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的医学成像支持工具，以显着提高检测率，即微妙的脑异常类型，这会导致复杂的大脑状况。具体来说，我们正在寻求开发提高我们可以比较跨种群脑扫描的精度的工具。这将使判断健康和非典型大脑之间的差异或检测患病组织之间的差异变得容易得多。这是具有挑战性的原因，是因为大脑非常复杂，由数十亿个细胞组成，每个细胞都可以看起来非常不同。这使得很难建立一个“健康”大脑应该是什么样的模型，因此很难发现疾病的证据。这些挑战意味着放射科医生需要多年的经验，审查无数例子，然后才能可靠地发现细微的脑部异常，即使如此疾病，以及诸如Focal Child epilepsies之类的疾病，甚至是exilepsies，and epilepsies ceserepsies case decection facection cantection cantection facection facection facection facection catection facection facection facection。出于类似的原因，自动化工具也经常挣扎：扫描的外观差异很大，以至于必须简化假设，从而导致解决方案。最大的假设是，所有大脑都具有共同的组织蓝图，其中负责不同功能的大脑领域以同一顺序出现。这样，如果每次大脑扫描都是拼图，则每块一个区域，形状可能会发生变化，但它们会以相同的方式一起进行。但是，实际上大脑在地形上有所不同，这意味着代表不同功能（例如语言）的区域可以交换位置。假设否则最终比较了跨个体大脑的完全不同区域的方法。每个区域看起来可能非常不同，对正常情况的定义不同。结果，这导致了混乱，限制了任何方法检测疾病迹象的能力。过去，方法特别有限，因为它们仅基于大脑折叠的模式，建立了区域组织模型。然而，事实证明，形状是一个相当粗糙且非特异性的大脑组织模型，并且大脑通常对同一功能区域具有截然不同的脑折叠模式。我们开发了一种新型的开放式访问工具，而是学会了如何将大脑映射到一个模型上，该模型不仅要考虑到一个模型，不仅是脑组织，而且还可以使大脑组织（Robinson Neurooimage obinson neurooimage 2014，2014年），2014年）。这导致了皮质组织（Glasser Nature 2016）和开发（Garcia PNAS 2018，O'Muircheartaigh 2020 2020）的新型，更准确的模型（Glasser Nature 2016）和开发模式，并改善了对大脑组织与行为之间联系的理解（Bijsterbosch Elife 2018）。我们将学习一个模型家庭（模式），而不是学习大脑组织的单一模型，这些模型（模式）试图描述我们的大脑的变化。这些将捕获所有与生物学上相关的变异模式，从而使单个脑部扫描在给定的位置仅与具有共同组织蓝图的其他位置进行比较。通过这种方式，我们将通过三项研究来验证方法的能力：1）在大脑中找到癫痫发作的来源（以支持手术计划）； 2）预测患有发育脑部病情的婴儿的认知结果； 3）确定大脑中可能有助于预测心理健康状况的生物学标记。最终，这些工具将支持研究人员，医生和医疗保健工作者建立更敏感的预测模型，并进行了微调以检测单个大脑内部异常的迹象。这将提高筛查率，并导致所有大脑条件的更准确诊断。