Digital Biomarkers for Vascular Cognitive Decline in Patients with Minor Stroke

轻微中风患者血管认知下降的数字生物标志物

• 批准号: 10525918
• 负责人: Elisabeth Breese Marsh
• 金额: $ 231.03万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-10 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10525918
• 关键词: Acute; Address; Affect; Age; Alzheimer' s Disease; Area; Attention; Biological Markers; Blood Vessels; Brain; Characteristics; Clinic; Clinical; Clinical Data; Clinical Engineering; Cognitive; Collaborations; Country; Data; Data Analyses; Databases; Dementia; Deterioration; Disease Progression; Early Diagnosis; Early Intervention; Effectiveness; Electroencephalography; Ensure; Evaluation; Exhibits; Frequencies; Functional disorder; Graph; Impaired cognition; Impairment; Individual; Infarction; Intervention; Ischemic Stroke; Lead; Learning; Lesion; Link; Location; Machine Learning; Magnetoencephalography; Measures; Memory; Minor; Modeling; Monitor; Neuropsychological Tests; Pathway interactions; Patient Monitoring; Patients; Pattern; Population; Predictive Value; Prevention strategy; Prognosis; Quality of life; Reaction Time; Recovery; Reporting; Research; Research Personnel; Rest; Screening procedure; Sensitivity and Specificity; Signal Transduction; Stroke; Techniques; Thrombectomy; Time; United States National Institutes of Health; Vascular Cognitive Impairment; Vascular Dementia; Work; base; cohort; cost; deep learning; deep learning model; design; digital; executive function; experience; high risk; improved; innovation; learning strategy; machine learning algorithm; mild cognitive impairment; multimodality; multitask; neuroimaging; neurophysiology; novel; post stroke; post stroke cognitive impairment; predictive marker; prevent; processing speed; prognostication; stroke outcome; stroke patient; success; tool; treatment trial

PROJECT SUMMARY/ABSTRACT Thrombectomy has significantly improved stroke outcomes. Nearly 80% of our clinic population now present with small strokes and low NIH Stroke Scale scores. However, greater than half endorse significant problems with attention, executive function, and processing speed. For many, significant recovery is seen by 6 months, but up to one third experience persistent vascular cognitive impairment. A biomarker to robustly predict who will exhibit long-standing deficits would enable us to initiate early interventions to slow or even prevent decline. Our work with MEG suggests global disruption of cognitive networks irrespective of stroke size or location; however, the compensatory mechanisms that allow some to recover but fail in others are poorly understood. There is a critical need for a noninvasive, inexpensive screening tool that can be widely implemented. The scientific premise of this proposal is two-fold: (i) using MEG and EEG we can determine functional network characteristics affecting both those with transient post-stroke cognitive impairment (psMCI) and persistent vascular cognitive impairment (VCI) as well as the compensatory mechanisms responsible for recovery, and (ii) a novel deep learning model that performs multimodal (MEG and EEG) learning to find shared signatures of VCI, but ultimately yields a model that needs affordable EEG-only data, will yield a powerful biomarker that can predict conversion of psMCI to VCI early after stroke. This proposal will pursue three specific aims. 1) Identify neurophysiologic similarities between transient psMCI and persistent VCI; 2) Identify specific features of functional connectivity that prognosticate conversion to VCI; 3) Design a digital biomarker that predicts conversion using functional brain networks that can be extended from MEG to EEG. To achieve these aims, we will collect both MEG and EEG data from 200 patients with minor stroke, evaluate their signals with expert neurophysiologists, and monitor the patient’s yearly conversion rate to VCI. We will then design and validate a deep learning model called Siamese Multiple Graph to Gauss (SMG2G), which performs multimodal learning on MEG and EEG network (graph) data but ultimately yields a model that needs EEG-only data to make predictions of conversion to VCI. The final product will be an EEG digital biomarker that can be readily measured and widely employed across the country. The research proposed in this application is innovative because it is the first to use functional network signals to design a biomarker for VCI that is inexpensive and widespread, yet robust, and achieves this by cutting edge machine learning. It is also significant because it will advance the field vertically both scientifically and clinically by enabling large-scale, early detection of VCI. Our team is well-prepared to undertake this project, with clinical and engineering expertise, strong collaborations, preliminary data supporting the aims, and institutional support. Patients with minor stroke have significant potential to fully recover. A biomarker to detect the high likelihood of conversion to VCI will allow us to design, implement, and monitor the effectiveness of targeted interventions to slow or even prevent cognitive decline.

项目摘要/摘要 血栓切除术显着改善了中风结果。现在近80％的诊所人口 小笔划和低NIH中风量表得分。但是，超过一半认可重大问题 随着注意力，执行功能和处理速度。对于许多人来说，可以看到6个月的重大恢复， 但是，多达三分之一的经历持续的血管认知障碍。一个生物标志物可靠地预测谁 将会遇到的长期定义将使我们能够开始早期干预措施，以减缓甚至防止下降。 我们与MEG的工作表明，无论中风大小或位置如何，全球认知网络的破坏； 但是，允许某些人恢复但失败的补偿机制知之甚少。 可以广泛实施的非侵入性，廉价的筛选工具的迫切需要。 该提案的科学前提是两个折叠：（i）使用MEG和EEG我们可以确定功能网络 影响瞬态后冲程后认知障碍（PSMCI）和持久性的特征 血管认知障碍（VCI）以及负责恢复的补偿机制 （ii）一种新颖的深度学习模型，该模型执行多模式（MEG和EEG）学习寻找共享的签名 VCI，但最终产生的模型需要负担得起的EEG数据，将产生一个强大的生物标志物，可以 中风后早期预测PSMCI向VCI的转化。该提案将追求三个具体目标。 1）识别 瞬时PSMCI和持续性VCI之间的神经生理相似性； 2）确定特定功能 功能连接性，将转换为VCI的功能连接性； 3）设计一个预测的数字生物标志物 使用可以从MEG扩展到脑电的功能性脑网络的转换。为了实现这些目标， 我们将从200个中风患者那里收集MEG和EEG数据，并评估他们的信号 神经生理学家，并监控患者的年度转化率到VCI。然后，我们将设计和验证 深度学习模型称为高斯（SMG2G）的暹罗多图，该模型执行多模式学习 在MEG和EEG网络（Graph）数据上，但最终产生了一个需要仅EEG数据的模型来制作 转换为VCI的预测。最终产品将是EEG数字生物标志物，很容易 在全国各地测量并广泛使用。本应用程序中提出的研究是创新的 因为它是第一个使用功能网络信号来设计VCI的生物标志物，该信号是便宜且 宽度，但稳健，并通过最先进的机器学习实现了这一目标。这也很重要，因为它将 通过实现大规模的VCI检测，垂直垂直地进行科学和临床上的领域。我们的 团队为通过临床和工程专业知识，强大的合作而做好准备，可以进行该项目。 支持目标和机构支持的初步数据。中风的患者有意义 完全恢复的潜力。一种生物标志物检测到VCI转换的可能性很高的生物标志物将使我们能够设计， 实施并监控有针对性干预措施减慢甚至预防认知能力下降的有效性。