Kit Melissa Larsen

The paper investigates how predictive processing networks change with age, finding that aging redistributes resources across subsystems rather than uniformly weakening them.

This study uses EEG data and machine learning to predict subclinical psychotic-like experiences on a continuum between healthy individuals and schizophrenia patients.

This study uses machine learning to predict the severity of specific psychotic symptoms from fMRI and behavioural data in schizophrenia patients.

This study investigates differences in auditory precision encoding connectivity between schizophrenia spectrum disorder patients, non-psychotic psychiatric inpatients, and healthy controls, finding group-specific and continuum-related connectivity changes.

This study examines neural responses to repeated sounds in individuals with 22q11.2 deletion syndrome, finding reduced adaptation but intact prediction, linked to negative symptom severity.