There has been a strong interest in the neuroimaging community to utilize information in imaging data to predict individual disease status and treatment response. We developed the following statistical prediction methods based on brain images: a prediction method based on a Bayesian hierarchical model for forecasting future neural activity based on a subject’s baseline brain images and other individual characteristics (Guo, Bowman and Kilts, 2008), which can potentially help select optimal treatment plans for individual patients; a weighted cluster kernel PCA model for predicting subjects’ cognitive state using brain images (Guo, 2010) ; we also propose a statistical method for predicting individual’s future functional connectivity in longitudinal studies by using information from individual's baseline fMRI scans along with relevant subject characteristics, such as disease or treatment status (Dai and Guo, 2017). The proposed prediction method improves the accuracy of individualized prediction of connectivity by combining information from both group-level connectivity patterns as well as individual-specific connectivity features. It also offers statistical inference tools such as predictive intervals that help quantify the uncertainty or variability of the predicted outcomes.

Fig1. Guo Y. et al. (2008): Individualized predicted and observed post-treatment rCBF measurements under the low load condition for four subjects in the working memory study. (a) Predicted maps. Notable differences exist between the patients’ predicted brain responses to treatment. (b) Observed maps. There is satisfactory agreement between the predicted and observed post-treatment rCBF.


  1. Dai, T and Guo, Y (2017). Predicting Individual Brain Functional Connectivity Using a Bayesian Hierarchical Model. NeuroImage. 147(15): 772–787. An earlier version of the paper was the Second-Place winner of the 2016 Student Paper Competition, American Statistical Association (ASA) Statistics in Imaging Section.

  2. Guo Y, Bowman FD, Kilts C (2008). Predicting the brain response to treatment using a Bayesian Hierarchical model . Human Brain Mapping, 29(9): 1092-1109.

  3. Guo Y (2010). A weighted cluster kernel PCA prediction model for multi-subject brain imaging data. Statistics And Its Interface. 3:103-111.

  4. Derado, G., Bowman, F. D., and Zhang, L. (2012). Predicting Brain Activity using a Bayesian Spatial Model. Statistical Methods in Medical Research (DOI: 10.1177/0962280212448972).