Following my list of resources for fresh neuroscientists, I figured I’d share something for those interested in exploring neuroscience but not quite ready to pick up a textbook, namely neurocomic.
Neurocomic is a Wellcome Trust supported project that aims to explain neuroscience ideas to a lay audience using comics. The brainchild of neuroscientists Hana Ros (UCL) and Matteo Farinella (who is also the artist), the story follows a man as he is trapped inside a brain and journeys to escape. The quest takes him through neuron forests, distinct brain regions and visual metaphors of common concepts in neuroscience and psychology, encountering various beasts and scientists along the way. Judging by its reviews, most people find it accessible and accurate, if a bit short (and short on women). Personally, I am particularly enthused by the medium.
The process and ideas behind the project are explained in the video below, and might be of interest to potential readers and to researchers considering tools for effective science communication.
Using illustrations to communicate science (or any information, really) can be powerful. I tend to draw quite a lot in my work, and find that visualising problems helps me work through them faster and see connections that might not be immediately apparent. It works great as a study method too (see this paper for example), helping students remember content better. As for communicating information, it is superb. I have a list of favourite science comics that have introduced me to new concepts more than once (I am particularly looking at you, xkcd, and your lovely comic explanation wiki). While three-panel strips rarely provide a full picture of a scientific concept, they can certainly offer a brief and memorable introduction. Similarly, neurocomic’s 150 pages are not enough to encompass the entire history and science of brain research, but it is a good place to start to get a flavour for some of the ideas in the field.
Neurocomic is on twitter @neurocomic
Analysis can be tricky at times, especially in neuroscience. Our business is one of maths and stats, and it can all be a bit complex, especially for those new to the field. Below is a short list of resources available to neuroscientists that may be useful when trying to make sense of the data:
1. The Q&A forum. There is a new resource in town for those new to neuroscience and stuck on some technical or analysis problem: https://neurostars.org/. It is a discussion forum where you can post your questions and (hopefully) get answers from the community. It has been open since December 2016, and from what I can tell, most questions posed on the forum has received at least one answer. It is not limited to any particular software library and it has a search function that can help you figure out if anyone has asked your question before. Whilst I have not used it myself (yet), it seems like a good resource.
2. The forum for (mostly) FSL. There is also FSL’s JISCmail site which works on the same principle as neurostars. As the name suggests, this is a discussion forum for FMRIB’s Software Library (FSL), but the topics span everything from the highly technical to basic model design, which means it could be useful for those favouring other software. As a FSL user, I’ve found this site immensely helpful whenever I’ve been stuck, and I can vouch for the quality of the answers on the forum. Records go back to 2001, making this a huge resource where you can be almost guaranteed to find the answer you need. However, make sure that the Q&A is not too old, as analysis tools do evolve.
3. The introduction to analysis. Finally, if you don’t really have a specific question, but rather would like an introduction to MRI analysis, I can thoroughly recommend Jeanette Mumford’s youtube channel, mumfordbrainstats. It’s clear and understandable, and it will make sure you understand what goes on under the bonnet of your analysis. She also created this nifty power calculator for fMRI – fmripower – which can give you a better indicator of power in your next experiment beyond “other people have used X subjects, so we’re going to go with that”. While it is not infallible in that it can only calculate power for a limited number of statistical tests, it is still a very useful starting point.
I’m willing to bet that at least one of the above will be able to help should you find that your analysis suddenly doesn’t work the way you expected it to.