Deep learning models for brain-behaviour associations

基于典型相关分析(CCA)的深度学习方法，如深度典型相关分析(DCCA，Andrew et al.2013)和深度规范相关自动编码器(DCCAE，Wang等人。2015)是机器学习技术，它可以学习相同样本/对象的两个视图在公共/潜在空间中的线性和非线性变换，其中学习到的表示高度相关。该项目旨在利用这些转换，通过使用大样本的健康个人以及精神健康障碍患者来创建多变量大脑模式与行为/临床模式之间的映射。然后，开发的生成模型将被用来测试与精神健康障碍有关的大脑行为关联的特定假设。例如，给定特定的临床/认知特征，该模型将能够生成相应的大脑模式，反之亦然。这一创新的建模方法有望阐明大脑行为关联的潜在机制。开发的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进行患者分层，以及使用生成性模型合成缺失数据以帮助预测性研究。

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.