Chatrin Suksasilp

Chatrin is most interested in understanding the heterogeneity of mental health conditions, to pave the way towards personalised mental healthcare: matching patients to the treatments most likely to work for them. Depression, for example, shows a bewildering diversity of possible symptoms, which likely reflect different underlying mechanisms in the brain and experience. Confusing this further, even identical symptoms might be caused by different mechanisms. Consequently, treatments for mental illness do not work for everyone, and clinicians currently have little to no tools for choosing the best treatment for a specific patient. Chatrin is most excited by emerging techniques in computational psychiatry - generative modelling and machine learning - that can address these challenges by developing more biologically valid and clinically predictive phenotypes.


Chatrin completed the BSc in Psychology and Language Sciences at UCL, and the MSc in Psychological Research at the University of Oxford. He became interested in mental health research by investigating interoception: how the brain senses and controls bodily states (such as hunger, temperature, and fatigue) to provide the building blocks for emotion. Altered interoception contributes to a wide range of mental health conditions, such as depression, anxiety, and schizophrenia. Chatrin is interested in shedding light on how this happens, which could lead to new mental health treatments that focus on brain-body interactions.