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Advanced machine learning algorithms that integrate multi-modal neuroimaging to quantify the heterogeneity in Alzheimer's Disease

先进的机器学习算法，集成多模式神经影像来量化阿尔茨海默病的异质性

基本信息

批准号：
10323673
负责人：
Aristeidis Sotiras
金额：
$ 53.56万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-01-15 至 2025-12-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10323673
关键词：
Address Affect Age Alzheimer&apos s Disease Alzheimer&apos s disease pathology American Amyloid Amyloid Proteins Anatomy Autopsy Biological Biological Markers Biometry Brain Brain Diseases Cerebrovascular Disorders Clinical Clinical Trials Cognition Cognitive Complex Data Data Collection Dementia Deposition Diagnosis Diagnostic Diagnostic Procedure Dimensions Disease Disease Marker Enabling Factors Factor Analysis Functional Magnetic Resonance Imaging Functional disorder Future Goals Healthcare Systems Heterogeneity Hospitals Image Image Analysis Impaired cognition Individual Intuition Label Letters Link Machine Learning Magnetic Resonance Imaging Measurement Measures Methodology Methods Modality Multimodal Imaging National Institute on Aging Nature Nerve Degeneration Neurodegenerative Disorders Neurology Outcome Participant Pathologic Pathologic Processes Pathology Patients Pattern Performance Population Positioning Attribute Positron-Emission Tomography Process Prognosis Regression Analysis Research Risk Factors Sampling Severities Subgroup System Techniques Testing Therapeutic Therapeutic Intervention Training Uncertainty Work age related aging brain base case control cognitive change cohort community burden comorbidity design disease heterogeneity disorder subtype healthy aging imaging modality improved in vivo innovation machine learning algorithm machine learning framework machine learning method multimodal data multimodal neuroimaging multimodality neuroimaging neuroinformatics novel patient stratification personalized medicine pre-clinical quantitative imaging supervised learning tau Proteins therapeutically effective tool unsupervised learning

项目摘要

Abstract Alzheimer's Disease (AD) affects over 5 million Americans posing a significant burden to the community and health care system. Machine learning (ML) methods have been crucial in detecting the disease and characterizing its progression. Due to the lack of an in vivo “ground truth” diagnosis, ML approaches have typically relied on clinically derived labels and a case-control design in their search for a single imaging pattern that optimally distinguishes between the two groups in the case-control design. However, heterogeneity within clinical labels may degrade performance and interpretability. The goal of this project is to address this limitation and accurately characterize heterogeneity in preclinical and symptomatic AD. Given that age is a major risk factor for developing dementia, we will characterize healthy aging using multimodal neuroimaging data and ML in Aim 1. To this end, we propose to develop a novel unsupervised multi-view machine learning tool that can integrate information from multiple imaging modalities (i.e., structural Magnetic Resonance Imaging, and amyloid and tau sensitive Positron Emission Tomography) in a principled way. This will enable us to define the normal trajectory of age- related changes across all modalities, providing the necessary context to understand AD pathology. We will characterize AD pathology using multimodal neuroimaging data and ML in Aim 2. To this end, we propose to develop a novel semi-supervised ML framework that integrates multimodal information and derives data-driven disease dimensions. This is achieved by identifying and quantifying at the individual level imaging patterns that capture neuroanatomical and neuropathological alterations. Our approach builds on our extensive prior work on using an advanced, unsupervised multivariate pattern analysis technique, termed orthonormal projective non-negative matrix factorization, for analyzing neuroimaging data. Importantly, our project leverages two large multimodal datasets, the Knight AD Research Center (ADRC) cohort and AD Neuroimaging Initiative (ADNI), which sample participants across the continuum of AD making them ideal for investigating heterogeneity of AD pathology using advanced ML techniques. If successful, our approaches could be used for studying any brain disorder and could be readily integrated into personalized medicine strategies in the future when rich, multimodal imaging data collection will become a routine diagnostic procedure in hospitals.

摘要