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• W2953374165 abstract "The brain can be modelled as a network with nodes and edges derived from a range of imaging modalities: the nodes correspond to spatially distinct regions and the edges to the interactions between them. Whole-brain connectivity studies typically seek to determine how network properties change with a given categorical phenotype such as age-group, disease condition or mental state. To do so reliably, it is necessary to determine the features of the connectivity structure that are common across a group of brain scans. Given the complex interdependencies inherent in network data, this is not a straightforward task. Some studies construct a group-representative network (GRN), ignoring individual differences, while other studies analyse networks for each individual independently, ignoring information that is shared across individuals. We propose a Bayesian framework based on exponential random graph models (ERGM) extended to multiple networks to characterise the distribution of a entire population of networks. Using resting-state fMRI data from the Cam-CAN project, a study on healthy ageing, we demonstrate how our method can be used to characterise and compare the brain's functional connectivity structure across a group of young individuals and a group of old individuals." @default.
• W2953374165 created "2019-06-27" @default.
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• W2953374165 date "2019-06-10" @default.
• W2953374165 modified "2023-09-27" @default.
• W2953374165 title "Characterising group-level brain connectivity: a framework using Bayesian exponential random graph models" @default.
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• W2953374165 doi "https://doi.org/10.1101/665398" @default.
• W2953374165 hasPublicationYear "2019" @default.
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