diff --git a/maps/bigbrain-cortical-labelled.json b/maps/bigbrain-cortical-labelled.json index 711c22c6..2acb0574 100644 --- a/maps/bigbrain-cortical-labelled.json +++ b/maps/bigbrain-cortical-labelled.json @@ -106,5 +106,15 @@ "label": 7 } ] - } + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/DatasetVersion": "13e6ec48-4fec-4e9a-9bcf-45036d3c8ef9" + }, + "publications": [ + { + "citation": "Wagstyl, K., Larocque, S., Cucurull, G., Lepage, C., Cohen, J. P., Bludau, S., Palomero-Gallagher, N., Lewis, L. B., Funck, T., Spitzer, H., Dickscheid, T., Fletcher, P. C., Romero, A., Zilles, K., Amunts, K., Bengio, Y., & Evans, A. C. (2020). BigBrain 3D atlas of cortical layers: Cortical and laminar thickness gradients diverge in sensory and motor cortices. PLOS Biology, 18(4), e3000678. https://doi.org/10.1371/journal.pbio.3000678", + "url": "https://doi.org/10.1371/journal.pbio.3000678" + } + ] } diff --git a/maps/bigbrain-isocortex-labelled.json b/maps/bigbrain-isocortex-labelled.json index 9109de02..bd9dc530 100644 --- a/maps/bigbrain-isocortex-labelled.json +++ b/maps/bigbrain-isocortex-labelled.json @@ -28,5 +28,25 @@ "label": 200 } ] - } + }, + "ebrains": { + "minds/core/species/v1.0.0": "0ea4e6ba-2681-4f7d-9fa9-49b915caaac9", + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd" + }, + "publications": [ + { + "name": "BigBrain: initial tissue classification and surface extraction (2014)", + "url": "https://bigbrain.loris.ca/papers/HBM2014poster.pdf", + "authors": [ + "Lindsay Lewis", + "Claude Lepage", + "Marc Fournier", + "Karl Zilles", + "Katrin Amunts", + "Alan Evans" + ], + "description": "The BigBrain is a 3D, high-resolution reference tool that provides a new level of neuroanatomical insight into the human brain, presents a variety of novel computational challenges. Here, we have extended its processing pipeline to intensity inhomogeneity correction and cortical surface segmentation. This dataset may be utilized for automated extraction of quantitative morphological indices of cortical substructure over the entire brain, and will allow the extraction of microscopic data for modeling and simulation applications.", + "citation": "Lewis LB, Lepage C, Fournier M et al. BigBrain: initial tissue classification and surface extraction. F1000Posters 2014, 5:933 (poster)" + } + ] } diff --git a/maps/bigbrain-jba29-labelled.json b/maps/bigbrain-jba29-labelled.json index 830d93c6..66b34c2f 100644 --- a/maps/bigbrain-jba29-labelled.json +++ b/maps/bigbrain-jba29-labelled.json @@ -1727,5 +1727,16 @@ "label": 1 } ] - } -} \ No newline at end of file + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "openminds/DatasetVersion": "a8932c7e-063c-4131-ab96-996d843998e9" + }, + "publications": [ + { + "citation": "Amunts, K., Mohlberg, H., Bludau, S., Zilles, K. (2020). Julich-Brain \u2013 A 3D probabilistic atlas of human brain\u2019s cytoarchitecture. Science 369, 988-992", + "url": "https://doi.org/10.1126/science.abb4588" + } + ] +} diff --git a/maps/colin27-dk-labelled.json b/maps/colin27-dk-labelled.json index 2506ec9e..c23bf3b7 100644 --- a/maps/colin27-dk-labelled.json +++ b/maps/colin27-dk-labelled.json @@ -423,5 +423,46 @@ "label": 2033 } ] - } + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd" + }, + "publications": [ + { + "name": "An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.", + "url": "https://doi.org/10.1016/j.neuroimage.2006.01.021", + "authors": [ + "Rahul S. Desikan", + "Florent S\u00e9gonne", + "Bruce Fischl", + "Brian T. Quinn", + "Bradford C. Dickerson", + "Deborah Blacker", + "Randy L. Buckner", + "Anders M. Dale", + "R. Paul Maguire", + "Bradley T. Hyman", + "Marilyn S. Albert", + "Ronald J. Killiany" + ], + "description": "In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1\u00a0mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.", + "citation": "Desikan RS, S\u00e9gonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage. 2006;31(3):968-980." + }, + { + "name": "Accurate nonlinear mapping between MNI volumetric and FreeSurfer surface coordinate systems", + "url": "https://doi.org/10.1002/hbm.24213", + "authors": [ + "Jianxiao Wu", + "Gia H. Ngo", + "Douglas Greve", + "Jingwei Li", + "Tong He", + "Bruce Fischl", + "Simon B. Eickhoff", + "B.T. Thomas Yeo" + ], + "description": "The results of most neuroimaging studies are reported in volumetric (e.g., MNI152) or surface (e.g., fsaverage) coordinate systems. Accurate mappings between volumetric and surface coordinate systems can facilitate many applications, such as projecting fMRI group analyses from MNI152/Colin27 to fsaverage for visualization or projecting resting-state fMRI parcellations from fsaverage to MNI152/Colin27 for volumetric analysis of new data. However, there has been surprisingly little research on this topic. Here, we evaluated three approaches for mapping data between MNI152/Colin27 and fsaverage coordinate systems by simulating the above applications: projection of group-average data from MNI152/Colin27 to fsaverage and projection of fsaverage parcellations to MNI152/Colin27. Two of the approaches are currently widely used. A third approach (registration fusion) was previously proposed, but not widely adopted. Two implementations of the registration fusion (RF) approach were considered, with one implementation utilizing the Advanced Normalization Tools (ANTs). We found that RF-ANTs performed the best for mapping between fsaverage and MNI152/Colin27, even for new subjects registered to MNI152/Colin27 using a different software tool (FSL FNIRT). This suggests that RF-ANTs would be useful even for researchers not using ANTs. Finally, it is worth emphasizing that the most optimal approach for mapping data to a coordinate system (e.g., fsaverage) is to register individual subjects directly to the coordinate system, rather than via another coordinate system. Only in scenarios where the optimal approach is not possible (e.g., mapping previously published results from MNI152 to fsaverage), should the approaches evaluated in this manuscript be considered. In these scenarios, we recommend RF-ANTs (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/registration/Wu2017_RegistrationFusion).", + "citation": "Wu J, Ngo GH, Greve D, Li J, He T, Fischl B, Eickhoff SB, Yeo BTT. Accurate nonlinear mapping between MNI volumetric and FreeSurfer surface coordinate systems. Hum Brain Mapp. 2018;39(9):3793-3808." + } + ] } diff --git a/maps/colin27-jba118-continuous.json b/maps/colin27-jba118-continuous.json index b002c24e..47a0cfd1 100644 --- a/maps/colin27-jba118-continuous.json +++ b/maps/colin27-jba118-continuous.json @@ -3550,5 +3550,24 @@ "label": null } ] - } + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "openminds/DatasetVersion": "4ac9f0bc-560d-47e0-8916-7b24da9bb0ce" + }, + "publications": [ + { + "citation": "Zilles K, Amunts K (2010) Centenary of Brodmann\u2019s map \u2013 conception and fate. Nature Reviews Neuroscience 11(2): 139-145 ", + "url": "https://doi.org/10.1038/nrn2776" + }, + { + "citation": "Amunts K, Schleicher A, Zilles K (2007) Cytoarchitecture of the cerebral cortex \u2013 more than localization. Neuroimage 37: 1061-1065", + "url": "https://doi.org/10.1016/j.neuroimage.2007.02.037" + }, + { + "citation": "Zilles K, Schleicher A, Palomero-Gallagher N, Amunts K (2002) Quantitative analysis of cyto- and receptor architecture of the human brain. In: /Brain Mapping: The Methods/, J. C. Mazziotta and A. Toga (eds.), USA: Elsevier, 2002, p. 573-602.", + "url": "http://dx.doi.org/10.1016/B978-012693019-1/50023-X" + } + ] } diff --git a/maps/colin27-jba118-labelled.json b/maps/colin27-jba118-labelled.json index 21acfb72..6fd7a205 100644 --- a/maps/colin27-jba118-labelled.json +++ b/maps/colin27-jba118-labelled.json @@ -1350,5 +1350,24 @@ "label": 219 } ] - } + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "openminds/DatasetVersion": "4ac9f0bc-560d-47e0-8916-7b24da9bb0ce" + }, + "publications": [ + { + "citation": "Zilles K, Amunts K (2010) Centenary of Brodmann\u2019s map \u2013 conception and fate. Nature Reviews Neuroscience 11(2): 139-145 ", + "url": "https://doi.org/10.1038/nrn2776" + }, + { + "citation": "Amunts K, Schleicher A, Zilles K (2007) Cytoarchitecture of the cerebral cortex \u2013 more than localization. Neuroimage 37: 1061-1065", + "url": "https://doi.org/10.1016/j.neuroimage.2007.02.037" + }, + { + "citation": "Zilles K, Schleicher A, Palomero-Gallagher N, Amunts K (2002) Quantitative analysis of cyto- and receptor architecture of the human brain. In: /Brain Mapping: The Methods/, J. C. Mazziotta and A. Toga (eds.), USA: Elsevier, 2002, p. 573-602.", + "url": "http://dx.doi.org/10.1016/B978-012693019-1/50023-X" + } + ] } diff --git a/maps/colin27-jba29-continuous.json b/maps/colin27-jba29-continuous.json index b906bd02..0aa6439d 100644 --- a/maps/colin27-jba29-continuous.json +++ b/maps/colin27-jba29-continuous.json @@ -5172,5 +5172,16 @@ "label": null } ] - } + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "openminds/DatasetVersion": "a8932c7e-063c-4131-ab96-996d843998e9" + }, + "publications": [ + { + "citation": "Amunts, K., Mohlberg, H., Bludau, S., Zilles, K. (2020). Julich-Brain \u2013 A 3D probabilistic atlas of human brain\u2019s cytoarchitecture. Science 369, 988-992", + "url": "https://doi.org/10.1126/science.abb4588" + } + ] } diff --git a/maps/colin27-jba29-labelled.json b/maps/colin27-jba29-labelled.json index 00944bd7..bc71ead3 100644 --- a/maps/colin27-jba29-labelled.json +++ b/maps/colin27-jba29-labelled.json @@ -1802,5 +1802,16 @@ "label": 367 } ] - } + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "openminds/DatasetVersion": "a8932c7e-063c-4131-ab96-996d843998e9" + }, + "publications": [ + { + "citation": "Amunts, K., Mohlberg, H., Bludau, S., Zilles, K. (2020). Julich-Brain \u2013 A 3D probabilistic atlas of human brain\u2019s cytoarchitecture. Science 369, 988-992", + "url": "https://doi.org/10.1126/science.abb4588" + } + ] } diff --git a/maps/colin27-jba30-labelled.json b/maps/colin27-jba30-labelled.json index 4cbaedab..ef883466 100644 --- a/maps/colin27-jba30-labelled.json +++ b/maps/colin27-jba30-labelled.json @@ -1901,5 +1901,25 @@ "label": 1157 } ] - } + }, + "ebrains": { + "openminds/DatasetVersion": "d69b70e2-3002-4eaf-9c61-9c56f019bbc8", + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "minds/core/species/v1.0.0": "0ea4e6ba-2681-4f7d-9fa9-49b915caaac9", + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd" + }, + "publications": [ + { + "citation": "Amunts, K., Mohlberg, H., Bludau, S., Zilles, K. (2020). Julich-Brain \u2013 A 3D probabilistic atlas of human brain\u2019s cytoarchitecture. Science 369, 988-992", + "url": "https://www.science.org/doi/10.1126/science.abb4588" + }, + { + "citation": "Alan C. Evans, Andrew L. Janke, D. Louis Collins, Sylvain Baillet, Brain templates and atlases, NeuroImage, Volume 62, Issue 2, 2012, Pages 911-922, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2012.01.024.", + "url": "https://doi.org/10.1016/j.neuroimage.2012.01.024" + }, + { + "citation": "Simon B. Eickhoff, Klaas E. Stephan, Hartmut Mohlberg, Christian Grefkes, Gereon R. Fink, Katrin Amunts, Karl Zilles, A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data, NeuroImage, Volume 25, Issue 4, 2005, Pages 1325-1335, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2004.12.034.", + "url": "https://doi.org/10.1016/j.neuroimage.2004.12.034" + } + ] } diff --git a/maps/colin27-jba30_157regions-continuous.json b/maps/colin27-jba30_157regions-continuous.json index 4809a70d..0ce3d5a2 100644 --- a/maps/colin27-jba30_157regions-continuous.json +++ b/maps/colin27-jba30_157regions-continuous.json @@ -4720,5 +4720,25 @@ "volume": 313 } ] - } + }, + "ebrains": { + "openminds/DatasetVersion": "d69b70e2-3002-4eaf-9c61-9c56f019bbc8", + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "minds/core/species/v1.0.0": "0ea4e6ba-2681-4f7d-9fa9-49b915caaac9", + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd" + }, + "publications": [ + { + "citation": "Amunts, K., Mohlberg, H., Bludau, S., Zilles, K. (2020). Julich-Brain \u2013 A 3D probabilistic atlas of human brain\u2019s cytoarchitecture. Science 369, 988-992", + "url": "https://www.science.org/doi/10.1126/science.abb4588" + }, + { + "citation": "Alan C. Evans, Andrew L. Janke, D. Louis Collins, Sylvain Baillet, Brain templates and atlases, NeuroImage, Volume 62, Issue 2, 2012, Pages 911-922, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2012.01.024.", + "url": "https://doi.org/10.1016/j.neuroimage.2012.01.024" + }, + { + "citation": "Simon B. Eickhoff, Klaas E. Stephan, Hartmut Mohlberg, Christian Grefkes, Gereon R. Fink, Katrin Amunts, Karl Zilles, A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data, NeuroImage, Volume 25, Issue 4, 2005, Pages 1325-1335, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2004.12.034.", + "url": "https://doi.org/10.1016/j.neuroimage.2004.12.034" + } + ] } diff --git a/maps/colin27-jba30_175regions-continuous.json b/maps/colin27-jba30_175regions-continuous.json index 5ccc6ee9..6c3980b0 100644 --- a/maps/colin27-jba30_175regions-continuous.json +++ b/maps/colin27-jba30_175regions-continuous.json @@ -5254,5 +5254,25 @@ "volume": 349 } ] - } + }, + "ebrains": { + "openminds/DatasetVersion": "d69b70e2-3002-4eaf-9c61-9c56f019bbc8", + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "minds/core/species/v1.0.0": "0ea4e6ba-2681-4f7d-9fa9-49b915caaac9", + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd" + }, + "publications": [ + { + "citation": "Amunts, K., Mohlberg, H., Bludau, S., Zilles, K. (2020). Julich-Brain \u2013 A 3D probabilistic atlas of human brain\u2019s cytoarchitecture. Science 369, 988-992", + "url": "https://www.science.org/doi/10.1126/science.abb4588" + }, + { + "citation": "Alan C. Evans, Andrew L. Janke, D. Louis Collins, Sylvain Baillet, Brain templates and atlases, NeuroImage, Volume 62, Issue 2, 2012, Pages 911-922, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2012.01.024.", + "url": "https://doi.org/10.1016/j.neuroimage.2012.01.024" + }, + { + "citation": "Simon B. Eickhoff, Klaas E. Stephan, Hartmut Mohlberg, Christian Grefkes, Gereon R. Fink, Katrin Amunts, Karl Zilles, A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data, NeuroImage, Volume 25, Issue 4, 2005, Pages 1325-1335, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2004.12.034.", + "url": "https://doi.org/10.1016/j.neuroimage.2004.12.034" + } + ] } diff --git a/maps/colin27-jba31-labelled.json b/maps/colin27-jba31-labelled.json index a3a88ae1..57aace4b 100644 --- a/maps/colin27-jba31-labelled.json +++ b/maps/colin27-jba31-labelled.json @@ -2920,5 +2920,25 @@ "fragment": "right hemisphere" } ] - } + }, + "ebrains": { + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "openminds/DatasetVersion": "f1fe19e8-99bd-44bc-9616-a52850680777", + "minds/core/species/v1.0.0": "0ea4e6ba-2681-4f7d-9fa9-49b915caaac9", + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd" + }, + "publications": [ + { + "citation": "Amunts, K., Mohlberg, H., Bludau, S., Zilles, K. (2020). Julich-Brain \u2013 A 3D probabilistic atlas of human brain\u2019s cytoarchitecture. Science 369, 988-992", + "url": "https://www.science.org/doi/10.1126/science.abb4588" + }, + { + "citation": "Alan C. Evans, Andrew L. Janke, D. Louis Collins, Sylvain Baillet, Brain templates and atlases, NeuroImage, Volume 62, Issue 2, 2012, Pages 911-922, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2012.01.024.", + "url": "https://doi.org/10.1016/j.neuroimage.2012.01.024" + }, + { + "citation": "Simon B. Eickhoff, Klaas E. Stephan, Hartmut Mohlberg, Christian Grefkes, Gereon R. Fink, Katrin Amunts, Karl Zilles, A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data, NeuroImage, Volume 25, Issue 4, 2005, Pages 1325-1335, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2004.12.034.", + "url": "https://doi.org/10.1016/j.neuroimage.2004.12.034" + } + ] } diff --git a/maps/colin27-jba31_207-continuous.json b/maps/colin27-jba31_207-continuous.json index 4509f44c..d86bd187 100644 --- a/maps/colin27-jba31_207-continuous.json +++ b/maps/colin27-jba31_207-continuous.json @@ -5066,5 +5066,25 @@ "volume": 413 } ] - } + }, + "ebrains": { + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "openminds/DatasetVersion": "f1fe19e8-99bd-44bc-9616-a52850680777", + "minds/core/species/v1.0.0": "0ea4e6ba-2681-4f7d-9fa9-49b915caaac9", + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd" + }, + "publications": [ + { + "citation": "Amunts, K., Mohlberg, H., Bludau, S., Zilles, K. (2020). Julich-Brain \u2013 A 3D probabilistic atlas of human brain\u2019s cytoarchitecture. Science 369, 988-992", + "url": "https://www.science.org/doi/10.1126/science.abb4588" + }, + { + "citation": "Alan C. Evans, Andrew L. Janke, D. Louis Collins, Sylvain Baillet, Brain templates and atlases, NeuroImage, Volume 62, Issue 2, 2012, Pages 911-922, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2012.01.024.", + "url": "https://doi.org/10.1016/j.neuroimage.2012.01.024" + }, + { + "citation": "Simon B. Eickhoff, Klaas E. Stephan, Hartmut Mohlberg, Christian Grefkes, Gereon R. Fink, Katrin Amunts, Karl Zilles, A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data, NeuroImage, Volume 25, Issue 4, 2005, Pages 1325-1335, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2004.12.034.", + "url": "https://doi.org/10.1016/j.neuroimage.2004.12.034" + } + ] } diff --git a/maps/colin27-jba31_227-continuous.json b/maps/colin27-jba31_227-continuous.json index eb4d773b..68b1e245 100644 --- a/maps/colin27-jba31_227-continuous.json +++ b/maps/colin27-jba31_227-continuous.json @@ -5536,5 +5536,25 @@ "volume": 453 } ] - } + }, + "ebrains": { + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "openminds/DatasetVersion": "f1fe19e8-99bd-44bc-9616-a52850680777", + "minds/core/species/v1.0.0": "0ea4e6ba-2681-4f7d-9fa9-49b915caaac9", + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd" + }, + "publications": [ + { + "citation": "Amunts, K., Mohlberg, H., Bludau, S., Zilles, K. (2020). Julich-Brain \u2013 A 3D probabilistic atlas of human brain\u2019s cytoarchitecture. Science 369, 988-992", + "url": "https://www.science.org/doi/10.1126/science.abb4588" + }, + { + "citation": "Alan C. Evans, Andrew L. Janke, D. Louis Collins, Sylvain Baillet, Brain templates and atlases, NeuroImage, Volume 62, Issue 2, 2012, Pages 911-922, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2012.01.024.", + "url": "https://doi.org/10.1016/j.neuroimage.2012.01.024" + }, + { + "citation": "Simon B. Eickhoff, Klaas E. Stephan, Hartmut Mohlberg, Christian Grefkes, Gereon R. Fink, Katrin Amunts, Karl Zilles, A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data, NeuroImage, Volume 25, Issue 4, 2005, Pages 1325-1335, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2004.12.034.", + "url": "https://doi.org/10.1016/j.neuroimage.2004.12.034" + } + ] } diff --git a/maps/colin27-marsatlas-labelled.json b/maps/colin27-marsatlas-labelled.json index 8baccf0f..2cf3c0b6 100644 --- a/maps/colin27-marsatlas-labelled.json +++ b/maps/colin27-marsatlas-labelled.json @@ -591,5 +591,18 @@ "label": 258 } ] - } -} \ No newline at end of file + }, + "publications": [ + { + "name": "MarsAtlas: A cortical parcellation atlas for functional mapping", + "url": "https://doi.org/10.1002/hbm.23121", + "authors": [ + "Guillaume Auzias", + "Olivier Coulon", + "Andrea Brovelli" + ], + "description": "The brainstem is one of the most densely packed areas of the central nervous system in terms of gray, but also white, matter structures and, therefore, is a highly functional hub. It has mainly been studied by the means of histological techniques, which requires several hundreds of slices with a loss of the 3D coherence of the whole specimen. Access to the inner structure of the brainstem is possible using Magnetic Resonance Imaging (MRI), but this method has a limited spatial resolution and contrast in vivo. Here, we scanned an ex vivo specimen using an ultra-high field (11.7T) preclinical MRI scanner providing data at a mesoscopic scale for anatomical T2-weighted (100\u00a0\u00b5m and 185\u00a0\u00b5m isotropic) and diffusion-weighted imaging (300\u00a0\u00b5m isotropic). We then proposed a hierarchical segmentation of the inner gray matter of the brainstem and defined a set of rules for each segmented anatomical class. These rules were gathered in a freely accessible web-based application, WIKIBrainStem (https://fibratlas.univ- tours.fr/brainstems/index.html), for 99 structures, from which 13 were subdivided into 29 substructures. This segmentation is, to date, the most detailed one developed from ex vivo MRI of the brainstem. This should be regarded as a tool that will be complemented by future results of alternative methods, such as Optical Coherence Tomography, Polarized Light Imaging or histology\u2026 This is a mandatory step prior to segmenting multiple specimens, which will be used to create a probabilistic automated segmentation method of ex vivo, but also in vivo, brainstem and may be used for targeting anatomical structures of interest in managing some degenerative or psychiatric disorders.", + "citation": "Auzias, G., Coulon, O. and Brovelli, A. (2016), MarsAtlas: A cortical parcellation atlas for functional mapping. Hum. Brain Mapp., 37: 1573-1592. https://doi.org/10.1002/hbm.23121" + } + ] +} diff --git a/maps/fsaverage-jba29-labelled.json b/maps/fsaverage-jba29-labelled.json index 64c6b042..8129eeea 100644 --- a/maps/fsaverage-jba29-labelled.json +++ b/maps/fsaverage-jba29-labelled.json @@ -1794,5 +1794,16 @@ "label": 367 } ] - } + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "openminds/DatasetVersion": "a8932c7e-063c-4131-ab96-996d843998e9" + }, + "publications": [ + { + "citation": "Amunts, K., Mohlberg, H., Bludau, S., Zilles, K. (2020). Julich-Brain \u2013 A 3D probabilistic atlas of human brain\u2019s cytoarchitecture. Science 369, 988-992", + "url": "https://doi.org/10.1126/science.abb4588" + } + ] } diff --git a/maps/fsaverage-jba30-labelled.json b/maps/fsaverage-jba30-labelled.json index b79262f0..1d1c7083 100644 --- a/maps/fsaverage-jba30-labelled.json +++ b/maps/fsaverage-jba30-labelled.json @@ -1812,5 +1812,25 @@ "fragment": "right hemisphere" } ] - } + }, + "ebrains": { + "openminds/DatasetVersion": "d69b70e2-3002-4eaf-9c61-9c56f019bbc8", + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "minds/core/species/v1.0.0": "0ea4e6ba-2681-4f7d-9fa9-49b915caaac9", + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd" + }, + "publications": [ + { + "citation": "Amunts, K., Mohlberg, H., Bludau, S., Zilles, K. (2020). Julich-Brain \u2013 A 3D probabilistic atlas of human brain\u2019s cytoarchitecture. Science 369, 988-992", + "url": "https://www.science.org/doi/10.1126/science.abb4588" + }, + { + "citation": "Alan C. Evans, Andrew L. Janke, D. Louis Collins, Sylvain Baillet, Brain templates and atlases, NeuroImage, Volume 62, Issue 2, 2012, Pages 911-922, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2012.01.024.", + "url": "https://doi.org/10.1016/j.neuroimage.2012.01.024" + }, + { + "citation": "Simon B. Eickhoff, Klaas E. Stephan, Hartmut Mohlberg, Christian Grefkes, Gereon R. Fink, Katrin Amunts, Karl Zilles, A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data, NeuroImage, Volume 25, Issue 4, 2005, Pages 1325-1335, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2004.12.034.", + "url": "https://doi.org/10.1016/j.neuroimage.2004.12.034" + } + ] } diff --git a/maps/fsaverage-jba31-labelled.json b/maps/fsaverage-jba31-labelled.json index 3e78caa6..c0113a37 100644 --- a/maps/fsaverage-jba31-labelled.json +++ b/maps/fsaverage-jba31-labelled.json @@ -2062,5 +2062,25 @@ "fragment": "right hemisphere" } ] - } + }, + "ebrains": { + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "openminds/DatasetVersion": "f1fe19e8-99bd-44bc-9616-a52850680777", + "minds/core/species/v1.0.0": "0ea4e6ba-2681-4f7d-9fa9-49b915caaac9", + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd" + }, + "publications": [ + { + "citation": "Amunts, K., Mohlberg, H., Bludau, S., Zilles, K. (2020). Julich-Brain \u2013 A 3D probabilistic atlas of human brain\u2019s cytoarchitecture. Science 369, 988-992", + "url": "https://www.science.org/doi/10.1126/science.abb4588" + }, + { + "citation": "Alan C. Evans, Andrew L. Janke, D. Louis Collins, Sylvain Baillet, Brain templates and atlases, NeuroImage, Volume 62, Issue 2, 2012, Pages 911-922, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2012.01.024.", + "url": "https://doi.org/10.1016/j.neuroimage.2012.01.024" + }, + { + "citation": "Simon B. Eickhoff, Klaas E. Stephan, Hartmut Mohlberg, Christian Grefkes, Gereon R. Fink, Katrin Amunts, Karl Zilles, A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data, NeuroImage, Volume 25, Issue 4, 2005, Pages 1325-1335, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2004.12.034.", + "url": "https://doi.org/10.1016/j.neuroimage.2004.12.034" + } + ] } diff --git a/maps/fsaverage-visfAtlas-labelled.json b/maps/fsaverage-visfAtlas-labelled.json index f5584871..e917c85c 100644 --- a/maps/fsaverage-visfAtlas-labelled.json +++ b/maps/fsaverage-visfAtlas-labelled.json @@ -253,5 +253,20 @@ "label": 16 } ] - } + }, + "publications": [ + { + "name": "A Probabilistic Functional Atlas of Human Occipito-Temporal Visual Cortex", + "url": "https://doi.org/10.1093/cercor/bhaa246", + "authors": [ + "Mona Rosenke", + "Rick van Hoof", + "Job van den Hurk", + "Kalanit Grill-Spector", + "Rainer Goebel" + ], + "description": "Human visual cortex contains many retinotopic and category- specific regions. These brain regions have been the focus of a large body of functional magnetic resonance imaging research, significantly expanding our understanding of visual processing. As studying these regions requires accurate localization of their cortical location, researchers perform functional localizer scans to identify these regions in each individual. However, it is not always possible to conduct these localizer scans. Here, we developed and validated a functional region of interest (ROI) atlas of early visual and category-selective regions in human ventral and lateral occipito-temporal cortex. Results show that for the majority of functionally defined ROIs, cortex-based alignment results in lower between- subject variability compared to nonlinear volumetric alignment. Furthermore, we demonstrate that 1) the atlas accurately predicts the location of an independent dataset of ventral temporal cortex ROIs and other atlases of place selectivity, motion selectivity, and retinotopy. Next, 2) we show that the majority of voxel within our atlas is responding mostly to the labeled category in a left-out subject cross-validation, demonstrating the utility of this atlas. The functional atlas is publicly available (download.brainvoyager.com/data/visfAtlas.zip) and can help identify the location of these regions in healthy subjects as well as populations (e.g., blind people, infants) in which functional localizers cannot be run.", + "citation": "Mona Rosenke, Rick van Hoof, Job van den Hurk, Kalanit Grill-Spector, Rainer Goebel A Probabilistic Functional Atlas of Human Occipito-Temporal Visual Cortex Cerebral Cortex, Volume 31, Issue 1, January 2021, Pages 603-619 https://doi.org/10.1093/cercor/bhaa246" + } + ] } diff --git a/maps/fsaverage6-jba29-labelled.json b/maps/fsaverage6-jba29-labelled.json index 2b0cd1f0..72ceec15 100644 --- a/maps/fsaverage6-jba29-labelled.json +++ b/maps/fsaverage6-jba29-labelled.json @@ -1794,5 +1794,16 @@ "label": 367 } ] - } + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "openminds/DatasetVersion": "a8932c7e-063c-4131-ab96-996d843998e9" + }, + "publications": [ + { + "citation": "Amunts, K., Mohlberg, H., Bludau, S., Zilles, K. (2020). Julich-Brain \u2013 A 3D probabilistic atlas of human brain\u2019s cytoarchitecture. Science 369, 988-992", + "url": "https://doi.org/10.1126/science.abb4588" + } + ] } diff --git a/maps/hcp32k-jba29-labelled.json b/maps/hcp32k-jba29-labelled.json index ae05bff2..5fba3682 100644 --- a/maps/hcp32k-jba29-labelled.json +++ b/maps/hcp32k-jba29-labelled.json @@ -1794,5 +1794,16 @@ "fragment": "right hemisphere" } ] - } + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "openminds/DatasetVersion": "a8932c7e-063c-4131-ab96-996d843998e9" + }, + "publications": [ + { + "citation": "Amunts, K., Mohlberg, H., Bludau, S., Zilles, K. (2020). Julich-Brain \u2013 A 3D probabilistic atlas of human brain\u2019s cytoarchitecture. Science 369, 988-992", + "url": "https://doi.org/10.1126/science.abb4588" + } + ] } diff --git a/maps/mni152-cort_thr0-labelled.json b/maps/mni152-cort_thr0-labelled.json index f606d5db..efb0fde1 100644 --- a/maps/mni152-cort_thr0-labelled.json +++ b/maps/mni152-cort_thr0-labelled.json @@ -591,5 +591,26 @@ "label": 96 } ] - } + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd" + }, + "publications": [ + { + "citation": "Makris N, Goldstein JM, Kennedy D, Hodge SM, Caviness VS, Faraone SV, Tsuang MT, Seidman LJ. Decreased volume of left and total anterior insular lobule in schizophrenia. Schizophr Res. 2006 Apr;83(2-3):155-71", + "url": "https://doi.org/10.1016/j.schres.2005.11.020" + }, + { + "citation": "Frazier JA, Chiu S, Breeze JL, Makris N, Lange N, Kennedy DN, Herbert MR, Bent EK, Koneru VK, Dieterich ME, Hodge SM, Rauch SL, Grant PE, Cohen BM, Seidman LJ, Caviness VS, Biederman J. Structural brain magnetic resonance imaging of limbic and thalamic volumes in pediatric bipolar disorder. Am J Psychiatry. 2005 Jul;162(7):1256-65", + "url": "https://doi.org/10.1176/appi.ajp.162.7.1256" + }, + { + "citation": "Desikan RS, S\u00e9gonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006 Jul 1;31(3):968-80.", + "url": "https://doi.org/10.1016/j.neuroimage.2006.01.021" + }, + { + "citation": "Goldstein JM, Seidman LJ, Makris N, Ahern T, O'Brien LM, Caviness VS Jr, Kennedy DN, Faraone SV, Tsuang MT. Hypothalamic abnormalities in schizophrenia: sex effects and genetic vulnerability. Biol Psychiatry. 2007 Apr 15;61(8):935-45", + "url": "https://doi.org/10.1016/j.biopsych.2006.06.027" + } + ] } diff --git a/maps/mni152-cort_thr25-labelled.json b/maps/mni152-cort_thr25-labelled.json index 07fa1df7..774dbe59 100644 --- a/maps/mni152-cort_thr25-labelled.json +++ b/maps/mni152-cort_thr25-labelled.json @@ -591,5 +591,26 @@ "label": 96 } ] - } + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd" + }, + "publications": [ + { + "citation": "Makris N, Goldstein JM, Kennedy D, Hodge SM, Caviness VS, Faraone SV, Tsuang MT, Seidman LJ. Decreased volume of left and total anterior insular lobule in schizophrenia. Schizophr Res. 2006 Apr;83(2-3):155-71", + "url": "https://doi.org/10.1016/j.schres.2005.11.020" + }, + { + "citation": "Frazier JA, Chiu S, Breeze JL, Makris N, Lange N, Kennedy DN, Herbert MR, Bent EK, Koneru VK, Dieterich ME, Hodge SM, Rauch SL, Grant PE, Cohen BM, Seidman LJ, Caviness VS, Biederman J. Structural brain magnetic resonance imaging of limbic and thalamic volumes in pediatric bipolar disorder. Am J Psychiatry. 2005 Jul;162(7):1256-65", + "url": "https://doi.org/10.1176/appi.ajp.162.7.1256" + }, + { + "citation": "Desikan RS, S\u00e9gonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006 Jul 1;31(3):968-80.", + "url": "https://doi.org/10.1016/j.neuroimage.2006.01.021" + }, + { + "citation": "Goldstein JM, Seidman LJ, Makris N, Ahern T, O'Brien LM, Caviness VS Jr, Kennedy DN, Faraone SV, Tsuang MT. Hypothalamic abnormalities in schizophrenia: sex effects and genetic vulnerability. Biol Psychiatry. 2007 Apr 15;61(8):935-45", + "url": "https://doi.org/10.1016/j.biopsych.2006.06.027" + } + ] } diff --git a/maps/mni152-difumo1024-continuous.json b/maps/mni152-difumo1024-continuous.json index 9e9e83ff..1b11760a 100644 --- a/maps/mni152-difumo1024-continuous.json +++ b/maps/mni152-difumo1024-continuous.json @@ -6164,5 +6164,15 @@ "volume": 0 } ] - } -} \ No newline at end of file + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/DatasetVersion": "164ef5c9-bec5-43c7-b258-80798cb0d57b" + }, + "publications": [ + { + "citation": "Dadi K, Varoquaux G, Machlouzarides-Shalit A, Gorgolewski KJ, Wassermann D, Thirion B, Mensch A. Fine-grain atlases of functional modes for fMRI analysis. NeuroImage. 2020;221:117126. doi:10.1016/j.neuroimage.2020.117126", + "url": "https://doi.org/10.1016/j.neuroimage.2020.117126" + } + ] +} diff --git a/maps/mni152-difumo1024-labelled.json b/maps/mni152-difumo1024-labelled.json index 459988c6..b4901905 100644 --- a/maps/mni152-difumo1024-labelled.json +++ b/maps/mni152-difumo1024-labelled.json @@ -6160,5 +6160,15 @@ "volume": 0 } ] - } -} \ No newline at end of file + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/DatasetVersion": "164ef5c9-bec5-43c7-b258-80798cb0d57b" + }, + "publications": [ + { + "citation": "Dadi K, Varoquaux G, Machlouzarides-Shalit A, Gorgolewski KJ, Wassermann D, Thirion B, Mensch A. Fine-grain atlases of functional modes for fMRI analysis. NeuroImage. 2020;221:117126. doi:10.1016/j.neuroimage.2020.117126", + "url": "https://doi.org/10.1016/j.neuroimage.2020.117126" + } + ] +} diff --git a/maps/mni152-difumo128-continuous.json b/maps/mni152-difumo128-continuous.json index 82335132..c4e30989 100644 --- a/maps/mni152-difumo128-continuous.json +++ b/maps/mni152-difumo128-continuous.json @@ -788,5 +788,15 @@ "volume": 0 } ] - } -} \ No newline at end of file + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/DatasetVersion": "b8d0ba16-5543-4594-a6f0-ecbacfc9fb04" + }, + "publications": [ + { + "citation": "Dadi K, Varoquaux G, Machlouzarides-Shalit A, Gorgolewski KJ, Wassermann D, Thirion B, Mensch A. Fine-grain atlases of functional modes for fMRI analysis. NeuroImage. 2020;221:117126. doi:10.1016/j.neuroimage.2020.117126", + "url": "https://doi.org/10.1016/j.neuroimage.2020.117126" + } + ] +} diff --git a/maps/mni152-difumo128-labelled.json b/maps/mni152-difumo128-labelled.json index 53c6887e..2cc906fd 100644 --- a/maps/mni152-difumo128-labelled.json +++ b/maps/mni152-difumo128-labelled.json @@ -784,5 +784,15 @@ "volume": 0 } ] - } -} \ No newline at end of file + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/DatasetVersion": "b8d0ba16-5543-4594-a6f0-ecbacfc9fb04" + }, + "publications": [ + { + "citation": "Dadi K, Varoquaux G, Machlouzarides-Shalit A, Gorgolewski KJ, Wassermann D, Thirion B, Mensch A. Fine-grain atlases of functional modes for fMRI analysis. NeuroImage. 2020;221:117126. doi:10.1016/j.neuroimage.2020.117126", + "url": "https://doi.org/10.1016/j.neuroimage.2020.117126" + } + ] +} diff --git a/maps/mni152-difumo256-continuous.json b/maps/mni152-difumo256-continuous.json index 8ff8f982..92b04cd4 100644 --- a/maps/mni152-difumo256-continuous.json +++ b/maps/mni152-difumo256-continuous.json @@ -1556,5 +1556,15 @@ "volume": 0 } ] - } -} \ No newline at end of file + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/DatasetVersion": "5438792c-ff2a-4554-9f85-af795f870741" + }, + "publications": [ + { + "citation": "Dadi K, Varoquaux G, Machlouzarides-Shalit A, Gorgolewski KJ, Wassermann D, Thirion B, Mensch A. Fine-grain atlases of functional modes for fMRI analysis. NeuroImage. 2020;221:117126. doi:10.1016/j.neuroimage.2020.117126", + "url": "https://doi.org/10.1016/j.neuroimage.2020.117126" + } + ] +} diff --git a/maps/mni152-difumo256-labelled.json b/maps/mni152-difumo256-labelled.json index 37a096df..6af9f97c 100644 --- a/maps/mni152-difumo256-labelled.json +++ b/maps/mni152-difumo256-labelled.json @@ -1552,5 +1552,15 @@ "volume": 0 } ] - } -} \ No newline at end of file + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/DatasetVersion": "5438792c-ff2a-4554-9f85-af795f870741" + }, + "publications": [ + { + "citation": "Dadi K, Varoquaux G, Machlouzarides-Shalit A, Gorgolewski KJ, Wassermann D, Thirion B, Mensch A. Fine-grain atlases of functional modes for fMRI analysis. NeuroImage. 2020;221:117126. doi:10.1016/j.neuroimage.2020.117126", + "url": "https://doi.org/10.1016/j.neuroimage.2020.117126" + } + ] +} diff --git a/maps/mni152-difumo512-continuous.json b/maps/mni152-difumo512-continuous.json index 5c7cf9a3..7bccd6ce 100644 --- a/maps/mni152-difumo512-continuous.json +++ b/maps/mni152-difumo512-continuous.json @@ -3092,5 +3092,15 @@ "volume": 0 } ] - } -} \ No newline at end of file + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/DatasetVersion": "2ab064dd-7ac5-44ca-8711-a72435f0672e" + }, + "publications": [ + { + "citation": "Dadi K, Varoquaux G, Machlouzarides-Shalit A, Gorgolewski KJ, Wassermann D, Thirion B, Mensch A. Fine-grain atlases of functional modes for fMRI analysis. NeuroImage. 2020;221:117126. doi:10.1016/j.neuroimage.2020.117126", + "url": "https://doi.org/10.1016/j.neuroimage.2020.117126" + } + ] +} diff --git a/maps/mni152-difumo512-labelled.json b/maps/mni152-difumo512-labelled.json index e206e485..273e05a2 100644 --- a/maps/mni152-difumo512-labelled.json +++ b/maps/mni152-difumo512-labelled.json @@ -3088,5 +3088,15 @@ "volume": 0 } ] - } -} \ No newline at end of file + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/DatasetVersion": "2ab064dd-7ac5-44ca-8711-a72435f0672e" + }, + "publications": [ + { + "citation": "Dadi K, Varoquaux G, Machlouzarides-Shalit A, Gorgolewski KJ, Wassermann D, Thirion B, Mensch A. Fine-grain atlases of functional modes for fMRI analysis. NeuroImage. 2020;221:117126. doi:10.1016/j.neuroimage.2020.117126", + "url": "https://doi.org/10.1016/j.neuroimage.2020.117126" + } + ] +} diff --git a/maps/mni152-difumo64-continuous.json b/maps/mni152-difumo64-continuous.json index 0ade23a0..138e8086 100644 --- a/maps/mni152-difumo64-continuous.json +++ b/maps/mni152-difumo64-continuous.json @@ -404,5 +404,15 @@ "volume": 0 } ] - } -} \ No newline at end of file + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/DatasetVersion": "e472a8c7-d9f9-4e75-9d0b-b137cecbc6a2" + }, + "publications": [ + { + "citation": "Dadi K, Varoquaux G, Machlouzarides-Shalit A, Gorgolewski KJ, Wassermann D, Thirion B, Mensch A. Fine-grain atlases of functional modes for fMRI analysis. NeuroImage. 2020;221:117126. doi:10.1016/j.neuroimage.2020.117126", + "url": "https://doi.org/10.1016/j.neuroimage.2020.117126" + } + ] +} diff --git a/maps/mni152-difumo64-labelled.json b/maps/mni152-difumo64-labelled.json index 7e21c61a..aeeadf91 100644 --- a/maps/mni152-difumo64-labelled.json +++ b/maps/mni152-difumo64-labelled.json @@ -400,5 +400,15 @@ "volume": 0 } ] - } -} \ No newline at end of file + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/DatasetVersion": "e472a8c7-d9f9-4e75-9d0b-b137cecbc6a2" + }, + "publications": [ + { + "citation": "Dadi K, Varoquaux G, Machlouzarides-Shalit A, Gorgolewski KJ, Wassermann D, Thirion B, Mensch A. Fine-grain atlases of functional modes for fMRI analysis. NeuroImage. 2020;221:117126. doi:10.1016/j.neuroimage.2020.117126", + "url": "https://doi.org/10.1016/j.neuroimage.2020.117126" + } + ] +} diff --git a/maps/mni152-dk-labelled.json b/maps/mni152-dk-labelled.json index 122a67b6..0bf75848 100644 --- a/maps/mni152-dk-labelled.json +++ b/maps/mni152-dk-labelled.json @@ -423,5 +423,46 @@ "label": 2033 } ] - } + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd" + }, + "publications": [ + { + "name": "An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.", + "url": "https://doi.org/10.1016/j.neuroimage.2006.01.021", + "authors": [ + "Rahul S. Desikan", + "Florent S\u00e9gonne", + "Bruce Fischl", + "Brian T. Quinn", + "Bradford C. Dickerson", + "Deborah Blacker", + "Randy L. Buckner", + "Anders M. Dale", + "R. Paul Maguire", + "Bradley T. Hyman", + "Marilyn S. Albert", + "Ronald J. Killiany" + ], + "description": "In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1\u00a0mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.", + "citation": "Desikan RS, S\u00e9gonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage. 2006;31(3):968-980." + }, + { + "name": "Accurate nonlinear mapping between MNI volumetric and FreeSurfer surface coordinate systems", + "url": "https://doi.org/10.1002/hbm.24213", + "authors": [ + "Jianxiao Wu", + "Gia H. Ngo", + "Douglas Greve", + "Jingwei Li", + "Tong He", + "Bruce Fischl", + "Simon B. Eickhoff", + "B.T. Thomas Yeo" + ], + "description": "The results of most neuroimaging studies are reported in volumetric (e.g., MNI152) or surface (e.g., fsaverage) coordinate systems. Accurate mappings between volumetric and surface coordinate systems can facilitate many applications, such as projecting fMRI group analyses from MNI152/Colin27 to fsaverage for visualization or projecting resting-state fMRI parcellations from fsaverage to MNI152/Colin27 for volumetric analysis of new data. However, there has been surprisingly little research on this topic. Here, we evaluated three approaches for mapping data between MNI152/Colin27 and fsaverage coordinate systems by simulating the above applications: projection of group-average data from MNI152/Colin27 to fsaverage and projection of fsaverage parcellations to MNI152/Colin27. Two of the approaches are currently widely used. A third approach (registration fusion) was previously proposed, but not widely adopted. Two implementations of the registration fusion (RF) approach were considered, with one implementation utilizing the Advanced Normalization Tools (ANTs). We found that RF-ANTs performed the best for mapping between fsaverage and MNI152/Colin27, even for new subjects registered to MNI152/Colin27 using a different software tool (FSL FNIRT). This suggests that RF-ANTs would be useful even for researchers not using ANTs. Finally, it is worth emphasizing that the most optimal approach for mapping data to a coordinate system (e.g., fsaverage) is to register individual subjects directly to the coordinate system, rather than via another coordinate system. Only in scenarios where the optimal approach is not possible (e.g., mapping previously published results from MNI152 to fsaverage), should the approaches evaluated in this manuscript be considered. In these scenarios, we recommend RF-ANTs (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/registration/Wu2017_RegistrationFusion).", + "citation": "Wu J, Ngo GH, Greve D, Li J, He T, Fischl B, Eickhoff SB, Yeo BTT. Accurate nonlinear mapping between MNI volumetric and FreeSurfer surface coordinate systems. Hum Brain Mapp. 2018;39(9):3793-3808." + } + ] } diff --git a/maps/mni152-dwm-continuous.json b/maps/mni152-dwm-continuous.json index 174d8e84..7aa6fe71 100644 --- a/maps/mni152-dwm-continuous.json +++ b/maps/mni152-dwm-continuous.json @@ -366,5 +366,16 @@ "label": null } ] - } -} \ No newline at end of file + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/Dataset": "2eff0f98-24e4-4b93-98ae-96cd82256951", + "openminds/DatasetVersion": "518b0d17-ed31-4a20-91c2-efbe2eb6545a" + }, + "publications": [ + { + "citation": "Guevara, P., Duclap, D., Poupon, C., Marrakchi-Kacem, L., Fillard, P., Le Bihan, D., Leboyer, M., Houenou, J., Mangin, J.- F (2012). Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas. NeuroImage, 61(4),1083-99.", + "url": "https://doi.org/10.1016/j.neuroimage.2012.02.071" + } + ] +} diff --git a/maps/mni152-dwm-labelled.json b/maps/mni152-dwm-labelled.json index 93c69c31..1dc82924 100644 --- a/maps/mni152-dwm-labelled.json +++ b/maps/mni152-dwm-labelled.json @@ -245,5 +245,16 @@ "label": 51 } ] - } + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/Dataset": "2eff0f98-24e4-4b93-98ae-96cd82256951", + "openminds/DatasetVersion": "518b0d17-ed31-4a20-91c2-efbe2eb6545a" + }, + "publications": [ + { + "citation": "Guevara, P., Duclap, D., Poupon, C., Marrakchi-Kacem, L., Fillard, P., Le Bihan, D., Leboyer, M., Houenou, J., Mangin, J.- F (2012). Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas. NeuroImage, 61(4),1083-99.", + "url": "https://doi.org/10.1016/j.neuroimage.2012.02.071" + } + ] } diff --git a/maps/mni152-jba118-continuous.json b/maps/mni152-jba118-continuous.json index 2b0dd802..67f04cfa 100644 --- a/maps/mni152-jba118-continuous.json +++ b/maps/mni152-jba118-continuous.json @@ -3598,5 +3598,24 @@ "label": null } ] - } + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "openminds/DatasetVersion": "4ac9f0bc-560d-47e0-8916-7b24da9bb0ce" + }, + "publications": [ + { + "citation": "Zilles K, Amunts K (2010) Centenary of Brodmann\u2019s map \u2013 conception and fate. Nature Reviews Neuroscience 11(2): 139-145 ", + "url": "https://doi.org/10.1038/nrn2776" + }, + { + "citation": "Amunts K, Schleicher A, Zilles K (2007) Cytoarchitecture of the cerebral cortex \u2013 more than localization. Neuroimage 37: 1061-1065", + "url": "https://doi.org/10.1016/j.neuroimage.2007.02.037" + }, + { + "citation": "Zilles K, Schleicher A, Palomero-Gallagher N, Amunts K (2002) Quantitative analysis of cyto- and receptor architecture of the human brain. In: /Brain Mapping: The Methods/, J. C. Mazziotta and A. Toga (eds.), USA: Elsevier, 2002, p. 573-602.", + "url": "http://dx.doi.org/10.1016/B978-012693019-1/50023-X" + } + ] } diff --git a/maps/mni152-jba118-labelled.json b/maps/mni152-jba118-labelled.json index 507cb582..18649203 100644 --- a/maps/mni152-jba118-labelled.json +++ b/maps/mni152-jba118-labelled.json @@ -1350,5 +1350,24 @@ "label": 219 } ] - } + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "openminds/DatasetVersion": "4ac9f0bc-560d-47e0-8916-7b24da9bb0ce" + }, + "publications": [ + { + "citation": "Zilles K, Amunts K (2010) Centenary of Brodmann\u2019s map \u2013 conception and fate. Nature Reviews Neuroscience 11(2): 139-145 ", + "url": "https://doi.org/10.1038/nrn2776" + }, + { + "citation": "Amunts K, Schleicher A, Zilles K (2007) Cytoarchitecture of the cerebral cortex \u2013 more than localization. Neuroimage 37: 1061-1065", + "url": "https://doi.org/10.1016/j.neuroimage.2007.02.037" + }, + { + "citation": "Zilles K, Schleicher A, Palomero-Gallagher N, Amunts K (2002) Quantitative analysis of cyto- and receptor architecture of the human brain. In: /Brain Mapping: The Methods/, J. C. Mazziotta and A. Toga (eds.), USA: Elsevier, 2002, p. 573-602.", + "url": "http://dx.doi.org/10.1016/B978-012693019-1/50023-X" + } + ] } diff --git a/maps/mni152-jba29-continuous.json b/maps/mni152-jba29-continuous.json index 80f72cee..92b73a09 100644 --- a/maps/mni152-jba29-continuous.json +++ b/maps/mni152-jba29-continuous.json @@ -5172,5 +5172,16 @@ "label": null } ] - } + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "openminds/DatasetVersion": "a8932c7e-063c-4131-ab96-996d843998e9" + }, + "publications": [ + { + "citation": "Amunts, K., Mohlberg, H., Bludau, S., Zilles, K. (2020). Julich-Brain \u2013 A 3D probabilistic atlas of human brain\u2019s cytoarchitecture. Science 369, 988-992", + "url": "https://doi.org/10.1126/science.abb4588" + } + ] } diff --git a/maps/mni152-jba29-labelled.json b/maps/mni152-jba29-labelled.json index a0484592..608aa811 100644 --- a/maps/mni152-jba29-labelled.json +++ b/maps/mni152-jba29-labelled.json @@ -1798,5 +1798,16 @@ "label": 367 } ] - } + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "openminds/DatasetVersion": "a8932c7e-063c-4131-ab96-996d843998e9" + }, + "publications": [ + { + "citation": "Amunts, K., Mohlberg, H., Bludau, S., Zilles, K. (2020). Julich-Brain \u2013 A 3D probabilistic atlas of human brain\u2019s cytoarchitecture. Science 369, 988-992", + "url": "https://doi.org/10.1126/science.abb4588" + } + ] } diff --git a/maps/mni152-jba30-labelled.json b/maps/mni152-jba30-labelled.json index 9a23c11a..61f744ff 100644 --- a/maps/mni152-jba30-labelled.json +++ b/maps/mni152-jba30-labelled.json @@ -1900,5 +1900,25 @@ "label": 1157 } ] - } + }, + "ebrains": { + "openminds/DatasetVersion": "d69b70e2-3002-4eaf-9c61-9c56f019bbc8", + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "minds/core/species/v1.0.0": "0ea4e6ba-2681-4f7d-9fa9-49b915caaac9", + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd" + }, + "publications": [ + { + "citation": "Amunts, K., Mohlberg, H., Bludau, S., Zilles, K. (2020). Julich-Brain \u2013 A 3D probabilistic atlas of human brain\u2019s cytoarchitecture. Science 369, 988-992", + "url": "https://www.science.org/doi/10.1126/science.abb4588" + }, + { + "citation": "Alan C. Evans, Andrew L. Janke, D. Louis Collins, Sylvain Baillet, Brain templates and atlases, NeuroImage, Volume 62, Issue 2, 2012, Pages 911-922, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2012.01.024.", + "url": "https://doi.org/10.1016/j.neuroimage.2012.01.024" + }, + { + "citation": "Simon B. Eickhoff, Klaas E. Stephan, Hartmut Mohlberg, Christian Grefkes, Gereon R. Fink, Katrin Amunts, Karl Zilles, A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data, NeuroImage, Volume 25, Issue 4, 2005, Pages 1325-1335, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2004.12.034.", + "url": "https://doi.org/10.1016/j.neuroimage.2004.12.034" + } + ] } diff --git a/maps/mni152-jba30_157regions-continuous.json b/maps/mni152-jba30_157regions-continuous.json index 2a6544d7..3f6c62a6 100644 --- a/maps/mni152-jba30_157regions-continuous.json +++ b/maps/mni152-jba30_157regions-continuous.json @@ -4720,5 +4720,25 @@ "volume": 313 } ] - } + }, + "ebrains": { + "openminds/DatasetVersion": "d69b70e2-3002-4eaf-9c61-9c56f019bbc8", + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "minds/core/species/v1.0.0": "0ea4e6ba-2681-4f7d-9fa9-49b915caaac9", + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd" + }, + "publications": [ + { + "citation": "Amunts, K., Mohlberg, H., Bludau, S., Zilles, K. (2020). Julich-Brain \u2013 A 3D probabilistic atlas of human brain\u2019s cytoarchitecture. Science 369, 988-992", + "url": "https://www.science.org/doi/10.1126/science.abb4588" + }, + { + "citation": "Alan C. Evans, Andrew L. Janke, D. Louis Collins, Sylvain Baillet, Brain templates and atlases, NeuroImage, Volume 62, Issue 2, 2012, Pages 911-922, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2012.01.024.", + "url": "https://doi.org/10.1016/j.neuroimage.2012.01.024" + }, + { + "citation": "Simon B. Eickhoff, Klaas E. Stephan, Hartmut Mohlberg, Christian Grefkes, Gereon R. Fink, Katrin Amunts, Karl Zilles, A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data, NeuroImage, Volume 25, Issue 4, 2005, Pages 1325-1335, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2004.12.034.", + "url": "https://doi.org/10.1016/j.neuroimage.2004.12.034" + } + ] } diff --git a/maps/mni152-jba30_175regions-continuous.json b/maps/mni152-jba30_175regions-continuous.json index 3ab35b9c..f119f05d 100644 --- a/maps/mni152-jba30_175regions-continuous.json +++ b/maps/mni152-jba30_175regions-continuous.json @@ -5254,5 +5254,25 @@ "volume": 349 } ] - } + }, + "ebrains": { + "openminds/DatasetVersion": "d69b70e2-3002-4eaf-9c61-9c56f019bbc8", + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "minds/core/species/v1.0.0": "0ea4e6ba-2681-4f7d-9fa9-49b915caaac9", + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd" + }, + "publications": [ + { + "citation": "Amunts, K., Mohlberg, H., Bludau, S., Zilles, K. (2020). Julich-Brain \u2013 A 3D probabilistic atlas of human brain\u2019s cytoarchitecture. Science 369, 988-992", + "url": "https://www.science.org/doi/10.1126/science.abb4588" + }, + { + "citation": "Alan C. Evans, Andrew L. Janke, D. Louis Collins, Sylvain Baillet, Brain templates and atlases, NeuroImage, Volume 62, Issue 2, 2012, Pages 911-922, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2012.01.024.", + "url": "https://doi.org/10.1016/j.neuroimage.2012.01.024" + }, + { + "citation": "Simon B. Eickhoff, Klaas E. Stephan, Hartmut Mohlberg, Christian Grefkes, Gereon R. Fink, Katrin Amunts, Karl Zilles, A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data, NeuroImage, Volume 25, Issue 4, 2005, Pages 1325-1335, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2004.12.034.", + "url": "https://doi.org/10.1016/j.neuroimage.2004.12.034" + } + ] } diff --git a/maps/mni152-jba31-labelled.json b/maps/mni152-jba31-labelled.json index a061c5fc..e853cc7c 100644 --- a/maps/mni152-jba31-labelled.json +++ b/maps/mni152-jba31-labelled.json @@ -2920,5 +2920,25 @@ "fragment": "right hemisphere" } ] - } + }, + "ebrains": { + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "openminds/DatasetVersion": "f1fe19e8-99bd-44bc-9616-a52850680777", + "minds/core/species/v1.0.0": "0ea4e6ba-2681-4f7d-9fa9-49b915caaac9", + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd" + }, + "publications": [ + { + "citation": "Amunts, K., Mohlberg, H., Bludau, S., Zilles, K. (2020). Julich-Brain \u2013 A 3D probabilistic atlas of human brain\u2019s cytoarchitecture. Science 369, 988-992", + "url": "https://www.science.org/doi/10.1126/science.abb4588" + }, + { + "citation": "Alan C. Evans, Andrew L. Janke, D. Louis Collins, Sylvain Baillet, Brain templates and atlases, NeuroImage, Volume 62, Issue 2, 2012, Pages 911-922, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2012.01.024.", + "url": "https://doi.org/10.1016/j.neuroimage.2012.01.024" + }, + { + "citation": "Simon B. Eickhoff, Klaas E. Stephan, Hartmut Mohlberg, Christian Grefkes, Gereon R. Fink, Katrin Amunts, Karl Zilles, A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data, NeuroImage, Volume 25, Issue 4, 2005, Pages 1325-1335, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2004.12.034.", + "url": "https://doi.org/10.1016/j.neuroimage.2004.12.034" + } + ] } diff --git a/maps/mni152-jba31_207-continuous.json b/maps/mni152-jba31_207-continuous.json index b958ff07..e7fe4baf 100644 --- a/maps/mni152-jba31_207-continuous.json +++ b/maps/mni152-jba31_207-continuous.json @@ -5066,5 +5066,25 @@ "volume": 413 } ] - } + }, + "ebrains": { + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "openminds/DatasetVersion": "f1fe19e8-99bd-44bc-9616-a52850680777", + "minds/core/species/v1.0.0": "0ea4e6ba-2681-4f7d-9fa9-49b915caaac9", + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd" + }, + "publications": [ + { + "citation": "Amunts, K., Mohlberg, H., Bludau, S., Zilles, K. (2020). Julich-Brain \u2013 A 3D probabilistic atlas of human brain\u2019s cytoarchitecture. Science 369, 988-992", + "url": "https://www.science.org/doi/10.1126/science.abb4588" + }, + { + "citation": "Alan C. Evans, Andrew L. Janke, D. Louis Collins, Sylvain Baillet, Brain templates and atlases, NeuroImage, Volume 62, Issue 2, 2012, Pages 911-922, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2012.01.024.", + "url": "https://doi.org/10.1016/j.neuroimage.2012.01.024" + }, + { + "citation": "Simon B. Eickhoff, Klaas E. Stephan, Hartmut Mohlberg, Christian Grefkes, Gereon R. Fink, Katrin Amunts, Karl Zilles, A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data, NeuroImage, Volume 25, Issue 4, 2005, Pages 1325-1335, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2004.12.034.", + "url": "https://doi.org/10.1016/j.neuroimage.2004.12.034" + } + ] } diff --git a/maps/mni152-jba31_227-continuous.json b/maps/mni152-jba31_227-continuous.json index e5f28689..f1583fe4 100644 --- a/maps/mni152-jba31_227-continuous.json +++ b/maps/mni152-jba31_227-continuous.json @@ -5536,5 +5536,25 @@ "volume": 453 } ] - } + }, + "ebrains": { + "openminds/Dataset": "5a16d948-8d1c-400c-b797-8a7ad29944b2", + "openminds/DatasetVersion": "f1fe19e8-99bd-44bc-9616-a52850680777", + "minds/core/species/v1.0.0": "0ea4e6ba-2681-4f7d-9fa9-49b915caaac9", + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd" + }, + "publications": [ + { + "citation": "Amunts, K., Mohlberg, H., Bludau, S., Zilles, K. (2020). Julich-Brain \u2013 A 3D probabilistic atlas of human brain\u2019s cytoarchitecture. Science 369, 988-992", + "url": "https://www.science.org/doi/10.1126/science.abb4588" + }, + { + "citation": "Alan C. Evans, Andrew L. Janke, D. Louis Collins, Sylvain Baillet, Brain templates and atlases, NeuroImage, Volume 62, Issue 2, 2012, Pages 911-922, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2012.01.024.", + "url": "https://doi.org/10.1016/j.neuroimage.2012.01.024" + }, + { + "citation": "Simon B. Eickhoff, Klaas E. Stephan, Hartmut Mohlberg, Christian Grefkes, Gereon R. Fink, Katrin Amunts, Karl Zilles, A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data, NeuroImage, Volume 25, Issue 4, 2005, Pages 1325-1335, ISSN 1053-8119, https://doi.org/10.1016/j.neuroimage.2004.12.034.", + "url": "https://doi.org/10.1016/j.neuroimage.2004.12.034" + } + ] } diff --git a/maps/mni152-sw_hcp-continuous.json b/maps/mni152-sw_hcp-continuous.json index 62dac84e..e2541437 100644 --- a/maps/mni152-sw_hcp-continuous.json +++ b/maps/mni152-sw_hcp-continuous.json @@ -10782,5 +10782,9 @@ "label": null } ] + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/DatasetVersion": "6a7e07ad-303c-4b1d-b444-ded9ee782225" } -} \ No newline at end of file +} diff --git a/maps/mni152-sw_hcp-labelled.json b/maps/mni152-sw_hcp-labelled.json index d20caef6..956d8f60 100644 --- a/maps/mni152-sw_hcp-labelled.json +++ b/maps/mni152-sw_hcp-labelled.json @@ -4059,5 +4059,9 @@ "label": 2308 } ] + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/DatasetVersion": "6a7e07ad-303c-4b1d-b444-ded9ee782225" } } diff --git a/maps/mni152-swm-continuous.json b/maps/mni152-swm-continuous.json index 7ff5023a..6de7593f 100644 --- a/maps/mni152-swm-continuous.json +++ b/maps/mni152-swm-continuous.json @@ -1614,5 +1614,15 @@ "label": null } ] - } -} \ No newline at end of file + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/DatasetVersion": "f58e4425-6614-4ad9-ac26-5e946b1296cb" + }, + "publications": [ + { + "citation": "Guevara, P., Duclap, D., Poupon, C., Marrakchi-Kacem, L., Fillard, P., Le Bihan, D., Leboyer, M., Houenou, J., Mangin, J.- F (2012). Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas. NeuroImage, 61(4),1083-99.", + "url": "https://doi.org/10.1016/j.neuroimage.2012.02.071" + } + ] +} diff --git a/maps/mni152-swm-labelled.json b/maps/mni152-swm-labelled.json index b4554ce6..40f7ac46 100644 --- a/maps/mni152-swm-labelled.json +++ b/maps/mni152-swm-labelled.json @@ -617,5 +617,15 @@ "label": 56 } ] - } + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd", + "openminds/DatasetVersion": "f58e4425-6614-4ad9-ac26-5e946b1296cb" + }, + "publications": [ + { + "citation": "Guevara, P., Duclap, D., Poupon, C., Marrakchi-Kacem, L., Fillard, P., Le Bihan, D., Leboyer, M., Houenou, J., Mangin, J.- F (2012). Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas. NeuroImage, 61(4),1083-99.", + "url": "https://doi.org/10.1016/j.neuroimage.2012.02.071" + } + ] } diff --git a/maps/mni152-vep-labelled.json b/maps/mni152-vep-labelled.json index 413c5181..92468a6c 100644 --- a/maps/mni152-vep-labelled.json +++ b/maps/mni152-vep-labelled.json @@ -1149,5 +1149,28 @@ "label": 72077 } ] - } + }, + "ebrains": { + "openminds/Species": "97c070c6-8e1f-4ee8-9d28-18c7945921dd" + }, + "publications": [ + { + "name": "VEP atlas: An anatomic and functional human brain atlas dedicated to epilepsy patients", + "url": "https://doi.org/10.1016/j.jneumeth.2020.108983", + "authors": [ + "Huifang E. Wang", + "Julia Scholly", + "Paul Triebkorn", + "Viktor Sip", + "Samuel Medina Villalon", + "Marmaduke M. Woodman", + "Arnaud Le Troter", + "Maxime Guye", + "Fabrice Bartolomei", + "Viktor Jirsa" + ], + "description": "Several automated parcellation atlases of the human brain have been developed over the past decades, based on various criteria, and have been applied in basic and clinical research. Here we present the Virtual Epileptic Patient (VEP) atlas that offers a new automated brain region parcellation and labeling, which has been developed for the specific use in the domains of epileptology and functional neurosurgery and is able to apply at individual patient\u2019s level.", + "citation": "Wang HE, Scholly J, Triebkorn P, Sip V, Medina Villalon S, Woodman MM, Le Troter A, Guye M, Bartolomei F, Jirsa V. VEP atlas: An anatomic and functional human brain atlas dedicated to epilepsy patients. J Neurosci Methods. 2021 Jan 15;348:108983. doi: 10.1016/j.jneumeth.2020.108983. Epub 2020 Oct 24. PMID: 33121983." + } + ] } diff --git a/maps/mni152-visfAtlas-labelled.json b/maps/mni152-visfAtlas-labelled.json index 4a9f9914..c0424b89 100644 --- a/maps/mni152-visfAtlas-labelled.json +++ b/maps/mni152-visfAtlas-labelled.json @@ -214,5 +214,20 @@ "label": 33 } ] - } -} \ No newline at end of file + }, + "publications": [ + { + "name": "A Probabilistic Functional Atlas of Human Occipito-Temporal Visual Cortex", + "url": "https://doi.org/10.1093/cercor/bhaa246", + "authors": [ + "Mona Rosenke", + "Rick van Hoof", + "Job van den Hurk", + "Kalanit Grill-Spector", + "Rainer Goebel" + ], + "description": "Human visual cortex contains many retinotopic and category- specific regions. These brain regions have been the focus of a large body of functional magnetic resonance imaging research, significantly expanding our understanding of visual processing. As studying these regions requires accurate localization of their cortical location, researchers perform functional localizer scans to identify these regions in each individual. However, it is not always possible to conduct these localizer scans. Here, we developed and validated a functional region of interest (ROI) atlas of early visual and category-selective regions in human ventral and lateral occipito-temporal cortex. Results show that for the majority of functionally defined ROIs, cortex-based alignment results in lower between- subject variability compared to nonlinear volumetric alignment. Furthermore, we demonstrate that 1) the atlas accurately predicts the location of an independent dataset of ventral temporal cortex ROIs and other atlases of place selectivity, motion selectivity, and retinotopy. Next, 2) we show that the majority of voxel within our atlas is responding mostly to the labeled category in a left-out subject cross-validation, demonstrating the utility of this atlas. The functional atlas is publicly available (download.brainvoyager.com/data/visfAtlas.zip) and can help identify the location of these regions in healthy subjects as well as populations (e.g., blind people, infants) in which functional localizers cannot be run.", + "citation": "Mona Rosenke, Rick van Hoof, Job van den Hurk, Kalanit Grill-Spector, Rainer Goebel A Probabilistic Functional Atlas of Human Occipito-Temporal Visual Cortex Cerebral Cortex, Volume 31, Issue 1, January 2021, Pages 603-619 https://doi.org/10.1093/cercor/bhaa246" + } + ] +} diff --git a/maps/mni152-vonEconomoKoskinas-labelled.json b/maps/mni152-vonEconomoKoskinas-labelled.json index 4804f522..553dd6e6 100644 --- a/maps/mni152-vonEconomoKoskinas-labelled.json +++ b/maps/mni152-vonEconomoKoskinas-labelled.json @@ -543,5 +543,21 @@ "label": 2044 } ] - } -} \ No newline at end of file + }, + "publications": [ + { + "name": "An MRI Von Economo \u2013 Koskinas atlas", + "url": "https://doi.org/10.1016/j.neuroimage.2016.12.069", + "authors": [ + "Lianne H. Scholtens", + "Marcel A. de Reus", + "Siemon C. de Lange", + "Ruben Schmidt", + "Martijn P. van den Heuvel" + ], + "license": "CC BY-NC-SA 4.0 Deed", + "description": "The cerebral cortex displays substantial variation in cellular architecture, a regional patterning that has been of great interest to anatomists for centuries. In 1925, Constantin von Economo and George Koskinas published a detailed atlas of the human cerebral cortex, describing a cytoarchitectonic division of the cortical mantle into over 40 distinct areas. Von Economo and Koskinas accompanied their seminal work with large photomicrographic plates of their histological slides, together with tables containing for each described region detailed morphological layer- specific information on neuronal count, neuron size and thickness of the cortical mantle. Here, we aimed to make this legacy data accessible and relatable to in vivo neuroimaging data by constructing a digital Von Economo \u2013 Koskinas atlas compatible with the widely used FreeSurfer software suite. In this technical note we describe the procedures used for manual segmentation of the Von Economo \u2013 Koskinas atlas onto individual T1 scans and the subsequent construction of the digital atlas. We provide the files needed to run the atlas on new FreeSurfer data, together with some simple code of how to apply the atlas to T1 scans within the FreeSurfer software suite. The digital Von Economo \u2013 Koskinas atlas is easily applicable to modern day anatomical MRI data and is made publicly available online.", + "citation": "Scholtens, L. H., de Reus, M. A., de Lange, S. C., Schmidt, R., & van den Heuvel, M. P. (2018). An mri von economo\u2013koskinas atlas. NeuroImage, 170, 249-256." + } + ] +} diff --git a/maps/monkey-mebrains-labelled.json b/maps/monkey-mebrains-labelled.json index 52c20259..fc9385b2 100644 --- a/maps/monkey-mebrains-labelled.json +++ b/maps/monkey-mebrains-labelled.json @@ -1136,5 +1136,17 @@ "label": 4000 } ] - } + }, + "publications": [ + { + "name": "MEBRAINS Multilevel Macaque Brain Atlas", + "url": "https://search.kg.ebrains.eu/instances/e39a0407-a98a-480e-9c63-4a2225ddfbe4", + "authors": [ + "" + ], + "license": "", + "description": "The MEBRAINS Multilevel Macaque Brain Atlas integrates multiple aspects of brain organisation spanning different spatial and temporal scales as revealed by complementary structural, functional and connectivity-based datasets. \nThe Atlas is centered around the MEBRAINS Template, a population-based template that represents an average of high-resolution structural T1 and T2 MRI scans as well as CT scans of 10 rhesus macaque monkey brains. The MEBRAINS Template not only serves as anchorage for structural and functional data within the Julich-Leuven Multilevel Macaque Brain Atlas, but can also be downloaded for the analysis of functional imaging and whole-brain connectivity studies. We also provide a manually curated segmentation of the cortical ribbon, the lateral ventricles and multiple subcortical structures.\nThe Julich Brain Macaque Maps are defined at the microscopic scale based on a multimodal quantitative architectonic analysis of one Macaca mulatta and three Macaca fascicularis post-mortem brains. They provide delineations of distinct cyto- and receptor architectonically identified areas for comparison with in vivo neuroimaging findings ([Rapan et al. 2021](https://doi.org/10.1016/j.neuroimage.2020.117574)). The criteria used for identification and delineation of each area are described in the publications associated with the corresponding delineation data set, and individual maps have also been registered to the Yerkes 19 reference space ([Donahue et al., 2016](https://www.jneurosci.org/content/36/25/6758)). The Julich Brain Macaque Maps are complemented with information on the densities of 14 different receptors for multiple classical neurotransmitters as revealed by means of in vitro quantitative receptor autoradiography ([Palomero & Zilles, 2018](https://www.sciencedirect.com/science/article/pii/B9780444636393000244?via%3Dihub); [Zilles et al. 2002](https://www.sciencedirect.com/science/article/abs/pii/S0924977X02001086?via%3Dihub)), representing a mesoscopical scale of brain organisation. \nThe Julich-Leuven Multilevel Macaque Brain Atlas will be continuously extended with new areas and populated by a growing selection of multimodal data spanning multiple structural and time scales (e.g., probabilistic retinotopic maps).", + "citation": "" + } + ] } diff --git a/maps/mouse-ccf2015-labelled.json b/maps/mouse-ccf2015-labelled.json index f880d966..4bb7c054 100644 --- a/maps/mouse-ccf2015-labelled.json +++ b/maps/mouse-ccf2015-labelled.json @@ -7876,5 +7876,33 @@ "label": 304325711 } ] - } + }, + "ebrains": { + "minds/core/species/v1.0.0": "cfc1656c-67d1-4d2c-a17e-efd7ce0df88c", + "openminds/Species": "d9875ebd-260e-4337-a637-b62fed4aa91d" + }, + "publications": [ + { + "citation": "Allen Reference Atlas - Mouse Brain [brain atlas]. Available from atlas.brain-map.org", + "url": "https://atlas.brain-map.org/" + }, + { + "name": "Neuroinformatics of the Allen Mouse Brain Connectivity Atlas", + "description": "The Allen Mouse Brain Connectivity Atlas is a mesoscale whole brain axonal projection atlas of the C57Bl/6J mouse brain. Anatomical trajectories throughout the brain were mapped into a common 3D space using a standardized platform to generate a comprehensive and quantitative database of inter-areal and cell-type-specific projections. This connectivity atlas has several desirable features, including brain-wide coverage, validated and versatile experimental techniques, a single standardized data format, a quantifiable and integrated neuroinformatics resource, and an open-access public online database (http://connectivity.brain-map.org/). Meaningful informatics data quantification and comparison is key to effective use and interpretation of connectome data. This relies on successful definition of a high fidelity atlas template and framework, mapping precision of raw data sets into the 3D reference framework, accurate signal detection and quantitative connection strength algorithms, and effective presentation in an integrated online application. Here we describe key informatics pipeline steps in the creation of the Allen Mouse Brain Connectivity Atlas and include basic application use cases.", + "citation": "Kuan L, Li Y, Lau C, Feng D, Bernard A, Sunkin SM, Zeng H, Dang C, Hawrylycz M, Ng L. Neuroinformatics of the Allen Mouse Brain Connectivity Atlas. Methods. 2015 Feb;73:4-17. doi: 10.1016/j.ymeth.2014.12.013. Epub 2014 Dec 20. PMID: 25536338.", + "url": "https://doi.org/10.1016/j.ymeth.2014.12.013", + "authors": [ + "Leonard Kuan", + "Yang Li", + "Chris Lau", + "David Feng", + "Amy Bernard", + "Susan M. Sunkin", + "Hongkui Zeng", + "Chinh Dang", + "Michael Hawrylycz", + "Lydia Ng" + ] + } + ] } diff --git a/maps/mouse-ccf2017-labelled.json b/maps/mouse-ccf2017-labelled.json index 9bfbe016..4490d738 100644 --- a/maps/mouse-ccf2017-labelled.json +++ b/maps/mouse-ccf2017-labelled.json @@ -7978,5 +7978,66 @@ "label": 304325711 } ] - } + }, + "ebrains": { + "minds/core/species/v1.0.0": "cfc1656c-67d1-4d2c-a17e-efd7ce0df88c", + "openminds/Species": "d9875ebd-260e-4337-a637-b62fed4aa91d" + }, + "publications": [ + { + "citation": "Allen Reference Atlas - Mouse Brain [brain atlas]. Available from atlas.brain-map.org", + "url": "https://atlas.brain-map.org/" + }, + { + "name": "The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas", + "description": "Recent large-scale collaborations are generating major surveys of cell types and connections in the mouse brain, collecting large amounts of data across modalities, spatial scales, and brain areas. Successful integration of these data requires a standard 3D reference atlas. Here, we present the Allen Mouse Brain Common Coordinate Framework (CCFv3) as such a resource. We constructed an average template brain at 10 \u03bcm voxel resolution by interpolating high resolution in-plane serial two-photon tomography images with 100 \u03bcm z-sampling from 1,675 young adult C57BL/6J mice. Then, using multimodal reference data, we parcellated the entire brain directly in 3D, labeling every voxel with a brain structure spanning 43 isocortical areas and their layers, 329 subcortical gray matter structures, 81 fiber tracts, and 8 ventricular structures. CCFv3 can be used to analyze, visualize, and integrate multimodal and multiscale datasets in 3D and is openly accessible (https://atlas.brain-map.org/).", + "citation": "Wang Q, Ding SL, Li Y, Royall J, Feng D, Lesnar P, Graddis N, Naeemi M, Facer B, Ho A, Dolbeare T, Blanchard B, Dee N, Wakeman W, Hirokawa KE, Szafer A, Sunkin SM, Oh SW, Bernard A, Phillips JW, Hawrylycz M, Koch C, Zeng H, Harris JA, Ng L. The Allen Mouse Brain Common Coordinate Framework: A 3D Reference Atlas. Cell. 2020 May 14;181(4):936-953.e20. doi: 10.1016/j.cell.2020.04.007. Epub 2020 May 7. PMID: 32386544; PMCID: PMC8152789.", + "url": "https://doi.org/10.1016/j.cell.2020.04.007", + "authors": [ + "Quanxin Wang", + "Song-Lin Ding", + "Yang Li", + "Josh Royall", + "David Feng", + "Phil Lesnar", + "Nile Graddis", + "Maitham Naeemi", + "Benjamin Facer", + "Anh Ho", + "Tim Dolbeare", + "Brandon Blanchard", + "Nick Dee", + "Wayne Wakeman", + "Karla E. Hirokawa", + "Aaron Szafer", + "Susan M. Sunkin", + "Seung Wook Oh", + "Amy Bernard", + "John W. Phillips", + "Michael Hawrylycz", + "Christof Koch", + "Hongkui Zeng", + "Julie A. Harris", + "Lydia Ng" + ] + }, + { + "name": "Neuroinformatics of the Allen Mouse Brain Connectivity Atlas", + "description": "The Allen Mouse Brain Connectivity Atlas is a mesoscale whole brain axonal projection atlas of the C57Bl/6J mouse brain. Anatomical trajectories throughout the brain were mapped into a common 3D space using a standardized platform to generate a comprehensive and quantitative database of inter-areal and cell-type-specific projections. This connectivity atlas has several desirable features, including brain-wide coverage, validated and versatile experimental techniques, a single standardized data format, a quantifiable and integrated neuroinformatics resource, and an open-access public online database (http://connectivity.brain-map.org/). Meaningful informatics data quantification and comparison is key to effective use and interpretation of connectome data. This relies on successful definition of a high fidelity atlas template and framework, mapping precision of raw data sets into the 3D reference framework, accurate signal detection and quantitative connection strength algorithms, and effective presentation in an integrated online application. Here we describe key informatics pipeline steps in the creation of the Allen Mouse Brain Connectivity Atlas and include basic application use cases.", + "citation": "Kuan L, Li Y, Lau C, Feng D, Bernard A, Sunkin SM, Zeng H, Dang C, Hawrylycz M, Ng L. Neuroinformatics of the Allen Mouse Brain Connectivity Atlas. Methods. 2015 Feb;73:4-17. doi: 10.1016/j.ymeth.2014.12.013. Epub 2014 Dec 20. PMID: 25536338.", + "url": "https://doi.org/10.1016/j.ymeth.2014.12.013", + "authors": [ + "Leonard Kuan", + "Yang Li", + "Chris Lau", + "David Feng", + "Amy Bernard", + "Susan M. Sunkin", + "Hongkui Zeng", + "Chinh Dang", + "Michael Hawrylycz", + "Lydia Ng" + ] + } + ] } diff --git a/maps/rat-waxholmv1_01-labelled.json b/maps/rat-waxholmv1_01-labelled.json index 9179dd2f..9aa4028c 100644 --- a/maps/rat-waxholmv1_01-labelled.json +++ b/maps/rat-waxholmv1_01-labelled.json @@ -478,5 +478,25 @@ "label": 44 } ] - } + }, + "ebrains": { + "minds/core/dataset/v1.0.0": "f40e466b-8247-463a-a4cb-56dfe68e7059", + "openminds/Species": "ab532423-1fd7-4255-8c6f-f99dc6df814f", + "openminds/Dataset": "82f91c95-6799-485a-ab9a-010c75f9e790", + "openminds/DatasetVersion": "f40e466b-8247-463a-a4cb-56dfe68e7059" + }, + "publications": [ + { + "citation": "https://www.nitrc.org/frs/?group_id=1081", + "url": "https://www.nitrc.org/frs/?group_id=1081" + }, + { + "citation": "Papp, E. A., Leergaard, T. B., Calabrese, E., Johnson, G. A., & Bjaalie, J. G. (2014). Waxholm Space atlas of the Sprague Dawley rat brain. NeuroImage, 97, 374-386. https://doi.org/10.1016/j.neuroimage.2014.04.001", + "url": "https://doi.org/10.1016/j.neuroimage.2014.04.001" + }, + { + "citation": "Papp, E. A., Leergaard, T. B., Calabrese, E., Allan Johnson, G., & Bjaalie, J. G. (2015). Addendum to \u201cWaxholm Space atlas of the Sprague Dawley rat brain\u201d [NeuroImage 97 (2014) 374-386]. NeuroImage, 105, 561-562. https://doi.org/10.1016/j.neuroimage.2014.10.017", + "url": "https://doi.org/10.1016/j.neuroimage.2014.10.017" + } + ] } diff --git a/maps/rat-waxholmv2-labelled.json b/maps/rat-waxholmv2-labelled.json index 47182db1..e96e290d 100644 --- a/maps/rat-waxholmv2-labelled.json +++ b/maps/rat-waxholmv2-labelled.json @@ -484,5 +484,21 @@ "label": 70 } ] - } + }, + "ebrains": { + "minds/core/dataset/v1.0.0": "2c8ec4fb-45ca-4fe7-accf-c41b5e92c43d", + "openminds/Species": "ab532423-1fd7-4255-8c6f-f99dc6df814f", + "openminds/Dataset": "82f91c95-6799-485a-ab9a-010c75f9e790", + "openminds/DatasetVersion": "2c8ec4fb-45ca-4fe7-accf-c41b5e92c43d" + }, + "publications": [ + { + "citation": "https://www.nitrc.org/frs/?group_id=1081", + "url": "https://www.nitrc.org/frs/?group_id=1081" + }, + { + "citation": "Kjonigsen LJ, Lillehaug S, Bjaalie JG, Witter MP, Leergaard TB (2015) Waxholm Space atlas of the rat brain hippocampal region: Three-dimensional delineations based on magnetic resonance and diffusion tensor imaging. NeuroImage 108, 441-449", + "url": "https://doi.org/10.1016/j.neuroimage.2014.12.080" + } + ] } diff --git a/maps/rat-waxholmv3-labelled.json b/maps/rat-waxholmv3-labelled.json index 66da59be..20ff18b7 100644 --- a/maps/rat-waxholmv3-labelled.json +++ b/maps/rat-waxholmv3-labelled.json @@ -724,5 +724,21 @@ "label": 162 } ] - } + }, + "ebrains": { + "minds/core/dataset/v1.0.0": "e80f9946-1aa9-494b-b81a-9048ca9afdbe", + "openminds/Species": "ab532423-1fd7-4255-8c6f-f99dc6df814f", + "openminds/Dataset": "82f91c95-6799-485a-ab9a-010c75f9e790", + "openminds/DatasetVersion": "e80f9946-1aa9-494b-b81a-9048ca9afdbe" + }, + "publications": [ + { + "citation": "https://www.nitrc.org/frs/?group_id=1081", + "url": "https://www.nitrc.org/frs/?group_id=1081" + }, + { + "citation": "Osen KK, Imad J, Wennberg AE, Papp EA, Leergaard TB (2019) Waxholm Space atlas of the rat brain auditory system: Three-dimensional delineations based on structural and diffusion tensor magnetic resonance imaging. NeuroImage 199, 38-56", + "url": "https://doi.org/10.1016/j.neuroimage.2019.05.016" + } + ] } diff --git a/maps/rat-waxholmv4-labelled.json b/maps/rat-waxholmv4-labelled.json index a30f1f8f..d13e1867 100644 --- a/maps/rat-waxholmv4-labelled.json +++ b/maps/rat-waxholmv4-labelled.json @@ -1354,5 +1354,28 @@ "label": 162 } ] - } + }, + "ebrains": { + "minds/core/dataset/v1.0.0": "413794ff-e7ad-4aef-b57c-0dab29c93c7e", + "minds/core/species/v1.0.0": "f3490d7f-8f7f-4b40-b238-963dcac84412", + "openminds/Species": "ab532423-1fd7-4255-8c6f-f99dc6df814f", + "openminds/DatasetVersion": "fadcd2cb-9e8b-4e01-9777-f4d4df8f1ebc", + "openminds/Dataset": "82f91c95-6799-485a-ab9a-010c75f9e790" + }, + "publications": [ + { + "name": "Waxholm Space atlas of the rat brain: a 3D atlas supporting data analysis and integration", + "description": "Volumetric brain atlases are increasingly used to integrate and analyze diverse experimental neuroscience data acquired from animal models, but until recently a publicly available digital atlas with complete coverage of the rat brain has been missing. Here we present an update of the Waxholm Space rat brain atlas, a comprehensive open-access volumetric atlas resource. This brain atlas features annotations of 222 structures, of which 112 are new and 57 revised compared to previous versions. It provides a detailed map of the cerebral cortex, hippocampal region, striatopallidal areas, midbrain dopaminergic system, thalamic cell groups, the auditory system and main fiber tracts. We document the criteria underlying the annotations and demonstrate how the atlas with related tools and workflows can be used to support interpretation, integration, analysis and dissemination of experimental rat brain data.", + "authors": [ + "Heidi Kleven", + "Ingvild E. Bjerke", + "Francisco Clasc\u00e1", + "Henk J. Groenewegen", + "Jan G. Bjaalie", + "Trygve B. Leergaard" + ], + "citation": "Kleven, H., Bjerke, I.E., Clasc\u00e1, F. et al. Waxholm Space atlas of the rat brain: a 3D atlas supporting data analysis and integration. Nat Methods (2023).", + "url": "https://doi.org/10.1038/s41592-023-02034-3" + } + ] }