Main Page

Welcome to the Brain Mapping Unit wiki

This is a wiki, a user-editable Web site. You can treat it as a perfectly ordinary Web site (read it), but you are also able to make changes to any page that has an edit link at the top of it. The more or you contribute, the more useful this page will become for current and future members of our group, so please feel free to add any relevant information!

How is it different from the BCNI wiki and the CBU?

When first joining the BMU, you may wonder how the BCNI, CBU and BMU are officially related. In fact, after 12 months at the BMU, I still haven't figured this out (to those of you who have... please feel free to edit this!). There are many resources that the BCNI, CBU and BMU have in common and it therefore makes sense for you to explore the BCNI and CBU websites and wikis when you have time (see details below). However, too much information can be problematic when you're starting out at a new place. This aim of this wiki is therefore to concentrate on the key knowledge and resources that you are most likely to need when starting out at the BMU. We aim to present these in a clear, well-organized and easy-to-navigate way. This wiki will also include some more specialized in-house knowledge such as network analysis and visualisation tools developed. For more general fMRI and MEG image analysis methods, the CBU wiki is probably the place to go.

Links to the BCNI and CBU wikis:

BCNI username: bcni password: re:cognition

CBU The CBU Imaging Wikis (one for fMRI and another for MEG) host a wealth of information on the design and analysis of imaging studies including highly popular tutorials.

Background Reading

Introductory papers

The first thing to do if you are starting out at the BMU is probably to read a couple of easy introductory papers on network science in general, as well as applied to neuroscience. Some useful papers to start with can be found at Introductory Papers

Publications by the rest of the group

You can see a list of the group's publications on the relevant page of the BMU group website

A non-exhaustive list of journals you may find intersting

Nature, Science, Nature Neuroscience, PNAS, Journal of Neuroscience, Brain, NeuroImage, Biological Psychiatry, PLoS Computational Biology, Cerebral Cortex, Frontiers in Systems Neuroscience...

Keeping up with new litterature

Most scientific journals allow you to sign up for 'e-alerts'. These are regular emails with either the journal's Table of Content (each time a new issue is published) or alerts about new papers from specific authors or containing specific keywords that you indicate you wish to follow. Just visit the webpage of your journal of interest and follow instructions to set up your e-alerts.

Courses, seminars and groups

Statistics, Programming and Neuroimaging Courses

The Department of Experimental Psychology has a useful list of courses on statistics, programming and neuroimaging here

Cambridge Networks Network (CNN)

CNN aims to bring together researchers from many different departments across Cambridge who share an interest in Complex Networks. You can learn more about it and join the CNN mailing list here

Sharing Data

In principle, we could put links to data located on the server so as to make it available to others in the group. Please make sure the data in question is free for you to share before doing this. Also, please try to document the data (a link to a paper describing the same dataset should do) and maybe add the name of the person that originally sourced the data.

Network Construction

In brain functional networks, each node corresponds to a different brain region, $i$, and edges or connections between nodes represent statistical associations, e.g., correlations, between the time series, $S_{i}(t)$, recorded at each of these regions. Once the $NxN$ association matrix of correlation coefficients has been evaluated (for $N$ brain regions), it is possible to draw a fully connected, weighted network where the weight of each link corresponds to the correlation strength between the pair $i,j$ of nodes it connects. This network, however, is not easy to analyze and contains many spurious connections resulting from noise rather than genuine correlations. It is therefore usually replaced by a sparser, unweighted network where, following the application of some filtering technique, only the most important connections have been retained as edges in a binary adjacency matrix. One difficulty arises from the fact that the method for filtering out less important connections is arbitrary and has a strong influence on the results obtained.

Beyond Correlations

This section discusses the various forms of statistical association that networks can be based on.

Filtering Methods

This section discusses the various filtering methods that can be used.

Network Analysis

All commonly used network measures have been implemented in MATLAB and bundled into a wonderful toolbox called the Brain Connectivity Toolbox by Olaf Sporns and collaborators. [[1]] They also welcome any additions to their toolbox, so feel free to contribute.

For the less computationally fluent: Yong Liu is developing a point-and-click toolbox of his own... it should be ready in late 2011.

Visualization Tools

Network Visualization

A variety of tools have been developed to generate efficient and pleasing graphical representations of networks:

• PAJEK is one of the most commonly used ones: [[2]]

• Gephi is looking increasingly poliched: [[3]]

• GUESS is my personal favourite [[4]]

Other Visualization tools

• Caret allows the creation of beautiful brain surface maps: [[5]]

How To Edit the Wiki

• You can find some help on editing this wiki on HowToEdit