Bruno Hebling Vieira

Predicting children's literacy from task-based fMRI: Neural heterogeneity and task-dependent performance

This study predicts children's literacy from task-based fMRI by comparing task types and contrasts, finding active tasks and multisensory learning tasks yield better predictive performance.

Toward Robust Neuroanatomical Normative Models: Influence of Sample Size and Covariates Distributions

This study evaluates how sample size and covariate distributions affect neuroanatomical normative models in Alzheimer's disease, showing that demographic alignment is crucial for reliable results.

Automating clinical assessments of memory deficits: Deep Learning based scoring of the Rey-Osterrieth Complex Figure

This paper develops a deep learning model to automate scoring of the Rey-Osterrieth Complex Figure (ROCF) drawings, outperforming human raters and clinicians in accuracy and consistency.

Beyond functional connectivity: deep learning applied to resting-state fMRI time series in the prediction of 58 human traits in the HCP

This study applies deep learning to resting-state fMRI time series to predict 58 human traits, finding comparable performance to kernel-based methods with some advantages in robustness to training set size reductions.

An open, analysis-ready, and quality controlled resource for pediatric brain white-matter research

The paper curates and provides analysis-ready diffusion MRI data for pediatric brain white-matter research, including automated quality control using a trained neural network.

On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting

This paper reviews methods for predicting human intelligence from neuroimaging data, finding differences in performance between general and fluid intelligence predictions.