[FieldTrip] SPM12 for segmentation and (inverse) normalization

Herbert J Gould (hgould) hgould at memphis.edu
Thu Sep 21 15:56:38 CEST 2017

I have retired please remove me from the mail list

Herbert Jay Gould
Professor Emeritus
The University of Memphis

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-------- Original message --------
From: Stephen Whitmarsh
Date:09/21/2017 7:43 AM (GMT-06:00)
To: FieldTrip discussion list
Subject: Re: [FieldTrip] SPM12 for segmentation and (inverse) normalization

Dear Sarang and Jan-Mathijs,

Thanks a lot. I am now able (after updating FT, which now includes SPM12 in /external), to use SPM12 for segmentation of my template and my subject MRI, by using cfg.spmversion = 'spm12'. 12 is definitely is a big improvement over 8 when it comes to brain-segmentation, which now does not require individual treatments anymore. It also outputs more compartments which gives me a little bit more to work with when dealing with scans that have bad delineation of the scalp for normalization.

Pleas note that defaults seems to differ - some FT functions default to spm8, others to spm12.

In fact, FT still reverts to spm8 in ft_volumenormalise when called in ft_prepare_sourcemodel, even when calling the latter with cfg.spmversion = 'spm12'. In other words the cfg.spmversion is not passed along.

Best wishes and thanks again!

On 21 September 2017 at 09:09, Schoffelen, J.M. (Jan Mathijs) <jan.schoffelen at donders.ru.nl<mailto:jan.schoffelen at donders.ru.nl>> wrote:
Hi Stephen,

Please note that FT now has full support for SPM12, both using the old-style segmentation, and the new one (the latter yielding 6 tissue types).


On 20 Sep 2017, at 17:03, Stephen Whitmarsh <stephen.whitmarsh at gmail.com<mailto:stephen.whitmarsh at gmail.com>> wrote:

Dear all,

I having some problems in normalizing MRIs for my study. Some have improper segmentation for which changing individual brain/scalp thresholds works in many cases but not all, e.g. when the scalp 'bleeds' into some noise outside of the head. Also, changing parameters in spm8 for normalization, such as number of iterations (directly in in spm_normalize, since FT does not pass these parameters) improves the transformation.

However, some scans I cannot deal with, either because they have noise from outsides of the head 'bleed' onto the scalp, thereby preventing optimal scalp-segmentation and thereby normalization. Others have an inappropriate contrast MRI sequence.

Some fMRI researchers advised me to use SPM12, because of its improved preprocessing procedures. However, it does not seem supported in FT yet. Does anyone have experience with this, and can perhaps share how they extracted the transformation matrix from the resulting nifti's?

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