Postnatal Development and Functional Connectivity of the Subiculum
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Position–Theta-Phase Model of Hippocampal Place Cell Activity Applied to Quantification of Running Speed Modulation of Firing Rate
Position–theta-phase model of hippocampal place cell activity applied to quantification of running speed modulation of firing rate Kathryn McClaina,b,1, David Tingleyb, David J. Heegera,c,1, and György Buzsákia,b,1 aCenter for Neural Science, New York University, New York, NY 10003; bNeuroscience Institute, New York University, New York, NY 10016; and cDepartment of Psychology, New York University, New York, NY 10003 Edited by Terrence J. Sejnowski, Salk Institute for Biological Studies, La Jolla, CA, and approved November 18, 2019 (received for review July 24, 2019) Spiking activity of place cells in the hippocampus encodes the Another challenge in understanding this system is the dynamic animal’s position as it moves through an environment. Within a interaction between the rate code and phase code. As these codes cell’s place field, both the firing rate and the phase of spiking in combine, different formats of information are conveyed simulta- the local theta oscillation contain spatial information. We propose a neously in place cell spiking. The interaction can produce unin- – position theta-phase (PTP) model that captures the simultaneous tuitive, although entirely predictable, results in traditional analyses expression of the firing-rate code and theta-phase code in place cell of place cell activity. These practical challenges have hindered the spiking. This model parametrically characterizes place fields to com- pare across cells, time, and conditions; generates realistic place cell effort to explain variability in hippocampal firing rates. A com- simulation data; and conceptualizes a framework for principled hy- putational tool is needed that accounts for the well-established pothesis testing to identify additional features of place cell activity. -
Abnormalities of Grey and White Matter [11C]Flumazenil Binding In
Brain (2002), 125, 2257±2271 Abnormalities of grey and white matter [11C]¯umazenil binding in temporal lobe epilepsy with normal MRI A. Hammers,1,2,3 M. J. Koepp,1,2,3 R. Hurlemann,2 M. Thom,2 M. P. Richardson,1,2,3 D. J. Brooks1 and J. S. Duncan1,2,3 1MRC Clinical Sciences Centre and Division of Correspondence to: Professor John S. Duncan, MA, DM, Neuroscience, Faculty of Medicine, Imperial College, FRCP, National Society for Epilepsy and Institute of 2Department of Clinical and Experimental Epilepsy, Neurology, 33 Queen Square, London WC1N 3BG, UK Institute of Neurology, University College London, London E-mail: [email protected] and 3National Society for Epilepsy MRI Unit, Chalfont St Peter, UK Summary In 20% of potential surgical candidates with refrac- the 16 patients with abnormalities, ®ndings were con- tory epilepsy, current optimal MRI does not identify cordant with EEG and clinical data, enabling further the cause. GABA is the principal inhibitory neuro- presurgical evaluation. Group ®ndings were: (i) transmitter in the brain, and GABAA receptors are decreased FMZ-Vd in the ipsilateral (Z = 3.01) and expressed by most neurones. [11C]Flumazenil (FMZ) contralateral (Z = 2.56) hippocampus; (ii) increased PET images the majority of GABAA receptor sub- FMZ-Vd in the ipsilateral (Z = 3.71) and contralat- types. We investigated abnormalities of FMZ binding eral TLWM (two clusters, Z = 3.11 and 2.79); and in grey and white matter in 18 patients with refrac- (iii) increased FMZ-Vd in the ipsilateral frontal lobe tory temporal lobe epilepsy (TLE) and normal quan- white matter between the superior and medial frontal titative MRI. -
Amygdaloid Projections to the Ventral Striatum in Mice: Direct and Indirect Chemosensory Inputs to the Brain Reward System
ORIGINAL RESEARCH ARTICLE published: 22 August 2011 NEUROANATOMY doi: 10.3389/fnana.2011.00054 Amygdaloid projections to the ventral striatum in mice: direct and indirect chemosensory inputs to the brain reward system Amparo Novejarque1†, Nicolás Gutiérrez-Castellanos2†, Enrique Lanuza2* and Fernando Martínez-García1* 1 Departament de Biologia Funcional i Antropologia Física, Facultat de Ciències Biològiques, Universitat de València, València, Spain 2 Departament de Biologia Cel•lular, Facultat de Ciències Biològiques, Universitat de València, València, Spain Edited by: Rodents constitute good models for studying the neural basis of sociosexual behavior. Agustín González, Universidad Recent findings in mice have revealed the molecular identity of the some pheromonal Complutense de Madrid, Spain molecules triggering intersexual attraction. However, the neural pathways mediating this Reviewed by: Daniel W. Wesson, Case Western basic sociosexual behavior remain elusive. Since previous work indicates that the dopamin- Reserve University, USA ergic tegmento-striatal pathway is not involved in pheromone reward, the present report James L. Goodson, Indiana explores alternative pathways linking the vomeronasal system with the tegmento-striatal University, USA system (the limbic basal ganglia) by means of tract-tracing experiments studying direct *Correspondence: and indirect projections from the chemosensory amygdala to the ventral striato-pallidum. Enrique Lanuza, Departament de Biologia Cel•lular, Facultat de Amygdaloid projections to the nucleus accumbens, olfactory tubercle, and adjoining struc- Ciències Biològiques, Universitat de tures are studied by analyzing the retrograde transport in the amygdala from dextran València, C/Dr. Moliner, 50 ES-46100 amine and fluorogold injections in the ventral striatum, as well as the anterograde labeling Burjassot, València, Spain. found in the ventral striato-pallidum after dextran amine injections in the amygdala. -
Glutamate Binding in Primate Brain
Journal of Neuroscience Research 27512-521 (1990) Quisqualate- and NMDA-Sensitive [3H]Glutamate Binding in Primate Brain A.B. Young, G.W. Dauth, Z. Hollingsworth, J.B. Penney, K. Kaatz, and S. Gilman Department of Neurology. University of Michigan, Ann Arbor Excitatory amino acids (EAA) such as glutamate and Young and Fagg, 1990). Because EAA are involved in aspartate are probably the neurotransmitters of a general cellular metabolic functions, they were not orig- majority of mammalian neurons. Only a few previous inally considered to be likcl y neurotransmitter candi- studies have been concerned with the distribution of dates. Electrophysiological studies demonstrated con- the subtypes of EAA receptor binding in the primate vincingly the potency of these substances as depolarizing brain. We examined NMDA- and quisqualate-sensi- agents and eventually discerned specific subtypes of tive [3H]glutamate binding using quantitative autora- EAA receptors that responded preferentially to EAA an- diography in monkey brain (Macaca fascicularis). alogs (Dingledine et al., 1988). Coincident with the elec- The two types of binding were differentially distrib- trophysiological studies, biochemical studies indicated uted. NMDA-sensitive binding was most dense in that EAA were released from slice and synaptosome dentate gyrus of hippocampus, stratum pyramidale preparations in a calcium-dependent fashion and were of hippocampus, and outer layers of cerebral cortex. accumulated in synaptosomc preparations by a high-af- Quisqualate-sensitive binding was most dense in den- finity transport system. With these methodologies, EAA tate gyrus of hippocampus, inner and outer layers of release and uptake were found to be selectively de- cerebral cortex, and molecular layer of cerebellum. -
Corticostriatal Interactions During Learning, Memory Processing, and Decision Making
The Journal of Neuroscience, October 14, 2009 • 29(41):12831–12838 • 12831 Mini-Symposium Corticostriatal Interactions during Learning, Memory Processing, and Decision Making Cyriel M. A. Pennartz,1 Joshua D. Berke,2,3 Ann M. Graybiel,4,5 Rutsuko Ito,6 Carien S. Lansink,1 Matthijs van der Meer,7 A. David Redish,7 Kyle S. Smith,4,5 and Pieter Voorn8 1University of Amsterdam, Swammerdam Institute for Life Sciences Center for Neuroscience, 1098 XH Amsterdam, The Netherlands, 2Department of Psychology and 3Neuroscience Program, University of Michigan, Ann Arbor, Michigan 48109, 4Department of Brain and Cognitive Sciences and 5McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, 6Department of Experimental Psychology, University of Oxford, Oxford OX1 3UD, United Kingdom, 7Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota 55455, and 8Department of Anatomy, Research Institute Neurosciences, Vrije Universiteit University Medical Center, 1007 MB Amsterdam, The Netherlands This mini-symposium aims to integrate recent insights from anatomy, behavior, and neurophysiology, highlighting the anatom- ical organization, behavioral significance, and information-processing mechanisms of corticostriatal interactions. In this sum- mary of topics, which is not meant to provide a comprehensive survey, we will first review the anatomy of corticostriatal circuits, comparing different ways by which “loops” of cortical–basal ganglia circuits communicate. Next, we will address the causal importance and systems-neurophysiological mechanisms of corticostriatal interactions for memory, emphasizing the communi- cation between hippocampus and ventral striatum during contextual conditioning. Furthermore, ensemble recording techniques have been applied to compare information processing in the dorsal and ventral striatum to predictions from reinforcement learning theory. -
The Hippocampus Marion Wright* Et Al
WikiJournal of Medicine, 2017, 4(1):3 doi: 10.15347/wjm/2017.003 Encyclopedic Review Article The Hippocampus Marion Wright* et al. Abstract The hippocampus (named after its resemblance to the seahorse, from the Greek ἱππόκαμπος, "seahorse" from ἵππος hippos, "horse" and κάμπος kampos, "sea monster") is a major component of the brains of humans and other vertebrates. Humans and other mammals have two hippocampi, one in each side of the brain. It belongs to the limbic system and plays important roles in the consolidation of information from short-term memory to long-term memory and spatial memory that enables navigation. The hippocampus is located under the cerebral cortex; (allocortical)[1][2][3] and in primates it is located in the medial temporal lobe, underneath the cortical surface. It con- tains two main interlocking parts: the hippocampus proper (also called Ammon's horn)[4] and the dentate gyrus. In Alzheimer's disease (and other forms of dementia), the hippocampus is one of the first regions of the brain to suffer damage; short-term memory loss and disorientation are included among the early symptoms. Damage to the hippocampus can also result from oxygen starvation (hypoxia), encephalitis, or medial temporal lobe epilepsy. People with extensive, bilateral hippocampal damage may experience anterograde amnesia (the inability to form and retain new memories). In rodents as model organisms, the hippocampus has been studied extensively as part of a brain system responsi- ble for spatial memory and navigation. Many neurons in the rat and mouse hippocampus respond as place cells: that is, they fire bursts of action potentials when the animal passes through a specific part of its environment. -
Distinct Transcriptomic Cell Types and Neural Circuits of the Subiculum and Prosubiculum Along 2 the Dorsal-Ventral Axis 3 4 Song-Lin Ding1,2,*, Zizhen Yao1, Karla E
bioRxiv preprint doi: https://doi.org/10.1101/2019.12.14.876516; this version posted December 15, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Distinct transcriptomic cell types and neural circuits of the subiculum and prosubiculum along 2 the dorsal-ventral axis 3 4 Song-Lin Ding1,2,*, Zizhen Yao1, Karla E. Hirokawa1, Thuc Nghi Nguyen1, Lucas T. Graybuck1, Olivia 5 Fong1, Phillip Bohn1, Kiet Ngo1, Kimberly A. Smith1, Christof Koch1, John W. Phillips1, Ed S. Lein1, 6 Julie A. Harris1, Bosiljka Tasic1, Hongkui Zeng1 7 8 1Allen Institute for Brain Science, Seattle, WA 98109, USA 9 10 2Lead Contact 11 12 *Correspondence: [email protected] (SLD) 13 14 15 Highlights 16 17 1. 27 transcriptomic cell types identified in and spatially registered to “subicular” regions. 18 2. Anatomic borders of “subicular” regions reliably determined along dorsal-ventral axis. 19 3. Distinct cell types and circuits of full-length subiculum (Sub) and prosubiculum (PS). 20 4. Brain-wide cell-type specific projections of Sub and PS revealed with specific Cre-lines. 21 22 23 In Brief 24 25 Ding et al. show that mouse subiculum and prosubiculum are two distinct regions with differential 26 transcriptomic cell types, subtypes, neural circuits and functional correlation. The former has obvious 27 topographic projections to its main targets while the latter exhibits widespread projections to many 28 subcortical regions associated with reward, emotion, stress and motivation. -
Beta Oscillations and Hippocampal Place Cell Learning During
Beta Oscillations and Hippocampal Place Cell Learning during Exploration of Novel Environments Stephen Grossberg1 1Department of Cognitive and Neural Systems Center for Adaptive Systems, and Center of Excellence for Learning in Education, Science and Technology Boston University 677 Beacon St Boston, Massachusetts 02215 [email protected] Telephone: 617-353-7858/7 Fax: 617-353-7755 http://www.cns.bu.edu/~steve July 29, 2008 Revised: November 24, 2008 CAS/CNS-TR-08-003 Hippocampus, in press Running head: Beta Oscillations during Hippocampal Place Cell Learning Corresponding author: Stephen Grossberg, [email protected] Keywords: grid cells, category learning, spatial navigation, adaptive resonance theory, entorhinal cortex 1 Abstract The functional role of synchronous oscillations in various brain processes has attracted a lot of experimental interest. Berke et al. (2008) reported beta oscillations during the learning of hippocampal place cell receptive fields in novel environments. Such place cell selectivity can develop within seconds to minutes, and can remain stable for months. Paradoxically, beta power was very low during the first lap of exploration, grew to full strength as a mouse traversed a lap for the second and third times, and became low again after the first two minutes of exploration. Beta oscillation power also correlated with the rate at which place cells became spatially selective, and not with theta oscillations. These beta oscillation properties are explained by a neural model of how place cell receptive fields may be learned and stably remembered as spatially selective categories due to feedback interactions between entorhinal cortex and hippocampus. This explanation allows the learning of place cell receptive fields to be understood as a variation of category learning processes that take place in many brain systems, and challenges hippocampal models in which beta oscillations and place cell stability cannot be explained. -
Hippocampal Subfield Volumes Are Uniquely Affected in PTSD and Depression
bioRxiv preprint doi: https://doi.org/10.1101/739094; this version posted August 21, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. Hippocampal subfields in PTSD and depression Hippocampal subfield volumes are uniquely affected in PTSD and depression: International analysis of 31 cohorts from the PGC-ENIGMA PTSD Working Group Lauren E. Salminen1, Philipp G. Sämann2, Yuanchao Zheng3, Emily L. Dennis1,4–6, Emily K. Clarke-Rubright7,8, Neda Jahanshad1, Juan E. Iglesias9–11, Christopher D. Whelan12,13, Steven E. Bruce14, Jasmeet P. Hayes15, Soraya Seedat16, Christopher L. Averill17, Lee A. Baugh18–20, Jessica Bomyea21,22, Joanna Bright1, Chanellé J. Buckle16, Kyle Choi23,24, Nicholas D. Davenport25,26, Richard J. Davidson27–29, Maria Densmore30,31, Seth G. Disner25,26, Stefan du Plessis16, Jeremy A. Elman22,32, Negar Fani33, Gina L. Forster18,19,34, Carol E. Franz22,32, Jessie L. Frijling35, Atilla Gonenc36–38, Staci A. Gruber36–38, Daniel W. Grupe27, Jeffrey P. Guenette39, Courtney C. Haswell7,8, David Hofmann40, Michael Hollifield41, Babok Hosseini42, Anna R. Hudson43, Jonathan Ipser44, Tanja Jovanovic45, Amy Kennedy-Krage42, Mitzy Kennis46,47, Anthony King48, Philipp Kinzel49, Saskia B. J. Koch35,50, Inga Koerte4, Sheri M. Koopowitz44, Mayuresh S. Korgaonkar51, William S. Kremen21,22,32, John Krystal17,52, Lauren A. M. Lebois37,53, Ifat Levy17,54, Michael J. Lyons55, Vincent A. Magnotta56, Antje Manthey57, Soichiro Nakahara58,59, Laura Nawijn35,60, Richard W. J. -
Cortical Layers: What Are They Good For? Neocortex
Cortical Layers: What are they good for? Neocortex L1 L2 L3 network input L4 computations L5 L6 Brodmann Map of Cortical Areas lateral medial 44 areas, numbered in order of samples taken from monkey brain Brodmann, 1908. Primary visual cortex lamination across species Balaram & Kaas 2014 Front Neuroanat Cortical lamination: not always a six-layered structure (archicortex) e.g. Piriform, entorhinal Larriva-Sahd 2010 Front Neuroanat Other layered structures in the brain Cerebellum Retina Complexity of connectivity in a cortical column • Paired-recordings and anatomical reconstructions generate statistics of cortical connectivity Lefort et al. 2009 Information flow in neocortical microcircuits Simplified version “computational Layer 2/3 layer” Layer 4 main output Layer 5 main input Layer 6 Thalamus e - excitatory, i - inhibitory Grillner et al TINS 2005 The canonical cortical circuit MAYBE …. DaCosta & Martin, 2010 Excitatory cell types across layers (rat S1 cortex) Canonical models do not capture the diversity of excitatory cell classes Oberlaender et al., Cereb Cortex. Oct 2012; 22(10): 2375–2391. Coding strategies of different cortical layers Sakata & Harris, Neuron 2010 Canonical models do not capture the diversity of firing rates and selectivities Why is the cortex layered? Do different layers have distinct functions? Is this the right question? Alternative view: • When thinking about layers, we should really be thinking about cell classes • A cells class may be defined by its input connectome and output projectome (and some other properties) • The job of different classes is to (i) make associations between different types of information available in each cortical column and/or (ii) route/gate different streams of information • Layers are convenient way of organising inputs and outputs of distinct cell classes Excitatory cell types across layers (rat S1 cortex) INTRATELENCEPHALIC (IT) | PYRAMIDAL TRACT (PT) | CORTICOTHALAMIC (CT) From Cereb Cortex. -
Slicer 3 Tutorial the SPL-PNL Brain Atlas
Slicer 3 Tutorial The SPL-PNL Brain Atlas Ion-Florin Talos, M.D. Surgical Planning Laboratory Brigham and Women’s Hospital -1- http://www.slicer.org Acknowledgments NIH P41RR013218 (Neuroimage Analysis Center) NIH U54EB005149 (NA-MIC) Surgical Planning Laboratory Brigham and Women’s Hospital -2- http://www.slicer.org Disclaimer It is the responsibility of the user of 3DSlicer to comply with both the terms of the license and with the applicable laws, regulations and rules. Surgical Planning Laboratory Brigham and Women’s Hospital -3- http://www.slicer.org Material • Slicer 3 http://www.slicer.org/pages/Special:Slicer_Downloads/Release Atlas data set http://wiki.na-mic.org/Wiki/index.php/Slicer:Workshops:User_Training_101 • MRI • Labels • 3D-models Surgical Planning Laboratory Brigham and Women’s Hospital -4- http://www.slicer.org Learning Objectives • Loading the atlas data • Creating and displaying customized 3D-views of neuroanatomy Surgical Planning Laboratory Brigham and Women’s Hospital -5- http://www.slicer.org Prerequisites • Slicer Training Slicer 3 Training 1: Loading and Viewing Data http://www.na-mic.org/Wiki/index.php/Slicer:Workshops:User_Training_101 Surgical Planning Laboratory Brigham and Women’s Hospital -6- http://www.slicer.org Overview • Part 1: Loading the Brain Atlas Data • Part 2: Creating and Displaying Customized 3D views of neuroanatomy Surgical Planning Laboratory Brigham and Women’s Hospital -7- http://www.slicer.org Loading the Brain Atlas Data Slicer can load: • Anatomic grayscale data (CT, MRI) ……… …………………………………. -
Perspectives
PERSPECTIVES reptiles, to birds and mammals, to primates OPINION and, finally, to humans — ascending from ‘lower’ to ‘higher’ intelligence in a chrono- logical series. They believed that the brains Avian brains and a new understanding of extant vertebrates retained ancestral structures, and, therefore, that the origin of of vertebrate brain evolution specific human brain subdivisions could be traced back in time by examining the brains of extant non-human vertebrates. In The Avian Brain Nomenclature Consortium* making such comparisons, they noted that the main divisions of the human CNS — Abstract | We believe that names have a pallium is nuclear, and the mammalian the spinal cord, hindbrain, midbrain, thala- powerful influence on the experiments we cortex is laminar in organization, the avian mus, cerebellum and cerebrum or telen- do and the way in which we think. For this pallium supports cognitive abilities similar cephalon — were present in all vertebrates reason, and in the light of new evidence to, and for some species more advanced than, (FIG. 1a). Edinger, however, noted that the about the function and evolution of the those of many mammals. To eliminate these internal organization of the telencephala vertebrate brain, an international consortium misconceptions, an international forum of showed the most pronounced differences of neuroscientists has reconsidered the neuroscientists (BOX 1) has, for the first time between species. In mammals, the outer traditional, 100-year-old terminology that is in 100 years, developed new terminology that part of the telencephalon was found to have used to describe the avian cerebrum. Our more accurately reflects our current under- prominently layered grey matter (FIG.