Dynamic functional connectivity during sleep in term and preterm infants
Poster No:
337
Submission Type:
Abstract Submission
Authors:
Katharine Lee1, Borja Blanco1, Rob Cooper2, Andrea Edwards3, Jem Hebden2, Kelle Pammenter3, Julie Uchitel1, Topun Austin3
Institutions:
1Department of Paediatrics, University of Cambridge, Cambridge, United Kingdom, 2Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, 3Cambridge University Hospitals NHS Foundation Trust, Cambridge, United Kingdom
First Author:
Co-Author(s):
Rob Cooper
Department of Medical Physics and Biomedical Engineering, University College London
London, United Kingdom
Department of Medical Physics and Biomedical Engineering, University College London
London, United Kingdom
Jem Hebden
Department of Medical Physics and Biomedical Engineering, University College London
London, United Kingdom
Department of Medical Physics and Biomedical Engineering, University College London
London, United Kingdom
Introduction:
Poor sleep early in life has been shown to negatively impact neurocognitive functions such as attention, inhibition, and learning (McCann, 2018). The impact of sleep on neuronal maturation is especially critical for preterm infants who experience environmental sleep disruptions early in development. Preterm birth has been associated with cognitive, social, and sleep difficulties later in life, outcomes that may be exacerbated by affected sleep (Gao, 2017, Stangenes, 2017). However, the relationship between sleep states, gestational age (GA), and functional brain development remains poorly understood. Studying the role of protected sleep in the NICU may reveal neuroprotective benefits and improve long-term clinical outcomes.
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 (Uchitel, 2023). Dynamic FC analysis investigates time-varying patterns in brain activity to shed light on the non-stationary nature of resting state brain functionality. One method proposed for this objective identifies recurring co-activation patterns (CAPs) using clustering algorithms which capture instantaneous brain configurations (Liu, 2018).
This study examines dynamic FC in term and preterm infants during sleep to better understand the functional relationship between sleep states and early brain connectivity.
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 (Uchitel, 2023). Dynamic FC analysis investigates time-varying patterns in brain activity to shed light on the non-stationary nature of resting state brain functionality. One method proposed for this objective identifies recurring co-activation patterns (CAPs) using clustering algorithms which capture instantaneous brain configurations (Liu, 2018).
This study examines dynamic FC in term and preterm infants during sleep to better understand the functional relationship between sleep states and early brain connectivity.
Methods:
HD-DOT data were acquired from sleeping newborns at the Rosie Hospital, Cambridge UK (term cohort: n = 44, GA = 40+0 weeks (median), 38+1 - 42+1 weeks (range); preterm cohort: n = 26, GA = 35+0 weeks (median), 29+1 - 36+6 weeks (range). Sleep state was labelled as AS/QS using synchronized video or electroencephalography. Frames were sorted by seed activity for three regions of interest (ROI), frontal, central, and parietal regions, and the top 15% frames were selected for k-means clustering. This threshold was chosen because the average of the top 15% of seed-selected frames strongly correlated with the seed-based correlation maps from the static analysis, validating the CAP procedure (see Figure 1). The clustered frames were averaged to create the CAP maps. Dynamic FC was compared across sleep states by calculating CAP consistency, in-participant fraction, dwell time, and transition likelihood for the term cohort. Additionally, regional bilateral activation was compared across sleep states within each CAP using a two proportion Z-test. The post-clustering analysis has been performed for the term cohort and will be applied to the preterm cohort for comparison.
·Figure 1. (i) Varying percentile threshold. (ii) Seed-based correlation maps are highly correlated with top 15% seed-selected frames. (a) Term seed analysis. (b) Preterm seed analysis.
Results:
Distinct CAPs were identified for each ROI (left ROI CAPs shown in Figure 2), presenting a novel dynamic characterization of known resting-state networks in term and preterm infants during sleep. These CAPs have high consistency scores, validating the efficacy of the fMRI-adapted CAP methodology in newborn HD-DOT data. Notably, several CAPs exhibit fronto-parietal connectivity, which previous studies relate to an immature and modular precursor of the default mode network. In the term analysis, nine CAPs had significantly different proportions of inter-hemispheric activity in AS versus QS (p-value < 0.016, corrected for 3 regional comparisons).
·Figure 2. Oxygenated hemoglobin co-activation patterns (CAPs) for (i) left frontal, (ii) left central, and (iii) left parietal region analyses. (a) CAPs for term cohort. (b) CAPs for preterm cohort.
Conclusions:
This study marks the first application of CAP analysis to term and preterm HD-DOT data revealing novel spatial patterns that dynamically characterize known brain networks. The bilateral activity analysis showed significant differences between sleep states, suggesting AS and QS may serve different purposes in early brain development. The upcoming analysis of the preterm cohort CAPs may reveal differences in CAP metrics (e.g. dwell time, in-participant fraction, and laterality) correlated with GA. The results of the remaining analysis will provide novel insight into the dynamic formation and maturation of brain networks in preterm infants.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Lifespan Development:
Normal Brain Development: Fetus to Adolescence
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Novel Imaging Acquisition Methods:
NIRS
Perception, Attention and Motor Behavior:
Sleep and Wakefulness 2
Keywords:
Computational Neuroscience
Development
Near Infra-Red Spectroscopy (NIRS)
Sleep
Other - Neonate
1|2Indicates the priority used for review
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Provide references using APA citation style.
Liu, X. (2018). Co-activation patterns in resting-state fMRI signals. NeuroImage, 180(Pt B), 485–494. https://doi.org/10.1016/j.neuroimage.2018.01.041
McCann, M. (2018). The relationship between sleep problems and working memory in children born very preterm. Child neuropsychology : a journal on normal and abnormal development in childhood and adolescence, 24(1), 124–144. https://doi.org/10.1080/09297049.2016.1235144
Stangenes, K. M. (2017). Children born extremely preterm had different sleeping habits at 11 years of age and more childhood sleep problems than term-born children. Acta Paediatrica, 106(12), 1966–1972. https://doi.org/10.1111/apa.13991
Uchitel, J. (2023). Cot-side imaging of functional connectivity in the developing brain using wearable high-density diffuse optical tomography. NeuroImage, 265, 119784. https://doi.org/10.1016/j.neuroimage.2022.119784