Abstract
When the brain tries to acquire an elaborate model of the world, multisensory integration should contribute to building predictions based on the various pieces of information, and deviance detection should repeatedly update these predictions by detecting “errors” from the actual sensory inputs. Accumulating evidence such as a hierarchical organization of the deviance-detection system indicates that the deviance-detection system can be interpreted in the predictive coding framework. Herein, we targeted mismatch negativity (MMN) as a type of prediction-error signal and investigated the relationship between multisensory integration and MMN. In particular, we studied whether and how cross-modal information processing affected MMN in rodents. We designed a new surface microelectrode array and simultaneously recorded visual and auditory evoked potentials from the visual and auditory cortices of rats under anesthesia. Then, we mapped MMNs for five types of deviant stimuli: single-modal deviants in (i) the visual oddball and (ii) auditory oddball paradigms, eliciting single-modal MMN; (iii) congruent audio-visual deviants, (iv) incongruent visual deviants, and (v) incongruent auditory deviants in the audio-visual oddball paradigm, eliciting cross-modal MMN. First, we demonstrated that visual MMN exhibited deviance detection properties and that the first-generation focus of visual MMN was localized in the visual cortex, as previously reported in human studies. Second, a comparison of MMN amplitudes revealed a non-linear relationship between single-modal and cross-modal MMNs. Moreover, congruent audio-visual MMN exhibited characteristics of both visual and auditory MMNs—its latency was similar to that of auditory MMN, whereas local blockage of N-methyl-D-aspartic acid receptors in the visual cortex diminished it as well as visual MMN. These results indicate that cross-modal information processing affects MMN without involving strong top-down effects, such as those of prior knowledge and attention. The present study is the first electrophysiological evidence of cross-modal MMN in animal models, and future studies on the neural mechanisms combining multisensory integration and deviance detection are expected to provide electrophysiological evidence to confirm the links between MMN and predictive coding theory.
Highlights
Prediction is an essential brain function required to understand the surrounding environment correctly
The multiple comparison and post hoc analyses showed that visual P1 (vP1) was larger in the putative visual cortex (Figure 2Ci; Kruskal-Wallis test, p = 5.6 × 10−6; post hoc Wilcoxon one-sided signedrank test with Bonferroni correction for three comparisons, p = 0.00024 for visual cortex vs. auditory cortex, and visual cortex vs. outer region, respectively), and that auditory P1 (aP1) was larger in the putative auditory cortex (Figure 2Cii; Kruskal-Wallis test, p = 5.6 × 10−6; post hoc Wilcoxon one-sided signed-rank test with Bonferroni correction for three comparisons, p = 0.00024 for auditory cortex vs. visual cortex, and auditory cortex vs. outer region, respectively)
The mean amplitude of audiovisual MMN (avMMN) in the visual cortex was significantly reduced (p = 0.038, Wilcoxon one-sided signed-rank test). This reduction in mean amplitude was not significant in the auditory cortex (p = 0.056); the maximum amplitude in this area was significantly reduced after application of the N-methyl-D-aspartic acid (NMDA) antagonist (p = 0.027, data not shown). These results show that NMDA receptor blockade in the visual cortex attenuated visual MMN (vMMN) in the single-modal oddball paradigm and avMMN in the congruent oddball paradigm
Summary
Prediction is an essential brain function required to understand the surrounding environment correctly. According to many theories, including Bayesian and Kahneman’s frameworks, the brain is thought to build predictions from various types of information and update these repeatedly by observing “errors” to acquire an elaborate model of the external world (Kahneman, 2011; Clark, 2013). In this prediction-building process, multisensory integration is thought to play an important role to obtain meaningful perceptual experiences by integrating information from different sensory modalities (Tononi et al, 1998; Alais et al, 2010). Combination of the multisensory integration and deviance-detecting system contribute to the brain building, maintaining, and renewing a model of the external environment
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