Hi Taylor,

Many thanks for the quick response!!

*You’re right- the documentation for the decoding methods is lacking, mostly because they haven’t gotten a lot of use yet. I can try to make improving the documentation on those methods a priority.*

Sounds really good, thank you!

*Yes, the probability values are posterior probabilities. The probForward values represent the probability of study selection given the presence of the label and the prior probability of having the label, while the probReverse values represent the probability of presence of the label given selection and the prior probability of having the label.*

*The prior probability of having the label in the Neurosynth approach is just a single a priori probability, by default 0.5 (50% chance of a brain experiencing the brain state described by the label). “Selection” refers to whatever procedure you use to identify the subset of studies you’re using to decode; perhaps the most common example being studies which report at least one coordinate in an ROI.*

Just to make sure, I got it right. This basically means the probReverse is:

„ ProbForward = P(Study Selection* | Label) ; prior = p(Label)“

*based on „at least one activation within an Roi“ within Roi

And the reverse inference is:

„ProbReverse = P(Label | Study Selection*) ; prior = p(Label)“

*based on „at least one activation within an Roi“ within Roi

Since we try to make more sense of the decoded data, we aim to calculate the Bayes Factor of the ProbReverse Values based on the posterior probability and the prior (as described in Poldrack, 2006, and in more detail in Goodman, 1999), it would be highly appreciated to get to know, whether the Nimare/Neurosynth-approach uses actually the Bayesian statistics to this effect.

I am just wondering, because the aforementioned uniformity and association tests seem to produce z-values. I am still not entirely sure how you actually calculate the probReverse and probForward values (since it is stated on the Neurosynth FAQ that the uniformity and association analysis differ from the Bayesian estimation analysis).

Via BrainMap the probReverse (e.g) is calculated according to Bayes Rule with P(Term|Activation = P(Activation|Term)*P(Term)/P(Activation). Where P(Activation|Term) = Experiments per specific Term within Roi/Experiments per specific Term in whole database. P(Activation) = Number of Experiments in Roi/Number of all Experiments in database. And p(Term) = Number of a specific Term in database/Number of all Experiments in database.

Would it be possible to get a documentation where it is shown, how probReverse via Neurosynth is calculated in more detail (e.g. as I did for BrainMap)? Just to make sure that we interpret the results of the decoding correctly.

Many thanks in advance!!

Best,

Ole