For the initial parcellation, we adapted a successful observer-independent semi-automated neuroanatomical approach for generating post-mortem architectonic parcellations 12, 13 to non-invasive neuroimaging. HUMAN BRAIN MAPPING 2016 MANUALPrevious parcellations typically used either fully automated algorithmic approaches, or else manual or partly automated neuroanatomical approaches in which neuroanatomists delineated areal borders, documented areal properties, and identified areas after consulting prior literature. HUMAN BRAIN MAPPING 2016 PLUSResting-state functional MRI (rfMRI) revealed functional connectivity of entire cortical areas plus topographic organization within some areas. Cortical function was measured using task functional MRI (tfMRI) contrasts from seven tasks 11. Architectural measures of relative cortical myelin content and cortical thickness were derived from T1-weighted (T1w) and T2-weighted (T2w) structural images 5, 9, 10. We analysed all four properties across all of neocortex in both hemispheres, using new or refined methods applied to the uniquely rich repository of exceptionally high-quality magnetic resonance imaging (MRI) data provided by the Human Connectome Project (HCP), which benefited from major advances in image acquisition and preprocessing 5 – 8. Combining multiple properties provides complementary as well as confirmatory information, as different properties distinguish different sets of areal boundaries, and more confidence can be placed in boundaries that are consistent across multiple independent properties. Most previous parcellations were based on only one neurobiological property (such as architecture, function, connectivity or topography), and many cover only part of the cortex. However, attaining a consensus whole-cortex parcellation has been difficult because of practical and technical challenges that we address here. 1) to ~200 ( refs 3, 4) areas per hemisphere. The human cerebral cortex has been estimated to contain anywhere from ~50 ( ref. Accurate parcellation provides a map of where we are in the brain, enabling efficient comparison of results across studies and communication among investigators as a foundation for illuminating the functional and structural organization of the brain and as a means to reduce data complexity while improving statistical sensitivity and power for many neuroimaging studies. Areas differ from their neighbours in microstructural architecture, functional specialization, connectivity with other areas, and/or orderly intra-area topographic organization (for example, the map of visual space in visual cortical areas) 1 – 3. Neuroscientists have long sought to subdivide the human brain into a mosaic of anatomically and functionally distinct, spatially contiguous areas (cortical areas and subcortical nuclei), as a prerequisite for understanding how the brain works. 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. 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. 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. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. 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. Making an accurate areal map has been a century-old objective in neuroscience. Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas.
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