Predicting medication response in ADHD through computational modeling of the Continuous Performance Test
ADHD is associated with negative consequences both for those diagnosed and the society in general.
Medicines can reduce symptoms, improve daily functioning and increase quality of life. However, it is estimated that about one in three patients with ADHD do not respond satisfactorily to ADHD-medication. Early detection of non-responders could significantly improve their treatment, but finding early indicators of medication response has proven difficult.
About the project
The current project takes a novel approach to prediction of medication response. By using computational modeling of decision making we will analyse already collected data from 250 adult ADHD patients. Patients in this study performed a computer-based test, the Continuous Performance Test (CPT), both before and 6 weeks after starting medication treatment. Preliminary results reveal that by analysing data with computational model, in contrast to standard analyses, responders and non-responders can be separated based on performance measures.
Adapting the analysis tool and further investigating the collected data will be performed under the supervision of Professor Michael Frank at Brown University. Dr. Frank is one of the most prominent researchers in Computational Psychiatry, a recent field in which computational models of cognition and brain function are used to better understand psychiatric disorders. Another goal for the project is to make the analysis tool available for other researchers to analyse CPT-data. The next phase of the projects involves using the modified model to analyse data from other clinical groups. This work will be performed through close collaboration with researchers at the Department of Psychology at the University of Oslo and at Oslo University Hospital. The goal of this phase is to investigate whether the model can provide new insight into how decision making is affected across psychiatric disorders.