Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article

Degree

Doctor of Philosophy

Program

Biomedical Engineering

Supervisor

Duerden, Emma G.

2nd Supervisor

St. Lawrence, Keith

Co-Supervisor

Abstract

Early brain injury in neonates is a common neurological complication associated with high mortality rates and long-term morbidities. Two common types of injury are intraventricular hemorrhage (IVH) and hypoxic ischemic encephalopathy (HIE). Currently used clinical neuroimaging tools for diagnosing or monitoring the injury have limitations, e.g., cranial ultrasound is not for continuous monitoring of the evolution of injury so it could miss the optimal timing for intervention, and magnetic resonance imaging (MRI) is not very accessible for infants in neonatal intensive care unit. Functional near-infrared spectroscopy (fNIRS) can be an alternative imaging technique as it can be used at the bedside for continuous monitoring. Previous research using functional MRI (fMRI) indicated that neonates with brain injury were reported to have altered resting-state functional connectivity (RSFC), which can be a potential biomarker for diagnosis. The current thesis examined and expanded upon the potential of fNIRS as a neuromonitoring tool for newborns with brain injury. Specifically, the goal was to determine if fNIRS can yield RSFC indices comparable to fMRI and whether differences in RSFC can be detected between neonates with and without brain injury. Also, predictive models based on machine learning were developed to address the cortical penetration depth of fNIRS, which is a major drawback limiting wider applications of the technology.

Neonates diagnosed with IVH or HIE, and healthy newborns were recruited and scanned with fNIRS and fMRI. RSFC was calculated from both modalities and compared. Then RSFC patterns were compared between neonates with and without injury. fNIRS and fMRI yielded comparable RSFC indices, and fNIRS also identified altered RSFC patterns in both IVH and HIE groups compared to healthy newborns. Then, graph convolutional networks (a subtype of artificial neural networks) were applied to predict subcortical connectivity from cortical connectivity, using either neonatal or adult fNIRS data, and gave good performance. In general, this thesis indicates that fNIRS has the potential to be a new tool for assessing brain injury and monitoring cerebral hemodynamics in neonates.

Summary for Lay Audience

Early brain injury occurs in both term and preterm born babies and seriously affects their health. Current tools for diagnosing and monitoring brain injury are limited. Namely ultrasound needs to be done at specific times after the injury and can delay treatments. Further, magnetic resonance imaging (MRI) cannot always be accessible for babies who are critically ill as time away from the intensive care unit can pose risks. Functional near-infrared spectroscopy (fNIRS) is a promising new tool that can monitor the impact of injury on blood flow to the brain as well as how brain regions are functionally connected, meaning how different brain regions communicate with one another. This thesis tested whether fNIRS can give us meaningful functional connectivity information and demonstrate differences between babies with and without brain injury. Also, fNIRS has a major limitation in that one cannot record from regions deep within the brain, which can be impacted by injury. To address this gap, computational methods were developed to infer brain connectivity in deep regions of the brain based on the connectivity in the outer regions of the brain (cortex).

Newborns with early brain injury and healthy babies were recruited and scanned with fNIRS. Functional connectivity was calculated and compared between babies with and without injury. fNIRS identified altered brain connectivity patterns in the injury groups compared to healthy babies. Then, a computational method based on machine learning was applied to predict activity in the deep regions of the brain, and tested on newborn and adult fNIRS data, and showed good performance.

In general, this study shows that fNIRS has the potential as a new tool for monitoring brain injury in newborns born critically ill.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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