Jaroslav Hlinka

This study uses fixel-based analysis to find reduced frontal fibre density in male first-episode psychosis patients compared to controls, with no significant differences in females.

This study presents a computational model of the motor pathway that reproduces individual MEP waveforms and reveals the critical role of spinal recurrent inhibition in shaping their temporal structure.

The paper examines altered attention network dynamics in resting-state fMRI of individuals with high smartphone addiction scores using dynamic functional connectivity methods.

This study compares parcellation strategies in schizophrenia rs-fMRI, finding that individualized parcellation better captures dynamic brain dynamics and symptom associations.

This paper introduces a method to analyze neural avalanches using optimal transport and dynamic time warping, revealing 12 propagation patterns and a shift toward stable patterns under psilocybin.

This study examines EEG microstate dynamics during psilocybin intoxication and finds correlations between acute neural changes and long-term psychological effects.

The paper identifies changes in EEG connectivity metrics as potential indicators of early antidepressant treatment response in major depressive disorder patients.

This study identifies cortical cell-type and receptor gradients that correlate with resting-state alpha-BOLD coupling patterns.

The paper attempts to replicate a study linking personality to functional connectomics but fails to replicate any of the reported neuroimaging correlates.

This paper introduces HORDCOIN, a software library for computing connected information using entropic constraints, demonstrating its effectiveness in detecting higher-order interactions in biomedical data.

This study evaluates methods to improve cross-site classification of first-episode schizophrenia using multimodal neuroimaging data, finding that domain adaptation strategies partially mitigate site-related performance degradation.

This study uses computational modeling and in vitro experiments to investigate how low-frequency electrical stimulation delays seizures by modulating ion dynamics and neuronal excitability in rat hippocampal slices.

The paper examines the relationship between personality traits and brain activity using naturalistic fMRI data, finding that higher Neuroticism/Harm Avoidance is associated with lower typicality in brain activity.

This study uses a computational model to show that ion dynamics underlie the temporal structure of resting-state functional connectivity.

This study uses fMRI to compare neural responses to ancestral, modern, and airborne threats, finding distinct activation patterns for each category.

This study uses fMRI and EEG to distinguish internally and externally oriented spontaneous thoughts during rest, finding distinct neural patterns in brain networks and alpha power.

This study investigates altered sense of agency in first-episode schizophrenia patients using neuroimaging, finding consistent DMN hypo-activation across two independent cohorts.

This study compares ROI-level and network-level functional connectivity approaches for early schizophrenia classification, finding that medium-resolution atlases provide the best accuracy.

This study uses a mathematical model of cortical microcircuits to show that input variability affects EEG-BOLD correlations, revealing negative alpha-BOLD and positive gamma-BOLD relationships.

This study examines structural connectivity disruptions in patients with first episode schizophrenia, finding reduced connectivity in specific brain regions linked to cognitive and language functions.

This paper compares EEG and fMRI data using spatio-spectral decomposition, finding that EEG patterns show weak but significant spatial similarity to fMRI BOLD signatures, with complementary information.

This paper introduces a framework using normative models to assess intra-individual longitudinal changes in neuroimaging data, revealing significant grey matter normalization in schizophrenia patients over time.

The paper quantifies non-linear brain connectivity across modalities and scales, finding that non-linear interactions are more prominent in microscale recordings than in non-invasive, large-scale modalities.

This study examines hyper- and hypoconnectivity in early schizophrenia using fMRI data, finding localized dysconnections and highlighting the impact of denoising strategies on results.

This study shows that cholinergic modulation dynamically switches resting-state networks by selectively suppressing the Default Mode Network in both rodent and human models.

The paper introduces Amplitude Entropy to detect seizure-related changes in EEG signals by analyzing chimera-like states, showing significant increases during seizures across patients and frequency bands.

This study compares microstate algorithms on EEG data and tests if linear EEG properties determine microstate dynamics, finding strong support for linear models.

The paper develops a method to infer group brain networks from intracranial EEG data, revealing interactions between visual streams and hippocampal connectivity during visual processing.

This study evaluates the use of persistent homology in topological data analysis for distinguishing brain states in epilepsy and schizophrenia using functional connectivity data.

This paper analyzes how perturbations can trigger or delay seizures in slow-fast relaxation oscillators, showing that perturbation amplitude, frequency, and timing determine seizure outcomes.