cool and important question!
I never used this functionality within TDT (BTW could you maybe add tags to all your future
posts on neurostars.org, as this allows for better documentation, indexing and searches, as well as pointing experts, that will most likely able to help you, into the direction of your question? For example, useful tags for your question would be: TDT, RSA, cross-validation). I assume it works comparable to the respective RSA toolbox functions?
I assume you already read Alex Walther’s paper on the reliability of dissimilarity measures? There is also a great introductory tutorial and a new preprint that maybe shed some light on your question. Full disclosure: I’m by far no expert on this. Basically, the occurrence of negative distances is not related to the euclidean distances per se, but their cross-validation. As you cross-validate between runs and assuming that the estimated noise is independent between them, their true distance should be zero if they only differ by noise (this is also related to the multivariate noise normalization). As mentioned in the tutorial, especially very small distances can sometimes become negative, which overall is “an inevitable characteristic of an unbiased estimator”. So, if you cross-validate across runs and then average across folds, chances are that your RDM estimates can contain negative values in the diagonal and off-diagonal.
Interpretation is another thing and I’m definitely not skilled enough to provide any advice on that. Regarding your 2. question: do you mean applying inferences directly on the matrix and its included distances or comparing those matrices with models?
I’m gonna include @Martin in this post and hope he has time to have a look, as he will be able to provide a way more helpful answer and more insights.
HTH, best, Peer