Evaluating the evolution and inter-individual variability of infant functional module development from 0 to 5 years old

TL;DR

This study introduces a Bayesian modeling approach to analyze infant brain network development from birth to age five, capturing individual variability and revealing gender differences in modular brain structure during sleep and wakefulness.

Contribution

The paper presents a novel Bayesian method for tracking infant brain module development that preserves individual differences, improving upon traditional group-averaged analyses.

Findings

Female infants show more distinct modular structures between sleep and wake states.

The new method more effectively detects brain modules compared to conventional techniques.

Gender differences in infant brain modularity are observed under different conditions.

Abstract

The segregation and integration of infant brain networks undergo tremendous changes due to the rapid development of brain function and organization. Traditional methods for estimating brain modularity usually rely on group-averaged functional connectivity (FC), often overlooking individual variability. To address this, we introduce a novel approach utilizing Bayesian modeling to analyze the dynamic development of functional modules in infants over time. This method retains inter-individual variability and, in comparison to conventional group averaging techniques, more effectively detects modules, taking into account the stationarity of module evolution. Furthermore, we explore gender differences in module development under awake and sleep conditions by assessing modular similarities. Our results show that female infants demonstrate more distinct modular structures between these two conditions, possibly implying relative quiet and restful sleep compared with male infants.