Data driven dynamic activity/connectivity methods for early detection of Alzheimer’s

用于早期检测阿尔茨海默病的数据驱动的动态活动/连接方法

基本信息

  • 批准号:
    10289991
  • 负责人:
  • 金额:
    $ 77.78万
  • 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/Abstract The development of biomarkers for identifying preclinical or prodromal Alzheimer’s disorder are of great in- terest. While some initial results based on resting fMRI have been presented, accuracy, robustness, and relia- bility are still relatively low. One highly promising direction is the development of dynamic functional activity and functional connectivity approaches. These approaches have been shown to be especially promising most likely due to the highly dynamic nature of the brain and the unconstrained nature of resting fMRI. Currently, there are no methods that can provide a full characterization of temporal, spatial, and spatio-temporal dynamics nor can most existing approaches characterize heterogenous subgroups or complex multiscale relationships. We will develop new methods that can effectively capture dynamic connectivity and provide summary metrics with a focus on individualized prediction of Alzheimer’s disease well prior to the onset of the illness. We propose a novel family of models that builds on the well-structured framework of joint blind source separation to capture a more complete characterization of (potentially nonlinear) spatio-temporal dynamics. Our models will also pro- duce a rich set of metrics to characterize the available dynamics and enable in depth comparison with currently available models. We show evidence that such measures are likely to be considerably more sensitive and more accurate in classifying individuals. We will extensively validate our approaches in a variety of ways including simulations, concurrent EEG/fMRI data, and evaluation on a large normative data set. We will apply the devel- oped methods to several large datasets including a large longitudinal sample of individuals who have been scanned at Emory University with resting fMRI who also have CSF amyloid and tau PET measures. We will use the developed markers to predict cognitive decline, amyloid, and tau levels in these data and include both a discovery data set as well as an independent replication data set. Successful completion of our aims will be an important first step towards providing an opportunity to develop and evaluate interventions early enough to have a positive impact on long-term prognosis. We will provide open source tools and release data throughout the duration of the project via GitHub, a web portal and the NITRC repository, hence enabling other investigators to compare their own methods with our own as well as to apply them to a large variety of brain disorders. Our tools also have wide application to the study of the healthy brain as well as many other diseases. 37
项目总结/文摘

项目成果

期刊论文数量(0)
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会议论文数量(0)
专利数量(0)

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TULAY ADALI其他文献

TULAY ADALI的其他文献

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{{ truncateString('TULAY ADALI', 18)}}的其他基金

Data-driven solutions for temporal, spatial, and spatiotemporal dynamic functional connectivity
用于时间、空间和时空动态功能连接的数据驱动解决方案
  • 批准号:
    10156006
  • 财政年份:
    2021
  • 资助金额:
    $ 77.78万
  • 项目类别:
Data-driven solutions for temporal, spatial, and spatiotemporal dynamic functional connectivity
用于时间、空间和时空动态功能连接的数据驱动解决方案
  • 批准号:
    10559654
  • 财政年份:
    2021
  • 资助金额:
    $ 77.78万
  • 项目类别:
Data-driven solutions for temporal, spatial, and spatiotemporal dynamic functional connectivity
用于时间、空间和时空动态功能连接的数据驱动解决方案
  • 批准号:
    10375496
  • 财政年份:
    2021
  • 资助金额:
    $ 77.78万
  • 项目类别:
Data driven dynamic activity/connectivity methods for early detection of Alzheimer’s
用于早期检测阿尔茨海默病的数据驱动的动态活动/连接方法
  • 批准号:
    10633189
  • 财政年份:
    2021
  • 资助金额:
    $ 77.78万
  • 项目类别:
Data driven dynamic activity/connectivity methods for early detection of Alzheimer’s
用于早期检测阿尔茨海默病的数据驱动的动态活动/连接方法
  • 批准号:
    10468956
  • 财政年份:
    2021
  • 资助金额:
    $ 77.78万
  • 项目类别:
Dynamic imaging-genomic models for characterizing and predicting psychosis and mood disorders
用于表征和预测精神病和情绪障碍的动态成像基因组模型
  • 批准号:
    9889183
  • 财政年份:
    2019
  • 资助金额:
    $ 77.78万
  • 项目类别:
Dynamic imaging-genomic models for characterizing and predicting psychosis and mood disorders
用于表征和预测精神病和情绪障碍的动态成像基因组模型
  • 批准号:
    10112311
  • 财政年份:
    2019
  • 资助金额:
    $ 77.78万
  • 项目类别:
Dynamic imaging-genomic models for characterizing and predicting psychosis and mood disorders
用于表征和预测精神病和情绪障碍的动态成像基因组模型
  • 批准号:
    10559628
  • 财政年份:
    2019
  • 资助金额:
    $ 77.78万
  • 项目类别:
Dynamic imaging-genomic models for characterizing and predicting psychosis and mood disorders
用于表征和预测精神病和情绪障碍的动态成像基因组模型
  • 批准号:
    10359205
  • 财政年份:
    2019
  • 资助金额:
    $ 77.78万
  • 项目类别:
Male/Female differences in psychosis and mood disorders:Dynamic imaging-genomic models for characterizing and predicting psychosis and mood d
精神病和情绪障碍的男性/女性差异:用于表征和预测精神病和情绪障碍的动态成像基因组模型
  • 批准号:
    10093861
  • 财政年份:
    2019
  • 资助金额:
    $ 77.78万
  • 项目类别:

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