Sex Differences in the Functional Topography of Brain Networks in Infants: A Longitudinal Study
Poster No:
1020
Submission Type:
Abstract Submission
Authors:
Ruyi Xiao1,2, Chunfeng Lian3,2, Xianjun Li4, Jian Yang4, Jianhua Ma2,1, Fan Wang1,2
Institutions:
1Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China, 2Research Center for Intelligent Medical Equipment and Devices, Xi'an Jiaotong University, Xi'an, China, 3School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China, 4Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
First Author:
Ruyi Xiao
Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University|Research Center for Intelligent Medical Equipment and Devices, Xi'an Jiaotong University
Xi'an, China|Xi'an, China
Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University|Research Center for Intelligent Medical Equipment and Devices, Xi'an Jiaotong University
Xi'an, China|Xi'an, China
Co-Author(s):
Chunfeng Lian
School of Mathematics and Statistics, Xi'an Jiaotong University|Research Center for Intelligent Medical Equipment and Devices, Xi'an Jiaotong University
Xi'an, China|Xi'an, China
School of Mathematics and Statistics, Xi'an Jiaotong University|Research Center for Intelligent Medical Equipment and Devices, Xi'an Jiaotong University
Xi'an, China|Xi'an, China
Xianjun Li
Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University
Xi'an, China
Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University
Xi'an, China
Jian Yang
Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University
Xi'an, China
Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University
Xi'an, China
Jianhua Ma
Research Center for Intelligent Medical Equipment and Devices, Xi'an Jiaotong University|Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University
Xi'an, China|Xi'an, China
Research Center for Intelligent Medical Equipment and Devices, Xi'an Jiaotong University|Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University
Xi'an, China|Xi'an, China
Fan Wang
Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University|Research Center for Intelligent Medical Equipment and Devices, Xi'an Jiaotong University
Xi'an, China|Xi'an, China
Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University|Research Center for Intelligent Medical Equipment and Devices, Xi'an Jiaotong University
Xi'an, China|Xi'an, China
Introduction:
Previous studies have demonstrated significant differences in personalized functional networks (PFNs) both across individuals and between sexes during adolescence and adulthood [1, 2]. However, it remains unknown whether there are sex differences in the topography of PFNs in infants. Herein, we utilized 905 high-resolution longitudinal resting-state functional MR images (rs-fMRIs) from 192 typically developing infants and toddlers (from birth to 24 months) to evaluate sex differences in functional topography. Our results show that sex differences in the functional topography of PFNs emerge early in infancy and become increasingly pronounced with age.
Methods:
T1w, T2w, and rs-fMRI scans of 192 healthy infants (86 males and 106 females) were collected from the UNC/UMN Baby Connectome Project (BCP) dataset. Each subject was scheduled to have longitudinal scans at 1, 3, 6, 9, 12, 18, and 24 months of age. rs-fMRIs were preprocessed as in [4], and cortical surfaces were reconstructed, aligned, and resampled using the UNC Infant Pipeline [5]. rs-fMRI signals were resampled onto the corresponding middle cortical surface [3].
Using the previously derived group consensus atlas [6] as a prior to ensure inter-individual correspondence, we derived each personalized functional network using sparsity-regularized non-negative matrix factorization (NMF) [7] based on the acquired group networks (10 × 32,492 loading matrix) as initialization and each individual's specific fMRI time series. To reduce complexity and minimize overfitting, the individualized network loading matrix was resampled to 2,562 vertices per hemisphere using Connectome Workbench [8]. The resampled matrix of each participant's PFNs was then used to train a linear support vector machine (SVM) to classify participants as male or female.
Using the previously derived group consensus atlas [6] as a prior to ensure inter-individual correspondence, we derived each personalized functional network using sparsity-regularized non-negative matrix factorization (NMF) [7] based on the acquired group networks (10 × 32,492 loading matrix) as initialization and each individual's specific fMRI time series. To reduce complexity and minimize overfitting, the individualized network loading matrix was resampled to 2,562 vertices per hemisphere using Connectome Workbench [8]. The resampled matrix of each participant's PFNs was then used to train a linear support vector machine (SVM) to classify participants as male or female.
Results:
Fig. 1 shows individual-specific functional topographic differences. While the spatial distribution of networks was consistent across participants, distinct person-specific topographic features were evident across individuals and sexes. Building on this observation, we investigated whether variability in the spatial distribution of PFNs from 3 to 24 months was associated with sex. Through sex classification experiments at different time points, classification performance improved progressively, with accuracy scores of 59.72%, 72.19%, 73.02%, 75.93%, 77.51%, and 80.90%, and AUC scores of 0.58, 0.79, 0.80, 0.81, 0.85, and 0.86 at 3, 6, 9, 12, 18, and 24 months, respectively. This suggests that sex differences in PFN topography become more pronounced with age.
Fig. 2 (a) shows changes in network contributions to sex classification. At 3–6 months, network contributions were evenly distributed with minimal differentiation. By 9-24 months, the networks contributing most to sex classification became more distinct, particularly the superior temporal, hand sensory motor, and peripheral visual networks. By 24 months, the posterior frontoparietal network (network 4) and the posterior default mode network (network 2) showed enhanced contributions, indicating the increasing importance of higher-order association networks in distinguishing sex differences.
Fig. 2 (b) shows the spatial distribution of vertices critical to sex classification. At 3–6 months, vertices critical for predicting participant sex were widely distributed across the cortex. Between 9 and 24 months, these vertices became increasingly concentrated in the superior temporal gyrus and precentral gyrus. By 24 months, the inclusion of the superior parietal lobule in these critical regions underscores the increasing role of higher-order areas in supporting sex classification.
Fig. 2 (a) shows changes in network contributions to sex classification. At 3–6 months, network contributions were evenly distributed with minimal differentiation. By 9-24 months, the networks contributing most to sex classification became more distinct, particularly the superior temporal, hand sensory motor, and peripheral visual networks. By 24 months, the posterior frontoparietal network (network 4) and the posterior default mode network (network 2) showed enhanced contributions, indicating the increasing importance of higher-order association networks in distinguishing sex differences.
Fig. 2 (b) shows the spatial distribution of vertices critical to sex classification. At 3–6 months, vertices critical for predicting participant sex were widely distributed across the cortex. Between 9 and 24 months, these vertices became increasingly concentrated in the superior temporal gyrus and precentral gyrus. By 24 months, the inclusion of the superior parietal lobule in these critical regions underscores the increasing role of higher-order areas in supporting sex classification.
Conclusions:
Sex classification performance improved with age, indicating increasing sex differentiation in the functional topography of PFNs during the first two years. Key networks and vertices evolved from diffuse patterns in infancy to more concentrated involvement in specific networks and regions.
Lifespan Development:
Normal Brain Development: Fetus to Adolescence 1
Modeling and Analysis Methods:
Classification and Predictive Modeling 2
Keywords:
Cortex
Development
FUNCTIONAL MRI
Machine Learning
Multivariate
PEDIATRIC
Sexual Dimorphism
Other - Longitudinal Study,personalized functional networks
1|2Indicates the priority used for review
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