Amir Omidvarnia

This study identifies a latent clinical-anatomical dimension linking metabolic syndrome severity to brain morphology and cognition in 40,087 individuals.

This study compares the predictive power of resting-state fMRI features and individual characteristics for cognitive phenotypes in 20,000 UK Biobank subjects, finding individual characteristics generally perform better.

Low phenotypic measurement reliability severely limits out-of-sample brain-behaviour prediction accuracy and prevents models from fully benefiting when neuroimaging sample sizes scale from hundreds to thousands of participants.

The paper evaluates how different levels of co-fluctuation in functional connectivity predict individual-level phenotypes using machine learning.

The paper validates that rsfMRI temporal complexity is reproducible and correlates with fluid intelligence across multiple brain networks.

The paper introduces a normalized participation coefficient to reduce module size bias in network analysis, demonstrating improved identification of high-participation nodes in various networks.

This paper develops an automatic EEG spike detection method for epilepsy studies, showing it can detect more spikes, identify additional brain regions, and speed up analysis compared to manual methods.

The paper models brain network switching using multilayer networks and finds it correlates with cognitive performance and sleep.

This study compares instantaneous phase synchrony analysis and correlation-based sliding window analysis for time-resolved fMRI connectivity, finding they are comparable with optimal parameters.