IDENTIFICATION OF NEUROMARKERS USING STRUCTURAL AND FUNCTIONAL NEUROIMAGING

<p>"One of the grand challenges in science is understanding the human brain. Recent advances in magnetic resonance imaging have opened unmatched opportunities to demystify neural circuitry. In this research, the spatially and temporally complex neuroimaging data were used to identify neuromarkers that aids in quantifying a brain’s health. In the first part, a comparison of static and dynamic functional connectivities was made to study their efficacies in identifying intrinsic individual connectivity patterns. Results show that the intrinsic individual connectivity pattern can be used as a ‘fingerprint’ to distinguish among and identify subjects and their biological sex and is more accurately captured with partial correlation and assuming static connectivity. The neuromarkers involved in identifying subjects and their sex were distinguished using edge consistency, variability, and differential power measures. The second part maps neuronal and functional complexities estimated using multi-scale entropy at various levels of the brain’s organization. The work introduces functional complexity, and neuromarkers were identified to predict fluid intelligence. The third part explores brain abnormalities associated with early life stress and how resilience helps in mitigating its effects. Results reveal that the neural correlates of reward processing may serve as neuroimaging phenotypes. The fourth part utilizes multimodal ensemble deep learning to predict disruptive behavior disorders in children. Results show the potential of the deep learning model to predict disruptive behavior disorders and to identify neuroimaging phenotypes using gradient class activation maps. Future research can build on this study to investigate brain neuromarkers in health, diseases and disorders"--Abstract, p. iv</p>

Name thesis:degree_name

Ph. D. in Mechanical Engineering

Grantor

Missouri University of Science and Technology

Author dc:creator

• Menon, Sreevalsan Sanathanan

• Engineering
• Mechanical Engineering

Repository record dc:identifier

https://scholarsmine.mst.edu/doctoral_dissertations/3184

OAI identifier oai:identifier

oai:scholarsmine.mst.edu:doctoral_dissertations-4189