Understanding Selectivity Mechanisms of Network Vulnerability and Resilience in Alzheimer's Disease by Establishing a Neurobiological Basis through Network Neuroscience
通过网络神经科学建立神经生物学基础,了解阿尔茨海默氏病网络脆弱性和恢复力的选择性机制
基本信息
- 批准号:10033069
- 负责人:
- 金额:$ 193.99万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-09-15 至 2024-08-31
- 项目状态:已结题
- 来源:
- 关键词:AffectAgingAlzheimer&aposs DiseaseAlzheimer&aposs disease pathologyAlzheimer’s disease biomarkerAmyloidAmyloid beta-ProteinAreaAtrophicBiological MarkersBrainBrain MappingBrain regionClinicCognitionCognitiveCommunitiesComputer softwareCross-Sectional StudiesDataDevelopmentDiagnosticDisciplineEducationEventEvolutionExhibitsFactor AnalysisGeneticGenetic MarkersGoalsGrainImpaired cognitionIndividualLeadLinkLongitudinal StudiesMeasurementMethodologyMethodsModelingMolecularNerve DegenerationNetwork-basedNeurobiologyNeurodegenerative DisordersNeurofibrillary TanglesNeuronsNeurosciencesOccupationsPathologicPathologyPathway AnalysisPathway interactionsPatternPhenotypePhysicsPositron-Emission TomographyProcessResearchRiskRisk FactorsRoleSenile PlaquesSingle Nucleotide PolymorphismSource CodeStructureSystemTestingThickVertebral columnbasebiophysical analysiscell typecomputerized toolsconnectomeflexibilityfluorodeoxyglucose positron emission tomographygenetic approachgenome wide association studyimprovedmodifiable risknetwork dysfunctionneurocognitive disorderneuroimagingneuron lossneuropathologynormal agingnovelprecision medicinepreservationprogramsresiliencespectral energystatisticstau Proteinstool
项目摘要
Abstract. A plethora of neuroscience and neuroimaging studies have shown that Alzheimer’s disease (AD)
differentially affects certain regions of the brain and specific cell types. Since AD-related pathological events
often propagate trans-neuronally, the selective vulnerability to neuron loss and structure damage also manifest
in the topological patterns of network alteration. Along with many other studies, the research team has found the
strong evidence that (1) AD preferentially affects hub nodes in the network that are densely connected in the
network, and (2) the propagation of neuropathological burdens such as amyloid plaques and neurofibrillary
tangles exhibit unique topological patterns that are governed by the self-organized harmonic bases. However,
the factors underlying this network vulnerability and the molecular mechanism regulating the selectivity in AD
remain unclear. In this regard, we aim to continue the development of cutting-edge network analysis tools with
a greater methodological understanding of how neuropathological events selectively affect certain harmonic
bases (harmonic-selective network vulnerability) and how brain networks counteract AD pathology (network
resilience). In this context, the backbone of this project is a harmonic factor analysis model that can be used as
a neurobiological basis to accurately characterize the whole-brain mapping of neurodegeneration at a system
level, where each harmonic factor explains how the ubiquitous propagation (wave) pattern of neuropathological
event emerges from the particular structural connectome pathway. In Aim 1, we will leverage the well-studied
biophysics concept of power and energy to identify a set of harmonic-selective vulnerable patterns that account
for network vulnerability between normal aging and AD. Also, we will associatethe identified network vulnerability
with couple factors from diverse research fields which include stochastics of selectivity (statistics), system
criticality (physics), network organization (network neuroscience), and cognitive domains (clinic). After that, we
will seek for the putative harmonic-genetics biomarker based on the discovered association between network
vulnerability and genetics factor in Aim 2 and develop a harmonic-genetic approach to capture network resilience
in Aim 3. In Aim 4, we will apply the computational approaches developed in Aim 1-3 to establish (1) a fine-
grain understanding of network vulnerability and resilience across A (amyloid-PET), T (Tau-PET), and N (FDG-
PET and cortical thickness) biomarkers, and (2) a longitudinal underpinning of the dynamics of network
vulnerability by investigating the longitudinal change of AT[N] biomarkers. The diagnostic power of our novel
harmonic-genetics biomarker and resilience will be evaluated in our current AD diagnostic engines. We will
release the software (both binary program and source code), to facilitate the other AD biomarker projects and
the neuroimaging studies of other neurocognitive disorders associated with brain network dysfunction.
摘要。大量的神经科学和神经影像学研究表明,阿尔茨海默病(AD)
项目成果
期刊论文数量(0)
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Guorong Wu其他文献
Guorong Wu的其他文献
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{{ truncateString('Guorong Wu', 18)}}的其他基金
Continuing Tool Development for Longitudinal Network Analysis: Enriching the Diagnostic Power of Disease-Specific Connectomic Biomarkers by Deep Graph Learning
纵向网络分析的持续工具开发:通过深度图学习丰富疾病特异性连接组生物标志物的诊断能力
- 批准号:
10359157 - 财政年份:2021
- 资助金额:
$ 193.99万 - 项目类别:
Uncovering the Heterogeneity of Neurodegeneration Trajectories in Alzheimer's Disease Using a Network Guided Reaction-Diffusion Model
使用网络引导反应扩散模型揭示阿尔茨海默病神经退行性轨迹的异质性
- 批准号:
10288783 - 财政年份:2021
- 资助金额:
$ 193.99万 - 项目类别:
Uncovering the Heterogeneity of Neurodegeneration Trajectories in Alzheimer's Disease Using a Network Guided Reaction-Diffusion Model
使用网络引导反应扩散模型揭示阿尔茨海默病神经退行性轨迹的异质性
- 批准号:
10461847 - 财政年份:2021
- 资助金额:
$ 193.99万 - 项目类别:
A Scalable Platform for Exploring and Analyzing Whole Brain Tissue Cleared Images
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- 批准号:
10463036 - 财政年份:2019
- 资助金额:
$ 193.99万 - 项目类别:
A Scalable Platform for Exploring and Analyzing Whole Brain Tissue Cleared Images
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- 批准号:
10370398 - 财政年份:2019
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$ 193.99万 - 项目类别:
A Scalable Platform for Exploring and Analyzing Whole Brain Tissue Cleared Images
用于探索和分析全脑组织清晰图像的可扩展平台
- 批准号:
10582669 - 财政年份:2019
- 资助金额:
$ 193.99万 - 项目类别:
A Scalable Platform for Exploring and Analyzing Whole Brain Tissue Cleared Images
用于探索和分析全脑组织清晰图像的可扩展平台
- 批准号:
10244882 - 财政年份:2019
- 资助金额:
$ 193.99万 - 项目类别:
A Scalable Platform for Exploring and Analyzing Whole Brain Tissue Cleared Images
用于探索和分析全脑组织清晰图像的可扩展平台
- 批准号:
9923760 - 财政年份:2019
- 资助金额:
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