| Autor: |
Xia J; Department of Computer Science and Technology, Shandong University, Shandong, China.; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA., Zhang C; Department of Computer Science and Technology, Shandong University, Shandong, China., Wang F; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA., Benkarim OM; DTIC, Universitat Pompeu Fabra, Barcelona, Spain., Sanroma G; DTIC, Universitat Pompeu Fabra, Barcelona, Spain., Piella G; DTIC, Universitat Pompeu Fabra, Barcelona, Spain., González Balleste MA; DTIC, Universitat Pompeu Fabra, Barcelona, Spain.; ICREA, Pg. Lluis Companys 23, 08010 Barcelona, Spain., Hahner N; Fetal i+D Fetal Medicine Research Center, BCNatal, Hospital Clínic and Hospital Sant Joan de Déu, Barcelona, Spain., Eixarch E; Fetal i+D Fetal Medicine Research Center, BCNatal, Hospital Clínic and Hospital Sant Joan de Déu, Barcelona, Spain., Shen D; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA., Li G; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA. |
| Abstrakt: |
Dividing the human cerebral cortex into structurally and functionally distinct regions is important in many neuroimaging studies. Although many parcellations have been created for adults, they are not applicable for fetal studies, due to dramatic differences in brain size, shape and folding between adults and fetuses, as well as dynamic growth of fetal brains. To address this issue, we propose a novel method to divide a population of fetal cortical surfaces into distinct regions based on the dynamic growth patterns of cortical properties, which indicate the underlying changes of microstructures. As microstructures determine the molecular organization and functional principles of the cortex, growth patterns enable an accurate definition of distinct regions in development, microstructure, and function. To comprehensively capture the similarities of cortical growth patterns among vertices, we construct two complementary similarity matrices. One is directly based on the growth trajectories of vertices and the other is based on the correlation profiles of vertices' growth trajectories in relation to those of reference points. Then, we nonlinearly fuse these two similarity matrices into a single one, which can better captures both their common and complementary information than by simply averaging them. Finally, based on this fused matrix, we perform spectral clustering to divide fetal cortical surfaces into distinct regions. We have applied our method on 25 normal fetuses from 26 to 29 gestational weeks and generated biologically meaningful parcellations. |
