Mapping brain network activity from structural connectivity using AI and Deep Learning

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The structure-function relationship is fundamental to natural sciences. The nervous system is organised as a hierarchy of progressively complex and interconnected neural populations. Traditionally, anatomical information provided high biological specificity and interpretability, yet it was inadequate for characterising the differences between individuals, in part because neural circuit functionality is influenced by tissue microstructure variations.Modern neuroimaging allows probing into, and highly detailed reconstructions of, the brain's structural and functional connectivity networks. Using these, early modelling proved the existence of functional coupling between structurally linked regions. Correlations between functional regions however depend both on the presence of pathways and signals received from the overall network. To account for this, models predicting function through higher order interactions between neural populations were developed, yet they either lacked biological plausibility or were hard to generalise across individuals. Recent statistical methods successfully used microstructure derived measures to predict functional connectivity variations while neural networks based techniques have seen some success in predicting functional connectivity; however, the goal of estimating this directly from structural information has remained elusive.This project builds on previous work highlighting correlations between structure and function using information extracted from diffusion MRI and resting-state functional MRI by developing a DL algorithm capable of translating between an individual's structural and functional connectivities, by learning the underlying relationships between them. Algorithms such as CNNs and GANs have been increasingly used in medical imaging due to their ability to learn complex features and relationships. This task involves both learning patterns and representations of direct signal and functional correlations between regions several synapses removed. The required biological insight and variability is achieved by using large clinical imaging datasets from UK Biobank. To retain the spatial contextual correlations of structural pathways and functional information, no data reductions are used before the joint between-modality modelling. This, together with the substantially higher dimensionality required for rich between-modality modelling stimulate the development of algorithms of much higher complexity than typical DL imaging applications.Initially, the inputs to the network are the summation of 27 white matter tracts obtained from subject-specific probabilistic diffusion tractography using standard-space protocols, ensuring sufficient spatial information is provided to allow the learning of relevant pathway and functional correlations. The targets are the rsfMRI spatial maps, with the Default Mode Network being initially used out of the 21 major functional sub-divisions obtained through group-level ICA. Additional modalities and data, such as an individual's genetics, lifestyle, cognitive and physical measures, will be later added as inputs while the outputs will incorporate all the functional sub-divisions, thus aiding in determining whether correlations can be established between them and an individual's functional connectivity (and the way in which it differs from the population average). Achieving this would contribute to the development of probabilistic models assessing an individual's deviation from the population distribution and identify specific brain regions which contribute to this. Moreover, this could also aid in the development of pre-surgical functional mapping methods for physically impaired subjects without the need for challenging explicit cognitive/motor tasks.This project falls within the EPSRC Medical Imaging research area, but also contributes to the Artificial Intelligence Technologies and Image and Vision Computing. It is a collaboration with F.Hoffmann-La Roche