Multi-layer large-scale functional connectome reveals infant brain developmental patterns

Han Zhang; Natalie Stanley; Peter J. Mucha; Weiyan Yin; Weili Lin; Dinggang Shen

doi:10.1007/978-3-030-00931-1_16

Multi-layer large-scale functional connectome reveals infant brain developmental patterns

Han Zhang, Natalie Stanley, Peter J. Mucha, Weiyan Yin, Weili Lin, Dinggang Shen

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

9 Citations (Scopus)

Abstract

Understanding human brain functional development in the very early ages is of great importance for charting normative development and detecting early neurodevelopmental disorders, but it is very challenging. We propose a group-constrained, robust community detection method for better understanding of developing brain functional connectome from neonate to two-year-old. For such a multi-subject, multi-age-group network topology study, we build a multi-layer functional network by adding inter-subject edges, and detect modular structure (communities) to explore topological changes of multiple functional systems at different ages and across subjects. This “Multi-Layer Inter-Subject-Constrained Modularity Analysis (MLISMA)” can detect group consistent modules without losing individual information, thus allowing assessment of individual variability in the brain modular topology, a key metric for developmental individualized fingerprinting. We propose a heuristic parameter optimization strategy to wisely determine the necessary parameters that define the modular configuration. Our method is validated to be feasible using longitudinal 0–1–2 year’s old infant brain functional MRI data, and reveals novel developmental trajectories of brain functional connectome.

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	136-144
Number of pages	9
ISBN (Print)	9783030009304
DOIs	https://doi.org/10.1007/978-3-030-00931-1_16
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

Publisher Copyright:
© Springer Nature Switzerland AG 2018.

Keywords

Brain network
Connectome
Development
Infant
Modularity

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-00931-1_16

Cite this

Zhang, H., Stanley, N., Mucha, P. J., Yin, W., Lin, W., & Shen, D. (2018). Multi-layer large-scale functional connectome reveals infant brain developmental patterns. 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. 136-144). (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_16

Multi-layer large-scale functional connectome reveals infant brain developmental patterns. / Zhang, Han; Stanley, Natalie; Mucha, Peter J. et al.
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. 136-144 (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

Zhang, H, Stanley, N, Mucha, PJ, Yin, W, Lin, W & Shen, D 2018, Multi-layer large-scale functional connectome reveals infant brain developmental patterns. 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. 136-144, 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_16

Zhang H, Stanley N, Mucha PJ, Yin W, Lin W, Shen D. Multi-layer large-scale functional connectome reveals infant brain developmental patterns. 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. 136-144. (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_16

Zhang, Han ; Stanley, Natalie ; Mucha, Peter J. et al. / Multi-layer large-scale functional connectome reveals infant brain developmental patterns. 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. 136-144 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Multi-layer large-scale functional connectome reveals infant brain developmental patterns

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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