Abstract

Precise cortical brain localization presents an important challenge in the literature. Brain atlases provide data-guided parcellation based on functional and structural brain metrics, and each atlas has its own unique benefits for localization. We offer a parcellation guided by intracranial electroencephalography, a technique which has historically provided pioneering advances in our understanding of brain structure–function relationships. We used a consensus boundary mapping approach combining anatomical designations in Duvernoy’s Atlas of the Human Brain, a widely recognized textbook of human brain anatomy, with the anatomy of the MNI152 template and the magnetic resonance imaging scans of an epilepsy surgery cohort. The Yale Brain Atlas consists of 690 one-square centimeter parcels based around conserved anatomical features and each with a unique identifier to communicate anatomically unambiguous localization. We report on the methodology we used to create the Atlas along with the findings of a neuroimaging study assessing the accuracy and clinical usefulness of cortical localization using the Atlas. We also share our vision for the Atlas as a tool in the clinical and research neurosciences, where it may facilitate precise localization of data on the cortex, accurate description of anatomical locations, and modern data science approaches using standardized brain regions.

Details

Title
High-resolution cortical parcellation based on conserved brain landmarks for localization of multimodal data to the nearest centimeter
Author
McGrath, Hari 1 ; Zaveri, Hitten P. 2 ; Collins, Evan 3 ; Jafar, Tamara 4 ; Chishti, Omar 5 ; Obaid, Sami 4 ; Ksendzovsky, Alexander 6 ; Wu, Kun 4 ; Papademetris, Xenophon 7 ; Spencer, Dennis D. 4 

 Yale School of Medicine, Department of Neurosurgery, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); King’s College London, GKT School of Medical Education, London, UK (GRID:grid.13097.3c) (ISNI:0000 0001 2322 6764) 
 Yale School of Medicine, Department of Neurology, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710) 
 Yale School of Medicine, Department of Neurosurgery, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale School of Engineering and Applied Science, New Haven, USA (GRID:grid.47100.32); Massachusetts Institute of Technology, Department of Biological Engineering, Cambridge, USA (GRID:grid.116068.8) (ISNI:0000 0001 2341 2786) 
 Yale School of Medicine, Department of Neurosurgery, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710) 
 Yale School of Medicine, Department of Neurosurgery, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale School of Engineering and Applied Science, New Haven, USA (GRID:grid.47100.32) 
 Yale School of Medicine, Department of Neurosurgery, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); University of Maryland School of Medicine, Department of Neurosurgery, Baltimore, USA (GRID:grid.411024.2) (ISNI:0000 0001 2175 4264) 
 Yale School of Medicine, Department of Radiology and Biomedical Engineering, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710); Yale School of Medicine, Department of Biomedical Engineering, New Haven, USA (GRID:grid.47100.32) (ISNI:0000000419368710) 
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2732139363
Copyright
© The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.