In this application, we will establish a novel paradigm of atlas-based brain MRI analysis of preclinical Alzheimer?s disease (AD), featuring in direct estimation of the patients? attributes with a unique knowledge-based information- retrieval technology. MRI atlases are widely used for automated image parcellation, especially, recent advances in multi-atlas methods have yielded superior segmentation accuracy. In conventional atlas-based analysis, atlases are used merely as templates to segment a patient image, and then volumes, shapes, intensities are extracted from the segmented structures to estimate the patient?s diagnostic attributes, such as cognitive status or clinical assessments. In contrast, in the proposed multi-atlas based direct estimation (MADE) approach, we utilize the multi-atlas library as a knowledge database that is associated with rich clinical information; anatomical similarity between the patient and atlas images will be used to weigh the information from multiple atlases, and a weighted decision will be made to directly estimate the patient attributes, bypassing the segmentation process. Our preliminary data have demonstrated the advantages of MADE-based analysis of T1-weighted images in dementia patients, compared to volumetric analysis. In addition, non-image demographic and clinical information of the patients can be readily incorporated into the MADE framework to further enhance the estimation accuracy. Our goals is to develop MADE-based computational tools and use them to improve diagnosis and prognosis in preclinical phase of AD. This study will be supported by the BIOCARD cohort, which is a well-designed longitudinal study that followed 350 participants over 20 years. Comprehensive cognitive assessments and MRI exams have been collected in these participants since 1995 when they were cognitive normal at enrollment. This unique database allows us to investigate brain degeneration in the long preclinical phase, and develop computational tools that can possibly assist diagnostic decisions in this critical phase.
In Aim 1, we will develop and optimize MADE-based brain structural MRI analysis diagram to estimate the patients? current cognitive status and disease stages, using both the ADNI and BIOCARD data. Once the tools become mature, we deploy them on a cloud-computing platform?the MRICloud, for public use.
In Aim 2, we use the optimized MADE pipeline to predict cognitive impairment in BIOCARD cohort. Specifically, we will used the MADE pipeline to predict patients? cognitive decline at 1-5 years after baseline; and also predict their probability of conversion from normal to cognitive impairment (MCI, AD or other types of abnormality) across the 20 years of follow-up, and estimate the time-to-diagnosis. The success of the proposed project could lead to the next generation of knowledge-based computer-aided diagnosis, and potentially improve early diagnosis and prognosis accuracy in preclinical AD patients.
We aim to develop a novel knowledge-based brain MRI analysis paradigm for computer-aided diagnosis and prognosis of preclinical Alzheimer?s disease (AD). The proposed multi-atlas based direct estimation (MADE) method is fundamentally different than conventional segmentation analysis; it searches the multi-atlas knowledge database and integrates the most relevant information to make a diagnostic estimation. We will use this unique approach to estimate cognitive outcomes in a large preclinical AD cohort?the BIOCARD study, and predict the conversion from normal to cognitive impairment in these subjects.
