Shape Analysis on Neuroimaging Data: Difference between revisions
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This data was first pre-processed using FreeSurfer - a freely available software offering a set of tools for analysis and visualization of brain imaging data. Among the available tools, FreeSurfer provides segmentation of white matter from the rest of the brain, skull stripping, registration of the cortical surface of an individual with a stereotaxic atlas, labeling of regions of the cortical surface. The output of this pre-processing step is a set of 35 different cortical segments per subject. | This data was first pre-processed using FreeSurfer - a freely available software offering a set of tools for analysis and visualization of brain imaging data. Among the available tools, FreeSurfer provides segmentation of white matter from the rest of the brain, skull stripping, registration of the cortical surface of an individual with a stereotaxic atlas, labeling of regions of the cortical surface. The output of this pre-processing step is a set of 35 different cortical segments per subject. | ||
2.Diffusion MRI + Tractography data | '''2.Diffusion MRI + Tractography data | ||
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== Methods == | == Methods == | ||
Revision as of 23:38, 20 March 2014
Tanya Glozman
Introduction
Understanding the relationships between structure and function in the brain is a key interest in neuroscience. Many studies indicate a correlation between structural/shape abnormalities and functional differences between subjects ranging from behavioral changes through functional differences to neurological disorders - an overview can be found in [3]. However, these studies focus mostly on cortical structures. In this project we explored the efficacy of projections in describing the shape of two types of neuroimaging data: cortical segments acquired from Structural MRI data and white matter fascicles (neuronal tracts) acquired from Diffusion Weighted MRI tractography data. We developed a new descriptor based on projections, and proved it's efficacy on these types of data by performing SVM-based classification of the different structures. We show that our simple descriptor greatly reduces the dimentionality of the problem while preserving fine shape information required to discriminate between different structures.
Data
Two types of neuroimaging data were explored:
1.Structural MRI data
Structural MRI data comes in the form of 2D images of axial cross-sections of the brain. MR RF-sequences are used to generate contrast between different tissues in the brain. Most commonly, T1-weighted images are acquired, showing good contrast between the white matter and the gray matter. This data was first pre-processed using FreeSurfer - a freely available software offering a set of tools for analysis and visualization of brain imaging data. Among the available tools, FreeSurfer provides segmentation of white matter from the rest of the brain, skull stripping, registration of the cortical surface of an individual with a stereotaxic atlas, labeling of regions of the cortical surface. The output of this pre-processing step is a set of 35 different cortical segments per subject.
2.Diffusion MRI + Tractography data
Methods
- Describe your algorithm or approach. - Detail any issues or problems that were particularly important. - Emphasize the parts of the project that you wrote (instead of ISET or downloaded code). - Describe the analysis in enough detail so that someone could understand and repeat your analysis. - What data and software did you use? What were the ideas of the algorithm and data analysis?
Results
- Organize your results in a good logical order (not necessarily historical order). - Include relevant graphs and/or images. Make sure graph axes are labeled. - Make sure you draw the reader's attention to the key element of the figure. - The key aspect should be the most visible element of the figure or graph. Help the reader by writing a clear figure caption.
Conclusions
- Describe what you learned. What worked? What didn't? Why? What would you do if you kept working on the project?
References
_ List references. Include links to papers that are online.
Appendix I
This project is ongoing. The data was acquired through collaboration with Dr. Franco Pestilli (for the connectome data )and Prof. Tony Norcia's group (for the structural MRI data. I am not free to share the data. Since we are hoping to publish the results of this work, we prefer to not share the code currently. Please email tanyagl@stanford.edu if you'd like to learn more.