Individualised modelling of dynamic brain networks in fMRI.

The brain is a complex organ. Functional magnetic resonance imaging (fMRI) is a powerful non-invasive tool to record brain activity in humans.

It detects the blood-oxygen-level-dependent (BOLD) signal as an indirect measure of neural activity, with high spatial resolution across different brain regions.

To study how these regions interact with each other in brain networks, scientists calculate functional connectivity, which in fMRI is the correlation between BOLD signals from distinct regions. Neuroscientists have identified brain networks that coordinate with each other in both rest and task.

Traditionally, functional connectivity is estimated using a static approach - calculating a single correlation value for each pair of brain regions using the entirety of a long scanning session.

Recently, there has been increasing interest in understanding brain network dynamics, i.e., dynamics in functional connectivity.

Brain activity is expected to be changing all the time, and hence time-varying network descriptions can capture information being missed by static approaches. Another important trend in this field is to consider subject variability. For example, the spatial locations of functional brain regions can vary over subjects.

Typically, functional brain regions are identified by grouping together voxels with similar activity.

A single timeseries is then extracted for the brain region and fed in as data to the dynamic approaches such as the Hidden Markov Modelling (HMM).

Obtaining these brain region time courses has previously been done using group-level Independent Component Analysis (ICA) combined with dual regression to give a subject-specific version of each subject's functional brain region.

However, this has been shown to underperform compared to newly developed methods such as PROFUMO, which explicitly model subject variability in a generative model.

Accounting for subject variability is critically important for understanding the human brain in both health and diseased; as, rather than just one overall vague group description, it provides more specific descriptions tuned to different types of brains. This is crucial for the delivery of personalised medicine in the era of big data.

The last decade has witnessed the development of large-scale publicly available neuroimaging data, such as the Human Connectome Project (HCP) and UK Biobank (UKB), and posed a great challenge: how do we understand functioning and malfunctioning of individual subject brain networks by using information from the large cohort?

This DPhil project faces up to the challenge from the perspective of dynamic brain networks. We aim to model network dynamics with subject-specific spatiotemporal variability in fMRI.

First, we will develop robust and trustworthy metrics to validate the different dynamic models such as the Hidden Markov Modelling (HMM) and Dynamic Network Modes (Dynemo).

We will explore Bayesian inference metrics (such as variational free energy) and machine learning methods (such as cross validation) to measure the ability of different models to reliably represent the brain network dynamics. This will inform model and hyperparameter selection for our future work. Second, we are going to explore the genetic basis of network dynamics.

Previous work has found that dynamics are highly heritable using twin structure in HCP data.

We will go beyond this work by exploring associations between single nucleotide polymorphisms and phenotypes obtained from existing dynamic network models in UK Biobank data. Finally, we will build a new dynamic network model that better handles subject variability.

Different from previous methods, this model will be end-to-end - combining these two steps into one large model with subject-specific descriptions on 100,000 subjects of UKB data.This project falls within the EPSRC research area of medical imaging - including medical image and vision computing. The industrial supervisor is Dr.

Stanislaw Adaszeewski from Roche.