[pymvpa] How to train a SurfaceQueryEngine?

[Nick Oosterhof]

Hi Swaroop,

On 4 January 2015 at 21:06, Swaroop Guntupalli <swaroopgj at gmail.com> wrote:

> I am trying to use SurfaceQueryEngine, but looks like training
> requires a dataset with node_indices as fa. How do I generate a
> dataset with surface information?


First of all: SQE is intended for surface-based searchlights where the
input is a surface dataset. (If the input is a volumetric dataset,
use SurfaceVerticesQueryEngine).

An existing surface-based dataset in AFNI NIML format can be loaded using
afni_niml_dset.py (in mvpa2/support/nibabel), and converted from and to the
PyMVPA dataset structure using niml.py (in mvpa2/datasets).

In other cases: if you already have a dataset but without .fa.node_indices,
you would have to set this yourself. In the case of a surface dataset for N
nodes with no missing data (i.e. all nodes have data associated with it)
and data in order (the k-th node on the surface corresponds to the k-th
feature in dataset), you can set it .fa.node_indices to [0, 1, ..., (N-1)].

Is there a helper function to
> achieve that?
>

Not that I know of, apart from the NIML support mentioned above.

>
> Since SQE is node-to-nodes mapping, can we skip training with volume
> dataset altogether?
>

Yes. Note that for the SQE, training with a *surface*-based dataset (with
.fa.node_indices) *is* required.
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