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Longitudinal predictive modeling for tau in Alzheimer's disease

阿尔茨海默病中 tau 蛋白的纵向预测模型

基本信息

批准号：
10632023
负责人：
Joyita Dutta
金额：
$ 53.9万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
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：
2021-09-01 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10632023
关键词：
Alzheimer&apos s Disease Amyloid Amyloid beta-Protein Anatomy Animal Model Artificial Intelligence Biological Biological Markers Brain Brain region Characteristics Clinical Clinical Trials Clinical Trials Design Cognitive Complex Computer Models Data Databases Dementia Diffusion Diffusion Magnetic Resonance Imaging Drug Exposure Elderly Ensure Epidemic Evolution Exhibits Fiber Future Goals Grant Graph Human Image Imaging Techniques Impaired cognition Individual Learning Length Machine Learning Magnetic Resonance Maps Mathematics Measures Medial Memory impairment Methods Modeling Monitor Neocortex Nerve Degeneration Neuroanatomy Neurobiology Neurofibrillary Tangles Outcome Measure Paper Pathologic Pathway interactions Pattern Phase Physics Pittsburgh Compound-B Positron-Emission Tomography Process Prognosis Proteins Research Research Project Grants Resolution Resources Rest Role Sample Size Senile Plaques Site Stereotyping Structure Temporal Lobe Therapeutic Trials Training Validation aging brain amyloid imaging biomarker development connectome deep learning demographics graph neural network human subject in vivo longitudinal positron emission tomography machine learning framework machine learning model member multimodality neocortical neural neuroimaging novel personalized predictions pre-clinical preclinical development predictive modeling prevent primary outcome prion-like prognostic tool progressive neurodegeneration rate of change serial imaging spatiotemporal tau Proteins tau aggregation tool white matter

项目摘要

PROJECT SUMMARY Alzheimer’s disease, the most common cause of dementia in the elderly, is characterized by a cognitively asymptomatic preclinical stage which is identified and monitored via longitudinal tracking of pathophysiological biomarkers, e.g., tau and amyloid. Since the aggregation of tau protein tangles in the medial temporal lobe is a key driver of memory impairment, accurate image-based longitudinal prediction of tau burden could fill a critical gap in biomarker development for preclinical Alzheimer’s disease. Tau tangles exhibit stereotypical neuroanatomical patterns of spatiotemporal spread that correlate strongly with the progression of neurodegeneration. Studies in animal models have suggested that the characteristic patterns of tau spread associated with Alzheimer’s progression are determined by neural connectivity rather than physical proximity between different brain regions. Graph-theoretic methods that utilize macroscale structural connectivity mapping in humans to predict future tau burden could lead to valuable prognostic tools for Alzheimer’s disease. The overarching research goal of this R01 Research Project Grant is to develop an interpretable machine learning model that uses individual structural connectomics to make personalized predictions of differential measures of tau from multimodal baseline data. Our approach relies on longitudinal 18F-Flortaucipir positron emission tomography (PET) for the imaging of tau tangles, 11C-Pittsburgh Compound B (PiB) for the imaging of amyloid plaques, and high-angular-resolution diffusion magnetic resonance (MR) imaging for individualized structural connectomics in human subjects. We will develop a physics-informed and interpretable graph neural network to predict the annual rate of change of the regional tau burden from multimodal inputs, including baseline tau, Aβ, and an array of structural connectivity metrics. We will also develop novel physics-based analytic models for tau progression, which will be used to effectively guide the machine learning framework. Finally, we will apply the machine learning model to investigate the earliest cortical site of tau aggregation, to examine the connectomic basis of early tau spread, and to leverage our model’s interpretability to discover and validate novel connectomic biomarkers to characterize preclinical Alzheimer’s disease. To validate the machine learning model, we will use serial tau PET data at two and three timepoints from the Harvard Aging Brain Study, one of the largest longitudinal imaging resources for preclinical Alzheimer’s disease. To ensure scientific rigor, secondary validation of the models will be performed using data from the Alzheimer’s Disease Neuroimaging Initiative database. The proposed personalized predictive model could significantly impact preclinical Alzheimer’s prognosis, facilitate ongoing clinical trials, and shed light on the neuroconnectomic and biological underpinnings of Alzheimer’s disease.

项目总结

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Artificial Intelligence Algorithms Need to Be Explainable-or Do They?

人工智能算法需要可解释——真的吗？

DOI：
10.2967/jnumed.122.264949
发表时间：
2023
期刊：
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
影响因子：
0
作者：
Bradshaw,TylerJ;McCradden,MelissaD;Jha,AbhinavK;Dutta,Joyita;Saboury,Babak;Siegel,EliotL;Rahmim,Arman
通讯作者：
Rahmim,Arman

Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem.

DOI：
10.2967/jnumed.121.263703
发表时间：
2023-02
期刊：
JOURNAL OF NUCLEAR MEDICINE
影响因子：
9.3
作者：
Saboury, Babak;Bradshaw, Tyler;Boellaard, Ronald;Buvat, Irene;Dutta, Joyita;Hatt, Mathieu;Jha, Abhinav K.;Li, Quanzheng;Liu, Chi;McMeekin, Helena;Morris, Michael A.;Scott, Peter J. H.;Siegel, Eliot;Sunderland, John J.;Pandit-Taskar, Neeta;Wahl, Richard L.;Zuehlsdorff, Sven;Rahmim, Arman
通讯作者：
Rahmim, Arman