This is a EPFL semester project doing visualization brain activity information with supervision by Benjamin Ricaud in Signal Processing Laboratory 2(http://lts2www.epfl.ch/) All the codes are open sourced and are available in my github:https://github.com/astro1860/visualization_project
Tools I used for this project:
- Ipython: command shell for interactive computing (http://ipython.org/)
- Networkx: a Python language software package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. (http://networkx.github.io/)
- other libraries for scientific computing: SciPy, NumPy etc.
The raw data used for visualization is obtained from fMRI(functional magnetic resonance imaging), with cooperation with researcher Patric Hagmann in CHUV(Centre hospitalier universitaire vaudois). As the fMRI data can indicate activity of different brain areas, thus can be used to represent the brain and its connections as a graph.
1. Network Analysis
We firstly go through mathematical background of network analysis with notion of two important concept: eigenvector and degrees.
2. Simple graphs
Then, we analysis two simple graphs(random graph and barbell graph) and performed brief visualizations.
3. Brain Visualization
Afterwards, we implemented “real” brain visualizations:
Play with data:
- force-directed layout with fisheye distortion:
– with position information:
- integrated interface showing centrality information
4. Social network visualization:
- Visualize with dynamic data which is able to see the changes of brain activity with time
- Display all visualization in my blog
- Using different visualization techniques such as sigma.js (sigmajs.org)
- Improve the final interface which could enable the opacity effect
. Patric Hagmann, Leila Cammoun(2008), Mapping the Structural Core of Human Cerebral Cortex. DOI: 10.1371/journal.pbio.0060159 view article
. Rahul S. Desikan,a Florent Segonne(2006), An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. DOI:10.1016/j.neuroimage.2006.01.021
. Analysis of twitter social graph visualization: https://github.com/JohnCoogan/twitter-graph-visualization
. Force-directed graph examples on d3.js: http://bl.ocks.org/mbostock/4062045
. Documentation of d3.js API: https://github.com/mbostock/d3/wiki
. Documentation of networkx: http://networkx.github.io/
. Scott Murray, Interactive Data Visualization for the Web by O’Reilly publication, 2013.