课题基金 / 基金详情

Characterising neural activation and functional connectivity differences between resilient and non-resilient young people

Characterising neural activation and functional connectivity differences between resilient and non-resilient young people
表征有弹性和无弹性年轻人之间的神经激活和功能连接差异
批准号:
2102307
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2018
资助国家:
英国
项目状态:
已结题
起止时间:
2018 至 --

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中文摘要
翻译
尽管遭受了极端的逆境或创伤,许多人仍然能够保持积极的心理健康,并成功地适应应对这些情况。研究人员强调了理解弹性对于提高我们对精神疾病和精神病理学的理解的重要性。大脑在恢复力方面的作用需要特别关注,因为它在调节对外部事件的行为反应方面起着关键作用。功能磁共振成像(fMRI)研究了弹性的潜在机制,建立了弹性组和非弹性组之间的神经激活和功能连接差异。然而,由于以前用于检测和测量这种结构的任意标准和截止值,准确地定义弹性带来了困难。量化创伤暴露的复杂性,如时间、持续时间和严重程度,在很大程度上被忽视了。该项目旨在提高我们对弹性的理解,并确定弹性年轻人与非弹性同龄人在神经激活和功能连接方面的差异。利用包含有关创伤和生活事件的详细信息的现有数据集,该项目旨在使用大型多层次数据集完善弹性结构,识别同一数据集中与弹性相关的神经激活和功能连接模式,并在包含类似信息的独立数据集中复制弹性结构和相关发现。
英文摘要
Despite exposure to extreme adversity or trauma, many individuals are able to maintain positive mental health and successfully adapt to cope in these situations. Researchers have highlighted the importance of understanding resilience to improve our understanding of mental illness and psychopathology. The brain's role in resilience requires particular attention as it is plays a critical role in moderating behavioural responses to external events. fMRI studies have examined the underlying mechanisms of resilience, establishing neural activation and functional connectivity differences between resilient and non-resilient groups. However, defining resilience accurately poses difficulties due to the arbitrary criteria and cut-offs previously applied to detect and measure this construct. The complexities of quantifying exposure to trauma such as timing, duration and severity, have largely been ignored.This project aims to improve our understanding of resilience and identify differences in neural activation and functional connectivity in resilient young people compared to their non-resilient peers. Using existing datasets that contain detailed information about trauma and life events, the project aims to refine the construct of resilience using a large multi-level dataset, identify patterns of neural activation and functional connectivity related to resilience in the same dataset and replicate the resilience constructs and associated findings in independent datasets which contain similar information.
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