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000904388 1001_ $$0P:(DE-Juel1)177936$$aPais, Patricia$$b0$$ufzj
000904388 245__ $$aContribution of animal models toward understanding resting state functional connectivity
000904388 260__ $$aOrlando, Fla.$$bAcademic Press$$c2021
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000904388 520__ $$aFunctional connectivity, which reflects the spatial and temporal organization of intrinsic activity throughout the brain, is one of the most studied measures in human neuroimaging research. The noninvasive acquisition of resting state functional magnetic resonance imaging (rs-fMRI) allows the characterization of features designated as functional networks, functional connectivity gradients, and time-varying activity patterns that provide insight into the intrinsic functional organization of the brain and potential alterations related to brain dysfunction. Functional connectivity, hence, captures dimensions of the brain's activity that have enormous potential for both clinical and preclinical research. However, the mechanisms underlying functional connectivity have yet to be fully characterized, hindering interpretation of rs-fMRI studies. As in other branches of neuroscience, the identification of the neurophysiological processes that contribute to functional connectivity largely depends on research conducted on laboratory animals, which provide a platform where specific, multi-dimensional investigations that involve invasive measurements can be carried out. These highly controlled experiments facilitate the interpretation of the temporal correlations observed across the brain. Indeed, information obtained from animal experimentation to date is the basis for our current understanding of the underlying basis for functional brain connectivity. This review presents a compendium of some of the most critical advances in the field based on the efforts made by the animal neuroimaging community.
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000904388 7001_ $$0P:(DE-HGF)0$$aMateo, Celine$$b1
000904388 7001_ $$0P:(DE-Juel1)188777$$aPan, Wen-Ju$$b2
000904388 7001_ $$0P:(DE-HGF)0$$aAcland, Ben$$b3
000904388 7001_ $$0P:(DE-HGF)0$$aKleinfeld, David$$b4
000904388 7001_ $$0P:(DE-HGF)0$$aSnyder, Lawrence H.$$b5
000904388 7001_ $$0P:(DE-HGF)0$$aYu, Xin$$b6
000904388 7001_ $$0P:(DE-HGF)0$$aKeilholz, Shella$$b7$$eCorresponding author
000904388 773__ $$0PERI:(DE-600)1471418-8$$a10.1016/j.neuroimage.2021.118630$$gVol. 245, p. 118630 -$$p118630 -$$tNeuroImage$$v245$$x1053-8119$$y2021
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