Predicting High-Resolution Brain Networks Using Hierarchically Embedded and Aligned Multi-resolution Neighborhoods

  • Kübra Cengiz
  • Islem RekikEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11843)


Several works have been dedicated to image super-resolution (i.e., synthesizing high-resolution data from low-resolution data). However, existing works only operate on images (e.g., predicting 7T-like magnetic resonance image (MRI) from 3T MRI) whereas brain connectivity network super-resolution remains unexplored. To fill this gap, we propose the first framework for predicting high-resolution (HR) brain networks from low-dimensional (LR) brain networks by hierarchically aligning and embedding LR neighborhood centered at the testing sample, along with its corresponding HR neighborhood. The proposed hierarchical embedding better preserves higher-order structural neighborhood of subjects within each domain. Recently, a seminal work was introduced for brain network prediction at a single resolution (or scale), where domain alignment was achieved using canonical correlation analysis followed by manifold learning to identify the most similar neighbors to the testing subject (i.e., testing neighborhood) in the source domain that can best predict the missing target network. Here, we inductively extend this idea by hierarchically learning the embedding and alignment of embedding of LR and HR neighborhoods. Our proposed framework achieved the best results in comparison with baseline methods.


  1. 1.
    Hollander, E., et al.: Striatal volume on magnetic resonance imaging and repetitive behaviors in autism. Biol. Psychiatry 58, 226–232 (2005)CrossRefGoogle Scholar
  2. 2.
    Rojas, D.C., Smith, J.A., Benkers, T.L., Camou, S.L., Reite, M.L., Rogers, S.J.: Hippocampus and amygdala volumes in parents of children with autistic disorder. Am. J. Psychiatry 161, 2038–2044 (2004)CrossRefGoogle Scholar
  3. 3.
    Hyde, K.K., et al.: Applications of supervised machine learning in autism spectrum disorder research: a review. Rev. J. Autism Dev. Disord. 6, 128–146 (2019)CrossRefGoogle Scholar
  4. 4.
    Rane, P., Cochran, D., Hodge, S.M., Haselgrove, C., Kennedy, D., Frazier, J.A.: Connectivity in autism: a review of MRI connectivity studies. Harv. Rev. Psychiatry 23, 223 (2015)CrossRefGoogle Scholar
  5. 5.
    Koshino, H., Carpenter, P.A., Minshew, N.J., Cherkassky, V.L., Keller, T.A., Just, M.A.: Functional connectivity in an fMRI working memory task in high-functioning autism. Neuroimage 24, 810–821 (2005)CrossRefGoogle Scholar
  6. 6.
    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). Scholar
  7. 7.
    Bahrami, K., Shi, F., Rekik, I., Shen, D.: Convolutional neural network for reconstruction of 7T-like images from 3T MRI using appearance and anatomical features. In: Carneiro, G., et al. (eds.) LABELS/DLMIA -2016. LNCS, vol. 10008, pp. 39–47. Springer, Cham (2016). Scholar
  8. 8.
    Tzourio-Mazoyer, N., et al.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273–289 (2002)CrossRefGoogle Scholar
  9. 9.
    Soussia, M., Rekik, I.: A review on image-and network-based brain data analysis techniques for Alzheimer’s disease diagnosis reveals a gap in developing predictive methods for prognosis. arXiv preprint arXiv:1808.01951 (2018)
  10. 10.
    Zhu, M., Rekik, I.: Multi-view brain network prediction from a source view using sample selection via CCA-based multi-kernel connectomic manifold learning. In: Rekik, I., Unal, G., Adeli, E., Park, S.H. (eds.) PRIME 2018. LNCS, vol. 11121, pp. 94–102. Springer, Cham (2018). Scholar
  11. 11.
    Blitzer, J., Kakade, S., Foster, D.: Domain adaptation with coupled subspaces. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 173–181 (2011)Google Scholar
  12. 12.
    Wang, B., Zhu, J., Pierson, E., Ramazzotti, D., Batzoglou, S.: Visualization and analysis of single-cell rna-seq data by kernel-based similarity learning. Nature Methods 14, 414 (2017)CrossRefGoogle Scholar
  13. 13.
    Zhu, X., Suk, H.I., Lee, S.W., Shen, D.: Canonical feature selection for joint regression and multi-class identification in Alzheimer’s disease diagnosis. Brain Imaging Behav. 10, 818–828 (2016)CrossRefGoogle Scholar
  14. 14.
    Haghighat, M., Abdel-Mottaleb, M., Alhalabi, W.: Fully automatic face normalization and single sample face recognition in unconstrained environments. Expert Syst. Appl. 47, 23–34 (2016)CrossRefGoogle Scholar
  15. 15.
    Fischl, B.: FreeSurfer. Neuroimage 62, 774–781 (2012)CrossRefGoogle Scholar
  16. 16.
    Mahjoub, I., Mahjoub, M.A., Rekik, I.: Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states. Sci. Rep. 8, 4103 (2018) CrossRefGoogle Scholar
  17. 17.
    Soussia, M., Rekik, I.: Unsupervised manifold learning using high-order morphological brain networks derived from T1-w MRI for autism diagnosis. Front. Neuroinform. 12 (2018)Google Scholar
  18. 18.
    Nebli, A., Rekik, I.: Gender differences in cortical morphological networks. Brain Imaging Behav. 1–9 (2019)Google Scholar
  19. 19.
    Wang, Y.H., Qiao, J., Li, J.B., Fu, P., Chu, S.C., Roddick, J.F.: Sparse representation-based MRI super-resolution reconstruction. Measurement 47, 946–953 (2014) CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.BASIRA Lab, Faculty of Computer and InformaticsIstanbul Technical UniversityIstanbulTurkey

Personalised recommendations