TY - GEN
T1 - Improving Phenotype Prediction Using Long-Range Spatio-Temporal Dynamics of Functional Connectivity
AU - Dahan, Simon
AU - Williams, Logan Z.J.
AU - Rueckert, Daniel
AU - Robinson, Emma C.
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Functional MRI
KW - Graph convolution networks
KW - Human connectome project
KW - Phenotyping
KW - Temporal dynamics
UR - http://www.scopus.com/inward/record.url?scp=85116313425&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-87586-2_15
DO - 10.1007/978-3-030-87586-2_15
M3 - Conference contribution
AN - SCOPUS:85116313425
SN - 9783030875855
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 145
EP - 154
BT - Machine Learning in Clinical Neuroimaging - 4th International Workshop, MLCN 2021, Held in Conjunction with MICCAI 2021, Proceedings
A2 - Abdulkadir, Ahmed
A2 - Kia, Seyed Mostafa
A2 - Habes, Mohamad
A2 - Kumar, Vinod
A2 - Rondina, Jane Maryam
A2 - Tax, Chantal
A2 - Tax, Chantal
A2 - Wolfers, Thomas
PB - Springer Science and Business Media Deutschland GmbH
T2 - 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
Y2 - 27 September 2021 through 27 September 2021
ER -