Heschl's Gyrus, Posterior Superior Temporal Gyrus, and Mid-Ventrolateral Prefrontal Cortex Have Different Roles in the Detection of Acoustic Changes

Cognitive Brain Research Unit, Department of Psychology, University of Helsinki, Helsinki, Finland.
Journal of Neurophysiology (Impact Factor: 2.89). 04/2007; 97(3):2075-82. DOI: 10.1152/jn.01083.2006
Source: PubMed


A part of the auditory system automatically detects changes in the acoustic environment. This preattentional process has been studied extensively, yet its cerebral origins have not been determined with sufficient accuracy to allow comparison to established anatomical and functional parcellations. Here we used event-related functional MRI and EEG in a parametric experimental design to determine the cortical areas in individual brains that participate in the detection of acoustic changes. Our results suggest that automatic change processing consists of at least three stages: initial detection in the primary auditory cortex, detailed analysis in the posterior superior temporal gyrus and planum temporale, and judgment of sufficient novelty for the allocation of attentional resources in the mid-ventrolateral prefrontal cortex.

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Available from: Satu Pakarinen
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    • "In DCM, Bayesian inference is used to optimise neural source dipoles based on a priori information about their locations. This information is available from studies investigating the sources underlying the MMN—using fMRI [Molholm et al., 2005; Rinne et al., 2005; Sch€ onwiesner et al., 2007], PET [Dittmann-Balçar et al., 2001; M€ uller et al., 2002], EEG/MEG [Deouell et al., 1998; Fulham et al., 2014; Jemel et al., 2002; Rinne et al., 2000; Tiitinen et al., 2006], and DCM [Garrido et al., 2007, 2008, 2009a]—show- ing that the MMN is generated by temporal and frontal sources. Using DCM, the model with the most evidence consists of a three-level hierarchy comprising bilateral primary auditory cortices (Heschl's gyrus, A1), bilateral superior temporal gyri (STG), and the right inferior frontal gyrus (rIFG). "
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    • "The ROI analysis aimed to increase sensitivity in detecting repetition suppression effects in brain areas that have been reported to process acoustic changes. The ROIs included Heschl's gyri (HGs), STGs, and IFGs bilaterally (Schönwiesner et al. 2007). We also chose to include the left IC based on the findings by Chandrasekaran et al. (2012), and the medial geniculate thalamic nuclei (MGB) since they relay acoustic information from the IC to cortical auditory areas (Javad et al. 2014). "
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    ABSTRACT: Do individuals differ in how efficiently they process non-native sounds? To what extent do these differences relate to individual variability in sound-learning aptitude? We addressed these questions by assessing the sound-learning abilities of Dutch native speakers as they were trained on non-native tone contrasts. We used fMRI repetition suppression to the non-native tones to measure participants' neuronal processing efficiency before and after training. Although all participants improved in tone identification with training, there was large individual variability in learning performance. A repetition suppression effect to tone was found in the bilateral inferior frontal gyri (IFGs) before training. No whole-brain effect was found after training; a region-of-interest analysis, however, showed that, after training, repetition suppression to tone in the left IFG correlated positively with learning. That is, individuals who were better in learning the non-native tones showed larger repetition suppression in this area. Crucially, this was true even before training. These findings add to existing evidence that the left IFG plays an important role in sound learning and indicate that individual differences in learning aptitude stem from differences in the neuronal efficiency with which non-native sounds are processed. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail:
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    • "Our results suggest that while different anterior regions participate in the encoding of features at the local level, posterior and hierarchically superior regions may be engaged in the encoding of more complex or global patterns. Despite converging evidence shows the existence of MMN generators in the frontal lobe [Doeller et al., 2003; Sch€ onwiesner et al., 2007], no frontal areas were observed in this study. Previous studies described the involvement of frontal regions when using listening tasks and by recording EEG or intracranial activity during global–local paradigms [Bekinschtein et al., 2009; Chennu et al., 2013]. "
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