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Functional Connectivity Analysis of FMRI Time-Series Data

Zhou, Dongli (2011) Functional Connectivity Analysis of FMRI Time-Series Data. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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The term ``functional connectivity' is used to denote correlations in activation among spatially-distinct brain regions, either in a resting state or when processing external stimuli. Functional connectivity has been extensively evaluated with several functional neuroimaging methods, particularly PET and fMRI. Yet these relationships have been quantified using very different measures and the extent to which they index the same constructs is unclear. We have implemented a variety of these functional connectivity measures in a new freely-available MATLAB toolbox. These measures are categorized into two groups: whole time-series and trial-based approaches. We evaluate these measures via simulations with different patterns of functional connectivity and provide recommendations for their use. We also apply these measures to a previously published fMRI dataset in which activity in dorsal anterior cingulate cortex (dACC) and dorsolateral prefrontal cortex (DLPFC) was evaluated in 32 healthy subjects during a digit sorting task. Though all implemented measures demonstrate functional connectivity between dACC and DLPFC activity during event-related tasks, different participants appeared to display qualitatively different relationships.We also propose a new methodology for exploring functional connectivity in slow event-related designs, where stimuli are presented at a sufficient separation to examine the dynamic responses in brain regions. Our methodology simultaneously determines the level of smoothing to obtain the underlying noise-free BOLD response and the functional connectivity among several regions. Smoothing is accomplished through an empirical basis via functional principal components analysis. The coefficients of the basis are assumed to be correlated across regions, and the nature and strength of functional connectivity is derived from this correlation matrix. The model is implemented within a Bayesian framework by specifying priors on the parameters and using a Markov Chain Monte Carlo (MCMC) Gibbs sampling algorithm. We demonstrate this new approach on a sample of clinically depressed subjects and healthy controls in examining relationships among three brain regions implicated in depression and emotion during emotional information processing. The results show that depressed subjects display decreased coupling between left amygdala and DLPFC compared to healthy subjects and this may potentially be due to inefficient functioning in mediating connectivity from the rostral portion Brodmann's area24 (BA24).


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Zhou, Donglidoz5@pitt.eduDOZ5
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairCheng, Yuyucheng@pitt.eduYUCHENG
Committee CoChairThompson,
Committee MemberSiegle, Greggsiegle@pitt.eduGSIEGLE
Committee MemberGleser, Leongleser@pitt.eduGLESER
Committee MemberKrafty, Robertkrafty@pitt.eduKRAFTY
Date: 31 January 2011
Date Type: Completion
Defense Date: 28 October 2010
Approval Date: 31 January 2011
Submission Date: 14 November 2010
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
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: B-spline; Functional data analysis; Mixed-effects model; Principal component; Reduced rank model
Other ID:, etd-11142010-093844
Date Deposited: 10 Nov 2011 20:04
Last Modified: 15 Nov 2016 13:51


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