Predicting the brain response to treatment using a Bayesian hierarchical model with application to a study of XXXX .

@entity1 @entity308 @entity146 @entity161 @entity195
In vivo functional neuroimaging, including functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), is becoming increasingly important in defining the pathophysiology of @entity146 such as @entity161 , @entity308 , and @entity195 . Furthermore, recent studies have begun to investigate the possibility of using functional neuroimaging to guide treatment selection for individual @entity1 . By studying the changes between a @entity1 '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 @entity146 . 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 @entity1 's pretreatment scans, coupled with relevant @entity1 characteristics, to forecast the @entity1 '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 @entity1 with @entity161 and an fMRI data example is also provided.
