354 - Examining dynamic functional connectivity during sleep in neonates using machine learning and high density diffuse optical tomography

Poster Number: 354
Publication Number: 354.2915

Background: Sleep is a critical factor in early brain development due to its impact on memory consolidation, synaptic plasticity, and neural network maintenance. High-Density Diffuse Optical Tomography (HD-DOT), a functional near-infrared spectroscopy (fNIRS) technology, has been used to investigate static resting state functional connectivity (FC) during active sleep (AS) and quiet sleep (QS) states in term-aged infants. Dynamic FC analysis investigates time-varying patterns in brain activity to shed light on the non-stationary nature of resting state brain functionality. One functional magnetic resonance imaging (fMRI) method proposed for this objective identifies recurring co-activation patterns (CAPs) using clustering algorithms. These CAPs represent instantaneous brain configurations at single time points and provide insight into the dynamics of spontaneous neural activity.

Objective: This HD-DOT study examines dynamic functional connectivity during newborn infant sleep to shed light on early brain development in relation to sleep states. Specifically, this study adapts CAP analysis, an fMRI approach that employs unsupervised machine learning, for HD-DOT data.

Design/Methods: In this observational study HD-DOT data was acquired from a cohort of term newborn infants while asleep, born at the Rosie Hospital, Cambridge UK (n=30, mean postmenstrual age=40+3). These datasets were classified as AS or QS based on behavioural analysis of synchronized video footage. The top 25% of frames of the seed signal for somato-motor and frontal networks were selected for each participant. Activation maps at these frames were clustered using the K-means algorithm into CAPs. CAP consistency was assessed by measuring intra-CAP spatial correlation. Other metrics such as CAP presence and CAP transition rate were compared in AS and QS datasets using rank sum t-tests to investigate potential effects of sleep state on resting state networks.

Results: Distinct CAPs were identified for AS and QS datasets, characterizing unique connectivity dynamics within somato-motor and frontal regions. These CAPs were found to have high consistency scores (left frontal region AS=0.51+/-0.03 and QS=0.50+/-0.04; left central region AS=0.53+/-0.04 and QS=0.51+/-0.04). Across iterations of CAP analysis, consistent trends emerged. Notably, CAPs with unilateral activation appear more frequently in the QS dataset.

Conclusion(s): Preliminary examination of CAP metrics reveal potential differences between dynamic FC during AS and QS. These differences may provide insight into the relationship between sleep and neuronal connectivity in the developing brain.