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

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:
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
项目状态:
未结题
起止时间:
2022 至 --

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中文摘要
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英文摘要
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.
期刊论文(7)
专著(0)
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会议论文
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
期刊:
影响因子: --
作者: [Mamalakis M]
通讯作者: 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
期刊:
影响因子: --
作者: [Seyedsalehi A]
通讯作者: Seyedsalehi A
DOI: 10.1101/2022.09.08.507084
发表时间: 2022-09
期刊: bioRxiv
影响因子: --
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]
通讯作者: 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
期刊:
影响因子: --
作者: [Jimenez-Mesa C]
通讯作者: Jimenez-Mesa C
Motivational processing, Mesolimbic and Mesostriatal Function in Neuropsychiatric Disease
  • 批准号:
    G0701911/1
  • 项目类别:
    Fellowship
  • 资助金额:
    $92.59万
  • 财政年份:
    2009
  • 负责人:
    Graham Murray
  • 依托单位:
海外基金