Hi Susanne, Sorry for the delay with the reply -- Monday happened to be too hectic to sit down to compose a reply with necessary load of details ;-) And I had to fix things up mvpa.atlas in pymvpa 0.6.rc*. Description of mvpa.atlases below might be of interest of others - and I would be eager to hear your opinion on either you see it useful and what features/interface should be optimal for the usecases you have.
> space, or simply do not like NIFTI ... not nice. So the only way is to > refer to some atlas in order to make sure you are referring to the very > same brain part. eh, I wish it was that simple... although knowing the nomenclature of brain imaging would be of great help in orienting yourself in your own findings and the descriptions of results by others, automagic labeling based on non subject-derived atlases, should be done with caution and only as preliminary step before eyeballing the results visually. For more on the topic please see Devlin, J. & Poldrack, R.A. (2007). In praise of tedious anatomy. Neuroimage, 37, 1033-41. http://www.poldracklab.org/Publications/pdfs/Neuroimage%202007%20Devlin.pdf > brain areas. Again an atlas has to be consulted to launch your MVPA > analysis straight away on that area of interest. that is, indeed, where I see a good use for those probabilistic atlases from FSL -- to constrain feature space to the ballpark of areas of interest. > '0.6.0~rc1' even seems to support using a standard FSL xml file for the > atlas (filename) and a custom image file in your subject space > (image_file). yeap -- and that was done for 2 reasons: * to overcome slowness of 'label_voxel|point' function calls which would possibly apply subject -> MNI (or Talairach transformation) per each call * to be able simply to spit out masks given a regular expression for the areas of interest, e.g.: >> fusiform_mask = atl.get_map(re.compile('Fusiform'), strategy='max')>40 which would gather all components for the Fusiform. And get_maps could be used to get a set of the "interesting" maps at once. > So is anybody of you already using the atlas module a lot and can give > me a hand with the following example: > - To label: searchlight results in subject space, lets call that > dataset sl_map At the moment it would require "manual" step, something like >> labels = [atl_hoc.label_voxel(x) for x in ds.fa.voxel_indices] and then cleaning up (selecting among multiple labels based on max probability). I guess that could be indeed a reason for a helper function -- given at atlas and strategy (e.g. max probability) to resolve multiple hits to assign some .fa . Alternatively, there is also yet another utility which you might have not discovered (yet): bin/atlaslabeler which, given a Nifti image file should provide you with a summary table... from its limited documentation: ,--- | Usage: /usr/bin/atlaslabeler [OPTIONS] [input_file.nii.gz] | Examples: | | /usr/bin/atlaslabeler -s -A talairach-dist -d 10 -R Closest\ Gray -l Structure,Brodmann\ area -cC mask.nii.gz | | produces a summary per each structure and brodmann area, for each voxel | looking within 10mm radius for the closest gray matter voxel. `--- unfortunately I have not used it much recently and just now adopted for nibabel as the backend to access Nifti/Analyze files. More testing is necessary and there are some outstanding issues with FSL: http://bugs.debian.org/cgi-bin/bugreport.cgi?bug=616006 to make it work flawlessly > - Preferably using Talairach (not my choice) from FSL Especially in the light of above reference, I would strongly discourage using an atlas derived from a single 60-years old female with known atypical properties. Although Talairach atlas has served as the basis for MNI templates, brains in the two (Talairach vs MNI) do not align, that is why additional transformation between two systems is necessary. And (surprise!) there is no known ultimate transformation. The most known/used one is a piece-wise linear from Matthew Brett. I have not checked but I think that is the one which was used by FSL folks. there are few more available in mvpa.atlases. Moreover, probably realizing that Talairach atlas is anyways mediocre, they (FSL guys) did not care much about providing adequate transformation from the original Talairach space into MNI space they have their other atlases in. To appreciate that, just open corresponding NIFTI file for the atlas and go through the slices to see something like: http://www.onerussian.com/tmp/gkrellShoot_03-01-11_084556.png although they seems have done reasonable job compensating for its small original size, so for any typical brain voxel you would get some areas. At some point long ago, whenever Atlas module was conceived (before PyMVPA existence) I also haven't liked the fact that FSL's atlas description labels by unique combination of different levels (answer for your > - what exactly does this levels option do? i.e.: <label index="19" x="28" y="58" z="14">Right Cerebrum.Limbic Lobe.Inferior Temporal Gyrus.Gray Matter.Brodmann area 20</label> that is why I came up with my own version of a Talairach atlas specified in Talairach space with all level separated. Having not received reply from Dr.Lancaster whom I asked about copyright/license terms for the original atlas from http://www.talairach.org/ not knowing that most probably data such as this atlas is not copyrightable at least in the states. The Debian package of that atlas is available from http://www.onerussian.com/tmp/python-rumba_0.0.10-1_all.deb and that atlas specifies different "levels": In [3]: atl_tl.levels Out[3]: {0: Labels Level: Laterality [0] , 1: Labels Level: Lobe [1] , 2: Labels Level: Structure [2] , 3: Labels Level: Matter [3] , 4: Labels Level: Brodmann area [4] , 'Brodmann area': Labels Level: Brodmann area [4] , 'Laterality': Labels Level: Laterality [0] , 'Lobe': Labels Level: Lobe [1] , 'Matter': Labels Level: Matter [3] , 'Structure': Labels Level: Structure [2] } Moreover, since I was interested in the 'Structure' level, but it is often quite misaligned to the actual subject anatomy, I came up with talairach-dist atlas, which provides capability to search for the nearest defined labeling at a given level, e.g. if we query talairach coordinates: In [6]: atl_tl.label_point([-22, -40, 8]) Out[6]: {'coord_queried': [-22, -40, 8], 'labels': [{'id': 'Laterality', 'index': 0, 'label': Label(Left Cerebrum, abbr='L', coord=('-29', '-15', '17'), count=652194, index=4)}, {'id': 'Lobe', 'index': 1, 'label': Label(Sub-lobar, abbr='sl', coord=('2', '-9', '8'), count=165879, index=11)}, {'id': 'Structure', 'index': 2, 'label': Label(Lateral Ventricle, abbr='LV', coord=('-2', '-9', '14'), count=17061, index=23)}, {'id': 'Matter', 'index': 3, 'label': Label(Cerebro-Spinal Fluid, abbr='CSF', coord=('0', '-16', '5'), count=18431, index=1)}, {'id': 'Brodmann area', 'index': 4, 'label': Label(None, abbr='', coord=('0', '-17', '12'), count=2203089, index=0)}], 'voxel_atlas': [112, 86, 80], 'voxel_queried': [112, 86, 80]} We hit CSF (cerebro-spinal fluid) and no broadman area, but if we use talairach-dist atlas looking for the closest defined 'Brodmann' area, it would hit corpus callosum (regardless of how fascinating that is) at 1mm distance: In [9]: atl_tld = Atlas(name='talairach-dist', reference_level='Closest Brodmann', distance=10) In [10]: atl_tld.label_point([-22, -40, 8]) Out[10]: {'coord_queried': [-22, -40, 8], 'distance': 1.0, 'labels': [{'id': 'Laterality', 'index': 0, 'label': Label(Left Cerebrum, abbr='L', coord=('-29', '-15', '17'), count=652194, index=4)}, {'id': 'Lobe', 'index': 1, 'label': Label(Sub-lobar, abbr='sl', coord=('2', '-9', '8'), count=165879, index=11)}, {'id': 'Structure', 'index': 2, 'label': Label(Extra-Nuclear, abbr='EN', coord=('2', '-11', '10'), count=75981, index=13)}, {'id': 'Matter', 'index': 3, 'label': Label(White Matter, abbr='WM', coord=('1', '-15', '18'), count=727061, index=3)}, {'id': 'Brodmann area', 'index': 4, 'label': Label(Corpus Callosum, abbr='CC', coord=('0', '-8', '18'), count=18443, index=49)}], 'voxel_atlas': [112, 86, 80], 'voxel_queried': array([111, 86, 80], dtype=uint8), 'voxel_referenced': [112, 86, 80]} So, that talairach-dist atlas might provide a bit more adequate labeling given some reasonable distance specification. > - the coordinate system of the atlas modul [26,26,26] would correspond > to the sl_map.fa.voxel_indices!? it should... although due to migration from zyx to xyz order change between pynifti and nibabel it requires more adition before becoming reliable -- I have just committed/pushed into our master branch some fixes and additional tests -- but more remains TODO . > - the other way round should be possible using the find option!? I > tried searching for "Cortex", but as soon as there are multiple matches > the option finishes in an error... eh, indeed documentation could be greatly improved here: In [19]: atl_hos.levels[0].find('Cortex', unique=False) Out[19]: [Label(Left Cerebral Cortex, coord=('70', '58', '40'), count=0, index=1), Label(Right Cerebral Cortex, coord=('20', '60', '43'), count=0, index=12)] > Those multiple sa options are just great. Super comfortable for our > kind of experiments! you are welcome ;-) So, after the dump above -- do you still think we should pursue mvpa.atlases module to make it easier to use? ;-) -- =------------------------------------------------------------------= Keep in touch www.onerussian.com Yaroslav Halchenko www.ohloh.net/accounts/yarikoptic _______________________________________________ Pkg-ExpPsy-PyMVPA mailing list [email protected] http://lists.alioth.debian.org/mailman/listinfo/pkg-exppsy-pymvpa
