title: Predicting the brain response to treatment using a Bayesian hierarchical model with application to a study of schizophrenia.
abstract: In vivo functional neuroimaging, including functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), is becoming increasingly important in defining the pathophysiology of psychiatric disorders such as schizophrenia, major depression, and Alzheimer's disease. Furthermore, recent studies have begun to investigate the possibility of using functional neuroimaging to guide treatment selection for individual patients. By studying the changes between a patient's pre- and post-treatment brain activity, investigators are gaining insights into the impact of treatment on behavior-related neural processing traits associated with particular psychiatric disorders. Furthermore, these studies may shed light on the neural basis of response and nonresponse to specific treatments. The practical limitation of such studies is that the post-treatment scans offer little guidance to treatment selection in clinical settings, since treatment decisions precede the availability of post-treatment brain scans. This shortcoming represents the impetus for developing statistical methodology that would provide clinicians with predictive information concerning the effect of treatment on brain activity and, ultimately, symptom-related behaviors. We present a prediction algorithm that uses a patient's pretreatment scans, coupled with relevant patient characteristics, to forecast the patient's brain activity following a specified treatment regimen. We derive our predictive method from a Bayesian hierarchical model constructed on the pre- and post-treatment scans of designated training data. We perform estimation using the expectation-maximization algorithm. We evaluate the accuracy of our proposed prediction method using K-fold cross-validation, quantifying the error using two new measures that we propose for neuroimaging data. The proposed method is applicable to both PET and fMRI studies. We illustrate its use with a PET study of working memory in patients with schizophrenia and an fMRI data example is also provided.
authors.affiliation: Department of Biostatistics, The Rollins School of Public Health, Emory University, Atlanta, Georgia, USA. yguo2@sph.emory.edu
authors.forename: Ying
authors.initials: Y
authors.lastname: Guo
authors.forename: F
authors.initials: F
authors.lastname: DuBois Bowman
authors.forename: Clinton
authors.initials: C
authors.lastname: Kilts
mesh_terms: D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D049268:Positron-Emission Tomography / Q000379:methods; D011237:Predictive Value of Tests; D012559:Schizophrenia / Q000503:physiopathology* / Q000628:therapy*