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Please use this identifier to cite or link to this item: http://hdl.handle.net/10277/817

Authors: Vergani, Alberto Arturo
Title: Contributions in computational intelligence with results in functional neuroimaging
Abstract: This thesis applies computational intelligence methodologies to study functional brain images. It is a state-of-the-art application relative to unsupervised learning domain to functional neuroimaging. There are also contributions related to computational intelligence on topics relative to clustering validation and spatio-temporal clustering analysis. Speci_cally, there are the presentation of a new separation measure based on fuzzy sets theory to establish the validity of the fuzzy clustering outcomes and the presentation of a framework to approach the parcellation of functional neuroimages taking in account both spatial and temporal patterns. These contributions have been applied to neuroimages obtained with functional Magnetic Resonance Imaging, using both active and passive paradigm and using both in-house data and fMRI repository. The results obtained shown, globally, an improvement on the quality of the neuroimaging analysis using the methodological contributions proposed.
Keywords: Unsupervised learning, fMRI
Issue Date: 2018
Language: eng
Doctoral course: Informatica e matematica del calcolo
Academic cycle: 31
Publisher: Università degli Studi dell'Insubria
Citation: Vergani, A.A.Contributions in computational intelligence with results in functional neuroimaging (Doctoral Thesis, Università degli Studi dell'Insubria, 2018).

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