Myelination and inhibitory neurons support the development of SFC during adolescence
Poster No:
971
Submission Type:
Abstract Submission
Authors:
Xiaoxi Dong1, Qiongling Li1, Yirong He1, Debin Zeng2, Lei Chu2, Shuyu Li1
Institutions:
1State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, 2Beihang University, Beijing, China
First Author:
Xiaoxi Dong
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
Co-Author(s):
Qiongling 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
Yirong He
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
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
Introduction:
Understanding how cortical neural activity arises from the interactions of thousands of neurons is a key question in neuroscience. Structural attributes, such as morphology(Dorfschmidt et al., 2024) and gray(Paquola et al., 2019) or white matter(Dong et al., 2024), impose varying constraints on functional signals. Current studies often rely on one or two structural indicators to assess structure-function coupling (SFC), but incorporating more comprehensive structural information could better reflect the relationship between structure and function. Childhood and adolescence are crucial for active brain development, during which SFC also evolves to support complex behaviors(Baum et al., 2020). Myelin and inhibitory neurons may shape the development of SFC by influencing the plasticity of neurons through axons and synapses. Exploring the effects of myelin and inhibitory neurons on SFC during adolescence can enhance our understanding of its heterogeneous distribution and developmental patterns(Larsen et al., 2023). Therefore, constructing SFC using comprehensive information (SC, MPC, and MSN) and examining its developmental changes and biological basis during childhood and adolescence is vital for unraveling the complex mechanisms of SFC.
Methods:
Dataset and preprocessing. The data used in this study are from the HCP-D Release 2.0 data. We constructed the SC based on the number of streamlines connecting pairs of regions in the Schaefer-400 atlas. For the MPC, we computed pairwise partial correlations of regional intensity profiles measured by the T1w/T2w ratio. The MSN was calculated using MIND proposed by (Sebenius et al. 2023). To construct FC, we computed pairwise Pearson correlations between regional time series.
Structure-Function Coupling (SFC). We constructed a multiple linear regression (MLR) model for each brain region of each subject, where the dependent variable is the FC of that region with all other regions, and the independent variables include the SC, MPC, and MSN. The fit accuracy R2 is used as the SFC.
The development of SFC. We utilized general additive models (GAMs) to capture the age-related changes of SFC. For each model, we set SFC as the dependent variable, with age as a smooth term, and sex and mean FD as covariates.
The influence of Myelin and inhibitory neurons on SFC. We also constructed MLR models for the different hierarchy of each subject, where the dependent variable is the SFC, and the independent variables include myelin and Hurst exponent (a proxy of inhibitory neurons)7. The weight of myelin is denoted as β1, while the Hurst is denoted as β2. We observed the development trajectories of β1 and β2 in different hierarchy.
Structure-Function Coupling (SFC). We constructed a multiple linear regression (MLR) model for each brain region of each subject, where the dependent variable is the FC of that region with all other regions, and the independent variables include the SC, MPC, and MSN. The fit accuracy R2 is used as the SFC.
The development of SFC. We utilized general additive models (GAMs) to capture the age-related changes of SFC. For each model, we set SFC as the dependent variable, with age as a smooth term, and sex and mean FD as covariates.
The influence of Myelin and inhibitory neurons on SFC. We also constructed MLR models for the different hierarchy of each subject, where the dependent variable is the SFC, and the independent variables include myelin and Hurst exponent (a proxy of inhibitory neurons)7. The weight of myelin is denoted as β1, while the Hurst is denoted as β2. We observed the development trajectories of β1 and β2 in different hierarchy.
Results:
The distribution of SFC, constructed by integrating multiple structural metrics (MPC, MSN, and SC) with FC, exhibited a hierarchical pattern that diminished with age, as transmodal regions developed more rapidly than primary regions, leading to less pronounced hierarchical differentiation. Whole-brain myelin changes significantly correlated with SFC variations, while inhibitory neurons showed no significant relationship. The contributions of myelin and inhibitory neurons to SFC evolved differently across developmental stages and cortical hierarchies. In the unimodal cortex, inhibitory contributions increased, while myelin's contributions initially rose and subsequently declined with age. In the middle cortex, myelin's contribution was initially positive but later decreased, whereas the impact of inhibitory neurons gradually diminished. In the transmodal cortex, inhibitory contributions declined with age, while myelin exhibited no significant change.
·Fig The hierarchical distribution of SFC
Conclusions:
Our analysis suggests that the distribution and the development of SFC is hierarchical across the neocortex. And myelin and inhibitory neurons underpin the development of SFC across different hierarchy. These findings facilitate our understanding of the complex mechanisms of SFC.
Lifespan Development:
Early life, Adolescence, Aging 1
Novel Imaging Acquisition Methods:
Anatomical MRI
BOLD fMRI
Diffusion MRI
Multi-Modal Imaging 2
Keywords:
Cortex
Development
Myelin
Other - hierarchy; inhibitory neurons; structure‒function coupling
1|2Indicates the priority used for review
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Provide references using APA citation style.
Dong, X., Li, Q., Wang, X., He, Y., Zeng, D., Chu, L., Zhao, K., & Li, S. (2024). How brain structure–function decoupling supports individual cognition and its molecular mechanism. Human Brain Mapping, 45(2), e26575. https://doi.org/10.1002/hbm.26575
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