Digital Biomarkers for Vascular Cognitive Decline in Patients with Minor Stroke

项目摘要/摘要 血栓切除术显著改善了中风的预后。我们现在有近80%的诊所人员在场 有较小的中风和低NIH中风量表得分。然而，超过一半的人认为存在重大问题 具有注意力、执行功能和处理速度。对于许多人来说，6个月后就能看到显著的复苏， 但多达三分之一的人经历了持续性的血管认知障碍。一种生物标志物，可以有力地预测 将显示长期的赤字，将使我们能够启动早期干预，以减缓甚至防止衰退。 我们对脑磁图的研究表明，无论中风的大小或位置如何，认知网络都会受到全球的破坏； 然而，人们对允许一些人恢复、而另一些人失败的补偿机制知之甚少。 迫切需要一种可以广泛实施的非侵入性、廉价的筛查工具。 这一建议的科学前提有两个：(I)利用脑磁图和脑电，我们可以确定功能网络 影响短暂性卒中后认知障碍(PsMCI)和持续性认知障碍的特征 血管认知损害(VCI)以及导致康复的代偿机制，以及 (Ii)一种新的深度学习模型，该模型执行多模式(脑磁图和脑电波)学习以找到共享签名 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记物，可以 预测卒中后早期psMCI向VCI的转换。这项提议将追求三个具体目标。1)识别 短暂性PSMCI和持续性VCI之间的神经生理学相似性；2)确定特定的特征 预测向VCI转变的功能连接性；3)设计预测VCI的数字生物标记物 使用可以从脑磁图扩展到脑电波的功能性大脑网络进行转换。为了实现这些目标， 我们将收集200例轻微中风患者的脑磁图和脑电数据，并与专家一起评估他们的信号 神经生理学家，并监测患者每年的VCI转换率。然后，我们将设计并验证 一种深度学习模型，称为暹罗多图到高斯(SMG2G)，它执行多模式学习 脑磁图和脑电网络(图)数据，但最终产生的模型只需要脑电数据来制作 向VCI转换的预测。最终产品将是一种脑电数字生物标记物，它可以很容易地 经过衡量，并在全国范围内广泛使用。本申请中提出的研究具有创新性。 因为它是第一个使用功能网络信号来设计VCI生物标记物的公司，而且价格低廉， 广泛，但健壮，并通过尖端的机器学习实现这一点。它还具有重要意义，因为它将 通过实现VCI的大规模早期检测，在科学和临床上垂直推进该领域的发展。我们的 团队已经做好了承担这个项目的准备，拥有临床和工程专业知识，强大的协作能力， 支持目标的初步数据，以及制度支持。轻度中风患者有显著的 完全恢复的可能性。一种检测转换为VCI的高可能性的生物标记物将允许我们设计， 实施并监测有针对性的干预措施的有效性，以减缓甚至防止认知能力下降。

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.