Reading minds with Deep Learning: predicting behavioural states from functional imaging data

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


Aim of the PhD Project:

The goal is to:

Develop tools for spatio-temporal Deep Learning of brain function
For prediction of neuro-developmental outcome in vulnerable preterm babies,
And development of biomarkers sensitive to risk of ADHD and Autism
Project Description / Background:

Precision diagnosis of complex cognitive disorders, such as Autism and ADHD, is extremely challenging since such disorders are characterised by a highly heterogeneous range of cognitive and behavioural traits. Such traits are extremely difficult to characterise as they reflect subtle features of the spatio-temporal dynamics of brain activity.

Currently, the most popular technique for analysing resting-state functional imaging data is to perform spatial-ICA (independent component analysis [1]). This models the brain as a macroscale network, formed from a set of functionally specialised regions, each associated with a time course. Network connectivity is then inferred by estimating similarities between time courses using correlation measures [1].

Although matrix factorisation approaches such as ICA, have significantly improved our understanding of how brain function relates to behaviour, they smooth out vital sources of inter-subject variation. Specifically, ICA analyses look at the average properties of brain states over time, whereas, it has been shown that many behavioural measures are better predicted by dynamic measures [2]. Further, studies assume a single global average model of cortical organisation; however, there is growing evidence that this is not the case [3,4].

What is required are tools that can learn temporal and spatial features from the data without requirement for prior modelling or spatial normalisation of the data. This problem lends itself to deep learning; we therefore seek to take inspiration from recent works on spatio-temporal convolutional deep learning for natural image processing [5,6], cardiac imaging [7] and functional Magnetic Resonance Imaging (fMRI [8]), in order to classify pathological brain states, and support precision diagnosis of neuro-developmental disorders.

Given that studies of cognition require precision analysis of the brain's surface (or cortex, [9,10]), a key objective will be to extend models to geometric deep learning [11,12], which trains on surface manifolds, rather than 2D or 3D grids. Significant emphasis will also be placed on the development of interpretable models [6]. This will support clinical interpretation.

The most suitable candidate for this project will have programming expertise in Python, and experience in Deep Learning. Experience in working with spatio-temporal data sets or geometric deep learning would be a significant plus.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S022104/1 01/10/2019 31/03/2028
2442178 Studentship EP/S022104/1 01/10/2020 30/09/2024 Simon Dahan