LORETA to Fieldtrip

arno arno at SALK.EDU
Tue Mar 28 12:05:43 CEST 2006

> Spatial clustering in FT requires that you specify the shape of the 3D
> array correctly. The best way of knowing that it is correct is by
> plotting it in FT. FT sourceplotting also has options for combining
> anatomy+functional+statistics in one display (using
> grey+color+tranparency).
> Why not ask around whether someone has read it into Matlab before. I
> suspect that there is a mailing list, or you can ask Roberto
> Pasqual-Marqui directly.
I do have a function to read LORETA results under Matlab and overlay
them onto a custom MRI file (normalized to the MNI brain using SPM2 for
example; MRI files can be read with the fieldtrip function
read_fcdc_mri). The attached file LORETAtalairach.xyz is part of the
LORETA distribution and contain the MNI coordinates of the LORETA voxels.

>> 2) Does sourcestatistics handle multiple timeframes i.e. does clustering
>> both in time and in space the same way as with scalpmaps? If yes, how
>> do I
>> specify the input for this? I haven't tried yet but I suspect that
>> would be
>> a pretty heavy operation to run.
> No, sofar I have not implemented Xvox-Yvox-Zvox-freq-time clustering,
> only Xvox-Yvox-Zvox clustering. We typically do not look at multiple
> frequencies or latencies, since on the channel level we can already
> decide (channel statistics) which time-frequency "tile" is
> significant. Subsequently we use multitapering on exactly that
> time-frequency tile to capture the complete effect in one TF-bin (with
> the desired width in both dimensions) and beam that single bin. So the
> standard way to deal with source data is that it is 3-D only. Actually
> it would not be too hard to implement the clustering in 4-D or 5-D,
> although I have no idea on the computational demands. I suspect it to
> be computationally feasible, since it uses the Matlab image processing
> toolbox, which is very efficient. For your LORETA data, the 4-D
> clustering does seem to be interesting so I could look at implementing
> it for N-D. The channel clustering is much more difficult, since the
> channel locations do not "code" a nice natural dimension in the data
> but  they have a weird neighbourhood structure.
> Robert

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