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