- 1 Welcome to the Brain Mapping Unit wiki
- 2 Become a Contributor
- 3 General Information
- 3.1 Booking the BCNI seminar room or the Craik Marshall room
- 3.2 Booking a room at HSB through MICAD
- 3.3 Getting a bcni/wbic account and access to the servers
- 3.4 Setting up a VNC connection
- 3.5 Transferring Files to and from Server
- 3.6 Who's who
- 3.7 Where's where
- 3.8 Mailing Lists
- 3.9 Lab meeting
- 3.10 Ordering supplies
- 3.11 If you're the last to leave...
- 4 Background Reading
- 5 Courses, seminars and groups
- 6 Conferences and Workshops
- 7 Sharing Data
- 8 Data Preprocessing and Analysis
- 9 Network Construction
- 10 Network Analysis
- 11 Statistical Methods
- 12 Visualization Tools
- 13 Using the HPHI
- 14 Using The High Performance Computing Service
- 15 How To Edit the Wiki
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!
The relationship between BMU, BCNI and CBU wikis
When you first join the BMU, you may wonder how the BMU, BCNI and CBU are related. Broadly speaking, the BMU (Brain Mapping Unit) is a constituent unit within the Department of Psychiatry, the BCNI (Behavioural and Clinical Neuroscience Institute) links six basic-science and clinical departments within the university and has a focus on translational research, and the CBU (Cognition and Brain Sciences Unit) is a standalone non-university-affiliated centre for cognitive science research.
There are many resources that the BMU, BCNI and CBU 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 arrive in a new group. The 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 often used in the group. For more general fMRI and MEG image analysis methods, the CBU wiki is probably the place to go.
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.
Become a Contributor
If you have just joined the BMU, it is the ideal time to become a wiki contributor. You can 'keep notes' of what you are learning by adding it to the wiki. This will both help you remember and help others learn faster. To learn how to edit the wiki just click on the link at the bottom of the page (it's dead easy and will only take 5 to 10 minutes). In order to be able to edit these pages, you first need to log in (top right corner) via raven.
For your first wiki-edit, try adding your name to this list of contributors: Lisa Ronan, Petra Vertes, Mika Rubinov, Prantik Kundu, Ameera Patel, Kirstie Whitaker, Rachel Moseley, Sarah Morgan...
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 BMU
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 interesting
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 literature
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
Cambridge Neuroscience Talks
Most neuroscience-related talks in Cambridge are advertised on this neat list.
Statistics, Programming and Neuroimaging Courses
The Department of Experimental Psychology has a useful list of courses on statistics, programming and neuroimaging here.
Tuesday Meetings - The Networks Group
Our weekly seminars are held on Tuesday mornings (11:00-12:00). These are principally for Ed Bullmore's students but also for other people within the BMU and associated groups with an interest in using networks to study the brain. For details about the structure of these meetings, see Lab meeting.
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.
A database of training courses run across departments. For BMU members, courses on programming (e.g.: Matlab, R, Python) or a crash course on neuroscience should be of particular interest. However, there are also courses on public engagement, writing and communication skills, and many other useful topics. For a schedule of events, see here.
Conferences and Workshops
The annual meeting of the Organization for Human Brain Mapping (OHBM) , held yearly around June.
The annual meeting of the Society for Neuroscience (SfN) , held yearly in the US around October/November.
The Brain Connectivity Workshop , usually held in (spatial and temporal) proximity to OHBM.
The biennial conference on Resting State and Brain Connectivity .
A one-off symposium on Connectomics across scales, held in New Mexico in March 2017 .
A yearly conference on networks (not necessarily brain networks) 
Sources of funding
The Guarantors of Brain (also behind Brain, the journal) provide a range of fellowships. The main is a conference travel fellowship provided to PhD students and postdoctoral fellows working in the UK. Awards range from a maximum of £400 (UK meetings) through £600 (Europe) to £800 (rest of world). Applications must be submitted at least 8 weeks before the meeting, through the website. For more information on these travel grants, see here. For general information about the charity and all of the grants they provide, see links on their main page.
The University of Cambridge Graduate School of Life Sciences (GSLS) has a few options available - especially the E. G. Earnside fund, which provides a maximum of £600 for conferences in Europe and £1000 for conferences beyond. For more information, see their website.
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.
Here are some existing public datasets (in matrix form) to get started: 
Data Preprocessing and Analysis
(Please note that the now outdated documentation of previous versions of our fMRI pipeline can still be found on the Archived_FMRI_pipeline page.)
Other useful bits of information in the great adventure of graph analyses
Useful Unix Commands
Kirstie has started a list of useful linux commands on her GitHub wiki. Please feel free to suggest additions!
In brain functional networks, each node corresponds to a different brain region, , and edges or connections between nodes represent statistical associations, e.g., correlations, between the time series, , recorded at each of these regions. Once the association matrix of correlation coefficients has been evaluated (for 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 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.
This section discusses the various forms of statistical association that networks can be based on.
This section discusses the various thresholding methods that can be used.
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.  They also welcome any additions to their toolbox, so feel free to contribute.
For people more inclined to just point and click, GAT provides and interface for the Brain Connectivity Toolbox, mostly aimed at use for structural covariance analysis (includes covariates , some plotting and permutation testing . Note that for fine-grained parcellations the current implementation (03-2016) is sub-optimal as it attempts to store all permuted matrices in one big mat file and tries to keep most of it in working memory (resulting in a nodes*nodes*permutations*densities 4D file).
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: 
- Gephi is looking increasingly polished: 
- GUESS is my personal favourite 
- BrainNet Viewer for visualising nodal and edge structures on brain meshes  and a short matlab wrapper for getting your data from matlab into the correct format along with a file containing the nodal information in matlab format 
Other Visualization tools
- Caret allows the creation of beautiful brain surface maps: 
- Kirstie also has code for plotting pretty pictures of brains available on GitHub: . This is an ongoing open project, feel free to contribute!
Using the HPHI
The HPHI is a new facility for processing and archiving data, and is the 'go to' place to set up and perform your data analyses. Information about the HPHI can be found at: , note you will need to login using Raven at the top right hand corner to be able to see these pages.
Once you've logged in, there is a link on the left hand side where you can apply for an account (or contact Guy Williams directly). There are also instructions for how to set up a remote link to the HPHI from your own computer.
MOST IMPORTANTLY: if you have problems with the HPHI, don't suffer in silence! Email 'support at hpc dot cam etc' and somebody should get back to you fairly quickly. This is the support email address for the whole (University-wide) HPC system, so you should say that you're using the HPHI, as well as giving a clear description of your problem, including the commands you are using and attaching any relevant code/output/error messages. The HPHI is new and there are still some issues, so if something's not working as you expected, it might be a problem with the system.
Other HPHI tips: (please feel free to add your own!)
- When you first login to the HPHI using X2Go, on the HPHI help pages it says to set the host to wbic-gate.vss... etc. If instead you put wbic-gate-X.vss...etc, where X is 1, 2, 3, or 4 (pick a number at random!), it will log you onto the same gate node each time. Which is a good idea because otherwise it will pick the gate node at random each time and if you lose internet connection there's no guarantee that when you go back you'll be on the same gate node as before.
- Transferring data- you should use rsync to transfer data, not scp (partly because rsync can keep the dates on your files, but also because it runs more checks on the data to help check it has transferred properly). The command you need will be something like: rsync -avtry /path/to/directory/you/want/to/transfer yourCRSID@wbic-gate.vss.cloud.private.cam.ac.uk:/home/yourCRSID/scratch/destination/for/data. You can easily learn more about the flags via Google.
- There are two ways to use the HPHI: interactively (e.g. if you want to run Matlab interactively) or by submitting jobs (e.g. if you want to run subjects through FreeSurfer). In the first case you'll need to request a graphical interface node- see that part of the HPHI website. In the second case you'll need to use the scheduling system- again, see that part of the website.
- Good news- lots of neuroimaging software is already installed on the HPHI! Different software packages can be loaded as modules- see the 'Software and Modules' section of the HPHI website (to load a module you'll need to run something like e.g. 'module load matlab'. To see a list of available modules type 'module avail'). So there's no need to download your own versions of software. If you do find that the package or version you want isn't there, contact the support email above and they're normally happy to add it for you.
- MATLAB- be warned that at the time of writing some people have had problems calling Matlab from the command line as part of a script. If you have problems, try changing your code so that it calls /applications/matlab/matlab2016a/bin-cs/matlab instead of calling matlab directly. Note this is not a problem if you want to open matlab interactively.
Using The High Performance Computing Service
The HPC is the University wide High Performance Computing Service. If you're looking for some helpful hints on how to use the HPC  please see High Performance Computing Service. However, most of the lab's computing work going forward will be done on the HPHI (see above), so that's probably a better place to start.
Alternatively, Ian Luff has also set up a Sun Grid Engine that everybody can use to submit jobs for parallel processing on the BCNI servers (please note the BCNI will be phased out at some point!).
How To Edit the Wiki
- You can find some help on editing this wiki on HowToEdit