Data Processing of Functional Magnetic Resonance Images of the Human Brain

Abstract

Study of the brain’s neural network architecture and function is complex yet fascinating. Among the state-of-the-art approaches to detecting and obtaining functional data from the brain, magnetic resonance imaging (MRI) provides unique techniques to investigate neural activities \textit{in vivo}. This thesis aims to implement a multi-layer processing pipeline on functional MRI datasets from healthy individual subjects. The report briefly introduces well-established functional MRI techniques and basic principles of Blood-Oxygen-Level-Dependent signals. Ultimately, the reader will get familiar with physical MR imaging and a much deeper understanding of MRI data processing and analysis. Furthermore, this work provides step-wise guidelines on fMRI data processing, preparations, third-party packages applications, and data retrieval from an open-access repository, the Human Connectome Project. Ultimately, the reader will get familiar with physical MR imaging and have a much deeper understanding of MRI data processing and analysis. Also, this thesis may be a good start for students aiming to work with MRI analysis projects, primarily related to the Human Connectome Project.

Study of the brain’s neural network architecture and function is complex yet fascinating. Among the state-of-the-art approaches to detecting and obtaining functional data from the brain, magnetic resonance imaging (MRI) provides unique techniques to investigate neural activities \textit{in vivo}. This thesis aims to implement a multi-layer processing pipeline on functional MRI datasets from healthy individual subjects. The report briefly introduces well-established functional MRI techniques and basic principles of Blood-Oxygen-Level-Dependent signals. Ultimately, the reader will get familiar with physical MR imaging and a much deeper understanding of MRI data processing and analysis. Furthermore, this work provides step-wise guidelines on fMRI data processing, preparations, third-party packages applications, and data retrieval from an open-access repository, the Human Connectome Project. Ultimately, the reader will get familiar with physical MR imaging and have a much deeper understanding of MRI data processing and analysis. Also, this thesis may be a good start for students aiming to work with MRI analysis projects, primarily related to the Human Connectome Project.