Development of Segregation and Integration of Functional Connectomes during the First 1000 Days
Presented During:
Thursday, June 27, 2024: 11:30 AM - 12:45 PM
COEX
Room:
ASEM Ballroom 202
Poster No:
1512
Submission Type:
Abstract Submission
Authors:
Qiongling Li1,2,3, Mingrui Xia1,2,3, Debin Zeng4, Yuehua Xu1,2,3, Lianglong Sun1,2,3, Xinyuan Liang1,2,3, Zhilei Xu1,2,3, Tengda Zhao1,2,3, Xuhong Liao5, Huishu Yuan6, Ying Liu6, Ran Huo6, Shuyu Li1, Yong He1,2,3,7
Institutions:
1State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, 2Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China, 3IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China, 4Beihang university, Beijing, China, 5School of Systems Science, Beijing Normal University, Beijing, China, 6Peking University Third Hospital, Beijing, China, 7Chinese Institute for Brain Research, Beijing, China
First Author:
Qiongling Li
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University|Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University|IDG/McGovern Institute for Brain Research, Beijing Normal University
Beijing, China|Beijing, China|Beijing, China
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University|Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University|IDG/McGovern Institute for Brain Research, Beijing Normal University
Beijing, China|Beijing, China|Beijing, China
Co-Author(s):
Mingrui Xia
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University|Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University|IDG/McGovern Institute for Brain Research, Beijing Normal University
Beijing, China|Beijing, China|Beijing, China
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University|Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University|IDG/McGovern Institute for Brain Research, Beijing Normal University
Beijing, China|Beijing, China|Beijing, China
Yuehua Xu
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University|Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University|IDG/McGovern Institute for Brain Research, Beijing Normal University
Beijing, China|Beijing, China|Beijing, China
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University|Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University|IDG/McGovern Institute for Brain Research, Beijing Normal University
Beijing, China|Beijing, China|Beijing, China
Lianglong Sun
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University|Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University|IDG/McGovern Institute for Brain Research, Beijing Normal University
Beijing, China|Beijing, China|Beijing, China
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University|Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University|IDG/McGovern Institute for Brain Research, Beijing Normal University
Beijing, China|Beijing, China|Beijing, China
Xinyuan Liang
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University|Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University|IDG/McGovern Institute for Brain Research, Beijing Normal University
Beijing, China|Beijing, China|Beijing, China
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University|Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University|IDG/McGovern Institute for Brain Research, Beijing Normal University
Beijing, China|Beijing, China|Beijing, China
Zhilei Xu
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University|Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University|IDG/McGovern Institute for Brain Research, Beijing Normal University
Beijing, China|Beijing, China|Beijing, China
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University|Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University|IDG/McGovern Institute for Brain Research, Beijing Normal University
Beijing, China|Beijing, China|Beijing, China
Tengda Zhao
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University|Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University|IDG/McGovern Institute for Brain Research, Beijing Normal University
Beijing, China|Beijing, China|Beijing, China
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University|Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University|IDG/McGovern Institute for Brain Research, Beijing Normal University
Beijing, China|Beijing, China|Beijing, China
Shuyu Li
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China
Yong He
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University|Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University|IDG/McGovern Institute for Brain Research, Beijing Normal University|Chinese Institute for Brain Research
Beijing, China|Beijing, China|Beijing, China|Beijing, China
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University|Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University|IDG/McGovern Institute for Brain Research, Beijing Normal University|Chinese Institute for Brain Research
Beijing, China|Beijing, China|Beijing, China|Beijing, China
Introduction:
The first 1000 days, from conception to the first 3 postnatal years1, are critical periods during which the human brain undergoes a remarkable process of growth and reorganization2. One of the most important aspects of this process is to unravel the developmental rules of segregation and integration of functional connectomes. Understanding how these processes mature during this critical period is a crucial step in elucidating the mechanisms underlying typical and atypical development. Here, we investigated the continuous, longitudinal developmental process of functional segregation and integration during the first 1000 days and the potential genetic contributions underlying connectome growth.
Methods:
Participants. This study used two publicly available longitudinal imaging datasets- the dHCP3 and the BCP4, comprised 930 scans from 665 infants. These infants underwent task-free functional MRI (tf-fMRI) scans at different ages, ranging from 28 post-conceptional weeks to 3 postnatal years. All MRI data were publicly available and anonymized.
Connectomic analysis. To capture the continuous maturational process of functional segregation and integration during the first 1000 days, we used a generalized additive mixed model (GAMM) with age as a smooth term and subject ID as a random effect. Linear covariates such as gender, head motion within scanners, and site were included in the model. By applying the GAMM to each brain voxel, we aimed to model the non-linear growth trajectories across the cortex. We also investigated whether the developmental trajectory of functional segregation and integration exhibited spatio-temporal heterogeneity across the cortex, indicating non-uniform maturation patterns. We also investigated the potential influence of differential spatio-temporal gene transcription on the development of the functional connectome.
Connectomic analysis. To capture the continuous maturational process of functional segregation and integration during the first 1000 days, we used a generalized additive mixed model (GAMM) with age as a smooth term and subject ID as a random effect. Linear covariates such as gender, head motion within scanners, and site were included in the model. By applying the GAMM to each brain voxel, we aimed to model the non-linear growth trajectories across the cortex. We also investigated whether the developmental trajectory of functional segregation and integration exhibited spatio-temporal heterogeneity across the cortex, indicating non-uniform maturation patterns. We also investigated the potential influence of differential spatio-temporal gene transcription on the development of the functional connectome.
Results:
Significant age-related changes in FCS during the first 1000 days were primarily observed in the primary cortical regions, and regions within default mode and executive control systems (Gaussian random field corrected, voxel-level p<0.001, cluster-level p<0.05). To illustrate the developmental trajectory of different functional systems, six seeds were selected from brain regions with significant changes (colored circles in Fig 1A). Perinatally, hubs were concentrated in the sensorimotor system shifting to the dorsal attention and visual systems postnatally, transitioning to the default mode at two years, potentially reorganizing by three years (Fig 1B). Specifically, the hub voxel ratio in the sensorimotor system initially decreased then increased. The dorsal attention system showed an increasing and then decreasing trajectory, while the default mode and visual systems continued to increase, with the default mode having a higher ratio (all p<0.001). Functional segregation and integration followed distinct developmental trajectories, spatially heterogeneous across the cortex and aligned with the A-P axis (Fig 1C). Module assignment across age is shown in Fig 1D revealing finer changes in the sensorimotor and occipital visual cortices, contrasting with minimal changes in the prefrontal cortex. Additionally, the sensorimotor and occipital visual cortices exhibited greater flexibility in module assignment compared to other regions (Fig 1E). Distinct transcriptomic trajectories between significant and non-significant regions in key neuronal metabolic pathways and dendritic development process were observed (Fig 2), with gene expression differences focusing on aerobic glycolysis (p<0.001, FDR-corrected) and dendritic development (p=0.02, FDR-corrected).
·Fig1
·Fig2
Conclusions:
Our analysis revealed a priority development of local segregation and hub relocation from primary to higher-order cortex. Regional developmental trajectories of functional segregation and integration diverged in a continuous manner across the A-P axis. The underlying mechanism may be the regulation of genes related to aerobic glycolysis and dendritic development.
Genetics:
Genetic Association Studies
Lifespan Development:
Early life, Adolescence, Aging
Normal Brain Development: Fetus to Adolescence 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Development
FUNCTIONAL MRI
1|2Indicates the priority used for review
Provide references using author date format
Gilmore, J.H., (2018). Imaging structural and functional brain development in early childhood. Nat. Rev. Neurosci. 19, 123–137. 10.1038/nrn.2018.1.
Edwards, A.D., (2022). The Developing Human Connectome Project Neonatal Data Release. Front. Neurosci. 16.
Howell, B.R., (2019). The UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development. NeuroImage 185, 891–905. 10.1016/j.neuroimage.2018.03.049.