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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Individual Predoctoral NRSA for M.D./Ph.D. Fellowships (ADAMHA) (F30)
Project #
1F30MH097312-01
Application #
8310694
Study Section
Special Emphasis Panel (ZRG1-F15-P (20))
Program Officer
Rosemond, Erica K
Project Start
2012-04-01
Project End
2015-03-31
Budget Start
2012-04-01
Budget End
2013-03-31
Support Year
1
Fiscal Year
2012
Total Cost
$28,480
Indirect Cost
Name
Washington University
Department
Neurosciences
Type
Schools of Medicine
DUNS #
068552207
City
Saint Louis
State
MO
Country
United States
Zip Code
63130
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
Salimi-Khorshidi, Gholamreza; Douaud, Gwenaƫlle; 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
Smith, Stephen M; Beckmann, Christian F; Andersson, Jesper et al. (2013) Resting-state fMRI in the Human Connectome Project. Neuroimage 80:144-68