Hi Swaroop, On 4 January 2015 at 21:06, Swaroop Guntupalli <[email protected]> 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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