- 1 Event Title
- 2 Key Event Overview
- 3 How this Key Event works
- 4 How it is Measured or Detected
- 5 Evidence Supporting Taxonomic Applicability
- 6 Regulatory Examples Using This Adverse Outcome
- 7 References
Key Event Overview
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AOPs Including This Key Event
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Level of Biological Organization
How this Key Event works
Learning can be defined as the process by which new information is acquired to establish knowledge by systematic study or by trial and error (Ono, 2009). Two types of learning are considered in neurobehavioral studies: a) associative learning and b) non-associative learning.
Associative learning is learning by making associations between different events. In associative learning, a subject learns the relationship among two different stimuli or between the stimulus and the subject’s behaviour. Classical conditioning, operant conditioning and category learning are some examples of associative learning. On the other hand, non-associative learning can be defined as an alteration in the behavioral response that occurs over time in response to a single type of stimulus. Habituation and sensitization are some examples of non-associative learning. Another important type of learning is emotional learning and the simplest form of emotional regulation is extinction (Quirk and Mueller, 2008). During extinction, conditioned response to a stimulus decreases when the reinforcer is omitted and fear conditioning experiments help to elucidate the underlined mechanism.
The memory to be formed requires acquisition, retention and retrieval of information in the brain, which is characterised by the non-conscious recall of information (Ono, 2009). Memory is considered very important as it allows the subjects to access the past, to form experience and consequently to acquire skills for surviving purposes. There are three main categories of memory, including sensory memory, short-term or working memory (up to a few hours) and long-term memory (up to several days or even much longer). At the cellular level the storage of long-term memory is associated with increased gene expression and protein synthesis as well as formation of novel synaptic connections (Lynch, 2004).
Learning-related processes require neural networks to detect correlations between events in the environment and store these as changes in synaptic strength (Abbott and Nelson, 2000). Long-term potentiation (LTP) and long-term depression (LTD) are two fundamental processes involved in cognitive functions (Abbott and Nelson, 2000; Malenka and Bear, 2004), which respectively, strengthen synaptic inputs that are effective at depolarizing the postsynaptic neuron and weaken inputs that are not, thus reinforcing useful pathways in the brain. Synapses that are strengthened become more effective at depolarizing the postsynaptic neuron, eventually driving neuronal activity to saturation (Abbott and Nelson, 2000). As correlated activity of presynaptic and postsynaptic neurons drives strengthening of specific synapses, the postsynaptic neuron will be driven more strongly, and so presynaptic inputs that were initially only poorly correlated with postsynaptic firing will be better able to trigger firing of the postsynaptic neuron. This implies that nervous systems must have a matching set of plasticity mechanisms that counteract these destabilizing forces. The cortical and hippocampal pyramidal neurons have a target firing rate, and synaptic strengths are regulated to maintain these rates relatively constant in the face of perturbations in input channel (Burrone et al., 2002). This provides a robust mechanism for generating stability in network function in the face of learning-related changes in synaptic input. In principle, neurons could maintain stable firing rates through homeostatic regulation of many aspects of neuronal excitability. These possibilities include balancing inward and outward voltage-dependent conductances that determine firing properties generally called “intrinsic excitability” (Marder and Goaillard, 2006; Zhang and Linden 2003), regulating inhibitory and/or excitatory synaptic strength (Turrigiano, 2011) or synapse number (Kirov et al., 1999) or by adjusting the ease with which other forms of plasticity can be induced, so-called “metaplasticity” (Abraham and Bear, 1996). Evidence suggests that all of these mechanisms can contribute to the homeostatic regulation of neuronal firing rates in central circuits. Activity-dependent alteration in synaptic strength is a fundamental property of the vertebrate central nervous system and is thought to underlie learning and memory.
A major expression mechanism of synaptic scaling is changes in the accumulation of synaptic glutamate receptors. Central synapses typically cluster both AMPA receptors and NMDA receptors. AMPA receptors are ionotropic and carry out the majority of excitatory synaptic current in the central nervous system; NMDA receptors are also ionotropic but open as a function of voltage, flux calcium, and mediate a number of calcium-dependent forms of synaptic plasticity (Malenka and Bear, 2004). Synaptic scaling results in postsynaptic changes in both types of glutamate receptors (Stellwagen and Malenka, 2006; Watt et al., 2000) and can therefore be monitored by measuring changes in receptor accumulation at synapses.
The best characterized form of LTP occurs in the CA1 region of the hippocampus, in which LTP is initiated by transient activation of receptors and is expressed as a persistent increase in synaptic transmission through AMPA receptors followed by activation of NMDARs. This increase is due, at least in part, to a postsynaptic modification of AMPA-receptor function; this modification could be caused by an increase in the number of receptors, their open probability, their kinetics or their single-channel conductance. Summing up activity-dependent alteration in synaptic strength is a fundamental property of the vertebrate central nervous system that underlies learning and memory processes.
It is appropriate to state that while much emphasis has been given on the key role of the hippocampus in memory, it would probably be simplistic to attribute memory deficits solely to hippocampal damage (Barker and Warburton, 2011). There is substantial evidence that fundamental memory functions are not mediated by hippocampus alone but require a network that includes, in addition to the hippocampus, anterior thalamic nuclei, mammillary bodies cortex, cerebellum and basal ganglia (Aggleton and Brown, 1999; Doya, 2000; Mitchell et al., 2002, Toscano and Guilarte, 2005). Each of these brain structures can be potentially damaged leading to more or less severe impairment of learning and memory.
Amnesia is defined as the impairment or loss of memory. Depending on the cause amnesia can be characterised as functional, organic amnesia or infantile amnesia. Dementia, is a brain disease that causes a long term and often gradual decrease in the ability to think and remember as well as problems with language, and a decrease in motivation (Solomon and Budson, 2011). It is an intellectual impairment observed mainly in elderly people due to the progress of a neudegenerative disease. In younger people this type of impairment is known as presenile dementia. The most common affected areas include memory, visual-spatial, language, attention, and executive function (problem solving). Therefore, very often, short-time memory, mind, speech and motor skills are affected. Certain forms of dementia can be treated, to some extent. The most common form of dementia is Alzheimer's disease, which accounts for between 50 and 60 percent of all cases. Other types include vascular dementia and Lewy body dementia (Burns, 2009). Initial symptoms in Alzheimer's disease is memory impairment (for review, Arhavsky, 2010), in particular short-term/episodic memory, which depends largely on hippocampal system (for review, Storandt et al., 2009; Daulatzai, 2013). This pathological and age-related memory decline is believed to be a result of reduced synaptic plasticity, including changes in the NR2 subunit composition of the NMDA receptor (for review, Wang et al., 2014). It can then evolve towards a global loss of cognitive functions defined as dementia (for review, Larson et al., 1992).
In the past, the study of infant memory has relied in models and tests used in adults and more specific amnesic patients with hippocampal damage. For this reason, the infant memory has been distinguished to declarative or explicit memory and nondeclarative or implicit memory. However, in recent years this distinction such as explicit/implicit are no longer accepted especially in relation to hippocampal function as new theories have been emerged (reviewed in Mullally and Maguire, 2014). Furthermore, there are findings that even very young infants have a more adept and flexible memory system than was previously thought and neurobiological data derived from non-humans provide support to the new hypotheses about hippocampal development that would facilitate to interpret infant memory data from humans.
How it is Measured or Detected
In humans: The neuropsychological tests have been used for neurosensory assessment of humans including identification of altered neurobehaviours in vulnerable populations such as children (Rohlman et al., 2008). Intelligence tests, perceptual motor tests, planning tests, and logical, spatial, short term, long term, and working memory tasks can be used in neurobehavioral studies to assess learning and memory. The same test is also used to identify risks from occupational exposure to chemicals.
In laboratory animals: Current behavioural tests used for evaluating learning and memory processes in rats such as the Morris water maze, Radial maze, Passive avoidance and Spontaneous alternation are characterized in the KE Decreased Neuronal Network Function.
Cognitive function including learning and memory is an important endpoint required by the US EPA and OECD Developmental Neurotoxicity (DNT) Guidelines (OCSPP 870.6300 or OECD 426). The methods applied to assess learning and memory have been reviewed (Markis et al., 2009) and discussed in the OECD Series on testing and assessment number 20, Guidance document for Neurotoxicity Testing (2004) . This document is considered an essential supplement to a substantial number of already existing OECD Test Guidelines relevant for neurotoxicity testing.
Evidence Supporting Taxonomic Applicability
Learning and memory have been studied in invertebrates such as gastropod molluscs and drosophila and vertebrates such as rodents and primates. Recently, larval zebrafish has also been suggested as a model for the study of learning and memory (Roberts et al., 2013).
Regulatory Examples Using This Adverse Outcome
Impairment of learning and memory is considered a chemically-induced adverse outcome that is used for risk assessment and management purposes. Neurotoxicity testing guidelines (OECD TG 424 and 426) are implemented on a number of occasions where the neurotoxic properties of a compound have to be assessed in order to comply with relevant EU regulations. These regulations are as follows: REACH regulation (EC, No 1907/2006), Plant protection products regulation (EC, No 1107/2009), Biocidal products regulation (EC, No 528/2012), Test methods regulation (EC, No 440/2008), Classification, labelling and packaging of substances and mixtures (EC, No 1272/2008) and Maximum residue levels of pesticides in or on food and feed of plant and animal origin regulation (EC, No 396/2005).
The US EPA and OECD Developmental Neurotoxicity (DNT) Guidelines (OCSPP 870.6300 or OECD 426) both require testing of learning and memory. These DNT Guidelines have been used to identify developmental neurotoxicity and adverse neurodevelopmental outcomes (Makris et al., 2009). Also in the frame of the OECD GD 43 (2008) on reproductive toxicity, learning and memory testing may have potential to be applied in the context of developmental neurotoxicity studies. However, many of the learning and memory tasks used in guideline studies may not readily detect subtle impairments in cognitive function associated with modest degrees of developmental thyroid disruption (Gilbert et al., 2012).
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Abraham HAJ, Bear MF. Bidirectional modification of CA1 synapses in the adult hippocampus in vivo. Nature. 1996 May 9;381(6578):163-6.
Aggleton JP, Brown MW. (1999) Episodic memory, amnesia, and the hippocampal-anterior thalamic axis. Behav Brain Sci. 22: 425-489.
Arshavsky YI. 2010. Why Alzheimer's disease starts with a memory impairment: neurophysiological insight. J Alzheimers Dis 20(1): 5-16.
Barker GR, Warburton EC. (2011) When is the hippocampus involved in recognition memory? J Neurosci. 31: 10721-10731.
Burns A, Iliffe, S. (2009). Dementia. BMJ (Clinical research ed.) 338: b75.
Burrone J, O’Byrne M, Murthy VN. (2002) Multiple forms of synaptic plasticity triggered by selective suppression of activity in individual neurons. Nature 420: 414–418.
Daulatzai MA. 2013. Neurotoxic saboteurs: straws that break the hippo's (hippocampus) back drive cognitive impairment and Alzheimer's Disease. Neurotox Res 24(3): 407-459.
Doya K. (2000) Complementary roles of basal ganglia and cerebellum in learning and motor control. Curr Opin Neurobiol. 10: 732-739.
Gilbert ME, Rovet J, Chen Z, Koibuchi N. (2012) Developmental thyroid hormone disruption: prevalence, environmental contaminants and neurodevelopmental consequences. Neurotoxicology 33: 842-52.
Kirov SA, Sorra KE, Harris KM. (1999) Slices have more synapses than perfusion-fixed hippocampus from both young and mature rats. J Neurosci. 19: 2876–2886.
Larson EB, Kukull WA, Katzman RL. 1992. Cognitive impairment: dementia and Alzheimer's disease. Annual review of public health 13: 431-449.
Lynch MA. (2004) Long-term potentiation and memory. Physiol Rev. 84: 87-136.
Makris SL, Raffaele K, Allen S, Bowers WJ, Hass U, Alleva E, Calamandrei G, Sheets L, Amcoff P, Delrue N, Crofton KM. (2009) A retrospective performance assessment of the developmental neurotoxicity study in support of OECD test guideline 426. Environ Health Perspect. 117:17-25.
Malenka RC, Bear MF (2004). LTP and LTD: An embarrassment of riches. Neuron 44: 5–21.
Marder E, Goaillard JM. (2006). Variability, compensation and homeostasis in neuron and network function. Nat Rev Neurosci. 7: 563–574.
Mitchell AS, Dalrymple-Alford JC, Christie MA. (2002) Spatial working memory and the brainstem cholinergic innervation to the anterior thalamus. J Neurosci. 22: 1922-1928.
Mullally SL, Maguire EA. (2014) Learning to remember: the early ontogeny of episodic memory. Dev Cogn Neurosci. 9: 12-29.
OECD (2004) Series on testing and assessment number 20, Guidance document for neurotoxicity testing.
OECD (2007). Test Guideline 426. OECD Guideline for Testing of Chemicals. Developmental Neurotoxicity Study. http://www.oecd.org/document/55/0,3343,en_2649_34377_2349687_1_1_ 1_1,00.html
OECD (2008) Nr 43 GUIDANCE DOCUMENT ON MAMMALIAN REPRODUCTIVE TOXICITY TESTING AND ASSESSMENT. ENV/JM/MONO(2008)16
Ono T. (2009) Learning and Memory. Encyclopedia of neuroscience. M D. Binder, N. Hirokawa and U. Windhorst (Eds). Springer-Verlag GmbH Berlin Heidelberg. pp 2129-2137.
Quirk GJ, Mueller D. (2008) Neural mechanisms of extinction learning and retrieval. Neuropsychopharmacology 33: 56-72.
Roberts AC, Bill BR, Glanzman DL. (2013) Learning and memory in zebrafish larvae. Front Neural Circuits 7: 126.
Rohlman DS, Lucchini R, Anger WK, Bellinger DC, van Thriel C. (2008) Neurobehavioral testing in human risk assessment. Neurotoxicology. 29: 556-567.
Solomon, Andrew E. Budson, Paul R. (2011). Memory loss : a practical guide for clinicians. Elsevier Saunders. ISBN 9781416035978
Stellwagen D, Malenka RC. (2006) Synaptic scaling mediated by glial TNF-α. Nature 440: 1054–1059.
Storandt M, Mintun MA, Head D, Morris JC. 2009. Cognitive decline and brain volume loss as signatures of cerebral amyloid-beta peptide deposition identified with Pittsburgh compound B: cognitive decline associated with Abeta deposition. Arch Neurol 66(12): 1476-1481.
Toscano CD, Guilarte TR. (2005) Lead neurotoxicity: From exposure to molecular effects. Brain Res Rev. 49: 529-554.
Turrigiano G. (2011) Too many cooks? Intrinsic and synaptic homeostatic mechanisms in cortical circuit refinement. Annu Rev Neurosci. 34: 89–103.
Wang D, Jacobs SA, Tsien JZ. 2014. Targeting the NMDA receptor subunit NR2B for treating or preventing age-related memory decline. Expert opinion on therapeutic targets 18(10): 1121-1130.
Watt AJ, van Rossum MC, MacLeod KM, Nelson SB, Turrigiano GG. (2000) Activity coregulates quantal AMPA and NMDA currents at neocortical synapses. Neuron 26: 659–670.
Zhang W, Linden DJ. (2003) The other side of the engram: Experience-driven changes in neuronal intrinsic excitability. Nat Rev Neurosci. 4: 885–900.