A novel deep learning framework on brain functional networks for early MCI diagnosis

Tae Eui Kam; Han Zhang; Dinggang Shen

doi:10.1007/978-3-030-00931-1_34

A novel deep learning framework on brain functional networks for early MCI diagnosis

Tae Eui Kam, Han Zhang, Dinggang Shen

Department of Brain and Cognitive Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

30 Citations (Scopus)

Abstract

Although alternations of brain functional networks (BFNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been considered as promising biomarkers for early Alzheimer’s disease (AD) diagnosis, it is still challenging to perform individualized diagnosis, especially at the very early stage of preclinical stage of AD, i.e., early mild cognitive impairment (eMCI). Recently, convolutional neural networks (CNNs) show powerful ability in computer vision and image analysis applications, but there is still a gap for directly applying CNNs to rs-fMRI-based disease diagnosis. In this paper, we propose a novel multiple-BFN-based 3D CNN framework that can automatically and deeply learn complex, high-level, hierarchical diagnostic features from various independent component analysis-derived BFNs. More importantly, the embedded features of different BFNs could comprehensively support each other towards a more accurate eMCI diagnosis in a unified model. The performance of the proposed method is validated by a large-sample, multisite, rigorously controlled publicly accessible dataset. The proposed framework can also be conveniently and straightforwardly applied to individualized diagnosis of various neurological and psychiatric diseases.

Original language	English
Title of host publication	Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
Editors	Alejandro F. Frangi, Christos Davatzikos, Gabor Fichtinger, Carlos Alberola-López, Julia A. Schnabel
Publisher	Springer Verlag
Pages	293-301
Number of pages	9
ISBN (Print)	9783030009304
DOIs	https://doi.org/10.1007/978-3-030-00931-1_34
Publication status	Published - 2018
Event	21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain Duration: 2018 Sept 16 → 2018 Sept 20

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11072 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Country/Territory	Spain
City	Granada
Period	18/9/16 → 18/9/20

Bibliographical note

Funding Information:
Acknowledgements. This work was partially supported by NIH grants (AG041721, AG049371, AG042599, and AG053867).

Publisher Copyright:
© Springer Nature Switzerland AG 2018.

Keywords

Brain networks
Convolutional neural networks
Deep learning
Diagnosis
Independent component analysis
Mild cognitive impairment
Resting-state functional MRI

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-00931-1_34

Cite this

Kam, T. E., Zhang, H., & Shen, D. (2018). A novel deep learning framework on brain functional networks for early MCI diagnosis. In A. F. Frangi, C. Davatzikos, G. Fichtinger, C. Alberola-López, & J. A. Schnabel (Eds.), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings (pp. 293-301). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11072 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-00931-1_34

A novel deep learning framework on brain functional networks for early MCI diagnosis. / Kam, Tae Eui; Zhang, Han; Shen, Dinggang.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. ed. / Alejandro F. Frangi; Christos Davatzikos; Gabor Fichtinger; Carlos Alberola-López; Julia A. Schnabel. Springer Verlag, 2018. p. 293-301 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11072 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Kam, TE, Zhang, H & Shen, D 2018, A novel deep learning framework on brain functional networks for early MCI diagnosis. in AF Frangi, C Davatzikos, G Fichtinger, C Alberola-López & JA Schnabel (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11072 LNCS, Springer Verlag, pp. 293-301, 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018, Granada, Spain, 18/9/16. https://doi.org/10.1007/978-3-030-00931-1_34

Kam TE, Zhang H, Shen D. A novel deep learning framework on brain functional networks for early MCI diagnosis. In Frangi AF, Davatzikos C, Fichtinger G, Alberola-López C, Schnabel JA, editors, Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. Springer Verlag. 2018. p. 293-301. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-00931-1_34

Kam, Tae Eui ; Zhang, Han ; Shen, Dinggang. / A novel deep learning framework on brain functional networks for early MCI diagnosis. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings. editor / Alejandro F. Frangi ; Christos Davatzikos ; Gabor Fichtinger ; Carlos Alberola-López ; Julia A. Schnabel. Springer Verlag, 2018. pp. 293-301 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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A novel deep learning framework on brain functional networks for early MCI diagnosis

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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