Automated brain-age prediction and its interpretation

自动脑年龄预测及其解释

• 批准号: 432015680
• 负责人: Professor Dr. Simon Eickhoff
• 金额: --
• 依托单位: Institut für Systemische Neurowissenschaften
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/432015680?language=en
• 关键词: Automated; brain; age; prediction; its

The biological age of a brain (brain-age) is a valuable summary of its structural or functional brain state. Importantly, elevated brain-age – the BrainAGE score – can serve as a biomarker to identify people with a higher risk of developing age-related diseases. However, brain-age cannot be measured and must be estimated, which calls for accurate methods based on non-invasive brain-imaging data. Although various machine learning (ML) based methods have been proposed for brain-age prediction, two crucial aspects remain notably understudied. Firstly, the impact of the manifold choices regarding data selection and representation or ML algorithms and their interaction with contextual aspects (e.g. restricted age range or a specific site) on prediction accuracy has not been systematically evaluated. As no single method can work well in all scenarios, there is a clear need to identify workflows (combinations of representation, ML algorithm and context) that may improve prediction accuracy. Secondly, localization of age-sensitive brain regions responsible for the predicted brain-age and in turn its deviation from the chronological age has only been coarsely explored. In addition, the impact of individual-specific factors (e.g. atypical aging in neurodegenerative diseases) remains poorly understood. In this work, we will address these issues by: (1) providing workflow design guidelines based on systematically evaluating a large number of workflows and contexts in a big-data framework, and (2) gaining neurobiological insights by testing and devising interpretation methods to localize age-sensitive brain regions and explain individual predictions.

大脑的生物年龄（brain-age）是对大脑结构或功能状态的一个有价值的总结。重要的是，脑年龄（BrainAGE评分）的升高可以作为一种生物标志物，用于识别患年龄相关疾病风险较高的人。然而，脑年龄无法测量，必须估计，这需要基于非侵入性脑成像数据的准确方法。尽管已经提出了各种基于机器学习（ML）的方法来预测大脑年龄，但两个关键方面仍然没有得到充分研究。首先，关于数据选择和表示或ML算法的多种选择及其与上下文方面（例如，限制年龄范围或特定站点）的相互作用对预测准确性的影响尚未得到系统评估。由于没有一种方法可以在所有场景中都很好地工作，因此显然需要确定可以提高预测准确性的工作流程（表示，ML算法和上下文的组合）。其次，负责预测脑年龄的年龄敏感脑区的定位，反过来又偏离实际年龄只进行了粗略的探索。此外，对个体特异性因素（如神经退行性疾病中的非典型衰老）的影响仍然知之甚少。在这项工作中，我们将通过以下方式解决这些问题：（1）在大数据框架中系统地评估大量工作流和上下文的基础上提供工作流设计指南，以及（2）通过测试和设计解释方法来定位年龄敏感的大脑区域并解释个人预测，从而获得神经生物学见解。