Building sensitive models of cognition using interpretable Deep Learning

目前用于从脑成像数据预测行为和认知特征的方法受限于需要应用简化的皮质组织模型以促进数据集之间的比较。另一方面，深度学习允许直接比较成像数据，而无需使用此类先验模型。因此，它有可能显着提高神经影像学研究的灵敏度，因为它已经为自然图像分析。不幸的是，深度网络进行预测的机制还没有得到很好的理解，这使得很难知道数据的哪些特征对每个预测任务最有区别。该项目的目标是开发用于深度学习算法的可视化和解释的新技术，以：设计深度网络，从大脑成像数据中学习有生物意义的表示，并使用这些特征来提高对认知和行为机制的理解。该项目的具体目标是开发用于图形卷积深度学习算法的可视化和解释的新技术，以：1）设计深度网络，从多模态皮层成像数据中学习有生物意义的表示。2)利用这些特征来提高对早产儿和有自闭症风险的婴儿认知障碍机制的理解。

Current approaches for predicting behavioural and cognitive traits from brain imaging data are limited by a need to apply simplified models of cortical organisation to facilitate comparison between datasets. Deep Learning, on the other hand, allows direct comparisons of imaging data without the use of such prior models. It thus has the potential to significantly improve the sensitivity of neuroimaging studies as it has done for natural image analysis. Unfortunately, the mechanisms by which deep networks make predictions are not well understood making it challenging to know what features of the data are most discriminative for each predictive task. The goal of this project is to develop novel techniques for visualisation and interpretation of Deep Learning algorithms to:design Deep Networks that learn biologically meaningful representations from brain imaging data, and use these features to improve understanding of the mechanisms of cognition and behaviour.The specific objectives of this project are to develop novel techniques for visualisation and interpretation of graph convolutional Deep Learning algorithms to:1) Design Deep Networks that learn biologically meaningful representations from multi-modality cortical imaging data. 2) Use these features to improve understanding of the mechanisms of cognitive impairment in preterm born infants and babies at risk of Autism.