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Dynamic causal modeling of subcortical connectivity of language.: DCM of a language network

Abstract : Subcortical-cortical interactions in the language network were investigated using dynamic causal modeling of magnetoencephalographic data recorded during auditory comprehension. Participants heard sentences that either were correct or contained violations. Sentences containing violations had syntactic or prosodic violations or both. We show that a hidden source, modeling magnetically silent deep nuclei, is required to explain the data best. This is in line with recent brain imaging studies and intracranial recordings suggesting an involvement of subcortical structures in language processing. Here, the processing of syntactic and prosodic violations elicited a global increase in the amplitude of evoked responses, both at the cortical and subcortical levels. As estimated by Bayesian model averaging, this was accompanied by various changes in cortical-cortical and subcortical-cortical connectivity. The most consistent findings in relation to violations were a decrease of reentrant inputs to Heschl's gyrus (HG) and of transcallosal lateral connections. These results suggest that in conditions where one hemisphere detects a violation, possibly via fast thalamocortical (HG) loops, the intercallosal connectivity is reduced to allow independent processing of syntax (left hemisphere) and of prosody (right hemisphere). This study is the first demonstration in cognitive neuroscience that subcortical-cortical loops can be empirically investigated using noninvasive electrophysiological recordings.
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https://www.hal.inserm.fr/inserm-00613112
Contributor : Olivier David <>
Submitted on : Thursday, November 10, 2011 - 6:14:22 PM
Last modification on : Friday, August 24, 2018 - 8:38:01 PM
Long-term archiving on: : Saturday, February 11, 2012 - 2:20:20 AM

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Olivier David, Burkhard Maess, Korinna Eckstein, Angela Friederici. Dynamic causal modeling of subcortical connectivity of language.: DCM of a language network. Journal of Neuroscience, Society for Neuroscience, 2011, 31 (7), pp.2712-7. ⟨10.1523/JNEUROSCI.3433-10.2011⟩. ⟨inserm-00613112⟩

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