Divergent Developmental Trajectories of Primary and Secondary Brain Sulci in Late Childhood
Poster No:
1307
Submission Type:
Abstract Submission
Authors:
Yijin Shan1, Yirong He2, Debin Zeng3, Xiaoxi Dong2, Lei Chu4, Shuyu Li5
Institutions:
1Beihang University, Beijing, Beijing, 2Beijing Normal University, Beijing, Beijing, 3Beihang university, Beijing, Beijing, 4Beihang University, BEIJING, Beijing, 5Beijing Normal University, Beijing, China
First Author:
Co-Author(s):
Introduction:
The primary sulcus can be identified after 25th GW, followed by the emergence of the secondary sulcus at the 32nd GW and the tertiary sulcus at the 36th GW (Chi, Dooling, and Gilles 1977). As cortical morphology continues to change during late childhood, there have been relatively few studies of developmental changes in specific sulcal morphology. What are the differences between the developmental patterns of the primary and secondary sulci in late childhood, and are such differences associated with cognition. To explore these questions, we used a widely used sulcus recognition toolbox to investigate the developmental changes of different primary and secondary sulci separately in children aged 6-14 years.
Methods:
Subjects
The dataset consisted of 312 typically developing children aged 6.1-13.9 years (F/M = 145/167) from the Children School Functions and Brain Development project (CBD, Beijing Cohort). Specifically, 47 children had 3 scans available, 97 children had 2 scans and 168 children had 1 for a total of 490 MRI scans.
MRI acquisition
The MRI data was acquired utilizing a 3T SIEMENS Prisma scanner(Fan et al. 2021). For every subject, T1-weighted structural images were acquired with the following parameters: TR = 2530 ms, TE = 2.98 ms, inversion time = 1100 ms, FA = 7°, FOV = 256 × 224 mm2, matrix size = 256 × 224, slice thickness = 1 mm, and scan time = 5 min and 58 s.
Image analyses
All T1w images were processed using FreeSurfer (v5.1), and the outputs were directly imported into the Morphologist toolbox in BrainVISA for sulcus classification and labeling according to predefined anatomical nomenclature (Borne et al. 2020). After filtering out the sulci with extraction success rate less than 75%, we retained the cortical thickness, sulcus width, surface area, maximum depth, mean depth, and sulcus length in standardized talairach space of a total of 91 sulci in the left and right hemisphere (Fig. 1).
Statistics The extracted morphological features were analyzed using a mixed-effects model (MEM), where effects affecting the dependent variable were categorized as either "fixed effects", such as the effects of age and sex, or "random effects", such as measurements of the same individual at different time points. After model fitting, the fitted models were selected by Bayesian Information Criterion (BIC).
Cognitive Data Acquisition and Analysis
Participants' cognitive performance was assessed using the classic numerical N-back Working Memory Task (WM) and a child-friendly version of the Attention Network Task (ANT) (Hao et al. 2021). To assess the interaction between cognitive scores and sulcus morphology a partial least squares correlation analysis (PLSC) was performed(Krishnan et al. 2011).
The dataset consisted of 312 typically developing children aged 6.1-13.9 years (F/M = 145/167) from the Children School Functions and Brain Development project (CBD, Beijing Cohort). Specifically, 47 children had 3 scans available, 97 children had 2 scans and 168 children had 1 for a total of 490 MRI scans.
MRI acquisition
The MRI data was acquired utilizing a 3T SIEMENS Prisma scanner(Fan et al. 2021). For every subject, T1-weighted structural images were acquired with the following parameters: TR = 2530 ms, TE = 2.98 ms, inversion time = 1100 ms, FA = 7°, FOV = 256 × 224 mm2, matrix size = 256 × 224, slice thickness = 1 mm, and scan time = 5 min and 58 s.
Image analyses
All T1w images were processed using FreeSurfer (v5.1), and the outputs were directly imported into the Morphologist toolbox in BrainVISA for sulcus classification and labeling according to predefined anatomical nomenclature (Borne et al. 2020). After filtering out the sulci with extraction success rate less than 75%, we retained the cortical thickness, sulcus width, surface area, maximum depth, mean depth, and sulcus length in standardized talairach space of a total of 91 sulci in the left and right hemisphere (Fig. 1).
Statistics The extracted morphological features were analyzed using a mixed-effects model (MEM), where effects affecting the dependent variable were categorized as either "fixed effects", such as the effects of age and sex, or "random effects", such as measurements of the same individual at different time points. After model fitting, the fitted models were selected by Bayesian Information Criterion (BIC).
Cognitive Data Acquisition and Analysis
Participants' cognitive performance was assessed using the classic numerical N-back Working Memory Task (WM) and a child-friendly version of the Attention Network Task (ANT) (Hao et al. 2021). To assess the interaction between cognitive scores and sulcus morphology a partial least squares correlation analysis (PLSC) was performed(Krishnan et al. 2011).
Results:
Similar to previous studies in late childhood, both primary and secondary sulci showed significant decreases in surface area, mean depth, and cortical thickness, with the primary sulcus showing the most significant changes (Fig. 2). Notably, sulcus width was significantly increased in the secondary sulcus but not in the primary. By plotting the t-values of the significant changes in each sulcus on a colormap at the sulcus level, it can be more intuitively found that the changes in sulcus morphology of the motor-sensory cortex are more obvious.
The PLSC results showed that although the morphology of both primary and secondary sulci interacted significantly with cognition, they were associated with different kinds of cognitive abilities.
The PLSC results showed that although the morphology of both primary and secondary sulci interacted significantly with cognition, they were associated with different kinds of cognitive abilities.
Conclusions:
The present work uses a extensive sample size to longitudinally and systematically investigate the trajectory of developmental changes across primary and secondary brain sulci in late childhood, suggesting that developmental changes in brain morphology during this period may be associated with different cognitive functions.
Lifespan Development:
Early life, Adolescence, Aging
Normal Brain Development: Fetus to Adolescence 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping 2
Normal Development
Novel Imaging Acquisition Methods:
Anatomical MRI
Keywords:
Cognition
Cortex
Development
Morphometrics
MRI
Other - Sulcus
1|2Indicates the priority used for review
Provide references using author date format
Chi, J. G., E. C. Dooling, and F. H. Gilles. 1977. ‘Gyral Development of the Human Brain’. Annals of Neurology 1 (1): 86–93.
Fan, Fengmei, Xuhong Liao, Tianyuan Lei, Tengda Zhao, Mingrui Xia, Weiwei Men, Yanpei Wang, et al. 2021. ‘Development of the Default-Mode Network during Childhood and Adolescence: A Longitudinal Resting-State fMRI Study’. NeuroImage 226 (February): 117581. https://doi.org/10.1016/j.neuroimage.2020.117581.
Hao, Lei, Lei Li, Menglu Chen, Jiahua Xu, Min Jiang, Yanpei Wang, Linhua Jiang, et al. 2021. ‘Mapping Domain- and Age-Specific Functional Brain Activity for Children’s Cognitive and Affective Development’. Neuroscience Bulletin 37 (6): 763–76.
Krishnan, Anjali, Lynne J. Williams, Anthony Randal McIntosh, and Hervé Abdi. 2011. ‘Partial Least Squares (PLS) Methods for Neuroimaging: A Tutorial and Review’. NeuroImage 56 (2): 455–75.