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 proceedingConference contribution

3 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 languageEnglish
Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
EditorsAlejandro F. Frangi, Christos Davatzikos, Gabor Fichtinger, Carlos Alberola-López, Julia A. Schnabel
PublisherSpringer Verlag
Pages136-144
Number of pages9
ISBN (Print)9783030009304
DOIs
Publication statusPublished - 2018 Jan 1
Externally publishedYes
Event21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: 2018 Sep 162018 Sep 20

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11072 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
CountrySpain
CityGranada
Period18/9/1618/9/20

Fingerprint

Multilayer
Brain
Topology
Community Detection
Heuristic Optimization
Fingerprinting
Community Structure
Parameter Optimization
Modularity
Network Topology
Disorder
Trajectories
Trajectory
Metric
Module
Configuration
Necessary

Keywords

  • Brain network
  • Connectome
  • Development
  • Infant
  • Modularity

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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.; Yin, Weiyan; Lin, Weili; 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. 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 proceedingConference 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)). https://doi.org/10.1007/978-3-030-00931-1_16
Zhang, Han ; Stanley, Natalie ; Mucha, Peter J. ; Yin, Weiyan ; Lin, Weili ; Shen, Dinggang. / 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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