Improving Phenotype Prediction Using Long-Range Spatio-Temporal Dynamics of Functional Connectivity
Dahan S, Williams LZJ, Rueckert D, Robinson EC (2021)
Publication Type: Conference contribution
Publication year: 2021
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 13001 LNCS
Pages Range: 145-154
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Strasbourg, FRA
ISBN: 9783030875855
DOI: 10.1007/978-3-030-87586-2_15
Abstract
The study of functional brain connectivity (FC) is important for understanding the underlying mechanisms of many psychiatric disorders. Many recent analyses adopt graph convolutional networks, to study non-linear interactions between functionally-correlated states. However, although patterns of brain activation are known to be hierarchically organised in both space and time, many methods have failed to extract powerful spatio-temporal features. To overcome those challenges, and improve understanding of long-range functional dynamics, we translate an approach, from the domain of skeleton-based action recognition, designed to model interactions across space and time. We evaluate this approach using the Human Connectome Project (HCP) dataset on sex classification and fluid intelligence prediction. To account for subject topographic variability of functional organisation, we modelled functional connectomes using multi-resolution dual-regressed (subject-specific) ICA nodes. Results show a prediction accuracy of 94.4% for sex classification (an increase of 6.2% compared to other methods), and an improvement of correlation with fluid intelligence of 0.325 vs 0.144, relative to a baseline model that encodes space and time separately. Results suggest that explicit encoding of spatio-temporal dynamics of brain functional activity may improve the precision with which behavioural and cognitive phenotypes may be predicted in the future.
Involved external institutions
King’s College London
United Kingdom (GB)
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
United Kingdom (GB)
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
How to cite
APA:
Dahan, S., Williams, L.Z.J., Rueckert, D., & Robinson, E.C. (2021). Improving Phenotype Prediction Using Long-Range Spatio-Temporal Dynamics of Functional Connectivity. In Ahmed Abdulkadir, Seyed Mostafa Kia, Mohamad Habes, Vinod Kumar, Jane Maryam Rondina, Chantal Tax, Chantal Tax, Thomas Wolfers (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 145-154). Strasbourg, FRA: Springer Science and Business Media Deutschland GmbH.
MLA:
Dahan, Simon, et al. "Improving Phenotype Prediction Using Long-Range Spatio-Temporal Dynamics of Functional Connectivity." Proceedings of the 4th International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2021 held in Conjunction with 24th International Conference on Medical Imaging Computing and Computer-Assisted Intervention, MICCAI 2021, Strasbourg, FRA Ed. Ahmed Abdulkadir, Seyed Mostafa Kia, Mohamad Habes, Vinod Kumar, Jane Maryam Rondina, Chantal Tax, Chantal Tax, Thomas Wolfers, Springer Science and Business Media Deutschland GmbH, 2021. 145-154.
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