Deep learning models for brain-behaviour associations

大脑行为关联的深度学习模型

• 批准号: 2269803
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2269803
• 关键词: Deep; learning; models; brain; behaviour

Deep learning approaches based on Canonical Correlation Analysis (CCA), such as Deep Canonical Correlation Analysis (DCCA, Andrew et al. 2013) and Deep Canonically Correlated Autoencoders (DCCAE, Wang et al. 2015), are machine learning techniques that can learn linear and non-linear transformations of two views of the same samples/subjects in a common/latent space, in which the learnt representations are highly correlated. This project will aim at employing these transformations to create mappings between multivariate brain patterns and behaviour/clinical patterns by using large samples of healthy individuals as well as individuals with mental health disorders. The developed generative models will then be used to test specific hypothesis of brain-behaviour associations related to mental health disorders. For example, given specific clinical/cognitive profiles, the model will be able to generate the corresponding brain patterns and vice-versa. This innovative modelling approach is expected to shed light on the underlying mechanisms of brain-behaviour association. Further applications of the developed framework will include utilising the latent space to perform patient stratification according to mental health disorders, and using the generative models to synthesize missing data to aid predictive studies.

基于典型相关分析（CCA）的深度学习方法，例如深度典型相关分析（DCCA，Andrew et al. 2013）和深度典型相关自动编码器（DCCAE，Wang et al. 2015），是机器学习技术，可以学习公共/潜在空间中相同样本/主题的两个视图的线性和非线性变换，其中学习到的表示是高度相关的。该项目旨在通过使用健康个体以及患有精神健康障碍的个体的大量样本，利用这些转换来创建多元大脑模式和行为/临床模式之间的映射。然后，开发的生成模型将用于测试与心理健康障碍相关的大脑行为关联的具体假设。例如，给定特定的临床/认知概况，该模型将能够生成相应的大脑模式，反之亦然。这种创新的建模方法有望揭示大脑行为关联的潜在机制。所开发框架的进一步应用将包括利用潜在空间根据精神健康障碍对患者进行分层，并使用生成模型合成缺失的数据以帮助预测研究。