Atlas for 1 year old babies

Dear Sylvain, dear François,

I’ve finished building the atlas for the 1 year old infants. You can obtain a copy here ( ) if you are interested in including it in Brainstorm. I wrapped the protocol with only a default anatomy since I don’t know how exactly you usually package your atlases. You have the “go” from the authors (i.e., Li et al.) for distribution within Brainstorm (with appropriate credits to their work, of course). Description of the procedure I used to build this Brainstorm-compatible atlas is as follow (including related credits for the original files):

The atlas is based on the work of Shi et al. (2011) who proposed an MRI template (grayscale average of 90 infants, recorded longitudinally after birth, at 1 year and at 2 years; only recordings taken at 1 year have been used here), tissue probability maps, and brain parcellation according to the division from Tzourio-Mazoyer et al. (2002). To make this atlas usable for EEG source reconstruction, its corresponding brain ribbon had to be reconstructed as a surface mesh. This process has been performed in a semi-automated fashion using the BrainVisa pipeline (version 2012). To provide at faithful reconstruction, BrainVisa had to be helped manually because of the poor white matter/grey matter discriminability at this age and the normal fuzziness due to inter-subject averaging of MRI volumes. Thus, we used a Python toolbox (NiBabel) to build the grey/white matter mask from the probabilistic maps provided by Shi et al. (2011) by merging the information from the two relevant maps (grey and white matter) in the following fashion: any voxel with an intensity lower than 25% of maximal intensity in the two maps was set to 0. All other voxels which intensity was higher in the probabilistic map of grey matter than in the white matter map was classified as grey matter. The remaining voxels were labeled as white matter. The MRI-space Tzourio-Mazoyed parcellation was propagated to the cortical mesh by coregistering every cortical vertex with the corresponding voxel. [François: Note that I used Brainstorm for that but I commented the two lines where you inflate (the cortex?) in the routine to import atlases based on MRI volumes. The result is that there are “unattributed” faces between cortical regions on the atlas mesh, but every vertex is associated with one and only one region. Thus, when averaging (or using other statistics) on vertexes of the scouts defined by these regions, the source activity computed at every vertex is used for the averaging of only one region. It is less nice visually (because it makes look the parcellation as if there were “gaps” between regions, but as far as vertexes are concerns, there are no such gaps. I guess you must have weighted the pros and cons of this “inflation” but it seems appropriate to leave it out for the usage I intended.]

BrainVisa provided a poor skull reconstruction. For this reason, Brainstorm was used for the reconstruction of the head. A 2.75 mm skull thickness was entered in its boundary-element method (BEM) algorithm for the reconstruction of scalp, outer skull interface, and inner skull interface. This value was based on the work of Li et al. (2015) who reported that skull thickness in one year old babies varies between 1.5 and 4 mm.

Li, Z., Park, B. K., Liu, W., Zhang, J., Reed, M. P., Rupp, J. D., . . . Hu, J. (2015). A statistical skull geometry model for children 0-3 years old. PLoS One, 10(5), e0127322. doi:10.1371/journal.pone.0127322

Shi, F., Yap, P. T., Wu, G., Jia, H., Gilmore, J. H., Lin, W., & Shen, D. (2011). Infant brain atlases from neonates to 1- and 2-year-olds. PLoS One, 6(4), e18746. doi:10.1371/journal.pone.0018746

Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., . . . Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15(1), 273-289. doi:10.1006/nimg.2001.0978

Of course, this methodology has some limitations and the results will probably be much more accurate with surface averaging than surface reconstruction of volume averaging. This has been proposed recently in of this age range, using FreeSurfer. However, their atlas does not seem to be available yet and the authors were unresponsive regarding when it should become available. Anyways, I think the atlas I’m proposing should do a satisfactory job for the relatively coarse process of computing EEG cortical sources based on atlases of infants and should already improve upon reporting only sensor level activity!



Hi Christian,

Thank you very much for this contribution.
I added your template to the list that can be downloaded easily form Brainstorm.
Update Brainstorm, create a new subject (default anatomy=no), right-click on the subject folder > Use template > Oreilly_1y.

I don’t know how you would like to document this. For now, the list templates is only briefly described on this page:
I will work on this page again when I’m done writing the new introduction tutorials.


Hi @christianoreilly @Francois,

Pardon the rekindling of this old thread. I was wondering if either of you are aware of the existence of a Brainstorm compatible atlas for children/adolescents aged 7-14. I’m working with a pediatric dataset, and was wondering if the process of projecting the individual anatomies to the default anatomy would affect my data analyses. I ask this question because children’s brains are in flux at that age, compared to the atlases/templates which use the brains of adults who are much more “stable” and have larger anatomies.

Would using the default Freesurfer atlases affect any downstream analyses? I should mention that I’ve been using them for a while now and I haven’t thought about it until it occurred to me this afternoon that there might be some “fitting” issues.

Thank you very much for your time and response.



Hi @AquilaAking,

I’m not sure about availability of Brainstorm compatible atlas for children/adolescents (maybe @Francois will be more informed on that). For your question “would an adult atlas (Freesurfer default atlas) affect any downstream analyses?”, I think you can answer it by answering “Is the macroscopic structure of the brain of children/adolescent different from the one of young adults?”. I would expect the answer to be “yes”. How much it would impact on the results is unfortunately difficult to know without more in-depth analysis and, I might be mistaken because I don’t have an up-to-date and thorough knowledge of this literature, but I think such questions have received relatively little attention. You can always start by checking how the brain and head size varies from 7 years old to adult (there should be data easy to find on that) and then check by which age the gyri/sulci structure becomes relatively stable. That should help you get an idea of the potential impact. Also, if you check for differences across the age, mind the fact that head/brain size will be correlated with age and will be a confounding factor… in any case, in the end, it all depends on the analysis you want to perform!

Hi Aquila,

I reference two previous posts on this forum:
MRI template for children
MRI template for Children (4-7 years)

If you want to see what kind of differences you may observe with different head sizes: create two subjects, one with the 1yr template and one with the default ICBM152 template. Import the same recordings and compute the sources in both subjects, compare the results.


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