BRAPH for Python: graph theory and graph neural networks for brain connectivity analysis

Lisa Sjöblom; Daniel Westerlund; Alice Deimante Neimantaite; Jonas Andersson

doi:10.1117/12.2581421

20 August 2020 BRAPH for Python: graph theory and graph neural networks for brain connectivity analysis

Lisa Sjöblom, Daniel Westerlund, Alice Deimante Neimantaite, Jonas Andersson

Proceedings Volume 11469, Emerging Topics in Artificial Intelligence 2020; 114691Y (2020) https://doi.org/10.1117/12.2581421
Event: SPIE Nanoscience + Engineering, 2020, Online Only

Conference Poster

Abstract

Syntronic AB collaborates with the Karolinska Institute and the University of Gothenburg in developing the software BRAPH (Brain Analysis with graPH theory). BRAPH is used for analyzing brain graphs derived from brain imaging data. With the increasing prevalence of neurodegenerative disorders, there is a need for new reliable methods for diagnosis to help implementing preventive treatments before damage is widespread. To this aim, Syntronic has developed a new Python version of BRAPH and investigated the potential use of implementing artificial intelligence to the software. In particular, the ability of different classes of Artificial Neural Networks (ANN) in detecting Alzheimer’s disease from MRI derived brain graphs has been studied. The study showed promising results using Graph Convolutional Neural Networks; a class of ANNs that generalize convolutions to graphs. The goal is to implement a new machine learning toolbox to BRAPH to exploit the benefits of these algorithms.

Conference Presentation

Citation Download Citation

Lisa Sjöblom, Daniel Westerlund, Alice Deimante Neimantaite, and Jonas Andersson "BRAPH for Python: graph theory and graph neural networks for brain connectivity analysis", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 114691Y (20 August 2020); https://doi.org/10.1117/12.2581421

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