To the extent possible under law, AOP-Wiki has waived all copyright and related or neighboring rights to KE:618
Key Event Title
Decreased, Neuronal network function in adult brain
|Level of Biological Organization|
Key Event Components
Key Event Overview
AOPs Including This Key Event
|AOP Name||Role of event in AOP||Point of Contact||Author Status||OECD Status|
|ionotropic glutamatergic receptors and cognition||KeyEvent||Allie Always (send email)||Open for citation & comment||WPHA/WNT Endorsed|
|Organo-Phosphate Chemicals leading to sensory axonal peripheral neuropathy and mortality||KeyEvent||Allie Always (send email)||Open for citation & comment||EAGMST Under Review|
Key Event Description
In the brain, neurons never work alone. They create a network where the activity of one cell directly influences many others. Each neuron is a specialized cell and when activated, it fires an electrochemical signal along the axon. A neuron fires only if the total signal received at the cell body from the dendrites exceeds a certain level (the firing threshold). The strength of the signal received by a neuron (and therefore its chances of firing) critically depends on the efficacy of the synapses. Each synapse actually contains a synaptic cleft with neurotransmitter that transmits a signal across the gap. During synaptic transmission neurotransmitters are released by a presynaptic neuron and bind to and activate the receptors of the postsynaptic neuron in response to a threshold of action potential. Synaptic transmission relies on: the availability of the neurotransmitter; the release of the neurotransmitter by exocytosis; the binding of the postsynaptic receptor by the neurotransmitter; the functional response of the postsynaptic cell; and the subsequent removal or deactivation of the neurotransmitter. Neurons form complex networks of synapses through which action potentials travel. When the nerve impulse arrives at the synapse, it may cause the release of neurotransmitters, which influence another (postsynaptic) neuron. The postsynaptic neurons receive inputs from many additional neurons, both excitatory and inhibitory. The excitatory and inhibitory influences are summed (neural summation) resulting in inhibition or "firing" (i.e., generate an action potential) if the threshold potential has been reached. The voltage at which an action potential is triggered happens if enough voltage-dependent sodium channels are activated and the net inward sodium current exceeds all outward currents (Kolb and Whishaw, 2003). Therefore, at the beginning of the action potential, the Na+ channels open and Na+ moves into the axon, causing depolarization. Re-polarization occurs when the K+ channels open and K+ moves out of the axon. This creates a change in polarity between the outside of the cell and the inside. The impulse travels down from the axon hillock in one direction only, to the axon terminal. Here, the neurotransmitter is released releasing neurotransmitter at the synaptic cleft to pass along information to another adjacent neuron. Excitatory inputs bring a neuron closer to a firing threshold, while inhibitory inputs bring the neuron farther from threshold. An action potential is an "all-or-none" event; neurons whose membranes have not reached threshold will not fire, while those that do, will fire. One of the most influential researchers into neurological systems (Donald Hebb) postulated that learning consisted principally in altering the "strength" of synaptic networking. Recent research in cognitive science, in particular in the area of non-conscious information processing, have further demonstrated the enormous capacity of the human mind to learn simple input-output co-variations from extremely complex stimuli. Consequently, the neurodegeneration and cell death disrupt the natural rhythms of brain network communication. Cognitive disorders are primarily associated with dysfunction of the neurons of the prefrontal cortex, hippocampus and with changes mainly in NMDARs function (Wang et al, 2015).
The interface through which neurons interact with their neighbours usually consists of several axon terminals connected via synapses to dendrites on other neurons. If the hippocampal or cortical neurons are damaged or killed by the over-activation of receptors for the excitatory neurotransmitter glutamate, such as the NMDA, kainate and AMPA receptors, the neuronal networking and number of synapses are decreased. Indeed, it has been proved that lesions of the hippocampus in humans prevent the acquisition of new episodic memories suggesting that hippocampus-dependent memory is mediated, at least in part, by hippocampal synaptic plasticity that is a prominent feature of hippocampal synapses of the neuronal network (Neves et al., 2008). Since the finding that the hippocampus plays a pivotal role in long-term memory consolidation (dogma, well established fact in the literature, described in the text books; e.g. Andersen et al., 2007; Byrne, 2008; Eichenbaum, 2002), many proposals have been made regarding its specific role. A prominent view of the mechanisms underlying consolidation of episodic memories involves fast formation (e.g., via Hebbian mechanisms) of strong associations between hippocampal sparse patterns of activity and distributed neocortical representations. Recent research on the primate prefrontal cortex discovered that the pyramidal cell circuits that generate the persistent firing underlying spatial working memory communicate through synapses on spines containing NMDARs with NR2B subunits (GluN2B) in the post-synaptic density. This contrasts with synapses in the hippocampus and primary visual cortex, where GluN2B receptors are both synaptic and extrasynaptic. Cholinergic stimulation of nicotinic α7 receptors within the glutamate synapse is necessary for NMDAR actions (Wang and Arnsten, 2015).
General role in biology:
Glutamatergic neurotransmission (NMDA, AMPA and KA receptors)
The network of glutamatergic neurons is heavily involved in long-term synaptic plasticity, the main process linked to learning and memory. At the same time over-activation of these neurons (excitotoxicity) leads to neuronal cell death that can be mediated by increased levels of extracellular glutamate or a molecule that behaves as its analogue. Glutamate acts at a variety of ionotropic receptors, including AMPARs, kainate receptors, and NMDARs. The NMDARs have been of particular interest due to their unique properties. They require neuronal depolarization to relieve their Mg++ block, and are permeable to Ca++ that can initiate second-messenger signalling events, such as mediating neuroplasticity or negative feedback through Ca++-sensitive K+ channels. There have been extensive studies on the glutamate NMDAR and AMPAR mechanisms underlying long-term synaptic plasticity in the primary visual cortex and in CA1 neurons of the hippocampus (Liu et al., 2004; Cho et al., 2009; Lüscher and Malenka, 2012). Neuronal network function and long-term plasticity is also regulated by the levels of AMPAR expression as the number of AMPARs inserted into the post-synaptic density can mediate the degree of spine depolarization and thus the NMDAR opening. Synaptic plasticity in the mature visual cortex appears to be governed by GluN2A subunits, which have faster kinetics than GluN2B. GluN2B receptors are expressed in synapses early in development, but many move to extra-synaptic locations in the mature visual cortex and hippocampus (Goebel-Goody et al., 2009). The actions of NMDARs on the dorsolateral prefrontal cortex neuronal circuitry network underlying spatial working memory in primates and it mechanism is described in detail by Wang and Arnsten (2015). In the hippocampus, there is some evidence that long-term potentiation (LTP) is mediated by synaptic GluN2A, while long-term depression is mediated by extrasynaptic GluN2B receptors (Liu et al., 2004). Kainate receptors (KARs) also play an important role in neuronal network function. They play a major function in the pre-synaptic terminal, in particular in the hippocampus. Activation of kainate receptors in have been shown to regulate glutamate release (Jane et al., 2009) and to both depress and factilitate transmission in different synapses. Pre-synaptic kainate receptors in the hippocampus facilitate AMPA and NMDA receptor-mediated transmission at mossy fibre-CA3 synapses (Lauri et al, 2005). Activation of post-synaptic KARs facilitates activation of NMDARs as it has been described in the context of DomA exposure.
Role of other neurotransmitters
It is important to stress that other classical neurotransmitter systems also play an important role in learning and memory processes (Blokland 1996). The role of the most critical neurotransmitters has been evaluated in a meta-analysis based on studies of four behavioral tasks relevant for evaluation of rat cognitive functions such as Morris water maze, radial maze, passive avoidance, and spontaneous alternation (Myhrer, 2003). Calculation of impact factors (percentage of significant effects of chemical agents like agonists, antagonists, neurotoxins) showed that glutamate was ranking highest (93), followed by GABA (81), dopamine (81), acetylcholine (81), serotonin (55), and norepinephrine (48).
GABA-ergic receptors: indeed, presynaptic GABA B receptors mediate GABA-dependent inhibition of glutamate release, impacting plasticity of hippocampal synapses and hippocampus-dependent memory function (Vigot et al., 2006). A critical link between GABABR heterodimer conformational dynamics and local regulation of release probability at hippocampal synapses has been recently proved (Laviv et al., 2010).
5-Hydroxytrytamine (serotonin) type 3A receptors (5-HT3ARs), as the only ligand-gated ion channels in the serotonin receptor family, are known to regulate neuronal excitation and release of GABA in hippocampal interneurons, playing also an important role in glutamatergic synaptic plasticity. Deletion of the 5-HT3AR gene in transgenic mice abolished NMDAR-dependent long-term depression (LTD) induced by low-frequency stimulation (LFS) in hippocampal CA1 synapses in slices. In addition, 5-HT3ARs disruption inhibited AMPARs internalization, without altering basal surface levels of AMPARs. These observations revealed an important role of 5-HT3ARs in NMDAR-dependent long-term depression, which is critical for learning behaviours (Yu et al., 2014).
The cholinergic hypothesis claims that the decline in cognitive functions in dementia is predominantly related to a decrease in cholinergic neurotransmission. This hypothesis has led to great interest in the putative involvement of the cholinergic neurotransmission in learning and memory processes (Blokland 1996; Bracco et al., 2014).
Dopamine plays diverse roles in human behaviour and cognition but it is mainly involved in motivation, decision-making, reward processing, attention, working memory and learning (Steinberg and Janak, 2012; Labudda et al., 2010).
Noradrenaline is associated with memory processing as it induces lasting changes in the brain that could sustain memories over time (Gazarini et al., 2013). As confirmed later on its neurotransmission indeed strengthens memory-related synaptic plasticity such as long-term potentiation, allowing memories to be formed and maintained in a more intense and enduring manner, a notion particularly valid for those with emotional content (Joëls et al. 2011). Like other types of memory, an emotional memory has to be consolidated to allow its later retrieval. Accumulating evidence has indicated that noradrenaline acts during these gradual stages to fine-tune the strength and/or persistence of a memory (Guzmán-Ramos et al. 2012; Gazarini et al., 2013).
How It Is Measured or Detected
Methods that have been previously reviewed and approved by a recognized authority should be included in the Overview section above. All other methods, including those well established in the published literature, should be described here. Consider the following criteria when describing each method: 1. Is the assay fit for purpose? 2. Is the assay directly or indirectly (i.e. a surrogate) related to a key event relevant to the final adverse effect in question? 3. Is the assay repeatable? 4. Is the assay reproducible?
Neuronal network activity is fundamental to brain function and now can be measured using in vitro and in vivo techniques such as:
1. Two-photon imaging of cell populations in vivo that are labelled with fluorescent calcium indicators. Two-photon imaging relies on fluorescence excitation and, in general, necessitates staining of cells with fluorescent dyes. Various staining methods have been developed for in vivo calcium measurements. Single cells can be filled with membrane-impermeable calcium indicators via intracellular recording electrodes or by single-cell electroporation. The basic aspects of in vivo calcium imaging and recent developments that allow evaluation of the neural circuits activity are described by Göbel et al., (2007a). With new imaging technology, scientists are now better able to visualize neural circuits connecting brain regions in humans. Advances in genetic engineering, microscopy, and computing are enabling scientists to begin to map the connections between individual nerve cells.
2. Optical detection of neuronal spikes both in vivo and in vitro. Assuming action potential (AP) as the only trigger of calcium influx, spike patterns are directly reflected in the trains of calcium transients. Each fluorescence trace is the convolution of the spike train with the single AP-evoked calcium transient plus added noise. The temporal resolution will be limited by the acquisition rate of the network scanning approach. In addition, the signal-to-noise ratio of fluorescence signals will be a decisive factor for the accuracy of the reconstruction.
3. Microelectrode array (MEA) recordings in primary cultures. Glutamate analogues effects on neuronal network activity can be assessed (Lantz et al., 2014) and neuronal spontaneous activity evaluation is already used for screening purposes (Valdivia et al., 2014).
4. To understand the function of a neural circuit, it is important to discriminate its sub-network components. This is possible through counterstaining of specific neuronal and glial cell types, especially in bulk loaded tissue where markers need not be calcium sensitive. In addition, transgenic mice with fluorescent protein expression in specific neuronal subsets, allow separation of functional signals into different neuronal subtypes (Göbel et al., 2007b).
5. Combined positron emission tomography (PET) and magnetic resonance imaging (MRI) is a new tool to study functional processes in the brain, including the response to a stimulus simultaneously using PET. Functional MRI (fMRI), is used to assesses at the same time fast vascular and oxygenation changes during activation. These results demonstrate the feasibility of combined PET-MRI for the simultaneous study of the brain at activation and rest, revealing comprehensive and complementary information to further decode brain function and brain networks (Wehrl et al., 2013).
6. Seed-based correlative analysis of [18F]fluorodeoxyglucose (FDG)-PET (FDG-PET) differences in images (resting state minus activation) is suitable to identify cerebral networks in rats. Using awake and freely moving animals enables functional network analysis of complex behavioral paradigms (Rohleder et al., 2015).
Although most experiments at present are carried out in anesthetized animals, several approaches for imaging in awake behaving animals have been devised that ultimately aim at directly correlating neuronal network dynamics with behaviour (Dombeck et al., 2007, Arenkiel et al., 2007). Finally, through expression of light-activated channel proteins, it might become possible in the future to not only read-out but also control neuronal networks in vivo (Garaschuk et al., 2006)since with the development of X-ray, CT, and MRI, deep neural networks involved in learning and memory processes can be studied in vivo (Cheng et al., .2014).
7. NMDAR overactivation-induced LTD that decrease number of spine density can be measured in vitro using GFP technology and by cofilin-F-actin quantification (Calabrese et al., 2014).
Current behavioural tests used for evaluating neural network function:
1. The Morris water maze: this test is developed to measure spatial orientation in rats The rat has to swim around the pool to search for a platform onto which he can escape from the water. In one condition, the platform is visible, rising 1 cm above the water surface. In a Second condition the rat has to learn to find the hidden platform provided it remains in the fixed position relative to distal room cues.
2. Radial maze: In the T-maze version of working memory, the animal has to remember only a single item for each trial. In the radial arm version of the working memory procedure rats have to learn multiple items.
3. Passive avoidance: fear-motivated avoidance tests are usually based on electric current as source of punishment.
4. Spontaneous alternation: spontaneous alternation is spatial alternation and represents a tendency to avoid stimulus re-exposure during exploratory behaviour. T-maze (simple or multiple), Y-maze, and radial maze are used to quantify an innate, unlearned response in rats.
These four behavioural tests are described in detail in the review by Myhrer (Myhrer et al., 2003).
Domain of Applicability
The ability to process complex spatiotemporal information through neuronal networking is a fundamental process underlying the behaviour of all higher organisms. The most studied are the neuronal networks of rodents (e.g Reig et al., 2015) and primates (e.g. Wang and Arnsten, 2015) and extremely large amount of the published data exist to support this topic. Invertebrates hold neural circuitries in various degrees of complexity and there are studies describing how neurons are organized into functional networks to generate behaviour. (Wong and Wong, 2004; Marder, 1994).
Andersen Per, Richard Morris, David Amaral, Tim Bliss and John O'Keefe, eds., The Hippocampus Book. Oxford University Press. ISBN, 2007, 978-0-19-510027-3.
Arenkiel BR, Peca J, Davison IG, Feliciano C, Deisseroth K, Augustine GJ, Ehlers MD, Feng G. In vivo light-induced activation of neural circuitry in transgenic mice expressing channelrhodopsin-2. Neuron, 2007, 54: 205–218.
Blokland A, Acetylcholine: a neurotransmitter for learning and memory? Brain Research Reviews, 1996, 21: 285-300.
Bracco L, Bessi V, Padiglioni S, Marini S1, Pepeu G., Do cholinesterase inhibitors act primarily on attention deficit? A naturalistic study in Alzheimer's disease patients. J Alzheimers Dis. 2014, 40(3):737-42.
Byrne John H, ed., Learning and Memory: A comprehensive reference. Elsevier. 2008, ISBN 978-0-12-370509-9.
Calabrese B., Saffin JM, Halpain SH. Activity-Dependent Dendritic Spine Shrinkage and Growth Involve Downregulation of Cofilin via Distinct Mechanisms, 2014 DOI: 10.1371/journal.pone.0094787
Cheng Da, Haixian Zhang, Yongsheng Sang. Brain CT Image Classification with Deep Neural Networks,Chapter from Proceedings of the 18th Asia Pacific Symposium on Intelligent and Evolutionary Systems, 2014, Volume 1 of the series Proceedings in Adaptation, Learning and Optimization pp 653-662.
Cho KK, Khibnik L, Philpot BD, Bear MF., The ratio of NR2A/B NMDA receptor subunits determines the qualities of ocular dominance plasticity in visual cortex. Proc Natl Acad Sci USA 2009, 106: 5377–5382.
Dombeck DA, Khabbaz AN, Collman F, Tank DW., Imaging large scale neural activity with cellular resolution in awake mobile mice. Neuron, 2007, 56: 43–57.
Eichenbaum Howard, The Cognitive Neuroscience of Memory. Oxford University Press US. 2002, ISBN 978-0-19-514175-7.
Gazarini L, Cristina A. Jark Stern, Antônio P., Carobrez and Leandro J. Bertoglio. 2013 Enhanced noradrenergic activity potentiates fear memory consolidation and reconsolidation by differentially recruiting α1- and β-adrenergic receptorsCurrent Issue Learning Memory, 2013, 20: 210-219.
Göbel W, Fritjof Helmchen, In Vivo Calcium Imaging of Neural Network Function, Physiology, 2007a, 22: 358-365.
Göbel W, Fritjof Helmchen, New Angles on Neuronal Dendrites In Vivo, Journal of Neurophysiology, 2007b, 98: 3770-3779.
Goebel-Goody SM, Davies KD, Alvestad Linger RM, Freund RK, Browning MD, Phospho-regulation of synaptic and extrasynaptic N-methyl-d-aspartate receptors in adult hippocampal slices. Neuroscience 2009, 158: 1446–1459.
Garaschuk O, Milos RI, Grienberger C, Marandi N, Adelsberger H, Konnerth A., Optical monitoring of brain function in vivo: from neurons to networks. Pflügers Arch., 2006, 453: 385–396.
Guzmán-Ramos K, Osorio-Gómez D, Moreno-Castilla P, Bermúdez-Rattoni F., Post-acquisition release of glutamate and norepinephrine in the amygdala is involved in taste-aversion memory consolidation. Learn Mem., 2012, 19: 231–238.
Jane DE, Lodge D, Collingridge GL. Kainate receptors: pharmacology, function and therapeutic potential. Neuropharmacology. 2009;56(1):90-113.
Joëls M, Fernandez G, Roozendaal B., Stress and emotional memory: A matter of timing. Trends Cogn Sci., 2011, 15: 280–288.
Kolb, Bryan; Whishaw, Ian Q., Fundamentals of Human Neuropsychology (5th ed.). 2003, Worth. pp. 102–104. ISBN 978-0-7167-5300-1.
Labudda K, Brand M, Mertens M, Ollech I, Markowitsch HJ and Woermann FG., Decision making under risk condition in patients with Parkinson’s disease: A behavioural and fMRI study. Behavioral Neurology. 2010, 23(3): 131-143.
Lantz SR1, Mack CM2, Wallace K2, Key EF3, Shafer TJ2, Casida JE3. 2014, Glufosinate binds N-methyl-D-aspartate receptors and increases neuronal network activity in vitro, Neurotoxicology. 2014, 45:38-47.
Lauri S.E., Mikael Segerstråle, Aino Vesikansa, Francois Maingret, Christophe Mulle, Graham L. Collingridge, John T. R. Isaac, Tomi Taira. Endogenous Activation of Kainate Receptors Regulates Glutamate Release and Network Activity in developing Hippocampus. The Journal of Neuroscience, 2005, 25(18): 4473-4484.
Laviv Tal, Inbal Riven, Iftach Dolev, Irena Vertkin, Bartosz Balana, Paul A. Slesinger, Inna Slutsky, Basal GABA Regulates GABABR Conformation and Release Probability at Single Hippocampal Synapses. Neuron, 2010, 67: 253–267.
Liu L, Wong TP, Pozza MF, Lingenhoehl K, Wang Y, Sheng M, et al., Role of NMDA receptor subtypes in governing the direction of hippocampal synaptic plasticity. Science 2004, 304: 1021–1024.
Lüscher C., Malenka RC. NMDA receptor-dependent longterm potentiation and long-term depression (LTP/LTD). Cold Spring Harb Perspect Biol 2012, 4: pii: a005710.
Marder E., Invertebrate neurobiology. Polymorphic neural networks. Curr Biol. 1994, 4: 752-4.
Myhrer T., Neurotransmitter systems involved in learning and memory in the rat: a meta-analysis based on studies of four behavioral tasks. Brain Res Brain Res Rev. 2003, 41(2-3):268-87.
Neves G., Sam F. Cooke & Tim V. P. Bliss, Synaptic plasticity, memory and the hippocampus: a neural network approach to causality. Nature Reviews Neuroscience, 2008, 9: 65-75.
Reig R, Zerlaut Y, Vergara R, Destexhe A, Sanchez-Vives MV., Gain modulation of synaptic inputs by network state in auditory cortex in vivo. J Neurosci. 2015, 35(6) :2689-702.
Rohleder Cathrin, F. Leweke, Bernd Neumaier, Alexander Drzezga, Heike Endepols Characterization of functional neural networks using [18F]fluorodeoxyglucose (FDG)-PET in awake rats. J Nucl Med May 1, 2015, 56 no. supplement 3 1542.
Steinberg EE and Janak PH., Establishing causality for dopamine in neural function and behavior with optogenetics. Brain Research, 2012, 9: 52-63.
Valdivia P., Matt Martin, William R. LeFew, James Ross, Keith A. Houck, Timothy J. Shafer, Multi-well microelectrode array recordings detect neuroactivity of ToxCast compounds, NeuroToxicology, 2014, 44: 204–217.
Vigot, R., Barbieri, S., Brauner-Osborne, H., Turecek, R., Shigemoto, R., Zhang, Y.P., Lujan, R., Jacobson, L.H., Biermann, B., Fritschy, J.M., et al., Differential compartmentalization and distinct functions of GABAB receptor variants. Neuron, 2006, 50: 589–601.
Wang M., Amy F T Arnsten, Contribution of NMDA receptors to dorsolateral prefrontal cortical networks in primates, Neurosci Bull April 1, 2015, 31(2): 191–197
Wehrl Hans F, Mosaddek Hossain, Konrad Lankes, Chih-Chieh Liu, Ilja Bezrukov, Petros Martirosian, Fritz Schick, Gerald Reischl, Bernd J Pichler, Simultaneous PET-MRI reveals brain function in activated and resting state on metabolic, hemodynamic and multiple temporal scales. Nature Medicine, 2013, 19: 1184–1189.
Wong Y.H. and Wong J.T.Y, Invertebrate Neural Networks, Neuro-Signals, 2004, 13, No. 1-2.
Yu Y, Cao DQ, Xu HY, Sun M, Huang ZL, Yung WH, Lu N, Huang Y6., 5-HT3A receptors are required in long-term depression and AMPA receptor internalization. Neuroscience. 2014, 278:105-12.