Laminar neural dynamics of auditory evoked responses: Computational modeling of local field potentials in auditory cortex of non-human primates.

Evoked neural responses to sensory stimuli have been extensively investigated in humans and animal models both to enhance our understanding of brain function and to aid in clinical diagnosis of neurological and neuropsychiatric conditions. Recording and imaging techniques such as electroencephalography (EEG), magnetoencephalography (MEG), local field potentials (LFPs), and calcium imaging provide complementary information about different aspects of brain activity at different spatial and temporal scales. Modeling and simulations provide a way to integrate these different types of information to clarify underlying neural mechanisms. In this study, we aimed to shed light on the neural dynamics underlying auditory evoked responses by fitting a rate-based model to LFPs recorded via multi-contact electrodes which simultaneously sampled neural activity across cortical laminae. Recordings included neural population responses to best-frequency (BF) and non-BF tones at four representative sites in primary auditory cortex (A1) of awake monkeys. The model considered major neural populations of excitatory, parvalbumin-expressing (PV), and somatostatin-expressing (SOM) neurons across layers 2/3, 4, and 5/6. Unknown parameters, including the connection strength between the populations, were fitted to the data. Our results revealed similar population dynamics, fitted model parameters, predicted equivalent current dipoles (ECD), tuning curves, and lateral inhibition profiles across recording sites and animals, in spite of quite different extracellular current distributions. We found that PV firing rates were higher in BF than in non-BF responses, mainly due to different strengths of tonotopic thalamic input, whereas SOM firing rates were higher in non-BF than in BF responses due to lateral inhibition. In conclusion, we demonstrate the feasibility of the model-fitting approach in identifying the contributions of cell-type specific population activity to stimulus-evoked LFPs across cortical laminae, providing a foundation for further investigations into the dynamics of neural circuits underlying cortical sensory processing.

Learning to predict: Neuronal signatures of auditory expectancy in human event-related potentials.

Learning to anticipate future states of the world based on statistical regularities in the environment is a key component of perception and is vital for the survival of many organisms. Such statistical learning and prediction are crucial for acquiring language and music appreciation. Importantly, learned expectations can be implicitly derived from exposure to sensory input, without requiring explicit information regarding contingencies in the environment. Whereas many previous studies of statistical learning have demonstrated larger neuronal responses to unexpected versus expected stimuli, the neuronal bases of the expectations themselves remain poorly understood. Here we examined behavioral and neuronal signatures of learned expectancy via human scalp-recorded event-related brain potentials (ERPs). Participants were instructed to listen to a series of sounds and press a response button as quickly as possible upon hearing a target noise burst, which was either reliably or unreliably preceded by one of three pure tones in low-, mid-, and high-frequency ranges. Participants were not informed about the statistical contingencies between the preceding tone 'cues' and the target. Over the course of a stimulus block, participants responded more rapidly to reliably cued targets. This behavioral index of learned expectancy was paralleled by a negative ERP deflection, designated as a neuronal contingency response (CR), which occurred immediately prior to the onset of the target. The amplitude and latency of the CR were systematically modulated by the strength of the predictive relationship between the cue and the target. Re-averaging ERPs with respect to the latency of behavioral responses revealed no consistent relationship between the CR and the motor response, suggesting that the CR represents a neuronal signature of learned expectancy or anticipatory attention. Our results demonstrate that statistical regularities in an auditory input stream can be implicitly learned and exploited to influence behavior. Furthermore, we uncover a potential 'prediction signal' that reflects this fundamental learning process.

Representation of speech in human auditory cortex: is it special?

Successful categorization of phonemes in speech requires that the brain analyze the acoustic signal along both spectral and temporal dimensions. Neural encoding of the stimulus amplitude envelope is critical for parsing the speech stream into syllabic units. Encoding of voice onset time (VOT) and place of articulation (POA), cues necessary for determining phonemic identity, occurs within shorter time frames. An unresolved question is whether the neural representation of speech is based on processing mechanisms that are unique to humans and shaped by learning and experience, or is based on rules governing general auditory processing that are also present in non-human animals. This question was examined by comparing the neural activity elicited by speech and other complex vocalizations in primary auditory cortex of macaques, who are limited vocal learners, with that in Heschl's gyrus, the putative location of primary auditory cortex in humans. Entrainment to the amplitude envelope is neither specific to humans nor to human speech. VOT is represented by responses time-locked to consonant release and voicing onset in both humans and monkeys. Temporal representation of VOT is observed both for isolated syllables and for syllables embedded in the more naturalistic context of running speech. The fundamental frequency of male speakers is represented by more rapid neural activity phase-locked to the glottal pulsation rate in both humans and monkeys. In both species, the differential representation of stop consonants varying in their POA can be predicted by the relationship between the frequency selectivity of neurons and the onset spectra of the speech sounds. These findings indicate that the neurophysiology of primary auditory cortex is similar in monkeys and humans despite their vastly different experience with human speech, and that Heschl's gyrus is engaged in general auditory, and not language-specific, processing. This article is part of a Special Issue entitled "Communication Sounds and the Brain: New Directions and Perspectives".

Searching for the mismatch negativity in primary auditory cortex of the awake monkey: deviance detection or stimulus specific adaptation?

The mismatch negativity (MMN) is a preattentive component of the auditory event-related potential that is elicited by a change in a repetitive acoustic pattern. While MMN has been extensively used in human electrophysiological studies of auditory processing, the neural mechanisms and brain regions underlying its generation remain unclear. We investigate possible homologs of the MMN in macaque primary auditory cortex (A1) using a frequency oddball paradigm in which rare "deviant" tones are randomly interspersed among frequent "standard" tones. Standards and deviants had frequencies equal to the best frequency (BF) of the recorded neural population or to a frequency that evoked a response half the amplitude of the BF response. Early and later field potentials, current source density components, multiunit activity, and induced high-gamma band responses were larger when elicited by deviants than by standards of the same frequency. Laminar analysis indicated that differences between deviant and standard responses were more prominent in later activity, thus suggesting cortical amplification of initial responses driven by thalamocortical inputs. However, unlike the human MMN, larger deviant responses were characterized by the enhancement of "obligatory" responses rather than the introduction of new components. Furthermore, a control condition wherein deviants were interspersed among many tones of variable frequency replicated the larger responses to deviants under the oddball condition. Results suggest that differential responses under the oddball condition in macaque A1 reflect stimulus-specific adaptation rather than deviance detection per se. We conclude that neural mechanisms of deviance detection likely reside in cortical areas outside of A1.

Neural correlates of auditory scene analysis based on inharmonicity in monkey primary auditory cortex.

Segregation of concurrent sounds in complex acoustic environments is a fundamental feature of auditory scene analysis. A powerful cue used by the auditory system to segregate concurrent sounds, such as speakers' voices at a cocktail party, is inharmonicity. This can be demonstrated when a component of a harmonic complex tone is perceived as a separate tone "popping out" from the complex as a whole when it is sufficiently mistuned from its harmonic value. The neural bases of perceptual "pop out" of mistuned harmonics are unclear. We recorded multiunit activity from primary auditory cortex (A1) of behaving monkeys elicited by harmonic complex tones that were either "in tune" or that contained a mistuned third harmonic set at the best frequency of the neural populations. Responses to mistuned sounds were enhanced relative to responses to "in-tune" sounds, thus correlating with the enhanced perceptual salience of the mistuned component. Consistent with human psychophysics of "pop out," response enhancements increased with the degree of mistuning, were maximal for neural populations tuned to the frequency of the mistuned component, and were not observed under comparable stimulus conditions that do not elicit perceptual "pop out." Mistuning was also associated with changes in neuronal temporal response patterns phase locked to "beats" in the stimuli. Intracortical auditory evoked potentials paralleled noninvasive neurophysiological correlates of perceptual "pop out" in humans, further augmenting the translational relevance of the results. Findings suggest two complementary neural mechanisms for "pop out," based on the detection of local differences in activation level or coherence of temporal response patterns across A1.

Spectrotemporal analysis of evoked and induced electroencephalographic responses in primary auditory cortex (A1) of the awake monkey.

Electroencephalography is increasingly being used to probe the functional organization of auditory cortex. Modulation of the electroencephalographic (EEG) signal by tones was examined in primary auditory cortex (A1) of awake monkeys. EEG data were measured at 4 laminar depths defined by current source density profiles evoked by best frequency (BF) tones. Midlaminar multiunit activity was used to define the tuning characteristics of A1 sites. Presentation of BF tones increased EEG power across the range of frequencies examined (4-290 Hz), with maximal effects evident within the first 100 ms after stimulus onset. The largest relative increases in EEG power generally occurred at very high gamma frequency bands (130-210 Hz). Increases in EEG power for frequencies less than 70 Hz primarily represented changes in phase-locked activity, whereas increases at higher frequencies primarily represented changes in non-phase-locked activity. Power increases in higher gamma bands were better correlated with the A1 tonotopic organization than power increases in lower frequency bands. Results were similar across the 4 laminar depths examined. These findings highlight the value of examining high-frequency EEG components in exploring the functional organization of auditory cortex and may enhance interpretation of related studies in humans.

Spectral resolution of monkey primary auditory cortex (A1) revealed with two-noise masking.

An important function of the auditory nervous system is to analyze the frequency content of environmental sounds. The neural structures involved in determining psychophysical frequency resolution remain unclear. Using a two-noise masking paradigm, the present study investigates the spectral resolution of neural populations in primary auditory cortex (A1) of awake macaques and the degree to which it matches psychophysical frequency resolution. Neural ensemble responses (auditory evoked potentials, multiunit activity, and current source density) evoked by a pulsed 60-dB SPL pure-tone signal fixed at the best frequency (BF) of the recorded neural populations were examined as a function of the frequency separation (DeltaF) between the tone and two symmetrically flanking continuous 80-dB SPL, 50-Hz-wide bands of noise. DeltaFs ranged from 0 to 50% of the BF, encompassing the range typically examined in psychoacoustic experiments. Responses to the signal were minimal for DeltaF = 0% and progressively increased with DeltaF, reaching a maximum at DeltaF = 50%. Rounded exponential functions, used to model auditory filter shapes in psychoacoustic studies of frequency resolution, provided excellent fits to neural masking functions. Goodness-of-fit was greatest for response components in lamina 4 and lower lamina 3 and least for components recorded in more superficial cortical laminae. Physiological equivalent rectangular bandwidths (ERBs) increased with BF, measuring nearly 15% of the BF. These findings parallel results of psychoacoustic studies in both monkeys and humans, and thus indicate that a representation of perceptual frequency resolution is available at the level of A1.