A hybrid method to select morphometric features using tensor completion and F-score rank for gifted children identification

Gifted children are able to learn in a more advanced way than others, probably due to neurophysiological differences in the communication efficiency in neural pathways. Topological features contribute to understanding the correlation between the brain structure and intelligence. Despite decades of neuroscience research using MRI, methods based on brain region connectivity patterns are limited by MRI artifacts, which therefore leads to revisiting MRI morphometric features, with the aim of using them to directly identify gifted children instead of using brain connectivity. However, the small, high dimensional morphometric feature dataset with outliers makes the task of finding good classification models challenging. To this end, a hybrid method is proposed that combines tensor completion and feature selection methods to handle outliers and then select the discriminative features. The proposed method can achieve a classification accuracy of 93.1%, higher than other existing algorithms, which is thus suitable for the small MRI datasets with outliers in supervised classification scenarios.Fil: Zhang, Jin. Nankai University; ChinaFil: Feng, Fan. Nankai University; ChinaFil: Han, TianYi. Nankai University; ChinaFil: Duan, Feng. Nankai University; ChinaFil: Sun, Zhe. Riken. Brain Science Institute; JapónFil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Solé Casals, Jordi. Central University of Catalonia; Españ

An Approximation of Label Distribution-Based Ensemble Learning Method for Online Educational Prediction

Online education becomes increasingly important since traditional learning is shocked heavily by COVID-19. To better develop personalized learning plans for students, it is necessary to build a model that can automatically evaluate students’ performance in online education. For this purpose, in this study we propose an ensemble learning method named light gradient boosting channel attention network (LGBCAN), which is based on label distribution estimation. First, the light gradient boosting machine (LightGBM) is used to predict the performance in online learning tasks. Then The Channel Attention Network (CAN) model further improves the function of LightGBM by focusing on better results in the K-fold CrossEntropy of LightGBM. The results are converted into predicted classes through post-processing methods named approximation of label distribution to complete the classification task. The experiments are employed on two datasets, data science bowl (DSB) and answer correctness prediction (ACP). The experimental results in both datasets suggest that our model has better robustness and generalization ability

Neurology of foreign language aptitude

This state-of-the art paper focuses on the poorly explored issue of foreign language aptitude, attempting to present the latest developments in this field and reconceptualizations of the construct from the perspective of neuroscience. In accordance with this goal, it first discusses general directions in neurolinguistic research on foreign language aptitude, starting with the earliest attempts to define the neurological substrate for talent, sources of difficulties in the neurolinguistic research on foreign language aptitude and modern research methods. This is followed by the discussion of the research on the phonology of foreign language aptitude with emphasis on functional and structural studies as well as their consequences for the knowledge of the concept. The subsequent section presents the studies which focus on lexical and morphosyntactic aspects of foreign language aptitude. The paper ends with a discussion of the limitations of contemporary research, the future directions of such research and selected methodological issues

Retrospective study in patients with HF ASD and Asperger syndrome: observations on clinical phenotypes

openIl disturbo dello spettro autistico è una condizione molto eterogenea, la cui eterogeneità è in parte determinata dalle differenze nel quoziente intellettivo (QI). Questo studio si concentra sulla parte di spettro senza deficit intellettivo (DI), che comprende l'autismo ad alto funzionamento (HFA o HF ASD) e la sindrome di Asperger (AS). Mentre “autismo ad alto funzionamento” è un'espressione non ufficiale usata per descrivere i pazienti autistici senza deficit intellettivo, il termine “sindrome di Asperger” ha avuto una breve esistenza all’interno della quarta edizione del Manuale diagnostico e statistico dei disturbi mentali (DSM-IV, 1995), prima di essere rimosso nella quinta edizione (DSM-5, 2013). Nonostante la sua breve durata, la sindrome di Asperger è riuscita a suscitare un enorme interesse e diverse controversie sulla sua validità diagnostica e sulla sua differenziazione dall’autismo ad alto funzionamento. Il presente studio si propone di esaminare AS e HFA e in particolare le loro differenze nei profili clinici. La popolazione dello studio è stata raccolta retrospettivamente tra i pazienti afferiti all'Unità di Neuropsichiatria del Dipartimento di Salute del Bambino e della Donna dell'Azienda Ospedaliera Universitaria di Padova, tra gennaio 2018 e gennaio 2022. Sono stati selezionati 43 pazienti che hanno ricevuto una diagnosi di disturbo dello spettro autistico secondo i criteri del DMS-5 e che non presentavano disabilità intellettiva. I pazienti sono stati poi suddivisi in due gruppi in base all’appartenenza ai sottogruppi HFA e AS. Sono state riscontrate differenze significative tra i due soprattutto per quanto riguarda l’età dei pazienti alla diagnosi, molti aspetti del linguaggio e della comunicazione, e le comorbilità (disturbi ansiosi e/o depressivi). Non sono state riscontrate invece differenze in molti altri aspetti, come la motricità e il sistema sensoriale, a riprova della forte somiglianza tra i due sottotipi. Da un punto di vista meramente clinico, le somiglianze sono apparse maggiori delle differenze.Autism spectrum disorder is a very heterogeneous condition, whose heterogeneity is in part determined by differences in intelligence quotient (IQ). This study focuses on the part of the spectrum without Intellectual disability (ID), which includes High Functioning Autism (HFA or HF ASD) and Asperger syndrome (AS). While “high functioning autism” is an unofficial expression used to describe autistic patients without intellectual deficiency (ID), the term “Asperger syndrome” had a brief existence as a diagnostic entity in the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV, 1995), before being removed in the fifth edition (DSM-5, 2013). Despite its short life span, Asperger syndrome still managed to arouse huge interest and controversy upon its diagnostic validity and its differentiation from HFA. The present study aims to examine AS and HFA and in particular their differences in clinical profiles. The population of the study was retrospectively collected among patients referred to the Neuropsychiatry Unit of Child and Woman Health Department, University Hospital of Padua, between January 2018 and January 2022. Forty-three patients, who received a diagnosis of autism spectrum disorder according to the DMS-5 criteria and who had no intellectual deficits, were selected. The patients were then divided into two groups based on the subtype: HFA and AS. Significant differences were found between the two, especially in the age of the patients at diagnosis, in many aspects of language and communication, as well as in comorbid disorders (anxiety and/or depressive disorders). No differences were found in many other aspects, such as motor and sensory systems, proving the strong similarity between the two subtypes. From a merely clinical point of view, similitudes appeared greater than differences.

Review of EEG and ERP studies of extraversion personality for baseline and cognitive tasks

According to psychological studies, the most fundamental personality is the extraversion personality. Most studies looking at differences between extroverts and introverts are pen and paper based studies. However, in a few studies, electrophysiological signals were involved. In this paper, we reviewed studies examining extraversion personality using electroencephalography (EEG) and event-related potentials (ERP). It was found that some of the EEG studies claimed that extroverts and introverts can be differentiated using baseline EEG, while some others claimed otherwise. Conflicting findings were also observed in the ERP studies; higher/lower P300 amplitude in extroverts compared to that of introverts in visual stimuli tasks. These various findings are probably due to differences in their experimental protocols, sample size, or age of subjects. Other possible reasons include no consideration given on the main feature of extraversion and the studies only focused on EEG power spectral analysis. We are thus suggesting for future investigations to involve the main feature such as sociability and/or to incorporate more EEG features in the analysis to produce more robust and reliable results. This review constitutes a guidance for research on brain-related conditions of extroverts and introverts and shall be useful in many areas

Graphonomics and your Brain on Art, Creativity and Innovation : Proceedings of the 19th International Graphonomics Conference (IGS 2019 – Your Brain on Art)

[Italiano]: “Grafonomia e cervello su arte, creatività e innovazione”. Un forum internazionale per discutere sui recenti progressi nell'interazione tra arti creative, neuroscienze, ingegneria, comunicazione, tecnologia, industria, istruzione, design, applicazioni forensi e mediche. I contributi hanno esaminato lo stato dell'arte, identificando sfide e opportunità, e hanno delineato le possibili linee di sviluppo di questo settore di ricerca. I temi affrontati includono: strategie integrate per la comprensione dei sistemi neurali, affettivi e cognitivi in ambienti realistici e complessi; individualità e differenziazione dal punto di vista neurale e comportamentale; neuroaesthetics (uso delle neuroscienze per spiegare e comprendere le esperienze estetiche a livello neurologico); creatività e innovazione; neuro-ingegneria e arte ispirata dal cervello, creatività e uso di dispositivi di mobile brain-body imaging (MoBI) indossabili; terapia basata su arte creativa; apprendimento informale; formazione; applicazioni forensi. / [English]: “Graphonomics and your brain on art, creativity and innovation”. A single track, international forum for discussion on recent advances at the intersection of the creative arts, neuroscience, engineering, media, technology, industry, education, design, forensics, and medicine. The contributions reviewed the state of the art, identified challenges and opportunities and created a roadmap for the field of graphonomics and your brain on art. The topics addressed include: integrative strategies for understanding neural, affective and cognitive systems in realistic, complex environments; neural and behavioral individuality and variation; neuroaesthetics (the use of neuroscience to explain and understand the aesthetic experiences at the neurological level); creativity and innovation; neuroengineering and brain-inspired art, creative concepts and wearable mobile brain-body imaging (MoBI) designs; creative art therapy; informal learning; education; forensics

Computational Creativity

In: Encyclopedia of Systems Biology, W. Dubitzky, O. Wolkenhauer, K-H Cho, H. Yokota (Eds.), Springer 2011Understanding brain processes behind creativity and modeling them using computational means is one of the grand challenges for systems biology. Computational creativity is a new field, inspired by cognitive psychology and neuroscience. In many respects human-level intelligence is far beyond what artificial intelligence can provide now, especially in regard to the high-level functions, involving thinking, reasoning, planning and the use of language. Intuition, insight, imagery and creativity are important aspects of all these functions