Predicting post-stroke dementia: mechanisms and markers

众所周知，中风会引发痴呆症发作加速的风险显著增加1，2。此外，虽然这些患者中的许多人存在中风后认知障碍，并且都接受了临床中风后扫描（CT或1.5T MR），但目前的方法缺乏区分将进一步下降的患者与将保留或恢复认知功能的患者的能力。高度敏感的认知评估将与机器学习（ML）算法结合使用，该算法从大型复杂的数据库中生成模式分类，以建立一种检测认知能力下降和痴呆高风险人群的方法。大脑中动脉（MCA）是急性卒中中最常见的动脉，MCA卒中非常频繁地影响下顶叶（IPL）并导致加速痴呆。IPL在注意力分配中的作用是公认的，并且在临床上与视觉空间忽视相关。然而，这个区域也是将记忆信息整合到主观记忆体验中的基础。IPL损伤可阻止与内侧颞叶（MTL）的连接，并损害记忆和记忆体验，从而显著促进卒中后认知障碍和加速痴呆发作。我们最近开发了一种新的方法，通过测量该区域对注意力分配的影响来估计IPL记忆功能。IPL注意力功能的双半球测试的表现与大脑依赖性识别记忆密切相关。因此，我们期望患者的注意力特征能够表明他们的IPL记忆功能和IPL-MTL连接的完整性，从而识别出有进一步认知能力下降风险的个体。除了认知下降的传统措施和我们的双半球IPL评估外，我们还将使用高度敏感的模式分离依赖性措施来跟踪顶叶卒中患者的认知，大脑皮层驱动的记忆功能。这种记忆过程在非常早期的痴呆症中特别脆弱，因此它的测量将使我们能够以高水平的灵敏度和特异性检测认知衰退的新兴记忆成分。我们将使用这些数据来开发监督ML算法（例如，随机森林分类器）来预测来自标准临床扫描的加速痴呆。由于中风患者很少接受高分辨率扫描（例如，3 T MR），使用标准的可用CT和1.5 MR数据在发展这种预后途径中最大化了转化价值。

It is well recognised that a stroke triggers a significantly increased risk of accelerated dementia onset1,2. Moreover, while many of these patients present with post-stroke cognitive impairment, and all receive clinical post-stroke scans (CT or 1.5T MR), current methods lack the ability to discriminate those who will decline further from those who will retain, or regain, cognitive function. Highly sensitive cognitive assessment will be used in combination with Machine learning (ML) algorithms, which generate pattern classifications from large, complex datasets3, to establish a method to detect those at high risk of cognitive decline and dementia. The middle cerebral artery (MCA) is the most common artery involved in acute stroke, and MCA strokes very frequently impact the inferior parietal lobe (IPL) and result in accelerated dementia. The IPL is recognised for its role in the allocation of attention, and clinically, is associated with visuospatial neglect. However, this region is also fundamental to integrating remembered information into a subjective memory experience4,5. IPL damage can prevent connectivity with the medial temporal lobe (MTL) and impair memory and memory experience, thus contributing substantially to post-stroke cognitive impairment and accelerated dementia onset. We have recently developed a new method for estimating IPL memory function by measuring the region's influence on attention allocation. Performance on this bi-hemispheric test of IPL attention function is closely associated with hippocampally-dependent recognition memory. We therefore expect a patient's attention profile to indicate the integrity of their IPL memory function and IPL-MTL connectivity, and thus identify individuals at risk of further cognitive decline. Alongside traditional measures of cognitive decline and our bi-hemispheric IPL assessments, we will track cognition using highly sensitive measures of pattern separation-dependent, hippocampally-driven memory function in parietal stroke patients. This memory process is particularly vulnerable in very early dementia, and its measurement will therefore allow us to detect the emerging memory-component of cognitive decline with high levels of sensitivity and specificity. We will use this data to develop supervised ML algorithms (e.g., random forest classifier) to predict accelerated dementia from standard clinical scans. As stroke patients rarely receive high-resolution scans (e.g., 3T MR), using standardly available CT and 1.5 MR data in the development of this prognostic pathway maximises translational value.