Given that a choice of a "canonical" HRF is nothing but a sample from a specific population (cats?) and region (early visual cortex?), I would say that it actually should be a "healthy practice" to introduce into the model a specification of area-specific (more plausible, based on a good prior) model of the response. , if prior assumptions exist (not just to include result of "fishing expedition/p-hacking"). Some models might even include/account for non-neural responses (e.g. to account/regress out mean blood influx effect correlating most IIRC 3 seconds after, if we assume that it is too distant from the neural effect of interest) which would require convolution with some custom kernel.
But I guess this issue is not about discussing best or worst practices, but rather about addressing use-cases existing in the wild, and since @mih ran into one upon a first shot -- there is a good number of them, since the tool allows for them. FWIW I would strongly advocate for extending the model to allow such flexibility in design specification to
make it applicable to real world cases (not the ones crafted specifically to be represented by NIDM-Results)
make it capable of describing more "sophisticated" designs
not restrict users and software developers seeking to support NIDM-Results into what might be "suboptimal" practices.
Thanks @yarikoptic ! +1 for richer NIDM Results. Our initial effort was limited simply by time... I think this took 3 years even when yoked to SPM/FSL/AFNI output. But I'm all for pushing it forward.
Original issue/concern/discussion: https://github.com/incf-nidash/nidmresults-fsl/issues/125#issuecomment-384307193 Filing on behalf of the shy @mih who might otherwise remain silent ;)
Given that a choice of a "canonical" HRF is nothing but a sample from a specific population (cats?) and region (early visual cortex?), I would say that it actually should be a "healthy practice" to introduce into the model a specification of area-specific (more plausible, based on a good prior) model of the response. , if prior assumptions exist (not just to include result of "fishing expedition/p-hacking"). Some models might even include/account for non-neural responses (e.g. to account/regress out mean blood influx effect correlating most IIRC 3 seconds after, if we assume that it is too distant from the neural effect of interest) which would require convolution with some custom kernel. But I guess this issue is not about discussing best or worst practices, but rather about addressing use-cases existing in the wild, and since @mih ran into one upon a first shot -- there is a good number of them, since the tool allows for them. FWIW I would strongly advocate for extending the model to allow such flexibility in design specification to