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000015664 0247_ $$2pmid$$apmid:21671731
000015664 0247_ $$2DOI$$a10.1162/jocn_a_00077
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000015664 037__ $$aPreJuSER-15664
000015664 041__ $$aeng
000015664 082__ $$a400
000015664 084__ $$2WoS$$aNeurosciences
000015664 084__ $$2WoS$$aPsychology, Experimental
000015664 1001_ $$0P:(DE-HGF)0$$aLaird, A.R.$$b0
000015664 245__ $$aBehavioral Interpretations of Intrinsic Connectivity Networks
000015664 260__ $$aCambridge, Mass.$$bMIT Pr. Journals$$c2011
000015664 300__ $$a4022 - 4037
000015664 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article
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000015664 3367_ $$2BibTeX$$aARTICLE
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000015664 440_0 $$03191$$aJournal of Cognitive Neuroscience$$v23$$x0898-929X$$y12
000015664 500__ $$aThis work was supported by NIMH grants R01-MH074457 (P. T. F. and A. R. L.) and R01-MH084812 (A. R. L. and J. A. T.) and the Helmholz Initiative on Systems-Biology (S. B. E.).
000015664 520__ $$aAn increasingly large number of neuroimaging studies have investigated functionally connected networks during rest, providing insight into human brain architecture. Assessment of the functional qualities of resting state networks has been limited by the task-independent state, which results in an inability to relate these networks to specific mental functions. However, it was recently demonstrated that similar brain networks can be extracted from resting state data and data extracted from thousands of task-based neuroimaging experiments archived in the BrainMap database. Here, we present a full functional explication of these intrinsic connectivity networks at a standard low order decomposition using a neuroinformatics approach based on the BrainMap behavioral taxonomy as well as a stratified, data-driven ordering of cognitive processes. Our results serve as a resource for functional interpretations of brain networks in resting state studies and future investigations into mental operations and the tasks that drive them.
000015664 536__ $$0G:(DE-Juel1)FUEK409$$2G:(DE-HGF)$$aFunktion und Dysfunktion des Nervensystems (FUEK409)$$cFUEK409$$x0
000015664 536__ $$0G:(DE-HGF)POF2-89571$$a89571 - Connectivity and Activity (POF2-89571)$$cPOF2-89571$$fPOF II T$$x1
000015664 588__ $$aDataset connected to Web of Science, Pubmed
000015664 650_2 $$2MeSH$$aBrain: physiology
000015664 650_2 $$2MeSH$$aBrain Mapping: methods
000015664 650_2 $$2MeSH$$aClassification: methods
000015664 650_2 $$2MeSH$$aCluster Analysis
000015664 650_2 $$2MeSH$$aDatabases, Factual
000015664 650_2 $$2MeSH$$aHumans
000015664 650_2 $$2MeSH$$aNerve Net: physiology
000015664 650_2 $$2MeSH$$aNeural Pathways: physiology
000015664 650_2 $$2MeSH$$aPsychomotor Performance: physiology
000015664 650_7 $$2WoSType$$aJ
000015664 7001_ $$0P:(DE-HGF)0$$aFox, P.M.$$b1
000015664 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, S.B.$$b2$$uFZJ
000015664 7001_ $$0P:(DE-HGF)0$$aTurner, J.A.$$b3
000015664 7001_ $$0P:(DE-HGF)0$$aRay, K.L.$$b4
000015664 7001_ $$0P:(DE-HGF)0$$aMcKay, D.R.$$b5
000015664 7001_ $$0P:(DE-HGF)0$$aGlahn, D.C.$$b6
000015664 7001_ $$0P:(DE-HGF)0$$aBeckmann, C.F.$$b7
000015664 7001_ $$0P:(DE-HGF)0$$aSmith, S.M.$$b8
000015664 7001_ $$0P:(DE-HGF)0$$aFox, P.T.$$b9
000015664 773__ $$0PERI:(DE-600)1475255-4$$a10.1162/jocn_a_00077$$gVol. 23, p. 4022 - 4037$$p4022 - 4037$$q23<4022 - 4037$$tJournal of cognitive neuroscience$$v23$$x0898-929X$$y2011
000015664 8567_ $$uhttp://dx.doi.org/10.1162/jocn_a_00077
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000015664 915__ $$0StatID:(DE-HGF)0010$$aJCR/ISI refereed
000015664 9141_ $$y2011
000015664 9132_ $$0G:(DE-HGF)POF3-571$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$vConnectivity and Activity$$x0
000015664 9131_ $$0G:(DE-HGF)POF2-89571$$1G:(DE-HGF)POF3-890$$2G:(DE-HGF)POF3-800$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bProgrammungebundene Forschung$$lohne Programm$$vConnectivity and Activity$$x1
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