The effect of NMDA-R antagonist, MK-801, on neuronal mismatch along the rat auditory thalamocortical pathway

Parras, Gloria G.; Valdés-Baizabal, Catalina; Harms, Lauren; Michie, Patricia T.; Malmierca, Manuel S.

doi:10.1038/s41598-020-68837-y

Cited by 19 publications

(14 citation statements)

References 95 publications

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“…Regarding the auditory system, no-repetition controls revealed that SSA could not fully account for the mismatch responses found in the nonlemniscal divisions of the IC and the MGB of anesthetized rats and awake mice. Hence, subcortical auditory nuclei seem to constitute the first levels of the predictive processing hierarchy, which is ultimately responsible for auditory deviance detection [ 39 , 60 , 61 ].…”

Section: Discussionmentioning

confidence: 99%

“…First, a multiunit frequency response area (FRA) was computed by randomly presenting pure tones of various frequency and intensity combinations that ranged from 1 to 44 kHz (in 4 to 6 frequency steps/octave) and from 0 to 70 dBs (10 dB steps) with 1 to 3 repetitions per tone. In our previous studies in the auditory system [39,60,61], we selected 10 tones at frequency steps of 0.5 octaves to generate our stimulation paradigms within the receptive field determined by the FRA. However, we could not determine clear receptive fields in the multiunit FRAs of the mPFC, so we had to choose the frequencies and intensity of our test sequences based on our observations during manual search, trying to maximize the auditory-evoked response when possible.…”

Section: Plos Biologymentioning

confidence: 99%

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Prediction error signaling explains neuronal mismatch responses in the medial prefrontal cortex

et al. 2020

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The mismatch negativity (MMN) is a key biomarker of automatic deviance detection thought to emerge from 2 cortical sources. First, the auditory cortex (AC) encodes spectral regularities and reports frequency-specific deviances. Then, more abstract representations in the prefrontal cortex (PFC) allow to detect contextual changes of potential behavioral relevance. However, the precise location and time asynchronies between neuronal correlates underlying this frontotemporal network remain unclear and elusive. Our study presented auditory oddball paradigms along with “no-repetition” controls to record mismatch responses in neuronal spiking activity and local field potentials at the rat medial PFC. Whereas mismatch responses in the auditory system are mainly induced by stimulus-dependent effects, we found that auditory responsiveness in the PFC was driven by unpredictability, yielding context-dependent, comparatively delayed, more robust and longer-lasting mismatch responses mostly comprised of prediction error signaling activity. This characteristically different composition discarded that mismatch responses in the PFC could be simply inherited or amplified downstream from the auditory system. Conversely, it is more plausible for the PFC to exert top-down influences on the AC, since the PFC exhibited flexible and potent predictive processing, capable of suppressing redundant input more efficiently than the AC. Remarkably, the time course of the mismatch responses we observed in the spiking activity and local field potentials of the AC and the PFC combined coincided with the time course of the large-scale MMN-like signals reported in the rat brain, thereby linking the microscopic, mesoscopic, and macroscopic levels of automatic deviance detection.

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Section: Discussionmentioning

confidence: 99%

Section: Plos Biologymentioning

confidence: 99%

Prediction error signaling explains neuronal mismatch responses in the medial prefrontal cortex

et al. 2020

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“…First, a multiunit FRA was computed by randomly presenting pure tones of various frequency and intensity combinations that ranged from 1 to 44 kHz (in 4–6 frequency steps/octave) and from 0 to 70 dBs (10 dB steps) with 1–3 repetitions per tone. In our previous studies in the auditory system [39,60,61], we selected 10 tones at frequency steps of 0.5 octaves to generate our stimulation paradigms within the receptive field determined by the FRA. However, we could not determine clear receptive fields in the multiunit FRAs of the mPFC, so we had to choose the frequencies and intensity of our test sequences based on our observations during manual search, trying to maximize the auditory-evoked response when possible.…”

Section: Methodsmentioning

confidence: 99%

“…Regarding the auditory system, no-repetition controls revealed that SSA could not fully account for the mismatch responses found in the nonlemniscal divisions of the IC and the MGB of the anesthetized rats and awake mice. Hence, subcortical auditory nuclei seem to constitute the first levels of the predictive processing hierarchy which is ultimately responsible for auditory deviance detection [39,60,61].…”

Section: Subcortical Middle Players Could Relay Pe Signals To the Pfcmentioning

confidence: 99%

Prediction error signaling explains neuronal mismatch responses in the medial prefrontal cortex

Casado-Román

Pérez-González

Malmierca

2019

Preprint

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32According to predictive coding theory, perception emerges through the interplay of neural circuits 33 that generate top-down predictions about environmental statistical regularities and those that 34 generate bottom-up error signals to sensory deviations. Prediction error signals are hierarchically 35 organized from subcortical structures to the auditory cortex. Beyond the auditory cortex, the 36 prefrontal cortices integrate error signals to update prediction models. Here, we recorded neuronal 37 activity in the medial prefrontal cortex of the anesthetized rat while presenting oddball and control 38 stimulus sequences, designed to separate prediction errors from repetition suppression effects of 39 mismatch responses. Robust mismatch signals were mostly due to prediction errors. The encoding of 40 a regularity representation and the repetition suppression effect over the course of repeated stimuli 41 were fast. Medial prefrontal cells encode stronger prediction errors than lower levels in the auditory 42 hierarchy. These neurons may, therefore, represent the neuronal basis of a fundamental mechanism 43 of hierarchical inference. 44 45 46 Keywords: auditory processing, predictive coding, prediction error, mismatch negativity, medial 47 prefrontal cortex, anaesthesia, neuronal activity 48 49 3 50 51 100 unpredictable deviations from the auditory background. These cells may, therefore, represent the 101 neuronal basis of predictive activity in FCs. 102 103 104 Results 105Context-dependent responses across fields in mPFC. 106 To seek experimental evidence for predictive coding signals in the mPFC, we recorded neuronal 107 activity across all fields in 33 urethane-anesthetized rats. We recorded 83 multiunits (AGM [medial 108 agranular cortex]: 25; ACC [anterior cingulate cortex]: 20; PL [prelimbic cortex]: 20; IL [infralimbic 109 cortex]: 18) and tested 384 tones (AGM: 13; ACC: 90; PL: 81; IL: 81) as part of oddball paradigms 110 and suitable control sequences, namely, cascade and many-standards conditions (Fig. 1). Figure 2 111 illustrates 5 examples of electrolytic lesions in three Nissl-stained sections at different recording sites 112 across the mPFC. Regardless of their anatomical location in all mPFC fields, we found sound-driven 113 neuronal activity to pure tones (0.6-42.5 kHz; 25-70 dB SPL). We assessed significantly increased 114 responses to sound by comparing baseline-corrected spike counts after stimulus presentation against 115 a simulated null peristimulus time histograms (PSTH) with a firing rate equal to the baseline 116 (detailed in Methods). We tested the neuronal response to each pure tone as either being deviant, 117 standard, or part of a control sequence and only included those frequency tones that demonstrated a 118 significant response to any condition. Thus, 86.2% (331/384) of deviant stimuli evoked a significant 119 neuronal discharge, while 26.6% (102/384) of standard stimuli, 26.6% (102/384) of many-standards 120 stimuli and 32.3% (124/384) of cascade stimuli evoked a firing rate ...

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“…Altogether, these findings support the influence of BDNF Val66Met on hippocampusdependent memory and cognitive processes, in line with the key role exerted by BDNF at the molecular level. Moreover, the influence of BDNF on glutamatergic transmission, which is crucial for the generation of MMN (Harms et al, 2020;Javitt et al, 1996;Parras et al, 2020;Tikhonravov et al, 2008), and the involvement of the hippocampus in novelty processing during oddball tasks (Crottaz-Herbette et al, 2005;Halgren et al, 1995a;Halgren et al, 1995b), suggest that differential levels of the neurotrophin could affect the MMN generation. This evidence, together with the implication of BDNF in the development and retuning of the auditory cortex, point to a possible influence of Val66Met in learning processes mediated by auditory predictions and, specifically, that such processes could be particularly enhanced upon long-term musical training.…”

Section: Introductionmentioning

confidence: 99%

BDNF Val66Met polymorphism as putative genetic substrate of music-induced plasticity in auditory prediction

Bruzzone

Bonetti

Paunio

et al. 2021

Preprint

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Predictive processing of sounds depends on the constant updating of priors based on exposure to posteriors, which through repeated exposure mediates learning. The result of such corrections to the model is seen in musicians, whose lifelong training results in measurable plasticity of audio-motor brain anatomy and functionality. It has been suggested that the plasticity of auditory predictive processes depends on the interaction between the environment and the individual genetic substrate. However, empirical evidence to this is still missing. BDNF is a critical genetic factor affecting learning and plasticity, and its widely studied functional variant Val66Met single-nucleotide polymorphism offers a unique opportunity to investigate neuroplastic functional changes occurring upon a years-long training. We hypothesised that BDNF gene variations would be driving neuroplasticity of the auditory cortex in musically trained human participants. To this goal, musicians and non-musicians were recruited and divided in Val/Val and Met carriers and their brain activity measured with magnetoencephalography (MEG) while they listened to a regular auditory sequence containing different types of prediction errors. The auditory cortex responses to prediction errors was enhanced in Val/Val carriers who underwent intensive musical training, compared to Met and non-musicians. Our results point at a role of gene-regulated neurotrophic factors in the neural adaptations of auditory processing after long-term training.

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The effect of NMDA-R antagonist, MK-801, on neuronal mismatch along the rat auditory thalamocortical pathway

Cited by 19 publications

References 95 publications

Prediction error signaling explains neuronal mismatch responses in the medial prefrontal cortex

Prediction error signaling explains neuronal mismatch responses in the medial prefrontal cortex

Prediction error signaling explains neuronal mismatch responses in the medial prefrontal cortex

BDNF Val66Met polymorphism as putative genetic substrate of music-induced plasticity in auditory prediction

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