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High-Dimensional Directed Network Analysis of Human Brains

Wang, Yaotian (2023) High-Dimensional Directed Network Analysis of Human Brains. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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The brain is the most complex organ in the human body. Studying the functional organization of such a complex organ is fascinating. As statisticians, we study the human brain’s functional organization by developing statistical modeling methods for brain data. Recent technological development brings opportunities and challenges: On one hand, enormous quantities of brain data in various modalities are produced in many fields, including biology, neurology, neuroscience, psychology, and psychiatry. On the other hand, human brain data bring new challenges to data analysts because their unique properties differ from conventional big data. This dissertation proposes novel statistical modeling methods to address these challenges and understand the brain’s functional organization.

The human brain is a high-dimensional directed network system consisting of many regions as network nodes that exert influence on each other. The directed influence from one region to another is called directed connectivity and corresponds to one directed edge in the directed brain network. We understand the brain’s functional organization by investigating how brain regions interact and form different network patterns when performing different brain functions. This dissertation illustrates two of our methods for revealing high-dimensional directed brain networks.

Chapter 2 explains a new Bayesian model for studying directed brain networks of patients with epilepsy using their intracranial electroencephalography (EEG) data. Epilepsy is a directed network disorder, as epileptic activity spreads from a seizure onset zone (SOZ) to many other regions after seizure onset. Intracranial EEG data are multivariate time series recordings of many brain regions. Using our model, we revealed the evolution of brain networks during seizure development and uncovered unique directed connectivity properties of the SOZ.

Chapter 3 presents a new Bayesian model for characterizing whole-brain directed networks of the healthy human population based on functional magnetic resonance imaging (fMRI) data. We also propose a computationally efficient algorithm to address the challenge of analyzing thousands of subjects’ fMRI data. Using our new model and algorithm, we analyzed the resting-state fMRI data of around one thousand subjects from the Human Connectome Project (HCP) and revealed both population-mean and subject-specific whole-brain directed networks of them.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Wang, Yaotianyaw87@pitt.eduyaw870000-0001-7242-4193
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairZhang,
Committee MemberYu,
Committee MemberIyengar,
Committee MemberYan,
Date: 6 September 2023
Date Type: Publication
Defense Date: 27 March 2023
Approval Date: 6 September 2023
Submission Date: 5 April 2023
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 99
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Statistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Brain network, directed connectivity, epileptic network, multi-subject fMRI, variational Bayes
Date Deposited: 07 Sep 2023 01:29
Last Modified: 07 Sep 2023 01:29


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