Co-Activation Patterns in Neonates using High-Density Diffuse Optical Tomography: Insights into Early Dynamic Functional Connectivity

Lee, K. E.; Austin, T.; Uchitel, J.; Caballero Gaudes, C.; Collins-Jones, L. H.; Cooper, R.; Edwards, A.; Hebden, J.; Kromm, G.; Pammenter, K.; Blanco, B.

doi:10.1101/2025.03.15.643385

bioRxiv · 10.1101/2025.03.15.643385

Co-Activation Patterns in Neonates using High-Density Diffuse Optical Tomography: Insights into Early Dynamic Functional Connectivity

Lee, K. E.·Austin, T.·Uchitel, J.·Caballero Gaudes, C.·Collins-Jones, L. H.·Cooper, R.·Edwards, A.·Hebden, J.·Kromm, G.·Pammenter, K.·Blanco, B.

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Abstract

SignificanceDynamic functional connectivity (FC) in neonates is a growing area of interest due to the developmental significance of early functional networks. There are several emerging techniques to measure dynamic FC, adding new perspectives to well-studied static FC networks. Recent dynamic FC studies suggest that adult resting-state networks are driven by key moments of dynamic activity rather than sustained correlations. Co-activation pattern (CAP) analysis leverages this theory, clustering high-activity frames to identify recurring configurations of significant activity. High-density diffuse optical tomography (HD-DOT) is an infant-friendly modality that measures hemodynamic changes in the cortex and has been used to investigate static FC in term-aged infants. CAP analysis has not yet been applied to neonatal HD-DOT and may reveal temporal features of early functional networks that are not apparent from static methods. AimThis study applies CAP analysis to neonatal HD-DOT to characterize transient co-activation states and provide new insight into early functional brain networks beyond what is known from static FC methods alone. ApproachTask-free HD-DOT data were acquired from a cohort of sleeping term newborns at the Rosie Hospital, Cambridge UK (n = 44, postmenstrual age = 40+3 (range: 38+2-42+6) weeks). In each recording, the top 15% of seed-selected frames were clustered using the K-means algorithm for three regions of interest (ROIs: frontal, central, and parietal) to identify significant seed-associated patterns of co-activation or co-deactivation. These co-activation patterns (CAPs) were characterized for each infant using four metrics: consistency, fractional occupancy, dwell time, and transition likelihood. ResultsDistinct CAPs, reflecting the dynamic organization of neonatal cortical networks, were identified for frontal, central, and parietal regions. These CAPs showed high consistency scores, reflecting high intra-cluster spatial correlation and validating the efficacy of CAP analysis for newborn HD-DOT data. The CAP decomposition revealed significant patterns not observed in conventional static functional connectivity analyses. Several neonatal CAPs exhibited frontoparietal co-activation, potentially reflecting early default mode network activity, which is immature and modular for the first year after birth. ConclusionsThis work demonstrates the utility of CAP analysis with newborn HD-DOT and provides new insight into the dynamics of neonatal functional connectivity.

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Lee, K. E., Austin, T., Uchitel, J., Caballero Gaudes, C., Collins-Jones, L. H., Cooper, R., Edwards, A., Hebden, J., Kromm, G., Pammenter, K., Blanco, B.. 2025-03-15. Co-Activation Patterns in Neonates using High-Density Diffuse Optical Tomography: Insights into Early Dynamic Functional Connectivity. https://doi.org/10.1101/2025.03.15.643385

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