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Principal Investigator  
Principal Investigator's Name: Feng Han
Institution: the Pennsylvania State University
Department: Department of Bioengineering
Country:
Proposed Analysis: 1. We first analyze the group difference of multiple modalities between AD, CN and MCI. The modalities include several measures from fMRI, such as functional connectivity and dynamics, MRI, such as cortical thickness, PET imaging, A-beta and tau, and behavioral data. This study may contribute to the AD biomarkers. 2. We also propose to use several biomarkers from the above to predict the occurrence rate of AD. For example, the specific pair of functional connection between brain seeds may show significant difference between AD and CN. Also, based on the co-activation pattern (CAP) method our group has proposed, we can look into the dynamics difference between the different group. This specific feature of dynamics may serve as other biomarkers so that we may derive a prediction model to predict the AD. 3. We recently try to get some fMRI data at baseline and derive several fMRI features, and correlate it with the longitudinal changes of A-beta from PET imaging, which may help to build the cross-modalities correspondence and also potential to be biomarker of AD. 2. We also propose to use several biomarkers from the above to predict the occurrence rate of AD. For example, the specific pair of functional connection between brain seeds may show significant difference between AD and CN. Also, based on the co-activaiton pattern (CAP) method
Additional Investigators  
Investigator's Name: Aaron Belkin-Rosen
Proposed Analysis: As proposed by Feng, we propose to use several biomarkers from the above to predict the occurrence rate of AD. For example, the specific pair of functional connection between brain seeds may show significant difference between AD and CN. Also, based on the co-activation pattern (CAP) method our group has proposed, we can look into the dynamics difference between the different group. This specific feature of dynamics may serve as other biomarkers so that we may derive a prediction model to predict the AD.