Mapping the principal components of aphasic language recovery onto brain structure and function

不幸的是，中风是一个主要的全球健康问题，世界上某个地方的人每两秒就会有第一次中风。中风的最大风险因素是年龄，因此在英国等人口老龄化的国家，中风是一个重大的医疗挑战。认知障碍(记忆力、注意力和语言方面的问题)在中风后非常常见。在中风后不久，每三个中风病例中就有一个出现语言问题，称为失语症，五分之一的病例在中风后持续一年以上。目前，我们不明白为什么有些人中风后失语症恢复得很好，而另一些人却长期存在语言问题。我们也不明白为什么有些人对言语和语言治疗反应良好，而另一些人却不能从中受益。这个项目的目标是将先进的神经成像和复杂的神经心理学结合起来回答这些问题。理想情况下，我们希望能够通过初步的脑部扫描来确定某人是否有可能从目前中风引起的损伤中恢复得很好。我们需要准确地判断卒中失语症患者的预后，以便将治疗资源有效地引导到最需要的人手中。我们还需要了解大脑结构和功能的哪些方面可以预测一个人对标准干预的反应，这样我们就可以在需要的时候专注于替代策略。为了实现这些目标，我们需要开发神经生物标记物，这些标记物可以可靠地用于确定康复和康复的预后。以前在康复过程中实现预后的尝试都取得了有限的成功，因为他们没有使用最先进的神经完整性测量方法，也没有关注神经功能的最相关方面。这个项目将利用Bioxdyn开发的解剖连接映射(ACM)方法，该公司为制药行业、医疗保健和学术界提供高价值的定量成像服务和诊断工具，并获得专利。ACM提供了一种独特的测量整个大脑的远程连接的方法，因此允许我们寻找与恢复和康复过程中表现改善相关的大脑结构的变化。我们目前对超过65名慢性中风失语症患者的队列研究显示，右脑连接性增加，这在任何其他大脑完整性的结构测量中都不明显。此外，我们最近发表的工作(Butler，Lambon Ralph和Woollams，2014，Brain)采用了一种新的方法来绘制病变症状图，使用主成分分析(PCA)来识别关键认知维度及其神经关联。目前的工作表明，这些区域的组织集中显著提高了我们的慢性中风失语样本中表现的预测。这个项目将首先使用来自神经区域的ACM信息，这些信息与通过PCA(语音、语义、流利度；Halai，Woollams和Lambon Ralph，in Press，Cortex)在我们的慢性中风队列中识别的特定语言功能相关，以证明考虑远程连接改善了表现的预测。我们还将从20名患者的子集中获得功能成像数据，以证明在PCA定义的区域激活提供了额外的预测能力。然后，我们将通过招募20名参与者在中风后的亚急性期进行测试和扫描，直接测试这些预测模型的预后能力。我们将使用他们在PCA衍生区域的ACM和功能激活来预测一年后重新测试时的恢复结果。如果这种方法被证明是有效的，使用Bioxdyn将有助于将其转化为临床实践，以预测预后，并提供用于临床试验的基线恢复轨迹。

Stroke is unfortunately a major global health problem, with a person somewhere in the world having their first stroke everytwo seconds. The greatest risk factor for stroke is age, hence it represents a significant healthcare challenge in countrieslike the UK with an aging population. Cognitive deficits (problems with memory, attention, and language) are very commonafter stroke. Problems with language, known as aphasia, are seen in one in every three stroke cases soon after the stroke,and persist for more than a year after stroke in one in five cases. At present, we do not understand why some people makea good recovery from aphasia after stroke while others are left with chronic language problems. Nor do we understand whysome people respond well to speech and language therapy while others fail to benefit. The goal of this project is tocombine advanced neuroimaging with sophisticated neuropsychology to answer these questions.Ideally, we would like to be able to determine from initial brain scans whether someone is likely to recover well from theircurrent stroke induced impairments. We need to determine prognosis for stroke aphasic patients accurately so we can thendirect therapeutic resources effectively to those who need them most. We also need to understand what aspects of brainstructure and function predict a person's response to standard interventions, as then we can focus on alternative strategieswhere needed. To achieve these goals, we need to develop neural biomarkers that can reliably be used to determineprognosis over recovery and rehabilitation. Previous attempts to achieve prognosis over the course of recovery have hadlimited success as they have not used the most advanced measures of neural integrity not have they focussed on the mostrelevant aspects of neural function.This project will exploit the Anatomical Connectivity Mapping (ACM) method developed and patented by Bioxydyn, aprovider of high value quantitative imaging services and diagnostic tools to the pharmaceutical industry, healthcare andacademia. ACM provides a unique measure of long range connectivity across the whole brain, and therefore allows us tosearch for changes in brain structure associated with improved performance over recovery and rehabilitation. Our currentwork on a cohort of over 65 chronic stroke aphasic patients has shown increases in right hemisphere connectivity that arenot apparent on any other structural measure of brain integrity. Moreover, our recently published work (Butler, Lambon Ralph, & Woollams, 2014, Brain) has adopted a novel approach to lesion symptom mapping by using PrincipleComponents Analysis (PCA) to identify key cognitive dimensions and their neural correlates. Current work has shown thattissue concentration in these areas significantly improves prediction of performance in our chronic stroke aphasic sample.This project will involve firstly using the ACM information from the neural regions associated with specific languagefunctions identified using PCA (phonology, semantics, fluency; Halai, Woollams, & Lambon Ralph, in press, Cortex) in ourchronic stroke cohort of more than 65 patients to demonstrate that consideration of long range connectivity improvesprediction of performance. We will also obtain functional imaging data from a subset of 20 of these patients to demonstratethat activation in the PCA defined regions offers additional predictive power. We will then test out the prognostic power ofthese predictive models directly by recruiting 20 participants for testing and scanning in the subacute stage after stroke. Wewill use their ACM and functional activation in the PCA derived regions to predict their recovery outcomes at retest afterone year. To the extent this approach proves effective, placement with Bioxydyn will facilitate its translation into clinicalpractice for prognosis and to provide baseline recovery trajectories for use in clinical trials.