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

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 identif...

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Published in:	Psychophysiology Vol. 58; no. 4; pp. e13769 - n/a
Main Authors:	Hajizadeh, Aida, Matysiak, Artur, Brechmann, André, König, Reinhard, May, Patrick J. C.
Format:	Journal Article
Language:	English
Published:	United States Blackwell Publishing Ltd 01.04.2021
Subjects:	Anatomy auditory cortex Auditory Cortex - anatomy & histology Auditory Cortex - physiology Biological Variation, Population - physiology Cognition computational modeling Computational neuroscience Computer Simulation Cortex (auditory) dynamics ERF event‐related field Evoked Potentials, Auditory - physiology Hearing Humans Latency Magnetoencephalography MEG N1m Neural Networks, Computer Physical characteristics dynamics auditory cortex event-related field anatomy latency ERF N1m computational modeling magnetoencephalography MEG
ISSN:	0048-5772, 1469-8986, 1469-8986, 1540-5958
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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. This study addresses the fundamental but overlooked question of why auditory event‐related fields (ERFs) are highly specific to each subject. Using magnetoencephalography measurements and computational modeling of auditory cortex, we conclude that ERF subject‐specificity arises from both the cortical gross anatomy and the neural dynamics being specific to each subject.
AbstractList	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. This study addresses the fundamental but overlooked question of why auditory event‐related fields (ERFs) are highly specific to each subject. Using magnetoencephalography measurements and computational modeling of auditory cortex, we conclude that ERF subject‐specificity arises from both the cortical gross anatomy and the neural dynamics being specific to each subject. 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. 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.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.
Author	May, Patrick J. C. König, Reinhard Brechmann, André Hajizadeh, Aida Matysiak, Artur
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Keywords	dynamics auditory cortex event-related field anatomy latency ERF N1m computational modeling magnetoencephalography MEG
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Snippet	Auditory event‐related fields (ERFs) measured with magnetoencephalography (MEG) are useful for studying the neuronal underpinnings of auditory cognition in... Auditory event-related fields (ERFs) measured with magnetoencephalography (MEG) are useful for studying the neuronal underpinnings of auditory cognition in...
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SubjectTerms	Anatomy auditory cortex Auditory Cortex - anatomy & histology Auditory Cortex - physiology Biological Variation, Population - physiology Cognition computational modeling Computational neuroscience Computer Simulation Cortex (auditory) dynamics ERF event‐related field Evoked Potentials, Auditory - physiology Hearing Humans Latency Magnetoencephalography MEG N1m Neural Networks, Computer Physical characteristics
Title	Why do humans have unique auditory event‐related fields? Evidence from computational modeling and MEG experiments
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