- Research article
- Open Access
Imbalanced pattern completion vs. separation in cognitive disease: network simulations of synaptic pathologies predict a personalized therapeutics strategy
© Hanson and Madison; licensee BioMed Central Ltd. 2010
- Received: 20 January 2010
- Accepted: 13 August 2010
- Published: 13 August 2010
Diverse Mouse genetic models of neurodevelopmental, neuropsychiatric, and neurodegenerative causes of impaired cognition exhibit at least four convergent points of synaptic malfunction: 1) Strength of long-term potentiation (LTP), 2) Strength of long-term depression (LTD), 3) Relative inhibition levels (Inhibition), and 4) Excitatory connectivity levels (Connectivity).
To test the hypothesis that pathological increases or decreases in these synaptic properties could underlie imbalances at the level of basic neural network function, we explored each type of malfunction in a simulation of autoassociative memory. These network simulations revealed that one impact of impairments or excesses in each of these synaptic properties is to shift the trade-off between pattern separation and pattern completion performance during memory storage and recall. Each type of synaptic pathology either pushed the network balance towards intolerable error in pattern separation or intolerable error in pattern completion. Imbalances caused by pathological impairments or excesses in LTP, LTD, inhibition, or connectivity, could all be exacerbated, or rescued, by the simultaneous modulation of any of the other three synaptic properties.
Because appropriate modulation of any of the synaptic properties could help re-balance network function, regardless of the origins of the imbalance, we propose a new strategy of personalized cognitive therapeutics guided by assay of pattern completion vs. pattern separation function. Simulated examples and testable predictions of this theorized approach to cognitive therapeutics are presented.
- Down Syndrome
- Stimulus Pattern
- Angelman Syndrome
- Pattern Separation
- Pattern Completion
Key Synaptic Phenotypes in Mouse Models of Diseased Cognition
Fragile × synd.
Tsc2 KO (rat)
Diverse disease models exhibit convergent synaptic and circuit alterations
Examples of neurodevelopmental diseases that can include memory deficits, where causative genes have been identified and mouse models have been created, include Angelman syndrome, Down syndrome, Fragile × syndrome, FRAXE Syndrome, Rett Syndrome, Neurofibromatosis, Tuberous Sclerosis, and various X-linked Mental Retardations (XLMR) (Table 1, top). Transgenic mouse models have also been created that are relevant to neuropsychiatric conditions including schizophrenia, a disease where memory impairment is an important endophenotype (Table 1, middle). In addition to Alzheimer's disease, other neurodegenerative diseases often more noted for their hallmark motor symptoms, also feature important cognitive phenotypes, and mouse models of neurodegenerative conditions with memory alterations include Amyotrophic Lateral Sclerosis (ALS), Huntington's disease, Parkinson's disease, and Spinocerebellar Ataxia (SCA) (Table 1, bottom). Together, these diverse mouse models provide a comparative window into potential substrates of memory impairment. In particular reoccurring points of pathological changes include: 1) Strength of LTP, 2) Strength of LTD, 3) Relative inhibition, and 4) Connectivity levels.
Neural Network Simulation of Autoassociation
The connectivity level of the recurrent excitatory synapses was set to a given value (for example 50%; Fig.3A), which was implemented by constraining the average number of randomly selected postsynaptic target neurons for each presynaptic neuron.
LTP and LTD
In the absence of any stored memories, all potential synaptic connections were silent with no AMPA receptor (AMPAR) conductance. LTP was implemented by potentiating synapses between presynaptic and postsynaptic neurons that both fired action potentials within the time window of a single gamma cycle during pattern storage. The strength of LTP was defined by the maximal excitatory synaptic conductance variable, gMaxAMPA. LTD was implemented by depressing synapses between presynaptic neurons and postsynaptic neurons that were active during storage of different stimulus patterns within a set of simultaneously stored patterns. The parameter, γLTD, defined the strength of LTD (see methods, Fig3B). The result of these plasticity mechanisms is that as larger numbers of patterns are simultaneously stored in the network, more silent synapses are potentiated, and more of those potentiated synapses are also reduced in strength by LTD due to accumulated overlap of the stimulus patterns (Fig.3C,D).
The strength of the feedback inhibitory conductance, gGABA, received by each pyramidal neuron was set relative to the average maximal excitatory conductance received by excitatory neurons, as defined by the Inhibition Ratio variable (see methods). Thus, the strength of LTP, LTD, connectivity, and inhibition could all be varied to simulate pathological conditions or therapeutic modulation via manipulation of single variables.
Measuring Pattern Completion and Pattern Separation
Wild-type Synaptic Properties
While biologically plausible values of each synaptic property serve as a good starting point for the baseline model, the necessary simplifications of the model make it difficult to predict exact wild-type values of synaptic properties. For example, relative inhibition is specified using the ratio of GABAA receptor (GABAAR) to AMPAR conductance in each spatially reduced neuron. However the dynamics of synaptic interaction in spatially complex neurons, can enhance the ability of inhibition to oppose excitation [21, 22], suggesting amplified GABAAR to AMPAR conductance ratios may be needed in the simplified simulation. Because of such considerations, we decided to determine wild-type network parameters via an empirical assessment of storage and recall performance. To avoid focusing on a non-unique set of optimal parameters we explored network performance over a broad range of parameter space by creating a database with 960,000 combinations of parameter values (gMaxAMPA, γLTD, Relative Inhibition, and Connectivity %) and memory storage conditions (see methods). While different metrics could be used to assess network performance, we defined optimal networks based on the maximal error rate, which was calculated as the greater of pattern completion or pattern separation error rates. Using this measure, pattern completion and pattern separation errors were equal in their ability to limit network performance.
Pattern Separation and Completion with Single Synaptic Pathologies
Interaction Between Multiple Synaptic Alterations
Substrates of Imbalanced Network Performance
To intuitively understand the basis of the interactions between the different points of synaptic pathology it is important to appreciate that the underlying substrates of successful pattern completion (and failed separation) necessarily converge at action potential generation, while essential to pattern separation (and failed completion) is lack of inappropriate spiking. Thus while the effects of LTP, LTD, inhibition and connectivity levels all impinge on the ability of synaptic inputs to drive output spiking, other perturbations that also effect input-output coupling will also alter the balance between completion and separation. For example, while beyond the scope of the present analysis, neuromodulatory influences that alter intrinsic excitability are also aberrant in disease states and are expected to alter pattern separation and completion functions [19, 31].
Theory of Personalized Therapeutics
That the four examined points of synaptic pathology all further converge in causing one of two distinct network imbalances provides a potential point of therapeutic intervention (Fig.8B). In particular, a pattern completion bias predicts one direction of therapeutic manipulation for each synaptic property, while a separation bias predicts therapeutic value for the opposite directions of manipulation (Fig.8C). If pattern completion vs. pattern separation performance were to be extensively evaluated in cognitive disease, two general possibilities exist: 1) Some causes of disease will consistently involve a separation bias, while other causes will always involve a completion bias, 2) Even within the same disease, the complex interaction of genetics and environment with disease progression will result in some patients with a completion bias and others with a separation bias.
Example Cross-Therapeutic Predictions for Overall Averages of Disease Populations
Disease or model
Memory rescue observed with
Other predicted targets
↑ LTP, ↑ connectivity, ↓ LTD
Down synd. mouse
↓ LTP, ↓ connectivity, ↑ LTD
Fragile × mouse
↓ Inhibition, ↓ LTD, ↑ connectivity
How pattern separation and pattern completion deficits in neural networks will read out in indirect behavioral measurements such as tests of prospective interference may be complicated, but could be determined by experiments correlating behavior and read-outs of neural network separation/completion function. However, the promise for implementing such an approach of assay-based therapeutic prescription is good, since non-invasive touchscreen-based memory tests already exist, including explicit measurement of pattern separation function, in both mouse models  and human patients [42, 43]. While higher-order processing strategies could confound behavioral read-outs of basic network functions , direct assay of autoassociative function can be performed in rodent models using recordings of neuronal ensemble activity [4–6], and has been demonstrated in humans using functional imaging . Although the current simulations focused on autoassociative function in the key region of CA3, interactions across multiple neural circuits are known to underlie cognitive behaviors like learning and memory. Nonetheless, much of the cortex is organized in recurrent circuits, and could process and store information in a sparser but analogous manner to CA3. Therefore, especially in cases contributed to by genetic disruptions with potentially widespread effects, the predicted manipulations aimed at rebalancing function could be broadly beneficial across neural networks underlying a pathologically extreme cognitive style. Ultimately tests of the types of predictions outlined in Fig.8 and detailed above, will support or refute this theorized approach of personalized cognitive therapeutics.
Network simulations were constructed using NeuroConstruct  and simulations were run using Neuron . Custom Matlab (Mathworks) scripts were used to generate stimulus pattern sets, calculate synaptic plasticity, and analyze simulation output. Each neuron was modeled as an isopotential sphere with a radius of 10 μm and had a membrane capacitance of 1.0 μF/cm2 and contained a leak conductance with Eleak = -67 mV (GLeak = 0.1 μS/cm2), and Fast Na+ (GNa = 100 μS/cm2), and Delayed Rectifier K+ (GK = 80 μS/cm2) conductances, based on a reduced model of hippocampal rhythm generation  (see Fig.2). Na+ current was calculated as: INa = GNam3h(Vm-ENa), with ENa = 90 mV, K+ current was calculated as: IK = GKn4(Vm-EK), with EK = -100. AMPA conductances of excitatory synapses had time courses described by GAMPA = exp(-t/tau2) - exp(-t/tau1), with tau1 = 1 and tau2 = 4, EAMPA = 0 mV, and GABAA conductances had time courses described by: GGABA = exp(-t/tau2)-exp(-t/tau1), with tau1 = 2 and tau2 = 8, EGABA = -80 mV. Synaptic delays were 1 ms and axonal conduction times were considered negligible.
For each simulation of a given connectivity level, three different connectivity profiles were generated using different random seeds in NeuroConstruct. The average performance of simulations using 10 different random sets of memories in each connectivity profile was calculated. Error rates are presented as mean ± SEM of the average performance in three connectivity profiles.
The weight of associational connections (W) following synaptic plasticity was determined using an adaptation of the equation used in the model of autoassociation we based our simulations on : Wij = nij11/(nij11*γ11+nij10*γ10+nij01* γ01), where nij11 is the number of patterns where presynaptic(i) and postsynaptic(j) neurons fire together, and nij10 and nij01 are the number of patterns where presynaptic or postsynaptic neurons fire independently within a set of stored patterns. For simplicity in our implementation, γ11 was set to a value of 1, and γ10 and γ01 shared the same value, γLTD. This yielded the equation; Wij= gMaxAMPA *nij11/(nij11+(nij10+nij01)*γLTD), where the normalized strength of maximal potentiation, gMaxAMPA, defines the strength of LTP, and the single parameter, γLTD, defines the strength of LTD (Fig.3B).
The strength of the excitatory synapses onto the inhibitory neuron was set to 90% of gMaxAMPA. The strength of inhibitory synaptic conductances in the network was set relative to the maximal total excitation received by a pyramidal neuron during a stimulus pattern involving 10 neurons such that: gGABA = gMaxAMPA*10*Connectivity Level*Relative Inhibition.
Parameter permutations consisting of 20 connectivity levels spaced between 5 and 100%, 20 gMaxAMPA values spaced between 2.78 and 55.56 nS, 20 logarithmically spaced Inhibition Ratio values between 0.01 and 100 (corresponding to 1.83 fold increments), and γLTD values logarithmically spaced between 0.1 and 10 (corresponding to 1.27 fold increments) were evaluated. For each of these 160,000 permutations of synaptic properties, 6 different sized pattern sets were assessed using multiple connectivity and stimulus pattern random seed conditions (see connectivity), for a total of 180 simulations per parameter combination. To facilitate the required large number of simulations, an approximation was made, based on the fact that the presence or absence of a spike in each individual neuron (which determines pattern storage or pattern completion success or failure), is determined entirely by, 1) the total excitatory conductance resulting from the properties of synaptic plasticity in the context of connectivity, and 2) the strength of inhibition. Therefore, a table of spike thresholds as a function of both total excitatory and inhibitory conductance received by a neuron was generated from a set of simulations with systematic variations in these properties. During creation of the database, pattern separation and pattern completion errors were assessed based on neuronal firing patterns determined by comparing values of total excitatory and inhibitory conductances in each neuron to the table of firing thresholds. Validation of this efficiency measure was performed by directly comparing several key parameter combinations using the threshold-table approximation with full explicit simulations. Optimal balanced networks were defined as the parameter combinations with the lowest maximal error (the higher error in either pattern separation or pattern completion), thus reflecting an even breakdown of pattern separation and completion for a given number of stored patterns.
This work was supported by grants from the National Institute of Mental Health (MH065541) and by The G. Harold and Leila Y. Mathers Charitable Foundation.
- Marr D: Simple memory: a theory for archicortex. Philos Trans R Soc Lond B Biol Sci. 1971, 262: 23-81. 10.1098/rstb.1971.0078.View ArticlePubMedGoogle Scholar
- McNaughton BL, Morris RGM: Hippocampal synaptic enhancement and information storage within a distributed memory system. Trends Neurosci. 1987, 10: 408-415. 10.1016/0166-2236(87)90011-7.View ArticleGoogle Scholar
- Treves A, Rolls ET: Computational analysis of the role of the hippocampus in memory. Hippocampus. 1994, 4: 374-391. 10.1002/hipo.450040319.View ArticlePubMedGoogle Scholar
- Lee I, Yoganarasimha D, Rao G, Knierim JJ: Comparison of population coherence of place cells in hippocampal subfields CA1 and CA3. Nature. 2004, 430: 456-459. 10.1038/nature02739.View ArticlePubMedGoogle Scholar
- Leutgeb S, Leutgeb JK, Treves A, Moser MB, Moser EI: Distinct ensemble codes in hippocampal areas CA3 and CA1. Science. 2004, 305: 1295-1298. 10.1126/science.1100265.View ArticlePubMedGoogle Scholar
- Vazdarjanova A, Guzowski JF: Differences in hippocampal neuronal population responses to modifications of an environmental context: evidence for distinct, yet complementary, functions of CA3 and CA1 ensembles. J Neurosci. 2004, 24: 6489-6496. 10.1523/JNEUROSCI.0350-04.2004.View ArticlePubMedGoogle Scholar
- Bakker A, Kirwan CB, Miller M, Stark CE: Pattern separation in the human hippocampal CA3 and dentate gyrus. Science. 2008, 319: 1640-1642. 10.1126/science.1152882.PubMed CentralView ArticlePubMedGoogle Scholar
- Guzowski JF, Knierim JJ, Moser EI: Ensemble dynamics of hippocampal regions CA3 and CA1. Neuron. 2004, 44: 581-584. 10.1016/j.neuron.2004.11.003.View ArticlePubMedGoogle Scholar
- McClelland JL, Goddard NH: Considerations arising from a complementary learning systems perspective on hippocampus and neocortex. Hippocampus. 1996, 6: 654-665. 10.1002/(SICI)1098-1063(1996)6:6<654::AID-HIPO8>3.0.CO;2-G.View ArticlePubMedGoogle Scholar
- O'Reilly RC, Rudy JW: Conjunctive representations in learning and memory: principles of cortical and hippocampal function. Psychol Rev. 2001, 108: 311-345. 10.1037/0033-295X.108.2.311.View ArticlePubMedGoogle Scholar
- O'Reilly RC, McClelland JL: Hippocampal conjunctive encoding, storage, and recall: avoiding a trade-off. Hippocampus. 1994, 4: 661-682. 10.1002/hipo.450040605.View ArticlePubMedGoogle Scholar
- Bennett MR, Gibson WG, Robinson J: Dynamics of the CA3 pyramidal neuron autoassociative memory network in the hippocampus. Philos Trans R Soc Lond B Biol Sci. 1994, 343: 167-187. 10.1098/rstb.1994.0019.View ArticlePubMedGoogle Scholar
- de Almeida L, Idiart M, Lisman JE: Memory retrieval time and memory capacity of the CA3 network: role of gamma frequency oscillations. Learn Mem. 2007, 14: 795-806. 10.1101/lm.730207.PubMed CentralView ArticlePubMedGoogle Scholar
- Jensen O, Idiart MA, Lisman JE: Physiologically realistic formation of autoassociative memory in networks with theta/gamma oscillations: role of fast NMDA channels. Learn Mem. 1996, 3: 243-256. 10.1101/lm.3.2-3.243.View ArticlePubMedGoogle Scholar
- Gloveli T, Dugladze T, Rotstein HG, Traub RD, Monyer H, Heinemann U, Whittington MA, Kopell NJ: Orthogonal arrangement of rhythm-generating microcircuits in the hippocampus. Proc Natl Acad Sci USA. 2005, 102: 13295-13300. 10.1073/pnas.0506259102.PubMed CentralView ArticlePubMedGoogle Scholar
- Hasselmo ME, Barkai E: Cholinergic modulation of activity-dependent synaptic plasticity in the piriform cortex and associative memory function in a network biophysical simulation. J Neurosci. 1995, 15: 6592-6604.PubMedGoogle Scholar
- Menschik ED, Finkel LH: Cholinergic neuromodulation and Alzheimer's disease: from single cells to network simulations. Prog Brain Res. 1999, 121: 19-45. full_text.View ArticlePubMedGoogle Scholar
- Hasselmo ME: A model of episodic memory: mental time travel along encoded trajectories using grid cells. Neurobiol Learn Mem. 2009, 92: 559-573. 10.1016/j.nlm.2009.07.005.PubMed CentralView ArticlePubMedGoogle Scholar
- Hasselmo ME, Wyble BP: Free recall and recognition in a network model of the hippocampus: simulating effects of scopolamine on human memory function. Behav Brain Res. 1997, 89: 1-34. 10.1016/S0166-4328(97)00048-X.View ArticlePubMedGoogle Scholar
- Norman KA, O'Reilly RC: Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach. Psychol Rev. 2003, 110: 611-646. 10.1037/0033-295X.110.4.611.View ArticlePubMedGoogle Scholar
- Mehaffey WH, Doiron B, Maler L, Turner RW: Deterministic multiplicative gain control with active dendrites. J Neurosci. 2005, 25: 9968-9977. 10.1523/JNEUROSCI.2682-05.2005.View ArticlePubMedGoogle Scholar
- Prescott SA, De Koninck Y: Gain control of firing rate by shunting inhibition: roles of synaptic noise and dendritic saturation. Proc Natl Acad Sci USA. 2003, 100: 2076-2081. 10.1073/pnas.0337591100.PubMed CentralView ArticlePubMedGoogle Scholar
- Amaral DG, Ishizuka N, Claiborne B: Neurons, numbers and the hippocampal network. Prog Brain Res. 1990, 83: 1-11. full_text.View ArticlePubMedGoogle Scholar
- Hanson JE, Blank M, Valenzuela RA, Garner CC, Madison DV: The functional nature of synaptic circuitry is altered in area CA3 of the hippocampus in a mouse model of Down's syndrome. J Physiol. 2007, 579: 53-67. 10.1113/jphysiol.2006.114868.PubMed CentralView ArticlePubMedGoogle Scholar
- Hanson JE, Madison DV: Presynaptic FMR1 genotype influences the degree of synaptic connectivity in a mosaic mouse model of fragile × syndrome. J Neurosci. 2007, 27: 4014-4018. 10.1523/JNEUROSCI.4717-06.2007.View ArticlePubMedGoogle Scholar
- Knafo S, Alonso-Nanclares L, Gonzalez-Soriano J, Merino-Serrais P, Fernaud-Espinosa I, Ferrer I, Defelipe J: Widespread Changes in Dendritic Spines in a Model of Alzheimer's Disease. Cereb Cortex. 2008Google Scholar
- Ivanco TL, Greenough WT: Altered mossy fiber distributions in adult Fmr1 (FVB) knockout mice. Hippocampus. 2002, 12: 47-54. 10.1002/hipo.10004.View ArticlePubMedGoogle Scholar
- Mineur YS, Sluyter F, de Wit S, Oostra BA, Crusio WE: Behavioral and neuroanatomical characterization of the Fmr1 knockout mouse. Hippocampus. 2002, 12: 39-46. 10.1002/hipo.10005.View ArticlePubMedGoogle Scholar
- Alpar A, Ueberham U, Seeger G, Arendt T, Gartner U: Effects of wild-type and mutant human amyloid precursor protein on cortical afferent network. Neuroreport. 2007, 18: 1247-1250. 10.1097/WNR.0b013e3282202829.View ArticlePubMedGoogle Scholar
- Palop JJ, Chin J, Roberson ED, Wang J, Thwin MT, Bien-Ly N, Yoo J, Ho KO, Yu GQ, Kreitzer A, et al.: Aberrant excitatory neuronal activity and compensatory remodeling of inhibitory hippocampal circuits in mouse models of Alzheimer's disease. Neuron. 2007, 55: 697-711. 10.1016/j.neuron.2007.07.025.View ArticlePubMedGoogle Scholar
- Hasselmo ME, Schnell E, Barkai E: Dynamics of learning and recall at excitatory recurrent synapses and cholinergic modulation in rat hippocampal region CA3. J Neurosci. 1995, 15: 5249-5262.PubMedGoogle Scholar
- Yoshiike Y, Kimura T, Yamashita S, Furudate H, Mizoroki T, Murayama M, Takashima A: GABA(A) receptor-mediated acceleration of aging-associated memory decline in APP/PS1 mice and its pharmacological treatment by picrotoxin. PLoS ONE. 2008, 3: e3029-10.1371/journal.pone.0003029.PubMed CentralView ArticlePubMedGoogle Scholar
- Rueda N, Florez J, Martinez-Cue C: Chronic pentylenetetrazole but not donepezil treatment rescues spatial cognition in Ts65Dn mice, a model for Down syndrome. Neurosci Lett. 2008, 433: 22-27. 10.1016/j.neulet.2007.12.039.View ArticlePubMedGoogle Scholar
- Fernandez F, Morishita W, Zuniga E, Nguyen J, Blank M, Malenka RC, Garner CC: Pharmacotherapy for cognitive impairment in a mouse model of Down syndrome. Nat Neurosci. 2007, 10: 411-413.PubMedGoogle Scholar
- Akbarian S: Restoring GABAergic signaling and neuronal synchrony in schizophrenia. Am J Psychiatry. 2008, 165: 1507-1509. 10.1176/appi.ajp.2008.08081225.View ArticlePubMedGoogle Scholar
- Lewis DA, Cho RY, Carter CS, Eklund K, Forster S, Kelly MA, Montrose D: Subunit-selective modulation of GABA type A receptor neurotransmission and cognition in schizophrenia. Am J Psychiatry. 2008, 165: 1585-1593. 10.1176/appi.ajp.2008.08030395.PubMed CentralView ArticlePubMedGoogle Scholar
- Hayashi ML, Rao BS, Seo JS, Choi HS, Dolan BM, Choi SY, Chattarji S, Tonegawa S: Inhibition of p21-activated kinase rescues symptoms of fragile × syndrome in mice. Proc Natl Acad Sci USA. 2007, 104: 11489-11494. 10.1073/pnas.0705003104.PubMed CentralView ArticlePubMedGoogle Scholar
- Lynch G, Kramar EA, Rex CS, Jia Y, Chappas D, Gall CM, Simmons DA: Brain-derived neurotrophic factor restores synaptic plasticity in a knock-in mouse model of Huntington's disease. J Neurosci. 2007, 27: 4424-4434. 10.1523/JNEUROSCI.5113-06.2007.View ArticlePubMedGoogle Scholar
- Simmons DA, Rex CS, Palmer L, Pandyarajan V, Fedulov V, Gall CM, Lynch G: Up-regulating BDNF with an ampakine rescues synaptic plasticity and memory in Huntington's disease knockin mice. Proc Natl Acad Sci USA. 2009, 106: 4906-4911. 10.1073/pnas.0811228106.PubMed CentralView ArticlePubMedGoogle Scholar
- van Woerden GM, Harris KD, Hojjati MR, Gustin RM, Qiu S, de Avila Freire R, Jiang YH, Elgersma Y, Weeber EJ: Rescue of neurological deficits in a mouse model for Angelman syndrome by reduction of alphaCaMKII inhibitory phosphorylation. Nat Neurosci. 2007, 10: 280-282. 10.1038/nn1845.View ArticlePubMedGoogle Scholar
- McTighe SM, Mar AC, Romberg C, Bussey TJ, Saksida LM: A new touchscreen test of pattern separation: effect of hippocampal lesions. Neuroreport. 2009, 20: 881-885. 10.1097/WNR.0b013e32832c5eb2.View ArticlePubMedGoogle Scholar
- Kirwan CB, Stark CE: Overcoming interference: an fMRI investigation of pattern separation in the medial temporal lobe. Learn Mem. 2007, 14: 625-633. 10.1101/lm.663507.PubMed CentralView ArticlePubMedGoogle Scholar
- Toner CK, Pirogovsky E, Kirwan CB, Gilbert PE: Visual object pattern separation deficits in nondemented older adults. Learn Mem. 2009, 16: 338-342. 10.1101/lm.1315109.View ArticlePubMedGoogle Scholar
- Gleeson P, Steuber V, Silver RA: neuroConstruct: a tool for modeling networks of neurons in 3D space. Neuron. 2007, 54: 219-235. 10.1016/j.neuron.2007.03.025.PubMed CentralView ArticlePubMedGoogle Scholar
- Hines ML, Carnevale NT: The NEURON simulation environment. Neural Comput. 1997, 9: 1179-1209. 10.1162/neco.1918.104.22.1689.View ArticlePubMedGoogle Scholar
- Jiang YH, Armstrong D, Albrecht U, Atkins CM, Noebels JL, Eichele G, Sweatt JD, Beaud , et al.: Mutation of the Angelman ubiquitin ligase in mice causes increased cytoplasmic p53 and deficits of contextual learning and long-term potentiation. Neuron. 1998, 21: 799-811. 10.1016/S0896-6273(00)80596-6.View ArticlePubMedGoogle Scholar
- Dindot SV, Antalffy BA, Bhattacharjee MB, Beaud , et al.: The Angelman syndrome ubiquitin ligase localizes to the synapse and nucleus, and maternal deficiency results in abnormal dendritic spine morphology. Hum Mol Genet. 2008, 17: 111-118. 10.1093/hmg/ddm288.View ArticlePubMedGoogle Scholar
- Costa AC, Grybko MJ: Deficits in hippocampal CA1 LTP induced by TBS but not HFS in the Ts65Dn mouse: a model of Down syndrome. Neurosci Lett. 2005, 382: 317-322. 10.1016/j.neulet.2005.03.031.View ArticlePubMedGoogle Scholar
- Kleschevnikov AM, Belichenko PV, Villar AJ, Epstein CJ, Malenka RC, Mobley WC: Hippocampal long-term potentiation suppressed by increased inhibition in the Ts65Dn mouse, a genetic model of Down syndrome. J Neurosci. 2004, 24: 8153-8160. 10.1523/JNEUROSCI.1766-04.2004.View ArticlePubMedGoogle Scholar
- Siarey RJ, Stoll J, Rapoport SI, Galdzicki Z: Altered long-term potentiation in the young and old Ts65Dn mouse, a model for Down Syndrome. Neuropharmacology. 1997, 36: 1549-1554. 10.1016/S0028-3908(97)00157-3.View ArticlePubMedGoogle Scholar
- Siarey RJ, Carlson EJ, Epstein CJ, Balbo A, Rapoport SI, Galdzicki Z: Increased synaptic depression in the Ts65Dn mouse, a model for mental retardation in Down syndrome. Neuropharmacology. 1999, 38: 1917-1920. 10.1016/S0028-3908(99)00083-0.View ArticlePubMedGoogle Scholar
- Belichenko PV, Masliah E, Kleschevnikov AM, Villar AJ, Epstein CJ, Salehi A, Mobley WC: Synaptic structural abnormalities in the Ts65Dn mouse model of Down Syndrome. J Comp Neurol. 2004, 480: 281-298. 10.1002/cne.20337.View ArticlePubMedGoogle Scholar
- Kurt MA, Kafa MI, Dierssen M, Davies DC: Deficits of neuronal density in CA1 and synaptic density in the dentate gyrus, CA3 and CA1, in a mouse model of Down syndrome. Brain Res. 2004, 1022: 101-109. 10.1016/j.brainres.2004.06.075.View ArticlePubMedGoogle Scholar
- Hu H, Qin Y, Bochorishvili G, Zhu Y, van Aelst L, Zhu JJ: Ras signaling mechanisms underlying impaired GluR1-dependent plasticity associated with fragile × syndrome. J Neurosci. 2008, 28: 7847-7862. 10.1523/JNEUROSCI.1496-08.2008.PubMed CentralView ArticlePubMedGoogle Scholar
- Lauterborn JC, Rex CS, Kramar E, Chen LY, Pandyarajan V, Lynch G, Gall CM: Brain-derived neurotrophic factor rescues synaptic plasticity in a mouse model of fragile × syndrome. J Neurosci. 2007, 27: 10685-10694. 10.1523/JNEUROSCI.2624-07.2007.View ArticlePubMedGoogle Scholar
- Pilpel Y, Kolleker A, Berberich S, Ginger M, Frick A, Mientjes E, Oostra BA, Seeburg PH: Synaptic ionotropic glutamate receptors and plasticity are developmentally altered in the CA1 field of Fmr1 knockout mice. J Physiol. 2009, 587: 787-804. 10.1113/jphysiol.2008.160929.PubMed CentralView ArticlePubMedGoogle Scholar
- Huber KM, Gallagher SM, Warren ST, Bear MF: Altered synaptic plasticity in a mouse model of fragile × mental retardation. Proc Natl Acad Sci USA. 2002, 99: 7746-7750. 10.1073/pnas.122205699.PubMed CentralView ArticlePubMedGoogle Scholar
- Curia G, Papouin T, Seguela P, Avoli M: Downregulation of Tonic GABAergic Inhibition in a Mouse Model of Fragile × Syndrome. Cereb Cortex. 2008Google Scholar
- D'Antuono M, Merlo D, Avoli M: Involvement of cholinergic and gabaergic systems in the fragile × knockout mice. Neuroscience. 2003, 119: 9-13. 10.1016/S0306-4522(03)00103-9.View ArticlePubMedGoogle Scholar
- El Idrissi A, Ding XH, Scalia J, Trenkner E, Brown WT, Dobkin C: Decreased GABA(A) receptor expression in the seizure-prone fragile × mouse. Neurosci Lett. 2005, 377: 141-146. 10.1016/j.neulet.2004.11.087.View ArticlePubMedGoogle Scholar
- Gu Y, McIlwain KL, Weeber EJ, Yamagata T, Xu B, Antalffy BA, Reyes C, Yuva-Paylor L, Armstrong D, Zoghbi H, et al.: Impaired conditioned fear and enhanced long-term potentiation in Fmr2 knock-out mice. J Neurosci. 2002, 22: 2753-2763.PubMedGoogle Scholar
- Cui Y, Costa RM, Murphy GG, Elgersma Y, Zhu Y, Gutmann DH, Parada LF, Mody I, Silva AJ: Neurofibromin regulation of ERK signaling modulates GABA release and learning. Cell. 2008, 135: 549-560. 10.1016/j.cell.2008.09.060.PubMed CentralView ArticlePubMedGoogle Scholar
- Costa RM, Federov NB, Kogan JH, Murphy GG, Stern J, Ohno M, Kucherlapati R, Jacks T, Silva AJ: Mechanism for the learning deficits in a mouse model of neurofibromatosis type 1. Nature. 2002, 415: 526-530. 10.1038/nature711.View ArticlePubMedGoogle Scholar
- Guilding C, McNair K, Stone TW, Morris BJ: Restored plasticity in a mouse model of neurofibromatosis type 1 via inhibition of hyperactive ERK and CREB. Eur J Neurosci. 2007, 25: 99-105. 10.1111/j.1460-9568.2006.05238.x.View ArticlePubMedGoogle Scholar
- Asaka Y, Jugloff DG, Zhang L, Eubanks JH, Fitzsimonds RM: Hippocampal synaptic plasticity is impaired in the Mecp2-null mouse model of Rett syndrome. Neurobiol Dis. 2006, 21: 217-227. 10.1016/j.nbd.2005.07.005.View ArticlePubMedGoogle Scholar
- Moretti P, Levenson JM, Battaglia F, Atkinson R, Teague R, Antalffy B, Armstrong D, Arancio O, Sweatt JD, Zoghbi HY: Learning and memory and synaptic plasticity are impaired in a mouse model of Rett syndrome. J Neurosci. 2006, 26: 319-327. 10.1523/JNEUROSCI.2623-05.2006.View ArticlePubMedGoogle Scholar
- Dani VS, Chang Q, Maffei A, Turrigiano GG, Jaenisch R, Nelson SB: Reduced cortical activity due to a shift in the balance between excitation and inhibition in a mouse model of Rett syndrome. Proc Natl Acad Sci USA. 2005, 102: 12560-12565. 10.1073/pnas.0506071102.PubMed CentralView ArticlePubMedGoogle Scholar
- Belichenko PV, Wright EE, Belichenko NP, Masliah E, Li HH, Mobley WC, Francke U: Widespread changes in dendritic and axonal morphology in Mecp2-mutant mouse models of Rett syndrome: evidence for disruption of neuronal networks. J Comp Neurol. 2009, 514: 240-258. 10.1002/cne.22009.View ArticlePubMedGoogle Scholar
- Smrt RD, Eaves-Egenes J, Barkho BZ, Santistevan NJ, Zhao C, Aimone JB, Gage FH, Zhao X: Mecp2 deficiency leads to delayed maturation and altered gene expression in hippocampal neurons. Neurobiol Dis. 2007, 27: 77-89. 10.1016/j.nbd.2007.04.005.PubMed CentralView ArticlePubMedGoogle Scholar
- von der Brelie C, Waltereit R, Zhang L, Beck H, Kirschstein T: Impaired synaptic plasticity in a rat model of tuberous sclerosis. Eur J Neurosci. 2006, 23: 686-692. 10.1111/j.1460-9568.2006.04594.x.View ArticlePubMedGoogle Scholar
- Meikle L, Pollizzi K, Egnor A, Kramvis I, Lane H, Sahin M, Kwiatkowski DJ: Response of a neuronal model of tuberous sclerosis to mammalian target of rapamycin (mTOR) inhibitors: effects on mTORC1 and Akt signaling lead to improved survival and function. J Neurosci. 2008, 28: 5422-5432. 10.1523/JNEUROSCI.0955-08.2008.PubMed CentralView ArticlePubMedGoogle Scholar
- Tavazoie SF, Alvarez VA, Ridenour DA, Kwiatkowski DJ, Sabatini BL: Regulation of neuronal morphology and function by the tumor suppressors Tsc1 and Tsc2. Nat Neurosci. 2005, 8: 1727-1734. 10.1038/nn1566.View ArticlePubMedGoogle Scholar
- Meng J, Meng Y, Hanna A, Janus C, Jia Z: Abnormal long-lasting synaptic plasticity and cognition in mice lacking the mental retardation gene Pak3. J Neurosci. 2005, 25: 6641-6650. 10.1523/JNEUROSCI.0028-05.2005.View ArticlePubMedGoogle Scholar
- D'Adamo P, Welzl H, Papadimitriou S, Raffaele di Barletta M, Tiveron C, Tatangelo L, Pozzi L, Chapman PF, Knevett SG, Ramsay MF, et al.: Deletion of the mental retardation gene Gdi1 impairs associative memory and alters social behavior in mice. Hum Mol Genet. 2002, 11: 2567-2580. 10.1093/hmg/11.21.2567.View ArticlePubMedGoogle Scholar
- Khelfaoui M, Alice P, Powell AD, Valnegri P, Cheong KW, Blandin Y, Passafaro M, Jefferys JG, Chelly J, Billuart P: Inhibition of RhoA pathway rescues the endocytosis defects in Oligophrenin1 mouse model of mental retardation. Hum Mol Genet. 2009Google Scholar
- Kvajo M, McKellar H, Arguello PA, Drew LJ, Moore H, MacDermott AB, Karayiorgou M, Gogos JA: A mutation in mouse Disc1 that models a schizophrenia risk allele leads to specific alterations in neuronal architecture and cognition. Proc Natl Acad Sci USA. 2008, 105: 7076-7081. 10.1073/pnas.0802615105.PubMed CentralView ArticlePubMedGoogle Scholar
- Weeber EJ, Beffert U, Jones C, Christian JM, Forster E, Sweatt JD, Herz J: Reelin and ApoE receptors cooperate to enhance hippocampal synaptic plasticity and learning. J Biol Chem. 2002, 277: 39944-39952. 10.1074/jbc.M205147200.View ArticlePubMedGoogle Scholar
- Qiu S, Korwek KM, Pratt-Davis AR, Peters M, Bergman MY, Weeber EJ: Cognitive disruption and altered hippocampus synaptic function in Reelin haploinsufficient mice. Neurobiol Learn Mem. 2006, 85: 228-242. 10.1016/j.nlm.2005.11.001.View ArticlePubMedGoogle Scholar
- Liu WS, Pesold C, Rodriguez MA, Carboni G, Auta J, Lacor P, Larson J, Condie BG, Guidotti A, Costa E: Down-regulation of dendritic spine and glutamic acid decarboxylase 67 expressions in the reelin haploinsufficient heterozygous reeler mouse. Proc Natl Acad Sci USA. 2001, 98: 3477-3482. 10.1073/pnas.051614698.PubMed CentralView ArticlePubMedGoogle Scholar
- Niu S, Yabut O, D'Arcangelo G: The Reelin signaling pathway promotes dendritic spine development in hippocampal neurons. J Neurosci. 2008, 28: 10339-10348. 10.1523/JNEUROSCI.1917-08.2008.PubMed CentralView ArticlePubMedGoogle Scholar
- Mukai J, Dhilla A, Drew LJ, Stark KL, Cao L, MacDermott AB, Karayiorgou M, Gogos JA: Palmitoylation-dependent neurodevelopmental deficits in a mouse model of 22q11 microdeletion. Nat Neurosci. 2008, 11: 1302-1310. 10.1038/nn.2204.PubMed CentralView ArticlePubMedGoogle Scholar
- Spalloni A, Geracitano R, Berretta N, Sgobio C, Bernardi G, Mercuri NB, Longone P, Ammassari-Teule M: Molecular and synaptic changes in the hippocampus underlying superior spatial abilities in pre-symptomatic G93A+/+ mice overexpressing the human Cu/Zn superoxide dismutase (Gly93 --> ALA) mutation. Exp Neurol. 2006, 197: 505-514. 10.1016/j.expneurol.2005.10.014.View ArticlePubMedGoogle Scholar
- Sgobio C, Trabalza A, Spalloni A, Zona C, Carunchio I, Longone P, Ammassari-Teule M: Abnormal medial prefrontal cortex connectivity and defective fear extinction in the presymptomatic G93A SOD1 mouse model of ALS. Genes Brain Behav. 2008, 7: 427-434. 10.1111/j.1601-183X.2007.00367.x.View ArticlePubMedGoogle Scholar
- Jacobsen JS, Wu CC, Redwine JM, Comery TA, Arias R, Bowlby M, Martone R, Morrison JH, Pangalos MN, Reinhart PH, Bloom FE: Early-onset behavioral and synaptic deficits in a mouse model of Alzheimer's disease. Proceedings of the National Academy of Sciences of the United States of America. 2006, 103: 5161-5166. 10.1073/pnas.0600948103.PubMed CentralView ArticlePubMedGoogle Scholar
- Saura CA, Choi SY, Beglopoulos V, Malkani S, Zhang D, Shankaranarayana Rao BS, Chattarji S, Kelleher RJ, Kandel ER, Duff K, et al.: Loss of presenilin function causes impairments of memory and synaptic plasticity followed by age-dependent neurodegeneration. Neuron. 2004, 42: 23-36. 10.1016/S0896-6273(04)00182-5.View ArticlePubMedGoogle Scholar
- Trinchese F, Liu S, Battaglia F, Walter S, Mathews PM, Arancio O: Progressive age-related development of Alzheimer-like pathology in APP/PS1 mice. Ann Neurol. 2004, 55: 801-814. 10.1002/ana.20101.View ArticlePubMedGoogle Scholar
- Lanz TA, Carter DB, Merchant KM: Dendritic spine loss in the hippocampus of young PDAPP and Tg2576 mice and its prevention by the ApoE2 genotype. Neurobiol Dis. 2003, 13: 246-253. 10.1016/S0969-9961(03)00079-2.View ArticlePubMedGoogle Scholar
- Moolman DL, Vitolo OV, Vonsattel JP, Shelanski ML: Dendrite and dendritic spine alterations in Alzheimer models. J Neurocytol. 2004, 33: 377-387. 10.1023/B:NEUR.0000044197.83514.64.View ArticlePubMedGoogle Scholar
- Tsai J, Grutzendler J, Duff K, Gan WB: Fibrillar amyloid deposition leads to local synaptic abnormalities and breakage of neuronal branches. Nat Neurosci. 2004, 7: 1181-1183. 10.1038/nn1335.View ArticlePubMedGoogle Scholar
- Murphy KP, Carter RJ, Lione LA, Mangiarini L, Mahal A, Bates GP, Dunnett SB, Morton AJ: Abnormal synaptic plasticity and impaired spatial cognition in mice transgenic for exon 1 of the human Huntington's disease mutation. The Journal of neuroscience : the official journal of the Society for Neuroscience. 2000, 20: 5115-5123.Google Scholar
- Usdin MT, Shelbourne PF, Myers RM, Madison DV: Impaired synaptic plasticity in mice carrying the Huntington's disease mutation. Hum Mol Genet. 1999, 8: 839-846. 10.1093/hmg/8.5.839.View ArticlePubMedGoogle Scholar
- Cummings DM, Milnerwood AJ, Dallerac GM, Waights V, Brown JY, Vatsavayai SC, Hirst MC, Murphy KP: Aberrant cortical synaptic plasticity and dopaminergic dysfunction in a mouse model of Huntington's disease. Hum Mol Genet. 2006, 15: 2856-2868. 10.1093/hmg/ddl224.View ArticlePubMedGoogle Scholar
- Milnerwood AJ, Cummings DM, Dallerac GM, Brown JY, Vatsavayai SC, Hirst MC, Rezaie P, Murphy KP: Early development of aberrant synaptic plasticity in a mouse model of Huntington's disease. Hum Mol Genet. 2006, 15: 1690-1703. 10.1093/hmg/ddl092.View ArticlePubMedGoogle Scholar
- Wang Y, Chandran JS, Cai H, Mattson MP: DJ-1 is essential for long-term depression at hippocampal CA1 synapses. Neuromolecular Med. 2008, 10: 40-45. 10.1007/s12017-008-8023-4.View ArticlePubMedGoogle Scholar
- Hanson JE, Orr AL, Madison DV: Altered Hippocampal Synaptic Physiology in Aged Parkin-Deficient Mice. Neuromolecular Med. 2010Google Scholar
- Wozniak DF, Xiao M, Xu L, Yamada KA, Ornitz DM: Impaired spatial learning and defective theta burst induced LTP in mice lacking fibroblast growth factor 14. Neurobiol Dis. 2007, 26: 14-26. 10.1016/j.nbd.2006.11.014.PubMed CentralView ArticlePubMedGoogle Scholar
- Xiao M, Xu L, Laezza F, Yamada K, Feng S, Ornitz DM: Impaired hippocampal synaptic transmission and plasticity in mice lacking fibroblast growth factor 14. Mol Cell Neurosci. 2007, 34: 366-377. 10.1016/j.mcn.2006.11.020.View ArticlePubMedGoogle Scholar
- Watase K, Gatchel JR, Sun Y, Emamian E, Atkinson R, Richman R, Mizusawa H, Orr HT, Shaw C, Zoghbi HY: Lithium therapy improves neurological function and hippocampal dendritic arborization in a spinocerebellar ataxia type 1 mouse model. PLoS Med. 2007, 4: e182-10.1371/journal.pmed.0040182.PubMed CentralView ArticlePubMedGoogle Scholar
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