František Váša

This study develops a deep learning model to enhance ultra-low-field MRI scans to near high-field resolution without paired high-field data, improving image quality and accessibility.

This paper presents a normative framework for brain asymmetry across the lifespan using a large neuroimaging dataset, revealing distinct spatial patterns and providing tools for clinical and research applications.

The paper uses kriging to show that task-induced brain activity increases can be predicted from activity decreases, revealing topographically determined antagonistic relationships.

This study evaluates the reliability and correspondence of ultra-low-field MRI morphometry to high-field MRI, showing high reliability and correspondence across scan resolutions and contrasts.

This study examines functional connectivity differences between preterm and term adults and identifies behavioral subgroups with distinct connectivity patterns.

This paper develops a deep learning model to enhance ultra-low-field MRI scans for pediatric neurodevelopmental research, achieving better reconstruction of brain structures than existing methods.

The paper examines how morphometric similarity in brain regions changes during adolescence, finding distinct developmental patterns in paralimbic versus neocortical areas.

This paper shows how heterogeneous spatial autocorrelation affects brain map comparisons and introduces a Bayesian spatial regression approach for valid statistical inference.

The paper uses variograms to show that function changes more rapidly in association cortex than in sensory and motor cortex, linked to intracortical myelin levels.

The paper investigates how resilient functioning in adolescents exposed to childhood adversity relates to structural brain network topology, finding lower nodal degree in specific brain regions associated with higher resilience.

This study compares EPImix and conventional T1-weighted MRI scans for tissue volume estimation and brain-age prediction, finding similar results but a systematic age offset that can be corrected.

The paper examines sex differences in brain network development during adolescence and links them to depression risk, finding divergent patterns in functional connectivity and gene expression.

This paper evaluates rapid processing of multicontrast EPImix scans and shows that quantitative neuroimaging data can be obtained within minutes, enabling adaptive imaging protocols.

The paper introduces an active learning approach to efficiently approximate multiverse analyses in neuroimaging, demonstrating its ability to identify optimal analysis pipelines for predicting participant ages.

The paper links genetic risks to spatial brain vulnerabilities in neurodevelopmental disorders using neuroimaging and gene expression data.

The paper identifies two modes of brain-behavior covariation in adolescents and young adults using canonical correlation analysis, linking brain connectivity to psychometric, IQ, and demographic data.

The paper identifies two modes of brain functional connectivity change during adolescence: conservative and disruptive, with distinct patterns in primary and association cortex.

The paper identifies age-related waves of cortical myelination and demyelination using micro-structural MRI markers in humans across the lifespan.

The paper introduces morphometric similarity networks to map cortical organization and shows they predict cognitive variation across individuals.

The paper constructs structural covariance networks from MRI data and links them to transcriptomic networks, finding coupling between network topology and gene expression in supragranular layers.

This study applies probabilistic thresholding to functional connectomes in schizophrenia, finding increased dysconnection and reduced consistency compared to healthy controls.

The paper examines age-related changes in structural brain networks during adolescence, finding decreased structural correlation and changes in nodal degree in association cortex.