Guido Alexander van Wingen

This study analyzes fMRI data from OCD patients and controls to identify age-specific differences in brain activation during inhibitory control and error processing.

This paper introduces a Hodge-Laplacian framework to analyze higher-order topology in OCD brain networks, identifying loop-level abnormalities not detectable by conventional functional connectivity analysis.

This study identifies brain abnormalities and developmental trajectories associated with the onset of six mental health problems in adolescents.

This study evaluates seven harmonization techniques on multi-site fMRI data to determine their impact on disorder classification, finding that they have little effect in standard settings but may be critical in imbalanced scenarios.

This study uses machine learning to predict 2-year MDD chronicity by integrating proteomics with clinical data, showing improved accuracy over psychiatrists' predictions.

This study shows that multimodal multilayer network centrality in the frontoparietal network is positively associated with executive functioning, outperforming unilayer centrality.

This study investigates the relationship between neural excitation/inhibition imbalance and treatment outcomes in severe depression, finding that ECT effectiveness is not tied to changes in inhibition but can be enhanced by pharmacotherapy under specific conditions.

The paper investigates subcortico-cortical connectivity development in ASD using dynamic causal modelling, finding delayed functional segregation and reduced subcortical influence on cortical processing in ASD compared to TD controls.

Deep brain stimulation improves mood and anxiety in obsessive-compulsive disorder by altering connectivity between the amygdala, insula and prefrontal cortex.

This study examines how acute stress affects neural network balance in OCD patients, finding blunted connectivity changes in the caudate nucleus linked to compulsivity.

The paper uses neuroimaging data to predict psychotherapy outcomes in PTSD, identifying specific resting-state networks that distinguish responders from non-responders.