Journal Article

PreJuSER-15399

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation

Eickhoff, S. B.FZJ* ; Bzdok, D.FZJ* ; Laird, A. R. ; Roski, C.FZJ* ; Caspers, S.FZJ* ; Zilles, K.FZJ* ; Fox, P. T.

2011
Academic Press Orlando, Fla.

This record in other databases:      

Please use a persistent id in citations: doi:10.1016/j.neuroimage.2011.05.021

Abstract: The organization of the cerebral cortex into distinct modules may be described along several dimensions, most importantly, structure, connectivity and function. Identification of cortical modules by differences in whole-brain connectivity profiles derived from diffusion tensor imaging or resting state correlations has already been shown. These approaches, however, carry no task-related information. Hence, inference on the functional relevance of the ensuing parcellation remains tentative. Here, we demonstrate, that Meta-Analytic Connectivity Modeling (MACM) allows the delineation of cortical modules based on their whole-brain co-activation pattern across databased neuroimaging results. Using a model free approach, two regions of the medial pre-motor cortex, SMA and pre-SMA were differentiated solely based on their functional connectivity. Assessing the behavioral domain and paradigm class meta-data of the experiments associated with the clusters derived from the co-activation based parcellation moreover allows the identification of their functional characteristics. The ensuing hypotheses about functional differentiation and distinct functional connectivity between pre-SMA and SMA were then explicitly tested and confirmed in independent datasets using functional and resting state fMRI. Co-activation based parcellation thus provides a new perspective for identifying modules of functional connectivity and linking them to functional properties, hereby generating new and subsequently testable hypotheses about the organization of cortical modules.

Keyword(s): Algorithms (MeSH) ; Brain: anatomy & histology (MeSH) ; Brain: physiology (MeSH) ; Brain Mapping: methods (MeSH) ; Cluster Analysis (MeSH) ; Humans (MeSH) ; Image Processing, Computer-Assisted: methods (MeSH) ; Magnetic Resonance Imaging (MeSH) ; Neural Pathways: anatomy & histology (MeSH) ; Neural Pathways: physiology (MeSH) ; J ; Database (auto) ; fMRI (auto) ; Areas (auto) ; Connectivity (auto) ; Action (auto) ; SMA (auto)

Note: This work was partly funded by the Human Brain Project (R01-MH074457-01A1; S.B.E., A.R.L., and P.T.F), the Initiative and Networking Fund of the Helmholtz Association within the Helmholtz Alliance on Systems Biology (Human Brain Model; K.Z., S.B.E.), the DFG (IRTG 1328, S.B.E., D.B.) and the Helmholtz Alliance for Mental Health in an Aging Society (HelMA; K.Z.).

---

Contributing Institute(s):

1. Molekulare Organisation des Gehirns (INM-2)

Research Program(s):

1. Funktion und Dysfunktion des Nervensystems (FUEK409) (FUEK409)
2. 89571 - Connectivity and Activity (POF2-89571) (POF2-89571)

Appears in the scientific report 2011

---