Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/66786
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Long, Hongquan | - |
dc.date.accessioned | 2016-04-26T03:54:28Z | - |
dc.date.available | 2016-04-26T03:54:28Z | - |
dc.date.issued | 2016 | - |
dc.identifier.uri | http://hdl.handle.net/10356/66786 | - |
dc.description.abstract | Developments in modern functional neuroimaging techniques, especially the advances in functional magnetic resonance imaging (fMRI) have enabled us to study and investigate functioning human brains. Recent studies have identified many functional networks of the brain from resting-state fMRI scans. However, whether functional connectivity of male and female brain are different has not been explored so far. By studying the functional connectivity, we are able to identify some specific regions with known functional brain systems. Those regions can possibly be further classified as functional organizations or functional modules. The regions in each functional module work cohesively in order to perform particular brain functions. The functional organizations of the human brain may exhibit diversity for different genders. By performing data analytics on the human brain images gathered from functional MRI Institution of Health (NIH), USA, we investigated features of functional module of male and female brain. We found that the network properties of default mode network (DMN) do not differ in male and female. And for other modules such as visual, fronto-parietal task control, sensory/somatomotor hand and cingulo-opercular task control modules, there exist statistically significant differences in male and female. | en_US |
dc.format.extent | 136 p. | en_US |
dc.language.iso | en | en_US |
dc.rights | Nanyang Technological University | - |
dc.subject | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision | en_US |
dc.title | Functional modules of the human brain from resting-state fMRI | en_US |
dc.type | Final Year Project (FYP) | en_US |
dc.contributor.supervisor | Rajapakse Jagath Chandana | en_US |
dc.contributor.school | School of Computer Engineering | en_US |
dc.description.degree | Bachelor of Engineering (Computer Science) | en_US |
dc.contributor.research | Centre for Computational Intelligence | en_US |
item.grantfulltext | restricted | - |
item.fulltext | With Fulltext | - |
Appears in Collections: | SCSE Student Reports (FYP/IA/PA/PI) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FYP_Report_Hongquan_Long.pdf Restricted Access | 24.32 MB | Adobe PDF | View/Open |
Google ScholarTM
Check
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.