Predictive model of spread of Parkinson's pathology using network diffusion

使用网络扩散预测帕金森病病理传播的模型

• 批准号: 9913596
• 负责人: Ajay Gupta
• 金额: $ 58.77万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-15 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9913596
• 关键词: Age; Algorithms; Alzheimer' s Disease; Anatomy; Atlases; Atrophic; Biological Markers; Bradykinesia; Brain; Brain Stem; Cells; Clinical; Clinical Data; Cognitive; Computer Assisted; Computer software; Corpus striatum structure; Coupled; Custom; Data; Data Set; Databases; Degenerative Disorder; Dementia; Development; Diagnostic; Diagnostic Imaging; Diffuse; Diffusion; Disease; Disease Progression; Disease model; Etiology; Exercise; Exhibits; Fiber; Future; Genetic; Goals; Image; Individual; Inflammatory; Joints; Lewy Bodies; Longitudinal Studies; Magnetic Resonance Imaging; Maps; Modeling; Motor; Neocortex; Neurocognitive; Neurodegenerative Disorders; Neurologist; Parkinson Disease; Pathologic; Pathology; Patients; Pattern; Positron-Emission Tomography; Process; Proteins; Regression Analysis; Reporting; Research; Resolution; Risk; Role; Severities; Severity of illness; Software Tools; Speed; Substantia nigra structure; Symptoms; Techniques; Technology; Testing; Travel; Tremor; Validation; age related; alpha synuclein; base; biophysical model; cerebral atrophy; clinical application; clinical care; clinical heterogeneity; cohort; connectome; diffusion weighted; disease transmission; disorder subtype; follow-up; human data; imaging biomarker; imaging study; in vivo; individual patient; insight; kinetic model; macrophage; mathematical model; misfolded protein; network models; neuroimaging; neuron loss; outcome forecast; pars compacta; posture instability; predictive modeling; predictive tools; prion-like; prognostic; prognostic tool; progression marker; radiotracer; raphe nuclei; regional atrophy; spatiotemporal; synuclein; synucleinopathy; technique development; tool; transmission process; uptake

PROJECT SUMMARY / ABSTRACT Project Summary Parkinson’s disease (PD) is a debilitating neurodegenerative disease characterized by progressive bradykinesia, rigidity, tremor and postural instability. The etiology, mechanism and progression of pathology and its relationship to clinical manifestations is not fully understood. These factors, coupled with its insidious onset, clinical heterogeneity, overlap with dementias, and the variability in speed and pattern of symptom progression, make a rigorous characterization and prognosis of PD difficult. Recent bench research on the trans-neuronal “prion-like” transmission of misfolded proteins is at last filling the gaps in the pathological context of PD, whereby misfolded alpha-synuclein protein can trigger misfolding in adjacent cells. If this spread mechanisms could be quantitatively modeled, it could enable accurate prediction of PD progression. This is the aim of our proposal. We will turn hitherto qualitative neuropathological insights into a rigorous “network-diffusion” model of disease spread. The model will be fed baseline in vivo MRI of PD patients, and will produce a deterministic and testable prediction for PD progression and conversion to dementia. By explicitly incorporating the brain’s connectivity network, our model will quantify the role of the brain’s anatomic connectivity network in disease transmission. We are targeting various applications, including diagnostic imaging biomarker, prognostic tool for assessing likely future patterns of disease and future neurocognitive status including likelihood of conversion to dementia. Relevance Parkinson’s Disease is a debilitating and common age-related degenerative disorder. The proposed network model will yield a validated deterministic and predictive model for PD progression, with applications in prediction of a patient’s future atrophy patterns, neurocognitive and motor scores.

项目概要/摘要 项目概要 帕金森病 (PD) 是一种使人衰弱的神经退行性疾病，其特征是进行性进展 运动迟缓、强直、震颤和姿势不稳定的病因、机制和病理进展。 这些因素及其与临床表现的关系尚不完全清楚。 发病、临床异质性、与痴呆的重叠以及症状的速度和模式的可变性 进展，使得对 PD 进行严格的表征和预后变得困难。 错误折叠蛋白的跨神经元“类朊病毒”传播最终填补了病理学的空白 PD 的背景下，如果这种情况扩散，错误折叠的 α-突触核蛋白可能会引发相邻细胞的错误折叠。 可以对机制进行定量建模，从而能够准确预测 PD 进展。 这是我们提案的目的，我们将把迄今为止的定性神经病理学见解转变为严格的。 疾病传播的“网络扩散”模型将输入 PD 患者的体内 MRI 基线，并将 明确地对 PD 进展和转化为痴呆症做出确定性且可测试的预测。 结合大脑的连接网络，我们的模型将量化大脑解剖学的作用 我们的目标是疾病传播中的连接网络，包括诊断。 成像生物标志物，用于评估未来可能的疾病模式和未来神经认知的预后工具 状况，包括转变为痴呆症的可能性。 关联 帕金森病是一种使人衰弱且常见的与年龄相关的退行性疾病。 模型将产生一个经过验证的 PD 进展确定性和预测模型，并在以下领域得到应用： 预测患者未来的萎缩模式、神经认知和运动评分。

项目成果

Origins of atrophy in Parkinson linked to early onset and local transcription patterns.

帕金森萎缩的起源与早期发病和局部转录模式有关。

• DOI: 10.1093/braincomms/fcaa065
• 发表时间: 2020
• 期刊: Brain communications
• 影响因子: 4.8
• 作者: Maia,PedroD;Pandya,Sneha;Freeze,Benjamin;Torok,Justin;Gupta,Ajay;Zeighami,Yashar;Raj,Ashish
• 通讯作者: Raj,Ashish

Combined Model of Aggregation and Network Diffusion Recapitulates Alzheimer's Regional Tau-Positron Emission Tomography.

聚集和网络扩散的组合模型概括了阿尔茨海默病的区域 Tau 正电子发射断层扫描。

• DOI: 10.1089/brain.2020.0841
• 发表时间: 2021
• 期刊: Brain connectivity
• 影响因子: 3.4
• 作者: Raj,Ashish;Tora,Veronica;Gao,Xiao;Cho,Hanna;Choi,JaeYong;Ryu,YoungHoon;Lyoo,ChulHyoung;Franchi,Bruno
• 通讯作者: Franchi,Bruno

Regional expression of genes mediating trans-synaptic alpha-synuclein transfer predicts regional atrophy in Parkinson disease.

• DOI: 10.1016/j.nicl.2018.01.009
• 发表时间: 2018
• 期刊: NeuroImage. Clinical
• 作者: Freeze B;Acosta D;Pandya S;Zhao Y;Raj A
• 通讯作者: Raj A

Computational Models in Electroencephalography.

• DOI: 10.1007/s10548-021-00828-2
• 发表时间: 2022-01
• 期刊: Brain topography
• 影响因子: 2.7
• 作者: Glomb K;Cabral J;Cattani A;Mazzoni A;Raj A;Franceschiello B
• 通讯作者: Franceschiello B

Preserved Structural Network Organization Mediates Pathology Spread in Alzheimer's Disease Spectrum Despite Loss of White Matter Tract Integrity.

• DOI: 10.3233/jad-170798
• 发表时间: 2018
• 期刊: Journal of Alzheimer's disease : JAD
• 作者: Powell F;Tosun D;Sadeghi R;Weiner M;Raj A;Alzheimer’s Disease Neuroimaging Initiative
• 通讯作者: Alzheimer’s Disease Neuroimaging Initiative