Dementia classification using a graph neural network on imaging of effective brain connectivity

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dc.contributor.authorCao, Jun
dc.contributor.authorYang, Lichao
dc.contributor.authorSarrigiannis, Ptolemaios Georgios
dc.contributor.authorBlackburn, Daniel
dc.contributor.authorZhao, Yifan
dc.date.accessioned2023-11-21T11:26:54Z
dc.date.available2023-11-21T11:26:54Z
dc.date.issued2023-11-18
dc.description.abstractAlzheimer's disease (AD) and Parkinson's disease (PD) are two of the most common forms of neurodegenerative diseases. The literature suggests that effective brain connectivity (EBC) has the potential to track differences between AD, PD and healthy controls (HC). However, how to effectively use EBC estimations for the research of disease diagnosis remains an open problem. To deal with complex brain networks, graph neural network (GNN) has been increasingly popular in very recent years and the effectiveness of combining EBC and GNN techniques has been unexplored in the field of dementia diagnosis. In this study, a novel directed structure learning GNN (DSL-GNN) was developed and performed on the imaging of EBC estimations and power spectrum density (PSD) features. In comparison to the previous studies on GNN, our proposed approach enhanced the functionality for processing directional information, which builds the basis for more efficiently performing GNN on EBC. Another contribution of this study is the creation of a new framework for applying univariate and multivariate features simultaneously in a classification task. The proposed framework and DSL-GNN are validated in four discrimination tasks and our approach exhibited the best performance, against the existing methods, with the highest accuracy of 94.0% (AD vs. HC), 94.2% (PD vs. HC), 97.4% (AD vs. PD) and 93.0% (AD vs. PD vs. HC). In a word, this research provides a robust analytical framework to deal with complex brain networks containing causal directional information and implies promising potential in the diagnosis of two of the most common neurodegenerative conditions.en_UK
dc.identifier.citationCao J, Yang L, Sarrigianis PG, et al., (2024) Dementia classification using a graph neural network on imaging of effective brain connectivity. Computers in Biology and Medicine, Volume 168, January 2024, Article number 107701en_UK
dc.identifier.issn0010-4825
dc.identifier.urihttps://doi.org/10.1016/j.compbiomed.2023.107701
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20567
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectEEGen_UK
dc.subjectAlzheimer's diseaseen_UK
dc.subjectParkinson's diseaseen_UK
dc.subjectDirected structure learning graph neural networken_UK
dc.subjectEffective brain connectivityen_UK
dc.titleDementia classification using a graph neural network on imaging of effective brain connectivityen_UK
dc.typeArticleen_UK

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