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Developmental Shifts in Functional Connectivity Across Age During Preadolescence

Poster No:

992 

Submission Type:

Abstract Submission 

Authors:

Haleh Falakshahi1, Hooman Rokham1, Tony Wilson2, Julia Stephen3, Yu-Ping Wang4, Vince Calhoun5

Institutions:

1Georgia State University, Atlanta, GA, 2Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE, 3Mind Research Network, Albuquerque, NM, 4Tulane University, New Orleans, LA, 5GSU/GATech/Emory, Atlanta, GA

First Author:

Haleh Falakshahi  
Georgia State University
Atlanta, GA

Co-Author(s):

Hooman Rokham  
Georgia State University
Atlanta, GA
Tony Wilson  
Institute for Human Neuroscience, Boys Town National Research Hospital
Omaha, NE
Julia Stephen  
Mind Research Network
Albuquerque, NM
Yu-Ping Wang  
Tulane University
New Orleans, LA
Vince Calhoun  
GSU/GATech/Emory
Atlanta, GA

Introduction:

The transition from childhood to adolescence is marked by profound changes in brain connectivity that underlie the maturation of cognitive functions. This study investigates developmental shifts in functional connectivity (FC) using resting-state and task-based fMRI data from typically developing children aged 6–13 years, part of the Developmental Multimodal Imaging of Neurocognitive Dynamics (Dev-MIND) study. Our analysis focuses on age-related changes in pairwise FC to capture detailed patterns of neural network reorganization across preadolescence.

Methods:

We analyzed functional connectivity (FC) in a cohort of 375 participants across resting-state and cognitive tasks, including Face Processing, designed to assess brain regions involved in recognizing facial features, emotions, and identities; the Local-Global task, which examines hierarchical information processing by requiring shifts in attention between local details and global structures; and the Multi-Source Interference Task (MSIT), which evaluates cognitive control by challenging participants to resolve conflicts between competing stimuli, measuring executive function and response inhibition. Data were preprocessed using the NeuroMark pipeline (Du et al., 2020) , which applies a priori-informed independent component analysis (ICA) with 53 intrinsic connectivity networks (ICNs) categorized into seven functional domains. FC matrices were generated using Pearson correlations between ICN time series, and pairwise t-tests were performed to identify significant differences between younger (ages 6–9) and older (ages 10–13) children.

Results:

Our findings reveal distinct age-related changes in FC across specific brain regions. Resting-state analyses showed that FC between the inferior parietal lobule and precuneus, as well as between the paracentral lobule and inferior parietal lobule, decreases with age, indicating reduced integration in these areas. Conversely, FC between the superior parietal lobule and postcentral gyrus, and between the putamen and middle frontal gyrus, increases with age, reflecting enhanced network efficiency and functional specialization in older children (Figure 1). These patterns suggest that certain brain connections strengthen while others weaken as part of typical neurodevelopmental trajectories.
Task-based analyses revealed further age-dependent differences in FC. During the Local-Global task, older children exhibited increased connectivity between cognitive control and sensorimotor networks, supporting improved attentional control and processing of hierarchical information. Similarly, in the MSIT conditions, older children displayed greater connectivity in cognitive control regions, highlighting their enhanced ability to manage interference and resolve conflicts. In contrast, younger children demonstrated higher connectivity in sensory and motor regions, suggesting reliance on different neural strategies for task performance.
Cluster analysis of resting-state FC revealed five distinct subgroups within the cohort, indicating variability in developmental trajectories. These clusters showed unique connectivity profiles, with older children more frequently associated with clusters characterized by stronger inter-network integration.
Supporting Image: Fig1.png
   ·Figure 1. The figure presents the average static functional connectivity matrix for resting-state fMRI data, illustrating differences between younger (6–9 years old) and older (10–13 years old) groups
 

Conclusions:

This study provides valuable insights into the developmental trajectories of FC across age, emphasizing the importance of analyzing individual FC pairs to understand the nuanced changes in brain network organization during preadolescence. Our findings highlight the interplay between functional specialization and integration, contributing to the neural underpinnings of cognitive maturation. These results enhance our understanding of typical developmental processes and provide a benchmark for identifying atypical patterns in developmental disorders. Future research should further explore how these age-related FC changes relate to cognitive and behavioral outcomes, particularly in diverse and clinically relevant populations.

Lifespan Development:

Early life, Adolescence, Aging 1

Modeling and Analysis Methods:

Connectivity (eg. functional, effective, structural) 2

Keywords:

FUNCTIONAL MRI

1|2Indicates the priority used for review

Abstract Information

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Please indicate below if your study was a "resting state" or "task-activation” study.

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Task-activation

Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Healthy subjects

Was this research conducted in the United States?

Yes

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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

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Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

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Please indicate which methods were used in your research:

Functional MRI

For human MRI, what field strength scanner do you use?

2.0T

Which processing packages did you use for your study?

SPM
FSL

Provide references using APA citation style.

Du, Y., Fu, Z., Sui, J., Gao, S., Xing, Y., Lin, D., Salman, M., Abrol, A., Rahaman, M. A., Chen, J., Hong, L. E., Kochunov, P., Osuch, E. A., Calhoun, V. D. & Initiative, for the A. D. N. (2020). NeuroMark: An automated and adaptive ICA based pipeline to identify reproducible fMRI markers of brain disorders. NeuroImage: Clinical, 28, 102375. https://doi.org/10.1016/j.nicl.2020.102375

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