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Multimodal Brain Connectomics and Its Applications

Guo, Lei (2023) Multimodal Brain Connectomics and Its Applications. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Over the last decade, connectomics has emerged as a new omics approach that can potentially revolutionize our understanding of the brain [1-11]. Currently, standard brain
connectome analyses commonly involve the comparison of summary graph-theoretical metrics such as clustering coefficient, path length, etc. While easy to compute and widely popular among connectome researchers, these simple scalar statistics, defined at either the nodal or the global
level, are problematic in that they do not take full advantage of connectomics encoding complex network inter-relationship for every intermediate scale between nodes (the most local level) and the entire graph. Although several new studies have been conducted on investigating higher-level
brain intrinsic geometry (e.g., modular community structure) for different types of brain network data, these existing techniques are incapable of taking into account other covariates (e.g., age, sex, etc.) as well as multi-scale and multi-view network data. The goal of this dissertation is to design efficient learning algorithms to address the challenges from multimodal brain connectome data integration and then demonstrate its usefulness via corresponding clinical applications. In this dissertation, we first describe some preliminary works on how to address the covariates’ effects in integrating diffusion MRI-derived multi-view structure connectome. Then, we formulate unified models to integrate multimodal brain connectomes from different MRI data (e.g., diffusion-weighted MRI and resting-state functional MRI. We applied new models to study brain cognitive decline and menstruation cycle, which can be beneficial for the disease diagnosis and woman's health. The contributions of the dissertation are expected to significantly contribute to brain research and public health.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Guo, Leileg80@pitt.eduleg80
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairHuang,
Committee MemberMao,
Committee MemberGao,
Committee MemberXiong,
Committee MemberChen,
Date: 14 September 2023
Date Type: Publication
Defense Date: 27 June 2023
Approval Date: 14 September 2023
Submission Date: 3 July 2023
Access Restriction: 2 year -- Restrict access to University of Pittsburgh for a period of 2 years.
Number of Pages: 105
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Electrical and Computer Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Brain Connectomics; Multimodal data mining.
Date Deposited: 14 Sep 2023 13:40
Last Modified: 14 Sep 2023 13:40


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