{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T21:30:42Z","timestamp":1740173442552,"version":"3.37.3"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,1,29]],"date-time":"2019-01-29T00:00:00Z","timestamp":1548720000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,1,29]],"date-time":"2019-01-29T00:00:00Z","timestamp":1548720000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Data"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Morphometric brain changes occur throughout the lifetime and are often investigated to understand healthy ageing and disease, to identify novel biomarkers, and to classify patient groups. Yet, to accurately characterise such changes, an accurate parcellation of the brain must be achieved. Here, we present a manually-parcellated dataset of the superior frontal, the supramarginal, and the cingulate gyri of 10 healthy middle-aged subjects along with a fully detailed protocol based on two anatomical atlases. Gyral parcels were hand-drawn then reviewed by specialists blinded from the protocol to ensure consistency. Importantly, we follow a procedure that allows accounting for anatomical variability beyond what is usually achieved by standard analysis packages and avoids mutually referring to neighbouring gyri when defining gyral edges. We also provide grey matter thickness, grey matter volume, and white matter surface area information for each parcel. This dataset and corresponding measurements are useful in assessing the accuracy of equivalent parcels and metrics generated by image analysis tools and their impact on morphometric studies.<\/jats:p>","DOI":"10.1038\/sdata.2019.1","type":"journal-article","created":{"date-parts":[[2019,1,29]],"date-time":"2019-01-29T14:05:27Z","timestamp":1548770727000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Manually-parcellated gyral data accounting for all known anatomical variability"],"prefix":"10.1038","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2490-9548","authenticated-orcid":false,"given":"Shadia S.","family":"Mikhael","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2189-443X","authenticated-orcid":false,"given":"Grant","family":"Mair","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria","family":"Valdes-Hernandez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Corn\u00e9","family":"Hoogendoorn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9812-6642","authenticated-orcid":false,"given":"Joanna M.","family":"Wardlaw","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark E.","family":"Bastin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4010-4632","authenticated-orcid":false,"given":"Cyril","family":"Pernet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,1,29]]},"reference":[{"key":"BFsdata20191_CR1","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1016\/j.neuroimage.2017.02.082","volume":"170","author":"S Mikhael","year":"2018","unstructured":"Mikhael, S., Hoogendoorn, C., Valdes-Hernandez, M. & Pernet, C. A critical analysis of neuroanatomical software protocols reveals clinically relevant differences in parcellation schemes. Neuroimage 170, 348\u2013364, https:\/\/doi.org\/10.1016\/j.neuroimage.2017.02.082 (2018).","journal-title":"Neuroimage"},{"key":"BFsdata20191_CR2","doi-asserted-by":"publisher","first-page":"12","DOI":"10.3389\/fnins.2012.00171","volume":"6","author":"A Klein","year":"2012","unstructured":"Klein, A. & Tourville, J. 101 labeled brain images and a consistent human cortical labeling protocol. Frontiers in Neuroscience 6, 12, https:\/\/doi.org\/10.3389\/fnins.2012.00171 (2012).","journal-title":"Frontiers in Neuroscience"},{"key":"BFsdata20191_CR3","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.pscychresns.2009.01.006","volume":"172","author":"ME Ranta","year":"2009","unstructured":"Ranta, M. E. et al. Manual MRI parcellation of the frontal lobe. Psychiatry Research-Neuroimaging 172, 147\u2013154, https:\/\/doi.org\/10.1016\/j.pscychresns.2009.01.006 (2009).","journal-title":"Psychiatry Research-Neuroimaging"},{"key":"BFsdata20191_CR4","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1371\/journal.pone.0166785","volume":"12","author":"F Bartel","year":"2017","unstructured":"Bartel, F. et al. Regional analysis of volumes and reproducibilities of automatic and manual hippocampal segmentations. Plos One 12, 19, https:\/\/doi.org\/10.1371\/journal.pone.0166785 (2017).","journal-title":"Plos One"},{"key":"BFsdata20191_CR5","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2015","unstructured":"Menze, B. H. et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). Ieee Transactions on Medical Imaging 34, 1993\u20132024, https:\/\/doi.org\/10.1109\/tmi.2014.2377694 (2015).","journal-title":"Ieee Transactions on Medical Imaging"},{"key":"BFsdata20191_CR6","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1016\/j.neuroimage.2007.09.031","volume":"39","author":"DW Shattuck","year":"2008","unstructured":"Shattuck, D. W. et al. Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage 39, 1064\u20131080, https:\/\/doi.org\/10.1016\/j.neuroimage.2007.09.031 (2008).","journal-title":"Neuroimage"},{"key":"BFsdata20191_CR7","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1038\/sdata.2015.59","volume":"2","author":"J Kulaga-Yoskovitz","year":"2015","unstructured":"Kulaga-Yoskovitz, J. et al. Multi-contrast submillimetric 3Tesla hippocampal subfield segmentation protocol and dataset. Scientific Data 2, 9, https:\/\/doi.org\/10.1038\/sdata.2015.59 (2015).","journal-title":"Scientific Data"},{"key":"BFsdata20191_CR8","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1371\/journal.pone.0007200","volume":"4","author":"JW Bohland","year":"2009","unstructured":"Bohland, J. W., Bokil, H., Allen, C. B. & Mitra, P. P. The Brain Atlas Concordance Problem: Quantitative Comparison of Anatomical Parcellations. Plos One 4, 18, https:\/\/doi.org\/10.1371\/journal.pone.0007200 (2009).","journal-title":"Plos One"},{"key":"BFsdata20191_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neuroimage.2016.01.038","volume":"129","author":"D Schoemaker","year":"2016","unstructured":"Schoemaker, D. et al. Hippocampus and amygdala volumes from magnetic resonance images in children: Assessing accuracy of FreeSurfer and FSL against manual segmentation. Neuroimage 129, 1\u201314, https:\/\/doi.org\/10.1016\/j.neuroimage.2016.01.038 (2016).","journal-title":"Neuroimage"},{"key":"BFsdata20191_CR10","doi-asserted-by":"publisher","unstructured":"Mikhael, S., Mair, G. & Pernet, C. A Manual Segmentation Protocol for Cortical Gyri (ed. Centre for Clinical Brain Sciences and Edinburgh Imaging University of Edinburgh. College of Medicine and Veterinary Medicine) https:\/\/doi.org\/10.7488\/ds\/2406 (Datashare: Edinburgh, 2018).","DOI":"10.7488\/ds\/2406"},{"key":"BFsdata20191_CR11","unstructured":"Ono, M., Kubik, S. & Abernathey, C. D. Atlas of the Cerebral Sulci. 1 edn, (Georg Thieme Verlag, 1990)."},{"key":"BFsdata20191_CR12","unstructured":"Duvernoy, H. M. The Human Brain: Surface, Blood Supply, and Three-Dimensional Section Anatomy. 1 edn, (Springer, 1999)."},{"key":"BFsdata20191_CR13","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.1016\/j.neuroimage.2010.04.258","volume":"52","author":"M Thambisetty","year":"2010","unstructured":"Thambisetty, M. et al. Longitudinal changes in cortical thickness associated with normal aging. Neuroimage 52, 1215\u20131223, https:\/\/doi.org\/10.1016\/j.neuroimage.2010.04.258 (2010).","journal-title":"Neuroimage"},{"key":"BFsdata20191_CR14","doi-asserted-by":"publisher","first-page":"1048","DOI":"10.1212\/01.wnl.0000340981.97664.2f","volume":"72","author":"A Bakkour","year":"2009","unstructured":"Bakkour, A., Morris, J. C. & Dickerson, B. C. The cortical signature of prodromal AD Regional thinning predicts mild AD dementia. Neurology 72, 1048\u20131055, https:\/\/doi.org\/10.1212\/01.wnl.0000340981.97664.2f (2009).","journal-title":"Neurology"},{"key":"BFsdata20191_CR15","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.neurobiolaging.2004.02.019","volume":"26","author":"M Boccardi","year":"2005","unstructured":"Boccardi, M. et al. Frontotemporal dementia as a neural system disease. Neurobiology of Aging 26, 37\u201344, https:\/\/doi.org\/10.1016\/j.neurobiolaging.2004.02.019 (2005).","journal-title":"Neurobiology of Aging"},{"key":"BFsdata20191_CR16","doi-asserted-by":"publisher","first-page":"1701","DOI":"10.1093\/cercor\/bhj105","volume":"16","author":"BF Jones","year":"2006","unstructured":"Jones, B. F. et al. Differential regional atrophy of the cingulate gyrus in Alzheimer disease: A volumetric MRI study. Cerebral Cortex 16, 1701\u20131708, https:\/\/doi.org\/10.1093\/cercor\/bhj105 (2006).","journal-title":"Cerebral Cortex"},{"key":"BFsdata20191_CR17","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1212\/WNL.58.2.198","volume":"58","author":"HJ Rosen","year":"2002","unstructured":"Rosen, H. J. et al. Patterns of brain atrophy in frontotemporal dementia and semantic dementia. Neurology 58, 198\u2013208, https:\/\/doi.org\/10.1212\/wnl.58.2.198 (2002).","journal-title":"Neurology"},{"key":"BFsdata20191_CR18","doi-asserted-by":"publisher","first-page":"1550","DOI":"10.1093\/cercor\/bhl066","volume":"17","author":"ER Sowell","year":"2007","unstructured":"Sowell, E. R. et al. Sex differences in cortical thickness mapped in 176 healthy individuals between 7 and 87 years of age. Cerebral Cortex 17, 1550\u20131560, https:\/\/doi.org\/10.1093\/cercor\/bhl066 (2007).","journal-title":"Cerebral Cortex"},{"key":"BFsdata20191_CR19","unstructured":"Weschler, D. A. (Psychological Corporation: San Antonio, TX, 2008)."},{"key":"BFsdata20191_CR20","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/S1361-8415(01)00036-6","volume":"5","author":"M Jenkinson","year":"2001","unstructured":"Jenkinson, M. & Smith, S. A global optimisation method for robust affine registration of brain images. Medical Image Analysis 5, 143\u2013156, https:\/\/doi.org\/10.1016\/s1361-8415(01)00036-6 (2001).","journal-title":"Medical Image Analysis"},{"key":"BFsdata20191_CR21","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1006\/nimg.2002.1132","volume":"17","author":"M Jenkinson","year":"2002","unstructured":"Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825\u2013841, https:\/\/doi.org\/10.1006\/nimg.2002.1132 (2002).","journal-title":"Neuroimage"},{"key":"BFsdata20191_CR22","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.neuroimage.2009.06.060","volume":"48","author":"DN Greve","year":"2009","unstructured":"Greve, D. N. & Fischl, B. Accurate and robust brain image alignment using boundary-based registration. Neuroimage 48, 63\u201372, https:\/\/doi.org\/10.1016\/j.neuroimage.2009.06.060 (2009).","journal-title":"Neuroimage"},{"key":"BFsdata20191_CR23","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1016\/j.mri.2012.05.001","volume":"30","author":"A Fedorov","year":"2012","unstructured":"Fedorov, A. et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magnetic Resonance Imaging 30, 1323\u20131341, https:\/\/doi.org\/10.1016\/j.mri.2012.05.001 (2012).","journal-title":"Magnetic Resonance Imaging"},{"key":"BFsdata20191_CR24","unstructured":"Zilles, K. The Human Nervous System. (Academic Press, 1990)."},{"key":"BFsdata20191_CR25","unstructured":"von Economo, C. The Cytoarchitectonics of the Human Cerebral Cortex. (Oxford Univ. Press, 1929)."},{"key":"BFsdata20191_CR26","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","volume":"31","author":"RS Desikan","year":"2006","unstructured":"Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968\u2013980, https:\/\/doi.org\/10.1016\/j.neuroimage.2006.01.021 (2006).","journal-title":"Neuroimage"},{"key":"BFsdata20191_CR27","unstructured":"(M2M), Masks2Metrics . 1.0: a Matlab tool for region-of-interest metrics v. 1.0. (Datashare, University of Edinburgh. Centre for Clinical Brain Sciences, 2018)."},{"issue":"22","key":"BFsdata20191_CR28","doi-asserted-by":"publisher","first-page":"436","DOI":"10.21105\/joss.00436","volume":"3","author":"S Mikhael","year":"2018","unstructured":"Mikhael, S. & Gray, C. Masks2Metrics (M2M): A Matlab Toolbox for Gold Standard Morphometrics. Journal of Open Source Software 3 (22), 436, https:\/\/doi.org\/10.21105\/joss.00436 (2018).","journal-title":"Journal of Open Source Software"},{"key":"BFsdata20191_CR29","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1038\/sdata.2016.44","volume":"3","author":"KJ Gorgolewski","year":"2016","unstructured":"Gorgolewski, K. J. et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data 3, 9, https:\/\/doi.org\/10.1038\/sdata.2016.44 (2016).","journal-title":"Scientific Data"},{"key":"BFsdata20191_CR30","doi-asserted-by":"publisher","first-page":"11050","DOI":"10.1073\/pnas.200033797","volume":"97","author":"B Fischl","year":"2000","unstructured":"Fischl, B. & Dale, A. M. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences of the United States of America 97, 11050\u201311055, https:\/\/doi.org\/10.1073\/pnas.200033797 (2000).","journal-title":"Proceedings of the National Academy of Sciences of the United States of America"},{"key":"BFsdata20191_CR31","doi-asserted-by":"publisher","first-page":"258","DOI":"10.3758\/s13428-010-0024-1","volume":"43","author":"IJ Deary","year":"2011","unstructured":"Deary, I. J., Liewald, D. & Nissan, J. A free, easy-to-use, computer-based simple and four-choice reaction time programme: The Deary-Liewald reaction time task. Behavior Research Methods 43, 258\u2013268, https:\/\/doi.org\/10.3758\/s13428-010-0024-1 (2011).","journal-title":"Behavior Research Methods"},{"key":"BFsdata20191_CR32","doi-asserted-by":"publisher","DOI":"10.7488\/ds\/2239","author":"M. Bastin","year":"2017","unstructured":"Bastin, M., Wardlaw, J., Pernet, C., & Mikhael, S. University of Edinburgh https:\/\/doi.org\/10.7488\/ds\/2239 (2017)"},{"key":"BFsdata20191_CR33","doi-asserted-by":"publisher","DOI":"10.7488\/ds\/2357","author":"S. Mikhael","year":"2018","unstructured":"Mikhael, S., & Pernet, C. University of Edinburgh https:\/\/doi.org\/10.7488\/ds\/2357 (2018)"}],"container-title":["Scientific Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/sdata20191.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/sdata20191","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/www.nature.com\/articles\/sdata20191","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"},{"URL":"https:\/\/www.nature.com\/articles\/sdata20191.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,17]],"date-time":"2022-12-17T05:02:08Z","timestamp":1671253328000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/sdata20191"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,29]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,3,12]]}},"alternative-id":["BFsdata20191"],"URL":"https:\/\/doi.org\/10.1038\/sdata.2019.1","relation":{"references":[{"id-type":"doi","id":"10.7488\/ds\/2239","asserted-by":"subject"},{"id-type":"doi","id":"10.7488\/ds\/2357","asserted-by":"subject"}]},"ISSN":["2052-4463"],"issn-type":[{"type":"electronic","value":"2052-4463"}],"subject":[],"published":{"date-parts":[[2019,1,29]]},"assertion":[{"value":"20 February 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 November 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"C.H. is employed by Canon Medical Research Europe which also partly funded this work.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"190001"}}