Linking structural and effective brain connectivity: structurally informed Parametric Empirical Bayes (si-PEB) View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-01

AUTHORS

Arseny A. Sokolov, Peter Zeidman, Michael Erb, Philippe Ryvlin, Marina A. Pavlova, Karl J. Friston

ABSTRACT

Despite the potential for better understanding functional neuroanatomy, the complex relationship between neuroimaging measures of brain structure and function has confounded integrative, multimodal analyses of brain connectivity. This is particularly true for task-related effective connectivity, which describes the causal influences between neuronal populations. Here, we assess whether measures of structural connectivity may usefully inform estimates of effective connectivity in larger scale brain networks. To this end, we introduce an integrative approach, capitalising on two recent statistical advances: Parametric Empirical Bayes, which provides group-level estimates of effective connectivity, and Bayesian model reduction, which enables rapid comparison of competing models. Crucially, we show that structural priors derived from high angular resolution diffusion imaging on a dynamic causal model of a 12-region network-based on functional MRI data from the same subjects-substantially improve model evidence (posterior probability 1.00). This provides definitive evidence that structural and effective connectivity depend upon each other in mediating distributed, large-scale interactions in the brain. Furthermore, this work offers novel perspectives for understanding normal brain architecture and its disintegration in clinical conditions. More... »

PAGES

205-217

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00429-018-1760-8

DOI

http://dx.doi.org/10.1007/s00429-018-1760-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1107498347

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30302538


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