Building sensitive models of cognition using interpretable Deep Learning

Lead Research Organisation: King's College London
Department Name: Imaging & Biomedical Engineering

Abstract

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.

Publications

10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S022104/1 01/10/2019 31/03/2028
2322101 Studentship EP/S022104/1 06/01/2020 06/01/2024 Mariana Ferreira Teixeira Da Silva