Investigating Neural Response Variability as a Single-patient Predictor of Successful CBT in Clinical Psychiatry

Many psychiatric patients are not sufficiently improved by current interventions. Functional magnetic imaging brain imaging (fMRI) has proven to be a promising method for predicting treatment outcomes in psychiatric treatment. Individuals moment-to-moment variability have not yet been evaluated as a predictor of treatment of three common forms of mental illness: depression, insomnia and health anxiety. The goal is to investigate whether objective measurements of brain function contribute to a better prediction of a patient's success in treatment than experiences and self-reports, e.g., treatment credibility and patients expectations about the treatment. The prediction model will be tested on internet-delivered CBT (iCBT) for depression, insomnia and social anxiety. Patients in each diagnostic group are asked for participation before treatment. The total number of participants in this study will amount to 225 participants. The goal is that 35% consists of healthy controls and that the remaining part is equally distributed between the three diagnostic patient groups. Being able to better predict how well a psychiatric treatment will work for an individual has great value from both an economic and a treatment perspective. The findings from this study may contribute to increased knowledge about neurobiological complications in mental illness. In the longer term, it can lead to improved routines and help in clinical decision-making when patients should be recommended treatment.