Transcranial direct current stimulation based metaplasticity protocols in working memory

It has been already shown that delivering tDCS that are spaced by an interval alters its impact on motor plasticity. These effects can be explained, based on metaplasticity in which a previous modification of activity in a neuronal network can change the effects of subsequent interventions in the same network. But to date there is limited data assessing metaplasticity effects in cognitive functioning.

Structural plasticity can produce metaplasticity.

BACKGROUND: Synaptic plasticity underlies many aspect of learning memory and development. The properties of synaptic plasticity can change as a function of previous plasticity and previous activation of synapses, a phenomenon called metaplasticity. Synaptic plasticity not only changes the functional connectivity between neurons but in some cases produces a structural change in synaptic spines; a change thought to form a basis for this observed plasticity. Here we examine to what extent structural plasticity of spines can be a cause for metaplasticity. This study is motivated by the observation that structural changes in spines are likely to affect the calcium dynamics in spines. Since calcium dynamics determine the sign and magnitude of synaptic plasticity, it is likely that structural plasticity will alter the properties of synaptic plasticity. METHODOLOGY/PRINCIPAL FINDINGS: In this study we address the question how spine geometry and alterations of N-methyl-D-aspartic acid (NMDA) receptors conductance may affect plasticity. Based on a simplified model of the spine in combination with a calcium-dependent plasticity rule, we demonstrated that after the induction phase of plasticity a shift of the long term potentiation (LTP) or long term depression (LTD) threshold takes place. This induces a refractory period for further LTP induction and promotes depotentiation as observed experimentally. That resembles the BCM metaplasticity rule but specific for the individual synapse. In the second phase, alteration of the NMDA response may bring the synapse to a state such that further synaptic weight alterations are feasible. We show that if the enhancement of the NMDA response is proportional to the area of the post synaptic density (PSD) the plasticity curves most likely return to the initial state. CONCLUSIONS/SIGNIFICANCE: Using simulations of calcium dynamics in synaptic spines, coupled with a biophysically motivated calcium-dependent plasticity rule, we find under what conditions structural plasticity can form the basis of synapse specific metaplasticity.

A biophysical model of synaptic plasticity and metaplasticity can account for the dynamics of the backward shift of hippocampal place fields.

Hippocampal place cells in the rat undergo experience-dependent changes when the rat runs stereotyped routes. One such change, the backward shift of the place field center of mass, has been linked by previous modeling efforts to spike-timing-dependent plasticity (STDP). However, these models did not account for the termination of the place field shift and they were based on an abstract implementation of STDP that ignores many of the features found in cortical plasticity. Here, instead of the abstract STDP model, we use a calcium-dependent plasticity (CaDP) learning rule that can account for many of the observed properties of cortical plasticity. We use the CaDP learning rule in combination with a model of metaplasticity to simulate place field dynamics. Without any major changes to the parameters of the original model, the present simulations account both for the initial rapid place field shift and for the subsequent slowing down of this shift. These results suggest that the CaDP model captures the essence of a general cortical mechanism of synaptic plasticity, which may underlie numerous forms of synaptic plasticity observed both in vivo and in vitro.

On the Biological Plausibility of Artificial Metaplasticity

The training algorithm studied in this paper is inspired by the biological metaplasticity property of neurons. Tested on different multidisciplinary applications, it achieves a more efficient training and improves Artificial Neural Network Performance. The algorithm has been recently proposed for Artificial Neural Networks in general, although for the purpose of discussing its biological plausibility, a Multilayer Perceptron has been used. During the training phase, the artificial metaplasticity multilayer perceptron could be considered a new probabilistic version of the presynaptic rule, as during the training phase the algorithm assigns higher values for updating the weights in the less probable activations than in the ones with higher probability

A Prediction Model to Diabetes using Artificial Metaplasticity

Diabetes is the most common disease nowadays in all populations and in all age groups. Different techniques of artificial intelligence has been applied to diabetes problem. This research proposed the artificial metaplasticity on multilayer perceptron (AMMLP) as prediction model for prediction of diabetes. The Pima Indians diabetes was used to test the proposed model AMMLP. The results obtained by AMMLP were compared with other algorithms, recently proposed by other researchers, that were applied to the same database. The best result obtained so far with the AMMLP algorithm is 89.93%

Artificial metaplasticity prediction model for cognitive rehabilitation outcome in acquired brain injury patients

Objective The main purpose of this research is the novel use of artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining tool for prediction the outcome of patients with acquired brain injury (ABI) after cognitive rehabilitation. The final goal aims at increasing knowledge in the field of rehabilitation theory based on cognitive affectation. Methods and materials The data set used in this study contains records belonging to 123 ABI patients with moderate to severe cognitive affectation (according to Glasgow Coma Scale) that underwent rehabilitation at Institut Guttmann Neurorehabilitation Hospital (IG) using the tele-rehabilitation platform PREVIRNEC©. The variables included in the analysis comprise the neuropsychological initial evaluation of the patient (cognitive affectation profile), the results of the rehabilitation tasks performed by the patient in PREVIRNEC© and the outcome of the patient after a 3–5 months treatment. To achieve the treatment outcome prediction, we apply and compare three different data mining techniques: the AMMLP model, a backpropagation neural network (BPNN) and a C4.5 decision tree. Results The prediction performance of the models was measured by ten-fold cross validation and several architectures were tested. The results obtained by the AMMLP model are clearly superior, with an average predictive performance of 91.56%. BPNN and C4.5 models have a prediction average accuracy of 80.18% and 89.91% respectively. The best single AMMLP model provided a specificity of 92.38%, a sensitivity of 91.76% and a prediction accuracy of 92.07%. Conclusions The proposed prediction model presented in this study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients. The ability to predict treatment outcomes may provide new insights toward improving effectiveness and creating personalized therapeutic interventions based on clinical evidence.

Impact d’une sieste sur plasticité cérébrale induite par stimulation magnétique transcrânienne

Chez l’humain, différents protocoles de stimulation magnétique transcrânienne répétée (SMTr) peuvent être utilisés afin de manipuler expérimentalement la plasticité cérébrale au niveau du cortex moteur primaire (M1). Ces techniques ont permis de mieux comprendre le rôle du sommeil dans la régulation de la plasticité cérébrale. Récemment, une étude a montré que lorsqu’une première session de stimulation SMTr au niveau de M1 est suivie d’une nuit de sommeil, l’induction subséquente de la plasticité par une deuxième session SMTr est augmentée. La présente étude a investigué si ce type de métaplasticité pouvait également bénéficier d’une sieste diurne. Quatorze sujets en santé ont reçu deux sessions de intermittent theta burst stimulation (iTBS) connue pour son effet facilitateur sur l’excitabilité corticale. Les sessions de stimulation étaient séparées par une sieste de 90 minutes ou par une période équivalente d’éveil. L’excitabilité corticale était quantifiée en terme d’amplitude des potentiels évoqués moteurs (PEM) mesurés avant et après chaque session de iTBS. Les résultats montrent que la iTBS n’est pas parvenue à augmenter de manière robuste l’amplitude des PEMs lors de la première session de stimulation. Lors de la deuxième session de stimulation, la iTBS a produit des changements plastiques variables et ce peu importe si les sujets ont dormi ou pas. Les effets de la iTBS sur l’excitabilité corticale étaient marqués par une importante variabilité inter et intra-individuelle dont les possibles causes sont discutées.

Spatial and temporal aspects of synaptic plasticity

The notion that changes in synaptic efficacy underlie learning and memory processes is now widely accepted even if definitive proof of the synaptic plasticity and memory hypothesis is still lacking. When learning occurs, patterns of neural activity representing the occurrence of events cause changes in the strength of synaptic connections within the brain. Reactivation of these altered connections constitutes the experience of memory for these events and for other events with which they may be associated. These statements summarize a long-standing theory of memory formation that we refer to as the synaptic plasticity and memory hypothesis. Since activity-dependent synaptic plasticity is induced at appropriate synapses during memory formation, and is both necessary and sufficient for the information storage, we can speculate that a methodological study of the synapse will help us understand the mechanism of learning. Random events underlie a wide range of biological processes as diverse as genetic drift and molecular diffusion, regulation of gene expression and neural network function. Additionally spatial variability may be important especially in systems with nonlinear behavior. Since synapse is a complex biological system we expect that stochasticity as well as spatial gradients of different enzymes may be significant for induction of plasticity. ^ In that study we address the question "how important spatial and temporal aspects of synaptic plasticity may be". We developed methods to justify our basic assumptions and examined the main sources of variability of calcium dynamics. Among them, a physiological method to estimate the number of postsynaptic receptors as well as a hybrid algorithm for simulating postsynaptic calcium dynamics. Additionally we studied how synaptic geometry may enhance any possible spatial gradient of calcium dynamics and how that spatial variability affect plasticity curves. Finally, we explored the potential of structural synaptic plasticity to provide a metaplasticity mechanism specific for the synapse. ^

Probabilistic versus incremental presynaptic learning in biological plausible synapses

In this paper, the presynaptic rule, a classical rule for hebbian learning, is revisited. It is shown that the presynaptic rule exhibits relevant synaptic properties like synaptic directionality, and LTP metaplasticity (long-term potentiation threshold metaplasticity). With slight modifications, the presynaptic model also exhibits metaplasticity of the long-term depression threshold, being also consistent with Artola, Brocher and Singer’s (ABS) influential model. Two asymptotically equivalent versions of the presynaptic rule were adopted for this analysis: the first one uses an incremental equation while the second, conditional probabilities. Despite their simplicity, both types of presynaptic rules exhibit sophisticated biological properties, specially the probabilistic version

Data mining for the diagnosis of type 2 diabetes

Diabetes is the most common disease nowadays in all populations and in all age groups. diabetes contributing to heart disease, increases the risks of developing kidney disease, blindness, nerve damage, and blood vessel damage. Diabetes disease diagnosis via proper interpretation of the diabetes data is an important classification problem. Different techniques of artificial intelligence has been applied to diabetes problem. The purpose of this study is apply the artificial metaplasticity on multilayer perceptron (AMMLP) as a data mining (DM) technique for the diabetes disease diagnosis. The Pima Indians diabetes was used to test the proposed model AMMLP. The results obtained by AMMLP were compared with decision tree (DT), Bayesian classifier (BC) and other algorithms, recently proposed by other researchers, that were applied to the same database. The robustness of the algorithms are examined using classification accuracy, analysis of sensitivity and specificity, confusion matrix. The results obtained by AMMLP are superior to obtained by DT and BC.

Implication du domaine intracellulaire du précurseur de la protéine amyloïde dans la modulation de la plasticité synaptique

Alzheimer's disease is the most common type of dementia in the elderly; it is characterized by early deficits in learning and memory formation and ultimately leads to a generalised loss of higher cognitive functions. While amyloid beta (Aβ) and tau are traditionally associated with the development of Alzheimer disease, recent studies suggest that other factors, like the intracellular domain (APP-ICD) of the amyloid precursor protein (APP), could play a role. In this study, we investigated whether APP-ICD could affect synaptic transmission and synaptic plasticity in the hippocampus, which is involved in learning and memory processes. Our results indicated that overexpression of APP-ICD in hippocampal CA1 neurons leads to a decrease in evoked AMPA-receptor and NMDA-receptor dependent synaptic transmission. Our study demonstrated that this effect is specific for APP-ICD since its closest homologue APLP2-ICD did not reproduce this effect. In addition, APP-ICD blocks the induction of long term potentiation (LTP) and leads to increased of expression and facilitated induction of long term depression (LTD), while APLP2-ICD shows neither of these effects. Our study showed that this difference observed in synaptic transmission and plasticity between the two intracellular domains resides in the difference of one alanine in the APP-ICD versus a proline in the APLP2-ICD. Exchanging this critical amino-acid through point-mutation, we observed that APP(PAV)-ICD had no longer an effect on synaptic plasticity. We also demonstrated that APLP2(AAV)-ICD mimic the effect of APP-ICD in regards of facilitated LTD. Next we showed that the full length APP-APLP2-APP (APP with a substitution of the Aβ component for its homologous APLP2 part) had no effect on synaptic transmission or synaptic plasticity when compared to the APP-ICD. However, by activating caspase cleavage prior to induction of LTD or LTP, we observed an LTD facilitation and a block of LTP with APP-APLP2-APP, effects that were not seen with the full length APLP2 protein. APP is phosphorylated at threonine 668 (Thr668), which is localized directly after the aforementioned critical alanine and the caspase cleavage site in APP-APLP2-APP. Mutating this Thr668 for an alanine abolishes the effects on LTD and restores LTP induction. Finally, we showed that the facilitation of LTD with APP-APLP2-APP involves ryanodine receptor dependent calcium release from intracellular stores. Taken together, we propose the emergence of a new APP intracellular domain, which plays a critical role in the regulation of synaptic plasticity and by extension, could play a role in the development of memory loss in Alzheimer’s disease.

Implication du domaine intracellulaire du précurseur de la protéine amyloïde dans la modulation de la plasticité synaptique

Alzheimer's disease is the most common type of dementia in the elderly; it is characterized by early deficits in learning and memory formation and ultimately leads to a generalised loss of higher cognitive functions. While amyloid beta (Aβ) and tau are traditionally associated with the development of Alzheimer disease, recent studies suggest that other factors, like the intracellular domain (APP-ICD) of the amyloid precursor protein (APP), could play a role. In this study, we investigated whether APP-ICD could affect synaptic transmission and synaptic plasticity in the hippocampus, which is involved in learning and memory processes. Our results indicated that overexpression of APP-ICD in hippocampal CA1 neurons leads to a decrease in evoked AMPA-receptor and NMDA-receptor dependent synaptic transmission. Our study demonstrated that this effect is specific for APP-ICD since its closest homologue APLP2-ICD did not reproduce this effect. In addition, APP-ICD blocks the induction of long term potentiation (LTP) and leads to increased of expression and facilitated induction of long term depression (LTD), while APLP2-ICD shows neither of these effects. Our study showed that this difference observed in synaptic transmission and plasticity between the two intracellular domains resides in the difference of one alanine in the APP-ICD versus a proline in the APLP2-ICD. Exchanging this critical amino-acid through point-mutation, we observed that APP(PAV)-ICD had no longer an effect on synaptic plasticity. We also demonstrated that APLP2(AAV)-ICD mimic the effect of APP-ICD in regards of facilitated LTD. Next we showed that the full length APP-APLP2-APP (APP with a substitution of the Aβ component for its homologous APLP2 part) had no effect on synaptic transmission or synaptic plasticity when compared to the APP-ICD. However, by activating caspase cleavage prior to induction of LTD or LTP, we observed an LTD facilitation and a block of LTP with APP-APLP2-APP, effects that were not seen with the full length APLP2 protein. APP is phosphorylated at threonine 668 (Thr668), which is localized directly after the aforementioned critical alanine and the caspase cleavage site in APP-APLP2-APP. Mutating this Thr668 for an alanine abolishes the effects on LTD and restores LTP induction. Finally, we showed that the facilitation of LTD with APP-APLP2-APP involves ryanodine receptor dependent calcium release from intracellular stores. Taken together, we propose the emergence of a new APP intracellular domain, which plays a critical role in the regulation of synaptic plasticity and by extension, could play a role in the development of memory loss in Alzheimer’s disease.