Why do humans have unique auditory event‐related fields? Evidence from computational modeling and MEG experiments

Abstract

Auditory event‐related fields (ERFs) measured with magnetoencephalography (MEG) are useful for studying the neuronal underpinnings of auditory cognition in human cortex. They have a highly subject‐specific morphology, albeit certain characteristic deflections (e.g., P1m, N1m, and P2m) can be identified in most subjects. Here, we explore the reason for this subject‐specificity through a combination of MEG measurements and computational modeling of auditory cortex. We test whether ERF subject‐specificity can predominantly be explained in terms of each subject having an individual cortical gross anatomy, which modulates the MEG signal, or whether individual cortical dynamics is also at play. To our knowledge, this is the first time that tools to address this question are being presented. The effects of anatomical and dynamical variation on the MEG signal is simulated in a model describing the core‐belt‐parabelt structure of the auditory cortex, and with the dynamics based on the leaky‐integrator neuron model. The experimental and simulated ERFs are characterized in terms of the N1m amplitude, latency, and width. Also, we examine the waveform grand‐averaged across subjects, and the standard deviation of this grand average. The results show that the intersubject variability of the ERF arises out of both the anatomy and the dynamics of auditory cortex being specific to each subject. Moreover, our results suggest that the latency variation of the N1m is largely related to subject‐specific dynamics. The findings are discussed in terms of how learning, plasticity, and sound detection are reflected in the auditory ERFs. The notion of the grand‐averaged ERF is critically evaluated.

The Adaptation Model Offers a Challenge for the Predictive Coding Account of Mismatch Negativity

“…As explained in May et al (2015), SSA on the single-unit level is only part of the explanation, with tuning to stimulus features also playing a major role. Omission responses (Experiment 1) are to be expected as resonance effects, given that interacting excitatory and inhibitory neural populations are dynamically equivalent to driven oscillators with damping (May and Tiitinen, 2001;Hajizadeh et al, 2019Hajizadeh et al, , 2021. In addition, the omission response could be enhanced or even caused by high-pass filtering acting on the sudden, omissionrelated drop in the sustained activity which is elicited by fast-rate stimulation (May and Tiitinen, 2010).…”

Auditory cortex modelled as a dynamical network of oscillators: understanding event-related fields and their adaptation

“…Our previous studies (Hajizadeh et al. 2019 , 2021 ) used spectral methods similar to the ones employed here (though without STSD) for investigating the normal modes in a system of 240 units representing cortical columns distributed over subcortical areas and 13 tonotopically organised cortical fields. Importantly, the current results open up the possibility of applying spectral methods for studying STSD modulation of AC dynamics in an expanded model with a much higher spatial resolution than here.…”

“…Each input is weighted by a connection-specific multiplier which depends on the connection type (feedforward, feedback, excitatory, inhibitory) (for more information, see Hajizadeh et al. 2019 , 2021 ). This topological information is expressed in the matrices and , whose structures are shown in Fig.…”

“…In our previous work (Hajizadeh et al. 2019 , 2021 ), we sought to understand the generation of the event-related field in terms of a dynamical system with the spatial organisation of the auditory cortex (Kaas and Hackett 2000 ; Hackett et al. 2014 ).…”

A Critical Review of the Deviance Detection Theory of Mismatch Negativity

“…Attempting to identify the neural source of an EEG response may be likened to the task of identifying an individual violinist playing among the chorus of a large orchestra with audio recordings from a single microphone positioned at the concert hall ceiling: far from a trivial problem. Furthermore, heterogeneity in cortical topology between individuals is thought to contribute to differences in auditory evoked electromagnetic fields measured from outside of the skull that are difficult to predict, effectively presenting another confounding biological factor [75]. While other neuroimaging technologies such as MEG and fMRI have been explored, the majority of early and contemporary MMN studies have used EEG to observe functional brain activity.…”