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.

The UK's world-leading position in biomedical research is critically dependent upon training scientists with the cutting-edge research skills and technological know-how needed to drive future scientific advances. Since 2009, the EPSRC and MRC CDT in Systems Approaches to Biomedical Science (SABS) has been working with its consortium of 22 industrial and institutional partners to meet this training need.

Over this period, our partners have identified a growing training need caused by the increasing reliance on computational approaches and research software. The new EPSRC CDT in Sustainable Approaches to Biomedical Science: Responsible and Reproducible Research - SABS:R^3 will address this need. By embedding a sustainable approach to software and computational model development into all aspects of the existing SABS training programme, we aim to foster a culture change in how the computational tools and research software that now underpin much of biomedical research are developed, and hence how quantitative and predictive translational biomedical research is undertaken.

As with all CDT Programmes, the future impact of SABS:R^3 will be through its alumni, and by the culture change that its training engenders. By these measures, our existing SABS CDT is already proving remarkably successful. Our alumni have gone on to a wide range of successful careers, 21 in academic research, 19 in industry (including 5 in SABS partner companies) and the other 10 working in organisations from the Office of National Statistics to the EPSRC. SABS' unique Open Innovation framework has facilitated new company connections and a high level of operational freedom, facilitating 14 multi-company, pre-competitive, collaborative doctoral research projects between 11 companies, each focused on a SABS student.

The impact of sustainable and open computational approaches on biomedical research is clear from existing SABS' student projects. Examples include SAbDab which resulted from the first-ever co-sponsored doctorate in SABS, by UCB and Roche. It was released as open source software, is embedded in the pipelines of several pharmaceutical companies (including UCB, Medimmune, GSK, and Lonza) and has resulted in 13 papers. The SABS student who developed SAbDab was initially seconded to MedImmune, sponsored by EPSRC IAA funding; he went on to work at Roche, and is now at BenevolentAI. Similarly, PanDDA, multi-dataset X-ray crystallographic software to detect ligand-bound states in protein complexes is in CCP4 and is an integral part of Diamond Light Source's XChem Pipeline. The SABS student who developed PanDDA was awarded an EMBO Fellowship.

Future SABS:R^3 students will undertake research supported by both our industrial partners and academic supervisors. These supervisors have a strong track record of high impact research through the release of open source software, computational tools, and databases, and through commercialisation and licensing of their research. All of this research has been undertaken in collaboration with industrial partners, with many examples of these tools now in routine use within partner companies.

The newly focused SABS:R^3 will permit new industrial collaborations. Six new partners have joined the consortium to support this new bid, ranging from major multinationals (e.g. Unilever) to SMEs (e.g. Lhasa). SABS:R^3 will continue to make all of its research and teaching resources publicly available and will continue to help to create other centres with similar aims. To promote a wider cultural change, the SABS:R^3 will also engage with the academic publishing industry (Elsevier, OUP, and Taylor & Francis). We will explore novel ways of disseminating the outputs of computational biomedical research, to engender trust in the released tools and software, facilitate more uptake and re-use.