Doctoral Dissertations

Abstract

"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

Advisor(s)

Krishnamurthy, K.

Committee Member(s)

Belfi, Amy M.
Midha, A. (Ashok)
Sarangapani, Jagannathan
Song, Yun Seong

Department(s)

Mechanical and Aerospace Engineering

Degree Name

Ph. D. in Mechanical Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2021

Pagination

xiv, 138 pages

Note about bibliography

Includes_bibliographical_references_(page 137)

Rights

© 2021 Sreevalsan Sanathanan Menon, All Rights Reserved

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 12179

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