EMOTIONAL INTELLIGENCE IN PRE-ADOLESCENT IMMIGRANTS OF SECOND GENERATION

The goal of this research has investigated if individual levels of EI in adolescent immigrants of second generation are related to different indicators of psychological adjustment such as individual well-being and peer relationships. To this aim, a group of 266 students of two schools in Palermo: 63 immigrants and 203 autochthonous aged from 10 to 18 (M=13.6, SD=25.3) EI was measured using a recently published Italian test IE-ACCME (D’Amico, 2013) addressed to preadolescents and adolescents and aimed at measuring the four branches of emotional intelligence described in Mayer & Salovey’s model (1997) using both self-report and performance measures. Psychological well-being was measured using the scale by Ryff (PWBS; Italian version by Ruini, 2003), and Moreno’s sociogram (Hofman, 2001) was used in order to explore social relationships among adolescents belonging to the same school classes. Finally, a reading comprehension test of Italian as second language (Center CILS, university for foreigners of Siena, Italy) has been selected as control instrument. These studies have an underlying reason which longs for investigating if: 1) individuals with poor adaptation could be characterized by low levels of emotional intelligence 2) emotional intelligence might be one of the key factors in social adjustment of immigrant preadolescents 3) improved emotional intelligence may have positive impact on social adjustment of immigrant preadolescents. In particular, the study no.1 investiagtes if the individual levels of emotional intelligence are related to other indicators of psychosocial adaptation, such as individual well-being, adaptation and success among peers.The study no.2 investigates whether training in emotional intelligence (IE) in pre-adolescent second generation immigrants, helps to improve individual well-being and social acceptance, promoting social inclusion. The results of Pearson correlational analysis revealed that positive significant correlations exist among EI, psychological well-being and social acceptance. Moreover, the outcomes evidenced that immigrant students have lower scores in some aspect of emotional intelligence, well-being and social acceptation

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: a systematic review

Introduction: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. Methods: We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. Results: A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. Discussion: The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. Highlights: There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias

Il ruolo dell’Intelligenza Emotiva nell’adattamento psico-sociale di adolescenti immigrati di seconda generazione.

L'obiettivo di questo studio è indagare se un training in intelligenza emotiva (IE) in pre-adolescenti immigrati di seconda generazione, aiuta a migliorare il benessere individuale e l’accettazione sociale, favorendo così l’inclusione sociale. A tal fine, sono state coinvolte due classi di un istituto comprensivo di Palermo: una sperimentale e una di controllo, per un totale di 38 partecipanti di cui 17 immigrati e 21 autoctoni, tra i 12 e 15 anni di etá. Il training ha integrato metodologie e attivitá dei programmi INTEMO (Ruiz-Aranda 2013) e “Sviluppare l’Intelligenza Emotiva” (D’Amico e De Caro, 2008). Entrambi i programmi si basano sul modello di ability IE di Mayer e Salovey, (1997). Gli strumenti di misura sono stati: Test IE-ACCME D'Amico, (2013), scala del benessere psicologico di Carol Ryff adattata da Ruini (2003), prova di comprensione di lettura della lingua italiana e test di Moreno. I risultati attesi, in linea con la letteratura, sono: miglioramento delle competenze emotive (Brackett y Katulak, 2007), del benessere individuale e delle relazioni sociali tra pari. I risultati dello studio ancora in corso, saranno discussi in sede congressuale

Theoretical review of emotional intelligence: models and measuring instruments

Abstract This theoretical review of emotional intelligence (EI) starts from the earliest conceptualizations made by Galton, Binet, Stemberg Thurstone and Thordinke Gardner, who, during their historical period, were successful in laying the basis for the birth of emotional intelligence as a theoretical construction which was introduced for the first time, in scientific literature, in 90’s. Since then, different EI models have been developed and investigative contributions have been increased. In this article, the most representative EI models will be described with details, such as: 1) Mental ability model (Ability Based) developed by Mayer and Salovey 2) Model of emotional competences by Goleman 3) Model of emotional social intelligence of Bar-On. The measuring instruments of EI will be described, based on the models mentioned before, as well the most representative experimental researches of EI and, as a final observation of the author, some researches based on immigrants in Europ

Does Emotional Intelligence play a role in psychosocial adjustment of adolescent immigrants of second generation?

The study is aimed at exploring if individuals’ levels of EI in adolescent immigrants of second generation are related to different indicators of psychosocial adjustment, such as individual well-being, social relationships with peers, scholastic performance and motivation. To this aim, a group of 307 adolescents aged 10-18 years, 237 autochthonous (153 females, 84 males) and 70 immigrants of second generation (39 females, 31 males), attending two secondary schools in Palermo, Italy, were involved in the research. EI was measured using a recently published Italian test IE-ACCME (D’Amico, 2013) addressed to preadolescents and adolescents and aimed at measuring the four branches of emotional intelligence described in Mayer & Salovey’s model (1997) using both self-report and performance measures. Psychological well-being was measured using the scale by Ryff (PWBS; Italian version by Ruini, 2003), and Moreno’s sociogram (1980) was used in order to explore social relationships among adolescents belonging to the same school classes. Moreover, students’ teachers were requested to evaluate both scholastic performance and motivated behavior at school. Since most of the tests presented to students are based on reading complex sentences or passages in Italian language, a control measure of reading comprehension of Italian as a second language was also administered (developed by the center for foreign students of the University of Siena, Italy). We expect to find a linear relationship, in all the adolescents involved in the study, among levels of EI and many of the indicators of psychosocial adjustment considered in the study. Moreover, we expect that these relationships are stronger in immigrant of second generation and that, particularly for newly arrived immigrants, emotional intelligence may represent a key factor for adjustment to the new culture. Final results of the study will be reported

3.4 Methods for large-scale EEG analyses (ConnEEGtome)

https://osf.io/h2wgv/ Multicentric initiatives based on high-density electroencephalography (hd-EEG) are urgently needed for classification and characterization of diseases subtypes in diverse and low-resource settings. These initiatives are, however, not without challenges, with sources of variability arising from differing data acquisition and harmonization methods, multiple preprocessing pipelines, and different theoretical modes and methods to compute source space/scalp functional connectivity. Our team developed a novel pipeline aimed at the harmonization of hd-EEG datasets and dementia classification. This pipeline handles data from recording to machine learning classification based on multi-metric measures of source space connectivity. A user interface is provided for those with limited background in MATLAB. Here, we present our pipeline and detail a comprehensive step-by-step example for prospective analysts reviewing the five main stages of the pipeline: data pre-processing; normalization; source transformation; connectivity metrics and dementia classification. This detailed step-by-step pipeline may improve the assessment of heterogenous, multicentric, and multi-methods approaches to functional connectivity in aging and dementia

A pipeline for large-scale assessments of dementia EEG connectivity across multicentric settings

Multicentric initiatives based on high-density electroencephalography (hd-EEG) are urgently needed for classification and characterization of diseases subtypes in diverse and low-resource settings. These initiatives are, however, not without challenges, with sources of variability arising from differing data acquisition and harmonization methods, multiple preprocessing pipelines, and different theoretical modes and methods to compute source space/scalp functional connectivity. Our team developed a novel pipeline aimed at the harmonization of hd-EEG datasets and dementia classification. This pipeline handles data from recording to machine learning classification based on multi-metric measures of source space connectivity. A user interface is provided for those with limited background in MATLAB. Here, we present our pipeline and detail a comprehensive step-by-step example for prospective analysts reviewing the five main stages of the pipeline: data pre-processing; normalization; source transformation; connectivity metrics and dementia classification. This detailed step-by-step pipeline may improve the assessment of heterogenous, multicentric, and multi-methods approaches to functional connectivity in aging and dementia