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:
  • 金额:
    $ 53.9万
  • 依托单位:
  • 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

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?
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:
  • 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
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Joyita Dutta其他文献

Joyita Dutta的其他文献

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{{ truncateString('Joyita Dutta', 18)}}的其他基金

Early Alzheimers Forecasting from Multimodal Data via Deep Transfer Learning, Evaluated on a Large-Scale Prospective Cohort Study
通过深度迁移学习从多模式数据预测早期阿尔茨海默病,并在大规模前瞻性队列研究中进行评估
  • 批准号:
    10732306
  • 财政年份:
    2023
  • 资助金额:
    $ 53.9万
  • 项目类别:
Super-Resolution Tau PET Imaging for Alzheimer's Disease
用于阿尔茨海默病的超分辨率 Tau PET 成像
  • 批准号:
    10724836
  • 财政年份:
    2022
  • 资助金额:
    $ 53.9万
  • 项目类别:
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
  • 批准号:
    10308208
  • 财政年份:
    2021
  • 资助金额:
    $ 53.9万
  • 项目类别:
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
  • 批准号:
    10471298
  • 财政年份:
    2021
  • 资助金额:
    $ 53.9万
  • 项目类别:
Sleep metrics from machine learning for Alzheimer's disease diagnostics
用于阿尔茨海默病诊断的机器学习睡眠指标
  • 批准号:
    10221599
  • 财政年份:
    2020
  • 资助金额:
    $ 53.9万
  • 项目类别:
Sleep metrics from machine learning for Alzheimer's disease diagnostics
用于阿尔茨海默病诊断的机器学习睡眠指标
  • 批准号:
    10042952
  • 财政年份:
    2020
  • 资助金额:
    $ 53.9万
  • 项目类别:
Sleep metrics from machine learning for Alzheimer's disease diagnostics
用于阿尔茨海默病诊断的机器学习睡眠指标
  • 批准号:
    10715006
  • 财政年份:
    2020
  • 资助金额:
    $ 53.9万
  • 项目类别:
Tau Quantitation in AD with High Resolution MRI and PET
使用高分辨率 MRI 和 PET 对 AD 中的 Tau 蛋白进行定量
  • 批准号:
    8949099
  • 财政年份:
    2015
  • 资助金额:
    $ 53.9万
  • 项目类别:

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  • 批准号:
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    2009
  • 资助金额:
    22.0 万元
  • 项目类别:
    地区科学基金项目

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A Possible Association Between Insulin and Alzheimer?s Disease: Examining the Consequences of Altered Insulin Signalling on the Expression of Human Amyloid-Beta in Caenorhabditis elegans
胰岛素与阿尔茨海默氏病之间的可能关联:检查胰岛素信号改变对秀丽隐杆线虫中人类β淀粉样蛋白表达的影响
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