Using neuroimaging and electroencephalography for prediction of treatment resistance in psychiatric disorders

Abstract

Earlier identification of psychiatric patients who are prone to treatment resistance could avoid the frustration of a trial-and-error approach and might facilitate the design of more optimized treatment regimens and setting of individualized level of care. Although current candidate biomarkers for psychiatric disorders await further validation, knowledge on candidate genomic and brain-based biomarkers is increasing rapidly. Thus, this chapter illustrates recent study findings regarding clinical application of brain-based biomarkers derived from patients for the prediction of response or resistance to treatment, as well as for improved design of clinical studies, to find more robust brain-based biomarkers of treatment response or resistance. First, for patients diagnosed with psychotic disorders, mood disorders, or anxiety disorders, changing patterns of structural-functional brain characteristics that result from treatment with pharmacotherapy, cognitive behavioral therapy, as well as direct brain stimulation will be reviewed. Second, we will show the brain-based predictors of treatment response at baseline. Third, we will turn from exploration based on groupwise predictive power to the individual-level prediction of treatment response and focus on the recent trends in machine learning-based studies in which brain-based biomarkers are applied as explanatory or predictive features.