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Eeg Characterization Of Sensorimotor Networks: Implications In Stroke, Dylan Blake Snyder
Eeg Characterization Of Sensorimotor Networks: Implications In Stroke, Dylan Blake Snyder
Dissertations (1934 -)
The purpose of this dissertation was to use electroencephalography (EEG) to characterize sensorimotor networks and examine the effects of stroke on sensorimotor networks. Sensorimotor networks play an essential role in completion of everyday tasks, and when damaged, as in stroke survivors, the successful completion of seemingly simple motor tasks becomes fantasy. When sensorimotor networks are impaired as a result of stroke, varying degrees of sensorimotor deficits emerge, most often including loss of sensation and difficulty generating upper extremity movements. Although sensory therapies, such as the application of tendon vibration, have been shown to reduce the sensorimotor deficits after stroke, the …
Characterization Of Neuroimage Coupling Between Eeg And Fmri Using Within-Subject Joint Independent Component Analysis, Nicholas Heugel
Characterization Of Neuroimage Coupling Between Eeg And Fmri Using Within-Subject Joint Independent Component Analysis, Nicholas Heugel
Dissertations (1934 -)
The purpose of this dissertation was to apply joint independent component analysis (jICA) to electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) to characterize the neuroimage coupling between the two modalities. EEG and fMRI are complimentary imaging techniques which have been used in conjunction to investigate neural activity. Understanding how these two imaging modalities relate to each other not only enables better multimodal analysis, but also has clinical implications as well. In particular, Alzheimer’s, Parkinson’s, hypertension, and ischemic stroke are all known to impact the cerebral blood flow, and by extension alter the relationship between EEG and fMRI. By characterizing …
Integration Of Eeg-Fmri In An Auditory Oddball Paradigm Using Joint Independent Component Analysis, Jain Mangalathu Arumana
Integration Of Eeg-Fmri In An Auditory Oddball Paradigm Using Joint Independent Component Analysis, Jain Mangalathu Arumana
Dissertations (1934 -)
The integration of event-related potential (ERP) and functional magnetic resonance imaging (fMRI) can contribute to characterizing neural networks with high temporal and spatial resolution. The overall objective of this dissertation is to determine the sensitivity and limitations of joint independent component analysis (jICA) within-subject for integration of ERP and fMRI data collected simultaneously in a parametric auditory oddball paradigm. The main experimental finding in this work is that jICA revealed significantly stronger and more extensive activity in brain regions associated with the auditory P300 ERP than a P300 linear regression analysis, both at the group level and within-subject. The results …