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| Hauptseite > Publikationsdatenbank > Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation > print |
| Hauptseite > Publikationsdatenbank > Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation > print |
| 001 | 15399 | ||
| 005 | 20210129210624.0 | ||
| 024 | 7 | _ | |2 pmid |a pmid:21609770 |
| 024 | 7 | _ | |2 pmc |a pmc:PMC3129435 |
| 024 | 7 | _ | |2 DOI |a 10.1016/j.neuroimage.2011.05.021 |
| 024 | 7 | _ | |2 WOS |a WOS:000292717900028 |
| 024 | 7 | _ | |a altmetric:21805965 |2 altmetric |
| 037 | _ | _ | |a PreJuSER-15399 |
| 041 | _ | _ | |a eng |
| 082 | _ | _ | |a 610 |
| 084 | _ | _ | |2 WoS |a Neurosciences |
| 084 | _ | _ | |2 WoS |a Neuroimaging |
| 084 | _ | _ | |2 WoS |a Radiology, Nuclear Medicine & Medical Imaging |
| 100 | 1 | _ | |0 P:(DE-Juel1)131678 |a Eickhoff, S.B. |b 0 |u FZJ |
| 245 | _ | _ | |a Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation |
| 260 | _ | _ | |a Orlando, Fla. |b Academic Press |c 2011 |
| 300 | _ | _ | |a 938 - 949 |
| 336 | 7 | _ | |0 PUB:(DE-HGF)16 |2 PUB:(DE-HGF) |a Journal Article |
| 336 | 7 | _ | |2 DataCite |a Output Types/Journal article |
| 336 | 7 | _ | |0 0 |2 EndNote |a Journal Article |
| 336 | 7 | _ | |2 BibTeX |a ARTICLE |
| 336 | 7 | _ | |2 ORCID |a JOURNAL_ARTICLE |
| 336 | 7 | _ | |2 DRIVER |a article |
| 440 | _ | 0 | |0 4545 |a NeuroImage |v 57 |x 1053-8119 |y 3 |
| 500 | _ | _ | |a 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.). |
| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |0 G:(DE-Juel1)FUEK409 |2 G:(DE-HGF) |x 0 |c FUEK409 |a Funktion und Dysfunktion des Nervensystems (FUEK409) |
| 536 | _ | _ | |0 G:(DE-HGF)POF2-89571 |a 89571 - Connectivity and Activity (POF2-89571) |c POF2-89571 |f POF II T |x 1 |
| 588 | _ | _ | |a Dataset connected to Web of Science, Pubmed |
| 650 | _ | 2 | |2 MeSH |a Algorithms |
| 650 | _ | 2 | |2 MeSH |a Brain: anatomy & histology |
| 650 | _ | 2 | |2 MeSH |a Brain: physiology |
| 650 | _ | 2 | |2 MeSH |a Brain Mapping: methods |
| 650 | _ | 2 | |2 MeSH |a Cluster Analysis |
| 650 | _ | 2 | |2 MeSH |a Humans |
| 650 | _ | 2 | |2 MeSH |a Image Processing, Computer-Assisted: methods |
| 650 | _ | 2 | |2 MeSH |a Magnetic Resonance Imaging |
| 650 | _ | 2 | |2 MeSH |a Neural Pathways: anatomy & histology |
| 650 | _ | 2 | |2 MeSH |a Neural Pathways: physiology |
| 650 | _ | 7 | |2 WoSType |a J |
| 653 | 2 | 0 | |2 Author |a Database |
| 653 | 2 | 0 | |2 Author |a fMRI |
| 653 | 2 | 0 | |2 Author |a Areas |
| 653 | 2 | 0 | |2 Author |a Connectivity |
| 653 | 2 | 0 | |2 Author |a Action |
| 653 | 2 | 0 | |2 Author |a SMA |
| 700 | 1 | _ | |0 P:(DE-Juel1)136848 |a Bzdok, D. |b 1 |u FZJ |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Laird, A.R. |b 2 |
| 700 | 1 | _ | |0 P:(DE-Juel1)VDB98850 |a Roski, C. |b 3 |u FZJ |
| 700 | 1 | _ | |0 P:(DE-Juel1)VDB53458 |a Caspers, S. |b 4 |u FZJ |
| 700 | 1 | _ | |0 P:(DE-Juel1)131714 |a Zilles, K. |b 5 |u FZJ |
| 700 | 1 | _ | |0 P:(DE-HGF)0 |a Fox, P.T. |b 6 |
| 773 | _ | _ | |0 PERI:(DE-600)1471418-8 |a 10.1016/j.neuroimage.2011.05.021 |g Vol. 57, p. 938 - 949 |p 938 - 949 |q 57<938 - 949 |t NeuroImage |v 57 |x 1053-8119 |y 2011 |
| 856 | 7 | _ | |2 Pubmed Central |u http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3129435 |
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| 914 | 1 | _ | |y 2011 |
| 915 | _ | _ | |0 StatID:(DE-HGF)0010 |a JCR/ISI refereed |
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