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Longitudinally-Consistent Parcellation of Infant Population Cortical Surfaces Based on Functional Connectivity

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Machine Learning in Medical Imaging (MLMI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10541))

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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.

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References

  1. Glasser, M.F., Coalson, T.S., Robinson, E.C., et al.: A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016)

    Article  Google Scholar 

  2. Fan, L., Li, H., Zhuo, J., et al.: The human brainnetome atlas: a new brain atlas based on connectional architecture. Cereb. Cortex 26, 3508–3526 (2016)

    Article  Google Scholar 

  3. Wang, D., Buckner, R.L., Fox, M.D., et al.: Parcellating cortical functional networks in individuals. Nat. Neurosci. 18, 1853–1860 (2015)

    Article  Google Scholar 

  4. Yeo, B.T., Krienen, F.M., Sepulcre, J., et al.: The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165 (2011)

    Article  Google Scholar 

  5. Li, G., Wang, L., Shi, F., et al.: Simultaneous and consistent labeling of longitudinal dynamic developing cortical surfaces in infants. Med. Image Anal. 18, 1274–1289 (2014)

    Article  Google Scholar 

  6. Li, G., Shen, D.: Consistent sulcal parcellation of longitudinal cortical surfaces. NeuroImage 57, 76–88 (2011)

    Article  Google Scholar 

  7. Li, G., Wang, L., Shi, F., et al.: Construction of 4D high-definition cortical surface atlases of infants: Methods and applications. Med. Image Anal. 25, 22–36 (2015)

    Article  Google Scholar 

  8. Gao, W., Alcauter, S., Elton, A., et al.: Functional network development during the first year: relative sequence and socioeconomic correlations. Cereb. Cortex 25, 2919–2928 (2015)

    Article  Google Scholar 

  9. von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17, 395–416 (2007)

    Article  MathSciNet  Google Scholar 

  10. Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 26, 1124–1137 (2004)

    Article  MATH  Google Scholar 

  11. Rand, W.M.: Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc. 66, 846–850 (1971)

    Article  Google Scholar 

  12. Gao, W., Gilmore, J.H., Giovanello, K.S., et al.: Temporal and spatial evolution of brain network topology during the first two years of life. PLoS ONE 6, e25278 (2011)

    Article  Google Scholar 

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Correspondence to Dinggang Shen .

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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: Wang, Q., Shi, Y., Suk, HI., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2017. Lecture Notes in Computer Science(), vol 10541. Springer, Cham. https://doi.org/10.1007/978-3-319-67389-9_23

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  • DOI: https://doi.org/10.1007/978-3-319-67389-9_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67388-2

  • Online ISBN: 978-3-319-67389-9

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