Machine learning enabled multivariate analysis of neuroimages
This project attempted to investigate various methods of multi-variant analysis in order to understand network effects of human brains. A literature review on forefront of research in the field of brain network was conducted, especially about functional connectivity analysis. The review was from cla...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
2016
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| Online Access: | http://hdl.handle.net/10356/66920 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | This project attempted to investigate various methods of multi-variant analysis in order to understand network effects of human brains. A literature review on forefront of research in the field of brain network was conducted, especially about functional connectivity analysis. The review was from classical statistical analysis to more advanced correlation-based analysis. It also covered the most recent breakthrough, brain fingerprinting which is to identify people by their neural activation profile.
This project also aimed to implement and conduct various multi-variant and machine learning methodologies on fMRI scans in order to investigate analysis methods of neuroimages. The neuroimages used in this project were designed to unravel the cause of dyslexia through fMRI scans of people under lexical stimulations. |
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