Precision Modelling of Cortical Variation and its Association with Neurological/Psychiatric disease

皮质变异的精确建模及其与神经/精神疾病的关系

• 批准号: MR/V03832X/1
• 负责人: Emma Robinson
• 金额: $ 68.44万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FV03832X%2F1
• 关键词: Precision; Modelling; Cortical; Variation; its

The aim of this proposal is to develop a novel medical imaging support tool to significantly improve rates of detection, of types of subtle brain abnormality, which give rise to complex brain conditions. Specifically, we are seeking to develop tools that improve the accuracy with which we can compare brain scans across populations. This will make it much easier to tell the difference between healthy and atypical brains, or detect diseased tissue.The reason that this is challenging is because brains are extremely complex, made of billions of cells, and each one can look very different. This makes it hard to build a single model of what "healthy" brains should look like, and as a result it becomes very difficult to spot evidence of disease.These challenges mean that radiologists require years of experience, reviewing countless examples, before they can reliably spot subtle brain abnormalities, and even so, for diseases such as focal childhood epilepsies, up to 30% of cases evade detection. For similar reasons, automated tools often also struggle: appearance of scans varies so extensively that simplifying assumptions must be made leading to coarse solutions.The largest assumption is that all brains share a common organisational blueprint, where areas of the brain responsible for different functions appear in the same order. Such that if each brain scan was a jigsaw, with each piece a region, the shapes might change but they would in go together in the same way. However, in reality brains vary topographically, which means that areas representing different functions (such as language) can swap location. Methods assuming otherwise end up comparing completely different areas of the brain across individuals. Each area may look very different, with different definitions of what is normal. As a result, this leads to confusion, limiting the ability of any method to detect signs of disease.In the past, methods were particularly limited as they built their model of regional organisation based simply on patterns of brain folding. However, it turns out that shape is a fairly coarse and non-specific model of brain organisation, and that brains often have very different patterns of brain folding for the same functional region.Recently we developed a novel open-access tool, which instead learns how to map brains onto a model which takes into account, not just shape but also function, and other aspects of brain organisation (Robinson Neuroimage 2014, 2018). This has led to new, more accurate, models of cortical organisation (Glasser Nature 2016) and development (Garcia PNAS 2018, O'Muircheartaigh Brain 2020) and improved understanding of the links between brain organisation and behaviour (Bijsterbosch Elife 2018).Now we propose to extend this tool, to account for variation of brain shape and appearance in a way that reflects the natural variation seen from one individual to another. Rather than learn a single model of brain organisation we will learn a family of models (modes) that try to describe how our brains vary. These will capture all biologically relevant modes of variation, allowing individual brain scans to be compared, for a given location, only against others with a common organisational blueprint. In this way we will support much more detailed comparison, than was ever possible before.We will validate the power of the approach through three studies: 1) finding the source of epileptic seizures in the brain (to support surgical planning); 2) predicting cognitive outcomes for babies with developmental brain conditions; 3) identifying biological markers in the brain that may help predict mental health conditions. Ultimately, these tools will support researchers, medical doctors and healthcare workers to build more sensitive predictive models, fine tuned to detect signs of abnormality within individual brains. This will improve screening detection rates and lead to more accurate diagnosis of all brain conditions.

该提案的目的是开发一种新型医学成像支持工具，以显着提高引起复杂大脑状况的微妙大脑异常类型的检测率。具体来说，我们正在寻求开发工具来提高我们可以比较不同人群的大脑扫描的准确性。这将使区分健康大脑和非典型大脑或检测患病组织变得更加容易。之所以具有挑战性，是因为大脑极其复杂，由数十亿个细胞组成，而且每个细胞看起来都非常不同。这使得建立“健康”大脑应该是什么样子的单一模型变得困难，因此发现疾病的证据变得非常困难。这些挑战意味着放射科医生需要多年的经验，审查无数的例子，然后才能能够可靠地发现细微的大脑异常，即便如此，对于局灶性儿童癫痫等疾病，高达 30% 的病例逃避检测。出于类似的原因，自动化工具也常常遇到困难：扫描的外观变化如此之大，以至于必须做出简化的假设，从而导致粗略的解决方案。最大的假设是所有大脑共享一个共同的组织蓝图，其中负责不同功能的大脑区域出现以相同的顺序。这样，如果每次大脑扫描都是一个拼图，每一块都是一个区域，形状可能会改变，但它们会以相同的方式组合在一起。然而，实际上大脑的地形各不相同，这意味着代表不同功能（例如语言）的区域可以交换位置。否则的方法最终会比较不同个体的大脑完全不同的区域。每个区域可能看起来非常不同，对正常的定义也不同。结果，这导致了混乱，限制了任何方法检测疾病迹象的能力。在过去，方法特别有限，因为他们仅根据大脑折叠模式建立区域组织模型。然而，事实证明，形状是一种相当粗糙且非特定的大脑组织模型，并且大脑对于同一功能区域通常具有非常不同的大脑折叠模式。最近我们开发了一种新颖的开放获取工具，它可以学习如何将大脑映射到一个模型上，该模型不仅考虑形状，还考虑功能以及大脑组织的其他方面（Robinson Neuroimage 2014，2018）。这导致了新的、更准确的皮质组织模型（Glasser Nature 2016）和发展（Garcia PNAS 2018，O'Muircheartaigh Brain 2020），并提高了对大脑组织和行为之间联系的理解（Bijsterbosch Elife 2018）。现在我们建议扩展这一工具，以反映个体之间自然差异的方式来解释大脑形状和外观的变化。我们不是学习单一的大脑组织模型，而是学习一系列试图描述我们的大脑如何变化的模型（模式）。这些将捕获所有生物学相关的变异模式，允许在给定位置将个体脑部扫描仅与具有共同组织蓝图的其他人进行比较。通过这种方式，我们将支持比以前更详细的比较。我们将通过三项研究验证该方法的功效：1）找到大脑中癫痫发作的根源（以支持手术计划）； 2）预测患有大脑发育疾病的婴儿的认知结果； 3）识别大脑中可能有助于预测心理健康状况的生物标记。最终，这些工具将支持研究人员、医生和医护人员建立更灵敏的预测模型，并进行微调以检测个体大脑内的异常迹象。这将提高筛查检出率，并更准确地诊断所有大脑疾病。