Longitudinally-consistent parcellation of infant population cortical surfaces based on functional connectivity

Junyi Yan, Yu Meng, Gang Li, Weili Lin, Dazhe Zhao, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Parcellation of the human cerebral cortex into functionally distinct and meaningful regions is important for understanding the human brain. Although there are plenty of studies focusing on functional parcellation for adults, longitudinally-consistent functional parcellation of the rapidly developing infant cerebral cortex at multiple ages is still critically missing for understanding early brain development. Due to the dramatic changes of the cortical structure and function in infants, it is challenging to both capture the meaningful changes of the boundaries of functional regions and keep the parcellation as longitudinally-consistent as possible. To address this problem, we propose a longitudinally-consistent framework to jointly parcellate a population of infant cortical surfaces at multiple ages. Specifically, first, a population-average representation of the functional connectivity profile is constructed at each vertex at each age. Second, the correlation of functional connectivity profiles between any two vertices on the average cortical surfaces is computed. Notably, this correlation computation is performed not only within the same age but also across different ages, weighted based on the age difference, thus forming a large comprehensive similarity matrix. Such similarity measurements encourage to assign similar vertices to the same parcels, even for the vertices on the average cortical surfaces from different ages, and thus hold the longitudinal consistency. Finally, we apply the spectral clustering method on the large similarity matrix to generate an initial joint parcellation for all average surfaces, and further employ a graph cuts method to produce the spatially-smooth longitudinally-consistent parcellations. The proposed method was applied to a longitudinal infant brain MRI dataset to jointly parcellate infant cortical surfaces at 7 different time points in the first 2 years of age. The results show that our parcellations not only capture the evolution of functional boundaries but also preserve the longitudinal consistency.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings
PublisherSpringer Verlag
Pages194-202
Number of pages9
Volume10541 LNCS
ISBN (Print)9783319673882
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: 2017 Sep 102017 Sep 10

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10541 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
CountryCanada
CityQuebec City
Period17/9/1017/9/10

Keywords

  • Functional connectivity
  • Infant brain
  • Longitudinal parcellation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Yan, J., Meng, Y., Li, G., Lin, W., Zhao, D., & Shen, D. (2017). Longitudinally-consistent parcellation of infant population cortical surfaces based on functional connectivity. In Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings (Vol. 10541 LNCS, pp. 194-202). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10541 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-67389-9_23