The New Jersey Institute of Technology's
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Title:	Neural correlates of post-traumatic brain injury (TBI) attention deficits in children
Author:	Cao, Meng
View Online:	njit-etd2023-018 (xv, 118 pages ~ 6.1 MB pdf)
Department:	Department of Biomedical Engineering
Degree:	Doctor of Philosophy
Program:	Biomedical Engineering
Document Type:	Dissertation
Advisory Committee:	Li, Xiaobo (Committee co-chair) Alvarez, Tara L. (Committee co-chair) Biswal, Bharat (Committee member) Gunal, Ozlem (Committee member) Kannurpatti, Sridhar (Committee member)
Date:	2023-05
Keywords:	Attention deficits Computational neuroscience Graph theoretical analysis Machine learning Magnetic resonance imaging Traumatic brain injury
Availability:	Unrestricted
Abstract:	Traumatic brain injury (TBI) in children is a major public health concern worldwide. Attention deficits are among the most common neurocognitive and behavioral consequences in children post-TBI which have significant negative impacts on their educational and social outcomes and compromise the quality of their lives. However, there is a paucity of evidence to guide the optimal treatment strategies of attention deficit related symptoms in children post-TBI due to the lack of understanding regarding its neurobiological substrate. Thus, it is critical to understand the neural mechanisms associated with TBI-induced attention deficits in children so that more refined and tailored strategies can be developed for diagnoses and long-term treatments and interventions. This dissertation is the first study to investigate neurobiological substrates associated with post-TBI attention deficits in children using both anatomical and functional neuroimaging data. The goals of this project are to discover the quantitatively measurable markers utilizing diffusion tensor imaging (DTI), structural magnetic resonance imaging (MRI), and functional MRI (fMRI) techniques, and to further identify the most robust neuroimaging features in predicting severe post-TBI attention deficits in children, by utilizing machine learning and deep learning techniques. A total of 53 children with TBI and 55 controls from age 9 to 17 are recruited. The results show that the systems-level topological properties in left frontal regions, parietal regions, and medial occipitotemporal regions in structural and functional brain network are significantly associated with inattentive and/or hyperactive/impulsive symptoms in children post-TBI. Semi-supervised deep learning modeling further confirms the significant contributions of these brain features in the prediction of elevated attention deficits in children post-TBI. The findings of this project provide valuable foundations for future research on developing neural markers for TBI-induced attention deficits in children, which may significantly assist the development of more effective and individualized diagnostic and treatment strategies.