More useful links… Open Sesame, the psychology of email, Inkscape, and others.

Another quickie post (it’s been ages since I’ve written anything substantive I know, bear with me just a little while longer…) with some links-of-interest for you.

First up is Open Sesame – this is an experiment-builder application with a nice graphical front-end, which also supports scripting in Python – nice. Looks like a possible alternative to PsychoPy with a fair few similar features. Also, it’s cross-platform, open-source and free – my three favourite things!

Next up is Inkscape – this is a free vector graphics editor (or drawing package), with similar features to Adobe Illustrator or Corel Draw. I tend to use Adobe Illustrator for a few specialised tasks, such as making posters for conferences, and this looks like a potentially really good free alternative.

Neuroimaging Made Easy is a blog I found a while ago that I’ve been meaning to share; it’s mostly a collection of tips and downloadable scripts to accomplish fairly specific tasks. They’re all pretty much optimised for Mac users (using AppleScript) and people who use BrainVoyager or FSL for their neuroimaging – SPM users are likely to be disappointed here (but they’re pretty used to that anyway, right?! Heh…). Really worth digging through the previous posts if you fall in the right segments of that Venn diagram though – I’ve been using a couple of their scripts for a while now.

Penultimately, I thought this recent article on Mind Hacks was really terrific – titled: “Psychological self-defence for the age of email”. It covers several relevant psychological principles and shows how they can be used to better cope with the onslaught of e-mail that many of us are often buried under.

Lastly, I hope you’ll pardon a modicum of self-promotion, but I recently did an interview over Skype with the lovely Ben Thomas of http://the-connectome.com/. Unfortunately the skype connection between London and Los Angeles was less than perfect which meant he couldn’t put it up as a podcast, but he heroically transcribed it instead – if you are so inclined, you can read it here.

TTFN.

The effects of hardware, software, and operating system on brain imaging results

A recent paper (Gronenschild et al., 2012) has caused a modicum of concern amongst neuroimaging researchers. The paper documents a set of results based on analysis of anatomical MRI images using a popular free software tool called FreeSurfer, and essentially reports that there are (sometimes quite substantive) differences in the results that it produces, depending on the exact version of the software used, and whether the analyses were carried out on a Mac (running OS X) or a Hewlett Packard PC (running Linux). In fact, even the exact version of OS X on the Mac systems was also shown to be important in replicating results precisely.

Figure 3 of Gronenschild et al. (2012) showing the effect of different versions of FreeSurfer on obtained grey-matter volume results. Percentage scale at the top, p-values on the bottom.

The fact that results differ from one version of FreeSurfer to another is perhaps not so surprising – after all, we expect that newer versions of software should be ‘improved’ in important ways, otherwise, what would be the point in releasing them? However, the fact that results differ between operating systems is a little more worrying – in theory any operating system capable of running the software should produce the same result. The authors recommendations are that 1) Researchers should not switch from one version/operating system/platform to another in the middle of a research project, and 2) that when reporting results software version numbers, and the workstation/OS used should all be documented. This seems broadly sensible.

It got me thinking about neuroimaging software more generally as well though. In general, people don’t do detailed evaluations of software of the kind reported by Gronenschild et al. (2012).  As an enthusiastic user of several fMRI-related packages (I’m currently using SPM, FSL and BrainVoyager, all on different projects) I’ve often wondered what the real differences were between them, in terms of the results they produce. Given how many people around the world use brain imaging software, you might think that some detailed evaluations would be floating around, but in fact there are very few.

I think there are several reasons for this:

1. It’s (perhaps understandably) regarded as a waste of time. After all, we (meaning researchers who use this software) are generally more interested in how the brain works, than by how software works. Neuroimaging is difficult and time-consuming and we all need to publish papers to survive – it makes more sense to spend our time on ‘real’ brain-related research.

2. Most people have one (or at most two) pieces of software that they like to use for neuroimaging, and they stick with it; I’m somewhat unusual in this respect. The fact that most people use just one package more-or-less exclusively means there’s a dearth of people who actually have the skills necessary to do cross-evaluation of packages. Again, this is understandable – why take the time to learn a new system, if you’re happy with the one you’re using?

3. The differences between the packages make precise comparison of end-results difficult. Even though all the packages use an application of the General Linear Model for basic analysis, other differences in pre-processing conceivably play a role. For instance, FSL handles the spatial transformation of functional data somewhat differently to other packages.

Having said that, there have been a few papers which have tried to do these kind of evaluations. Two examples are here (on motion correction) and here (on segmentation). Another somewhat instructive paper is this one, which summarises the results of a functional-imaging analysis contest held as part of the Human Brain Mapping meeting in Toronto in 2005; developers of popular neuroimaging software were all given the same set of data and asked to analyse it as best they could. Interesting stuff, but as the contestants all used somewhat different methods to get the most out of the data, it’s hard to draw direct comparisons.

If there’s a moral to this story, it’s that (as the recent Gronenschild et al. paper demonstrates) we need to pay close attention to this kind of thing. As responsible researchers we cannot simply assume our results will be replicable with different hardware and software, and detailed reporting of not just the analysis procedures, but also the tools used to achieve the results seems a simple and robust way of at least acknowledging the issue and enabling more precise replicability. Actually solving the issues involved is a substantially more difficult problem, and may be a job for future generations of researchers and developers.

See also:
My previous post on comparisons of different fMRI software: Here, here and here.
Neuroskeptic has also written a short piece on the recent paper mentioned above.

TTFN.

Which fMRI analysis software (SPM, BrainVoyager, FSL)?

This one’s a bit advanced for the kind of information I generally want to include on here, but I thought it might be useful to somebody, so I’d put it up. I was recently asked to do a talk on fMRI software, so put together a presentation comparing three popular choices for analysing brain imaging data: SPM, BrainVoyager and FSL. I’ve used all three packages in the past for my work, although I’m not so expert with the new versions of SPM as I used to be. The talk was pretty basic, and focussed more on features and the UI experience of the three applications, rather than any technical details.

Anyway, the slides are available for download here (PDF, 3.6Mb) if anyone’s interested. All content is my personal opinion, your mileage may vary, etc. etc.