Methods in Neuroscience

By Fabiana Kubke 16/10/2009 2


There are, in my opinion, two types of scientists. Those who adapt the questions to the methods they use, and those who choose the methods based on the questions they need to ask. This is not a value judgement, both approaches lead to important results.

Science magazine has published a focus on neuroscience methods that made me think about this. They highlight (among others) 4 methodological approaches (neuronal modelling, molecular and cellular approaches to the study of memory, functional neuroimaging, and optogenetic methods), and they do so very well by highlighting not only the advances in knowledge that each method allows, but also the limitations inherent to the methods themselves. And it is the latter that I found most useful when going through these articles.

It is very tempting as a scientist to jump on the wagon of a newly developed and ‘sexy’ method that promises to be the best thing since sliced bread. It makes us look ‘up to date’. But, as highlighted in these articles, the methods (and the derived results) are meaningless if the scientific question is not properly framed, taking into account, among other things, the assumptions inherent to the methodology itself. And it is precisely here where some of the science fails: It doesn’t matter how good you are ‘at’ the method, unless the right question has been asked.

So, are we choosing the method or are we choosing the question?

The brain and its function have many dimensions and understanding it requires being able to move along that landscape: gene expression, neuronal morphology, molecular interactions, connectivity, etc. We will not be able to understand it unless we are able to integrate across the different levels of organisation that make it function as a whole. And many of these answers are better found with old traditional methodological approaches. There is nothing wrong with adopting new methods, but the risk is that there is a tendency to assume that data that is obtained with older more traditional methods is less important or less relevant (or even less accurate). The fads that come with these ‘new and shiny’ methods can lead us along a narrow path: we either narrow the questions that we ask, or we ask the wrong questions and we erroneously interpret the results (this is my favourite artifact). So before adopting a method (or funding a study that uses it) the question should be: What is the question you are trying to ask? The answer may sometimes be found in a great optogenetic approach, or, in other cases, in a good old Golgi stain.

References:

Molecular and Cellular Approaches to Memory Allocation in Neural Circuits. Alcino J. Silva, Yu Zhou, Thomas Rogerson, Justin Shobe, and J. Balaji (16 October 2009) Science 326 (5951), 391. [DOI: 10.1126/science.1174519]
The Optogenetic Catechism. Gero Miesenböck (16 October 2009) Science 326 (5951), 395. [DOI: 10.1126/science.1174520]
Modalities, Modes, and Models in Functional Neuroimaging. Karl J. Friston (16 October 2009) Science 326 (5951), 399. [DOI: 10.1126/science.1174521]
How Good Are Neuron Models? Wulfram Gerstner and Richard Naud (16 October 2009) Science (5951), 399. [326 (5951), 379. [DOI: 10.1126/science.1181936]


2 Responses to “Methods in Neuroscience”

  • I’d throw developing new methods into the mix, as opposed to selecting ones that already exist. By developing new methods, I mean from ground up, not tweaking an existing method. That’d add two more kinds of scientists:

    1. Those who develop methods, and that’s about all they do.

    2. Those that are focused on a question, but will develop new methods if that’s what is needed to address the question.

    Personally—given a choice in the matter!—I prefer to work the latter way. The biologist part of me likes to work from the questions, exploring the biology. The computational part of me likes to develop new algorithms and code them myself, rather than “merely” munge the data through someone else’s software (while cursing the lack of decent documentation for most academic software…!)

  • The makers of methods are always fun to follow! There is also the MacGyvers, that can rummage through the rubbish and build a lab! (Those are heaps of fun too!)