Item Details

title

METHODS FOR INVESTIGATING STATIC AND DYNAMIC BRAIN NETWORKS: ADVANCED APPROACHES AND ANALYTICAL STRATEGIES

author

Khodaei, Mohammadreza

abstract

The study of functional brain networks began with the static networks, in which one brain network demonstrates the connectivity between brain regions over the full scan time. However, attention has shifted to dynamic functional connectivity in recent years, stating that connectivity between brain regions is not static and changes over time. Although dynamic brain connectivity analysis is in its infancy, it has shown its potential for helping us understand brain function and its alteration during neurological disorders. One of the new topics in the study of dynamic connectivity is the study of brain state dynamics, referring to patterns of connectivity that repeat over time. The hidden semi-Markov model (HSMM) has been one of the promising methods for studying these states. However, despite its advantages, it has several limitations that have limited its use by the neuroscience community. First, it requires specifying the number of hidden states in advance. Second, its performance needs to be evaluated using more realistic simulated data sets. Finally, due to its complex setup, its implementation is challenging for a broad range of scientists in the neuroscience community, many of whom may not have experience with computational coding. This work focused on addressing these limitations to make its use widespread across the neuroscience community.

subject

Dynamic Connectivity

fMRI

Functional Connectivity

HSMM

contributor

Simpson, Sean L. (advisor)

Shappell, Heather M. (committee member)

LaConte, Stephen M. (committee member)

Wu, Guorong (committee member)

date

2025-06-24T08:36:42Z (accessioned)

2025-06-24T08:36:42Z (available)

2025 (issued)

degree

Biomedical Engineering (discipline)

identifier

http://hdl.handle.net/10339/111060 (uri)

language

en (iso)

publisher

Wake Forest University

type

Dissertation