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dc.contributor.advisorHu, Xiaoping
dc.contributor.advisorKeilholz, Shella
dc.contributor.authorChen, Shiyang
dc.date.accessioned2019-01-16T17:20:09Z
dc.date.available2019-01-16T17:20:09Z
dc.date.created2017-12
dc.date.issued2017-10-13
dc.date.submittedDecember 2017
dc.identifier.urihttp://hdl.handle.net/1853/60677
dc.description.abstractResting state fMRI (rfMRI) has been widely used to study functional connectivity of human brains. Although most of the analysis methods to date have assumed the resting state to be stationary, this assumption is invalid when significant changes in functional connectivity occur within a short period of time, as indicated by recent literature. Hence, resting state analysis can be improved by accounting for temporal changes. Recent literature has reached a consensus that the brain is frequently switching among a number of quasi-stable states. To capture these temporal dynamics and derive both spatial and temporal characteristics of the states simultaneously, we propose two machine learning models that incorporate spatial and temporal information. First, Gaussian hidden Markov model (GHMM) has been applied to systematically model the brain state switching processing in rfMRI. We have shown its stability and reproducibility on healthy controls and then demonstrate its application in deriving biomarkers of prenatal alcohol exposure. Second, we have introduced recurrent neural network to investigating individual uniqueness using fMRI and visualized the features that can distinguish individuals, which is considered as a fingerprint of the brain’s function.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectFunctional magnetic resonance imaging
dc.subjectResting-state
dc.subjectDynamic model
dc.subjectIndividual identification
dc.subjectGated recurrent unit
dc.subjectRecurrent neural network
dc.titleSpatiotemporal modeling of brain dynamics using machine learning approaches
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentBiomedical Engineering (Joint GT/Emory Department)
thesis.degree.levelDoctoral
dc.contributor.committeeMemberLee, Chin-Hui
dc.contributor.committeeMemberSathian, Krish
dc.contributor.committeeMemberTing, Lena
dc.contributor.committeeMemberQiu, Deqiang
dc.date.updated2019-01-16T17:20:09Z


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