TY - GEN
T1 - Dynamic Adaptive Spatio-Temporal Graph Convolution for fMRI Modelling
AU - el-Gazzar, Ahmed
AU - Thomas, Rajat Mani
AU - van Wingen, Guido
N1 - Funding Information: Acknowledgement. This work was supported by the Netherlands Organization for Scientific Research (NWO; 628.011.023), Philips Research, AAA Data Science Program, and ZonMW (Vidi; 016.156.318). Publisher Copyright: © 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - The characterisation of the brain as a functional network in which the connections between brain regions are represented by correlation values across time series has been very popular in the last years. Although this representation has advanced our understanding of brain function, it represents a simplified model of brain connectivity that has a complex dynamic spatio-temporal nature. Oversimplification of the data may hinder the merits of applying advanced non-linear feature extraction algorithms. To this end, we propose a dynamic adaptive spatio-temporal graph convolution (DAST-GCN) model to overcome the shortcomings of pre-defined static correlation-based graph structures. The proposed approach allows end-to-end inference of dynamic connections between brain regions via layer-wise graph structure learning module while mapping brain connectivity to a phenotype in a supervised learning framework. This leverages the computational power of the model, data and targets to represent brain connectivity, and could enable the identification of potential biomarkers for the supervised target in question. We evaluate our pipeline on the UKBiobank dataset for age and gender classification tasks from resting-state functional scans and show that it outperforms currently adapted linear and non-linear methods in neuroimaging. Further, we assess the generalizability of the inferred graph structure by transferring the pre-trained graph to an independent dataset for the same task. Our results demonstrate the task-robustness of the graph against different scanning parameters and demographics.
AB - The characterisation of the brain as a functional network in which the connections between brain regions are represented by correlation values across time series has been very popular in the last years. Although this representation has advanced our understanding of brain function, it represents a simplified model of brain connectivity that has a complex dynamic spatio-temporal nature. Oversimplification of the data may hinder the merits of applying advanced non-linear feature extraction algorithms. To this end, we propose a dynamic adaptive spatio-temporal graph convolution (DAST-GCN) model to overcome the shortcomings of pre-defined static correlation-based graph structures. The proposed approach allows end-to-end inference of dynamic connections between brain regions via layer-wise graph structure learning module while mapping brain connectivity to a phenotype in a supervised learning framework. This leverages the computational power of the model, data and targets to represent brain connectivity, and could enable the identification of potential biomarkers for the supervised target in question. We evaluate our pipeline on the UKBiobank dataset for age and gender classification tasks from resting-state functional scans and show that it outperforms currently adapted linear and non-linear methods in neuroimaging. Further, we assess the generalizability of the inferred graph structure by transferring the pre-trained graph to an independent dataset for the same task. Our results demonstrate the task-robustness of the graph against different scanning parameters and demographics.
KW - Adaptive graph structure learning
KW - Functional connectivity
KW - Spatio-temporal graph convolution
KW - UKBiobank
UR - http://www.scopus.com/inward/record.url?scp=85116354050&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-030-87586-2_13
DO - https://doi.org/10.1007/978-3-030-87586-2_13
M3 - Conference contribution
SN - 9783030875855
VL - 13001 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 125
EP - 134
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 - 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 -