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Ceccarelli F, Ferrucci L, Londei F, Ramawat S, Brunamonti E, Genovesio A. Static and dynamic coding in distinct cell types during associative learning in the prefrontal cortex. Nat Commun 2023; 14:8325. [PMID: 38097560 PMCID: PMC10721651 DOI: 10.1038/s41467-023-43712-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 11/17/2023] [Indexed: 12/17/2023] Open
Abstract
The prefrontal cortex maintains information in memory through static or dynamic population codes depending on task demands, but whether the population coding schemes used are learning-dependent and differ between cell types is currently unknown. We investigate the population coding properties and temporal stability of neurons recorded from male macaques in two mapping tasks during and after stimulus-response associative learning, and then we use a Strategy task with the same stimuli and responses as control. We identify a heterogeneous population coding for stimuli, responses, and novel associations: static for putative pyramidal cells and dynamic for putative interneurons that show the strongest selectivity for all the variables. The population coding of learned associations shows overall the highest stability driven by cell types, with interneurons changing from dynamic to static coding after successful learning. The results support that prefrontal microcircuitry expresses mixed population coding governed by cell types and changes its stability during associative learning.
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Affiliation(s)
- Francesco Ceccarelli
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
| | - Lorenzo Ferrucci
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
| | - Fabrizio Londei
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
- PhD program in Behavioral Neuroscience, Sapienza University, Rome, Italy
| | - Surabhi Ramawat
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
| | - Emiliano Brunamonti
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy
| | - Aldo Genovesio
- Department of Physiology and Pharmacology, Sapienza University, 00185, Rome, Italy.
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2
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Jung YJ, Sun SH, Almasi A, Yunzab M, Meffin H, Ibbotson MR. Characterization of extracellular spike waveforms recorded in wallaby primary visual cortex. Front Neurosci 2023; 17:1244952. [PMID: 37746137 PMCID: PMC10517629 DOI: 10.3389/fnins.2023.1244952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 08/25/2023] [Indexed: 09/26/2023] Open
Abstract
Extracellular recordings were made from 642 units in the primary visual cortex (V1) of a highly visual marsupial, the Tammar wallaby. The receptive field (RF) characteristics of the cells were objectively estimated using the non-linear input model (NIM), and these were correlated with spike shapes. We found that wallaby cortical units had 68% regular spiking (RS), 12% fast spiking (FS), 4% triphasic spiking (TS), 5% compound spiking (CS) and 11% positive spiking (PS). RS waveforms are most often associated with recordings from pyramidal or spiny stellate cell bodies, suggesting that recordings from these cell types dominate in the wallaby cortex. In wallaby, 70-80% of FS and RS cells had orientation selective RFs and had evenly distributed linear and nonlinear RFs. We found that 47% of wallaby PS units were non-orientation selective and they were dominated by linear RFs. Previous studies suggest that the PS units represent recordings from the axon terminals of non-orientation selective cells originating in the lateral geniculate nucleus (LGN). If this is also true in wallaby, as strongly suggested by their low response latencies and bursty spiking properties, the results suggest that significantly more neurons in wallaby LGN are already orientation selective. In wallaby, less than 10% of recorded spikes had triphasic (TS) or sluggish compound spiking (CS) waveforms. These units had a mixture of orientation selective and non-oriented properties, and their cellular origins remain difficult to classify.
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Affiliation(s)
- Young Jun Jung
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
- National Vision Research Institute, Australian College of Optometry Carlton, Carlton, VIC, Australia
- Department of Optometry and Vision Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Shi H. Sun
- National Vision Research Institute, Australian College of Optometry Carlton, Carlton, VIC, Australia
| | - Ali Almasi
- National Vision Research Institute, Australian College of Optometry Carlton, Carlton, VIC, Australia
| | - Molis Yunzab
- National Vision Research Institute, Australian College of Optometry Carlton, Carlton, VIC, Australia
| | - Hamish Meffin
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Michael R. Ibbotson
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
- National Vision Research Institute, Australian College of Optometry Carlton, Carlton, VIC, Australia
- Department of Optometry and Vision Sciences, The University of Melbourne, Melbourne, VIC, Australia
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3
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Druga R, Salaj M, Al-Redouan A. Parvalbumin - Positive Neurons in the Neocortex: A Review. Physiol Res 2023; 72:S173-S191. [PMID: 37565421 PMCID: PMC10660579 DOI: 10.33549/physiolres.935005] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 02/02/2023] [Indexed: 12/01/2023] Open
Abstract
The calcium binding protein parvalbumin (PV) in the mammalian neocortex is expressed in a subpopulation of cortical GABAergic inhibitory interneurons. PV - producing interneurons represent the largest subpopulation of neocortical inhibitory cells, exhibit mutual chemical and electrical synaptic contacts and are well known to generate gamma oscillation. This review summarizes basic data of the distribution, afferent and efferent connections and physiological properties of parvalbumin expressing neurons in the neocortex. Basic data about participation of PV-positive neurons in cortical microcircuits are presented. Autaptic connections, metabolism and perineuronal nets (PNN) of PV positive neurons are also discussed.
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Affiliation(s)
- R Druga
- Department of Anatomy, 2nd Medical Faculty, Charles University Prague, Czech Republic.
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Lukacs IP, Francavilla R, Field M, Hunter E, Howarth M, Horie S, Plaha P, Stacey R, Livermore L, Ansorge O, Tamas G, Somogyi P. Differential effects of group III metabotropic glutamate receptors on spontaneous inhibitory synaptic currents in spine-innervating double bouquet and parvalbumin-expressing dendrite-targeting GABAergic interneurons in human neocortex. Cereb Cortex 2023; 33:2101-2142. [PMID: 35667019 PMCID: PMC9977385 DOI: 10.1093/cercor/bhac195] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 04/27/2022] [Accepted: 04/28/2022] [Indexed: 11/12/2022] Open
Abstract
Diverse neocortical GABAergic neurons specialize in synaptic targeting and their effects are modulated by presynaptic metabotropic glutamate receptors (mGluRs) suppressing neurotransmitter release in rodents, but their effects in human neocortex are unknown. We tested whether activation of group III mGluRs by L-AP4 changes GABAA receptor-mediated spontaneous inhibitory postsynaptic currents (sIPSCs) in 2 distinct dendritic spine-innervating GABAergic interneurons recorded in vitro in human neocortex. Calbindin-positive double bouquet cells (DBCs) had columnar "horsetail" axons descending through layers II-V innervating dendritic spines (48%) and shafts, but not somata of pyramidal and nonpyramidal neurons. Parvalbumin-expressing dendrite-targeting cell (PV-DTC) axons extended in all directions innervating dendritic spines (22%), shafts (65%), and somata (13%). As measured, 20% of GABAergic neuropil synapses innervate spines, hence DBCs, but not PV-DTCs, preferentially select spine targets. Group III mGluR activation paradoxically increased the frequency of sIPSCs in DBCs (to median 137% of baseline) but suppressed it in PV-DTCs (median 92%), leaving the amplitude unchanged. The facilitation of sIPSCs in DBCs may result from their unique GABAergic input being disinhibited via network effect. We conclude that dendritic spines receive specialized, diverse GABAergic inputs, and group III mGluRs differentially regulate GABAergic synaptic transmission to distinct GABAergic cell types in human cortex.
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Affiliation(s)
- Istvan P Lukacs
- Department of Pharmacology, University of Oxford, Oxford OX1 3QT, UK
| | | | - Martin Field
- Department of Pharmacology, University of Oxford, Oxford OX1 3QT, UK
| | - Emily Hunter
- Department of Pharmacology, University of Oxford, Oxford OX1 3QT, UK
| | - Michael Howarth
- Department of Pharmacology, University of Oxford, Oxford OX1 3QT, UK
| | - Sawa Horie
- Department of Pharmacology, University of Oxford, Oxford OX1 3QT, UK
| | - Puneet Plaha
- Department of Neurosurgery, John Radcliffe Hospital, OUH NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Richard Stacey
- Department of Neurosurgery, John Radcliffe Hospital, OUH NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Laurent Livermore
- Department of Neurosurgery, John Radcliffe Hospital, OUH NHS Foundation Trust, Oxford OX3 9DU, UK
| | - Olaf Ansorge
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - Gabor Tamas
- Department of Physiology, Anatomy and Neuroscience, University of Szeged, 6726 Szeged, Hungary
| | - Peter Somogyi
- Department of Pharmacology, University of Oxford, Oxford OX1 3QT, UK
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Histamine Release in the Prefrontal Cortex Excites Fast-Spiking Interneurons while GABA Released from the Same Axons Inhibits Pyramidal Cells. J Neurosci 2023; 43:187-198. [PMID: 36639899 PMCID: PMC9838703 DOI: 10.1523/jneurosci.0936-22.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 09/06/2022] [Accepted: 11/03/2022] [Indexed: 12/13/2022] Open
Abstract
We studied how histamine and GABA release from axons originating from the hypothalamic tuberomammillary nucleus (TMN) and projecting to the prefrontal cortex (PFC) influence circuit processing. We optostimulated histamine/GABA from genetically defined TMN axons that express the histidine decarboxylase gene (TMNHDC axons). Whole-cell recordings from PFC neurons in layer 2/3 of prelimbic, anterior cingulate, and infralimbic regions were used to monitor excitability before and after optostimulated histamine/GABA release in male and female mice. We found that histamine-GABA release influences the PFC through actions on distinct neuronal types: the histamine stimulates fast-spiking interneurons; and the released GABA enhances tonic (extrasynaptic) inhibition on pyramidal cells (PyrNs). For fast-spiking nonaccommodating interneurons, histamine released from TMNHDC axons induced additive gain changes, which were blocked by histamine H1 and H2 receptor antagonists. The excitability of other fast-spiking interneurons in the PFC was not altered. In contrast, the GABA released from TMNHDC axons predominantly produced divisive gain changes in PyrNs, increasing their resting input conductance, and decreasing the slope of the input-output relationship. This inhibitory effect on PyrNs was not blocked by histamine receptor antagonists but was blocked by GABAA receptor antagonists. Across the adult life span (from 3 to 18 months of age), the GABA released from TMNHDC axons in the PFC inhibited PyrN excitability significantly more in older mice. For individuals who maintain cognitive performance into later life, the increases in TMNHDC GABA modulation of PyrNs during aging could enhance information processing and be an adaptive mechanism to buttress cognition.SIGNIFICANCE STATEMENT The hypothalamus controls arousal state by releasing chemical neurotransmitters throughout the brain to modulate neuronal excitability. Evidence is emerging that the release of multiple types of neurotransmitters may have opposing actions on neuronal populations in key cortical regions. This study demonstrates for the first time that the neurotransmitters histamine and GABA are released in the prefrontal cortex from axons originating from the tuberomammillary nucleus of the hypothalamus. This work demonstrates how hypothalamic modulation of neuronal excitability is maintained throughout adult life, highlighting an unexpected aspect of the aging process that may help maintain cognitive abilities.
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Dienel SJ, Schoonover KE, Lewis DA. Cognitive Dysfunction and Prefrontal Cortical Circuit Alterations in Schizophrenia: Developmental Trajectories. Biol Psychiatry 2022; 92:450-459. [PMID: 35568522 PMCID: PMC9420748 DOI: 10.1016/j.biopsych.2022.03.002] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 02/28/2022] [Accepted: 03/04/2022] [Indexed: 01/01/2023]
Abstract
Individuals with schizophrenia (SZ) exhibit cognitive performance below expected levels based on familial cognitive aptitude. One such cognitive process, working memory (WM), is robustly impaired in SZ. These WM impairments, which emerge over development during the premorbid and prodromal stages of SZ, appear to reflect alterations in the neural circuitry of the dorsolateral prefrontal cortex. Within the dorsolateral prefrontal cortex, a microcircuit formed by reciprocal connections between excitatory layer 3 pyramidal neurons and inhibitory parvalbumin basket cells (PVBCs) appears to be a key neural substrate for WM. Postmortem human studies indicate that both layer 3 pyramidal neurons and PVBCs are altered in SZ, suggesting that levels of excitation and inhibition are lower in the microcircuit. Studies in monkeys indicate that features of both cell types exhibit distinctive postnatal developmental trajectories. Together, the results of these studies suggest a model in which 1) genetic and/or early environmental insults to excitatory signaling in layer 3 pyramidal neurons give rise to cognitive impairments during the prodromal phase of SZ and evoke compensatory changes in inhibition that alter the developmental trajectories of PVBCs, and 2) synaptic pruning during adolescence further lowers excitatory activity to a level that exceeds the compensatory capacity of PVBC inhibition, leading to a failure of the normal maturational improvements in WM during the prodromal and early clinical stages of SZ. Findings that support as well as challenge this model are discussed.
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Affiliation(s)
- Samuel J Dienel
- Translational Neuroscience Program, Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania; Medical Scientist Training Program, University of Pittsburgh, Pittsburgh, Pennsylvania; Department of Neuroscience, Dietrich School of Arts and Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania; Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - Kirsten E Schoonover
- Translational Neuroscience Program, Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - David A Lewis
- Translational Neuroscience Program, Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania; Department of Neuroscience, Dietrich School of Arts and Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania; Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania.
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7
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Fontanier V, Sarazin M, Stoll FM, Delord B, Procyk E. Inhibitory control of frontal metastability sets the temporal signature of cognition. eLife 2022; 11:63795. [PMID: 35635439 PMCID: PMC9200403 DOI: 10.7554/elife.63795] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 05/27/2022] [Indexed: 11/13/2022] Open
Abstract
Cortical dynamics are organized over multiple anatomical and temporal scales. The mechanistic origin of the temporal organization and its contribution to cognition remain unknown. Here we demonstrate the cause of this organization by studying a specific temporal signature (time constant and latency) of neural activity. In monkey frontal areas, recorded during flexible decisions, temporal signatures display specific area-dependent ranges, as well as anatomical and cell-type distributions. Moreover, temporal signatures are functionally adapted to behaviorally relevant timescales. Fine-grained biophysical network models, constrained to account for experimentally observed temporal signatures, reveal that after-hyperpolarization potassium and inhibitory GABA-B conductances critically determine areas' specificity. They mechanistically account for temporal signatures by organizing activity into metastable states, with inhibition controlling state stability and transitions. As predicted by models, state durations non-linearly scale with temporal signatures in monkey, matching behavioral timescales. Thus, local inhibitory-controlled metastability constitutes the dynamical core specifying the temporal organization of cognitive functions in frontal areas.
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Affiliation(s)
| | - Matthieu Sarazin
- Institute of Intelligent Systems and Robotics (ISIR) - UMR 7222, Sorbonne Université, CNRS, Paris, France
| | - Frederic M Stoll
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, United States
| | - Bruno Delord
- Institute of Intelligent Systems and Robotics (ISIR) - UMR 7222, Sorbonne Université, CNRS, Paris, France
| | - Emmanuel Procyk
- Stem Cell and Brain Research Institute U1208, Inserm, Lyon, France
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Malkin SL, Khachatryan VA, Fedorov EV, Zaitsev AV. The Electrophysiological Properties of Cortical Neurons in the Epileptic Foci of Children with Refractory Temporal Lobe Epilepsy. J EVOL BIOCHEM PHYS+ 2022. [DOI: 10.1134/s0022093022010197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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9
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Bakken TE, Jorstad NL, Hu Q, Lake BB, Tian W, Kalmbach BE, Crow M, Hodge RD, Krienen FM, Sorensen SA, Eggermont J, Yao Z, Aevermann BD, Aldridge AI, Bartlett A, Bertagnolli D, Casper T, Castanon RG, Crichton K, Daigle TL, Dalley R, Dee N, Dembrow N, Diep D, Ding SL, Dong W, Fang R, Fischer S, Goldman M, Goldy J, Graybuck LT, Herb BR, Hou X, Kancherla J, Kroll M, Lathia K, van Lew B, Li YE, Liu CS, Liu H, Lucero JD, Mahurkar A, McMillen D, Miller JA, Moussa M, Nery JR, Nicovich PR, Niu SY, Orvis J, Osteen JK, Owen S, Palmer CR, Pham T, Plongthongkum N, Poirion O, Reed NM, Rimorin C, Rivkin A, Romanow WJ, Sedeño-Cortés AE, Siletti K, Somasundaram S, Sulc J, Tieu M, Torkelson A, Tung H, Wang X, Xie F, Yanny AM, Zhang R, Ament SA, Behrens MM, Bravo HC, Chun J, Dobin A, Gillis J, Hertzano R, Hof PR, Höllt T, Horwitz GD, Keene CD, Kharchenko PV, Ko AL, Lelieveldt BP, Luo C, Mukamel EA, Pinto-Duarte A, Preissl S, Regev A, Ren B, Scheuermann RH, Smith K, Spain WJ, White OR, Koch C, Hawrylycz M, Tasic B, Macosko EZ, McCarroll SA, Ting JT, et alBakken TE, Jorstad NL, Hu Q, Lake BB, Tian W, Kalmbach BE, Crow M, Hodge RD, Krienen FM, Sorensen SA, Eggermont J, Yao Z, Aevermann BD, Aldridge AI, Bartlett A, Bertagnolli D, Casper T, Castanon RG, Crichton K, Daigle TL, Dalley R, Dee N, Dembrow N, Diep D, Ding SL, Dong W, Fang R, Fischer S, Goldman M, Goldy J, Graybuck LT, Herb BR, Hou X, Kancherla J, Kroll M, Lathia K, van Lew B, Li YE, Liu CS, Liu H, Lucero JD, Mahurkar A, McMillen D, Miller JA, Moussa M, Nery JR, Nicovich PR, Niu SY, Orvis J, Osteen JK, Owen S, Palmer CR, Pham T, Plongthongkum N, Poirion O, Reed NM, Rimorin C, Rivkin A, Romanow WJ, Sedeño-Cortés AE, Siletti K, Somasundaram S, Sulc J, Tieu M, Torkelson A, Tung H, Wang X, Xie F, Yanny AM, Zhang R, Ament SA, Behrens MM, Bravo HC, Chun J, Dobin A, Gillis J, Hertzano R, Hof PR, Höllt T, Horwitz GD, Keene CD, Kharchenko PV, Ko AL, Lelieveldt BP, Luo C, Mukamel EA, Pinto-Duarte A, Preissl S, Regev A, Ren B, Scheuermann RH, Smith K, Spain WJ, White OR, Koch C, Hawrylycz M, Tasic B, Macosko EZ, McCarroll SA, Ting JT, Zeng H, Zhang K, Feng G, Ecker JR, Linnarsson S, Lein ES. Comparative cellular analysis of motor cortex in human, marmoset and mouse. Nature 2021; 598:111-119. [PMID: 34616062 PMCID: PMC8494640 DOI: 10.1038/s41586-021-03465-8] [Show More Authors] [Citation(s) in RCA: 386] [Impact Index Per Article: 96.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 03/17/2021] [Indexed: 12/11/2022]
Abstract
The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch-seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.
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Affiliation(s)
| | | | - Qiwen Hu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Blue B Lake
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Wei Tian
- The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Brian E Kalmbach
- Allen Institute for Brain Science, Seattle, WA, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Megan Crow
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Fenna M Krienen
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | | | - Jeroen Eggermont
- LKEB, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Andrew I Aldridge
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Anna Bartlett
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | | | - Rosa G Castanon
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | | | | | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nikolai Dembrow
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
- Epilepsy Center of Excellence, Department of Veterans Affairs Medical Center, Seattle, WA, USA
| | - Dinh Diep
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | | | - Weixiu Dong
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Rongxin Fang
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA
| | - Stephan Fischer
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Melissa Goldman
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Brian R Herb
- Institute for Genomes Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Xiaomeng Hou
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Jayaram Kancherla
- Department of Computer Science, University of Maryland College Park, College Park, MD, USA
| | | | - Kanan Lathia
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Baldur van Lew
- LKEB, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Yang Eric Li
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
| | - Christine S Liu
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
- Biomedical Sciences Program, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Hanqing Liu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | - Anup Mahurkar
- Institute for Genomes Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | | | | | - Joseph R Nery
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | | | - Sheng-Yong Niu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Computer Science and Engineering Program, University of California, San Diego, La Jolla, CA, USA
| | - Joshua Orvis
- Institute for Genomes Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Julia K Osteen
- The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Scott Owen
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Carter R Palmer
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
- Biomedical Sciences Program, School of Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Thanh Pham
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nongluk Plongthongkum
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Olivier Poirion
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Nora M Reed
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | | | - Angeline Rivkin
- The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - William J Romanow
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | | | - Kimberly Siletti
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | | | - Josef Sulc
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Michael Tieu
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Herman Tung
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Xinxin Wang
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
| | - Fangming Xie
- Department of Physics, University of California, San Diego, La Jolla, CA, USA
| | | | - Renee Zhang
- J. Craig Venter Institute, La Jolla, CA, USA
| | - Seth A Ament
- Institute for Genomes Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - Hector Corrada Bravo
- Department of Computer Science, University of Maryland College Park, College Park, MD, USA
| | - Jerold Chun
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | | | - Jesse Gillis
- Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Ronna Hertzano
- Departments of Otorhinolaryngology, Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Patrick R Hof
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Thomas Höllt
- Computer Graphics and Visualization Group, Delt University of Technology, Delft, The Netherlands
| | - Gregory D Horwitz
- Department of Physiology and Biophysics, Washington National Primate Research Center, University of Washington, Seattle, WA, USA
| | - C Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Peter V Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Andrew L Ko
- Department of Neurological Surgery, University of Washington School of Medicine, Seattle, WA, USA
- Regional Epilepsy Center, Harborview Medical Center, Seattle, WA, USA
| | - Boudewijn P Lelieveldt
- LKEB, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Pattern Recognition and Bioinformatics group, Delft University of Technology, Delft, The Netherlands
| | - Chongyuan Luo
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Eran A Mukamel
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA
| | | | - Sebastian Preissl
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Aviv Regev
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Bing Ren
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
| | - Richard H Scheuermann
- J. Craig Venter Institute, La Jolla, CA, USA
- Department of Pathology, University of California, San Diego, CA, USA
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, USA
| | | | - William J Spain
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
- Epilepsy Center of Excellence, Department of Veterans Affairs Medical Center, Seattle, WA, USA
| | - Owen R White
- Institute for Genomes Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | | | | | | | | | - Steven A McCarroll
- Department of Genetics, Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jonathan T Ting
- Allen Institute for Brain Science, Seattle, WA, USA
- Department of Physiology and Biophysics, University of Washington, Seattle, WA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Kun Zhang
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Guoping Feng
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joseph R Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Sten Linnarsson
- Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, USA.
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10
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Piette C, Vandecasteele M, Bosch-Bouju C, Goubard V, Paillé V, Cui Y, Mendes A, Perez S, Valtcheva S, Xu H, Pouget P, Venance L. Intracellular Properties of Deep-Layer Pyramidal Neurons in Frontal Eye Field of Macaque Monkeys. Front Synaptic Neurosci 2021; 13:725880. [PMID: 34621162 PMCID: PMC8490863 DOI: 10.3389/fnsyn.2021.725880] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 08/24/2021] [Indexed: 11/13/2022] Open
Abstract
Although many details remain unknown, several positive statements can be made about the laminar distribution of primate frontal eye field (FEF) neurons with different physiological properties. Most certainly, pyramidal neurons in the deep layer of FEF that project to the brainstem carry movement and fixation signals but clear evidence also support that at least some deep-layer pyramidal neurons projecting to the superior colliculus carry visual responses. Thus, deep-layer neurons in FEF are functionally heterogeneous. Despite the useful functional distinctions between neuronal responses in vivo, the underlying existence of distinct cell types remain uncertain, mostly due to methodological limitations of extracellular recordings in awake behaving primates. To substantiate the functionally defined cell types encountered in the deep layer of FEF, we measured the biophysical properties of pyramidal neurons recorded intracellularly in brain slices issued from macaque monkey biopsies. Here, we found that biophysical properties recorded in vitro permit us to distinguish two main subtypes of regular-spiking neurons, with, respectively, low-resistance and low excitability vs. high-resistance and strong excitability. These results provide useful constraints for cognitive models of visual attention and saccade production by indicating that at least two distinct populations of deep-layer neurons exist.
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Affiliation(s)
- Charlotte Piette
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, PSL University, Paris, France
| | - Marie Vandecasteele
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, PSL University, Paris, France
| | - Clémentine Bosch-Bouju
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, PSL University, Paris, France
| | - Valérie Goubard
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, PSL University, Paris, France
| | - Vincent Paillé
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, PSL University, Paris, France
| | - Yihui Cui
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, PSL University, Paris, France
| | - Alexandre Mendes
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, PSL University, Paris, France
| | - Sylvie Perez
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, PSL University, Paris, France
| | - Silvana Valtcheva
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, PSL University, Paris, France
| | - Hao Xu
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, PSL University, Paris, France
| | - Pierre Pouget
- INSERM, CNRS, Institut du Cerveau, Sorbonne Université, Paris, France
| | - Laurent Venance
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology (CIRB), College de France, CNRS, INSERM, PSL University, Paris, France
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11
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Lee EK, Balasubramanian H, Tsolias A, Anakwe SU, Medalla M, Shenoy KV, Chandrasekaran C. Non-linear dimensionality reduction on extracellular waveforms reveals cell type diversity in premotor cortex. eLife 2021; 10:e67490. [PMID: 34355695 PMCID: PMC8452311 DOI: 10.7554/elife.67490] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 08/04/2021] [Indexed: 11/13/2022] Open
Abstract
Cortical circuits are thought to contain a large number of cell types that coordinate to produce behavior. Current in vivo methods rely on clustering of specified features of extracellular waveforms to identify putative cell types, but these capture only a small amount of variation. Here, we develop a new method (WaveMAP) that combines non-linear dimensionality reduction with graph clustering to identify putative cell types. We apply WaveMAP to extracellular waveforms recorded from dorsal premotor cortex of macaque monkeys performing a decision-making task. Using WaveMAP, we robustly establish eight waveform clusters and show that these clusters recapitulate previously identified narrow- and broad-spiking types while revealing previously unknown diversity within these subtypes. The eight clusters exhibited distinct laminar distributions, characteristic firing rate patterns, and decision-related dynamics. Such insights were weaker when using feature-based approaches. WaveMAP therefore provides a more nuanced understanding of the dynamics of cell types in cortical circuits.
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Affiliation(s)
- Eric Kenji Lee
- Psychological and Brain Sciences, Boston UniversityBostonUnited States
| | - Hymavathy Balasubramanian
- Bernstein Center for Computational Neuroscience, Bernstein Center for Computational NeuroscienceBerlinGermany
| | - Alexandra Tsolias
- Department of Anatomy and Neurobiology, Boston UniversityBostonUnited States
| | | | - Maria Medalla
- Department of Anatomy and Neurobiology, Boston UniversityBostonUnited States
| | - Krishna V Shenoy
- Department of Electrical Engineering, Stanford UniversityStanfordUnited States
- Department of Bioengineering, Stanford UniversityStanfordUnited States
- Department of Neurobiology, Stanford UniversityStanfordUnited States
- Wu Tsai Neurosciences Institute, Stanford UniversityStanfordUnited States
- Bio-X Institute, Stanford UniversityStanfordUnited States
- Howard Hughes Medical Institute, Stanford UniversityStanfordUnited States
| | - Chandramouli Chandrasekaran
- Psychological and Brain Sciences, Boston UniversityBostonUnited States
- Department of Anatomy and Neurobiology, Boston UniversityBostonUnited States
- Center for Systems Neuroscience, Boston UniversityBostonUnited States
- Department of Biomedical Engineering, Boston UniversityBostonUnited States
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12
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Banaie Boroujeni K, Tiesinga P, Womelsdorf T. Interneuron-specific gamma synchronization indexes cue uncertainty and prediction errors in lateral prefrontal and anterior cingulate cortex. eLife 2021; 10:69111. [PMID: 34142661 PMCID: PMC8248985 DOI: 10.7554/elife.69111] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 06/17/2021] [Indexed: 12/27/2022] Open
Abstract
Inhibitory interneurons are believed to realize critical gating functions in cortical circuits, but it has been difficult to ascertain the content of gated information for well-characterized interneurons in primate cortex. Here, we address this question by characterizing putative interneurons in primate prefrontal and anterior cingulate cortex while monkeys engaged in attention demanding reversal learning. We find that subclasses of narrow spiking neurons have a relative suppressive effect on the local circuit indicating they are inhibitory interneurons. One of these interneuron subclasses showed prominent firing rate modulations and (35–45 Hz) gamma synchronous spiking during periods of uncertainty in both, lateral prefrontal cortex (LPFC) and anterior cingulate cortex (ACC). In LPFC, this interneuron subclass activated when the uncertainty of attention cues was resolved during flexible learning, whereas in ACC it fired and gamma-synchronized when outcomes were uncertain and prediction errors were high during learning. Computational modeling of this interneuron-specific gamma band activity in simple circuit motifs suggests it could reflect a soft winner-take-all gating of information having high degree of uncertainty. Together, these findings elucidate an electrophysiologically characterized interneuron subclass in the primate, that forms gamma synchronous networks in two different areas when resolving uncertainty during adaptive goal-directed behavior.
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Affiliation(s)
| | - Paul Tiesinga
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Thilo Womelsdorf
- Department of Psychology, Vanderbilt University, Nashville, United States.,Department of Biology, Centre for Vision Research, York University, Toronto, Canada
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13
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Lemon RN, Baker SN, Kraskov A. Classification of Cortical Neurons by Spike Shape and the Identification of Pyramidal Neurons. Cereb Cortex 2021; 31:5131-5138. [PMID: 34117760 PMCID: PMC8491674 DOI: 10.1093/cercor/bhab147] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Many investigators who make extracellular recordings from populations of cortical neurons are now using spike shape parameters, and particularly spike duration, as a means of classifying different neuronal sub-types. Because of the nature of the experimental approach, particularly that involving nonhuman primates, it is very difficult to validate directly which spike characteristics belong to particular types of pyramidal neurons and interneurons, as defined by modern histological approaches. This commentary looks at the way antidromic identification of pyramidal cells projecting to different targets, and in particular, pyramidal tract neurons (PTN), can inform the utility of spike width classification. Spike duration may provide clues to a diversity of function across the pyramidal cell population, and also highlights important differences that exist across species. Our studies suggest that further electrophysiological and optogenetic approaches are needed to validate spike duration as a means of cell classification and to relate this to well-established histological differences in neocortical cell types.
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Affiliation(s)
- Roger N Lemon
- Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK
| | - Stuart N Baker
- Biosciences Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Alexander Kraskov
- Biosciences Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
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14
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Mosher CP, Wei Y, Kamiński J, Nandi A, Mamelak AN, Anastassiou CA, Rutishauser U. Cellular Classes in the Human Brain Revealed In Vivo by Heartbeat-Related Modulation of the Extracellular Action Potential Waveform. Cell Rep 2021; 30:3536-3551.e6. [PMID: 32160555 DOI: 10.1016/j.celrep.2020.02.027] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Revised: 12/23/2019] [Accepted: 02/05/2020] [Indexed: 01/01/2023] Open
Abstract
Determining cell types is critical for understanding neural circuits but remains elusive in the living human brain. Current approaches discriminate units into putative cell classes using features of the extracellular action potential (EAP); in absence of ground truth data, this remains a problematic procedure. We find that EAPs in deep structures of the brain exhibit robust and systematic variability during the cardiac cycle. These cardiac-related features refine neural classification. We use these features to link bio-realistic models generated from in vitro human whole-cell recordings of morphologically classified neurons to in vivo recordings. We differentiate aspiny inhibitory and spiny excitatory human hippocampal neurons and, in a second stage, demonstrate that cardiac-motion features reveal two types of spiny neurons with distinct intrinsic electrophysiological properties and phase-locking characteristics to endogenous oscillations. This multi-modal approach markedly improves cell classification in humans, offers interpretable cell classes, and is applicable to other brain areas and species.
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Affiliation(s)
- Clayton P Mosher
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Yina Wei
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Jan Kamiński
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Division of Biology and Biological Engineering, Caltech, Pasadena, CA 91125, USA
| | - Anirban Nandi
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Adam N Mamelak
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Costas A Anastassiou
- Allen Institute for Brain Science, Seattle, WA 98109, USA; Division of Neurology, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
| | - Ueli Rutishauser
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Center for Neural Science and Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Division of Biology and Biological Engineering, Caltech, Pasadena, CA 91125, USA.
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15
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Burns TF, Rajan R. Sensing and processing whisker deflections in rodents. PeerJ 2021; 9:e10730. [PMID: 33665005 PMCID: PMC7906041 DOI: 10.7717/peerj.10730] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 12/17/2020] [Indexed: 11/20/2022] Open
Abstract
The classical view of sensory information mainly flowing into barrel cortex at layer IV, moving up for complex feature processing and lateral interactions in layers II and III, then down to layers V and VI for output and corticothalamic feedback is becoming increasingly undermined by new evidence. We review the neurophysiology of sensing and processing whisker deflections, emphasizing the general processing and organisational principles present along the entire sensory pathway—from the site of physical deflection at the whiskers to the encoding of deflections in the barrel cortex. Many of these principles support the classical view. However, we also highlight the growing number of exceptions to these general principles, which complexify the system and which investigators should be mindful of when interpreting their results. We identify gaps in the literature for experimentalists and theorists to investigate, not just to better understand whisker sensation but also to better understand sensory and cortical processing.
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Affiliation(s)
- Thomas F Burns
- Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia
| | - Ramesh Rajan
- Biomedicine Discovery Institute, Monash University, Melbourne, Victoria, Australia
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16
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Ravasz L, Kékesi KA, Mittli D, Todorov MI, Borhegyi Z, Ercsey-Ravasz M, Tyukodi B, Wang J, Bártfai T, Eberwine J, Juhász G. Cell Surface Protein mRNAs Show Differential Transcription in Pyramidal and Fast-Spiking Cells as Revealed by Single-Cell Sequencing. Cereb Cortex 2021; 31:731-745. [PMID: 32710103 DOI: 10.1093/cercor/bhaa195] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 05/27/2020] [Accepted: 06/28/2020] [Indexed: 12/12/2022] Open
Abstract
The prefrontal cortex (PFC) plays a key role in higher order cognitive functions and psychiatric disorders such as autism, schizophrenia, and depression. In the PFC, the two major classes of neurons are the glutamatergic pyramidal (Pyr) cells and the GABAergic interneurons such as fast-spiking (FS) cells. Despite extensive electrophysiological, morphological, and pharmacological studies of the PFC, the therapeutically utilized drug targets are restricted to dopaminergic, glutamatergic, and GABAergic receptors. To expand the pharmacological possibilities as well as to better understand the cellular and network effects of clinically used drugs, it is important to identify cell-type-selective, druggable cell surface proteins and to link developed drug candidates to Pyr or FS cell targets. To identify the mRNAs of such cell-specific/enriched proteins, we performed ultra-deep single-cell mRNA sequencing (19 685 transcripts in total) on electrophysiologically characterized intact PFC neurons harvested from acute brain slices of mice. Several selectively expressed transcripts were identified with some of the genes that have already been associated with cellular mechanisms of psychiatric diseases, which we can now assign to Pyr (e.g., Kcnn2, Gria3) or FS (e.g., Kcnk2, Kcnmb1) cells. The earlier classification of PFC neurons was also confirmed at mRNA level, and additional markers have been provided.
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Affiliation(s)
- Lilla Ravasz
- ELTE NAP Neuroimmunology Research Group, Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University, Budapest H-1117, Hungary.,Laboratory of Proteomics, Institute of Biology, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | - Katalin Adrienna Kékesi
- ELTE NAP Neuroimmunology Research Group, Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University, Budapest H-1117, Hungary.,Laboratory of Proteomics, Institute of Biology, ELTE Eötvös Loránd University, Budapest H-1117, Hungary.,Department of Physiology and Neurobiology, Institute of Biology, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | - Dániel Mittli
- Laboratory of Proteomics, Institute of Biology, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | - Mihail Ivilinov Todorov
- Laboratory of Proteomics, Institute of Biology, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | - Zsolt Borhegyi
- ELTE NAP Neuroimmunology Research Group, Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University, Budapest H-1117, Hungary.,Laboratory of Proteomics, Institute of Biology, ELTE Eötvös Loránd University, Budapest H-1117, Hungary
| | - Mária Ercsey-Ravasz
- Faculty of Physics, Babeș-Bolyai University, Cluj-Napoca RO-400084, Romania.,Transylvanian Institute of Neuroscience, Cluj-Napoca RO-400157, Romania
| | - Botond Tyukodi
- Faculty of Physics, Babeș-Bolyai University, Cluj-Napoca RO-400084, Romania.,Martin Fisher School of Physics, Brandeis University, Waltham, MA 02451, USA
| | - Jinhui Wang
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Tamás Bártfai
- Department of Biochemistry and Biophysics, Stockholm University, Stockholm SE-106 91, Sweden
| | - James Eberwine
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Gábor Juhász
- ELTE NAP Neuroimmunology Research Group, Department of Biochemistry, Institute of Biology, ELTE Eötvös Loránd University, Budapest H-1117, Hungary.,Laboratory of Proteomics, Institute of Biology, ELTE Eötvös Loránd University, Budapest H-1117, Hungary.,CRU Hungary Ltd., H-2131 Göd, Hungary
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17
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Ding C, Emmenegger V, Schaffrath K, Feldmeyer D. Layer-Specific Inhibitory Microcircuits of Layer 6 Interneurons in Rat Prefrontal Cortex. Cereb Cortex 2021; 31:32-47. [PMID: 32829414 PMCID: PMC7727376 DOI: 10.1093/cercor/bhaa201] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 06/06/2020] [Accepted: 07/02/2020] [Indexed: 12/15/2022] Open
Abstract
GABAergic interneurons in different cortical areas play important roles in diverse higher-order cognitive functions. The heterogeneity of interneurons is well characterized in different sensory cortices, in particular in primary somatosensory and visual cortex. However, the structural and functional properties of the medial prefrontal cortex (mPFC) interneurons have received less attention. In this study, a cluster analysis based on axonal projection patterns revealed four distinct clusters of L6 interneurons in rat mPFC: Cluster 1 interneurons showed axonal projections similar to Martinotti-like cells extending to layer 1, cluster 2 displayed translaminar projections mostly to layer 5, and cluster 3 interneuron axons were confined to the layer 6, whereas those of cluster 4 interneurons extend also into the white matter. Correlations were found between neuron location and axonal distribution in all clusters. Moreover, all cluster 1 L6 interneurons showed a monotonically adapting firing pattern with an initial high-frequency burst. All cluster 2 interneurons were fast-spiking, while neurons in cluster 3 and 4 showed heterogeneous firing patterns. Our data suggest that L6 interneurons that have distinct morphological and physiological characteristics are likely to innervate different targets in mPFC and thus play differential roles in the L6 microcircuitry and in mPFC-associated functions.
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Affiliation(s)
- Chao Ding
- Institute of Neuroscience and Medicine, INM-10 Function of Cortical Microcircuits Group, Research Centre Jülich, 52425 Jülich, Germany
| | - Vishalini Emmenegger
- Institute of Neuroscience and Medicine, INM-10 Function of Cortical Microcircuits Group, Research Centre Jülich, 52425 Jülich, Germany
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Kim Schaffrath
- Institute of Neuroscience and Medicine, INM-10 Function of Cortical Microcircuits Group, Research Centre Jülich, 52425 Jülich, Germany
- Department of Ophthalmology, RWTH Aachen University Hospital, Medical School, 52074 Aachen, Germany
| | - Dirk Feldmeyer
- Institute of Neuroscience and Medicine, INM-10 Function of Cortical Microcircuits Group, Research Centre Jülich, 52425 Jülich, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University Hospital, 52074 Aachen, Germany
- JARA-Translational Brain Medicine, 52074 Aachen, Germany
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18
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Yuste R, Hawrylycz M, Aalling N, Aguilar-Valles A, Arendt D, Armañanzas R, Ascoli GA, Bielza C, Bokharaie V, Bergmann TB, Bystron I, Capogna M, Chang Y, Clemens A, de Kock CPJ, DeFelipe J, Dos Santos SE, Dunville K, Feldmeyer D, Fiáth R, Fishell GJ, Foggetti A, Gao X, Ghaderi P, Goriounova NA, Güntürkün O, Hagihara K, Hall VJ, Helmstaedter M, Herculano-Houzel S, Hilscher MM, Hirase H, Hjerling-Leffler J, Hodge R, Huang J, Huda R, Khodosevich K, Kiehn O, Koch H, Kuebler ES, Kühnemund M, Larrañaga P, Lelieveldt B, Louth EL, Lui JH, Mansvelder HD, Marin O, Martinez-Trujillo J, Chameh HM, Mohapatra AN, Munguba H, Nedergaard M, Němec P, Ofer N, Pfisterer UG, Pontes S, Redmond W, Rossier J, Sanes JR, Scheuermann RH, Serrano-Saiz E, Staiger JF, Somogyi P, Tamás G, Tolias AS, Tosches MA, García MT, Wozny C, Wuttke TV, Liu Y, Yuan J, Zeng H, Lein E. A community-based transcriptomics classification and nomenclature of neocortical cell types. Nat Neurosci 2020; 23:1456-1468. [PMID: 32839617 PMCID: PMC7683348 DOI: 10.1038/s41593-020-0685-8] [Citation(s) in RCA: 160] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.
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Affiliation(s)
| | | | | | | | - Detlev Arendt
- European Molecular Biology Laboratory, Heidelberg, Germany
| | - Ruben Armañanzas
- George Mason University, Fairfax, VA, USA
- BrainScope Company Inc., Bethesda, MD, USA
| | | | | | - Vahid Bokharaie
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | | | | | - Marco Capogna
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - YoonJeung Chang
- Department of Genetics, Harvard Medical School, Boston, MA, USA
| | | | | | | | | | | | | | - Richárd Fiáth
- Research Centre for Natural Sciences, Budapest, Hungary
| | | | | | - Xuefan Gao
- European Molecular Biology Laboratory, Hamburg, Germany
| | - Parviz Ghaderi
- École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | | | | | - Kenta Hagihara
- Friedrich Miescher Institute for Biological Research, Basel, Switzerland
| | | | | | | | - Markus M Hilscher
- Karolinska Institutet, Stockholm, Sweden
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden
| | | | | | | | - Josh Huang
- Cold Spring Harbor Laboratory, Laurel Hollow, NY, USA
| | - Rafiq Huda
- WM Keck Center for Collaborative Neuroscience, Department of Cell Biology and Neuroscience, Rutgers University - New Brunswick, Piscataway, NJ, USA
| | | | - Ole Kiehn
- Department of Neuroscience, University of Copenhagen, Copenhagen, Denmark
| | | | - Eric S Kuebler
- Robarts Research Institute, Western University, London, Ontario, Canada
| | | | | | | | | | - Jan H Lui
- Stanford University, Stanford, CA, USA
| | | | | | - Julio Martinez-Trujillo
- Schulich School of Medicine and Dentistry, Departments of Physiology, Pharmacology and Psychiatry, University of Western Ontario, London, Ontario, Canada
| | | | | | | | | | | | | | | | | | | | | | | | - Richard H Scheuermann
- J. Craig Venter Institute, La Jolla, CA, USA
- Department of Pathology, University of California, San Diego, CA, USA
| | | | - Jochen F Staiger
- Institute for Neuroanatomy, University of Göttingen, Göttingen, Germany
| | | | | | | | | | | | - Christian Wozny
- University of Strathclyde, Glasgow, UK
- MSH Medical School, Hamburg, Germany
| | - Thomas V Wuttke
- Departments of Neurosurgery and of Neurology and Epileptology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Yong Liu
- University of Copenhagen, Copenhagen, Denmark
| | - Juan Yuan
- Karolinska Institutet, Stockholm, Sweden
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA.
| | - Ed Lein
- Allen Institute for Brain Science, Seattle, WA, USA.
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19
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Ofer N, Shefi O, Yaari G. Axonal Tree Morphology and Signal Propagation Dynamics Improve Interneuron Classification. Neuroinformatics 2020; 18:581-590. [PMID: 32346847 DOI: 10.1007/s12021-020-09466-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Neurons are diverse and can be differentiated by their morphological, electrophysiological, and molecular properties. Current morphology-based classification approaches largely rely on the dendritic tree structure or on the overall axonal projection layout. Here, we use data from public databases of neuronal reconstructions and membrane properties to study the characteristics of the axonal and dendritic trees for interneuron classification. We show that combining signal propagation patterns observed by biophysical simulations of the activity along ramified axonal trees with morphological parameters of the axonal and dendritic trees, significantly improve classification results compared to previous approaches. The classification schemes introduced here can be utilized for robust neuronal classification. Our work paves the way for understanding and utilizing form-function principles in realistic neuronal reconstructions.
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Affiliation(s)
- Netanel Ofer
- Faculty of Engineering, Bar Ilan University, Ramat Gan, 5290002, Israel.,Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, 5290002, Israel
| | - Orit Shefi
- Faculty of Engineering, Bar Ilan University, Ramat Gan, 5290002, Israel. .,Bar Ilan Institute of Nanotechnologies and Advanced Materials, Bar Ilan University, Ramat Gan, 5290002, Israel.
| | - Gur Yaari
- Faculty of Engineering, Bar Ilan University, Ramat Gan, 5290002, Israel.
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20
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Duan ZRS, Che A, Chu P, Modol L, Bollmann Y, Babij R, Fetcho RN, Otsuka T, Fuccillo MV, Liston C, Pisapia DJ, Cossart R, De Marco García NV. GABAergic Restriction of Network Dynamics Regulates Interneuron Survival in the Developing Cortex. Neuron 2019; 105:75-92.e5. [PMID: 31780329 DOI: 10.1016/j.neuron.2019.10.008] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 07/23/2019] [Accepted: 10/01/2019] [Indexed: 12/15/2022]
Abstract
During neonatal development, sensory cortices generate spontaneous activity patterns shaped by both sensory experience and intrinsic influences. How these patterns contribute to the assembly of neuronal circuits is not clearly understood. Using longitudinal in vivo calcium imaging in un-anesthetized mouse pups, we show that spatially segregated functional assemblies composed of interneurons and pyramidal cells are prominent in the somatosensory cortex by postnatal day (P) 7. Both reduction of GABA release and synaptic inputs onto pyramidal cells erode the emergence of functional topography, leading to increased network synchrony. This aberrant pattern effectively blocks interneuron apoptosis, causing increased survival of parvalbumin and somatostatin interneurons. Furthermore, the effect of GABA on apoptosis is mediated by inputs from medial ganglionic eminence (MGE)-derived but not caudal ganglionic eminence (CGE)-derived interneurons. These findings indicate that immature MGE interneurons are fundamental for shaping GABA-driven activity patterns that balance the number of interneurons integrating into maturing cortical networks.
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Affiliation(s)
- Zhe Ran S Duan
- Center for Neurogenetics, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA; Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program, New York, NY 10021, USA
| | - Alicia Che
- Center for Neurogenetics, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
| | - Philip Chu
- Center for Neurogenetics, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
| | - Laura Modol
- Aix Marseille University, INSERM, INMED, Turing Center for Living Systems, Marseille, France
| | - Yannick Bollmann
- Aix Marseille University, INSERM, INMED, Turing Center for Living Systems, Marseille, France
| | - Rachel Babij
- Center for Neurogenetics, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA; Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program, New York, NY 10021, USA
| | - Robert N Fetcho
- Center for Neurogenetics, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA; Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program, New York, NY 10021, USA
| | - Takumi Otsuka
- Center for Neurogenetics, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
| | - Marc V Fuccillo
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Conor Liston
- Center for Neurogenetics, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
| | - David J Pisapia
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10065, USA
| | - Rosa Cossart
- Aix Marseille University, INSERM, INMED, Turing Center for Living Systems, Marseille, France
| | - Natalia V De Marco García
- Center for Neurogenetics, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA.
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21
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Cell class-specific modulation of attentional signals by acetylcholine in macaque frontal eye field. Proc Natl Acad Sci U S A 2019; 116:20180-20189. [PMID: 31527242 PMCID: PMC6778228 DOI: 10.1073/pnas.1905413116] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Attention is critical to high-level cognition, and attentional deficits are a hallmark of cognitive dysfunction. A key transmitter for attentional control is acetylcholine, but its cellular actions in attention-controlling areas remain poorly understood. Here we delineate how muscarinic and nicotinic receptors affect basic neuronal excitability and attentional control signals in different cell types in macaque frontal eye field. We found that broad spiking and narrow spiking cells both require muscarinic and nicotinic receptors for normal excitability, thereby affecting ongoing or stimulus-driven activity. Attentional control signals depended on muscarinic, not nicotinic receptors in broad spiking cells, while they depended on both muscarinic and nicotinic receptors in narrow spiking cells. Cluster analysis revealed that muscarinic and nicotinic effects on attentional control signals were highly selective even for different subclasses of narrow spiking cells and of broad spiking cells. These results demonstrate that cholinergic receptors are critical to establish attentional control signals in the frontal eye field in a cell type-specific manner.
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22
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Introducing double bouquet cells into a modular cortical associative memory model. J Comput Neurosci 2019; 47:223-230. [PMID: 31502234 PMCID: PMC6879442 DOI: 10.1007/s10827-019-00729-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 08/02/2019] [Accepted: 08/21/2019] [Indexed: 01/01/2023]
Abstract
We present an electrophysiological model of double bouquet cells and integrate them into an established cortical columnar microcircuit model that has previously been used as a spiking attractor model for memory. Learning in that model relies on a Hebbian-Bayesian learning rule to condition recurrent connectivity between pyramidal cells. We here demonstrate that the inclusion of a biophysically plausible double bouquet cell model can solve earlier concerns about learning rules that simultaneously learn excitation and inhibition and might thus violate Dale’s principle. We show that learning ability and resulting effective connectivity between functional columns of previous network models is preserved when pyramidal synapses onto double bouquet cells are plastic under the same Hebbian-Bayesian learning rule. The proposed architecture draws on experimental evidence on double bouquet cells and effectively solves the problem of duplexed learning of inhibition and excitation by replacing recurrent inhibition between pyramidal cells in functional columns of different stimulus selectivity with a plastic disynaptic pathway. We thus show that the resulting change to the microcircuit architecture improves the model’s biological plausibility without otherwise impacting the model’s spiking activity, basic operation, and learning abilities.
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23
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Trainito C, von Nicolai C, Miller EK, Siegel M. Extracellular Spike Waveform Dissociates Four Functionally Distinct Cell Classes in Primate Cortex. Curr Biol 2019; 29:2973-2982.e5. [PMID: 31447374 DOI: 10.1016/j.cub.2019.07.051] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2019] [Revised: 06/21/2019] [Accepted: 07/17/2019] [Indexed: 11/19/2022]
Abstract
Understanding the function of different neuronal cell types is key to understanding brain function. However, cell-type diversity is typically overlooked in electrophysiological studies in awake behaving animals. Here, we show that four functionally distinct cell classes can be robustly identified from extracellular recordings in several cortical regions of awake behaving monkeys. We recorded extracellular spiking activity from dorsolateral prefrontal cortex (dlPFC), the frontal eye field (FEF), and the lateral intraparietal area of macaque monkeys during a visuomotor decision-making task. We employed unsupervised clustering of spike waveforms, which robustly dissociated four distinct cell classes across all three brain regions. The four cell classes were functionally distinct. They showed different baseline firing statistics, visual response dynamics, and coding of visual information. Although cell-class-specific baseline statistics were consistent across brain regions, response dynamics and information coding were regionally specific. Our results identify four functionally distinct spike-waveform-based cell classes in primate cortex. This opens a new window to dissect and study the cell-type-specific function of cortical circuits.
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Affiliation(s)
- Caterina Trainito
- Centre for Integrative Neuroscience, University of Tübingen, Otfried-Müller-Strasse 25, 72076 Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen, Otfried-Müller-Strasse 27, 72076 Tübingen, Germany; MEG Center, University of Tübingen, Otfried-Müller-Strasse 47, 72076 Tübingen, Germany; IMPRS for Cognitive and Systems Neuroscience, University of Tübingen, Österbergstrasse 3, 72074 Tübingen, Germany
| | - Constantin von Nicolai
- Centre for Integrative Neuroscience, University of Tübingen, Otfried-Müller-Strasse 25, 72076 Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen, Otfried-Müller-Strasse 27, 72076 Tübingen, Germany; MEG Center, University of Tübingen, Otfried-Müller-Strasse 47, 72076 Tübingen, Germany
| | - Earl K Miller
- The Picower Institute for Learning and Memory and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139, USA
| | - Markus Siegel
- Centre for Integrative Neuroscience, University of Tübingen, Otfried-Müller-Strasse 25, 72076 Tübingen, Germany; Hertie Institute for Clinical Brain Research, University of Tübingen, Otfried-Müller-Strasse 27, 72076 Tübingen, Germany; MEG Center, University of Tübingen, Otfried-Müller-Strasse 47, 72076 Tübingen, Germany.
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24
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Zhang Y, Li S, Jiang D, Chen A. Response Properties of Interneurons and Pyramidal Neurons in Macaque MSTd and VPS Areas During Self-Motion. Front Neural Circuits 2018; 12:105. [PMID: 30532695 PMCID: PMC6265351 DOI: 10.3389/fncir.2018.00105] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2018] [Accepted: 11/05/2018] [Indexed: 11/29/2022] Open
Abstract
To perceive self-motion perception, the brain needs to integrate multi-modal sensory signals such as visual, vestibular and proprioceptive cues. Self-motion perception is very complex and involves multi candidate areas. Previous studies related to self-motion perception during passive motion have revealed that some of the areas show selective response to different directions for both visual (optic flow) and vestibular stimuli, such as the dorsal subdivision of the medial superior temporal area (MSTd) and the visual posterior sylvian fissure (VPS), although MSTd is dominated by visual signals and VPS is dominated by vestibular signals. However, none of studies related to self-motion perception have distinguished the different neuron types with distinct neuronal properties in cortical microcircuitry, which limited our understanding of the local circuits for self-motion perception. In the current study, we classified the recorded MSTd and VPS neurons into putative pyramidal neurons and putative interneurons based on the extracellular action potential waveforms and spontaneous firing rates. We found that: (1) the putative interneurons exhibited obviously broader direction tuning than putative pyramidal neurons in response to their dominant (visual for MSTd; vestibular for VPS) stimulation type; (2) either in visual or vestibular condition, the putative interneurons were more responsive but with larger variability than the putative pyramidal neurons for both MSTd and VPS areas; and (3) the timing of vestibular and visual peak directional tuning was earlier in the putative interneurons than that of the putative pyramidal neurons for both MSTd and VPS areas. Based on these findings we speculated that, within the microcircuitry, several adjacent putative interneurons with broad direction tuning receive earlier strong but variable signals, which might act feedforward input to shape the direction tuning of the target putative pyramidal neuron, but each interneuron may participate in several microcircuitries, targeting different output neurons.
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Affiliation(s)
| | | | | | - Aihua Chen
- Key Laboratory of Brain Functional Genomics (Ministry of Education), East China Normal University, Shanghai, China
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25
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Lowe KA, Schall JD. Functional Categories of Visuomotor Neurons in Macaque Frontal Eye Field. eNeuro 2018; 5:ENEURO.0131-18.2018. [PMID: 30406195 PMCID: PMC6220589 DOI: 10.1523/eneuro.0131-18.2018] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 09/20/2018] [Accepted: 09/21/2018] [Indexed: 12/11/2022] Open
Abstract
Frontal eye field (FEF) in macaque monkeys contributes to visual attention, visual-motor transformations and production of eye movements. Traditionally, neurons in FEF have been classified by the magnitude of increased discharge rates following visual stimulus presentation, during a waiting period, and associated with eye movement production. However, considerable heterogeneity remains within the traditional visual, visuomovement, and movement categories. Cluster analysis is a data-driven method of identifying self-segregating groups within a dataset. Because many cluster analysis techniques exist and outcomes vary with analysis assumptions, consensus clustering aggregates over multiple analyses, identifying robust groups. To describe more comprehensively the neuronal composition of FEF, we applied a consensus clustering technique for unsupervised categorization of patterns of spike rate modulation measured during a memory-guided saccade task. We report 10 functional categories, expanding on the traditional 3 categories. Categories were distinguished by latency, magnitude, and sign of visual response; the presence of sustained activity; and the dynamics, magnitude and sign of saccade-related modulation. Consensus clustering can include other metrics and can be applied to datasets from other brain regions to provide better information guiding microcircuit models of cortical function.
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Affiliation(s)
- Kaleb A Lowe
- Department of Psychology, Center for Integrative and Cognitive Neuroscience, Vanderbilt Vision Research Center, Vanderbilt University, Nashville, Tennessee 37240
| | - Jeffrey D Schall
- Department of Psychology, Center for Integrative and Cognitive Neuroscience, Vanderbilt Vision Research Center, Vanderbilt University, Nashville, Tennessee 37240
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26
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Quantitative Association of Anatomical and Functional Classes of Olfactory Bulb Neurons. J Neurosci 2018; 38:7204-7220. [PMID: 29976625 PMCID: PMC6096045 DOI: 10.1523/jneurosci.0303-18.2018] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2018] [Revised: 06/04/2018] [Accepted: 06/22/2018] [Indexed: 12/04/2022] Open
Abstract
Juxtaglomerular cells (JGCs) of the olfactory bulb (OB) glomerular layer (GL) play a fundamental role in olfactory information processing. Their variability in morphology, physiology, and connectivity suggests distinct functions. The quantitative understanding of population-wise morphological and physiological properties and a comprehensive classification based on quantitative parameters, however, is still lacking, impeding the analysis of microcircuits. Here, we provide multivariate clustering of 95 in vitro sampled cells from the GL of the mouse (male or female C57BL/6) OB and perform detailed morphological and physiological characterization for the seven computed JGC types. Using a classifier based on a subselection of parameters, we identified the neuron types in paired recordings to characterize their functional connectivity. We found that 4 of the 7 clusters comply with prevailing concepts of GL cell types, whereas the other 3 represent own distinct entities. We have labeled these entities horizontal superficial tufted cell (hSTC), vertical superficial tufted cell, and microglomerular cell (MGC): The hSTC is a tufted cell with a lateral dendrite that much like mitral cells and tufted cells receives excitatory inputs from the external tufted cell but likewise serves as an excitatory element for glomerular interneurons. The vertical superficial tufted cell, on the other hand, represents a tufted cell type with vertically projecting basal dendrites. We further define the MGC, characterized by a small dendritic tree and plateau action potentials. In addition to olfactory nerve-driven and external tufted cell driven interneurons, these MGCs represent a third functionally distinct type, the hSTC-driven interneurons. The presented correlative analysis helps to bridge the gap between branching patterns and cellular functional properties, permitting the integration of results from in vivo recordings, advanced morphological tools, and connectomics. SIGNIFICANCE STATEMENT The variance of neuron properties is a feature across mammalian cerebral circuits, contributing to signal processing and adding computational robustness to the networks. It is particularly noticeable in the glomerular layer of the olfactory bulb, the first site of olfactory information processing. We provide the first unbiased population-wise multivariate analysis to correlate morphological and physiological parameters of juxtaglomerular cells. We identify seven cell types, including four previously described neuron types, and identify further three distinct classes. The presented correlative analysis of morphological and physiological parameters gives an opportunity to predict morphological classes from physiological measurements or the functional properties of neurons from morphology and opens the way to integrate results from in vivo recordings, advanced morphological tools, and connectomics.
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27
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Standage D, Paré M. Slot-like capacity and resource-like coding in a neural model of multiple-item working memory. J Neurophysiol 2018; 120:1945-1961. [PMID: 29947585 DOI: 10.1152/jn.00778.2017] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
For the past decade, research on the storage limitations of working memory has been dominated by two fundamentally different hypotheses. On the one hand, the contents of working memory may be stored in a limited number of "slots," each with a fixed resolution. On the other hand, any number of items may be stored but with decreasing resolution. These two hypotheses have been invaluable in characterizing the computational structure of working memory, but neither provides a complete account of the available experimental data or speaks to the neural basis of the limitations it characterizes. To address these shortcomings, we simulated a multiple-item working memory task with a cortical network model, the cellular resolution of which allowed us to quantify the coding fidelity of memoranda as a function of memory load, as measured by the discriminability, regularity, and reliability of simulated neural spiking. Our simulations account for a wealth of neural and behavioral data from human and nonhuman primate studies, and they demonstrate that feedback inhibition lowers both capacity and coding fidelity. Because the strength of inhibition scales with the number of items stored by the network, increasing this number progressively lowers fidelity until capacity is reached. Crucially, the model makes specific, testable predictions for neural activity on multiple-item working memory tasks. NEW & NOTEWORTHY Working memory is the ability to keep information in mind and is fundamental to cognition. It is actively debated whether the storage limitations of working memory reflect a small number of storage units (slots) or a decrease in coding resolution as a limited resource is allocated to more items. In a cortical model, we found that slot-like capacity and resource-like neural coding resulted from the same mechanism, offering an integrated explanation for storage limitations.
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Affiliation(s)
- Dominic Standage
- Centre for Neuroscience Studies, Queen's University , Kingston, Ontario , Canada
| | - Martin Paré
- Centre for Neuroscience Studies, Queen's University , Kingston, Ontario , Canada
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28
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Vijayraghavan S, Major AJ, Everling S. Muscarinic M1 Receptor Overstimulation Disrupts Working Memory Activity for Rules in Primate Prefrontal Cortex. Neuron 2018; 98:1256-1268.e4. [PMID: 29887340 DOI: 10.1016/j.neuron.2018.05.027] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2018] [Revised: 04/10/2018] [Accepted: 05/17/2018] [Indexed: 10/14/2022]
Abstract
Acetylcholine release in the prefrontal cortex (PFC), acting through muscarinic receptors, has an essential role in regulating flexible behavior and working memory (WM). General muscarinic receptor blockade disrupts PFC WM representations, while selective stimulation of muscarinic receptor subtypes is of great interest for the treatment of cognitive dysfunction in Alzheimer's disease. Here, we tested selective stimulation and blockade of muscarinic M1 receptors (M1Rs) in macaque PFC, during performance of a cognitive control task in which rules maintained in WM specified saccadic responses. We hypothesized that M1R blockade and stimulation would disrupt and enhance rule representation in WM, respectively. Unexpectedly, M1R blockade did not consistently affect PFC neuronal rule selectivity. Moreover, M1R stimulation suppressed PFC activity, and at higher doses, degraded rule representations. Our results suggest that, in primates, the deleterious effects of general muscarinic blockade on PFC WM activity are not mediated by M1Rs, while their overstimulation deteriorates PFC rule maintenance.
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Affiliation(s)
- Susheel Vijayraghavan
- Department of Physiology and Pharmacology, The University of Western Ontario, London, ON N6A 5B7, Canada; Robarts Research Institute, The University of Western Ontario, London, ON N6A 5B7, Canada
| | - Alex James Major
- Graduate Program in Neuroscience, The University of Western Ontario, London, ON N6A 5B7, Canada
| | - Stefan Everling
- Department of Physiology and Pharmacology, The University of Western Ontario, London, ON N6A 5B7, Canada; Robarts Research Institute, The University of Western Ontario, London, ON N6A 5B7, Canada; Graduate Program in Neuroscience, The University of Western Ontario, London, ON N6A 5B7, Canada.
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29
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Ferrante M, Tahvildari B, Duque A, Hadzipasic M, Salkoff D, Zagha EW, Hasselmo ME, McCormick DA. Distinct Functional Groups Emerge from the Intrinsic Properties of Molecularly Identified Entorhinal Interneurons and Principal Cells. Cereb Cortex 2018; 27:3186-3207. [PMID: 27269961 DOI: 10.1093/cercor/bhw143] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Inhibitory interneurons are an important source of synaptic inputs that may contribute to network mechanisms for coding of spatial location by entorhinal cortex (EC). The intrinsic properties of inhibitory interneurons in the EC of the mouse are mostly undescribed. Intrinsic properties were recorded from known cell types, such as, stellate and pyramidal cells and 6 classes of molecularly identified interneurons (regulator of calcineurin 2, somatostatin, serotonin receptor 3a, neuropeptide Y neurogliaform (NGF), neuropeptide Y non-NGF, and vasoactive intestinal protein) in acute brain slices. We report a broad physiological diversity between and within cell classes. We also found differences in the ability to produce postinhibitory rebound spikes and in the frequency and amplitude of incoming EPSPs. To understand the source of this intrinsic variability we applied hierarchical cluster analysis to functionally classify neurons. These analyses revealed physiologically derived cell types in EC that mostly corresponded to the lines identified by biomarkers with a few unexpected and important differences. Finally, we reduced the complex multidimensional space of intrinsic properties to the most salient five that predicted the cellular biomolecular identity with 81.4% accuracy. These results provide a framework for the classification of functional subtypes of cortical neurons by their intrinsic membrane properties.
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Affiliation(s)
- Michele Ferrante
- Center for Memory and Brain.,Center for Systems Neuroscience.,Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215, USA
| | - Babak Tahvildari
- Department of Neurobiology.,Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06520-8001, USA
| | - Alvaro Duque
- Department of Neurobiology.,Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06520-8001, USA
| | - Muhamed Hadzipasic
- Interdepartmental Program in Neuroscience, Yale School of Medicine, New Haven, CT 06520-8001, USA
| | - David Salkoff
- Department of Neurobiology.,Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06520-8001, USA
| | - Edward William Zagha
- Department of Neurobiology.,Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06520-8001, USA
| | - Michael E Hasselmo
- Center for Memory and Brain.,Center for Systems Neuroscience.,Department of Psychological and Brain Sciences, Boston University, Boston, MA 02215, USA
| | - David A McCormick
- Department of Neurobiology.,Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, CT 06520-8001, USA
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30
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Differences in reward processing between putative cell types in primate prefrontal cortex. PLoS One 2017; 12:e0189771. [PMID: 29261734 PMCID: PMC5736196 DOI: 10.1371/journal.pone.0189771] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2017] [Accepted: 11/26/2017] [Indexed: 11/19/2022] Open
Abstract
Single-unit studies in monkeys have demonstrated that neurons in the prefrontal cortex predict the reward type, reward amount or reward availability associated with a stimulus. To examine contributions of pyramidal cells and interneurons in reward processing, single-unit activity was extracellularly recorded in prefrontal cortices of four monkeys performing a reward prediction task. Based on their shapes of spike waveforms, prefrontal neurons were classified into broad-spike and narrow-spike units that represented putative pyramidal cells and interneurons, respectively. We mainly observed that narrow-spike neurons showed higher firing rates but less bursty discharges than did broad-spike neurons. Both narrow-spike and broad-spike cells selectively responded to the stimulus, reward and their interaction, and the proportions of each type of selective neurons were similar between the two cell classes. Moreover, the two types of cells displayed equal reliability of reward or stimulus discrimination. Furthermore, we found that broad-spike and narrow-spike cells showed distinct mechanisms for encoding reward or stimulus information. Broad-spike neurons raised their firing rate relative to the baseline rate to represent the preferred reward or stimulus information, whereas narrow-spike neurons inhibited their firing rate lower than the baseline rate to encode the non-preferred reward or stimulus information. Our results suggest that narrow-spike and broad-spike cells were equally involved in reward and stimulus processing in the prefrontal cortex. They utilized a binary strategy to complementarily represent reward or stimulus information, which was consistent with the task structure in which the monkeys were required to remember two reward conditions and two visual stimuli.
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31
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Anatomical and Electrophysiological Clustering of Superficial Medial Entorhinal Cortex Interneurons. eNeuro 2017; 4:eN-NWR-0263-16. [PMID: 29085901 PMCID: PMC5659260 DOI: 10.1523/eneuro.0263-16.2017] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Revised: 09/19/2017] [Accepted: 09/29/2017] [Indexed: 01/03/2023] Open
Abstract
Local GABAergic interneurons regulate the activity of spatially-modulated principal cells in the medial entorhinal cortex (MEC), mediating stellate-to-stellate connectivity and possibly enabling grid formation via recurrent inhibitory circuitry. Despite the important role interneurons seem to play in the MEC cortical circuit, the combination of low cell counts and functional diversity has made systematic electrophysiological studies of these neurons difficult. For these reasons, there remains a paucity of knowledge on the electrophysiological profiles of superficial MEC interneuron populations. Taking advantage of glutamic acid decarboxylase 2 (GAD2)-IRES-tdTomato and PV-tdTomato transgenic mice, we targeted GABAergic interneurons for whole-cell patch-clamp recordings and characterized their passive membrane features, basic input/output properties and action potential (AP) shape. These electrophysiologically characterized cells were then anatomically reconstructed, with emphasis on axonal projections and pial depth. K-means clustering of interneuron anatomical and electrophysiological data optimally classified a population of 106 interneurons into four distinct clusters. The first cluster is comprised of layer 2- and 3-projecting, slow-firing interneurons. The second cluster is comprised largely of PV+ fast-firing interneurons that project mainly to layers 2 and 3. The third cluster contains layer 1- and 2-projecting interneurons, and the fourth cluster is made up of layer 1-projecting horizontal interneurons. These results, among others, will provide greater understanding of the electrophysiological characteristics of MEC interneurons, help guide future in vivo studies, and may aid in uncovering the mechanism of grid field formation.
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Feldmeyer D, Qi G, Emmenegger V, Staiger JF. Inhibitory interneurons and their circuit motifs in the many layers of the barrel cortex. Neuroscience 2017; 368:132-151. [PMID: 28528964 DOI: 10.1016/j.neuroscience.2017.05.027] [Citation(s) in RCA: 87] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2017] [Revised: 05/11/2017] [Accepted: 05/12/2017] [Indexed: 10/19/2022]
Abstract
Recent years have seen substantial progress in studying the structural and functional properties of GABAergic interneurons and their roles in the neuronal networks of barrel cortex. Although GABAergic interneurons represent only about 12% of the total number of neocortical neurons, they are extremely diverse with respect to their structural and functional properties. It has become clear that barrel cortex interneurons not only serve the maintenance of an appropriate excitation/inhibition balance but also are directly involved in sensory processing. In this review we present different interneuron types and their axonal projection pattern framework in the context of the laminar and columnar organization of the barrel cortex. The main focus is here on the most prominent interneuron types, i.e. basket cells, chandelier cells, Martinotti cells, bipolar/bitufted cells and neurogliaform cells, but interneurons with more unusual axonal domains will also be mentioned. We describe their developmental origin, their classification with respect to molecular, morphological and intrinsic membrane and synaptic properties. Most importantly, we will highlight the most prominent circuit motifs these interneurons are involved in and in which way they serve feed-forward inhibition, feedback inhibition and disinhibition. Finally, this will be put into context to their functional roles in sensory signal perception and processing in the whisker system and beyond.
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Affiliation(s)
- Dirk Feldmeyer
- Institute of Neuroscience and Medicine, INM-2, Research Center Jülich, D-52425 Jülich, Germany; Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, D-52074 Aachen, Germany; Jülich Aachen Research Alliance, Translational Brain Medicine (JARA Brain), D-52074 Aachen, Germany.
| | - Guanxiao Qi
- Institute of Neuroscience and Medicine, INM-2, Research Center Jülich, D-52425 Jülich, Germany
| | - Vishalini Emmenegger
- Institute of Neuroscience and Medicine, INM-2, Research Center Jülich, D-52425 Jülich, Germany; Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, D-52074 Aachen, Germany
| | - Jochen F Staiger
- Institute for Neuroanatomy, University Medical Center Göttingen, Georg-August-University, Göttingen D-37075, Germany.
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Chung DW, Fish KN, Lewis DA. Pathological Basis for Deficient Excitatory Drive to Cortical Parvalbumin Interneurons in Schizophrenia. Am J Psychiatry 2016; 173:1131-1139. [PMID: 27444795 PMCID: PMC5089927 DOI: 10.1176/appi.ajp.2016.16010025] [Citation(s) in RCA: 115] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Deficient excitatory drive to parvalbumin-containing cortical interneurons is proposed as a key neural substrate for altered gamma oscillations and cognitive dysfunction in schizophrenia. However, a pathological entity producing such a deficit has not been identified. The authors tested the hypothesis that cortical parvalbumin interneurons receive fewer excitatory synaptic inputs in individuals with schizophrenia. METHOD Fluorescent immunohistochemistry, confocal microscopy, and post-image processing techniques were used to quantify the number of putative excitatory synapses (i.e., the overlap of vesicular glutamate transporter 1-positive [VGlut1+] puncta and postsynaptic density protein 95-positive [PSD95+] puncta) per surface area of parvalbumin-positive (PV+) or calretinin-positive (CR+) neurons in the dorsolateral prefrontal cortex from schizophrenia subjects and matched unaffected comparison subjects. RESULTS Mean density of VGlut1+/PSD95+ puncta on PV+ neurons was 18% lower in schizophrenia, a significant difference. This deficit was not influenced by methodological confounds or schizophrenia-associated comorbid factors, not present in monkeys chronically exposed to antipsychotic medications, and not present in CR+ neurons. Mean density of VGlut1+/PSD95+ puncta on PV+ neurons predicted the activity-dependent expression levels of parvalbumin and glutamic acid decarboxylase 67 (GAD67) in schizophrenia subjects but not comparison subjects. CONCLUSIONS To the authors' knowledge, this is the first demonstration that excitatory synapse density is lower selectively on parvalbumin interneurons in schizophrenia and predicts the activity-dependent down-regulation of parvalbumin and GAD67. Because the activity of parvalbumin interneurons is required for generation of cortical gamma oscillations and working memory function, these findings reveal a novel pathological substrate for cortical dysfunction and cognitive deficits in schizophrenia.
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Affiliation(s)
- Daniel W. Chung
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA,Medical Scientist Training Program, University of Pittsburgh School of Medicine, Pittsburgh, PA
| | - Kenneth N. Fish
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA
| | - David A. Lewis
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA
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Wang B, Ke W, Guang J, Chen G, Yin L, Deng S, He Q, Liu Y, He T, Zheng R, Jiang Y, Zhang X, Li T, Luan G, Lu HD, Zhang M, Zhang X, Shu Y. Firing Frequency Maxima of Fast-Spiking Neurons in Human, Monkey, and Mouse Neocortex. Front Cell Neurosci 2016; 10:239. [PMID: 27803650 PMCID: PMC5067378 DOI: 10.3389/fncel.2016.00239] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2016] [Accepted: 09/30/2016] [Indexed: 12/13/2022] Open
Abstract
Cortical fast-spiking (FS) neurons generate high-frequency action potentials (APs) without apparent frequency accommodation, thus providing fast and precise inhibition. However, the maximal firing frequency that they can reach, particularly in primate neocortex, remains unclear. Here, by recording in human, monkey, and mouse neocortical slices, we revealed that FS neurons in human association cortices (mostly temporal) could generate APs at a maximal mean frequency (Fmean) of 338 Hz and a maximal instantaneous frequency (Finst) of 453 Hz, and they increase with age. The maximal firing frequency of FS neurons in the association cortices (frontal and temporal) of monkey was even higher (Fmean 450 Hz, Finst 611 Hz), whereas in the association cortex (entorhinal) of mouse it was much lower (Fmean 215 Hz, Finst 342 Hz). Moreover, FS neurons in mouse primary visual cortex (V1) could fire at higher frequencies (Fmean 415 Hz, Finst 582 Hz) than those in association cortex. We further validated our in vitro data by examining spikes of putative FS neurons in behaving monkey and mouse. Together, our results demonstrate that the maximal firing frequency of FS neurons varies between species and cortical areas.
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Affiliation(s)
- Bo Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, School of Brain and Cognitive Sciences, The Collaborative Innovation Center for Brain Science, Beijing Normal UniversityBeijing, China; Institute of Neuroscience and State Key Laboratory of Neuroscience, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, and University of Chinese Academy of SciencesShanghai, China
| | - Wei Ke
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, School of Brain and Cognitive Sciences, The Collaborative Innovation Center for Brain Science, Beijing Normal University Beijing, China
| | - Jing Guang
- Institute of Neuroscience and State Key Laboratory of Neuroscience, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, and University of Chinese Academy of Sciences Shanghai, China
| | - Guang Chen
- Institute of Neuroscience and State Key Laboratory of Neuroscience, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, and University of Chinese Academy of Sciences Shanghai, China
| | - Luping Yin
- Institute of Neuroscience and State Key Laboratory of Neuroscience, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, and University of Chinese Academy of Sciences Shanghai, China
| | - Suixin Deng
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, School of Brain and Cognitive Sciences, The Collaborative Innovation Center for Brain Science, Beijing Normal University Beijing, China
| | - Quansheng He
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, School of Brain and Cognitive Sciences, The Collaborative Innovation Center for Brain Science, Beijing Normal University Beijing, China
| | - Yaping Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, School of Brain and Cognitive Sciences, The Collaborative Innovation Center for Brain Science, Beijing Normal University Beijing, China
| | - Ting He
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, School of Brain and Cognitive Sciences, The Collaborative Innovation Center for Brain Science, Beijing Normal University Beijing, China
| | - Rui Zheng
- Institute of Neuroscience and State Key Laboratory of Neuroscience, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, and University of Chinese Academy of Sciences Shanghai, China
| | - Yanbo Jiang
- Institute of Neuroscience and State Key Laboratory of Neuroscience, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, and University of Chinese Academy of Sciences Shanghai, China
| | - Xiaoxue Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, School of Brain and Cognitive Sciences, The Collaborative Innovation Center for Brain Science, Beijing Normal University Beijing, China
| | - Tianfu Li
- Department of Neurosurgery, Brain Institute, and Department of Neurology, Epilepsy Center, Beijing Sanbo Brain Hospital, Capital Medical University Beijing, China
| | - Guoming Luan
- Department of Neurosurgery, Brain Institute, and Department of Neurology, Epilepsy Center, Beijing Sanbo Brain Hospital, Capital Medical University Beijing, China
| | - Haidong D Lu
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, School of Brain and Cognitive Sciences, The Collaborative Innovation Center for Brain Science, Beijing Normal University Beijing, China
| | - Mingsha Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, School of Brain and Cognitive Sciences, The Collaborative Innovation Center for Brain Science, Beijing Normal University Beijing, China
| | - Xiaohui Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, School of Brain and Cognitive Sciences, The Collaborative Innovation Center for Brain Science, Beijing Normal University Beijing, China
| | - Yousheng Shu
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, School of Brain and Cognitive Sciences, The Collaborative Innovation Center for Brain Science, Beijing Normal University Beijing, China
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Leopoldo AS, Lima-Leopoldo AP, Nascimento AF, Luvizotto RAM, Sugizaki MM, Campos DHS, da Silva DCT, Padovani CR, Cicogna AC. Classification of different degrees of adiposity in sedentary rats. Braz J Med Biol Res 2016; 49:e5028. [PMID: 26909787 PMCID: PMC4792506 DOI: 10.1590/1414-431x20155028] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2015] [Accepted: 11/17/2015] [Indexed: 01/23/2023] Open
Abstract
In experimental studies, several parameters, such as body weight, body mass index,
adiposity index, and dual-energy X-ray absorptiometry, have commonly been used to
demonstrate increased adiposity and investigate the mechanisms underlying obesity and
sedentary lifestyles. However, these investigations have not classified the degree of
adiposity nor defined adiposity categories for rats, such as normal, overweight, and
obese. The aim of the study was to characterize the degree of adiposity in rats fed a
high-fat diet using cluster analysis and to create adiposity intervals in an
experimental model of obesity. Thirty-day-old male Wistar rats were fed a normal
(n=41) or a high-fat (n=43) diet for 15 weeks. Obesity was defined based on the
adiposity index; and the degree of adiposity was evaluated using cluster analysis.
Cluster analysis allowed the rats to be classified into two groups (overweight and
obese). The obese group displayed significantly higher total body fat and a higher
adiposity index compared with those of the overweight group. No differences in
systolic blood pressure or nonesterified fatty acid, glucose, total cholesterol, or
triglyceride levels were observed between the obese and overweight groups. The
adiposity index of the obese group was positively correlated with final body weight,
total body fat, and leptin levels. Despite the classification of sedentary rats into
overweight and obese groups, it was not possible to identify differences in the
comorbidities between the two groups.
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Affiliation(s)
- A S Leopoldo
- Departamento de Desportos, Centro de Educação Física e Esportes, Universidade Federal do Espírito Santo, Vitória, ES, Brasil
| | - A P Lima-Leopoldo
- Departamento de Desportos, Centro de Educação Física e Esportes, Universidade Federal do Espírito Santo, Vitória, ES, Brasil
| | - A F Nascimento
- Instituto de Ciências da Saúde, Universidade Federal do Mato Grosso, Sinop, MT, Brasil
| | - R A M Luvizotto
- Instituto de Ciências da Saúde, Universidade Federal do Mato Grosso, Sinop, MT, Brasil
| | - M M Sugizaki
- Instituto de Ciências da Saúde, Universidade Federal do Mato Grosso, Sinop, MT, Brasil
| | - D H S Campos
- Departamento de Clínica Médica, Faculdade de Medicina, Universidade Estadual Paulista, Botucatu, SP, Brasil
| | - D C T da Silva
- Departamento de Clínica Médica, Faculdade de Medicina, Universidade Estadual Paulista, Botucatu, SP, Brasil
| | - C R Padovani
- Departamento de Bioestatística, Instituto de Biociências, Universidade Estadual Paulista, Botucatu, SP, Brasil
| | - A C Cicogna
- Departamento de Clínica Médica, Faculdade de Medicina, Universidade Estadual Paulista, Botucatu, SP, Brasil
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Crockett T, Wright N, Thornquist S, Ariel M, Wessel R. Turtle Dorsal Cortex Pyramidal Neurons Comprise Two Distinct Cell Types with Indistinguishable Visual Responses. PLoS One 2015; 10:e0144012. [PMID: 26633877 PMCID: PMC4669164 DOI: 10.1371/journal.pone.0144012] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2015] [Accepted: 11/12/2015] [Indexed: 11/25/2022] Open
Abstract
A detailed inventory of the constituent pieces in cerebral cortex is considered essential to understand the principles underlying cortical signal processing. Specifically, the search for pyramidal neuron subtypes is partly motivated by the hypothesis that a subtype-specific division of labor could create a rich substrate for computation. On the other hand, the extreme integration of individual neurons into the collective cortical circuit promotes the hypothesis that cellular individuality represents a smaller computational role within the context of the larger network. These competing hypotheses raise the important question to what extent the computational function of a neuron is determined by its individual type or by its circuit connections. We created electrophysiological profiles from pyramidal neurons within the sole cellular layer of turtle visual cortex by measuring responses to current injection using whole-cell recordings. A blind clustering algorithm applied to these data revealed the presence of two principle types of pyramidal neurons. Brief diffuse light flashes triggered membrane potential fluctuations in those same cortical neurons. The apparently network driven variability of the visual responses concealed the existence of subtypes. In conclusion, our results support the notion that the importance of diverse intrinsic physiological properties is minimized when neurons are embedded in a synaptic recurrent network.
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Affiliation(s)
- Thomas Crockett
- Department of Physics, Washington University in St. Louis, St. Louis, Missouri, United States of America
- * E-mail:
| | - Nathaniel Wright
- Department of Physics, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Stephen Thornquist
- Department of Physics, Washington University in St. Louis, St. Louis, Missouri, United States of America
| | - Michael Ariel
- Department of Pharmacology and Physiology, Saint Louis University School of Medicine, St. Louis, Missouri, United States of America
| | - Ralf Wessel
- Department of Physics, Washington University in St. Louis, St. Louis, Missouri, United States of America
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Markram H, Muller E, Ramaswamy S, Reimann MW, Abdellah M, Sanchez CA, Ailamaki A, Alonso-Nanclares L, Antille N, Arsever S, Kahou GAA, Berger TK, Bilgili A, Buncic N, Chalimourda A, Chindemi G, Courcol JD, Delalondre F, Delattre V, Druckmann S, Dumusc R, Dynes J, Eilemann S, Gal E, Gevaert ME, Ghobril JP, Gidon A, Graham JW, Gupta A, Haenel V, Hay E, Heinis T, Hernando JB, Hines M, Kanari L, Keller D, Kenyon J, Khazen G, Kim Y, King JG, Kisvarday Z, Kumbhar P, Lasserre S, Le Bé JV, Magalhães BRC, Merchán-Pérez A, Meystre J, Morrice BR, Muller J, Muñoz-Céspedes A, Muralidhar S, Muthurasa K, Nachbaur D, Newton TH, Nolte M, Ovcharenko A, Palacios J, Pastor L, Perin R, Ranjan R, Riachi I, Rodríguez JR, Riquelme JL, Rössert C, Sfyrakis K, Shi Y, Shillcock JC, Silberberg G, Silva R, Tauheed F, Telefont M, Toledo-Rodriguez M, Tränkler T, Van Geit W, Díaz JV, Walker R, Wang Y, Zaninetta SM, DeFelipe J, Hill SL, Segev I, Schürmann F. Reconstruction and Simulation of Neocortical Microcircuitry. Cell 2015; 163:456-92. [PMID: 26451489 DOI: 10.1016/j.cell.2015.09.029] [Citation(s) in RCA: 823] [Impact Index Per Article: 82.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2014] [Revised: 05/04/2015] [Accepted: 09/11/2015] [Indexed: 02/03/2023]
Affiliation(s)
- Henry Markram
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland; Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland.
| | - Eilif Muller
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Srikanth Ramaswamy
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Michael W Reimann
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Marwan Abdellah
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Carlos Aguado Sanchez
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Anastasia Ailamaki
- Data-Intensive Applications and Systems Lab, EPFL, 1015 Lausanne, Switzerland
| | - Lidia Alonso-Nanclares
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Madrid, Spain; Instituto Cajal (CSIC) and CIBERNED, 28002 Madrid, Spain
| | - Nicolas Antille
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Selim Arsever
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Guy Antoine Atenekeng Kahou
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Thomas K Berger
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Ahmet Bilgili
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Nenad Buncic
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Athanassia Chalimourda
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Giuseppe Chindemi
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Jean-Denis Courcol
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Fabien Delalondre
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Vincent Delattre
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Shaul Druckmann
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel; Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Raphael Dumusc
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - James Dynes
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Stefan Eilemann
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Eyal Gal
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Michael Emiel Gevaert
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Jean-Pierre Ghobril
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Albert Gidon
- Department of Neurobiology, Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Joe W Graham
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Anirudh Gupta
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Valentin Haenel
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Etay Hay
- Department of Neurobiology, Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel; The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Thomas Heinis
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland; Data-Intensive Applications and Systems Lab, EPFL, 1015 Lausanne, Switzerland; Imperial College London, London SW7 2AZ, UK
| | - Juan B Hernando
- CeSViMa, Centro de Supercomputación y Visualización de Madrid, Universidad Politécnica de Madrid, 28223 Madrid, Spain
| | - Michael Hines
- Department of Neurobiology, Yale University, New Haven, CT 06510 USA
| | - Lida Kanari
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Daniel Keller
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - John Kenyon
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Georges Khazen
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Yihwa Kim
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - James G King
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Zoltan Kisvarday
- MTA-Debreceni Egyetem, Neuroscience Research Group, 4032 Debrecen, Hungary
| | - Pramod Kumbhar
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Sébastien Lasserre
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland; Laboratoire d'informatique et de visualisation, EPFL, 1015 Lausanne, Switzerland
| | - Jean-Vincent Le Bé
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Bruno R C Magalhães
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Angel Merchán-Pérez
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Madrid, Spain; Instituto Cajal (CSIC) and CIBERNED, 28002 Madrid, Spain
| | - Julie Meystre
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Benjamin Roy Morrice
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Jeffrey Muller
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Alberto Muñoz-Céspedes
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Madrid, Spain; Instituto Cajal (CSIC) and CIBERNED, 28002 Madrid, Spain
| | - Shruti Muralidhar
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Keerthan Muthurasa
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Daniel Nachbaur
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Taylor H Newton
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Max Nolte
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Aleksandr Ovcharenko
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Juan Palacios
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Luis Pastor
- Modeling and Virtual Reality Group, Universidad Rey Juan Carlos, 28933 Móstoles, Madrid, Spain
| | - Rodrigo Perin
- Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Rajnish Ranjan
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland; Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Imad Riachi
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - José-Rodrigo Rodríguez
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Madrid, Spain; Instituto Cajal (CSIC) and CIBERNED, 28002 Madrid, Spain
| | - Juan Luis Riquelme
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Christian Rössert
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Konstantinos Sfyrakis
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Ying Shi
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland; Laboratory of Neural Microcircuitry, Brain Mind Institute, EPFL, 1015 Lausanne, Switzerland
| | - Julian C Shillcock
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Gilad Silberberg
- Department of Neuroscience, Karolinska Institutet, Stockholm 17177, Sweden
| | - Ricardo Silva
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Farhan Tauheed
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland; Data-Intensive Applications and Systems Lab, EPFL, 1015 Lausanne, Switzerland
| | - Martin Telefont
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | | | - Thomas Tränkler
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Werner Van Geit
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Jafet Villafranca Díaz
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Richard Walker
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Yun Wang
- Key Laboratory of Visual Science and National Ministry of Health, School of Optometry and Opthalmology, Wenzhou Medical College, Wenzhou 325003, China; Caritas St. Elizabeth's Medical Center, Genesys Research Institute, Tufts University, Boston, MA 02111, USA
| | - Stefano M Zaninetta
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Javier DeFelipe
- Laboratorio Cajal de Circuitos Corticales, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28223 Madrid, Spain; Instituto Cajal (CSIC) and CIBERNED, 28002 Madrid, Spain
| | - Sean L Hill
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
| | - Idan Segev
- Department of Neurobiology, Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel; The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
| | - Felix Schürmann
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL) Biotech Campus, 1202 Geneva, Switzerland
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Converging models of schizophrenia--Network alterations of prefrontal cortex underlying cognitive impairments. Prog Neurobiol 2015; 134:178-201. [PMID: 26408506 DOI: 10.1016/j.pneurobio.2015.09.010] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2015] [Revised: 09/10/2015] [Accepted: 09/17/2015] [Indexed: 02/08/2023]
Abstract
The prefrontal cortex (PFC) and its connections with other brain areas are crucial for cognitive function. Cognitive impairments are one of the core symptoms associated with schizophrenia, and manifest even before the onset of the disorder. Altered neural networks involving PFC contribute to cognitive impairments in schizophrenia. Both genetic and environmental risk factors affect the development of the local circuitry within PFC as well as development of broader brain networks, and make the system vulnerable to further insults during adolescence, leading to the onset of the disorder in young adulthood. Since spared cognitive functions correlate with functional outcome and prognosis, a better understanding of the mechanisms underlying cognitive impairments will have important implications for novel therapeutics for schizophrenia focusing on cognitive functions. Multidisciplinary approaches, from basic neuroscience to clinical studies, are required to link molecules, circuitry, networks, and behavioral phenotypes. Close interactions among such fields by sharing a common language on connectomes, behavioral readouts, and other concepts are crucial for this goal.
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Rotaru DC, Olezene C, Miyamae T, Povysheva NV, Zaitsev AV, Lewis DA, Gonzalez-Burgos G. Functional properties of GABA synaptic inputs onto GABA neurons in monkey prefrontal cortex. J Neurophysiol 2015; 113:1850-61. [PMID: 25540225 PMCID: PMC4359991 DOI: 10.1152/jn.00799.2014] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2014] [Accepted: 12/23/2014] [Indexed: 11/22/2022] Open
Abstract
In rodent cortex GABAA receptor (GABAAR)-mediated synapses are a significant source of input onto GABA neurons, and the properties of these inputs vary among GABA neuron subtypes that differ in molecular markers and firing patterns. Some features of cortical interneurons are different between rodents and primates, but it is not known whether inhibition of GABA neurons is prominent in the primate cortex and, if so, whether these inputs show heterogeneity across GABA neuron subtypes. We thus studied GABAAR-mediated miniature synaptic events in GABAergic interneurons in layer 3 of monkey dorsolateral prefrontal cortex (DLPFC). Interneurons were identified on the basis of their firing pattern as fast spiking (FS), regular spiking (RS), burst spiking (BS), or irregular spiking (IS). Miniature synaptic events were common in all of the recorded interneurons, and the frequency of these events was highest in FS neurons. The amplitude and kinetics of miniature inhibitory postsynaptic potentials (mIPSPs) also differed between DLPFC interneuron subtypes in a manner correlated with their input resistance and membrane time constant. FS neurons had the fastest mIPSP decay times and the strongest effects of the GABAAR modulator zolpidem, suggesting that the distinctive properties of inhibitory synaptic inputs onto FS cells are in part conferred by GABAARs containing α1 subunits. Moreover, mIPSCs differed between FS and RS interneurons in a manner consistent with the mIPSP findings. These results show that in the monkey DLPFC GABAAR-mediated synaptic inputs are prominent in layer 3 interneurons and may differentially regulate the activity of different interneuron subtypes.
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Affiliation(s)
- Diana C Rotaru
- Department of Neuroscience, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Cameron Olezene
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Takeaki Miyamae
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Nadezhda V Povysheva
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania; and
| | - Aleksey V Zaitsev
- Sechenov Institute of Evolutionary Physiology and Biochemistry of the Russian Academy of Sciences, St. Petersburg, Russia
| | - David A Lewis
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Guillermo Gonzalez-Burgos
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania;
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40
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Gregoriou GG, Paneri S, Sapountzis P. Oscillatory synchrony as a mechanism of attentional processing. Brain Res 2015; 1626:165-82. [PMID: 25712615 DOI: 10.1016/j.brainres.2015.02.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2014] [Revised: 01/24/2015] [Accepted: 02/01/2015] [Indexed: 01/11/2023]
Abstract
The question of how the brain selects which stimuli in our visual field will be given priority to enter into perception, to guide our actions and to form our memories has been a matter of intense research in studies of visual attention. Work in humans and animal models has revealed an extended network of areas involved in the control and maintenance of attention. For many years, imaging studies in humans constituted the main source of a systems level approach, while electrophysiological recordings in non-human primates provided insight into the cellular mechanisms of visual attention. Recent technological advances and the development of sophisticated analytical tools have allowed us to bridge the gap between the two approaches and assess how neuronal ensembles across a distributed network of areas interact in visual attention tasks. A growing body of evidence suggests that oscillatory synchrony plays a crucial role in the selective communication of neuronal populations that encode the attended stimuli. Here, we discuss data from theoretical and electrophysiological studies, with more emphasis on findings from humans and non-human primates that point to the relevance of oscillatory activity and synchrony for attentional processing and behavior. These findings suggest that oscillatory synchrony in specific frequencies reflects the biophysical properties of specific cell types and local circuits and allows the brain to dynamically switch between different spatio-temporal patterns of activity to achieve flexible integration and selective routing of information along selected neuronal populations according to behavioral demands. This article is part of a Special Issue entitled SI: Prediction and Attention.
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Affiliation(s)
- Georgia G Gregoriou
- University of Crete, Faculty of Medicine, 71003 Heraklion, Crete, Greece; Foundation for Research and Technology Hellas, Institute of Applied and Computational Mathematics, 70013 Heraklion, Crete, Greece.
| | - Sofia Paneri
- University of Crete, Faculty of Medicine, 71003 Heraklion, Crete, Greece; Foundation for Research and Technology Hellas, Institute of Applied and Computational Mathematics, 70013 Heraklion, Crete, Greece.
| | - Panagiotis Sapountzis
- Foundation for Research and Technology Hellas, Institute of Applied and Computational Mathematics, 70013 Heraklion, Crete, Greece.
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41
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Bitanihirwe BKY, Woo TUW. Transcriptional dysregulation of γ-aminobutyric acid transporter in parvalbumin-containing inhibitory neurons in the prefrontal cortex in schizophrenia. Psychiatry Res 2014; 220:1155-9. [PMID: 25312391 PMCID: PMC4447488 DOI: 10.1016/j.psychres.2014.09.016] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2014] [Revised: 08/15/2014] [Accepted: 09/23/2014] [Indexed: 12/18/2022]
Abstract
Parvalbumin (PV)-containing neurons are functionally compromised in schizophrenia. Using double in situ hybridization in postmortem human prefrontal cortex, we found that the messenger RNA (mRNA) for the γ-aminobutyric acid (GABA) transporter GAT-1 was undetectable in 22-41% of PV neurons in layers 3-4 in schizophrenia. In the remaining PV neurons with detectable GAT-1 mRNA, transcript expression was decreased by 26% in layer 3. Hence, the dysfunction of PV neurons involves the molecular dysregulation of presynaptic GABA reuptake.
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Affiliation(s)
- Byron K. Y. Bitanihirwe
- System and Cell Biology of Neurodegeneration, University of Zürich, Zürich, Switzerland,Program in Cellular Neuropathology, McLean Hospital, Belmont, Massachusetts, USA
| | - Tsung-Ung W. Woo
- Program in Cellular Neuropathology, McLean Hospital, Belmont, Massachusetts, USA,Department of Psychiatry, Harvard Medical School, Boston, Massachusetts, USA,Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
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42
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Localising and classifying neurons from high density MEA recordings. J Neurosci Methods 2014; 233:115-28. [DOI: 10.1016/j.jneumeth.2014.05.037] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2014] [Revised: 05/29/2014] [Accepted: 05/30/2014] [Indexed: 11/18/2022]
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43
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Lado WE, Spanswick DC, Lewis JE, Trudeau VL. Electrophysiological characterization of male goldfish (Carassius auratus) ventral preoptic area neurons receiving olfactory inputs. Front Neurosci 2014; 8:185. [PMID: 25071430 PMCID: PMC4074913 DOI: 10.3389/fnins.2014.00185] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2014] [Accepted: 06/11/2014] [Indexed: 01/28/2023] Open
Abstract
Chemical communication via sex pheromones is critical for successful reproduction but the underlying neural mechanisms are not well-understood. The goldfish is a tractable model because sex pheromones have been well-characterized in this species. We used male goldfish forebrain explants in vitro and performed whole-cell current clamp recordings from single neurons in the ventral preoptic area (vPOA) to characterize their membrane properties and synaptic inputs from the olfactory bulbs (OB). Principle component and cluster analyses based on intrinsic membrane properties of vPOA neurons (N = 107) revealed five (I–V) distinct cell groups. These cells displayed differences in their input resistance (Rinput: I < II < IV < III = V), time constant (TC: I = II < IV < III = V), and threshold current (Ithreshold: I > II = IV > III = V). Evidence from electrical stimulation of the OB and application of receptor antagonists suggests that vPOA neurons receive monosynaptic glutamatergic inputs via the medial olfactory tract, with connectivity varying among neuronal groups [I (24%), II (40%), III (0%), IV (34%), and V (2%)].
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Affiliation(s)
- Wudu E Lado
- Department of Biology, University of Ottawa Ottawa, ON, Canada ; Department of Cell and Systems Biology, University of Toronto Toronto, ON, Canada
| | - David C Spanswick
- Warwick Medical School, University of Warwick Coventry, UK ; Department of Physiology, Monash University Clayton, VIC, Australia
| | - John E Lewis
- Department of Biology, University of Ottawa Ottawa, ON, Canada
| | - Vance L Trudeau
- Department of Biology, University of Ottawa Ottawa, ON, Canada
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44
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Gonzalez-Burgos G, Miyamae T, Pafundo DE, Yoshino H, Rotaru DC, Hoftman G, Datta D, Zhang Y, Hammond M, Sampson AR, Fish KN, Ermentrout GB, Lewis DA. Functional Maturation of GABA Synapses During Postnatal Development of the Monkey Dorsolateral Prefrontal Cortex. Cereb Cortex 2014; 25:4076-93. [PMID: 24904071 DOI: 10.1093/cercor/bhu122] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Development of inhibition onto pyramidal cells may be crucial for the emergence of cortical network activity, including gamma oscillations. In primate dorsolateral prefrontal cortex (DLPFC), inhibitory synaptogenesis starts in utero and inhibitory synapse density reaches adult levels before birth. However, in DLPFC, the expression levels of γ-aminobutyric acid (GABA) synapse-related gene products changes markedly during development until young adult age, suggesting a highly protracted maturation of GABA synapse function. Therefore, we examined the development of GABA synapses by recording GABAAR-mediated inhibitory postsynaptic currents (GABAAR-IPSCs) from pyramidal cells in the DLPFC of neonatal, prepubertal, peripubertal, and adult macaque monkeys. We found that the decay of GABAAR-IPSCs, possibly including those from parvalbumin-positive GABA neurons, shortened by prepubertal age, while their amplitude increased until the peripubertal period. Interestingly, both GABAAR-mediated quantal response size, estimated by miniature GABAAR-IPSCs, and the density of GABAAR synaptic appositions, measured with immunofluorescence microscopy, were stable with age. Simulations in a computational model network with constant GABA synapse density showed that the developmental changes in GABAAR-IPSC properties had a significant impact on oscillatory activity and predicted that, whereas DLPFC circuits can generate gamma frequency oscillations by prepubertal age, mature levels of gamma band power are attained at late stages of development.
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Affiliation(s)
- Guillermo Gonzalez-Burgos
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Takeaki Miyamae
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Diego E Pafundo
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA Current address: Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, USA
| | - Hiroki Yoshino
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA Current address: Department of Psychiatry, Nara Medical University, Japan
| | - Diana C Rotaru
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA Current address: Department of Integrative Neurophysiology, Vrije Universiteit, Amsterdam, Netherlands
| | - Gil Hoftman
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Dibyadeep Datta
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - Yun Zhang
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mahjub Hammond
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Allan R Sampson
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kenneth N Fish
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
| | - G Bard Ermentrout
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, USA
| | - David A Lewis
- Translational Neuroscience Program, Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15261, USA
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45
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Medalla M, Barbas H. Specialized prefrontal "auditory fields": organization of primate prefrontal-temporal pathways. Front Neurosci 2014; 8:77. [PMID: 24795553 PMCID: PMC3997038 DOI: 10.3389/fnins.2014.00077] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2014] [Accepted: 03/27/2014] [Indexed: 12/14/2022] Open
Abstract
No other modality is more frequently represented in the prefrontal cortex than the auditory, but the role of auditory information in prefrontal functions is not well understood. Pathways from auditory association cortices reach distinct sites in the lateral, orbital, and medial surfaces of the prefrontal cortex in rhesus monkeys. Among prefrontal areas, frontopolar area 10 has the densest interconnections with auditory association areas, spanning a large antero-posterior extent of the superior temporal gyrus from the temporal pole to auditory parabelt and belt regions. Moreover, auditory pathways make up the largest component of the extrinsic connections of area 10, suggesting a special relationship with the auditory modality. Here we review anatomic evidence showing that frontopolar area 10 is indeed the main frontal “auditory field” as the major recipient of auditory input in the frontal lobe and chief source of output to auditory cortices. Area 10 is thought to be the functional node for the most complex cognitive tasks of multitasking and keeping track of information for future decisions. These patterns suggest that the auditory association links of area 10 are critical for complex cognition. The first part of this review focuses on the organization of prefrontal-auditory pathways at the level of the system and the synapse, with a particular emphasis on area 10. Then we explore ideas on how the elusive role of area 10 in complex cognition may be related to the specialized relationship with auditory association cortices.
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Affiliation(s)
- Maria Medalla
- Department of Anatomy and Neurobiology, Boston University Boston, MA, USA ; Neural Systems Laboratory, Department of Health Sciences, Boston University Boston, MA, USA
| | - Helen Barbas
- Department of Anatomy and Neurobiology, Boston University Boston, MA, USA ; Neural Systems Laboratory, Department of Health Sciences, Boston University Boston, MA, USA ; Department of Health Sciences, Boston University Boston, MA, USA
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46
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Time course of functional connectivity in primate dorsolateral prefrontal and posterior parietal cortex during working memory. PLoS One 2013; 8:e81601. [PMID: 24260582 PMCID: PMC3834341 DOI: 10.1371/journal.pone.0081601] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2013] [Accepted: 10/15/2013] [Indexed: 12/04/2022] Open
Abstract
The dorsolateral prefrontal and posterior parietal cortex play critical roles in mediating attention, working memory, and executive function. Despite proposed dynamic modulation of connectivity strength within each area according to task demands, scant empirical data exist about the time course of the strength of effective connectivity, particularly in tasks requiring information to be sustained in working memory. We investigated this question by performing time-resolved cross-correlation analysis for pairs of neurons recorded simultaneously at distances of 0.2–1.5 mm apart of each other while monkeys were engaged in working memory tasks. The strength of effective connectivity determined in this manner was higher throughout the trial in the posterior parietal cortex than the dorsolateral prefrontal cortex. Significantly higher levels of parietal effective connectivity were observed specifically during the delay period of the task. These differences could not be accounted for by differences in firing rate, or electrode distance in the samples recorded in the posterior parietal and prefrontal cortex. Differences were present when we restricted our analysis to only neurons with significant delay period activity and overlapping receptive fields. Our results indicate that dynamic changes in connectivity strength are present but area-specific intrinsic organization is the predominant factor that determines the strength of connections between neurons in each of the two areas.
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47
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Response selectivity is correlated to dendritic structure in parvalbumin-expressing inhibitory neurons in visual cortex. J Neurosci 2013; 33:11724-33. [PMID: 23843539 DOI: 10.1523/jneurosci.2196-12.2013] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Inhibitory neurons have been shown to perform a variety of functions within brain circuits, including shaping response functions in target cells. Still, how the properties of specific inhibitory neuron classes relate to their local circuits remains unclear. To better understand the distribution and origins of orientation selectivity in inhibitory neurons expressing the calcium binding protein parvalbumin (PV) in the mouse primary visual cortex, we labeled PV(+) neurons with red fluorescent protein (RFP) and targeted them for cell-attached electrophysiological recordings. PV(+) neurons could be broadly tuned or sharply tuned for orientation but tended to be more broadly tuned than unlabeled neurons on average. The dendritic morphology of PV(+) cells, revealed by intracellular labeling, was strongly correlated with tuning: highly tuned PV(+) neurons had shorter dendrites that branched nearer to the soma and had smaller dendritic fields overall, whereas broadly tuned PV(+) neurons had longer dendrites that branched farther from the soma, producing larger dendritic fields. High-speed two-photon calcium imaging of visual responses showed that the orientation preferences of highly tuned PV(+) neurons resembled the preferred orientations of neighboring cells. These results suggest that the diversity of the local neighborhood and the nature of dendritic sampling may both contribute to the response selectivity of PV(+) neurons.
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48
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Electrophysiological heterogeneity of fast-spiking interneurons: chandelier versus basket cells. PLoS One 2013; 8:e70553. [PMID: 23950961 PMCID: PMC3741302 DOI: 10.1371/journal.pone.0070553] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2013] [Accepted: 06/19/2013] [Indexed: 11/21/2022] Open
Abstract
In the prefrontal cortex, parvalbumin-positive inhibitory neurons play a prominent role in the neural circuitry that subserves working memory, and alterations in these neurons contribute to the pathophysiology of schizophrenia. Two morphologically distinct classes of parvalbumin neurons that target the perisomatic region of pyramidal neurons, chandelier cells (ChCs) and basket cells (BCs), are generally thought to have the same “fast-spiking” phenotype, which is characterized by a short action potential and high frequency firing without adaptation. However, findings from studies in different species suggest that certain electrophysiological membrane properties might differ between these two cell classes. In this study, we assessed the physiological heterogeneity of fast-spiking interneurons as a function of two factors: species (macaque monkey vs. rat) and morphology (chandelier vs. basket). We showed previously that electrophysiological membrane properties of BCs differ between these two species. Here, for the first time, we report differences in ChCs membrane properties between monkey and rat. We also found that a number of membrane properties differentiate ChCs from BCs. Some of these differences were species-independent (e.g., fast and medium afterhyperpolarization, firing frequency, and depolarizing sag), whereas the differences in the first spike latency between ChCs and BCs were species-specific. Our findings indicate that different combinations of electrophysiological membrane properties distinguish ChCs from BCs in rodents and primates. Such electrophysiological differences between ChCs and BCs likely contribute to their distinctive roles in cortical circuitry in each species.
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49
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Zaitsev AV, Lewis DA. Functional properties and short-term dynamics of unidirectional and reciprocal synaptic connections between layer 2/3 pyramidal cells and fast-spiking interneurons in juvenile rat prefrontal cortex. Eur J Neurosci 2013; 38:2988-98. [PMID: 23834038 DOI: 10.1111/ejn.12294] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2012] [Revised: 05/29/2013] [Accepted: 06/04/2013] [Indexed: 11/28/2022]
Abstract
The interactions between inhibitory fast-spiking (FS) interneurons and excitatory pyramidal neurons contribute to the fundamental properties of cortical networks. An important role for FS interneurons in mediating rapid inhibition in local sensory and motor cortex microcircuits and processing thalamic inputs to the cortex has been shown in multiple reports; however, studies in the prefrontal cortex, a key neocortical region supporting working memory, are less numerous. In the present work, connections between layer 2/3 pyramidal cells and FS interneurons were studied with paired whole-cell recordings in acute neocortical slices of the medial prefrontal cortex from juvenile rats. The connection rate between FS interneurons and pyramidal neurons was about 40% in each direction with 16% of pairs connected reciprocally. Excitatory and inhibitory connections had a high efficacy and a low neurotransmission failure rate. Sustained presynaptic activity decreased the amplitude of responses and increased the failure rate more in excitatory connections than in inhibitory connections. In the reciprocal connections between the FS and pyramidal neurons, inhibitory and excitatory neurotransmission was more efficient and had a lower failure rate than in the unidirectional connections; the differences increased during the train stimulation. These results suggest the presence of distinct preferential subnetworks between FS interneurons and pyramidal cells in the rat prefrontal cortex that might be specific for this cortical area.
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Affiliation(s)
- A V Zaitsev
- Sechenov Institute of Evolutionary Physiology and Biochemistry of the Russian Academy of Sciences, Torez Prospect 44, Saint-Petersburg 194223, Russia. ,Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - D A Lewis
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.,Department of Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
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50
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Courtin J, Bienvenu T, Einarsson E, Herry C. Medial prefrontal cortex neuronal circuits in fear behavior. Neuroscience 2013; 240:219-42. [DOI: 10.1016/j.neuroscience.2013.03.001] [Citation(s) in RCA: 66] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2012] [Revised: 02/28/2013] [Accepted: 03/01/2013] [Indexed: 01/01/2023]
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