Abstract #0334

A framework for graph theory analyses of functional connectivity within resting state networks (RSNs) of neonates

Ndivhuwo Magondo^1,2, Fleur Warton^1,2, Jia Fan^1,2, Barbara Laughton³, Andre van der Kouwe^4,5, and Ernesta Meintjes^1,2,6

¹Biomedical Engineering Research Centre, Division of Biomedical Engineering, Department of Human Biology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa, ²Neuroscience Institute, University of Cape Town, Cape Town, South Africa, ³Tygerberg Children’s Hospital, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa, ⁴A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ⁵Department of Radiology, Harvard Medical School, Boston, MA, United States, ⁶Cape Universities Body Imaging Centre, University of Cape Town, Cape Town, South Africa

Synopsis

Keywords: Functional Connectivity, Neonatal

Motivation: The topological organisation of RSNs can be studied with graph theory. While graph nodes can be defined using atlases in adults, infant atlases are not readily available.

Goal(s): To create a framework to define nodes and edges for graph theory analyses of infant RSNs.

Approach: We resampled the original template voxel size and created evenly distributed nodes within RSNs.

Results: We present a mask comprising 605 evenly-spaced spheres to discretize neonatal RSNs. Graph theory demonstrated lower global and/or nodal efficiency in 4 networks in HEU neonates compared to HUU, indicating decreased information transmission throughout and regionally within affected networks.

Impact: The proposed method may enable more comprehensive analyses of the topological organisation of RSNs in infant cohorts. This will advance knowledge on how functional networks process and distribute information from birth.

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