Improving Phenotype Prediction Using Long-Range Spatio-Temporal Dynamics of Functional Connectivity

Simon Dahan, Logan Z.J. Williams, Daniel Rueckert, Emma C. Robinson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

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.

Original languageEnglish
Title of host publicationMachine Learning in Clinical Neuroimaging - 4th International Workshop, MLCN 2021, Held in Conjunction with MICCAI 2021, Proceedings
EditorsAhmed Abdulkadir, Seyed Mostafa Kia, Mohamad Habes, Vinod Kumar, Jane Maryam Rondina, Chantal Tax, Chantal Tax, Thomas Wolfers
PublisherSpringer Science and Business Media Deutschland GmbH
Pages145-154
Number of pages10
ISBN (Print)9783030875855
DOIs
StatePublished - 2021
Externally publishedYes
Event4th 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, France
Duration: 27 Sep 202127 Sep 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13001 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th 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
Country/TerritoryFrance
CityStrasbourg
Period27/09/2127/09/21

Keywords

  • Functional MRI
  • Graph convolution networks
  • Human connectome project
  • Phenotyping
  • Temporal dynamics

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