Search

Your search keyword '"Vanello, N."' showing total 177 results
Start Over Author "Vanello, N."
177 results on '"Vanello, N."'

1. DataBrain: a web-accessible database for three-dimensional reconstructions and quantitative morphometrics of neurons

Author

Magliaro, C., Callara, A. L., Arami, N., De Maria, C., Ferrari, V., Vanello, N., Tognetti, A., Mangione, M., Viaggi, M. C., Vaglini, F., Castagna, M., De Rossi, D., Landini, L., Ahluwalia, A., Magjarevic, Ratko, Editor-in-chief, Ładyżyński, Piotr, Series editor, Ibrahim, Fatimah, Series editor, Lacković, Igor, Series editor, Rock, Emilio Sacristan, Series editor, Eskola, Hannu, editor, Väisänen, Outi, editor, Viik, Jari, editor, and Hyttinen, Jari, editor

Published
2018
Full Text
View/download PDF

4. Human body odour modulates neural processing of faces: Effective connectivity analysis using EEG

Author

Ferdowsi, S, Ognibene, D, Foulsham, T, Greco, A, Callara, A, Cervera-Torres, S, Alcaniz, M, Vanello, N, Citi, L, Callara, AL, Ferdowsi, S, Ognibene, D, Foulsham, T, Greco, A, Callara, A, Cervera-Torres, S, Alcaniz, M, Vanello, N, Citi, L, and Callara, AL

Abstract

Facial emotion processing by the brain plays a decisive role in human social interactions. This signal helps us interpret and predict people's behaviours. However, other social signals such as human voices or human body odours may facilitate or impair the identification of facial expressions. Here we studied the effects of emotional human body odours on face processing by measuring evoked neural responses and brain connectivity using the electroencephalogram (EEG). We used an emotion recognition task in which the participants attributed an emotion (i.e. happy vs fearful) to a presented face image while simultaneously exposed to emotional body odours. First, we measured face related potentials (FRP)s including P100 and N170 components. Statistical analyses revealed significant differences among FRPs recorded in different odour conditions. Second, we used a hierarchical Bayesian approach including a group dynamic causal model (DCM) followed by parametric empirical Bayes (PEB) to characterize the brain network explaining differences between FRPs. Our preliminary results suggested that different brain networks contribute to neutral face processing in the presence of different emotional body odours.

Published
2023

9. DataBrain: a web-accessible database for three-dimensional reconstructions and quantitative morphometrics of neurons

Author

Magliaro, C., primary, Callara, A. L., additional, Arami, N., additional, De Maria, C., additional, Ferrari, V., additional, Vanello, N., additional, Tognetti, A., additional, Mangione, M., additional, Viaggi, M. C., additional, Vaglini, F., additional, Castagna, M., additional, De Rossi, D., additional, Landini, L., additional, and Ahluwalia, A., additional

Published
2017
Full Text
View/download PDF

10. Transmural voltage gradient dispersion and heterogeneity in brugada syndrome phenotype - a novel workflow for advanced mapping using endocardial unipolar electrograms J-point elevation

Author

Rossi, A, primary, Hartwig, V, additional, Morelli, M S, additional, Martini, N, additional, Zaurino, N, additional, Notarstefano, P, additional, Nesti, M, additional, Giannoni, A, additional, Mansi, G, additional, Mirizzi, G, additional, Panchetti, L, additional, Garibaldi, S, additional, Startari, U, additional, Piacenti, M, additional, and Vanello, N, additional

Published
2022
Full Text
View/download PDF

17. Electrodermal activity and speech features as predictors for arousal level changes after affective word pronunciation

Author

CLAUDIA MARZI, Greco, A., Scilingo, E. P., and Vanello, N.

Subjects
regression model, arousal, speech, electrodermal activity, word pronunciation
Abstract

This work explores the possibility of estimating subject arousal through the analysis of speech and electrodermal activity (EDA). One critical issue to be clarified is the reliability of EDA signal during speech production. To accomplish this task, a relation among EDA, speech activity and subject arousal during isolated affective word pronunciation task, will be investigated. The results show that significant information on subject arousal can be still obtained by analyzing EDA during speech. In fact, a significant relationship between EDA features and self-reported arousal can be observed. In addition, a quantitative linear model relating EDA- and speech-related features could be identified. These preliminary results indicate how the analysis of concurrent acquisition of EDA and speech deserves further attention and could offer a valid approach for the prediction of subject arousal during speech production, as a method for validating self-assessment ratings.

Published
2019

34. Design of a quadrature surface coil for hyperpolarized13C MRS cardiac metabolism studies in pigs

Author

Giovannetti, G., primary, Frijia, F., additional, Hartwig, V., additional, Attanasio, S., additional, Menichetti, L., additional, Vanello, N., additional, Positano, V., additional, Ardenkjaer-Larsen, J.H., additional, Lionetti, V., additional, Aquaro, G.D., additional, Marchi, D. De, additional, Schulte, R.F., additional, Wiesinger, F., additional, Landini, L., additional, Lombardi, M., additional, and Santarelli, M.F., additional

Published
2013
Full Text
View/download PDF

35. Analysis of Residual Dependencies of Independent Components Extracted from fMRI Data.

Author

Vanello, N., Ricciardi, E., and Landini, L.

Subjects
*FUNCTIONAL magnetic resonance imaging, *INDEPENDENT component analysis, *ESTIMATION theory, *HISTOGRAMS, *ALGORITHMS
Abstract

Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data can be employed as an exploratory method. The lack in the ICA model of strong a priori assumptions about the signal or about the noise leads to difficult interpretations of the results. Moreover, the statistical independence of the components is only approximated. Residual dependencies among the components can reveal informative structure in the data. A major problem is related to model order selection, that is, the number of components to be extracted. Specifically, overestimation may lead to component splitting. In this work, a method based on hierarchical clustering of ICA applied to fMRI datasets is investigated. The clustering algorithm uses a metric based on the mutual information between the ICs. To estimate the similarity measure, a histogram-based technique and one based on kernel density estimation are tested on simulated datasets. Simulations results indicate that the method could be used to cluster components related to the same task and resulting from a splitting process occurring at different model orders. Different performances of the similarity measures were found and discussed. Preliminary results on real data are reported and show that the method can group task related and transiently task related components. [ABSTRACT FROM AUTHOR]

Published
2015
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results