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Neural Networks models to predict individual behaviour from Multimodal MRI Data

   UKRI Centre for Doctoral Training in Socially Intelligent Artificial Agents

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  Mr Jared de Bruin  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

For instructions on how to apply, please see: PhD Studentships: UKRI Centre for Doctoral Training in Socially Intelligent Artificial Agents.


  • Cassandra Sampaio Batista: School of Psychology
  • Tanaya Guha: School of Computing Science

Understanding and predicting individual complex behaviour in healthy and pathological conditions is a key goal in neuroscience and psychology. Magnetic Resonance Imaging (MRI) allows us to image functional and structural human brain properties in vivo and to relate them to behavioural performance. Furthermore, multimodal MRI, such as functional MRI (fMRI), Diffusion Tensor Imaging (DTI) and Multiparameter Mapping (MPM) can be acquired in the same session, capturing different brain tissue properties (Lazari et al., 2021). However, multimodal MRI data remains underused to explore brain-behaviour relationships. The majority of unimodal MRI studies use simple correlation methods either voxel-wise or based on region of interest (ROI).

Machine Learning (ML) methods have seen major breakthroughs in the last decade in the domain of natural image understanding, making its way to medical image analysis. So far, most ML-based MRI analysis use large data sets with hundreds or thousands of individuals, making it less than ideal for MRI-based studies that, with some exceptions (e.g. Human Connectome project, Biobank), rely on small sample sizes. Recent advances in ML, in an attempt to address the criticism for being ‘data-hungry’, focus on learning from smaller datasets through approaches like self-supervised/unsupervised learning and data augmentation.

In this PhD project the student will leverage multimodal MRI (task and resting- state fmri, DTI, MPM, T1-weighted) using ML methods to perform data augmentation and to discover participant-specific attributes (biomarkers) that relate to the performance in different cognitive-motor tasks in healthy individuals in small sample studies.

The main objectives of this project are as follows:

Objective 1: To identify the biomarkers from multimodal MRI that relate to behaviour/impairment in small sample studies

Objective 2: How to effectively fuse information from multiple modalities to achieve objective 1

Building on this initial project the student will then develop models to predict impairment in stroke survivors from multimodal MRI.

Expected outcome/impact

This project will develop tools and models that can be applied to small sample studies to understand individual differences in complex behaviour and to patient studies, that are typically small, to make predictions about prognosis and recovery. The resulting predictive models can potentially be used to understand how brain traits relate to individual behavioural and learning characteristics.


Lazari, A., Salvan, P., Cottaar, M., Papp, D., Jens van der Werf, O., Johnstone, A., Sanders, Z.B., Sampaio-Baptista, C., Eichert, N., Miyamoto, K., Winkler, A., Callaghan, M.F., Nichols, T.E., Stagg, C.J., Rushworth, M.F.S., Verhagen, L., Johansen-Berg, H., 2021. Reassessing associations between white matter and behaviour with multimodal microstructural imaging. Cortex 145, 187-200.
Doersch, C., Zisserman, A. 2017. Multi-task Self-Supervised Visual Learning. IEEE International Conference on Computer Vision (ICCV). 2070–2079.
Shorten, Connor; Khoshgoftaar, Taghi M. (2019). “A survey on Image Data Augmentation for Deep Learning”. Mathematics and Computers in Simulation. springer. 6: 60. doi:10.1186/s40537-019-0197-0
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