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dc.contributor.advisorDeshpande, Gopikrishna
dc.contributor.authorSreenivasan, Karthik Ramakrishnan
dc.date.accessioned2014-04-25T16:40:11Z
dc.date.available2014-04-25T16:40:11Z
dc.date.issued2014-04-25
dc.identifier.urihttp://hdl.handle.net/10415/4063
dc.description.abstractSince the inception of functional magnetic resonance imaging (fMRI) there has been a steady growth in the number of studies utilizing this non-invasive method to gain insights into the brain function in both healthy and clinical populations. In this thesis we address two key issues associated with fMRI data analysis arising due to the fact that the fMRI signal is considered to be the convolution of the hemodynamic response function (HRF) and a latent neuronal response and hence is not a direct measure of the neuronal activity. First, in order to deconvolve voxel-specific HRF and recover the latent neuronal response, highly parameterized blind deconvolution models have been recently proposed. We investigate whether these models are susceptible to over-fitting by proposing a non-parametric method to perform blind hemodynamic deconvolution. We also compare the performance of our method with an already existing parametric method using both simulations and experimental data. We tested the hypothesis that if the performance of these two methods were similar, then we can conclude that the parametric models are probably not susceptible to over-fitting. The results of the both simulations and experimental data supported our hypothesis, and we found that the neuronal responses were estimated effectively by both these methods and their results were very similar. The second issue is related to the use of raw fMRI data in effective connectivity (EC) analysis using Granger Causality. Non-neuronal spatial variability of the HRF has a confounding effect on the inferences that one could obtain from EC analysis using raw fMRI data. Therefore we describe a novel EC model which can be used to perform directional connectivity analysis using latent neuronal variables (as opposed to raw fMRI data) obtained by blind hemodynamic deconvolution methods and also demonstrate the utility of the proposed EC model in a diverse set of fMRI studies.en_US
dc.rightsEMBARGO_NOT_AUBURNen_US
dc.subjectElectrical Engineeringen_US
dc.titleConnectivity analysis of functional MRI data in the latent neuronal space: Applications in science and medicineen_US
dc.typethesisen_US
dc.embargo.lengthMONTHS_WITHHELD:12en_US
dc.embargo.statusEMBARGOEDen_US
dc.embargo.enddate2015-04-25en_US


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