Cannabinoid signaling is involved in different brain functions and it is mediated by the cannabinoid receptor 1 (CNR1), which is encoded by the CNR1 gene. Previous evidence suggests an association between cognition and cannabis use. The logical interaction between genetically determined cannabinoid signaling and cannabis use has not been determined. Therefore, we investigated whether CNR1 variation predicts CNR1 prefrontal mRNA expression in postmortem prefrontal human tissue. Then, we studied whether functional variation in CNR1 and cannabis exposure interact in modulating prefrontal function and related behavior during working memory processing. Thus, 208 healthy subjects (113 males) were genotyped for the relevant functional SNP and were evaluated for cannabis use by the Cannabis Experience Questionnaire. All individuals performed the 2-back working memory task during functional magnetic resonance imaging. CNR1 rs1406977 was associated with prefrontal mRNA and individuals carrying a G allele had reduced CNR1 prefrontal mRNA levels compared with AA subjects. Moreover, functional connectivity MRI demonstrated that G carriers who were also cannabis users had greater functional connectivity in the left ventrolateral prefrontal cortex and reduced working memory behavioral accuracy during the 2-back task compared with the other groups. Overall, our results indicate that the deleterious effects of cannabis use are more evident on a specific genetic background related to its receptor expression.
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http://dx.doi.org/10.1038/npp.2014.213 | DOI Listing |
Humans excel at applying learned behavior to unlearned situations. A crucial component of this generalization behavior is our ability to compose/decompose a whole into reusable parts, an attribute known as compositionality. One of the fundamental questions in robotics concerns this characteristic: How can linguistic compositionality be developed concomitantly with sensorimotor skills through associative learning, particularly when individuals only learn partial linguistic compositions and their corresponding sensorimotor patterns? To address this question, we propose a brain-inspired neural network model that integrates vision, proprioception, and language into a framework of predictive coding and active inference on the basis of the free-energy principle.
View Article and Find Full Text PDFCogn Emot
January 2025
Equipe de Recherche Contextes et Acteurs de l'Education (ERCAé), Université d'Orléans, Orléans, France.
Recent research has revealed the widespread effects of emotion on cognitive functions and memory. However, the influence of emotional valence on verbal short-term memory remains largely unexplored, especially in children. This study measured the effect of emotional valence on word immediate serial recall in 4-6-year-old French children ( = 124).
View Article and Find Full Text PDFFront Neurosci
January 2025
Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, United States.
Introduction: , a protein kinase located on human chromosome 21, plays a role in postembryonic neuronal development and degeneration. Alterations to have been consistently associated with cognitive functioning and neurodevelopmental disorders (e.g.
View Article and Find Full Text PDFClin Neuropsychiatry
December 2024
IRCCS Stella Maris Foundation, Pisa, Italy.
Objective: To describe the relationship between executive functions (EF) and symptom's severity, behavioral problems, and adaptive functioning in autistic preschoolers.
Method: Seventy-six autistic preschoolers (age-range: 37-72 months; SD: 8.67 months) without intellectual disability were assessed.
Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi
December 2024
Anhui Provincial Center for Disease Control and Prevention, Hefei, Anhui 230601, China.
Objective: To predict the areas of snail spread in Anhui Province from 1977 to 2023 using machine learning models, and to compare the effectiveness of different machine learning models for prediction of areas of snail spread, so as to provide insights into investigating the trends in areas of snail spread.
Methods: Data pertaining to snail spread in Anhui Province from 1977 to 2023 were collected and a database was created. Five machine learning models were created using the software Matlab R2019b, including support vector regression (SVR), nonlinear autoregressive (NAR) neural network, back propagation (BP) neural network, gated recurrent unit (GRU) neural network and long short-term memory (LSTM) neural network models, and the model fitting effect was evaluated with mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination ().
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