Development of a Bayesian multimodal model to detect biomarkers in neuroimaging studies - Bhaumik, Dulal K.; Wang, Yue; Yen, Pei-Shan; Ajilore, Olusola A. (Frontiers in Neuroimaging, 2023)
We develop a Bayesian multimodal model to detect biomarkers (or neuromarkers) using resting-state functional and structural data while comparing the late-life depression group with a healthy control group. Biomarker detection helps determine a target for treatment intervention to get the optimal therapeutic benefit for treatment-resistant patients. The borrowing strength of the structural connectivity has been quantified while detecting the biomarker for functional activity. In the biomarker searching process, thousands of hypotheses are generated and tested simultaneously using our novel method to control the false discovery rate for small samples. Several existing statistical approaches, frequently used in analyzing neuroimaging data have been investigated and compared via simulation with the proposed approach to show its excellent performance. Results are illustrated with a live data set generated in a late-life depression study. The role of detected biomarkers in terms of cognitive function has been explored.