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2019 | OriginalPaper | Chapter

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

Authors: Kübra Cengiz, Islem Rekik

Published in: Predictive Intelligence in Medicine

Publisher: Springer International Publishing

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Abstract

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.

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Footnotes
1
http://fcon_1000.projects.nitrc.org/indi/abide/.
 
Literature
1.
go back to reference Hollander, E., et al.: Striatal volume on magnetic resonance imaging and repetitive behaviors in autism. Biol. Psychiatry 58, 226–232 (2005) CrossRef Hollander, E., et al.: Striatal volume on magnetic resonance imaging and repetitive behaviors in autism. Biol. Psychiatry 58, 226–232 (2005) CrossRef
2.
go back to reference 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) CrossRef 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) CrossRef
3.
go back to reference 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) CrossRef 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) CrossRef
4.
go back to reference 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) CrossRef 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) CrossRef
5.
go back to reference 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) CrossRef 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) CrossRef
8.
go back to reference 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) CrossRef 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) CrossRef
9.
go back to reference 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) 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)
11.
go back to reference 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) 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)
12.
go back to reference 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) CrossRef 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) CrossRef
13.
go back to reference 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) CrossRef 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) CrossRef
14.
go back to reference 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) CrossRef 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) CrossRef
16.
go back to reference Mahjoub, I., Mahjoub, M.A., Rekik, I.: Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states. Sci. Rep. 8, 4103 (2018) CrossRef Mahjoub, I., Mahjoub, M.A., Rekik, I.: Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states. Sci. Rep. 8, 4103 (2018) CrossRef
17.
go back to reference 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) 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)
18.
go back to reference Nebli, A., Rekik, I.: Gender differences in cortical morphological networks. Brain Imaging Behav. 1–9 (2019) Nebli, A., Rekik, I.: Gender differences in cortical morphological networks. Brain Imaging Behav. 1–9 (2019)
19.
go back to reference 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) CrossRef 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) CrossRef
Metadata
Title
Predicting High-Resolution Brain Networks Using Hierarchically Embedded and Aligned Multi-resolution Neighborhoods
Authors
Kübra Cengiz
Islem Rekik
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-030-32281-6_12

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