Graduation Semester and Year
2023
Language
English
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Biomedical Engineering
Department
Bioengineering
First Advisor
Hanli Liu
Abstract
Electroencephalogram (EEG) can detect and monitor neuro-electrophysiological signals in the human brain, including assessing brain function in newborns at risk of neurological injury and healthy adults undergoing intervention with prefrontal transcranial photobiomodulation (tPBM). Moreover, EEG-based brain functional connectivity can be assessed in either resting-state or task-based measurements using graph-theoretical network modeling. However, the management of newborns with mild hypoxic ischemic encephalopathy (HIE) is controversial, and no study has investigated the EEG-based brain network and information flow resulting from HIE. Also, the underlying electrophysiological mechanism of tPBM is still unclear, and further research is needed to determine optimal parameters for tPBM applications. My dissertation targets these gaps to (1) evaluate the potential of predicting neurodevelopmental outcomes of newborns with HIE using the brain state of newborn (BSN) measured within the first day of life; (2) investigate the brain network in newborns with HIE; and (3) compare electrophysiological modulations of the human brain in response to left and right prefrontal tPBM using 800-nm laser. In Chapter 2, I aimed to predict neurodevelopmental outcomes at two years of age using BSN that was derived from EEG data collected on the first day of life. The results showed that BSN can distinguish normal and HIE cases and has strong correlation with a clinical assessment score (i.e., the concomitant Total Sanart Score). BSN were also differentiated between neonates with normal and abnormal neurodevelopmental outcomes at the age of two years. Additionally, higher BSN values indicate a reduction in the odds of HIE occurrence and of abnormal neurodevelopmental outcomes in global, cognitive, language, and motor skills. The findings confirm that BSN is a sensitive real-time biomarker for monitoring the dynamic progression of neonatal encephalopathy. In Chapter 3, I targeted the assessment of brain network in newborns with HIE. Based on the first 30 minutes of available clean eight-channel EEG data, I quantified the global brain connectivity parameters in newborns with HIE, followed by comparisons with those from healthy newborns and adults. Furthermore, nodal graphical brain connectivity and region-wise networks were also investigated. The major findings indicate that the neural networks of neonates affected by HIE exhibited a notable reduction of overall efficiency compared to both healthy neonates and adults. However, significant distinctions in these fundamental metrics were not observed between the mild and moderate HIE cohorts, implying the necessity for prompt and efficacious medical intervention even for newborns with mild HIE to mitigate potential adverse outcomes. In Chapter 4, I explored electrophysiological modulations of the brain in response to left/right prefrontal 800-nm tPBM. Recent literature supports tPBM's capacity to enhance cerebral blood flow and oxygenation and thus to improve cognitive performance. A total of 26 subjects underwent 7-min resting-state 19-channel EEG recordings before and after tPBM/sham stimulation on the left/right forehead, in a single-blind crossover design with randomized sham and tPBM sequences. Global and regional GTA-derived brain networks were assessed and compared between the tPBM and sham conditions. My results indicated site-specific effects of tPBM, with distinct EEG network changes induced by left and right prefrontal tPBM.
Keywords
Hypoxic-ischemic encephalopathy, Transcranial photobiomodulation, Neurodevelopmental outcome, Brain network, Electroencephalography, Biomarker, Therapeutic hypothermia, HIE, tPBM
Disciplines
Biomedical Engineering and Bioengineering | Engineering
License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Recommended Citation
Kang, Shu, "Quantification and identification of neuro-electrophysiological markers and brain network for biomedical applications" (2023). Bioengineering Dissertations. 158.
https://mavmatrix.uta.edu/bioengineering_dissertations/158
Comments
Degree granted by The University of Texas at Arlington