Abstract
Characterizations of atypical patterns of static functional connectivity (FC) have been widely observed in individuals with autism spectrum disorder (ASD). In recent years, some studies have hypothesized the stationary assumption and revealed the relevance of the time-varying anomaly in FC to the autistic traits. While most existing work focus on exploring properties of static FC (sFC) and dynamic FC (dFC) separately, little efforts have been made to investigate the correlation among these two modalities and combine their information to diagnose ASD. In this paper, we propose a multi-modal dynamic hypergraph learning framework for childhood autism diagnosis using both sFCs and dFCs. We collect a childhood ASD dataset including 91 ASD patients and 76 healthy controls (HC). After extracting features from the sFC and dFC for each subject, two hypergraphs are constructed to represent the complex correlation among different subjects under static and dynamic modalities, respectively. To further moderate inappropriate or even wrong connections, a multi-modal dynamic hypergraph learning process is conducted to jointly learn the data correlation and predict the subject labels, i.e., HC or ASD. Experimental results demonstrate that our method can achieve 75.6% accuracy with 5-fold cross validation and consistently outperform the conventional classifiers for autism diagnosis.
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References
Allen, G., Courchesne, E.: Differential effects of developmental cerebellar abnormality on cognitive and motor functions in the cerebellum: an fMRI study of autism. Am. J. Psychiatry 160(2), 262–273 (2003)
Amaral, D.G., Schumann, C.M., Nordahl, C.W.: Neuroanatomy of autism. Trends Neurosci. 31(3), 137–145 (2008)
Boddaert, N., Chabane, N., Gervais, H., Good, C., Bourgeois, M., Plumet, M., et al.: Superior temporal sulcus anatomical abnormalities in childhood autism: a voxel-based morphometry MRI study. Neuroimage 23(1), 364–369 (2004)
Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013)
Cerliani, L., Mennes, M., Thomas, R.M., Di Martino, A., Thioux, M., Keysers, C.: Increased functional connectivity between subcortical and cortical resting-state networks in autism spectrum disorder. JAMA Psychiat. 72(8), 767–777 (2015)
Chen, H., Nomi, J.S., Uddin, L.Q., Duan, X., Chen, H.: Intrinsic functional connectivity variance and state-specific under-connectivity in autism. Hum. Brain Mapp. 38(11), 5740–5755 (2017)
Chen, X., Zhang, H., Gao, Y., Wee, C.Y., Li, G., Shen, D.: High-order resting-state functional connectivity network for MCI classification. Hum. Brain Mapp. 37(9), 3282–3296 (2016)
Demirtas, M., et al.: Dynamic functional connectivity reveals altered variability in functional connectivity among patients with major depressive disorder. Hum. Brain Mapp. 37(8), 2918–2930 (2016)
Falahpour, M., Thompson, W.K., Abbott, A.E., Jahedi, A., Mulvey, M.E., et al.: Underconnected, but not broken? dynamic functional connectivity MRI shows underconnectivity in autism is linked to increased intra-individual variability across time. Brain Connectivity 6(5), 403–414 (2016)
Gao, Y., Wang, M., Tao, D., Ji, R., Dai, Q.: 3-D object retrieval and recognition with hypergraph analysis. IEEE Trans. Image Process. 21(9), 4290–4303 (2012)
von dem Hagen, E.A., Stoyanova, R.S., Baron-Cohen, S., Calder, A.J.: Reduced functional connectivity within and between ‘social’ resting state networks in autism spectrum conditions. Soc. Cogn. Affect. Neurosci. 8(6), 694–701 (2012)
Huang, Y., Liu, Q., Zhang, S., Metaxas, D.N.: Image retrieval via probabilistic hypergraph ranking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3376–3383 (2010)
Jung, M., et al.: Default mode network in young male adults with autism spectrum disorder: relationship with autism spectrum traits. Molecular Autism 5(1), 35 (2014)
Keown, C.L., Shih, P., Nair, A., Peterson, N., Mulvey, M.E., Müller, R.A.: Local functional overconnectivity in posterior brain regions is associated with symptom severity in autism spectrum disorders. Cell Rep. 5(3), 567–572 (2013)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Ktena, S.I., et al.: Metric learning with spectral graph convolutions on brain connectivity networks. Neuroimage 169, 431–442 (2018)
Laufs, H., Rodionov, R., Thornton, R., Duncan, J.S., Lemieux, L., Tagliazucchi, E.: Altered fMRI connectivity dynamics in temporal lobe epilepsy might explain seizure semiology. Front. Neurol. 5, 175 (2014)
Monk, C.S., et al.: Abnormalities of intrinsic functional connectivity in autism spectrum disorders. Neuroimage 47(2), 764–772 (2009)
Parisot, S., et al.: Spectral graph convolutions for population-based disease prediction. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 177–185. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_21
Parisot, S., et al.: Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer’s disease. Med. Image Anal. 48, 117–130 (2018)
Price, T., Wee, C.-Y., Gao, W., Shen, D.: Multiple-network classification of childhood autism using functional connectivity dynamics. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8675, pp. 177–184. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10443-0_23
Radulescu, E., et al.: Abnormalities in Fronto-striatal connectivity within language networks relate to differences in grey-matter heterogeneity in Asperger syndrome. NeuroImage Clin. 2, 716–726 (2013)
Salmi, J., et al.: The brains of high functioning autistic individuals do not synchronize with those of others. NeuroImage Clin. 3, 489–497 (2013)
Supekar, K., et al.: Brain hyperconnectivity in children with autism and its links to social deficits. Cell Rep. 5(3), 738–747 (2013)
Watanabe, T., Rees, G.: Brain network dynamics in high-functioning individuals with autism. Nat. Commun. 8, 16048 (2017)
Wee, C.Y., Yap, P.T., Shen, D.: Diagnosis of autism spectrum disorders using temporally distinct resting-state functional connectivity networks. CNS Neurosci. Therapeutics 22(3), 212–9 (2016)
Yerys, B.E., et al.: Default mode network segregation and social deficits in autism spectrum disorder: evidence from non-medicated children. NeuroImage Clin. 9, 223–232 (2015)
Yu, Q., et al.: Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy controls and patients with schizophrenia. Neuroimage 107, 345–355 (2015)
Zhang, Z., Lin, H., Gao, Y.: Dynamic hypergraph structure learning. In: Proceedings of International Joint Conference on Artificial Intelligence, pp. 3162–3169. International Joint Conferences on Artificial Intelligence Organization (2018)
Zhao, W., et al.: Learning to map social network users by unified manifold alignment on hypergraph. IEEE Trans. Neural Netw. Learn. Syst. 29(12), 5834–5846 (2018)
Zhu, L., Shen, J., Jin, H., Zheng, R., Xie, L.: Content-based visual landmark search via multimodal hypergraph learning. IEEE Trans. Cybern. 45(12), 2756–2769 (2015)
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This work was supported by National Natural Science Funds of China (U1701262).
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Zhang, Z., Liu, J., Li, B., Gao, Y. (2021). Diagnosis of Childhood Autism Using Multi-modal Functional Connectivity via Dynamic Hypergraph Learning. In: Fang, L., Chen, Y., Zhai, G., Wang, J., Wang, R., Dong, W. (eds) Artificial Intelligence. CICAI 2021. Lecture Notes in Computer Science(), vol 13069. Springer, Cham. https://doi.org/10.1007/978-3-030-93046-2_11
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