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000021838 0247_ $$2DOI$$a10.1016/j.neuroimage.2012.04.013
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000021838 041__ $$aeng
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000021838 084__ $$2WoS$$aNeurosciences
000021838 084__ $$2WoS$$aNeuroimaging
000021838 084__ $$2WoS$$aRadiology, Nuclear Medicine & Medical Imaging
000021838 1001_ $$0P:(DE-Juel1)VDB103707$$aBezgin, G.$$b0$$uFZJ
000021838 245__ $$aHundreds of brain maps in one atlas: Registering coordinate-independent primate neuro-anatomical data to a standard brain
000021838 260__ $$aOrlando, Fla.$$bAcademic Press$$c2012
000021838 300__ $$a67 - 76
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000021838 440_0 $$04545$$aNeuroImage$$v62$$x1053-8119$$y1
000021838 500__ $$3POF3_Assignment on 2016-02-29
000021838 500__ $$aGB and RM acknowledge McDonnell Baycrest grant 737100401. RB is supported by the German INCF Node (BMBF grant 01GQ0801), the Helmholtz Alliance on Systems Biology, JUGENE grant JINB33, the Next-Generation Supercomputer Project of MEXT, Japan, EU grant 269921 (BrainScaleS). We thank David Van Essen, Donna Dierker, John Harwell, Stan Gielen, Alfi Afadiyanti, Jimmy Shen, Kelly Shen. Thanks to Rolf Kotter and his family.
000021838 520__ $$aNon-invasive measuring methods such as EEG/MEG, fMRI and DTI are increasingly utilised to extract quantitative information on functional and anatomical connectivity in the human brain. These methods typically register their data in Euclidean space, so that one can refer to a particular activity pattern by specifying its spatial coordinates. Since each of these methods has limited resolution in either the time or spatial domain, incorporating additional data, such as those obtained from invasive animal studies, would be highly beneficial to link structure and function. Here we describe an approach to spatially register all cortical brain regions from the macaque structural connectivity database CoCoMac, which contains the combined tracing study results from 459 publications (http://cocomac.g-node.org). Brain regions from 9 different brain maps were directly mapped to a standard macaque cortex using the tool Caret (Van Essen and Dierker, 2007). The remaining regions in the CoCoMac database were semantically linked to these 9 maps using previously developed algebraic and machine-learning techniques (Bezgin et al., 2008; Stephan et al., 2000). We analysed neural connectivity using several graph-theoretical measures to capture global properties of the derived network, and found that Markov Centrality provides the most direct link between structure and function. With this registration approach, users can query the CoCoMac database by specifying spatial coordinates. Availability of deformation tools and homology evidence then allow one to directly attribute detailed anatomical animal data to human experimental results.
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000021838 536__ $$0G:(EU-Grant)269921$$aBRAINSCALES - Brain-inspired multiscale computation in neuromorphic hybrid systems (269921)$$c269921$$fFP7-ICT-2009-6$$x1
000021838 588__ $$aDataset connected to Web of Science, Pubmed
000021838 65320 $$2Author$$aBrain atlas
000021838 65320 $$2Author$$aStructural connectivity
000021838 65320 $$2Author$$aPrimate brain
000021838 65320 $$2Author$$aTract tracing
000021838 65320 $$2Author$$aAnimal model
000021838 65320 $$2Author$$aNetwork analysis
000021838 650_2 $$2MeSH$$aAnimals
000021838 650_2 $$2MeSH$$aBrain: anatomy & histology
000021838 650_2 $$2MeSH$$aComputer Simulation
000021838 650_2 $$2MeSH$$aDatabases, Factual: standards
000021838 650_2 $$2MeSH$$aImage Interpretation, Computer-Assisted: methods
000021838 650_2 $$2MeSH$$aMacaca: anatomy & histology
000021838 650_2 $$2MeSH$$aModels, Anatomic
000021838 650_2 $$2MeSH$$aModels, Neurological
000021838 650_2 $$2MeSH$$aNerve Net: anatomy & histology
000021838 650_2 $$2MeSH$$aReference Values
000021838 650_2 $$2MeSH$$aSoftware
000021838 650_2 $$2MeSH$$aSubtraction Technique
000021838 650_7 $$2WoSType$$aJ
000021838 7001_ $$0P:(DE-Juel1)VDB107328$$aVakorin, V.A.$$b1$$uFZJ
000021838 7001_ $$0P:(DE-Juel1)VDB107329$$avan Opstal, A.J.$$b2$$uFZJ
000021838 7001_ $$0P:(DE-Juel1)VDB103713$$aMcIntosh, A.R.$$b3$$uFZJ
000021838 7001_ $$0P:(DE-Juel1)145578$$aBakker, R.$$b4$$uFZJ
000021838 773__ $$0PERI:(DE-600)1471418-8$$a10.1016/j.neuroimage.2012.04.013$$gVol. 62, p. 67 - 76$$p67 - 76$$q62<67 - 76$$tNeuroImage$$v62$$x1053-8119$$y2012
000021838 8567_ $$uhttp://dx.doi.org/10.1016/j.neuroimage.2012.04.013
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