Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2011-08, Vol.57 (3), p.938-949
• Main Authors: Eickhoff, Simon B., Bzdok, Danilo, Laird, Angela R., Roski, Christian, Caspers, Svenja, Zilles, Karl, Fox, Peter T.
• Format: Article
• Language: English
• Subjects: Action; Algorithms; Alliances; Areas; Brain - anatomy & histology; Brain - physiology; Brain Mapping - methods; Cluster Analysis; Connectivity; Cortex; Experiments; fMRI; Humans; Hypotheses; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging; Medical imaging; Methods; Neural Pathways - anatomy & histology; Neural Pathways - physiology; SMA; Studies
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2011.05.021

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. ► Metaanalytical connectivity mapping (MACM) may yield voxel-wise connectivity patterns. ► Co-activation based parcellation allows a model free identification of cortical areas. ► Reference to underlying databased experiments provides a functional characterization. ► MACM and co-activation based parcellation provide testable hypotheses.