Conference paper
Comparing Structural Brain Connectivity by the Infinite Relational Model
The growing focus in neuroimaging on analyzing brain connectivity calls for powerful and reliable statistical modeling tools. We examine the Infinite Relational Model (IRM) as a tool to identify and compare structure in brain connectivity graphs by contrasting its performance on graphs from the same subject versus graphs from different subjects.
The inferred structure is most consistent between graphs from the same subject, however, the model is able to predict links in graphs from different subjects on par with results within a subject. The framework proposed can be used as a statistical modeling tool for the identification of structure and quantification of similarity in graphs of brain connectivity in general.
| Language: | English |
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| Publisher: | IEEE |
| Year: | 2013 |
| Pages: | 50-53 |
| Proceedings: | 3rd International Workshop on Pattern Recognition in NeuroImaging (PRNI 2013) |
| ISBN: | 0769550614 , 1479909289 , 9780769550619 and 9781479909285 |
| Types: | Conference paper |
| DOI: | 10.1109/PRNI.2013.22 |
| ORCIDs: | Herlau, Tue , Schmidt, Mikkel Nørgaard and Mørup, Morten |
Area measurement Bayes methods Bayesian Methods Brain models Complex networks IRM tool Imaging Mutual information Neuroimaging Predictive models Relational Modelling Structural Connectivity biomedical imaging brain connectivity graph structure graph similarity quantification infinite relational model link prediction neuroimaging nonparametric Bayesian generative model statistical analysis statistical modeling tools structural brain connectivity analysis structure identification
