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Network dysfunction in Alzheimer's disease: does synaptic scaling drive disease progression?
Opinion
Network dysfunction in Alzheimer’s disease: does synaptic scaling drive disease progression? David H. Small Menzies Research Institute, University of Tasmania, Hobart 7000, Tasmania, Australia
Accumulation of b-amyloid protein (Ab) in the brain is a key feature of Alzheimer’s disease (AD). The build-up of aggregated forms of Ab leads to synaptic loss and to cognitive dysfunction. Although the pathways controlling production and aggregation of Ab are well studied, the mechanisms that drive the spread of neurodegeneration in the brain are unclear. Here, the idea is presented that AD progresses as a consequence of synaptic scaling, a type of neuronal plasticity that helps maintain synaptic signal strength. Recent studies indicate that brain-derived neurotrophic factor, tumour necrosis factor-a and a7 nicotinic acetylcholine receptors (a7 nAChRs) regulate synaptic scaling in the AD brain. It is suggested that further studies on synaptic scaling in AD could reveal new targets for therapeutic drug development. Introduction In this Opinion article, a relatively new, emerging view of the mechanism of cognitive dysfunction in Alzheimer’s disease (AD) (see Glossary) is presented. Until recently, most studies on neurodegeneration in AD have focussed on the biochemical mechanisms that cause neuronal dysfunction in single cells. These diverse studies have provided many clues to the aetiology of AD and have helped to explain some aspects of the disease. However, as will be discussed here, it is becoming increasingly clear that cognitive decline should be examined from a much broader perspective. Neurons do not function in isolation and any attempt to understand AD at the level of a single neuron gives an incomplete picture of the disease. Cognition arises from the behaviour of functional assemblies of connected neurons or neural networks. By understanding how neurodegenerative processes affect these neural networks, we can begin to understand those cellular mechanisms that might be important in disease causation. One phenomenon that has been revealed by studies on neural networks is known as synaptic scaling. Synaptic scaling is a relatively slow form of neuronal plasticity that helps to maintain signalling in neural networks by modifying neuronal excitability of synapses. In this article, studies on synaptic scaling are reviewed along with other studies that demonstrate that synaptic scaling is an important phenomenon in AD. As will be described, the identification of biochemical mechanisms that control Corresponding author: Small, D.H. ([email protected]).
synaptic scaling provides clues to the forces that drive disease progression. Pathogenesis of AD AD is the most common cause of dementia in the elderly. At the early stages of the disease, wandering and disorientation are common. However, as the disease progresses, symptoms worsen and patients become increasingly bedridden. Typically, memory loss occurs progressively [1]. Initially, patients might have trouble remembering recent events. However, as the disease spreads, older more established memories are lost. The degree of neurodegeneration in the brain, as measured by neurofibrillary tangle (NFT) pathology, correlates approximately with the cognitive decline [2]. Typically, many degenerating neurons exhibit abnormal morphology and contain NFTs made up from intracellular aggregates of hyperphosphorylated tau protein. NFT
Glossary Alzheimer’s disease (AD): a progressive neurodegenerative disease that is the most common cause of dementia in the elderly. Sporadic forms of AD normally occur after the age of 65 years. Familial forms of the disease are rarer and can affect individuals as early as the fourth or fifth decades of life. AD is characterized typically by memory problems and confusion in its early stages. Neuropathological analysis of the brain typically shows amyloid plaques, made up from the Ab protein and neurofibrillary tangles composed of a hyperphosphorylated form of the microtubule-associated protein tau. Dementia: loss of intellectual abilities, such as memory, language or reasoning. Mild cognitive impairment: a condition in which thinking abilities are mildly impaired. It is still debated whether mild cognitive impairment is a preclinical form of AD or an entirely separate condition. A percentage of patients diagnosed with mild cognitive impairment will eventually go on to develop AD. Neural network: an entity made up of interconnected units (‘neurons’) in which weighted (‘synaptic’) interconnections control the degree of unit activation. Neural networks can be biological (i.e. composed of real neurons). However, the term is used commonly to apply to artificial or theoretical structures modelled on a computer (an artificial neural network). Synaptic plasticity: the ability of a connection or synapse between two neurons to change in signal strength. Synaptic plasticity provides the basis for all types of learning and memory. Long-term potentiation and long-term depression are two types of synaptic plasticity. Synaptic scaling: a form of synaptic plasticity in which homeostatic mechanisms compensate for changes in synaptic signalling by altering either the level of postsynaptic receptors or the efficacy of release of presynaptic neurotransmitters. b-amyloid protein (Ab): a small neurotoxic polypeptide that is the major constituent of amyloid deposits in the AD brain. Accumulation of Ab in the brain is thought to be the key event that causes Alzheimer’s disease. Ab can aggregate to form high molecular-weight amyloid fibrils. However, it is now recognized that low molecular-weight oligomeric forms of Ab are probably the most neurotoxic species.
1471-4914/$ – see front matter ß 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.molmed.2007.12.006 Available online 11 February 2008
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Opinion pathology is often found initially in the trans-entorhinal cortex and then it spreads to the hippocampus and neocortex. As the disease progresses further, association areas of the neocortex become involved. Motor and visual cortices are often spared until the later stages of the disease [3]. The cognitive decline in AD is also correlated with accumulation of b-amyloid protein (Ab) in the brain. Ab is produced by proteolytic cleavage of the b-amyloid precursor protein (APP), which is an integral type I transmembrane glycoprotein [4]. The b-secretase (BACE1) cleaves APP at a site within the ectodomain, close to the membrane, to yield a 99-residue C-terminal fragment (C99). This fragment is further cleaved by the g-secretase complex to yield Ab and a smaller APP intracellular domain fragment (AICD) (Figure 1). Ab can aggregate spontaneously and it is now generally accepted that a build-up of oligomeric Ab is a major contributor to the neurodegeneration in AD [5]. Oligomeric Ab is toxic to neurons because it can disrupt a wide variety of signal-transduction pathways. For example, Ab can alter calcium homeostasis, increase the level of reactive oxygen species, induce endoplasmic reticulum stress and activate kinases, caspases and calpains [6]. Among these effects, disruption of calcium homeostasis is one of the most important factors for neurotoxicity [7] because calcium influx can alter synaptic function and activate cell-death pathways [8]. Some regions of the brain are more susceptible to Ab toxicity than others [9]. One possible explanation for this variability is that there are regional intrinsic factors that regulate neuronal susceptibility to toxicity [9]. For example, Cecci et al. [10] suggest that different intrinsic abilities to buffer cytoplasmic calcium might be one factor that determines the degree of Ab toxicity. Alternatively, the differential susceptibility might be owing to extrinsic factors, such as the number and type of synaptic inputs that neurons receive in vivo. For example, intracellular calcium levels are controlled by neuronal excitability, which, in turn, is related to the total input neurons receive from other excitatory and inhibitory neurons.
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Although studies over the last few years have gone a long way toward explaining some of the basic biochemical mechanisms of AD, the precise mechanisms that drive disease progression and that define the differential susceptibility of certain neurons or brain regions to neurodegeneration have, until recently, remained obscure. AD and synaptic plasticity It is increasingly recognized that a defect in synaptic plasticity underlies the cognitive decline in AD [6,11]. Neuropathological studies suggest that synapse loss (or dysfunction), rather than cell death, is closely correlated to cognitive decline [12]. The accumulation of Ab in the AD brain is associated with a loss of functional of synapses and with neuritic dystrophy. These synaptic changes are likely to have profound effects on learning and memory [6,11,12]. A central characteristic of Ab toxicity is that it causes a decrease in neuronal excitability. For example, recent studies [13] suggest that Ab induces loss of a-amino-3hydroxy-5-methylisoxazole-4-propionic acid (AMPA) receptors. This decrease in AMPA receptors underlies Ab-induced synaptic depression and dendritic-spine loss [13]. Ab-induced synaptic depression can lead to disruption of long-term potentiation, which is an important form of synaptic plasticity associated with learning and memory [14]. On the basis of these studies [6,11–14], it is now clear that synaptic dysfunction is the key event in AD pathogenesis. A neural network perspective on dementia The effects of Ab on synaptic function and cognition cannot be understood solely at the level of a single synapse. The contribution of Ab to synaptic dysfunction and to cognitive decline can only be understood using broad, integrated approaches, in which the effects of Ab on assemblies of many networked neurons are examined. For example, cognitive decline in AD can be assessed using a computational approach. Many aspects of memory and learning can be modelled in relatively simple neural networks, known as Hopfield-type attractor neural
Figure 1. Generation of Ab from APP and the mechanism of Ab neurotoxicity. Ab is formed by the sequential actions of the b- and g-secretases, which cleave APP in a region overlapping the transmembrane domain. Cleavage of APP by the b-secretase (BACE1) results in a 99-amino acid residue C-terminal fragment (C99), which is further cleaved by the g-secretase to yield Ab. Ab can aggregate into dimers and low molecular-weight diffusible oligomeric species, which are thought to be the most neurotoxic species. Oligomeric forms of Ab can disrupt calcium homeostasis, induce ER stress, lead to the formation of reactive oxygen species (ROS) and alter tau phosphorylation and mitogen-activated protein (MAP) kinase and p21 pathways [6,43,44]. Amyloid plaques can also form from higher molecular-weight fibrils but they are not considered to be the most toxic form of Ab.
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Opinion networks, which are assemblies of model neurons connected by synapses [15]. These attractor neural networks can be trained to learn and store memories in a fashion similar to that which occurs in vivo. Experiments with artificial neural networks demonstrate that synaptic dysfunction causes severe memory loss [16]. However, although the ablation of synapses in artificial neural networks can cause memory loss, the pattern of amnesia does not completely mimic that which is seen in neurodegenerative diseases [16]. In neural network models, memory is often preserved until a critical percentage of loss is reached, whereupon memory loss is sudden and catastrophic. However, in AD, memory loss is gradual. Recent memories are often lost first, whereas older, more consolidated, memories remain relatively preserved until the disease progresses. To mimic the type of amnesia that occurs in AD, a mechanism of synaptic compensation or scaling has to be introduced into neural networks. In these models, loss of signal strength at a particular synapse is compensated for by an increase in signalling as the number of synaptic inputs decreases [16]. The fact that the introduction of such a mechanism can reproduce the same gradual decline that is seen in AD supports the notion that synaptic scaling is an important phenomenon in AD. Synaptic scaling is a well documented type of synaptic plasticity that involves homeostatic changes at synapses, which maintain signal strength when input is either increased or decreased [17,18]. Synaptic scaling can be effected by several different mechanisms (Figure 2). In mammalian central neurons, raising or lowering activity results in a respective decrease or increase in the number of postsynaptic AMPA receptors. These changes are accom-
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panied by changes in the turnover of AMPA receptorassociated proteins. Synaptic scaling can also be regulated presynaptically by changing the efficacy of neurotransmitter release [17,18]. For example, mutant Drosophila, which overexpress the cell adhesion molecule fasciclin II, have hyperinnervated tissues in which synaptic efficacy (the probability of acetylcholine release at cholinergic synapses) is decreased [19,20]. By contrast, hypoinnervated synapses upregulate the level of cholinergic receptors. Brain-derived neurotrophic factor (BDNF) is thought to have an important role in synaptic plasticity and homeostasis in the cortex [21,22]. BDNF is released from cortical pyramidal neurons in an activity-dependent fashion and, on release, BDNF can act through two different mechanisms to regulate synaptic activity. Under conditions of decreased pyramidal neuron activity, there is a coordinated decrease in BDNF release, which leads to an increase in signalling strength among pyramidal neurons (Figure 2). By contrast, an increase in neuronal activity leads to an increase in extracellular BDNF, which causes inhibitory interneurons to fire and leads to the inhibition of excitatory signalling among pyramidal neurons [21,22]. Recent studies [23,24] have shown that tumour necrosis factor (TNF)-a also regulates synaptic scaling (Figure 2). Astrocyte-derived TNF-a can regulate neuronal excitability through an action on AMPA-receptor trafficking. Stellwagen et al. [23,24] have shown that a decrease in neuronal activity can result in an increase in the release of glial TNF-a, which, in turn, can act on the neuronal plasma membrane to increase the number of AMPA receptors. This increase in receptor density leads to a compensatory increase in excitability (scaling).
Figure 2. Possible mechanisms of synaptic scaling in the AD brain. In response to Ab-induced synaptic dysfunction, neurons can regulate signalling homeostatically through several different mechanisms. Oligomeric Ab causes dendritic abnormalities and leads to a decrease in signalling through the downregulation of AMPA receptors ( ). Under conditions of reduced neuronal activity, levels of BDNF are decreased, causing an increase in neuronal excitability in adjacent healthy cells (+). TNF-a is released from astrocytes in response to decreased neuronal activity and also causes an increase in excitation in adjacent neurons (+). In AD, an Ab-induced decrease in cortical synaptic activity might cause an increase in a7 nAChRs and might lead to increased production of acetylcholine (ACh) in basal forebrain cholinergic neurons (BFC). Abbreviation: AMPAR, AMPA receptor.
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Opinion Synaptic scaling mechanisms in AD There is evidence that synaptic scaling occurs in AD. Because Ab neurotoxicity causes synaptic loss or dystrophy, synaptic scaling is likely to have an important role in maintaining signal strength in the remaining healthy neurons. For example, recent studies demonstrate that abnormal excitatory neuronal activity occurs in association with Ab-induced changes to hippocampal circuits in a transgenic mouse model of AD [25]. Increased activity of basal forebrain cholinergic neurons might be one mechanism that contributes to synaptic scaling in the AD brain [26]. Although a decrease in cholinergic activity in the brain is viewed generally as contributing to cognitive decline in AD, cholinergic activity might be increased during the very early (preclinical) stages of the disease [27]. A decrease in cholinergic activity might occur only in later stages of AD when clinical symptoms become apparent [26]. Studies by DeKosky et al. [27] show that, in cases of mild-cognitive impairment, levels of choline acetyltransferase (ChAT) are increased in the brain of individuals who are likely to develop early-stage AD. Studies of APP-transgenic mice support the view that cholinergic activity might be activated as a consequence of Ab accumulation in the brain. APP transgenic mice show elevated levels of the cholinergic markers choline acetyltransferase (ChAT), acetylcholinesterase (AChE) and the a7 nicotinic acetylcholine receptor (a7 nAChR) around amyloid plaques without apparent loss of cholinergic cell bodies [26,28–31]. For both AChE [32] and the a7 nAChR [30], this increase occurs at a very early stage in the development of transgenic mice, well before amyloidplaque formation, suggesting that it might be related to the overproduction of oligomeric Ab [32]. BDNF and TNF-a might also contribute to synaptic scaling in AD. BDNF levels might be decreased in AD [33,34], possibly as a consequence of an Ab-induced decrease in neuronal activity. Significantly, this decrease in BDNF might occur very early in the course of the disease because a decline in BDNF levels can also be detected in patients with mild cognitive impairment [35]. In contrast to BDNF, which is an inhibitor of neuronal excitability, an increase in secreted TNF-a leads to an increase in neuronal excitability. Indeed, several studies have found that the cytokine is increased in post-mortem AD tissue [36,37]. Although this increase is presumed generally to be related to the cytokine’s role in inflammation, a direct role in synaptic scaling also seems likely. The source of the TNF-a is unclear because it could be derived either from astrocytes or microglia. However, interestingly, TNF-a is also increased in APP-transgenic mice, in which the overall amount of microglial activation and inflammatory change is relatively low [38]. In summary, these studies provide clear evidence that, in AD, or in transgenic mouse models of AD, synaptic hypoactivity caused by Ab neurotoxicity results in several compensatory changes that are consistent with synaptic scaling. Do homeostatic mechanisms drive disease progression in AD? Although synaptic scaling might help to preserve memory in the short term, it might have long-term effects that lead to the spread of the disease to otherwise healthy neurons in 106
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the network. An Ab-induced decrease in synaptic signalling should cause a compensatory increase (scaling) in the excitability of adjacent healthy neurons. The increase in excitability would, in turn, be expected to raise intracellular calcium levels in the healthy neurons that are connected within the same network. Because calcium is a key mediator of Ab neurotoxicity [7,39,40], an increase in cytosolic calcium could increase the vulnerability of the healthy neurons to Ab toxicity [10] (Figure 3). In synaptic scaling, lowered input from one neuron within a network is compensated for by an increase in presynaptic neurotransmitter (glutamate) release from adjacent neurons within the network. This increase in glutamate release causes increased excitation, which leads to an increase in cytosolic calcium levels within the adjacent neurons. Because neural networks gravitate towards a relatively stable (attractor) state in which multiple neurons fire within the network at the same time [15], the compensatory increase in excitation is shared among
Figure 3. A hypothetical model of synaptic scaling and disease progression in AD. AD is triggered by a combination of genetic and environmental risk factors that, in combination with age, lead to an imbalance in Ab or APP metabolism and an increase in oligomeric Ab species in the brain. Ab-induced neurotoxicity causes synaptic dysfunction, a consequent decrease in neuronal excitation and a compensatory increase occurs in neuronal excitation in adjacent healthy neurons (synaptic scaling). This increase in excitation and the subsequent rise in cytosolic calcium in these healthy neurons lead to an increased susceptibility to Ab toxicity.
Opinion many neurons within the network. As shown by Cecci et al. [10], an increase in cytosolic calcium might raise the susceptibility of neurons to Ab toxicity. Therefore, as neurons within a network compensate for the decreased output of degenerating neurons within the network, their own susceptibility to toxicity increases. According to this model, in response to Ab-induced synaptic loss in one region, increased neurotransmitter release occurs from adjacent healthy neurons, exciting many healthy neurons within the network, destabilizing calcium homeostasis and rendering the neurons vulnerable to toxicity. As these healthy neurons degenerate, other neurons in the network become susceptible and so neurodegeneration progresses (Figure 3). Does a neural network model explain the pattern of neurodegeneration? On the basis of the model proposed in Figure 3, it is possible to explain some features of AD neuropathology. First, the pattern of neurodegeneration in AD approximately follows the circuitry involved in memory processing [3]. Neurons of the trans-entorhinal cortex send fibres through the perforant pathway to synapse onto granule cells in the dentate gyrus of the hippocampus. These cells then send mossy fibre axons to the CA3 region and CA1 region of the hippocampus, which make connections with cortical pyramidal neurons. In AD, tangle neuropathology follows a similar pathway because it is often seen first in the transentorhinal cortex and then in the hippocampus [3], before spreading to the associated areas of the cortex. The model predicts that excitatory neurons, which are highly networked, would be more vulnerable than other neurons, which are less highly networked. Indeed, pyramidal neurons, which are predominantly glutamatergic and which make multiple synaptic connections within the cortex, are particularly vulnerable in AD [2,3,41]. Similarly, non-glutamatergic neurons, which increase their excitation state in response to Ab neurotoxicity, should also be susceptible to Ab toxicity. This might explain why cholinergic neurons are vulnerable in AD [42]. Increased cholinergic excitation in response to Ab-induced synaptic loss within the cortex [26,27] could increase the susceptibility of cholinergic neurons to Ab toxicity. Thus, an increase in cholinergic activity in the early stages of the disease would be followed by cholinergic loss in the later stages, exactly as has been described [26,27]. Therefore, we can conclude that the pattern of neurodegeneration in the AD brain is, at the very least, consistent with a model in which synaptic scaling is the driving force for disease progression. Synaptic scaling: a target for AD therapy? It is worth considering whether synaptic scaling mechanisms might be a valid target for AD therapy. Most of the current effort in the field of AD therapeutics has been directed at developing drugs that inhibit the production, aggregation, clearance or neurotoxicity of Ab [43]. However, inhibition of disease progression could also be a strategy for therapy. If synaptic scaling drives the spread of neurodegeneration, then it might be possible to slow down the spread of the disease. For example, it might be
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Box 1. Outstanding questions What are the major unidentified mechanisms of synaptic scaling in the brain? To what extent does synaptic scaling contribute to the overall cognitive profile in AD? How do drugs that block the neurotoxic effects of Ab on isolated neurons in culture affect the function of networked neurons? Are the mechanisms of synaptic scaling useful targets for the development of drugs that block the progression of A D or will drugs acting on these targets adversely affect cognition?
possible to administer BDNF or to inhibit TNF-a as a means of inhibiting disease progression. However, synaptic scaling is a fundamental mechanism that helps to preserve the function of neural networks [16]. Therefore, although inhibition of synaptic scaling might help to decrease the spread of neurodegeneration, it might also increase synaptic depression and cognitive dysfunction, at least in the short term. Thus, long-term benefits of such an approach might have to be weighed against shortterm disadvantages. The potential of this sort of strategy will be difficult to assess without clinical trial data. Concluding remarks It is now becoming clear that, to understand the mechanism of disease causation in AD, we need to understand how Ab affects neural networks. Although Ab has many biochemical effects, some of these effects might contribute more than others to cognitive dysfunction. Synaptic scaling undoubtedly has an important role in helping to preserve the activity of neural networks in the AD brain (Box 1). However, as described here, it seems likely that synaptic scaling might also contribute to the spread of neurodegeneration in the brain and to the progression of the disease. Whether mechanisms of synaptic scaling will prove to be useful targets for drug development is unclear because inhibition of synaptic scaling might adversely affect cognitive performance. Nevertheless, even if synaptic scaling is not an appropriate therapeutic target, the use of an integrated (neural network) approach to assess drug efficacy seems likely to be an appropriate strategy for other therapeutic targets. We are now entering an exciting period in AD research. Eventually, it might be possible not only to design drugs that block Ab toxicity but also to examine how these drugs broadly affect neural network function. Such a strategy might help to speed up the process of drug-candidate optimization and provide a more rational basis for moving forward to clinical trials. This might be the next big step in AD drug development. References 1 Storey, E. et al. (2001) The neuropsychological diagnosis of Alzheimer’s disease. J. Alzheimers Dis. 3, 261–285 2 Probst, A. et al. (1991) Alzheimer’s disease: a description of the structural lesions. Brain Pathol. 1, 229–239 3 Braak, H. and Braak, E. (1995) Staging of Alzheimer’s disease-related neurofibrillary changes. Neurobiol. Aging 16, 271–278 4 Nunan, J. and Small, D.H. (2000) Regulation of APP cleavage by alpha-, beta- and gamma-secretases. FEBS Lett. 483, 6–10 5 Walsh, D.M. and Selkoe, D.J. (2007) A beta oligomers – a decade of discovery. J. Neurochem. 101, 1172–1184 107
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6 Small, D.H. et al. (2001) Alzheimer’s disease and Ab toxicity: from top to bottom. Nat. Rev. Neurosci. 2, 595–598 7 LaFerla, F.M. (2002) Calcium dyshomeostasis and intracellular signalling in Alzheimer’s disease. Nat. Rev. Neurosci. 3, 862–872 8 Duchen, M.R. (2000) Mitochondria and calcium: from cell signalling to cell death. J. Physiol. 529, 57–68 9 Romito-DiGiacomo, R.R. et al. (2007) Effects of Alzheimer’s disease on different cortical layers: the role of intrinsic differences in Ab susceptibility. J. Neurosci. 27, 8496–8504 10 Cecchi, C. et al. (2005) Insights into the molecular basis of the differing susceptibility of varying cell types to the toxicity of amyloid aggregates. J. Cell Sci. 118, 3459–3470 11 Palop, J.J. et al. (2006) A network dysfunction perspective on neurodegenerative diseases. Nature 443, 768–773 12 Terry, R.D. (2000) Cell death or synaptic loss in Alzheimer disease. J. Neuropathol. Exp. Neurol. 59, 1118–1119 13 Hsieh, H. et al. (2006) AMPAR removal underlies Ab-induced synaptic depression and dendritic spine loss. Neuron 52, 831–843 14 Yuste, R. and Bonhoeffer, T. (2001) Morphological changes in dendritic spines associated with long-term synaptic plasticity. Annu. Rev. Neurosci. 24, 1071–1089 15 Hopfield, J.J. (1982) Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. U. S. A. 79, 2554–2558 16 Ruppin, E. and Reggia, J.A. (1995) A neural model of memory impairment in diffuse cerebral atrophy. Br. J. Psychiatry 166, 19–28 17 Turrigiano, G.G. (1999) Homeostatic plasticity in neuronal networks: the more things change, the more they stay the same. Trends Neurosci. 22, 221–227 18 Turrigiano, G.G. and Nelson, S.B. (2004) Homeostatic plasticity in the developing nervous system. Nat. Rev. Neurosci. 5, 97–107 19 Davis, G.W. and Goodman, C.S. (1998) Genetic analysis of synaptic development and plasticity: homeostatic regulation of synaptic efficacy. Curr. Opin. Neurobiol. 8, 149–156 20 Davis, G.W. and Goodman, C.S. (1998) Synapse-specific control of synaptic efficacy at the terminals of a single neuron. Nature 392, 82–86 21 Rutherford, L.C. et al. (1998) BDNF has opposite effects on the quantal amplitude of pyramidal neuron and interneuron excitatory synapses. Neuron 21, 521–530 22 Rutherford, L.C. et al. (1997) Brain-derived neurotrophic factor mediates the activity-dependent regulation of inhibition in neocortical cultures. J. Neurosci. 17, 4527–4535 23 Stellwagen, D. and Malenka, R.C. (2006) Synaptic scaling mediated by glial TNF-alpha. Nature 440, 1054–1059 24 Stellwagen, D. et al. (2005) Differential regulation of AMPA receptor and GABA receptor trafficking by tumor necrosis factor-alpha. J. Neurosci. 25, 3219–3228 25 Palop, J.J. et al. (2007) Aberrant excitatory neuronal activity and compensatory remodeling of inhibitory hippocampal circuits in mouse models of Alzheimer’s disease. Neuron 55, 697–711 26 Small, D.H. (2004) Do acetylcholinesterase inhibitors boost synaptic scaling in Alzheimer’s disease? Trends Neurosci. 27, 245–249
27 DeKosky, S.T. et al. (2002) Upregulation of choline acetyltransferase activity in hippocampus and frontal cortex of elderly subjects with mild cognitive impairment. Ann. Neurol. 51, 145–155 28 Luth, H.J. et al. (2003) Degeneration of beta-amyloid-associated cholinergic structures in transgenic APP SW mice. Brain Res. 977, 16–22 29 Saez-Valero, J. et al. (1999) Molecular isoform distribution and glycosylation of acetylcholinesterase are altered in brain and cerebrospinal fluid of patients with Alzheimer’s disease. J. Neurochem. 72, 1600–1608 30 Dineley, K.T. et al. (2002) Accelerated plaque accumulation, associative learning deficits, and up-regulation of alpha 7 nicotinic receptor protein in transgenic mice co-expressing mutant human presenilin 1 and amyloid precursor proteins. J. Biol. Chem. 277, 22768–22780 31 Hu, L. et al. (2003) The impact of Ab-plaques on cortical cholinergic and non-cholinergic presynaptic boutons in Alzheimer’s disease-like transgenic mice. Neuroscience 121, 421–432 32 Fodero, L.R. et al. (2002) Altered glycosylation of acetylcholinesterase in APP(SW) Tg2576 transgenic mice occurs prior to amyloid plaque deposition. J. Neurochem. 81, 441–448 33 Tong, L. et al. (2004) Beta-amyloid peptide at sublethal concentrations downregulates brain-derived neurotrophic factor functions in cultured cortical neurons. J. Neurosci. 24, 6799–6809 34 Garzon, D.J. and Fahnestock, M. (2007) Oligomeric amyloid decreases basal levels of brain-derived neurotrophic factor (BDNF) mRNA via specific downregulation of BDNF transcripts IV and V in differentiated human neuroblastoma cells. J. Neurosci. 27, 2628–2635 35 Peng, S. et al. (2005) Precursor form of brain-derived neurotrophic factor and mature brain-derived neurotrophic factor are decreased in the pre-clinical stages of Alzheimer’s disease. J. Neurochem. 93, 1412– 1421 36 Jia, J.P. et al. (2005) Cerebrospinal fluid tau, Ab1-42 and inflammatory cytokines in patients with Alzheimer’s disease and vascular dementia. Neurosci. Lett. 383, 12–16 37 Ho, G.J. et al. (2005) Mechanisms of cell signaling and inflammation in Alzheimer’s disease. Curr. Drug Targets Inflamm. Allergy 4, 247–256 38 Quadros, A. et al. (2003) Increased TNFa production and Cox-2 activity in organotypic brain slice cultures from APPsw transgenic mice. Neurosci. Lett. 353, 66–68 39 Mattson, M.P. et al. (1992) b-Amyloid peptides destabilize calcium homeostasis and render human cortical neurons vulnerable to excitotoxicity. J. Neurosci. 12, 376–389 40 Marx, J. (2007) Fresh evidence points to an old suspect: calcium. Science 318, 384–385 41 Morrison, J.H. and Hof, P.R. (2002) Selective vulnerability of corticocortical and hippocampal circuits in aging and Alzheimer’s disease. Prog. Brain Res. 136, 467–486 42 Bartus, R.T. et al. (1982) The cholinergic hypothesis of geriatric memory dysfunction. Science 217, 408–414 43 Golde, T.E. (2006) Disease-modifying therapy for AD? J. Neurochem. 99, 689–707 44 Zhao, L. et al. (2006) Role of p21-activated kinase pathway defects in the cognitive deficits of Alzheimer’s disease. Nat. Neurosci. 9, 234–242
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Network dysfunction in Alzheimer’s disease: does synaptic scaling drive disease progression? David H. Small Menzies Research Institute, University of Tasmania, Hobart 7000, Tasmania, Australia
Accumulation of b-amyloid protein (Ab) in the brain is a key feature of Alzheimer’s disease (AD). The build-up of aggregated forms of Ab leads to synaptic loss and to cognitive dysfunction. Although the pathways controlling production and aggregation of Ab are well studied, the mechanisms that drive the spread of neurodegeneration in the brain are unclear. Here, the idea is presented that AD progresses as a consequence of synaptic scaling, a type of neuronal plasticity that helps maintain synaptic signal strength. Recent studies indicate that brain-derived neurotrophic factor, tumour necrosis factor-a and a7 nicotinic acetylcholine receptors (a7 nAChRs) regulate synaptic scaling in the AD brain. It is suggested that further studies on synaptic scaling in AD could reveal new targets for therapeutic drug development. Introduction In this Opinion article, a relatively new, emerging view of the mechanism of cognitive dysfunction in Alzheimer’s disease (AD) (see Glossary) is presented. Until recently, most studies on neurodegeneration in AD have focussed on the biochemical mechanisms that cause neuronal dysfunction in single cells. These diverse studies have provided many clues to the aetiology of AD and have helped to explain some aspects of the disease. However, as will be discussed here, it is becoming increasingly clear that cognitive decline should be examined from a much broader perspective. Neurons do not function in isolation and any attempt to understand AD at the level of a single neuron gives an incomplete picture of the disease. Cognition arises from the behaviour of functional assemblies of connected neurons or neural networks. By understanding how neurodegenerative processes affect these neural networks, we can begin to understand those cellular mechanisms that might be important in disease causation. One phenomenon that has been revealed by studies on neural networks is known as synaptic scaling. Synaptic scaling is a relatively slow form of neuronal plasticity that helps to maintain signalling in neural networks by modifying neuronal excitability of synapses. In this article, studies on synaptic scaling are reviewed along with other studies that demonstrate that synaptic scaling is an important phenomenon in AD. As will be described, the identification of biochemical mechanisms that control Corresponding author: Small, D.H. ([email protected]).
synaptic scaling provides clues to the forces that drive disease progression. Pathogenesis of AD AD is the most common cause of dementia in the elderly. At the early stages of the disease, wandering and disorientation are common. However, as the disease progresses, symptoms worsen and patients become increasingly bedridden. Typically, memory loss occurs progressively [1]. Initially, patients might have trouble remembering recent events. However, as the disease spreads, older more established memories are lost. The degree of neurodegeneration in the brain, as measured by neurofibrillary tangle (NFT) pathology, correlates approximately with the cognitive decline [2]. Typically, many degenerating neurons exhibit abnormal morphology and contain NFTs made up from intracellular aggregates of hyperphosphorylated tau protein. NFT
Glossary Alzheimer’s disease (AD): a progressive neurodegenerative disease that is the most common cause of dementia in the elderly. Sporadic forms of AD normally occur after the age of 65 years. Familial forms of the disease are rarer and can affect individuals as early as the fourth or fifth decades of life. AD is characterized typically by memory problems and confusion in its early stages. Neuropathological analysis of the brain typically shows amyloid plaques, made up from the Ab protein and neurofibrillary tangles composed of a hyperphosphorylated form of the microtubule-associated protein tau. Dementia: loss of intellectual abilities, such as memory, language or reasoning. Mild cognitive impairment: a condition in which thinking abilities are mildly impaired. It is still debated whether mild cognitive impairment is a preclinical form of AD or an entirely separate condition. A percentage of patients diagnosed with mild cognitive impairment will eventually go on to develop AD. Neural network: an entity made up of interconnected units (‘neurons’) in which weighted (‘synaptic’) interconnections control the degree of unit activation. Neural networks can be biological (i.e. composed of real neurons). However, the term is used commonly to apply to artificial or theoretical structures modelled on a computer (an artificial neural network). Synaptic plasticity: the ability of a connection or synapse between two neurons to change in signal strength. Synaptic plasticity provides the basis for all types of learning and memory. Long-term potentiation and long-term depression are two types of synaptic plasticity. Synaptic scaling: a form of synaptic plasticity in which homeostatic mechanisms compensate for changes in synaptic signalling by altering either the level of postsynaptic receptors or the efficacy of release of presynaptic neurotransmitters. b-amyloid protein (Ab): a small neurotoxic polypeptide that is the major constituent of amyloid deposits in the AD brain. Accumulation of Ab in the brain is thought to be the key event that causes Alzheimer’s disease. Ab can aggregate to form high molecular-weight amyloid fibrils. However, it is now recognized that low molecular-weight oligomeric forms of Ab are probably the most neurotoxic species.
1471-4914/$ – see front matter ß 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.molmed.2007.12.006 Available online 11 February 2008
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Opinion pathology is often found initially in the trans-entorhinal cortex and then it spreads to the hippocampus and neocortex. As the disease progresses further, association areas of the neocortex become involved. Motor and visual cortices are often spared until the later stages of the disease [3]. The cognitive decline in AD is also correlated with accumulation of b-amyloid protein (Ab) in the brain. Ab is produced by proteolytic cleavage of the b-amyloid precursor protein (APP), which is an integral type I transmembrane glycoprotein [4]. The b-secretase (BACE1) cleaves APP at a site within the ectodomain, close to the membrane, to yield a 99-residue C-terminal fragment (C99). This fragment is further cleaved by the g-secretase complex to yield Ab and a smaller APP intracellular domain fragment (AICD) (Figure 1). Ab can aggregate spontaneously and it is now generally accepted that a build-up of oligomeric Ab is a major contributor to the neurodegeneration in AD [5]. Oligomeric Ab is toxic to neurons because it can disrupt a wide variety of signal-transduction pathways. For example, Ab can alter calcium homeostasis, increase the level of reactive oxygen species, induce endoplasmic reticulum stress and activate kinases, caspases and calpains [6]. Among these effects, disruption of calcium homeostasis is one of the most important factors for neurotoxicity [7] because calcium influx can alter synaptic function and activate cell-death pathways [8]. Some regions of the brain are more susceptible to Ab toxicity than others [9]. One possible explanation for this variability is that there are regional intrinsic factors that regulate neuronal susceptibility to toxicity [9]. For example, Cecci et al. [10] suggest that different intrinsic abilities to buffer cytoplasmic calcium might be one factor that determines the degree of Ab toxicity. Alternatively, the differential susceptibility might be owing to extrinsic factors, such as the number and type of synaptic inputs that neurons receive in vivo. For example, intracellular calcium levels are controlled by neuronal excitability, which, in turn, is related to the total input neurons receive from other excitatory and inhibitory neurons.
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Although studies over the last few years have gone a long way toward explaining some of the basic biochemical mechanisms of AD, the precise mechanisms that drive disease progression and that define the differential susceptibility of certain neurons or brain regions to neurodegeneration have, until recently, remained obscure. AD and synaptic plasticity It is increasingly recognized that a defect in synaptic plasticity underlies the cognitive decline in AD [6,11]. Neuropathological studies suggest that synapse loss (or dysfunction), rather than cell death, is closely correlated to cognitive decline [12]. The accumulation of Ab in the AD brain is associated with a loss of functional of synapses and with neuritic dystrophy. These synaptic changes are likely to have profound effects on learning and memory [6,11,12]. A central characteristic of Ab toxicity is that it causes a decrease in neuronal excitability. For example, recent studies [13] suggest that Ab induces loss of a-amino-3hydroxy-5-methylisoxazole-4-propionic acid (AMPA) receptors. This decrease in AMPA receptors underlies Ab-induced synaptic depression and dendritic-spine loss [13]. Ab-induced synaptic depression can lead to disruption of long-term potentiation, which is an important form of synaptic plasticity associated with learning and memory [14]. On the basis of these studies [6,11–14], it is now clear that synaptic dysfunction is the key event in AD pathogenesis. A neural network perspective on dementia The effects of Ab on synaptic function and cognition cannot be understood solely at the level of a single synapse. The contribution of Ab to synaptic dysfunction and to cognitive decline can only be understood using broad, integrated approaches, in which the effects of Ab on assemblies of many networked neurons are examined. For example, cognitive decline in AD can be assessed using a computational approach. Many aspects of memory and learning can be modelled in relatively simple neural networks, known as Hopfield-type attractor neural
Figure 1. Generation of Ab from APP and the mechanism of Ab neurotoxicity. Ab is formed by the sequential actions of the b- and g-secretases, which cleave APP in a region overlapping the transmembrane domain. Cleavage of APP by the b-secretase (BACE1) results in a 99-amino acid residue C-terminal fragment (C99), which is further cleaved by the g-secretase to yield Ab. Ab can aggregate into dimers and low molecular-weight diffusible oligomeric species, which are thought to be the most neurotoxic species. Oligomeric forms of Ab can disrupt calcium homeostasis, induce ER stress, lead to the formation of reactive oxygen species (ROS) and alter tau phosphorylation and mitogen-activated protein (MAP) kinase and p21 pathways [6,43,44]. Amyloid plaques can also form from higher molecular-weight fibrils but they are not considered to be the most toxic form of Ab.
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Opinion networks, which are assemblies of model neurons connected by synapses [15]. These attractor neural networks can be trained to learn and store memories in a fashion similar to that which occurs in vivo. Experiments with artificial neural networks demonstrate that synaptic dysfunction causes severe memory loss [16]. However, although the ablation of synapses in artificial neural networks can cause memory loss, the pattern of amnesia does not completely mimic that which is seen in neurodegenerative diseases [16]. In neural network models, memory is often preserved until a critical percentage of loss is reached, whereupon memory loss is sudden and catastrophic. However, in AD, memory loss is gradual. Recent memories are often lost first, whereas older, more consolidated, memories remain relatively preserved until the disease progresses. To mimic the type of amnesia that occurs in AD, a mechanism of synaptic compensation or scaling has to be introduced into neural networks. In these models, loss of signal strength at a particular synapse is compensated for by an increase in signalling as the number of synaptic inputs decreases [16]. The fact that the introduction of such a mechanism can reproduce the same gradual decline that is seen in AD supports the notion that synaptic scaling is an important phenomenon in AD. Synaptic scaling is a well documented type of synaptic plasticity that involves homeostatic changes at synapses, which maintain signal strength when input is either increased or decreased [17,18]. Synaptic scaling can be effected by several different mechanisms (Figure 2). In mammalian central neurons, raising or lowering activity results in a respective decrease or increase in the number of postsynaptic AMPA receptors. These changes are accom-
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panied by changes in the turnover of AMPA receptorassociated proteins. Synaptic scaling can also be regulated presynaptically by changing the efficacy of neurotransmitter release [17,18]. For example, mutant Drosophila, which overexpress the cell adhesion molecule fasciclin II, have hyperinnervated tissues in which synaptic efficacy (the probability of acetylcholine release at cholinergic synapses) is decreased [19,20]. By contrast, hypoinnervated synapses upregulate the level of cholinergic receptors. Brain-derived neurotrophic factor (BDNF) is thought to have an important role in synaptic plasticity and homeostasis in the cortex [21,22]. BDNF is released from cortical pyramidal neurons in an activity-dependent fashion and, on release, BDNF can act through two different mechanisms to regulate synaptic activity. Under conditions of decreased pyramidal neuron activity, there is a coordinated decrease in BDNF release, which leads to an increase in signalling strength among pyramidal neurons (Figure 2). By contrast, an increase in neuronal activity leads to an increase in extracellular BDNF, which causes inhibitory interneurons to fire and leads to the inhibition of excitatory signalling among pyramidal neurons [21,22]. Recent studies [23,24] have shown that tumour necrosis factor (TNF)-a also regulates synaptic scaling (Figure 2). Astrocyte-derived TNF-a can regulate neuronal excitability through an action on AMPA-receptor trafficking. Stellwagen et al. [23,24] have shown that a decrease in neuronal activity can result in an increase in the release of glial TNF-a, which, in turn, can act on the neuronal plasma membrane to increase the number of AMPA receptors. This increase in receptor density leads to a compensatory increase in excitability (scaling).
Figure 2. Possible mechanisms of synaptic scaling in the AD brain. In response to Ab-induced synaptic dysfunction, neurons can regulate signalling homeostatically through several different mechanisms. Oligomeric Ab causes dendritic abnormalities and leads to a decrease in signalling through the downregulation of AMPA receptors ( ). Under conditions of reduced neuronal activity, levels of BDNF are decreased, causing an increase in neuronal excitability in adjacent healthy cells (+). TNF-a is released from astrocytes in response to decreased neuronal activity and also causes an increase in excitation in adjacent neurons (+). In AD, an Ab-induced decrease in cortical synaptic activity might cause an increase in a7 nAChRs and might lead to increased production of acetylcholine (ACh) in basal forebrain cholinergic neurons (BFC). Abbreviation: AMPAR, AMPA receptor.
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Opinion Synaptic scaling mechanisms in AD There is evidence that synaptic scaling occurs in AD. Because Ab neurotoxicity causes synaptic loss or dystrophy, synaptic scaling is likely to have an important role in maintaining signal strength in the remaining healthy neurons. For example, recent studies demonstrate that abnormal excitatory neuronal activity occurs in association with Ab-induced changes to hippocampal circuits in a transgenic mouse model of AD [25]. Increased activity of basal forebrain cholinergic neurons might be one mechanism that contributes to synaptic scaling in the AD brain [26]. Although a decrease in cholinergic activity in the brain is viewed generally as contributing to cognitive decline in AD, cholinergic activity might be increased during the very early (preclinical) stages of the disease [27]. A decrease in cholinergic activity might occur only in later stages of AD when clinical symptoms become apparent [26]. Studies by DeKosky et al. [27] show that, in cases of mild-cognitive impairment, levels of choline acetyltransferase (ChAT) are increased in the brain of individuals who are likely to develop early-stage AD. Studies of APP-transgenic mice support the view that cholinergic activity might be activated as a consequence of Ab accumulation in the brain. APP transgenic mice show elevated levels of the cholinergic markers choline acetyltransferase (ChAT), acetylcholinesterase (AChE) and the a7 nicotinic acetylcholine receptor (a7 nAChR) around amyloid plaques without apparent loss of cholinergic cell bodies [26,28–31]. For both AChE [32] and the a7 nAChR [30], this increase occurs at a very early stage in the development of transgenic mice, well before amyloidplaque formation, suggesting that it might be related to the overproduction of oligomeric Ab [32]. BDNF and TNF-a might also contribute to synaptic scaling in AD. BDNF levels might be decreased in AD [33,34], possibly as a consequence of an Ab-induced decrease in neuronal activity. Significantly, this decrease in BDNF might occur very early in the course of the disease because a decline in BDNF levels can also be detected in patients with mild cognitive impairment [35]. In contrast to BDNF, which is an inhibitor of neuronal excitability, an increase in secreted TNF-a leads to an increase in neuronal excitability. Indeed, several studies have found that the cytokine is increased in post-mortem AD tissue [36,37]. Although this increase is presumed generally to be related to the cytokine’s role in inflammation, a direct role in synaptic scaling also seems likely. The source of the TNF-a is unclear because it could be derived either from astrocytes or microglia. However, interestingly, TNF-a is also increased in APP-transgenic mice, in which the overall amount of microglial activation and inflammatory change is relatively low [38]. In summary, these studies provide clear evidence that, in AD, or in transgenic mouse models of AD, synaptic hypoactivity caused by Ab neurotoxicity results in several compensatory changes that are consistent with synaptic scaling. Do homeostatic mechanisms drive disease progression in AD? Although synaptic scaling might help to preserve memory in the short term, it might have long-term effects that lead to the spread of the disease to otherwise healthy neurons in 106
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the network. An Ab-induced decrease in synaptic signalling should cause a compensatory increase (scaling) in the excitability of adjacent healthy neurons. The increase in excitability would, in turn, be expected to raise intracellular calcium levels in the healthy neurons that are connected within the same network. Because calcium is a key mediator of Ab neurotoxicity [7,39,40], an increase in cytosolic calcium could increase the vulnerability of the healthy neurons to Ab toxicity [10] (Figure 3). In synaptic scaling, lowered input from one neuron within a network is compensated for by an increase in presynaptic neurotransmitter (glutamate) release from adjacent neurons within the network. This increase in glutamate release causes increased excitation, which leads to an increase in cytosolic calcium levels within the adjacent neurons. Because neural networks gravitate towards a relatively stable (attractor) state in which multiple neurons fire within the network at the same time [15], the compensatory increase in excitation is shared among
Figure 3. A hypothetical model of synaptic scaling and disease progression in AD. AD is triggered by a combination of genetic and environmental risk factors that, in combination with age, lead to an imbalance in Ab or APP metabolism and an increase in oligomeric Ab species in the brain. Ab-induced neurotoxicity causes synaptic dysfunction, a consequent decrease in neuronal excitation and a compensatory increase occurs in neuronal excitation in adjacent healthy neurons (synaptic scaling). This increase in excitation and the subsequent rise in cytosolic calcium in these healthy neurons lead to an increased susceptibility to Ab toxicity.
Opinion many neurons within the network. As shown by Cecci et al. [10], an increase in cytosolic calcium might raise the susceptibility of neurons to Ab toxicity. Therefore, as neurons within a network compensate for the decreased output of degenerating neurons within the network, their own susceptibility to toxicity increases. According to this model, in response to Ab-induced synaptic loss in one region, increased neurotransmitter release occurs from adjacent healthy neurons, exciting many healthy neurons within the network, destabilizing calcium homeostasis and rendering the neurons vulnerable to toxicity. As these healthy neurons degenerate, other neurons in the network become susceptible and so neurodegeneration progresses (Figure 3). Does a neural network model explain the pattern of neurodegeneration? On the basis of the model proposed in Figure 3, it is possible to explain some features of AD neuropathology. First, the pattern of neurodegeneration in AD approximately follows the circuitry involved in memory processing [3]. Neurons of the trans-entorhinal cortex send fibres through the perforant pathway to synapse onto granule cells in the dentate gyrus of the hippocampus. These cells then send mossy fibre axons to the CA3 region and CA1 region of the hippocampus, which make connections with cortical pyramidal neurons. In AD, tangle neuropathology follows a similar pathway because it is often seen first in the transentorhinal cortex and then in the hippocampus [3], before spreading to the associated areas of the cortex. The model predicts that excitatory neurons, which are highly networked, would be more vulnerable than other neurons, which are less highly networked. Indeed, pyramidal neurons, which are predominantly glutamatergic and which make multiple synaptic connections within the cortex, are particularly vulnerable in AD [2,3,41]. Similarly, non-glutamatergic neurons, which increase their excitation state in response to Ab neurotoxicity, should also be susceptible to Ab toxicity. This might explain why cholinergic neurons are vulnerable in AD [42]. Increased cholinergic excitation in response to Ab-induced synaptic loss within the cortex [26,27] could increase the susceptibility of cholinergic neurons to Ab toxicity. Thus, an increase in cholinergic activity in the early stages of the disease would be followed by cholinergic loss in the later stages, exactly as has been described [26,27]. Therefore, we can conclude that the pattern of neurodegeneration in the AD brain is, at the very least, consistent with a model in which synaptic scaling is the driving force for disease progression. Synaptic scaling: a target for AD therapy? It is worth considering whether synaptic scaling mechanisms might be a valid target for AD therapy. Most of the current effort in the field of AD therapeutics has been directed at developing drugs that inhibit the production, aggregation, clearance or neurotoxicity of Ab [43]. However, inhibition of disease progression could also be a strategy for therapy. If synaptic scaling drives the spread of neurodegeneration, then it might be possible to slow down the spread of the disease. For example, it might be
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Box 1. Outstanding questions What are the major unidentified mechanisms of synaptic scaling in the brain? To what extent does synaptic scaling contribute to the overall cognitive profile in AD? How do drugs that block the neurotoxic effects of Ab on isolated neurons in culture affect the function of networked neurons? Are the mechanisms of synaptic scaling useful targets for the development of drugs that block the progression of A D or will drugs acting on these targets adversely affect cognition?
possible to administer BDNF or to inhibit TNF-a as a means of inhibiting disease progression. However, synaptic scaling is a fundamental mechanism that helps to preserve the function of neural networks [16]. Therefore, although inhibition of synaptic scaling might help to decrease the spread of neurodegeneration, it might also increase synaptic depression and cognitive dysfunction, at least in the short term. Thus, long-term benefits of such an approach might have to be weighed against shortterm disadvantages. The potential of this sort of strategy will be difficult to assess without clinical trial data. Concluding remarks It is now becoming clear that, to understand the mechanism of disease causation in AD, we need to understand how Ab affects neural networks. Although Ab has many biochemical effects, some of these effects might contribute more than others to cognitive dysfunction. Synaptic scaling undoubtedly has an important role in helping to preserve the activity of neural networks in the AD brain (Box 1). However, as described here, it seems likely that synaptic scaling might also contribute to the spread of neurodegeneration in the brain and to the progression of the disease. Whether mechanisms of synaptic scaling will prove to be useful targets for drug development is unclear because inhibition of synaptic scaling might adversely affect cognitive performance. Nevertheless, even if synaptic scaling is not an appropriate therapeutic target, the use of an integrated (neural network) approach to assess drug efficacy seems likely to be an appropriate strategy for other therapeutic targets. We are now entering an exciting period in AD research. Eventually, it might be possible not only to design drugs that block Ab toxicity but also to examine how these drugs broadly affect neural network function. Such a strategy might help to speed up the process of drug-candidate optimization and provide a more rational basis for moving forward to clinical trials. This might be the next big step in AD drug development. References 1 Storey, E. et al. (2001) The neuropsychological diagnosis of Alzheimer’s disease. J. Alzheimers Dis. 3, 261–285 2 Probst, A. et al. (1991) Alzheimer’s disease: a description of the structural lesions. Brain Pathol. 1, 229–239 3 Braak, H. and Braak, E. (1995) Staging of Alzheimer’s disease-related neurofibrillary changes. Neurobiol. Aging 16, 271–278 4 Nunan, J. and Small, D.H. (2000) Regulation of APP cleavage by alpha-, beta- and gamma-secretases. FEBS Lett. 483, 6–10 5 Walsh, D.M. and Selkoe, D.J. (2007) A beta oligomers – a decade of discovery. J. Neurochem. 101, 1172–1184 107
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