Barbora Rehák Bučková

This paper updates the normative modelling protocol for computational psychiatry, including new methods, software tools, and models for neuroimaging data analysis.

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 longitudinal normative modelling of white matter fractional anisotropy in the EU-AIMS LEAP cohort to show individual deviations in white matter development are linked to clinical variation in autism, with significant cross-sectional and longitudinal brain-behaviour associations.

This paper addresses structured missingness in biomedical data by introducing a hierarchical imputation framework and advocating for alternative evaluation metrics.

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.

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

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.