Predicting cortical-thalamic functional connectivity using functional near-infrared spectroscopy and graph convolutional networks.

Functional near-infrared spectroscopy (fNIRS) measures cortical hemodynamic changes, yet it cannot collect this information from subcortical structures, such as the thalamus, which is involved in several key functional networks. To address this drawback, we propose a machine-learning-based approach to predict cortical-thalamic functional connectivity using cortical fNIRS data. We applied graph convolutional networks (GCN) to two datasets obtained from healthy adults and neonates with early brain injuries, respectively.

Altered functional connectivity in preterm neonates with intraventricular hemorrhage assessed using functional near-infrared spectroscopy.

Intraventricular hemorrhage (IVH) is a common neurological injury following very preterm birth. Resting-state functional connectivity (RSFC) using functional magnetic resonance imaging (fMRI) is associated with injury severity; yet, fMRI is impractical for use in intensive care settings. Functional near-infrared spectroscopy (fNIRS) measures RSFC through cerebral hemodynamics and has greater bedside accessibility than fMRI.

Altered resting-state functional connectivity in newborns with hypoxic ischemic encephalopathy assessed using high-density functional near-infrared spectroscopy.

Hypoxic-ischemic encephalopathy (HIE) results from a lack of oxygen to the brain during the perinatal period. HIE can lead to mortality and various acute and long-term morbidities. Improved bedside monitoring methods are needed to identify biomarkers of brain health.