Multi-Modality Image Data Fusion and Machine Learning Approaches for Personalized Diagnostics and Prognostics of MCI due to AD

Alzheimer s Disease AD is the most common form of dementia and the sixth leading cause of death in the US More than million people in the US currently have AD and the direct health care cost is over $ billion per year Detection of early phase of AD namely Mild Cognitive Impairment MCI can delay prevent and treat this serious disease The project will develop a clinically feasible system for Mild Cognitive Impairment MCI diagnostics and prognostics by integrating multi modality imaging data such as MRI and PET as well as non imaging data such as clinical assessments biomarkers demographics and genetic information This project involves three Aims In Aim we will develop the system by designing diagnostic and prognostic modeling using cross sectionally incomplete multi modality data by multitask learning Our multitask learning approach that will simultaneously model multiple related tasks by allowing effective knowledge and data sharing to jointly estimate the diagnostic prognostic models for each patient cohort In Aim we will update diagnostic and prognostic model using longitudinally incomplete multi modality data by transfer learning We will integrate the model of an old domain e g the diagnostic prognostic model obtained at an earlier time point and the data of a new domain e g new data obtained at the a follow up visit in order to obtain an updated model with better accuracy This can take care of incomplete longitudinal data due to patient drop off because it transfers the old domain model not the data In Aim we will conduct validation for the proposed models using the MCI data collected by Alzheimerandapos s Disease Neuroimaging Initiative ADNI for all phases of AD The current project is novel in creating a first of its kind clinically feasible technology for personalized MCI diagnostics and prognostics as well as in using multitask learning and transfer learning machine learning methods for modeling cross sectionally and longitudinally incomplete multi modality data It is innovative in using multitask learning to model incomplete cross sectional data e g baseline data and using transfer learning to model the incomplete longitudinal data Project Narrative The project will develop a clinically feasible system for Mild Cognitive Impairment MCI diagnostics and prognostics by integrating multi modality imaging data such as MRI and PET as well as non imaging data such as clinical assessments biomarkers demographics and genetic information Successful development of this system can help overcome several limitations in existing methods and help address the current and growing public health concern regarding Alzheimer s Disease AD affecting over millions people in US