Rogers F. Silva

This paper introduces MSIVA, a method to capture latent relationships between brain structure and function, showing it identifies phenotype-associated brain regions in neuroimaging data.

The paper links functional change patterns to brain structure using multimodal fusion in adolescents, revealing sex-specific differences in neural dynamics.

This paper uses aNy-way ICA to identify a cortico-thalamo-cerebellar circuit in schizophrenia, showing altered connectivity and cognitive impairments across multiple datasets.

The paper introduces a voxel-centric framework using information theory to analyze multi-way brain network interactions, revealing reduced Hamming weights in auditory, visual, and motor networks in schizophrenia.

This paper develops a method to detect linked features across neuroimaging modalities and identifies markers related to age, sex, cognition, and schizophrenia in large datasets.

The paper introduces dcSBM, a decentralized constrained source-based morphometry method for multivariate brain structure analysis, showing it reliably identifies group differences in brain morphology across sites.

This study uses a Siamese CNN framework to predict individual identity from resting-state fMRI data, achieving 88% accuracy in subject discrimination using spatial network pairs.

This study compares deep learning and standard machine learning on brain imaging data, showing deep learning's superior predictive power and robustness in age and gender classification.

The paper analyzes resting-state fMRI data across different frequency bins to assess functional network connectivity and finds distinct patterns at higher frequencies.

This paper introduces spICA to link genomic and brain imaging data, identifying a SNP and GMV association in ADHD linked to working memory deficits.

This paper introduces a method to fuse functional and structural MRI data by integrating temporal information, identifying linked variability and covariation in schizophrenia.

This paper introduces a sparse infomax algorithm using Hoyer projection for improved pattern recovery in simulated structural MRI and SNP data, showing better sparseness and performance under low signal-to-noise ratio conditions.