Abstract
Major depressive disorder (MDD) is a common and costly mental illness whose pathophysiology is difficult to clarify. Resting-state functional MRI (rs-fMRI) provides a non-invasive solution for the study of functional brain network abnormalities in MDD patients. Existing studies have shown that multiple indexes derived from rs-fMRI, such as fractional amplitude of low-frequency fluctuations (fALFF), voxel-mirrored homotopic connectivity (VMHC), and degree centrality (DC) help depict functional mechanisms of brain disorders from different perspectives. However, previous methods generally treat these indexes independently, without considering their potentially complementary relationship. Moreover, it is usually very challenging to effectively fuse multi-index representations for disease analysis, due to the significant heterogeneity among indexes in the feature distribution. In this paper, we propose a tensor-based multi-index representation learning (TMRL) framework for fMRI-based MDD detection. In TMRL, we first generate multi-index representations (i.e., fALFF, VMHC and DC) for each subject, followed by patch selection via group comparison for each index. We further develop a tensor-based multi-task learning model (with a tensor-based regularizer) to align multi-index representations into a common latent space, followed by MDD prediction. Experimental results on 533 subjects with rs-fMRI data demonstrate that the TMRL outperforms several state-of-the-art methods in MDD identification.
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References
Otte, C., et al.: Major depressive disorder. Nat. Rev. Dis. Primers 2(1), 1–20 (2016)
Gray, J.P., Müller, V.I., Eickhoff, S.B., Fox, P.T.: Multimodal abnormalities of brain structure and function in major depressive disorder: a meta-analysis of neuroimaging studies. Am. J. Psychiatry 177(5), 422–434 (2020)
Yao, D., et al.: A mutual multi-scale triplet graph convolutional network for classification of brain disorders using functional or structural connectivity. IEEE Trans. Med. Imaging 40(4), 1279–1289 (2021)
Wang, M., Lian, C., Yao, D., Zhang, D., Liu, M., Shen, D.: Spatial-temporal dependency modeling and network hub detection for functional MRI analysis via convolutional-recurrent network. IEEE Trans. Biomed. Eng. 67(8), 2241–2252 (2019)
Guo, X., et al.: Shared and distinct resting functional connectivity in children and adults with attention-deficit/hyperactivity disorder. Transl. Psychiatry 10(1), 1–12 (2020)
Zhi, D., et al.: Aberrant dynamic functional network connectivity and graph properties in major depressive disorder. Front. Psychiatry 9, 339 (2018)
Wang, M., Zhang, D., Huang, J., Yap, P.T., Shen, D., Liu, M.: Identifying autism spectrum disorder with multi-site fMRI via low-rank domain adaptation. IEEE Trans. Med. Imaging 39(3), 644–655 (2019)
Yao, D., Sui, J., Yang, E., Yap, P.-T., Shen, D., Liu, M.: Temporal-adaptive graph convolutional network for automated identification of major depressive disorder using resting-state fMRI. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds.) MLMI 2020. LNCS, vol. 12436, pp. 1–10. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59861-7_1
Zou, Q.H., et al.: An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF. J. Neurosci. Methods 172(1), 137–141 (2008)
Zuo, X.N., et al.: Growing together and growing apart: regional and sex differences in the lifespan developmental trajectories of functional homotopy. J. Neurosci. 30(45), 15034–15043 (2010)
Bonacich, P.: Factoring and weighting approaches to status scores and clique identification. J. Math. Soc. 2(1), 113–120 (1972)
Wei, J., et al.: Voxel-mirrored homotopic connectivity of resting-state functional magnetic resonance imaging in blepharospasm. Front. Psychol. 9, 1620 (2018)
Sun, H., et al.: Regional homogeneity and functional connectivity patterns in major depressive disorder, cognitive vulnerability to depression and healthy subjects. J. Affect. Disord. 235, 229–235 (2018)
Liu, W., et al.: Abnormal degree centrality of functional hubs associated with negative coping in older Chinese adults who lost their only child. Biol. Psychol. 112, 46–55 (2015)
Banerjee, A., Merugu, S., Dhillon, I.S., Ghosh, J., Lafferty, J.: Clustering with Bregman divergences. J. Mach. Learn. Res. 6(10), 1705–1749 (2005)
Lu, C., Feng, J., Chen, Y., Liu, W., Lin, Z., Yan, S.: Tensor robust principal component analysis with a new tensor nuclear norm. IEEE Trans. Pattern Anal. Mach. Intell. 42(4), 925–938 (2019)
Lin, Z., Chen, M., Ma, Y.: The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. arXiv preprint arXiv:1009.5055 (2010)
Boyd, S., Parikh, N., Chu, E.: Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers. Now Publishers Inc, Boston (2011)
Yang, L., Jin, R.: Distance metric learning: a comprehensive survey. Mich. State Univ. 2(2), 4 (2006)
Lauriola, I., Aiolli, F.: MKLpy: A python-based framework for multiple kernel learning. arXiv preprint arXiv:2007.09982 (2020)
Févotte, C., Idier, J.: Algorithms for nonnegative matrix factorization with the \(\beta \)-divergence. Neural Comput. 23(9), 2421–2456 (2011)
Zhao, H., Ding, Z., Fu, Y.: Multi-view clustering via deep matrix factorization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)
Zhang, C., Fu, H., Hu, Q., Zhu, P., Cao, X.: Flexible multi-view dimensionality co-reduction. IEEE Trans. Image Process. 26(2), 648–659 (2016)
Beneyto, M., Kristiansen, L.V., Oni-Orisan, A., McCullumsmith, R.E., Meador-Woodruff, J.H.: Abnormal glutamate receptor expression in the medial temporal lobe in schizophrenia and mood disorders. Neuropsychopharmacology 32(9), 1888–1902 (2007)
Caetano, S.C., et al.: Medial temporal lobe abnormalities in pediatric unipolar depression. Neurosci. Lett. 427(3), 142–147 (2007)
Liao, Y., et al.: Is depression a disconnection syndrome? Meta-analysis of diffusion tensor imaging studies in patients with MDD. J. Psychiatry Neurosci. JPN 38(1), 49 (2013)
Gu, L., Huang, L., Yin, F., Cheng, Y.: Classification of depressive disorder based on rs-fMRI using multivariate pattern analysis with multiple features. In: 2017 4th IAPR Asian Conference on Pattern Recognition (ACPR), pp. 61–66. IEEE (2017)
Li, M., et al.: Clinical utility of a short resting-state MRI scan in differentiating bipolar from unipolar depression. Acta Psychiatrica Scandinavica 136(3), 288–299 (2017)
Jie, N.F., et al.: Discriminating bipolar disorder from major depression based on SVM-FoBa: efficient feature selection with multimodal brain imaging data. IEEE Trans. Auton. Ment. Dev. 7(4), 320–331 (2015)
Guo, W., et al.: Decreased interhemispheric resting-state functional connectivity in first-episode, drug-naive major depressive disorder. Prog. Neuropsychopharmacol. Biol. Psychiatry 41, 24–29 (2013)
Zhou, M., et al.: Intrinsic cerebral activity at resting state in adults with major depressive disorder: a meta-analysis. Prog. Neuropsychopharmacol. Biol. Psychiatry 75, 157–164 (2017)
Fan, H., Yang, X., Zhang, J., Chen, Y., Li, T., Ma, X.: Analysis of voxel-mirrored homotopic connectivity in medication-free, current major depressive disorder. J. Affect. Disord. 240, 171–176 (2018)
Acknowledgements
This work was finished when D. Yao was visiting the University of North Carolina at Chapel Hill. D. Yao and M. Liu was partly supported by NIH grant (No. AG041721). Z. Zhang was partly supported by the National Key Research and Development Program of China (No. 2016YFD0700100).
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Yao, D., Yang, E., Guan, H., Sui, J., Zhang, Z., Liu, M. (2021). Tensor-Based Multi-index Representation Learning for Major Depression Disorder Detection with Resting-State fMRI. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12905. Springer, Cham. https://doi.org/10.1007/978-3-030-87240-3_17
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