Brain sulcal folding and mental illness: investigating causal associations and stratification approaches for psychosis

脑沟折叠和精神疾病：调查精神病的因果关系和分层方法

• 批准号: MR/W020025/1
• 负责人: Graham Murray
• 金额: $ 114.87万
• 依托单位: University of Cambridge
• 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%2FW020025%2F1
• 关键词: Brain; sulcal; folding; mental; illness

The folding of the outer surface of the brain is highly variable across individuals. Variations in folding are associated with brain function, potentially contributing to mental illness. Some folds of the brain shows a particularly high level of variability between individuals, including the cingulate fold (or known to scientists as the cingulate gyrus and its associated furrow, the cingulate sulcus), which presents as a single fold in some people and a double fold in others. Variability in cingulate folding is an almost uniquely human feature. Whilst the cingulate fold is present in other mammals, humans and chimpanzees are the only animals that can have a double cingulate fold. Initial evidence suggests that the folding pattern may also be important for vulnerability to mental health problems, but providing definitive answers has been limited by small samples sizes and technical challenges in measuring the degree of folding on brain scans.To drive this work forward, we have improved the way we can measure variable folding on brain scans. Currently the gold standard way to measure it is for a trained expert to spend about 30 minutes per scan per variable sulcus, looking at a brain scan from various points of view on a screen and measuring it using a computer mouse to draw lines on the scan. This method is prone to human error and is too slow to do at scale. We have developed a new computer program to measure cingulate folding rapidly, reliably and accurately, and we will refine this program, extending it to measure folding in other parts of the brain, and make it freely available for other scientists and clinicians to use in future. We will run the program on the brain scans in over 50,000 people (from the large UK Biobank and USA ABCD studies), giving each participant summary measures of several variable folds. We will deposit the results in the relevant database so that researchers studying this acclaimed resource can easily access the results and relate cingulate folding to other measures of interest. It is not at all known what causes folding variability. We will conduct the first study to examine the molecular genetic basis of sulcal folding variation, and we will use a genetic technique called mendelian randomisation that uses genetic information to check whether particular brain folding variants are contributory causes to mental illness. We will illustrate the clinical importance of folding by testing whether it can predict an important clinical outcome in schizophrenia, namely response to treatment. If we can identify, at first presentation, those patients who will need to treatments that are usually only introduced as last resort, we could potentially improve early outcomes.Our project will clarify the importance of folding for psychological function and mental illness in much greater detail and in much larger numbers than has been done before. To do this we will take advantage of several existing high quality studies that have already collected data that we will use to clarify the role of the PCS in health and illness. In particular, we will look at cingulate folding in relation to hallucinations using scans from over 1100 patients with schizophrenia in studies drawn from Europe and the USA - a sample five time larger than the largest previous study. We will examine the effects of cingulate folding in the general population in large studies of over 50,000 people. We will use data from these studies to see if the cingulate folding pattern is linked to vulnerability to certain psychiatric symptoms, psychological abilities, and the wiring patterns of the brain. This line of work is exciting: our computer program could in future be used on brain scans to calculate "biomarkers" that help doctors and patients make better treatment decisions, and will provide new knowledge on how the brain folds and what difference this makes to our thinking and vulnerability to mental illness.

大脑外表面的折叠在个体之间是高度可变的。折叠的变化与大脑功能有关，可能导致精神疾病。大脑的某些褶皱在个体之间表现出特别高的变异性，包括扣带回褶皱（或被科学家称为扣带回及其相关沟，扣带回沟），在某些人中表现为单褶皱，在其他人中表现为双褶皱。扣带回折叠的可变性几乎是人类独有的特征。虽然扣带回褶皱存在于其他哺乳动物中，但人类和黑猩猩是唯一可以拥有双重扣带回褶皱的动物。最初的证据表明，折叠模式可能对精神健康问题的脆弱性也很重要，但由于样本量小和测量大脑扫描折叠程度的技术挑战，提供明确的答案受到限制。为了推动这项工作，我们改进了测量大脑扫描可变折叠的方法。目前，测量它的黄金标准方法是由受过训练的专家每次扫描每个变量沟花费大约30分钟，从屏幕上的各个角度观察大脑扫描，并使用计算机鼠标在扫描上绘制线条进行测量。这种方法容易出现人为错误，而且速度太慢，无法大规模使用。我们已经开发了一种新的计算机程序来快速，可靠和准确地测量扣带折叠，我们将改进该程序，将其扩展到测量大脑其他部位的折叠，并使其免费提供给其他科学家和临床医生在未来使用。我们将在超过50，000人的大脑扫描上运行该程序（来自大型英国生物银行和美国ABCD研究），为每个参与者提供几个变量折叠的摘要测量。我们将存款的结果在相关的数据库，使研究人员研究这一著名的资源可以很容易地访问的结果，并与扣带折叠的其他措施的利益。目前还不清楚是什么原因导致了折叠变异性。我们将进行第一项研究，以检查脑沟折叠变异的分子遗传基础，我们将使用一种称为孟德尔随机化的遗传技术，该技术使用遗传信息来检查特定的大脑折叠变异是否是精神疾病的促成因素。 我们将通过测试折叠是否可以预测精神分裂症的重要临床结局，即对治疗的反应，来说明折叠的临床重要性。如果我们能在第一次就诊时就识别出那些需要治疗的患者，而这些治疗通常只是作为最后的手段引入的，那么我们就有可能改善早期的结果。我们的项目将比以前更详细、更大量地阐明折叠对心理功能和精神疾病的重要性。为此，我们将利用现有的几项高质量研究，这些研究已经收集了数据，我们将使用这些数据来阐明PCS在健康和疾病中的作用。特别是，我们将使用来自欧洲和美国的研究中超过1100名精神分裂症患者的扫描来研究与幻觉有关的扣带回折叠-样本比以前最大的研究大五倍。我们将在超过50，000人的大型研究中检查扣带回折叠对普通人群的影响。我们将使用这些研究的数据来观察扣带折叠模式是否与某些精神症状、心理能力和大脑布线模式的脆弱性有关。这项工作令人兴奋：我们的计算机程序将来可以用于大脑扫描，以计算“生物标志物”，帮助医生和患者做出更好的治疗决策，并将提供有关大脑如何折叠的新知识，以及这对我们的思维和精神疾病的脆弱性有什么影响。

项目成果

A 3D explainability framework to uncover learning patterns and crucial sub-regions in variable sulci recognition

用于揭示可变脑沟识别中的学习模式和关键子区域的 3D 可解释性框架

• DOI: 10.48550/arxiv.2309.00903
• 发表时间: 2023
• 作者: Mamalakis M

Educational attainment, structural brain reserve and Alzheimer's disease: a Mendelian randomization analysis.

教育程度、结构性大脑储备和阿尔茨海默病：孟德尔随机分析。

• DOI: 10.17863/cam.90771
• 发表时间: 2023
• 作者: Seyedsalehi A

The genetics of cortical organisation and development: a study of 2,347 neuroimaging phenotypes

• DOI: 10.1101/2022.09.08.507084
• 发表时间: 2022-09
• 期刊: bioRxiv
• 作者: V. Warrier;E. Stauffer;Q. Huang;E. Wigdor;E. Slob;J. Seidlitz;L. Ronan;S. Valk;T. Mallard;A. Grotzinger;R. Romero-García;S. Baron-Cohen;D. Geschwind;Madeline A. Lancaster;G. Murray;M. Gandal;A. Alexander-Bloch;H. Won;H. Martin;E. Bullmore;R. Bethlehem

Machine learning in small sample neuroimaging studies: Novel measures for schizophrenia analysis.

小样本神经影像研究中的机器学习：精神分裂症分析的新方法。

• DOI: 10.17863/cam.104313
• 发表时间: 2024
• 作者: Jimenez-Mesa C