Unique evolution of middle frontal gyrus in adolescents from a view of source-based morphometry
Poster No:
1805
Submission Type:
Abstract Submission
Authors:
liqiang zhang1, Shiyun Wang1, Dongmei Zhi1, Peng Wang1, Na Luo2, Vince Calhoun3, Jing Sui1
Institutions:
1State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, 2Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 3GSU/GATech/Emory, Atlanta, GA
First Author:
liqiang zhang
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):
Shiyun Wang
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
Dongmei Zhi
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
Peng Wang
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
Jing Sui
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:
Exploring the relationship between brain structure and function is a key area in neuroscience[1]. There's limited understanding of how this relationship evolves with age, especially during adolescence, which could offer insights for typical neurodevelopmental trajectories and help identify early biomarkers and intervention targets for neuropsychiatric disorders.
Methods:
We utilize healthy controls from the ABCD dataset at baseline (N = 2,259, discovery dataset) and followup (N = 2,387, replication dataset, fig 2a). Derived resting-state functional components from NeuroMark[2] (53 independent components) and decomposed structural components into 75 ICs using the Infomax independent component analysis (ICA) algorithm to minimize confounding factors. Performed dimensionality reduction by principal component analysis to extract 100 principal components, repeated 20 times with ICASSO software for result stability and selected the best run for further analysis (fig 1a). Constructed structural-functional coupling networks with a Pearson correlation coefficient |r| > 0.25 and mutual information value |MI| > 0.2 between 75 structural ICs and 53 functional ICs by a greedy correlation algorithm[3]. Also analyzed peak activation regions of different components at two time points and explored their developmental similarities and differences (fig 1c).
Results:
In total, 39 structural ICs matched 35 functional ICs (p-value < 0.01) in seven networks (subcortical, visual, sensorimotor, cognitive control, default, and cerebellar) in the discovery dataset (fig 1b). In the replication dataset, 40 structural ICs matched 36 functional ICs, with 39 structural ICs consistent with those in the discovery dataset (fig 2b), indicating reliability and robustness. We recorded the changes in gray matter volume from ages 9 to 14 every 4 months. We found that most brain regions exhibited a decrease in gray matter volume over this age range[4] (as shown in the top three images of Fig. 2c), with the exception of the middle frontal gyrus, where the gray matter volume increased (as shown in the bottom image of Fig. 2c). The Orbital Frontal Gyrus (OFG) component at the baseline was associated with picture vocabulary ability (r = 0.045, p = 0.034), and the Superior Frontal Gyrus (SFG) component at the followup was linked to pattern comparison processing speed (r = 0.043, p = 0.036), illustrating the gradual development of brain functions from language and semantic processing to emotional regulation and then to advanced cognitive control. It also highlights brain region differentiation, functional takeover, and the formation of complex cross-domain functional networks via enhanced connectivity.
Conclusions:
Here we constructed a structural MRI template for adolescents based on source-based morphometry and the Neuromark fMRI template. Observation from the grey matter volume identified different developmental trajectory of MFG and SFG[5], revealing that the progression of brain GMV differs among different functional regions. While MFG is associated with picture vocabulary ability and SFG is associated with pattern comparison processing speed, each exhibits a unique trend. These findings provide new insights into the structure-function evolution in adolescents, which need further validation from independent cohorts and a more diverse population.
Lifespan Development:
Normal Brain Development: Fetus to Adolescence 2
Neuroinformatics and Data Sharing:
Brain Atlases 1
Keywords:
Atlasing
Cognition
FUNCTIONAL MRI
STRUCTURAL MRI
Other - ICA, adolescent
1|2Indicates the priority used for review
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[2] Du, Y. (2020). NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders. NeuroImage: Clinical, 28.
[3] Luo, N. (2020). Structural Brain Architectures Match Intrinsic Functional Networks and Vary across Domains: A Study from 15 000+ Individuals. Cerebral cortex, 30(10), 5460-5470.
[4] Bethlehem, R. A. I. (2022). Brain charts for the human lifespan. Nature, 604(7906), 525-533.
[5] Fu, Z. (2024). Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities. Neuroimage, 292.