Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/2546
Title: Brain connectivity analysis with ICA
Authors: Yang, Kanyan
Keywords: DRNTU::Engineering::Computer science and engineering::Computing methodologies::Pattern recognition
DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
DRNTU::Engineering::Computer science and engineering::Computer applications::Life and medical sciences
Issue Date: 2005
Source: Yang, K. (2005). Brain connectivity analysis with ICA. Master’s thesis, Nanyang Technological University, Singapore.
Abstract: Functional Magnetic Resonance Imaging (fMRI) is increasingly utilized to explore brain networks and neuronal interactions underlying brain functions. Although the concept of functional connectivity has been introduced to analyze brain connections for many years, this is a measure relying on the pattern of temporal correlations that exist between distinct neuronal units. In this research, we are going to extend the definition of brain connectivity into a higher-order statistical sense. Apart from this, two more contributions are made including a novel restoration model and a fully exploratory approach to investigating effective connectivity.
URI: https://hdl.handle.net/10356/2546
DOI: 10.32657/10356/2546
Rights: Nanyang Technological University
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Theses

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