Computational Tracking of Mental Health in Youth

Mota N., Copelli M., Ribeiro S.

The early onset of mental disorders can lead to serious cognitive damage, and timely interventions are needed in order to prevent them. In patients of low socioeconomic status, as is common in Latin America, it can be hard to identify children at risk. Here, we briefly introduce the problem by reviewing the scarce epidemiological data from Latin America regarding the onset of mental disorders, and discussing the difficulties associated with early diagnosis. Then we present computational psychiatry, a new field to which we and other Latin American researchers have contributed methods particularly relevant for the quantitative investigation of psychopathologies manifested during childhood. We focus on new technologies that help to identify mental disease and provide prodromal evaluation, so as to promote early differential diagnosis and intervention. To conclude, we discuss the application of these methods to clinical and educational practice. A comprehensive and quantitative characterization of verbal behavior in children, from hospitals and laboratories to homes and schools, may lead to more effective pedagogical and medical intervention.

Machine Learning Algorithms for Automatic Classification of Marmoset Vocalizations

Hjalmar K. Turesson, Sidarta Ribeiro, Danillo R. Pereira, João P. Papa, Victor Hugo C. de Albuquerque

Automatic classification of vocalization type could potentially become a useful tool for acoustic the monitoring of captive colonies of highly vocal primates. However, for classification to be useful in practice, a reliable algorithm that can be successfully trained on small datasets is necessary. In this work, we consider seven different classification algorithms with the goal of finding a robust classifier that can be successfully trained on small datasets. We found good classification performance (accuracy > 0.83 and F1-score > 0.84) using the Optimum Path Forest classifier. Dataset and algorithms are made publicly available.

Bolsas para trabalhar com Arquitetura e Desenvolvimento de Jogos

O Centro de Pesquisa, Inovação e Difusão em Neuromatemática (NeuroMat) está oferecendo bolsas de treinamento técnico da FAPESP para profissionais interessados em aplicar seus conhecimentos de computação no desenvolvimento de pesquisa científica de alto nível. Os bolsistas irão interagir com pesquisadores da USP e demais cientistas e colaboradores do NeuroMat em atividades de desenvolvimento, adaptação, manutenção e instalação de software de suporte à pesquisa científica do centro.

Direct PhD position (no MA required)

The doctoral program in Statistics (Statistics and Probability) of the Institute of Mathematics and Statistics, University of São Paulo, Brazil, has 3 fellowships for a full time PhD student without master's degree. These are full fellowships for a period of 48 months, starting from February 2017. These positions are focused on research in probability theory and inference in stochastic processes with emphasis on stochastic modeling of neuron biological data.

NeuroMat na construção de rede para capacitar pessoas com doença de Parkinson na formação de estratégias terapêuticas

Como parte das atividades clínicas propostas pelo Centro de Pesquisa, Inovação e Difusão da FAPESP para Neuromatemática (CEPID NeuroMat), foi lançado neste mês de setembro o projeto "Rede Amparo", uma iniciativa que irá promover a integração de pacientes, familiares e profissionais de saúde que vivenciam a doença de Parkinson.

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O Centro de Pesquisa, Inovação e Difusão em Neuromatemática está sediado na Universidade de São Paulo e é financiado pela FAPESP (Fundação de Amparo à Pesquisa do Estado de São Paulo).

 

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