Ben Ridley

I'm interested in evaluting the evidence in the literature I've collected for spatial/anatomical variation in an MRI measure. The data consists of mean values, standard deviations and sample sizes, with widely varying numbers of data points for different regions.

They're not RCTs, but as per these answers:

and this package:

I've understood I can use the mean as an outcome measure, and can use the other data to calculate the sampling variances. My issue is I'm not sure of the best way to approach the widely different numbers/quality of studies for different regions. For example, I have estimates for overall grey matter or white matter from up to 30 studies, but for other regions I might have as few as one.

Broadly, I'm interested in two questions, that seem to me to imply different approaches:

1) To what extent does the existing literature support the idea that there IS regional variability? This would evaluate the evidence against the null hypothesis that there is no regional variability. I'd also like to evaluate the contribution of potential demographic and MRI-related confounders/ covariates, and if they prove to be significant, normalise the data with respect to them or otherwise account for them. This seems to imply a kind of regression across studies, but I'm not sure how best to account for the different contributions of different studies across regions.

2) What is the 'best' estimate (the most supported) of the value of my MRI measure in the literature. The object is partly to indicate the level of evidence for the 'best' estimate, in the hopes of encouraging better study of it in larger populations. I also want to compare the 'best extimate' to the values computed from a toy model informed by histology of what the value 'should' be. This seems to point to separate meta-analyses of each region, becuase the the level of evidence for each region is likely to be different.

I'm hoping for pointers as to the best method and approach to take. I'd thought to use R for the analysis, but if anyone has advice about other (preferably free) software that is suited to the task that would be useful too.

Many thanks

I’ve found that certain issues with scientific English come up again and again. I thought I would share these as a resource for those trying to improve their skills. Given the area I work in, the subject tends to be Neuroscience, and the first language of the writers tends to be French. However, these are general points for anybody writing scientific content

For the first in this series on Scientific English see the new post at NeurOnToSomething:

When you study, if you’re anything like me,  it can be difficult to retain details in the form of lists of obscurely related facts. I find it’s helpful to know a bit of the history of the thing you are studying, beyond just immediate facts, as a kind of scaffold to hook the details onto: Why were they interested in that? What was the motivation? Why was this approach used?

I’ve long been on the lookout for a good single volume history of neuroscience.  I found it recently in the form  of @Andrew P. Wickens' A History of the Brain: from Stone Age surgery to modern neuroscience.

Find out more at:

Accidental or deliberate erasure of memories is a recurrent theme in films and science fiction and often paints a picture where amnesia can be induced without impacts on wider functioning. Find out how some portrayals get that wrong and other things they get right (Unfortunately!) at the new blog post at NeurOnToSomething

They say you only understand something when you can explain it in simple terms. How about explaining complicated neuroanatomy using only the 1000 most used words in english? Find out more with the new blog post at NeurOn2Something

What does MRI and the quenching button have to do with terminators and space magic?