A multi-modal parcellation of human cerebral cortex

By Matthew F. Glasser, Timothy S. Coalson, Emma Claire Robinson, C. Hacker, John W. Harwell, Essa Yacoub, Kamil Ugurbil, Jesper L. R. Andersson, Christian F. Beckmann, Mark Jenkinson and 2 more. Nature, 2016

4,736 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/5155e8de5c38375b4eefea72d8a09833dc3a03c0

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Citations in the corpus

The UNC/UMN Baby Connectome Project (BCP): An Overview of the Study Design and Protocol Development (2018, positive): “Indeed, the approach to acquisition and processing, and the quality of HCP and HCP-inspired data has advanced our knowledge of the structural and functional network architecture of the brain (Glasser et al., 2016b; Tavor et al., 2016).”
CAT: a computational anatomy toolbox for the analysis of structural MRI data (2024, neutral): “Furthermore , C AT is easy to integrate with non-SPM software pac ka ges and also supports the Br ain Ima ging Data Structur e (BIDS) standards [ 60 ].”
Recruiting the ABCD sample: Design considerations and procedures (2018, neutral): “With the relatively recent creation of large-sample, neuroscientific studies such as IMAGEN (Schumann et al., 2010), PING (Jernigan et al., 2016), NCANDA (Brown et al., 2015), HCP (Glasser et al., 2016), UK Biobank (Sudlow et al., 2015), Generation R (Kooijman et al., 2016), GUSTO (Soh et al., 2014)”
Morphometric Similarity Networks Detect Microscale Cortical Organisation And Predict Inter-Individual Cognitive Variation (2017, neutral): “(Glasser et al., 2016) which align closely with known histological gradients in nonhuman primates (Wagstyl et al.”
neuromaps: structural and functional interpretation of brain maps (2022, methodology): “Subject-level surfaces were aligned to one another using an MSM procedure (MSMAll) 30 .”
Towards a Universal Taxonomy of Macro-scale Functional Human Brain Networks (2019, background): “Note that the dorsal and ventral visual streams (Goodale and Milner 1992) likely originate from this core occipital network.”
Precision functional mapping of individual human brains (2017, positive): “Spatially Variable Myelin Features Closely Conform to RSFC Network Maps in Individuals Cortical myelin content can be derived from the ratio between T1- and T2-weighted images (Glasser and Van Essen, 2011; Glasser et al., 2016).”
Mindboggling morphometry of human brains (2016, background): “Machine learning has recently been demonstrated to recognize the multi-modal ‘fingerprint’ of cortical areas [24].”
The Developing Human Connectome Project: a Minimal Processing Pipeline for Neonatal Cortical Surface Reconstruction (2017, methodology): “…or functional connectivity, using methods such as Arslan et al. (2015); Craddock et al. (2012); Gordon et al. (2014); Parisot et al. (2016a,b), or indeed train a classifier that will propagate the Glasser et al. (2016a) method for multi-modal parcellation of the adult cortex onto neonatal brains.”
The Lifespan Human Connectome Project in Development: A large-scale study of brain connectivity development in 5–21 year olds (2018, methodology): “For participants 8 years and older, we acquire 26min of resting state scanning in four runs, consistent with recent findings and recommendations about obtaining robust connectivity estimates from rfMRI data (Glasser et al., 2016b; Laumann et al., 2017; Pannunzi et al., 2017).”