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.
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Notes
- 1.
The code for this experiment will be made available at: http://www.github.com/metrics-lab/ST-fMRI/.
References
Beckmann, C., Smith, S.: Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans. Med. Imaging 23(2), 137–152 (2004)
Bijsterbosch, J.D., Woolrich, M.W., Glasser, M.F., Robinson, E.C., Beckmann, C.F., et al.: The relationship between spatial configuration and functional connectivity of brain regions. Elife (2018)
Dsouza, N.S., Nebel, M.B., Crocetti, D., Robinson, J., Mostofsky, S., et al.: M-GCN: a multimodal graph convolutional network to integrate functional and structural connectomics data to predict multidimensional phenotypic characterizations (2021)
Finn, E.S., Shen, X., Scheinost, D., Rosenberg, M.D., Huang, J., et al.: Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat. Neurosci. 18(11), 1664–1671 (2015)
Gadgil, S., Zhao, Q., Pfefferbaum, A., Sullivan, E.V., Adeli, E., et al.: Spatio-temporal graph convolution for resting-state fMRI analysis (2021)
Glasser, M.F., Coalson, T.S., Robinson, E.C., Hacker, C.D., Harwell, J., et al.: A multi-modal parcellation of human cerebral cortex. Nature 7615, 171–178 (2016)
Glasser, M.F., Sotiropoulos, S.N., Wilson, J.A., Coalson, T.S., Fischl, B., et al.: The minimal preprocessing pipelines for the human connectome project. NeuroImage 80, 105–124 (2013)
Harrison, S.J., Bijsterbosch, J.D., Segerdahl, A.R., Fitzgibbon, S.P., Farahibozorg, S.R., et al.: Modelling subject variability in the spatial and temporal characteristics of functional modes. NeuroImage 222, 117226 (2020)
Huang, Z.A., Zhu, Z., Yau, C.H., Tan, K.C.: Identifying autism spectrum disorder from resting-state fMRI using deep belief network. IEEE Trans. Neural Netw. Learn. Syst. (2020)
Kim, B.H., Ye, J.C., Kim, J.J.: Learning dynamic graph representation of brain connectome with spatio-temporal attention (2021)
Kong, R., Li, J., Orban, C., Sabuncu, M.R., Liu, H., et al.: Spatial topography of individual-specific cortical networks predicts human cognition, personality, and emotion. Cereb. Cortex 29(6), 2533–2551 (2019)
Kong, R., Yang, Q., Gordon, E., Xue, A., Yan, X., et al.: Individual-specific areal-level parcellations improve functional connectivity prediction of behavior. bioRxiv (2021)
Ktena, S.I., Parisot, S., Ferrante, E., Rajchl, M., Lee, M., et al.: Metric learning with spectral graph convolutions on brain connectivity networks. NeuroImage 169, 431–442 (2018)
Li, X., Zhou, Y., Gao, S., Dvornek, N., Zhang, M., et al.: BrainGNN: interpretable brain graph neural network for fMRI analysis. bioRxiv (2020)
Liégeois, R., Li, J., Kong, R., Orban, C., Van De Ville, D., et al.: Resting brain dynamics at different timescales capture distinct aspects of human behavior. Nat. Commun. 10(1), 1–9 (2019)
Liu, Z., Zhang, H., Chen, Z., Wang, Z., Ouyang, W.: Disentangling and unifying graph convolutions for skeleton-based action recognition (2020)
Marquand, A.F., Kia, S.M., Zabihi, M., Wolfers, T., Buitelaar, J.K., et al.: Conceptualizing mental disorders as deviations from normative functioning. Mol. Psychiatry 10, 1415–1424 (2019)
Marquand, A.F., Rezek, I., Buitelaar, J., Beckmann, C.F.: Understanding heterogeneity in clinical cohorts using normative models: beyond case-control studies. Biol. Psychiatry 80(7), 552–561 (2016)
Pervaiz, U., Vidaurre, D., Gohil, C., Smith, S.M., Woolrich, M.W.: Multi-dynamic modelling reveals strongly time-varying resting fMRI correlations. bioRxiv (2021)
Pervaiz, U., Vidaurre, D., Woolrich, M.W., Smith, S.M.: Optimising network modelling methods for fMRI. NeuroImage 211, 116604 (2020)
Robinson, E.C., Garcia, K., Glasser, M.F., Chen, Z., Coalson, T.S., et al.: Multimodal surface matching with higher-order smoothness constraints. NeuroImage 167, 453–465 (2018)
Robinson, E.C., Jbabdi, S., Glasser, M.F., Andersson, J., Burgess, G.C., et al.: MSM: a new flexible framework for multimodal surface matching. NeuroImage 100, 414–426 (2014)
Smith, S.M., Nichols, T.E., Vidaurre, D., Winkler, A.M., Behrens, T.E., et al.: A positive-negative mode of population covariation links brain connectivity, demographics and behavior (2015)
Smith, S.M., Vidaurre, D., Beckmann, C.F., Glasser, M.F., Jenkinson, M., et al.: Functional connectomics from resting-state fMRI. Trends Cogn. Sci. 17(12), 666–682 (2013)
Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E., Yacoub, E., et al.: The WU-Minn human connectome project: an overview. NeuroImage 80, 62–79 (2013)
Vidaurre, D., Abeysuriya, R., Becker, R., Quinn, A.J., Alfaro-Almagro, F., et al.: Discovering dynamic brain networks from big data in rest and task. NeuroImage 180, 646–656 (2018)
Vidaurre, D., Smith, S.M., Woolrich, M.W.: Brain network dynamics are hierarchically organized in time. Proc. Natl. Acad. Sci. 114(48), 12827–12832 (2017)
Wolfers, T., Rokicki, J., Alnæs, D., Berthet, P., Agartz, I., et al.: Replicating extensive brain structural heterogeneity in individuals with schizophrenia and bipolar disorder. Hum. Brain Mapp. 42(8), 2546–2555 (2021)
Yan, S., Xiong, Y., Lin, D.: Spatial temporal graph convolutional networks for skeleton-based action recognition (2018)
Acknowledgments
Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University [25].
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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: Abdulkadir, A., et al. Machine Learning in Clinical Neuroimaging. MLCN 2021. Lecture Notes in Computer Science(), vol 13001. Springer, Cham. https://doi.org/10.1007/978-3-030-87586-2_15
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