Specific exercise patterns generate an epigenetic molecular memory window that drives long-term memory formation and identifies ACVR1C as a bidirectional regulator of memory in mice.

Exercise has beneficial effects on cognition throughout the lifespan. Here, we demonstrate that specific exercise patterns transform insufficient, subthreshold training into long-term memory in mice. Our findings reveal a potential molecular memory window such that subthreshold training within this window enables long-term memory formation. We performed RNA-seq on dorsal hippocampus and identify genes whose expression correlate with conditions in which exercise enables long-term memory formation. Among these genes we found Acvr1c, a member of the TGF ß family. We find that exercise, in any amount, alleviates epigenetic repression at the Acvr1c promoter during consolidation. Additionally, we find that ACVR1C can bidirectionally regulate synaptic plasticity and long-term memory in mice. Furthermore, Acvr1c expression is impaired in the aging human and mouse brain, as well as in the 5xFAD mouse model, and over-expression of Acvr1c enables learning and facilitates plasticity in mice. These data suggest that promoting ACVR1C may protect against cognitive impairment.

Behavioral Analysis of NMDAR Function in Rodents: Tests of Long-Term Spatial Memory.

NMDAR-dependent forms of synaptic plasticity in brain regions like the hippocampus are widely believed to provide the neural substrate for long-term associative memory formation. However, the experimental data are equivocal at best and may suggest a more nuanced role for NMDARs and synaptic plasticity in memory. Much of the experimental data available comes from studies in genetically modified mice in which NMDAR subunits have been deleted or mutated in order to disrupt NMDAR function. Behavioral assessment of long-term memory in these mice has involved tests like the Morris watermaze and the radial arm maze. Here we describe these behavioral tests and some of the different testing protocols that can be used to assess memory performance. We discuss the importance of distinguishing selective effects on learning and memory processes from nonspecific effects on sensorimotor or motivational aspects of performance.

Measurement invariance across countries of the Test of Memory Strategies (TMS): A contribution to the cross-national validity study.

Previous literature showed a complex interpretation of recall tasks due to the complex relationship between Executive Functions (EF) and Long Term Memory (M). The Test of Memory Strategies (TMS) could be useful for assessing this issue, because it evaluates EF and M simultaneously. This study aims to explore the validity of the TMS structure, comparing the models proposed by Vaccaro et al. (2022) and evaluating the measurement invariance according to three countries (Italy, Spain, and Portugal) through Confirmatory Factor Analysis (CFA). Four hundred thirty-one healthy subjects (Age mean = 54.84, sd = 20.43; Education mean = 8.85, sd =4.05; M = 177, F = 259) were recruited in three countries (Italy, Spain, and Portugal). Measurement invariance across three country groups was evaluated through Structural Equation modeling. Also, convergent and divergent validity were examined through the correlation between TMS and classical neuropsychological tests. CFA outcomes suggested that the best model was the three-dimensional model, in which list 1 and list2 reflect EF, list 3 reflects a mixed factor of EF and M (EFM) and list4 and list5 reflect M. This result is in line with the theory that TMS decreases EF components progressively. TMS was metric invariant to the country, but scalar invariance was not tenable. Finally, the factor scores of TMS showed convergent validity with the classical neuropsychological tests. The overall results support cross-validation of TMS in the three countries considered.

Leveraging transfer learning with deep learning for crime prediction.

Crime remains a crucial concern regarding ensuring a safe and secure environment for the public. Numerous efforts have been made to predict crime, emphasizing the importance of employing deep learning approaches for precise predictions. However, sufficient crime data and resources for training state-of-the-art deep learning-based crime prediction systems pose a challenge. To address this issue, this study adopts the transfer learning paradigm. Moreover, this study fine-tunes state-of-the-art statistical and deep learning methods, including Simple Moving Averages (SMA), Weighted Moving Averages (WMA), Exponential Moving Averages (EMA), Long Short Term Memory (LSTM), Bi-directional Long Short Term Memory (BiLSTMs), and Convolutional Neural Networks and Long Short Term Memory (CNN-LSTM) for crime prediction. Primarily, this study proposed a BiLSTM based transfer learning architecture due to its high accuracy in predicting weekly and monthly crime trends. The transfer learning paradigm leverages the fine-tuned BiLSTM model to transfer crime knowledge from one neighbourhood to another. The proposed method is evaluated on Chicago, New York, and Lahore crime datasets. Experimental results demonstrate the superiority of transfer learning with BiLSTM, achieving low error values and reduced execution time. These prediction results can significantly enhance the efficiency of law enforcement agencies in controlling and preventing crime.

Developing an Improved Cycle Architecture for AI-Based Generation of New Structures Aimed at Drug Discovery.

Drug discovery involves a crucial step of optimizing molecules with the desired structural groups. In the domain of computer-aided drug discovery, deep learning has emerged as a prominent technique in molecular modeling. Deep generative models, based on deep learning, play a crucial role in generating novel molecules when optimizing molecules. However, many existing molecular generative models have limitations as they solely process input information in a forward way. To overcome this limitation, we propose an improved generative model called BD-CycleGAN, which incorporates BiLSTM (bidirectional long short-term memory) and Mol-CycleGAN (molecular cycle generative adversarial network) to preserve the information of molecular input. To evaluate the proposed model, we assess its performance by analyzing the structural distribution and evaluation matrices of generated molecules in the process of structural transformation. The results demonstrate that the BD-CycleGAN model achieves a higher success rate and exhibits increased diversity in molecular generation. Furthermore, we demonstrate its application in molecular docking, where it successfully increases the docking score for the generated molecules. The proposed BD-CycleGAN architecture harnesses the power of deep learning to facilitate the generation of molecules with desired structural features, thus offering promising advancements in the field of drug discovery processes.

Class I histone deacetylases inhibition reverses memory impairment induced by acute stress in mice.

While chronic stress induces learning and memory impairments, acute stress may facilitate or prevent memory consolidation depending on whether it occurs during the learning event or before it, respectively. On the other hand, it has been shown that histone acetylation regulates long-term memory formation. This study aimed to evaluate the effect of two inhibitors of class I histone deacetylases (HDACs), 4-phenylbutyrate (PB) and IN14 (100 mg/kg/day, ip for 2 days), on memory performance in mice exposed to a single 15-min forced swimming stress session. Plasma corticosterone levels were determined 30 minutes after acute swim stress in one group of mice. In another experimental series, independent groups of mice were trained in one of three different memory tasks: Object recognition test, Elevated T maze, and Buried food location test. Subsequently, the hippocampi were removed to perform ELISA assays for histone deacetylase 2 (HDAC2) expression. Acute stress induced an increase in plasma corticosterone levels, as well as hippocampal HDAC2 content, along with an impaired performance in memory tests. Moreover, PB and IN14 treatment prevented memory loss in stressed mice. These findings suggest that HDAC2 is involved in acute stress-induced cognitive impairment. None of the drugs improved memory in non-stressed animals, indicating that HDACs inhibitors are not cognitive boosters, but rather potentially useful drugs for mitigating memory deficits.

Episodic long-term memory formation during slow-wave sleep.

We are unresponsive during slow-wave sleep but continue monitoring external events for survival. Our brain wakens us when danger is imminent. If events are non-threatening, our brain might store them for later consideration to improve decision-making. To test this hypothesis, we examined whether novel vocabulary consisting of simultaneously played pseudowords and translation words are encoded/stored during sleep, and which neural-electrical events facilitate encoding/storage. An algorithm for brain-state-dependent stimulation selectively targeted word pairs to slow-wave peaks or troughs. Retrieval tests were given 12 and 36 hr later. These tests required decisions regarding the semantic category of previously sleep-played pseudowords. The sleep-played vocabulary influenced awake decision-making 36 hr later, if targeted to troughs. The words' linguistic processing raised neural complexity. The words' semantic-associative encoding was supported by increased theta power during the ensuing peak. Fast-spindle power ramped up during a second peak likely aiding consolidation. Hence, new vocabulary played during slow-wave sleep was stored and influenced decision-making days later.

Spaced training activates Miro/Milton-dependent mitochondrial dynamics in neuronal axons to sustain long-term memory.

Neurons have differential and fluctuating energy needs across distinct cellular compartments, shaped by brain electrochemical activity associated with cognition. In vitro studies show that mitochondria transport from soma to axons is key to maintaining neuronal energy homeostasis. Nevertheless, whether the spatial distribution of neuronal mitochondria is dynamically adjusted in vivo in an experience-dependent manner remains unknown. In Drosophila, associative long-term memory (LTM) formation is initiated by an early and persistent upregulation of mitochondrial pyruvate flux in the axonal compartment of neurons in the mushroom body (MB). Through behavior experiments, super-resolution analysis of mitochondria morphology in the neuronal soma and in vivo mitochondrial fluorescence recovery after photobleaching (FRAP) measurements in the axons, we show that LTM induction, contrary to shorter-lived memories, is sustained by the departure of some mitochondria from MB neuronal soma and increased mitochondrial dynamics in the axonal compartment. Accordingly, impairing mitochondrial dynamics abolished the increased pyruvate consumption, specifically after spaced training and in the MB axonal compartment, thereby preventing LTM formation. Our results thus promote reorganization of the mitochondrial network in neurons as an integral step in elaborating high-order cognitive processes.

Mrj is a chaperone of the Hsp40 family that regulates Orb2 oligomerization and long-term memory in Drosophila.

Orb2 the Drosophila homolog of cytoplasmic polyadenylation element binding (CPEB) protein forms prion-like oligomers. These oligomers consist of Orb2A and Orb2B isoforms and their formation is dependent on the oligomerization of the Orb2A isoform. Drosophila with a mutation diminishing Orb2A's prion-like oligomerization forms long-term memory but fails to maintain it over time. Since this prion-like oligomerization of Orb2A plays a crucial role in the maintenance of memory, here, we aim to find what regulates this oligomerization. In an immunoprecipitation-based screen, we identify interactors of Orb2A in the Hsp40 and Hsp70 families of proteins. Among these, we find an Hsp40 family protein Mrj as a regulator of the conversion of Orb2A to its prion-like form. Mrj interacts with Hsp70 proteins and acts as a chaperone by interfering with the aggregation of pathogenic Huntingtin. Unlike its mammalian homolog, we find Drosophila Mrj is neither an essential gene nor causes any gross neurodevelopmental defect. We observe a loss of Mrj results in a reduction in Orb2 oligomers. Further, Mrj knockout exhibits a deficit in long-term memory and our observations suggest Mrj is needed in mushroom body neurons for the regulation of long-term memory. Our work implicates a chaperone Mrj in mechanisms of memory regulation through controlling the oligomerization of Orb2A and its association with the translating ribosomes.

The Intellectual Disability Risk Gene Kdm5b Regulates Long-Term Memory Consolidation in the Hippocampus.

The histone lysine demethylase KDM5B is implicated in recessive intellectual disability disorders, and heterozygous, protein-truncating variants in KDM5B are associated with reduced cognitive function in the population. The KDM5 family of lysine demethylases has developmental and homeostatic functions in the brain, some of which appear to be independent of lysine demethylase activity. To determine the functions of KDM5B in hippocampus-dependent learning and memory, we first studied male and female mice homozygous for a Kdm5b Δ ARID allele that lacks demethylase activity. Kdm5b Δ ARID/ Δ ARID mice exhibited hyperactivity and long-term memory deficits in hippocampus-dependent learning tasks. The expression of immediate early, activity-dependent genes was downregulated in these mice and hyperactivated upon a learning stimulus compared with wild-type (WT) mice. A number of other learning-associated genes were also significantly dysregulated in the Kdm5b Δ ARID/ Δ ARID hippocampus. Next, we knocked down Kdm5b specifically in the adult, WT mouse hippocampus with shRNA. Kdm5b knockdown resulted in spontaneous seizures, hyperactivity, and hippocampus-dependent long-term memory and long-term potentiation deficits. These findings identify KDM5B as a critical regulator of gene expression and synaptic plasticity in the adult hippocampus and suggest that at least some of the cognitive phenotypes associated with KDM5B gene variants are caused by direct effects on memory consolidation mechanisms.

The small conductance Ca2+-activated K+ channel activator GW542573X impairs hippocampal memory in C57BL/6J mice.

Small conductance Ca2+-activated K+ (SK) channels, expressed throughout the CNS, are comprised of SK1, SK2 and SK3 subunits, assembled as homotetrameric or heterotetrameric proteins. SK channels expressed somatically modulate the excitability of neurons by mediating the medium component of the afterhyperpolarization. Synaptic SK channels shape excitatory postsynaptic potentials and synaptic plasticity. Such SK-mediated effects on neuronal excitability and activity-dependent synaptic strength likely underlie the modulatory influence of SK channels on memory encoding. Converging evidence indicates that several forms of long-term memory are facilitated by administration of the SK channel blocker, apamin, and impaired by administration of the pan-SK channel activator, 1-EBIO, or by overexpression of the SK2 subunit. The selective knockdown of dendritic SK2 subunits facilitates memory to a similar extent as that observed after systemic apamin. SK1 subunits co-assemble with SK2; yet the functional significance of SK1 has not been clearly defined. Here, we examined the effects of GW542573X, a drug that activates SK1 containing SK channels, as well as SK2/3, on several forms of long-term memory in male C57BL/6J mice. Our results indicate that pre-training, but not post-training, systemic GW542573X impaired object memory and fear memory in mice tested 24 h after training. Pre-training direct bilateral infusion of GW542573X into the CA1 of hippocampus impaired object memory encoding. These data suggest that systemic GW542573X impairs long-term memory. These results add to growing evidence that SK2 subunit-, and SK1 subunit-, containing SK channels can regulate behaviorally triggered synaptic plasticity necessary for encoding hippocampal-dependent memory.

Octopamine integrates the status of internal energy supply into the formation of food-related memories.

The brain regulates food intake in response to internal energy demands and food availability. However, can internal energy storage influence the type of memory that is formed? We show that the duration of starvation determines whether Drosophila melanogaster forms appetitive short-term or longer-lasting intermediate memories. The internal glycogen storage in the muscles and adipose tissue influences how intensely sucrose-associated information is stored. Insulin-like signaling in octopaminergic reward neurons integrates internal energy storage into memory formation. Octopamine, in turn, suppresses the formation of long-term memory. Octopamine is not required for short-term memory because octopamine-deficient mutants can form appetitive short-term memory for sucrose and to other nutrients depending on the internal energy status. The reduced positive reinforcing effect of sucrose at high internal glycogen levels, combined with the increased stability of food-related memories due to prolonged periods of starvation, could lead to increased food intake.

Deciding what and how much to eat is a complex biological process which involves balancing many types of information such as the levels of internal energy storage, the amount of food previously available in the environment, the perceived value of certain food items, and how these are remembered. At the molecular level, food contains carbohydrates that are broken down to produce glucose, which is then delivered to cells under the control of a hormone called insulin. There, glucose molecules are either immediately used or stored as glycogen until needed. Insulin signalling is also known to interact with the brain's decision-making systems that control eating behaviors; however, how our brains balance food intake with energy storage is poorly understood. Berger et al. set out to investigate this question using fruit flies as an experimental model. These insects also produce insulin-like molecules which help to relay information about glycogen levels to the brain's decision-making system. In particular, these signals reach a population of neurons that produce a messenger known as octopamine similar to the human noradrenaline, which helps regulate how much the flies find consuming certain types of foods rewarding. Berger et al. were able to investigate the role of octopamine in helping to integrate information about internal and external resource levels, memory formation and the evaluation of different food types. When the insects were fed normally, increased glycogen levels led to foods rich in carbohydrates being rated as less rewarding by the decision-making cells, and therefore being consumed less. Memories related to food intake were also short-lived ­ in other words, long-term 'food memory' was suppressed, re-setting the whole system after every meal. In contrast, long periods of starvation in insects with high carbohydrates resources produced a stable, long-term memory of food and hunger which persisted even after the flies had fed again. This experience also changed their food rating system, with highly nutritious foods no longer being perceived as sufficiently rewarding. As a result, the flies overate. This study sheds new light on the mechanisms our bodies may use to maintain energy reserves when food is limited. The persistence of 'food memory' after long periods of starvation may also explain why losing weight is difficult, especially during restrictive diets. In the future, Berger et al. hope that this knowledge will contribute to better strategies for weight management.

W-WaveNet: A multi-site water quality prediction model incorporating adaptive graph convolution and CNN-LSTM.

Water quality prediction is of great significance in pollution control, prevention, and management. Deep learning models have been applied to water quality prediction in many recent studies. However, most existing deep learning models for water quality prediction are used for single-site data, only considering the time dependency of water quality data and ignoring the spatial correlation among multi-sites. This research defines and analyzes the non-aligned spatial correlations that exist in multi-site water quality data. Then deploy spatial-temporal graph convolution to process water quality data, which takes into account both the temporal and spatial correlation of multi-site water quality data. A multi-site water pollution prediction method called W-WaveNet is proposed that integrates adaptive graph convolution and Convolutional Neural Network, Long Short-Term Memory (CNN-LSTM). It integrates temporal and spatial models by interleaved stacking. Theoretical analysis shows that the method can deal with non-aligned spatial correlations in different time spans, which is suitable for water quality data processing. The model validates water quality data generated on two real river sections that have multiple sites. The experimental results were compared with the results of Support Vector Regression, CNN-LSTM, and Spatial-Temporal Graph Convolutional Networks (STGCN). It shows that when W-WaveNet predicts water quality over two river sections, the average Mean Absolute Error is 0.264, which is 45.2% lower than the commonly used CNN-LSTM model and 23.8% lower than the STGCN. The comparison experiments also demonstrate that W-WaveNet has a more stable performance in predicting longer sequences.

Gold price prediction by a CNN-Bi-LSTM model along with automatic parameter tuning.

Banking and stock markets consider gold to be an important component of their economic and financial status. There are various factors that influence the gold price trend and its fluctuations. Accurate and reliable prediction of the gold price is an essential part of financial and portfolio management. Moreover, it could provide insights about potential buy and sell points in order to prevent financial damages and reduce the risk of investment. In this paper, different architectures of deep neural network (DNN) have been proposed based on long short-term memory (LSTM) and convolutional-based neural networks (CNN) as a hybrid model, along with automatic parameter tuning to increase the accuracy, coefficient of determination, of the forecasting results. An illustrative dataset from the closing gold prices for 44 years, from 1978 to 2021, is provided to demonstrate the effectiveness and feasibility of this method. The grid search technique finds the optimal set of DNNs' parameters. Furthermore, to assess the efficiency of DNN models, three statistical indices of RMSE, RMAE, and coefficient of determination (R2), were calculated for the test set. Results indicate that the proposed hybrid model (CNN-Bi-LSTM) outperforms other models in total bias, capturing extreme values and obtaining promising results. In this model, CNN is used to extract features of input dataset. Furthermore, Bi-LSTM uses CNN's outputs to predict the daily closing gold price.

Prediction model of land surface settlement deformation based on improved LSTM method: CEEMDAN-ICA-AM-LSTM (CIAL) prediction model.

The uneven settlement of the surrounding ground surface caused by subway construction is not only complicated but also liable to cause casualties and property damage, so a timely understanding of the ground settlement deformation in the subway excavation and its prediction in real time is of practical significance. Due to the complex nonlinear relationship between subway settlement deformation and numerous influencing factors, as well as the existence of a time lag effect and the influence of various factors in the process, the prediction performance and accuracy of traditional prediction methods can no longer meet industry demands. Therefore, this paper proposes a surface settlement deformation prediction model by combining noise reduction and attention mechanism (AM) with the long short-term memory (LSTM). The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and independent component analysis (ICA) methods are used to denoise the input original data and then combined with AM and LSTM for prediction to obtain the CEEMDAN-ICA-AM-LSTM (CIAL) prediction model. Taking the settlement monitoring data of the construction site of Urumqi Rail Transit Line 1 as an example for analysis reveals that the model in this paper has better effectiveness and applicability in the prediction of surface settlement deformation than multiple prediction models. The RMSE, MAE, and MAPE values of the CIAL model are 0.041, 0.033 and 0.384%; R2 is the largest; the prediction effect is the best; the prediction accuracy is the highest; and its reliability is good. The new method is effective for monitoring the safety of surface settlement deformation.

Modeling opening price spread of Shanghai Composite Index based on ARIMA-GRU/LSTM hybrid model.

In the dynamic landscape of financial markets, accurate forecasting of stock indices remains a pivotal yet challenging task, essential for investors and policymakers alike. This study is motivated by the need to enhance the precision of predicting the Shanghai Composite Index's opening price spread, a critical measure reflecting market volatility and investor sentiment. Traditional time series models like ARIMA have shown limitations in capturing the complex, nonlinear patterns inherent in stock price movements, prompting the exploration of advanced methodologies. The aim of this research is to bridge the gap in forecasting accuracy by developing a hybrid model that integrates the strengths of ARIMA with deep learning techniques, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. This novel approach leverages the ARIMA model's proficiency in linear trend analysis and the deep learning models' capability in modeling nonlinear dependencies, aiming to provide a comprehensive tool for market prediction. Utilizing a comprehensive dataset covering the period from December 20, 1990, to June 2, 2023, the study develops and assesses the efficacy of ARIMA, LSTM, GRU, ARIMA-LSTM, and ARIMA-GRU models in forecasting the Shanghai Composite Index's opening price spread. The evaluation of these models is based on key statistical metrics, including Mean Squared Error (MSE) and Mean Absolute Error (MAE), to gauge their predictive accuracy. The findings indicate that the hybrid models, ARIMA-LSTM and ARIMA-GRU, perform better in forecasting the opening price spread of the Shanghai Composite Index than their standalone counterparts. This outcome suggests that combining traditional statistical methods with advanced deep learning algorithms can enhance stock market prediction. The research contributes to the field by providing evidence of the potential benefits of integrating different modeling approaches for financial forecasting, offering insights that could inform investment strategies and financial decision-making.

The fading memories of youth.

The mystery of "infantile amnesia" suggests memory works differently in the developing brain.

Astrocytes control recent and remote memory strength by affecting the recruitment of the CA1→ACC projection to engrams.

The maturation of engrams from recent to remote time points involves the recruitment of CA1 neurons projecting to the anterior cingulate cortex (CA1→ACC). Modifications of G-protein-coupled receptor pathways in CA1 astrocytes affect recent and remote recall in seemingly contradictory ways. To address this inconsistency, we manipulated these pathways in astrocytes during memory acquisition and tagged c-Fos-positive engram cells and CA1→ACC cells during recent and remote recall. The behavioral results were coupled with changes in the recruitment of CA1→ACC projection cells to the engram: Gq pathway activation in astrocytes caused enhancement of recent recall alone and was accompanied by earlier recruitment of CA1→ACC projecting cells to the engram. In contrast, Gi pathway activation in astrocytes resulted in the impairment of only remote recall, and CA1→ACC projecting cells were not recruited during remote memory. Finally, we provide a simple working model, hypothesizing that Gq and Gi pathway activation affect memory differently, by modulating the same mechanism: CA1→ACC projection.

An acute bout of resistance exercise increases BDNF in hippocampus and restores the long-term memory of insulin-resistant rats.

A sedentary lifestyle, inadequate diet, and obesity are substantial risk factors for Type 2 diabetes mellitus (T2DM) development. A major picture of T2DM is insulin resistance (IR), which causes many impairments in brain physiology, such as increased proinflammatory state and decreased brain-derived neurotrophic factor (BDNF) concentration, hence reducing cognitive function. Physical exercise is a non-pharmacological tool for managing T2DM/IR and its complications. Thus, this study investigated the effects of IR induction and the acute effects of resistance exercise (RE) on memory, neurotrophic, and inflammatory responses in the hippocampus and prefrontal cortex of insulin-resistant rats. IR was induced by a high-fat diet and fructose-rich beverage. Insulin-resistant rats performed acute resistance exercise (IR.RE; vertical ladder climb at 50-100% of the maximum load) or rest (IR.REST; 20 min). Cognitive parameters were assessed by novel object recognition (NOR) tasks, and biochemical analyses were performed to assess BDNF concentrations and inflammatory profile in the hippocampus and prefrontal cortex. Insulin-resistant rats had 20% worse long-term memory (LTM) (p < 0.01) and lower BDNF concentration in the hippocampus (-14.6%; p < 0.05) when compared to non-insulin-resistant rats (CON). An acute bout of RE restored LTM (-9.7% pre vs. post; p > 0.05) and increased BDNF concentration in the hippocampus (9.1%; p < 0.05) of insulin-resistant rats compared to REST. Thus, an acute bout of RE can attenuate the adverse effects of IR on memory and neurotrophic factors in rats, representing a therapeutic tool to alleviate the IR impact on the brain.

Long-term memory consolidation of new words in children with self-limited epilepsy with centro-temporal spikes.

Accelerated long-term forgetting has been studied and demonstrated in adults with epilepsy. In contrast, the question of long-term consolidation (delays > 1 day) in children with epilepsy shows conflicting results. However, childhood is a period of life in which the encoding and long-term storage of new words is essential for the development of knowledge and learning. The aim of this study was therefore to investigate long-term memory consolidation skills in children with self-limited epilepsy with centro-temporal spikes (SeLECTS), using a paradigm exploring new words encoding skills and their long-term consolidation over one-week delay. As lexical knowledge, working memory skills and executive/attentional skills has been shown to contribute to long-term memory/new word learning, we added standardized measures of oral language and executive/attentional functions to explore the involvement of these cognitive skills in new word encoding and consolidation. The results showed that children with SeLECTS needed more repetitions to encode new words, struggled to encode the phonological forms of words, and when they finally reached the level of the typically developing children, they retained what they had learned, but didn't show improved recall skills after a one-week delay, unlike the control participants. Lexical knowledge, verbal working memory skills and phonological skills contributed to encoding and/or recall abilities, and interference sensitivity appeared to be associated with the number of phonological errors during the pseudoword encoding phase. These results are consistent with the functional model linking working memory, phonology and vocabulary in a fronto-temporo-parietal network. As SeLECTS involves perisylvian dysfunction, the associations between impaired sequence storage (phonological working memory), phonological representation storage and new word learning are not surprising. This dual impairment in both encoding and long-term consolidation may result in large learning gap between children with and without epilepsy. Whether these results indicate differences in the sleep-induced benefits required for long-term consolidation or differences in the benefits of retrieval practice between the epilepsy group and healthy children remains open. As lexical development is associated with academic achievement and comprehension, the impact of such deficits in learning new words is certainly detrimental.