Advertisement

Developing Novel Weighted Correlation Kernels for Convolutional Neural Networks to Extract Hierarchical Functional Connectivities from fMRI for Disease Diagnosis

  • Biao Jie
  • Mingxia Liu
  • Chunfeng Lian
  • Feng Shi
  • Dinggang Shen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11046)

Abstract

Functional magnetic resonance imaging (fMRI) has been widely applied to analysis and diagnosis of brain diseases, including Alzheimer’s disease (AD) and its prodrome, i.e., mild cognitive impairment (MCI). Traditional methods usually construct connectivity networks (CNs) by simply calculating Pearson correlation coefficients (PCCs) between time series of brain regions, and then extract low-level network measures as features to train the learning model. However, the valuable observation information in network construction (e.g., specific contributions of different time points) and high-level (i.e., high-order) network properties are neglected in these methods. In this paper, we first define a novel weighted correlation kernel (called wc-kernel) to measure the correlation of brain regions, by which weighting factors are determined in a data-driven manner to characterize the contribution of each time point, thus conveying the richer interaction information of brain regions compared with the PCC method. Furthermore, we propose a wc-kernel based convolutional neural network (CNN) (called wck-CNN) framework for extracting the hierarchical (i.e., from low-order to high-order) functional connectivities for disease diagnosis, by using fMRI data. Specifically, we first define a layer to build dynamic CNs (DCNs) using the defined wc-kernels. Then, we define three layers to extract local (region specific), global (network specific) and temporal high-order properties from the constructed low-order functional connectivities as features for classification. Results on 174 subjects (a total of 563 scans) with rs-fMRI data from ADNI suggest that the our method can not only improve the performance compared with state-of-the-art methods, but also provide novel insights into the interaction patterns of brain activities and their changes in diseases.

Notes

Acknowledgements

This study was supported by NSFC (61573023, 61703301), NIH grants (EB006733, EB008374, EB009634, MH100217, AG041721, AG042599, AG010129, AG030514).

References

  1. 1.
    Greicius, M.D., Krasnow, B., Reiss, A.L., Menon, V.: Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc. Natl. Acad. Sci. 100(1), 253–258 (2003)CrossRefGoogle Scholar
  2. 2.
    Jie, B., Liu, M., Zhang, D., Shen, D.: Sub-network kernels for measuring similarity of brain connectivity networks in disease diagnosis. IEEE Trans. Image Process. 27(5), 2340–2353 (2018)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Thompson, G.J., et al.: Short-time windows of correlation between large-scale functional brain networks predict vigilance intraindividually and interindividually. Hum. Brain Mapp. 34(12), 3280–3298 (2013)CrossRefGoogle Scholar
  4. 4.
    Hutchison, R.M., et al.: Dynamic functional connectivity: promise, issues, and interpretations. NeuroImage 80, 360–378 (2013)CrossRefGoogle Scholar
  5. 5.
    Jie, B., Liu, M., Shen, D.: Integration of temporal and spatial properties of dynamic connectivity networks for automatic diagnosis of brain disease. Med. Image Anal. 47, 81–94 (2018)CrossRefGoogle Scholar
  6. 6.
    Van Dijk, K.R., Sabuncu, M.R., Buckner, R.L.: The influence of head motion on intrinsic functional connectivity MRI. NeuroImage 59(1), 431–438 (2012)CrossRefGoogle Scholar
  7. 7.
    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(1), 273–289 (2002)CrossRefGoogle Scholar
  8. 8.
    Yao, H., et al.: Decreased functional connectivity of the amygdala in Alzheimer’s disease revealed by resting-state fMRI. Eur. J. Radiol. 82(9), 1531–1538 (2013)CrossRefGoogle Scholar
  9. 9.
    Jie, B., Wee, C.Y., Shen, D., Zhang, D.: Hyper-connectivity of functional networks for brain disease diagnosis. Med. Image Anal. 32, 84–100 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Biao Jie
    • 1
    • 2
  • Mingxia Liu
    • 1
  • Chunfeng Lian
    • 1
  • Feng Shi
    • 3
  • Dinggang Shen
    • 1
  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.Department of Computer Science and TechnologyAnhui Normal UniversityAnhuiChina
  3. 3.Shanghai United Imaging Intelligence Co., Ltd.ShanghaiChina

Personalised recommendations