A multi-modal parcellation of human cerebral cortex

Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal ‘fingerprint’ of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease. A detailed parcellation (map) of the human cerebral cortex has been obtained by integrating multi-modal imaging data, including functional magnetic resonance imaging (fMRI), and the resulting freely available resources will enable detailed comparative studies of the human brain in health, ageing and disease. For more than a century, neuroscientists have sought to subdivide the human cerebral cortex into a patchwork of anatomically and functionally distinct areas. Until now such maps have relied largely on only a single property such as micro-architecture or functional imaging, have been based on a relatively small number of individuals, and have usually been blurry due to misalignment of brain areas from person to person. Matthew Glasser, David Van Essen and colleagues have tackled these deficiencies in a new more 'universal' map of the human cerebral cortex by integrating multi-modal imaging data obtained from 210 healthy subjects and validated on 210 other individuals. The authors propose a total of 180 areas per cerebral hemisphere (97 of them previously unknown) and apply a machine-learning classifier to automatically identify these areas in new subjects, even in individuals with atypical parcellations. This freely available resource will enhance the anatomical accuracy and interpretability of future structural and functional studies of the human brain in health and disease.