, Sapporo, 060-8638, Japan
| | - Masahiko Watanabe
- Department of Anatomy, Hokkaido University Graduate School of Medicine, Sapporo, 060-8638, Japan
| | - Kayo Nishizawa
- Department of Molecular Genetics, Institute of Biomedical Sciences, Fukushima Medical University, Fukushima, 960-1295, Japan
| | - Kazuto Kobayashi
- Department of Molecular Genetics, Institute of Biomedical Sciences, Fukushima Medical University, Fukushima, 960-1295, Japan
| | - Fumino Fujiyama
- Laboratory of Neural Circuitry, Graduate School of Brain Science, Doshisha University, Kyotanabe, 610-0394, Japan.
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90
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Graph Theoretic and Motif Analyses of the Hippocampal Neuron Type Potential Connectome. eNeuro 2016; 3:eN-NWR-0205-16. [PMID: 27896314 PMCID: PMC5114701 DOI: 10.1523/eneuro.0205-16.2016] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Revised: 10/21/2016] [Accepted: 10/25/2016] [Indexed: 01/11/2023] Open
Abstract
We computed the potential connectivity map of all known neuron types in the rodent hippocampal formation by supplementing scantly available synaptic data with spatial distributions of axons and dendrites from the open-access knowledge base Hippocampome.org. The network that results from this endeavor, the broadest and most complete for a mammalian cortical region at the neuron-type level to date, contains more than 3200 connections among 122 neuron types across six subregions. Analyses of these data using graph theory metrics unveil the fundamental architectural principles of the hippocampal circuit. Globally, we identify a highly specialized topology minimizing communication cost; a modular structure underscoring the prominence of the trisynaptic loop; a core set of neuron types serving as information-processing hubs as well as a distinct group of particular antihub neurons; a nested, two-tier rich club managing much of the network traffic; and an innate resilience to random perturbations. At the local level, we uncover the basic building blocks, or connectivity patterns, that combine to produce complex global functionality, and we benchmark their utilization in the circuit relative to random networks. Taken together, these results provide a comprehensive connectivity profile of the hippocampus, yielding novel insights on its functional operations at the computationally crucial level of neuron types.
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91
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Gulyás AI, Freund TF, Káli S. The Effects of Realistic Synaptic Distribution and 3D Geometry on Signal Integration and Extracellular Field Generation of Hippocampal Pyramidal Cells and Inhibitory Neurons. Front Neural Circuits 2016; 10:88. [PMID: 27877113 PMCID: PMC5099150 DOI: 10.3389/fncir.2016.00088] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2016] [Accepted: 10/17/2016] [Indexed: 12/02/2022] Open
Abstract
In vivo and in vitro multichannel field and somatic intracellular recordings are frequently used to study mechanisms of network pattern generation. When interpreting these data, neurons are often implicitly considered as electrotonically compact cylinders with a homogeneous distribution of excitatory and inhibitory inputs. However, the actual distributions of dendritic length, diameter, and the densities of excitatory and inhibitory input are non-uniform and cell type-specific. We first review quantitative data on the dendritic structure and synaptic input and output distribution of pyramidal cells (PCs) and interneurons in the hippocampal CA1 area. Second, using multicompartmental passive models of four different types of neurons, we quantitatively explore the effect of differences in dendritic structure and synaptic distribution on the errors and biases of voltage clamp measurements of inhibitory and excitatory postsynaptic currents. Finally, using the 3-dimensional distribution of dendrites and synaptic inputs we calculate how different inhibitory and excitatory inputs contribute to the generation of local field potential in the hippocampus. We analyze these effects at different realistic background activity levels as synaptic bombardment influences neuronal conductance and thus the propagation of signals in the dendritic tree. We conclude that, since dendrites are electrotonically long and entangled in 3D, somatic intracellular and field potential recordings miss the majority of dendritic events in some cell types, and thus overemphasize the importance of perisomatic inhibitory inputs and belittle the importance of complex dendritic processing. Modeling results also suggest that PCs and inhibitory neurons probably use different input integration strategies. In PCs, second- and higher-order thin dendrites are relatively well-isolated from each other, which may support branch-specific local processing as suggested by studies of active dendritic integration. In the electrotonically compact parvalbumin- and cholecystokinincontaining interneurons, synaptic events are visible in the whole dendritic arbor, and thus the entire dendritic tree may form a single integrative element. Calretinin-containing interneurons were found to be electrotonically extended, which suggests the possibility of complex dendritic processing in this cell type. Our results also highlight the need for the integration of methods that allow the measurement of dendritic processes into studies of synaptic interactions and dynamics in neural networks.
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Affiliation(s)
- Attila I Gulyás
- Laboratory of Cerebral Cortex Research, Institute of Experimental Medicine, Hungarian Academy of Sciences Budapest, Hungary
| | - Tamás F Freund
- Laboratory of Cerebral Cortex Research, Institute of Experimental Medicine, Hungarian Academy of Sciences Budapest, Hungary
| | - Szabolcs Káli
- Laboratory of Cerebral Cortex Research, Institute of Experimental Medicine, Hungarian Academy of Sciences Budapest, Hungary
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92
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McDougal RA, Bulanova AS, Lytton WW. Reproducibility in Computational Neuroscience Models and Simulations. IEEE Trans Biomed Eng 2016; 63:2021-35. [PMID: 27046845 PMCID: PMC5016202 DOI: 10.1109/tbme.2016.2539602] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
OBJECTIVE Like all scientific research, computational neuroscience research must be reproducible. Big data science, including simulation research, cannot depend exclusively on journal articles as the method to provide the sharing and transparency required for reproducibility. METHODS Ensuring model reproducibility requires the use of multiple standard software practices and tools, including version control, strong commenting and documentation, and code modularity. RESULTS Building on these standard practices, model-sharing sites and tools have been developed that fit into several categories: 1) standardized neural simulators; 2) shared computational resources; 3) declarative model descriptors, ontologies, and standardized annotations; and 4) model-sharing repositories and sharing standards. CONCLUSION A number of complementary innovations have been proposed to enhance sharing, transparency, and reproducibility. The individual user can be encouraged to make use of version control, commenting, documentation, and modularity in development of models. The community can help by requiring model sharing as a condition of publication and funding. SIGNIFICANCE Model management will become increasingly important as multiscale models become larger, more detailed, and correspondingly more difficult to manage by any single investigator or single laboratory. Additional big data management complexity will come as the models become more useful in interpreting experiments, thus increasing the need to ensure clear alignment between modeling data, both parameters and results, and experiment.
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93
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Puighermanal E, Cutando L, Boubaker-Vitre J, Honoré E, Longueville S, Hervé D, Valjent E. Anatomical and molecular characterization of dopamine D1 receptor-expressing neurons of the mouse CA1 dorsal hippocampus. Brain Struct Funct 2016; 222:1897-1911. [PMID: 27678395 PMCID: PMC5406422 DOI: 10.1007/s00429-016-1314-x] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2016] [Accepted: 09/15/2016] [Indexed: 12/21/2022]
Abstract
In the hippocampus, a functional role of dopamine D1 receptors (D1R) in synaptic plasticity and memory processes has been suggested by electrophysiological and pharmacological studies. However, comprehension of their function remains elusive due to the lack of knowledge on the precise localization of D1R expression among the diversity of interneuron populations. Using BAC transgenic mice expressing enhanced green fluorescent protein under the control of D1R promoter, we examined the molecular identity of D1R-containing neurons within the CA1 subfield of the dorsal hippocampus. In agreement with previous findings, our analysis revealed that these neurons are essentially GABAergic interneurons, which express several neurochemical markers, including calcium-binding proteins, neuropeptides, and receptors among others. Finally, by using different tools comprising cell type-specific isolation of mRNAs bound to tagged-ribosomes, we provide solid data indicating that D1R is present in a large proportion of interneurons expressing dopamine D2 receptors. Altogether, our study indicates that D1Rs are expressed by different classes of interneurons in all layers examined and not by pyramidal cells, suggesting that CA1 D1R mostly acts via modulation of GABAergic interneurons.
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Affiliation(s)
- Emma Puighermanal
- CNRS UMR 5203, Institut de Génomique Fonctionnelle, 141 rue de la Cardonille, 34094, Montpellier Cedex 05, France.,INSERM, U1191, Montpellier, 34094, France.,Université de Montpellier, UMR 5203, Montpellier, 34094, France
| | - Laura Cutando
- CNRS UMR 5203, Institut de Génomique Fonctionnelle, 141 rue de la Cardonille, 34094, Montpellier Cedex 05, France.,INSERM, U1191, Montpellier, 34094, France.,Université de Montpellier, UMR 5203, Montpellier, 34094, France
| | - Jihane Boubaker-Vitre
- CNRS UMR 5203, Institut de Génomique Fonctionnelle, 141 rue de la Cardonille, 34094, Montpellier Cedex 05, France.,INSERM, U1191, Montpellier, 34094, France.,Université de Montpellier, UMR 5203, Montpellier, 34094, France
| | - Eve Honoré
- CNRS UMR 5203, Institut de Génomique Fonctionnelle, 141 rue de la Cardonille, 34094, Montpellier Cedex 05, France.,INSERM, U1191, Montpellier, 34094, France.,Université de Montpellier, UMR 5203, Montpellier, 34094, France
| | - Sophie Longueville
- Inserm, UMR-S 839, 75005, Paris, France.,Université Pierre et Marie Curie-Paris 6, 75005, Paris, France.,Institut du Fer à Moulin, 75005, Paris, France
| | - Denis Hervé
- Inserm, UMR-S 839, 75005, Paris, France.,Université Pierre et Marie Curie-Paris 6, 75005, Paris, France.,Institut du Fer à Moulin, 75005, Paris, France
| | - Emmanuel Valjent
- CNRS UMR 5203, Institut de Génomique Fonctionnelle, 141 rue de la Cardonille, 34094, Montpellier Cedex 05, France. .,INSERM, U1191, Montpellier, 34094, France. .,Université de Montpellier, UMR 5203, Montpellier, 34094, France.
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94
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Ascoli GA, Wheeler DW. In search of a periodic table of the neurons: Axonal-dendritic circuitry as the organizing principle: Patterns of axons and dendrites within distinct anatomical parcels provide the blueprint for circuit-based neuronal classification. Bioessays 2016; 38:969-76. [PMID: 27516119 DOI: 10.1002/bies.201600067] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
No one knows yet how to organize, in a simple yet predictive form, the knowledge concerning the anatomical, biophysical, and molecular properties of neurons that are accumulating in thousands of publications every year. The situation is not dissimilar to the state of Chemistry prior to Mendeleev's tabulation of the elements. We propose that the patterns of presence or absence of axons and dendrites within known anatomical parcels may serve as the key principle to define neuron types. Just as the positions of the elements in the periodic table indicate their potential to combine into molecules, axonal and dendritic distributions provide the blueprint for network connectivity. Furthermore, among the features commonly employed to describe neurons, morphology is considerably robust to experimental conditions. At the same time, this core classification scheme is suitable for aggregating biochemical, physiological, and synaptic information.
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Affiliation(s)
- Giorgio A Ascoli
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA.
| | - Diek W Wheeler
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
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95
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Name-calling in the hippocampus (and beyond): coming to terms with neuron types and properties. Brain Inform 2016; 4:1-12. [PMID: 27747821 PMCID: PMC5319951 DOI: 10.1007/s40708-016-0053-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2016] [Accepted: 05/24/2016] [Indexed: 01/25/2023] Open
Abstract
Widely spread naming inconsistencies in neuroscience pose a vexing obstacle to effective communication within and across areas of expertise. This problem is particularly acute when identifying neuron types and their properties. Hippocampome.org is a web-accessible neuroinformatics resource that organizes existing data about essential properties of all known neuron types in the rodent hippocampal formation. Hippocampome.org links evidence supporting the assignment of a property to a type with direct pointers to quotes and figures. Mining this knowledge from peer-reviewed reports reveals the troubling extent of terminological ambiguity and undefined terms. Examples span simple cases of using multiple synonyms and acronyms for the same molecular biomarkers (or other property) to more complex cases of neuronal naming. New publications often use different terms without mapping them to previous terms. As a result, neurons of the same type are assigned disparate names, while neurons of different types are bestowed the same name. Furthermore, non-unique properties are frequently used as names, and several neuron types are not named at all. In order to alleviate this nomenclature confusion regarding hippocampal neuron types and properties, we introduce a new functionality of Hippocampome.org: a fully searchable, curated catalog of human and machine-readable definitions, each linked to the corresponding neuron and property terms. Furthermore, we extend our robust approach to providing each neuron type with an informative name and unique identifier by mapping all encountered synonyms and homonyms.
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96
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Skene NG, Grant SGN. Identification of Vulnerable Cell Types in Major Brain Disorders Using Single Cell Transcriptomes and Expression Weighted Cell Type Enrichment. Front Neurosci 2016; 10:16. [PMID: 26858593 PMCID: PMC4730103 DOI: 10.3389/fnins.2016.00016] [Citation(s) in RCA: 165] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Accepted: 01/12/2016] [Indexed: 11/13/2022] Open
Abstract
The cell types that trigger the primary pathology in many brain diseases remain largely unknown. One route to understanding the primary pathological cell type for a particular disease is to identify the cells expressing susceptibility genes. Although this is straightforward for monogenic conditions where the causative mutation may alter expression of a cell type specific marker, methods are required for the common polygenic disorders. We developed the Expression Weighted Cell Type Enrichment (EWCE) method that uses single cell transcriptomes to generate the probability distribution associated with a gene list having an average level of expression within a cell type. Following validation, we applied EWCE to human genetic data from cases of epilepsy, Schizophrenia, Autism, Intellectual Disability, Alzheimer's disease, Multiple Sclerosis and anxiety disorders. Genetic susceptibility primarily affected microglia in Alzheimer's and Multiple Sclerosis; was shared between interneurons and pyramidal neurons in Autism and Schizophrenia; while intellectual disabilities and epilepsy were attributable to a range of cell-types, with the strongest enrichment in interneurons. We hypothesized that the primary cell type pathology could trigger secondary changes in other cell types and these could be detected by applying EWCE to transcriptome data from diseased tissue. In Autism, Schizophrenia and Alzheimer's disease we find evidence of pathological changes in all of the major brain cell types. These findings give novel insight into the cellular origins and progression in common brain disorders. The methods can be applied to any tissue and disorder and have applications in validating mouse models.
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Affiliation(s)
- Nathan G Skene
- Centre for Clinical Brain Sciences, Edinburgh University Edinburgh, UK
| | - Seth G N Grant
- Centre for Clinical Brain Sciences, Edinburgh University Edinburgh, UK
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97
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Skene NG, Grant SGN. Identification of Vulnerable Cell Types in Major Brain Disorders Using Single Cell Transcriptomes and Expression Weighted Cell Type Enrichment. Front Neurosci 2016; 10:16. [PMID: 26858593 DOI: 10.3389/fnins.2016.00016/bibtex] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2015] [Accepted: 01/12/2016] [Indexed: 05/23/2023] Open
Abstract
The cell types that trigger the primary pathology in many brain diseases remain largely unknown. One route to understanding the primary pathological cell type for a particular disease is to identify the cells expressing susceptibility genes. Although this is straightforward for monogenic conditions where the causative mutation may alter expression of a cell type specific marker, methods are required for the common polygenic disorders. We developed the Expression Weighted Cell Type Enrichment (EWCE) method that uses single cell transcriptomes to generate the probability distribution associated with a gene list having an average level of expression within a cell type. Following validation, we applied EWCE to human genetic data from cases of epilepsy, Schizophrenia, Autism, Intellectual Disability, Alzheimer's disease, Multiple Sclerosis and anxiety disorders. Genetic susceptibility primarily affected microglia in Alzheimer's and Multiple Sclerosis; was shared between interneurons and pyramidal neurons in Autism and Schizophrenia; while intellectual disabilities and epilepsy were attributable to a range of cell-types, with the strongest enrichment in interneurons. We hypothesized that the primary cell type pathology could trigger secondary changes in other cell types and these could be detected by applying EWCE to transcriptome data from diseased tissue. In Autism, Schizophrenia and Alzheimer's disease we find evidence of pathological changes in all of the major brain cell types. These findings give novel insight into the cellular origins and progression in common brain disorders. The methods can be applied to any tissue and disorder and have applications in validating mouse models.
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Affiliation(s)
- Nathan G Skene
- Centre for Clinical Brain Sciences, Edinburgh University Edinburgh, UK
| | - Seth G N Grant
- Centre for Clinical Brain Sciences, Edinburgh University Edinburgh, UK
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98
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Ferrante M, Ascoli GA. Distinct and synergistic feedforward inhibition of pyramidal cells by basket and bistratified interneurons. Front Cell Neurosci 2015; 9:439. [PMID: 26594151 PMCID: PMC4633480 DOI: 10.3389/fncel.2015.00439] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2015] [Accepted: 10/22/2015] [Indexed: 01/14/2023] Open
Abstract
Feedforward inhibition (FFI) enables pyramidal cells in area CA1 of the hippocampus (CA1PCs) to remain easily excitable while faithfully representing a broad range of excitatory inputs without quickly saturating. Despite the cortical ubiquity of FFI, its specific function is not completely understood. FFI in CA1PCs is mediated by two physiologically and morphologically distinct GABAergic interneurons: fast-spiking, perisomatic-targeting basket cells and regular-spiking, dendritic-targeting bistratified cells. These two FFI pathways might create layer-specific computational sub-domains within the same CA1PC, but teasing apart their specific contributions remains experimentally challenging. We implemented a biophysically realistic model of CA1PCs using 40 digitally reconstructed morphologies and constraining synaptic numbers, locations, amplitude, and kinetics with available experimental data. First, we validated the model by reproducing the known combined basket and bistratified FFI of CA1PCs at the population level. We then analyzed how the two interneuron types independently affected the CA1PC spike probability and timing as a function of inhibitory strength. Separate FFI by basket and bistratified respectively modulated CA1PC threshold and gain. Concomitant FFI by both interneuron types synergistically extended the dynamic range of CA1PCs by buffering their spiking response to excitatory stimulation. These results suggest testable hypotheses on the precise effects of GABAergic diversity on cortical computation.
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Affiliation(s)
- Michele Ferrante
- Computational Neurophysiology Laboratory, Center for Memory and Brain, Psychology Department, Boston University Boston, MA, USA
| | - Giorgio A Ascoli
- Krasnow Institute for Advanced Study, George Mason University Fairfax, VA, USA
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