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• W3094046734 endingPage "117480" @default.
• W3094046734 startingPage "117480" @default.
• W3094046734 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 an 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.
• W3094046734 created "2020-10-29" @default.
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• W3094046734 date "2021-01-01" @default.
• W3094046734 modified "2023-10-13" @default.
• W3094046734 title "Characterising group-level brain connectivity: A framework using Bayesian exponential random graph models" @default.
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• W3094046734 doi "https://doi.org/10.1016/j.neuroimage.2020.117480" @default.
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