Bruno Hebling Vieira

Bruno Hebling Vieira
University of Zurich | UZH · Psychologisches Institut

Doutor em Ciências (PhD)
Postdoktorand at the University of Zurich using machine-learning to understand progression of neurodegenerative diseases

About

24
Publications
1,980
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33
Citations
Introduction
Bruno Hebling Vieira is currently in a Postdoctoral research position at the Department of Psychology at the University of Zurich, Switzerland. Areas of expertise include Neuroimaging, Machine learning, and Data Analysis.
Additional affiliations
March 2018 - November 2021
University of São Paulo
Position
  • PhD Student
Education
March 2018 - November 2021
Universidade de São Paulo
Field of study
  • Physics Applied to Medicine and Biology
March 2016 - February 2018
Universidade de São Paulo
Field of study
  • Physics Applied to Medicine and Biology
March 2012 - December 2015
Universidade de São Paulo
Field of study
  • Medical Physics

Publications

Publications (24)
Article
Full-text available
Reviews and meta-analyses have proved to be fundamental to establish neuroscientific theories on intelligence. The prediction of intelligence using invivo neuroimaging data and machine learning has become a widely accepted and replicated result. We present a systematic review of this growing area of research, based on studies that employ structural...
Preprint
Memory deficits are a hallmark of many different neurological and psychiatric conditions. The Rey-Osterrieth complex figure (ROCF) is the state-of-the-art assessment tool for neuropsychologists across the globe to assess the degree of non-verbal visual memory deterioration. To obtain a score, a trained clinician inspects a patient's ROCF drawing an...
Article
Full-text available
Previous literature has focused on predicting a diagnostic label from structural brain imaging. Since subtle changes in the brain precede cognitive decline in healthy and pathological aging, our study predicts future decline as a continuous trajectory instead. Here, we tested whether baseline multimodal neuroimaging data improve the prediction of f...
Article
Full-text available
Logistic growth regressions present high uncertainties when data are not past their inflection points. In such conditions, the uncertainty in the estimated carrying capacity K, for example, can be of the order of K. Here, we present a method for uncertainty reduction in logistic growth regression using data from a surrogate logistic growth process....
Preprint
Full-text available
Human intelligence is one of the main objects of study in cognitive neuroscience. Reviews and meta-analyses have proved to be fundamental to establish and cement neuroscientific theories on intelligence. The prediction of intelligence using in vivo neuroimaging data and machine learning has become a widely accepted and replicated result. Here, we p...
Article
Full-text available
Prediction of cognitive ability latent factors such as general intelligence from neuroimaging has elucidated questions pertaining to their neural origins. However, predicting general intelligence from functional connectivity limit hypotheses to that specific domain, being agnostic to time‐distributed features and dynamics. We used an ensemble of re...
Article
Full-text available
Brain aging is a complex process, entailing alterations at the most diverse levels of brain structure and functioning. On the macroscopical scale, gray matter atrophy is one of its most prominent markers, but it remains to be elucidated why some regions are more affected by it than others. In this work, we aimed to explore how age affects the morph...
Article
Full-text available
Human intelligence has always been a fascinating subject for scientists. Since the inception of Spearman's general intelligence in the early 1900s, there has been significant progress towards characterizing different aspects of intelligence and its relationship with structural and functional features of the brain. In recent years, the invention of...
Preprint
Full-text available
Logistic regressions are subject to high uncertainty when the data are not past the inflection point. For example, for logistic regressions estimated with data up to or before inflection point, uncertainties in the upper asymptotic value $K$ can be of the same order of magnitude of the population under analysis. This paper presents a method for unc...
Article
Full-text available
Canonical resting state networks (RSNs) can be obtained through independent component analysis (ICA). RSNs are reproducible across subjects but also present inter-individual differences, which can be used to individualize regions-of-interest (ROI) definition, thus making fMRI analyses more accurate. Unfortunately, no automatic tool for defining sub...
Article
Full-text available
Normal aging incurs functional and anatomical alterations in the brain. Cortical thinning, age-related alterations in resting-state functional connectivity (RSFC) and reductions in fractional amplitude of low frequency fluctuations (fALFF) are key components of brain aging that can be studied by neuroimaging. However, the level of association betwe...
Preprint
Full-text available
Normal aging incurs functional and anatomical alterations in the brain. Cor-tical thinning, age-related alterations in resting-state functional connectivity (RSFC) and reductions in fractional amplitude of low frequency fluctuations (fALFF) are key components of brain aging that can be studied by neu-roimaging. However, the level of association bet...
Article
Background Generalized Partial Directed Coherence (GPDC) is a multivariate measure of predictability between functional timeseries defined in the frequency domain. However, analysis has often been constrained by its compositional nature. Specifically, the squared GPDC from a node region to all nodes in any given frequency must sum to one. New metho...
Preprint
Full-text available
Background: Generalized Partial Directed Coherence (GPDC) is a multivariate measure of predictability between functional timeseries defined in the frequency domain. However, analysis has often been constrained by its compositional nature. Specifically, the squared GPDC from a node region to all nodes in any given frequency must sum to one. New Meth...
Article
Full-text available
Introduction: The interpretation of brain tumors and abscesses MR spectra is complex and subjective. In clinical practice, different experimental conditions such as field strength or echo time (TE) reveal different metabolite information. Our study aims to show in which scenarios magnetic resonance spectroscopy can differentiate among brain tumors,...
Conference Paper
Full-text available
Corpus Callosum (CC) is a white matter structure connecting cerebral right and left hemispheres, through which major neural fiber tracts across over. The brain structure and function changes across lifespan and MRI is a useful tool to quantify this alterations. The structural connectivity can be characterized by regional cortical thickness and diff...
Conference Paper
Full-text available
Introduction: The aging process causes structural and functional alterations in the healthy brain. From Diffusion Tensor Imaging (DTI) data, tractography allows virtual visualization of the architecture of white matter (WM) tracts and the estimation of structural connectivity parameters, as fiber density (FD) and fractional anisotropy (FA). Both pa...

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Projects

Projects (2)
Project
To augment the current understanding of the neural bases of human intelligence inter-subject differences. To this end, we employ machine learning and fMRI.