Pamela Guevara

Developing emotional competencies has been identified as a key component of personal well-being, school coexistence, and significant learning during childhood and adolescence. The aim of this study was to map and categorise the quantitative scientific evidence available between 2019 and 2024 regarding the use of artificial intelligence technologies and algorithms applied to emotional education in school-age populations. This scoping review followed the PRISMA Extension for Scoping Reviews (PRISMA-ScR) and searched seven databases (Web of Science, Scopus, PubMed, PsycNet, ERIC, IEEE Xplore and the ACM Digital Library). The reviewed studies were published between the years 2019 and 2024. Eighteen studies met the inclusion criteria required for the final analysis. The results show that the most trained competence was machine-based emotional recognition using convolutional neural networks (CNNs). The goal of using them was to provide teacher support, specifically during initial or diagnostic assessments of students’ competencies. However, there remains a gap in its integration into the teaching and training of complex human skills, such as empathy. More longitudinal research is required, as well as more robust experimental designs that go beyond basic emotion models.

Summary: Adaptive behavior requires weighting incoming feedback against more stable action policies, but the neural dynamics controlling this balance during social interaction remain unclear. Across 296 experimental sessions in 219 participants, we combined computational modeling, EEG, fMRI, diffusion MRI, and fMRI-guided rhythmic TMS during repeated social bargaining. Behavior was captured by complementary feedback-based updating and reputation-based adjustment, with reproducible individual differences in how instructed social context reweighted feedback. Feedback-based updating showed the clearest convergent neural signature: trial-by-trial updating was associated with alpha–beta dynamics and multivoxel representational structure in right temporoparietal cortex. Critically, rhythmic alpha-TMS over individually localized TPJ sites selectively reduced feedback-based updating relative to active arrhythmic stimulation, while increasing baseline offers and modulating anticipatory alpha activity. Functional and structural connectivity analyses further implicated a distributed temporoparietal–frontal network. Together, these findings link a specific computational operation—feedback weighting—to temporoparietal alpha dynamics and show that rhythmic perturbation alters its behavioral expression.

The role of digital media in early childhood development remains highly debated, particularly regarding its impact on language acquisition. While excessive or unsupervised screen exposure has been linked to poorer outcomes, less is known about whether structured and interactive uses of technology can support learning. Building on previous research, we evaluated a brief, educator-supervised tablet-based intervention in 246 children aged 2-5 years from low- to middle-socioeconomic backgrounds attending public early education centers. Using a pre-post design with matched study and control groups, children completed 4-8 short training sessions (15 minutes each) involving interactive word-image associations spanning multiple linguistic categories. Preschoolers additionally engaged in prompted vocalization. Across age groups (2-3, 3-4, and 4-5 years), children in the intervention showed greater gains in language comprehension than controls, including receptive language in toddlers (β = 0.49, p = 0.009), vocabulary and morphology in younger preschoolers (β = 0.59-0.68, all p < 0.05), and grammar comprehension in older preschoolers (β = 0.30, p = 0.038). These effects were consistent after accounting for child and parental characteristics. Together, these findings suggest that the developmental impact of digital media depends less on exposure itself than on how it is used. When embedded in structured, socially guided interactions, even brief tablet-based activities may support early language development

Inférence d'un modèle des faisceaux de fibre du cerveau humain à partir de l'imagerie de diffusion à haute résolution angulaire: La structure et l'organisation de la substance blanche du cerveau humain ne sont pas encore complètement connues. L'Imagerie par Résonance Magnétique de diffusion (IRMd) offre une approche unique pour étudier in vivo la structure des tissus cérébraux, permettant la reconstruction non invasive des trajectoires des faisceaux de fibres du cerveau en utilisant la tractographie. Aujourd'hui, les techniques récentes d'IRMd avec haute résolution angulaire (HARDI) ont largement amélioré la qualité de la tractographie par rapport à l'imagerie du tenseur de diffusion standard (DTI). Toutefois, les jeux de données de tractographie résultant sont très complexes et comprennent des millions de fibres, ce qui nécessite une nouvelle génération de méthodes d'analyse. Au-delà de la cartographie des principales voies de la substance blanche, cette nouvelle technologie ouvre la voie à l'étude des faisceaux d'association courts, qui ont rarement été étudiés avant et qui sont au centre de cette thèse. L'objectif est d'inférer un atlas des faisceaux de fibres du cerveau humain et une méthode qui permet le mappage de cet atlas à tout nouveau cerveau.Afin de surmonter la limitation induite par la taille et la complexité des jeux de données de tractographie, nous proposons une stratégie à deux niveaux, qui enchaîne des regroupements de fibres intra- et inter-sujet. Le premier niveau, un regroupement intra-sujet, est composé par plusieurs étapes qui effectuent un regroupement hiérarchique et robuste des fibres issues de la tractographie, pouvant traiter des jeux de données contenant des millions de fibres. Le résultat final est un ensemble de quelques milliers de faisceaux de fibres homogènes représentant la structure du jeu de données de tractographie dans sa totalité. Cette représentation simplifiée de la substance blanche peut être utilisée par plusieurs études sur la structure des faisceaux individuels ou des analyses de groupe. La robustesse et le coût de l'extensibilité de la méthode sont vérifiés à l'aide de jeux de fibres simulés. Le deuxième niveau, un regroupement inter-sujet, rassemble les faisceaux obtenus dans le premier niveau pour une population de sujets et effectue un regroupement après normalisation spatiale. Il produit en sortie un modèle composé d'une liste de faisceaux de fibres génériques qui peuvent être détectés dans la plupart de la population. Une validation avec des jeux de données simulées est appliqué afin d'étudier le comportement du regroupement inter-sujet sur une population de sujets alignés avec une transformation affine. La méthode a été appliquée aux jeux de fibres calculés à partir des données HARDI de douze cerveaux adultes. Un nouveau atlas des faisceaux HARDI multi-sujet, qui représente la variabilité de la forme et la position des faisceaux à travers les sujets, a été ainsi inféré. L'atlas comprend 36 faisceaux de la substance blanche profonde, dont certains représentent quelques subdivisions des faisceaux connus, et 94 faisceaux d'association courts de la substance blanche superficielle. Enfin, nous proposons une méthode de segmentation automatique de mappage de cet atlas à tout nouveau sujet.

Docosahexaenoic acid (DHA) is a polyunsaturated fatty acid enriched in neuronal membranes and myelin and associated with cognitive performance. However, nutritional interventions show inconsistent cognitive effects, partly due to limited knowledge of the neural pathways linking DHA status to human cognition during sensitive periods of white-matter maturation, such as adolescence. We addressed this gap by studying 99 adolescents drawn from both extremes of performance on a national scholastic examination. Participants completed assessments of scholastic achievement (SA) and intellectual ability (IA), provided erythrocyte DHA samples, and underwent multimodal MRI, including diffusion, T1-weighted, and T2-weighted imaging. Independent component analysis and Bayesian multivariate LASSO models identified brain components jointly associated with DHA and cognition. Across four MRI modalities, a single deep white-matter component consistently emerged as the strongest shared pathway linking DHA with cognition. Tract-resolved analyses highlighted predominant contributions from the fornix and thalamus–temporal fasciculus, with additional subcortical and cortical involvement. In joint models, these components predicted SA and IA after accounting for DHA and other fatty acids, consistent with an indirect, mediation-like pathway. These findings move beyond DHA–behavior correlations by identifying specific neuroanatomical pathways through which a modifiable dietary factor relates to adolescent learning and intellectual performance, offering mechanistic insight relevant to neuroscience, nutrition, and education. Significance Statement: Adolescence is a sensitive period for the maturation of white-matter pathways that support learning and reasoning. Docosahexaenoic acid (DHA), an essential dietary fatty acid enriched in neuronal membranes and myelin, has been linked to cognitive performance, yet the neural mechanisms underlying this association remain unclear. Using multimodal MRI and Bayesian multivariate modeling in adolescents with high or low scholastic performance, identify a specific deep white-matter pathway—centered on the fornix and the thalamus–temporal fasciculus—as the principal route connecting DHA status with scholastic achievement and intellectual ability. Additional subcortical and cortical contributions reveal a coordinated system-level architecture. These findings move beyond correlations by providing mechanistic insight into how a modifiable nutritional factor relates to cognitive development, with implications for neuroscience, public health, and education.

INPLASY2025100064 Emotional Literacy in the Digital Era: Role of AI Tools for Social-emotional training in school populations Keywords: Artificial Intelligence, emotional competencies, social-emoti…

Persistent cognitive symptoms following SARS-CoV-2 infection have been widely reported, yet their underlying neural mechanisms remain poorly understood. Here, we applied an integrative framework—combining EEG, diffusion MRI, and computational cognitive modeling—to investigate brain–behavior relationships in patients recovered from COVID-19 (n=70) and other non-COVID respiratory infections (n=26). Participants completed tasks probing cognitive control, working memory, and decision-making. Across tasks, individuals with prior COVID-19 showed altered low-frequency oscillatory activity and performance deficits, despite mild or moderate acute illness. These oscillatory alterations were linked to reduced white matter integrity in distinct long-range tracts, including the cingulum and thalamo-occipital fasciculi, depending on the cognitive computation involved. Crucially, model-based clustering revealed two distinct neurophysiological profiles among post-COVID participants, reflecting different combinations of structural and functional alterations. These profiles were not explained by clinical severity markers or other symptoms like anosmia, suggesting the emergence of neurocognitive phenotypes beyond binary classifications. Our findings identify a generalizable principle whereby specific brain network disruptions underlie cognitive dysfunction, highlighting biomarkers that may guide personalized intervention strategies. Beyond COVID-19, this multimodal framework offers a scalable approach for uncovering structure–function–computation coupling in other post-infectious or neuroinflammatory conditions, with broad relevance for the prevention and treatment of cognitive impairment.

Subjective tinnitus is the perception of sound in the absence of an external source, with a neurobiological basis that remains poorly understood. Importantly, tinni-tus prevalence increases with aging, reaching up to 25 % in adults aged above 65 years. This study examines white matter tract alterations in older adults with tinnitus using diffusion magnetic resonance imaging. The research involved 96 individuals from the Chilean ANDES cohort, including 56 patients with tinnitus and 40 controls. Thirty-six deep white matter (DWM) and 84 superficial white matter (SWM) bundles were segmented. For each bundle, we extracted four diffu-sion tensor imaging metrics: axial diffusivity (AD), mean diffusivity (MD), radial diffusivity (RD), and fractional anisotropy (FA). With these features, we trained an Extreme Gradient Boosting classifier to predict tinnitus, achieving an AUC of 0.93, highlighting the relevance of AD. Key DWM bundles included the ante-rior arcuate fasciculus, inferior longitudinal fasciculus, and cingulum short fibers (right hemisphere), and the superior motor thalamic radiation (left hemisphere). Significant SWM bundles in the left hemisphere included the superior parietal, cuneus, lingual, superior temporal, caudal middle frontal, precentral, fusiform, postcentral, supramarginal, rostral middle frontal, and superior frontal regions. Tinnitus patients showed decreased AD, MD, and RD, and increased FA, sug-gesting microstructural reorganization. These changes may reflect adaptive or maladaptive plasticity. Increased FA could signal compensatory responses, while decreased AD might indicate axonal damage. Meanwhile, the decrease in MD and RD could indicate an increase in myelin integrity. This is the first study to investigate SWM bundle alterations in tinnitus patients.