Prior atlases have been created to optimize cortical localization, including the Destrieux atlas of common neuroanatomy and the Talairach stereotaxic atlas 14, 15, 16, 17. This limits the resolution of many atlases 1. Precise cortical localization poses a major challenge in atlas mapping due to inter-individual anatomical variability, which necessitates larger parcels to ensure that atlases can fit common anatomy. Some studies then link this localization to previously identified connectomic, functional, or architectonic patterns, while other studies report solely on the topographic localization for instance, studies mapping cortical function based on surgical data 7, 8, 9, studies mapping seizure onset and spread in epilepsy 10, 11, and studies mapping the anatomical distribution of brain tumors 12, 13. Many studies, particularly those in the clinical sciences, require precise cortical localization to map the topographic location of features in the brain. Several widely-used atlases have used similarly robust methods, including the Human Brainnetome Atlas 4, the Yeo Atlas 5, and the Schaefer Atlas 6. One example is the HCP-MMP1 (Glasser) atlas, where the authors used multiple imaging modalities along with a semi-automated parcellation algorithm to capture four cortical properties which derived the parcellation: architecture, function, connectivity, and topography 3. 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.īrain atlas mapping is a method for localizing data in a common space according to biomarkers of brain structure and function 1, 2. 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. 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 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. We offer a parcellation guided by intracranial electroencephalography, a technique which has historically provided pioneering advances in our understanding of brain structure–function relationships. Brain atlases provide data-guided parcellation based on functional and structural brain metrics, and each atlas has its own unique benefits for localization. ![]() Precise cortical brain localization presents an important challenge in the literature.
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