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
- 来源:
- 关键词:AddressAdultAgeAgingAlgorithmsAlzheimer disease detectionAlzheimer&aposs DiseaseAlzheimer’s disease biomarkerAmyloidBaltimoreBiological MarkersBrainBrain DiseasesCognitiveCommunitiesComplexCouplingDataData DiscoveryData SetDementiaDetectionDevelopmentDiseaseDisease ProgressionDocumentationEarly DiagnosisEarly InterventionEnsureEvaluationExhibitsFamilyFrequenciesFunctional Magnetic Resonance ImagingFutureGoalsGrowthHeterogeneityImpaired cognitionIndividualIntuitionJointsLongitudinal StudiesMeasuresMethodsModelingNatureOnset of illnessPatternPhasePositron-Emission TomographyPrognosisPythonsResearch PersonnelRestSamplingScanningSleepSourceSpecificityStructureStudy modelsSubgroupTestingTimeUniversitiesValidationWorkbasebiomarker developmentblindflexibilityfunctional magnetic resonance imaging/electroencephalographyimprovedinnovationinterestlarge datasetsnovelnovel markeropen sourceopen source toolpersonalized predictionspre-clinicalprodromal Alzheimer&aposs diseaserepositorysimulationspatiotemporaltau Proteinstooluser-friendlyweb portal
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
项目概要/摘要
用于识别临床前或前驱阿尔茨海默病的生物标志物的开发具有重要意义。
兴趣。虽然已经提出了一些基于静息功能磁共振成像的初步结果,但准确性、稳健性和可靠性
能力还是比较低的。一个非常有前途的方向是动态功能活动的发展和
功能连接方法。这些方法已被证明特别有前途
由于大脑的高度动态特性和静息功能磁共振成像的不受约束的特性。目前,有
没有任何方法可以提供时间、空间和时空动力学的完整表征,也不能
大多数现有方法都描述异质子组或复杂的多尺度关系。我们将
开发新方法,可以有效捕获动态连接并提供摘要指标
重点关注阿尔茨海默病发病前的个体化预测。我们提出一个
新颖的模型系列,建立在联合盲源分离的结构良好的框架之上,以捕获
(潜在非线性)时空动力学的更完整的表征。我们的模型也将亲
产生一组丰富的指标来描述可用动态并能够与当前进行深入比较
可用型号。我们提供的证据表明,此类措施可能更加敏感、更加有效。
准确地对个体进行分类。我们将通过多种方式广泛验证我们的方法,包括
模拟、并发脑电图/功能磁共振成像数据以及对大型规范数据集的评估。我们将应用该开发
对几个大型数据集进行了操作方法,其中包括已被研究过的个体的大型纵向样本
在埃默里大学进行静息功能磁共振成像扫描,同时进行脑脊液淀粉样蛋白和 tau PET 测量。我们将使用
开发的标记物可预测这些数据中的认知衰退、淀粉样蛋白和 tau 蛋白水平,并包括
发现数据集以及独立的复制数据集。成功完成我们的目标将是
重要的第一步是提供尽早制定和评估干预措施的机会,以便
对长期预后产生积极影响。我们将全程提供开源工具并发布数据
通过 GitHub、门户网站和 NITRC 存储库了解项目的持续时间,从而使其他研究人员能够
将他们自己的方法与我们自己的方法进行比较,并将其应用于多种脑部疾病。我们的工具
也广泛应用于健康大脑以及许多其他疾病的研究。
37
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
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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