Author ORCID Identifier
0000-0001-7466-1520
Document Type
Dissertation
Date of Award
5-31-2025
Degree Name
Doctor of Philosophy in Biomedical Engineering - (Ph.D.)
Department
Biomedical Engineering
First Advisor
Xiaobo Li
Second Advisor
Bharat Biswal
Third Advisor
Mesut Sahin
Fourth Advisor
Ozlem Gunal
Fifth Advisor
Soha Saleh
Abstract
Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent neurodevelopmental disorder, characterized by developmentally inappropriate levels of inattention, hyperactivity, and impulsivity. Children with family history of ADHD are at an elevated risk of having ADHD as well as a higher risk of persistent ADHD into adulthood, reflecting a source of etiological heterogeneity in ADHD. This heterogeneity in terms of both biological and environmental risk factors may explain differences in neural correlates, outcomes, cognitive, behavioral as well as developmental trajectories. It is therefore critical to understand the influence of having, or not having positive family risk factors on the neuroanatomical structures of the brain in children with ADHD. However, the field lacks systems-level investigations of neuroanatomical structures in a biologically more homogeneous subgroups of children, ADHD patients with positive family history.
This dissertation is the first study to investigate quantitatively measurable neuroanatomical markers in the brain for familial ADHD (ADHD-F), non-familial ADHD (ADHD-NF), and typically developing children (TDC) by utilizing structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI), and to further identify the most robust neuroimaging features for constructing predictive models to accurately classify these groups using conventional statistical methods and machine/deep learning techniques. This study proposes to use the baseline data in the Adolescent Brain Cognitive Development (ABCD) study, a large study that took part in a major collaboration between multiple sites across the United States. The three specific aims are 1) to study the gray matter (GM) alterations in children with ADHD, and investigate the anatomical differences between ADHD-F and ADHD-NF subgroups; 2) to determine the white matter (WM) alterations in children with ADHD, and identify the WM biomarkers in differentiating ADHD-F and ADHD-NF; 3) to construct prediction model using the identified brain markers for ADHD-F and ADHD-NF from Aim 1 and Aim 2, and machine/deep learning techniques. To achieve Aim 1, cortical thickness-, surface area- and volume-based GM measures extracted and compared in a total of 606 participants, including 132, 165 and 309 in groups of ADHD-F, ADHD-NF, and typically developed children, respectively. In aim 2, Fractional Anisotropy and volume of region-of-interest (ROI)-based probabilistic tractography among cortico-cortical and subcortico-cortical pathways will be calculated during DTI data analyses for each individual. In aim 3, deep learning-based classification analysis using a binary hypothesis testing framework and autoencoding techniques will be conducted using structural and diffusion imaging measures in Aim 1 and Aim 2 as features, selected using a combinations of multiple feature selections methods. Based on existing findings, the present study hypothesized that neuroanatomical alterations in the frontal and parietal lobes and related circuits and subnetworks in ADHD may have a strong heritable familial origin. The findings presented in this dissertation have considerable value to facilitate the development of novel early preventions and inventions that target differential underlying neurodevelopmental anomalies characteristics of these subgroups of ADHD.
Recommended Citation
Baboli, Rahman, "Childhood neuroanatomical markers of familial and nonfamilial attention-deficit/hyperactivity disorder" (2025). Dissertations. 1830.
https://digitalcommons.njit.edu/dissertations/1830
