Cortical areas constitute an important and accessible level of functional and anatomical organization of the human cortex. In invasive studies of nonhuman primates, cortical areas have been defined using differences in Function (neural activity patterns in response to external or internal stimuli), Architecture (the organization of clls and connections within each area of the cortex), Connectivity (differences in connections between different areas), and Topography (maps of space, the body, etc present in some cortical areas). Based on our extensive knowledge in the macaque and limited knowledge in the human, the human cerebral cortex contains between 150 and 200 cortical areas that can best be identified using a mutli-modal neuroimaging approach. The project aims are twofold: 1) To improve upon a non-invasive MRI-based architectonic technique, myelin mapping, that we recently showed can define many cortical areas in group-average data. Artifacts corrupt some regions of the group average data and individuals have more artifacts. We will use improved algorithms to detect and correct these artifacts in higher quality data. The anticipated outcomes are better myelin maps and the ability to define cortical areas in individuals. 2) To identify reproducible cortical areas and areal boundaries in resting state fMRI (R-fMRI) data. We will improve gradient-based R-fMRI parcellation and apply it to high-resolution 7T R-fMRI data acquired by the Human Connectome Project.
This aim should yield more detailed maps of cortical areas and areal borders that show consistency across scans, within subjects, across subjects, and when compared to other modalities. The anticipated outcome is a more detailed map of cortical areas defined by R-fMRI functional connectivity and related to published anatomical and functional studies. The study of brain connectivity and function in heath and disease will benefit greatly from improved methods for localizing anatomical and functional data to the cortical areas. Accurately defining cortical areas in individuals and groups will allow scientists studying mental disorders to better compare their results across individuals, groups, and studies. It will also enable specific study of those cortical areas directly implicated in the disease. The NIMH Strategic Plan mentions neuroimaging and human brain areas repeatedly. Interpreting neuroimaging patterns will be greatly aided by improved maps of human cortical areas across populations and the ability to accurately define areas in the individuals being studied non-invasively. Accurate definition of cortical areas also allows for their use as biomarkers of diseases (or as neurobiologically grounded landmarks for measuring other non-invasive biomarkers such as myelin content, functional connectivity, or diffusion tractography). The NIMH Priorities for Basic Brain and Behavioral Science also emphasizes the use of good anatomical information in neuroimaging studies, which this proposal aims to provide.

Public Health Relevance

To understand the cerebral cortex in health and disease at a systems level, we need to know the identities of the component units, the cortical areas, and have a way of localizing experimental results to them. This project aims to provide advances towards both these goals. These advances will make it easier for investigators studying the brain in disease states to interpret their results and relate them to what is known about the cortical areas of interest.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Individual Predoctoral NRSA for M.D./Ph.D. Fellowships (ADAMHA) (F30)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-F15-P (20))
Program Officer
Rosemond, Erica K
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Washington University
Schools of Medicine
Saint Louis
United States
Zip Code
Glasser, Matthew F; Coalson, Timothy S; Robinson, Emma C et al. (2016) A multi-modal parcellation of human cerebral cortex. Nature 536:171-8
Donahue, Chad J; Sotiropoulos, Stamatios N; Jbabdi, Saad et al. (2016) Using Diffusion Tractography to Predict Cortical Connection Strength and Distance: A Quantitative Comparison with Tracers in the Monkey. J Neurosci 36:6758-70
Harrison, Samuel J; Woolrich, Mark W; Robinson, Emma C et al. (2015) Large-scale probabilistic functional modes from resting state fMRI. Neuroimage 109:217-31
Smith, Stephen M; Nichols, Thomas E; Vidaurre, Diego et al. (2015) A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nat Neurosci 18:1565-7
Salimi-Khorshidi, Gholamreza; Douaud, Gwenaëlle; Beckmann, Christian F et al. (2014) Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. Neuroimage 90:449-68
Abdollahi, Rouhollah O; Kolster, Hauke; Glasser, Matthew F et al. (2014) Correspondences between retinotopic areas and myelin maps in human visual cortex. Neuroimage 99:509-24
Van Essen, David C; Glasser, Matthew F (2014) In vivo architectonics: a cortico-centric perspective. Neuroimage 93 Pt 2:157-64
Robinson, Emma C; Jbabdi, Saad; Glasser, Matthew F et al. (2014) MSM: a new flexible framework for Multimodal Surface Matching. Neuroimage 100:414-26
Glasser, Matthew F; Goyal, Manu S; Preuss, Todd M et al. (2014) Trends and properties of human cerebral cortex: correlations with cortical myelin content. Neuroimage 93 Pt 2:165-75
Glasser, Matthew F; Sotiropoulos, Stamatios N; Wilson, J Anthony et al. (2013) The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80:105-24

Showing the most recent 10 out of 16 publications