Saliency mapping of RS-fMRI data in GCNs for sex and brain age prediction

Kevin Andersson; Eric Lindgren

doi:10.1117/12.2594436

1 August 2021 Saliency mapping of RS-fMRI data in GCNs for sex and brain age prediction

Kevin Andersson, Eric Lindgren

Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 1180428 (2021) https://doi.org/10.1117/12.2594436
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States

Conference Poster

Abstract

Understanding what happens in the brain during healthy ageing is important for several reasons ranging from its correlation to cognitive function to the research of neurodegenerative diseases and their biomarkers. In recent years, studies have linked certain biomarkers to both ageing and neurodegenerative diseases, but there is still more to be found and concluded. Inspired by current developments in machine learning, the use of artificial neural networks (ANN) as a method for extracting new informational biomarkers has been investigated. The brain consists of several functionally connected regions and can thus be modeled and analyzed using graph theory. ANNs and graph theory can be combined into graph neural networks, which when applied on human brain data have shown great promise. We aim to investigate the usage of GNNs on age-related data, in the search for biomarkers and other important features related to the ageing brain.

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Citation Download Citation

Kevin Andersson and Eric Lindgren "Saliency mapping of RS-fMRI data in GCNs for sex and brain age prediction", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 1180428 (1 August 2021); https://doi.org/10.1117/12.2594436

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