The human cerebral cortex develops dynamically during the early postnatal stage, reflecting the underlying rapid changes of cortical microstructures and their connections, which jointly determine the functional principles of cortical regions. Hence, the dynamic cortical developmental patterns are ideal for defining the distinct cortical regions in microstructure and function for neurodevelopmental studies. Moreover, given the remarkable inter-subject variability in terms of cortical structure/function and their developmental patterns, the individualized cortical parcellation based on each infant’s own developmental patterns is critical for precisely localizing personalized distinct cortical regions and also understanding inter-subject variability. To this end, we propose a novel method for individualized parcellation of the infant cortical surface into distinct and meaningful regions based on each individual’s cortical developmental patterns. Specifically, to alleviate the effects of cortical measurement errors and also make the individualized cortical parcellation comparable across subjects, we first create a population-based cortical parcellation to capture the general developmental landscape of the cortex in an infant population. Then, this population-based parcellation is leveraged to guide the individualized parcellation based on each infant’s own cortical developmental patterns in an iterative manner. At each iteration, the individualized parcellation is gradually updated based on (1) the prior information of the population-based parcellation, (2) the individualized parcellation at the previous iteration, and also (3) the developmental patterns of all vertices. Experiments on fifteen healthy infants, each with longitudinal MRI scans acquired at six time points (i.e., 1, 3, 6, 9, 12 and 18 months of age), show that our method generates a reliable and meaningful individualized cortical parcellation based on each infant’s own developmental patterns.
|Title of host publication||Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings|
|Editors||Maxime Descoteaux, Simon Duchesne, Alfred Franz, Pierre Jannin, D. Louis Collins, Lena Maier-Hein|
|Number of pages||9|
|Publication status||Published - 2017|
|Event||20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada|
Duration: 2017 Sept 11 → 2017 Sept 13
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017|
|Period||17/9/11 → 17/9/13|
Bibliographical noteFunding Information:
Acknowledgements. This work was supported in part by NIH grants (MH100217, MH107815, MH108914, MH109773, and MH110274).
© 2017, Springer International Publishing AG.
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)