Natália B. Mota, Renata Callipo, Lígia Leite, Ana R. Torres, Janaína Weissheimer, Silvia A. Bunge, Mauro Copelli and Sidarta Ribeiro
Formal thought organization obtained from free speech, a key feature for psychiatric evaluations, has been poorly investigated during typical development. Computational tools such as speech graph connectedness (LSC) currently allow for an accurate quantification in naturalistic settings. LSC's typical development is better predicted by years of education than by age. Among beginning readers, the LSC of stories composed of short‐term memory predicted reading independently from IQ. Here we set out to test a longitudinal sample (6–8 years old, n = 45, followed for 2 years) to verify whether the LSC is predictive of various memory measures, and whether such relations can explain the correlation with reading. The LSC was specifically correlated with verbal short‐term memory performance. The results support the notion that the short‐term storage of verbal information is necessary to plan a story. Given the limited sample size, the relationship of this interaction with reading remains inconclusive.
The whole paper is available here.
The Research, Innovation and Dissemination Center for Neuromathematics (NeuroMat), hosted by the University of São Paulo, Brazil, and funded by the São Paulo Research Foundation (FAPESP), is offering two post-doctoral fellowships for recent PhDs with outstanding research potential. The research will involve collaborations with experimental and theoretical groups and laboratories associated to NeuroMat.
The Fellows Programme is part of the Frictionless Data for Reproducible Research project at Open Knowledge Foundation, a global, non-profit network that promotes and shares information at no charge, including both content and data. This project, funded by the Sloan Foundation, applies to work in Frictionless Data to data-driven research disciplines, in order to facilitate data workflows in research contexts. During the first half of 2019, Neuroscience Experiments System (NES) was selected to be a funded project of Frictionless Data. As you may know, NES is an open-source tool being developed that aims to assist neuroscience research laboratories in routine procedures for data collection. NES was developed to store a large amount of data in a structured way, allowing researchers to seek and share data and metadata of neuroscience experiments. To the best of our knowledge, there are no open-source software tools which provide a way to record data and metadata involved in all steps of an electrophysiological experiment and also register experimental data and its fundamental provenance information. With the anonymization of sensitive information, the data collected using NES can be publicly available through the NeuroMat Open Database, which allows any researcher to reproduce the experiment or simply use the data in a different study.
The Research, Innovation and Dissemination Center for Neuromathematics (RIDC NeuroMat) will launch in August the podcast "A Matemática do Cérebro" --in Portuguese, Mathematics of the Brain. This resource will be available on the most important podcast technologies and also hosted on its own website. The production of the podcast is led by NeuroMat director Antonio Galves and the newest member of the RIDC, Eduardo Vicente, from the University of São Paulo School of Communications and Arts.
Zacharias L. R., Peres A. S. C., Souza V. H., Conforto A. B. and Baffa O.
Small variations in TMS parameters, such as pulse frequency and amplitude may elicit distinct neurophysiological responses. Assessing the mismatch between nominal and experimental parameters of TMS stimulators is essential for safe application and comparisons of results across studies.
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