Victor M. Vergara

This paper introduces a new framework called 'flownectome' to analyze BOLD fMRI dynamics by capturing spatial signal propagation and propagative attractor patterns, revealing significant relationships with schizophrenia.

This paper introduces a method to analyze spatial propagation in BOLD fMRI using Markov processes, revealing functional attractors that differ between schizophrenia patients and controls.

The paper introduces EVOdFNCs, a method to analyze dynamic brain connectivity patterns in rs-fMRI data, revealing associations with schizophrenia symptoms.

This paper introduces a framework for analyzing localized brain dynamics within a whole-brain HMM framework, identifying group differences in entropy and dynamics in schizophrenia patients compared to controls.

The paper introduces a toolbox to automatically label and order brain activity maps with high accuracy using a pretrained model and integration of existing techniques.

This paper uses machine learning to classify resting-state functional network connectivity data from rats to detect prenatal alcohol exposure, achieving higher accuracy in females than males.

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

This study analyzes age-related changes in brain structure and function across 5967 individuals from 13 to 72 years old, revealing nonlinear patterns in gray matter volume, structural network correlation, and functional network connectivity.