Mapping the spatiotemporal continuum of structural connectivity development across the human connect
Poster No:
1015
Submission Type:
Abstract Submission
Authors:
Xiaoyu Xu1,2,3, Hang Yang3,2, Jing Cong3,1,2, Valerie Sydnor4, Kangcheng Wang5, Shaoling Zhao3,2, Haoshu Xu3,2,6, Yang Li3,2, Zaixu Cui3,2
Institutions:
1State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, 2Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China, 3Chinese Institute for Brain Research, Beijing, Beijing, China, 4University of Pittsburgh, Pittsburgh, PA, 5School of Psychology, Shandong Normal University, Jinan, China, 6Academy for Advanced Interdisciplinary Studies, Beijing, China
First Author:
Xiaoyu Xu
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University|Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College|Chinese Institute for Brain Research, Beijing
Beijing, China|Beijing, China|Beijing, China
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University|Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College|Chinese Institute for Brain Research, Beijing
Beijing, China|Beijing, China|Beijing, China
Co-Author(s):
Hang Yang
Chinese Institute for Brain Research, Beijing|Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College
Beijing, China|Beijing, China
Chinese Institute for Brain Research, Beijing|Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College
Beijing, China|Beijing, China
Jing Cong
Chinese Institute for Brain Research, Beijing|State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University|Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College
Beijing, China|Beijing, China|Beijing, China
Chinese Institute for Brain Research, Beijing|State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University|Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College
Beijing, China|Beijing, China|Beijing, China
Shaoling Zhao
Chinese Institute for Brain Research, Beijing|Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College
Beijing, China|Beijing, China
Chinese Institute for Brain Research, Beijing|Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College
Beijing, China|Beijing, China
Haoshu Xu
Chinese Institute for Brain Research, Beijing|Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College|Academy for Advanced Interdisciplinary Studies
Beijing, China|Beijing, China|Beijing, China
Chinese Institute for Brain Research, Beijing|Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College|Academy for Advanced Interdisciplinary Studies
Beijing, China|Beijing, China|Beijing, China
Yang Li
Chinese Institute for Brain Research, Beijing|Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College
Beijing, China|Beijing, China
Chinese Institute for Brain Research, Beijing|Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College
Beijing, China|Beijing, China
Zaixu Cui
Chinese Institute for Brain Research, Beijing|Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College
Beijing, China|Beijing, China
Chinese Institute for Brain Research, Beijing|Beijing Institute for Brain Research, Chinese Academy of Medical Sciences & Peking Union Medical College
Beijing, China|Beijing, China
Introduction:
Childhood and adolescence are characterized by protracted developmental remodeling of white matter structural connectivity. Histological and animal studies illustrate that the refinement of white matter happens heterogeneously across different regions (de Faria, 2021). However, the spatiotemporal variability of this developmental pattern across human connectome remains poorly understood. This study aims to provide a detailed developmental chart for large-scale structural connectivity and investigate whether the variability in refinement across the human connectome aligns with a predefined sensorimotor-to-association (S-A) axis (Sydnor, 2021) of the human connectome.
Methods:
We utilized data from four independent developmental datasets, the Lifespan Human Connectome Project in Development (HCP-D) (Somerville, 2018), the Adolescent Brain Cognitive Development (ABCD) study (Casey, 2018), a Chinese cohort, and the Healthy Brain Network (HBN) study (Alexander, 2018). Structural connectivity networks were reconstructed for each participant, with nodes defined as large-scale cortical systems generated by equally dividing the cortex into 12 fractions along the S-A cortical axis (Figure 1a). The weights were defined as the structural connectivity strength computed by counting the SIFT2-weighted (Smith, 2015) number of white matter streamlines linking pairs of cortical systems.
Developmental models for 78 large-scale structural connections were fitted using generalized additive models (GAMs) and general additive mixed models (GAMMs). Partial R2 was calculated to assess the overall age effect, and the average second derivatives of age were used to quantify the curvature of developmental trajectories. Spearman's rank correlation was utilized to evaluate the alignment between developmental heterogeneity and the S-A connectional axis. Associations between structural connectivity, higher-order cognition, and general psychopathological symptoms were also examined using GAMs and GAMMs.
Developmental models for 78 large-scale structural connections were fitted using generalized additive models (GAMs) and general additive mixed models (GAMMs). Partial R2 was calculated to assess the overall age effect, and the average second derivatives of age were used to quantify the curvature of developmental trajectories. Spearman's rank correlation was utilized to evaluate the alignment between developmental heterogeneity and the S-A connectional axis. Associations between structural connectivity, higher-order cognition, and general psychopathological symptoms were also examined using GAMs and GAMMs.
Results:
The developmental trajectories of structural connectivity are shown in Figure 1b, revealing heterogeneous developmental patterns across connections. To evaluate whether these patterns are organized along the S-A connectional axis, we defined the connectional axis rank as the quadratic sum of the S-A cortical axis ranks of the pair-wise nodes (Figure 1c). Notably, the averaged second derivatives of age show a similar spatial pattern with the S-A connectional axis (Figure 1d), with a high correlation coefficient (rho=0.79, P<0.001, Figure 1e). Specifically, connections close to the sensorimotor end show negative second derivatives (concave upward curves), while connections close to the association end have positive derivatives (concave downward curves). Further analysis illustrated how developmental rates evolved with age (Figure 1f) and quantified the alignment between developmental rates and the S-A connectional axis. This alignment shifted from strong negative to strong positive over development, reaching zero alignment at approximately 15.5 years (Figure 1g,h). Developmental results are represented only for the HCP-D dataset due to word limit, while these findings are robust across datasets (http://connectcharts.cibr.ac.cn). Additionally, the S-A connectional axis captured spatial variations in the associations between structural connectivity and higher-order cognition (Figure 2a,b,c) and general psychopathology (Figure 2e,f,g). Moreover, connectivity developmental trajectories vary with cognitive (Figure 2d) and psychopathological levels (Figure 2h), with psychopathological effects predominantly observed in association connections. Cognitive results are only shown for the ABCD dataset, though they are also robust in the HBN dataset.
·Figure 1. Developmental patterns of structural connectivity align with the S-A connectional axis.
·Figure 2. Spatial variations in the associations between structural connectivity and higher-order cognition and general psychopathology.
Conclusions:
Our findings map a spatiotemporal continuum of structural connectivity development, offering a normative reference for quantifying developmental variability across different cognitive levels and psychiatric disorders.
Higher Cognitive Functions:
Higher Cognitive Functions Other
Lifespan Development:
Early life, Adolescence, Aging
Normal Brain Development: Fetus to Adolescence 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Diffusion MRI Modeling and Analysis
Keywords:
Cognition
Development
MRI
Open Data
Open-Source Code
STRUCTURAL MRI
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
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Provide references using APA citation style.
Casey, B. J. (2018). The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Dev Cogn Neurosci, 32, 43-54.
de Faria, O. (2021). Periods of synchronized myelin changes shape brain function and plasticity. Nat Neurosci, 24(11), 1508-1521.
Smith, R. E. (2015). SIFT2: Enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. Neuroimage, 119, 338-351.
Somerville, L. H. (2018). The Lifespan Human Connectome Project in Development: A large-scale study of brain connectivity development in 5-21 year olds. Neuroimage, 183, 456-468.
Sydnor, V. J. (2021). Neurodevelopment of the association cortices: Patterns, mechanisms, and implications for psychopathology. Neuron, 109(18), 2820-2846.