A multi-institutional machine learning algorithm for prognosticating facial nerve injury following microsurgical resection of vestibular schwannoma.

Vestibular schwannomas (VS) are the most common tumor of the skull base with available treatment options that carry a risk of iatrogenic injury to the facial nerve, which can significantly impact patients' quality of life. As facial nerve outcomes remain challenging to prognosticate, we endeavored to utilize machine learning to decipher predictive factors relevant to facial nerve outcomes following microsurgical resection of VS. A database of patient-, tumor- and surgery-specific features was constructed via retrospective chart review of 242 consecutive patients who underwent microsurgical resection of VS over a 7-year study period. This database was then used to train non-linear supervised machine learning classifiers to predict facial nerve preservation, defined as House-Brackmann (HB) I vs. facial nerve injury, defined as HB II-VI, as determined at 6-month outpatient follow-up. A random forest algorithm demonstrated 90.5% accuracy, 90% sensitivity and 90% specificity in facial nerve injury prognostication. A random variable (rv) was generated by randomly sampling a Gaussian distribution and used as a benchmark to compare the predictiveness of other features. This analysis revealed age, body mass index (BMI), case length and the tumor dimension representing tumor growth towards the brainstem as prognosticators of facial nerve injury. When validated via prospective assessment of facial nerve injury risk, this model demonstrated 84% accuracy. Here, we describe the development of a machine learning algorithm to predict the likelihood of facial nerve injury following microsurgical resection of VS. In addition to serving as a clinically applicable tool, this highlights the potential of machine learning to reveal non-linear relationships between variables which may have clinical value in prognostication of outcomes for high-risk surgical procedures.

Changes in neural readout of response magnitude during auditory streaming do not correlate with behavioral choice in the auditory cortex.

A fundamental goal of the auditory system is to group stimuli from the auditory environment into a perceptual unit (i.e., "stream") or segregate the stimuli into multiple different streams. Although previous studies have clarified the psychophysical and neural mechanisms that may underlie this ability, the relationship between these mechanisms remains elusive. Here, we recorded multiunit activity (MUA) from the auditory cortex of monkeys while they participated in an auditory-streaming task consisting of interleaved low- and high-frequency tone bursts. As the streaming stimulus unfolded over time, MUA amplitude habituated; the magnitude of this habituation was correlated with the frequency difference between the tone bursts. An ideal-observer model could classify these time- and frequency-dependent changes into reports of "one stream" or "two streams" in a manner consistent with the behavioral literature. However, because classification was not modulated by the monkeys' behavioral choices, this MUA habituation may not directly reflect perceptual reports.

Time as a supervisor: temporal regularity and auditory object learning.

Sensory systems appear to learn to transform incoming sensory information into perceptual representations, or "objects," that can inform and guide behavior with minimal explicit supervision. Here, we propose that the auditory system can achieve this goal by using time as a supervisor, i.e., by learning features of a stimulus that are temporally regular. We will show that this procedure generates a feature space sufficient to support fundamental computations of auditory perception. In detail, we consider the problem of discriminating between instances of a prototypical class of natural auditory objects, i.e., rhesus macaque vocalizations. We test discrimination in two ethologically relevant tasks: discrimination in a cluttered acoustic background and generalization to discriminate between novel exemplars. We show that an algorithm that learns these temporally regular features affords better or equivalent discrimination and generalization than conventional feature-selection algorithms, i.e., principal component analysis and independent component analysis. Our findings suggest that the slow temporal features of auditory stimuli may be sufficient for parsing auditory scenes and that the auditory brain could utilize these slowly changing temporal features.

Functional network properties of the auditory cortex.

The auditory system transforms auditory stimuli from the external environment into perceptual auditory objects. Recent studies have focused on the contribution of the auditory cortex to this transformation. Other studies have yielded important insights into the contributions of neural activity in the auditory cortex to cognition and decision-making. However, despite this important work, the relationship between auditory-cortex activity and behavior/perception has not been fully elucidated. Two of the more important gaps in our understanding are (1) the specific and differential contributions of different fields of the auditory cortex to auditory perception and behavior and (2) the way networks of auditory neurons impact and facilitate auditory information processing. Here, we focus on recent work from non-human-primate models of hearing and review work related to these gaps and put forth challenges to further our understanding of how single-unit activity and network activity in different cortical fields contribution to behavior and perception.

Cortical circuit-based lossless neural integrator for perceptual decision-making: A computational modeling study.

The intrinsic uncertainty of sensory information (i.e., evidence) does not necessarily deter an observer from making a reliable decision. Indeed, uncertainty can be reduced by integrating (accumulating) incoming sensory evidence. It is widely thought that this accumulation is instantiated via recurrent rate-code neural networks. Yet, these networks do not fully explain important aspects of perceptual decision-making, such as a subject's ability to retain accumulated evidence during temporal gaps in the sensory evidence. Here, we utilized computational models to show that cortical circuits can switch flexibly between "retention" and "integration" modes during perceptual decision-making. Further, we found that, depending on how the sensory evidence was readout, we could simulate "stepping" and "ramping" activity patterns, which may be analogous to those seen in different studies of decision-making in the primate parietal cortex. This finding may reconcile these previous empirical studies because it suggests these two activity patterns emerge from the same mechanism.

Rule-based and stimulus-based cues bias auditory decisions via different computational and physiological mechanisms.

Expectations, such as those arising from either learned rules or recent stimulus regularities, can bias subsequent auditory perception in diverse ways. However, it is not well understood if and how these diverse effects depend on the source of the expectations. Further, it is unknown whether different sources of bias use the same or different computational and physiological mechanisms. We examined how rule-based and stimulus-based expectations influenced behavior and pupil-linked arousal, a marker of certain forms of expectation-based processing, of human subjects performing an auditory frequency-discrimination task. Rule-based cues consistently biased choices and response times (RTs) toward the more-probable stimulus. In contrast, stimulus-based cues had a complex combination of effects, including choice and RT biases toward and away from the frequency of recently presented stimuli. These different behavioral patterns also had: 1) distinct computational signatures, including different modulations of key components of a novel form of a drift-diffusion decision model and 2) distinct physiological signatures, including substantial bias-dependent modulations of pupil size in response to rule-based but not stimulus-based cues. These results imply that different sources of expectations can modulate auditory processing via distinct mechanisms: one that uses arousal-linked, rule-based information and another that uses arousal-independent, stimulus-based information to bias the speed and accuracy of auditory perceptual decisions.

Computational and neurophysiological principles underlying auditory perceptual decisions.

A fundamental scientific goal in auditory neuroscience is identifying what mechanisms allow the brain to transform an unlabeled mixture of auditory stimuli into distinct perceptual representations. This transformation is accomplished by a complex interaction of multiple neurocomputational processes, including Gestalt grouping mechanisms, categorization, attention, and perceptual decision-making. Despite a great deal of scientific energy devoted to understanding these principles of hearing, we still do not understand either how auditory perception arises from neural activity or the causal relationship between neural activity and auditory perception. Here, we review the contributions of cortical and subcortical regions to auditory perceptual decisions with an emphasis on those studies that simultaneously measure behavior and neural activity. We also put forth challenges to the field that must be faced if we are to further our understanding of the relationship between neural activity and auditory perception.

A modular high-density µECoG system on macaque vlPFC for auditory cognitive decoding.

OBJECTIVE: A fundamental goal of the auditory system is to parse the auditory environment into distinct perceptual representations. Auditory perception is mediated by the ventral auditory pathway, which includes the ventrolateral prefrontal cortex (vlPFC). Because large-scale recordings of auditory signals are quite rare, the spatiotemporal resolution of the neuronal code that underlies vlPFC's contribution to auditory perception has not been fully elucidated. Therefore, we developed a modular, chronic, high-resolution, multi-electrode array system with long-term viability in order to identify the information that could be decoded from µECoG vlPFC signals. APPROACH: We molded three separate µECoG arrays into one and implanted this system in a non-human primate. A custom 3D-printed titanium chamber was mounted on the left hemisphere. The molded 294-contact µECoG array was implanted subdurally over the vlPFC. µECoG activity was recorded while the monkey participated in a 'hearing-in-noise' task in which they reported hearing a 'target' vocalization from a background 'chorus' of vocalizations. We titrated task difficulty by varying the sound level of the target vocalization, relative to the chorus (target-to-chorus ratio, TCr). MAIN RESULTS: We decoded the TCr and the monkey's behavioral choices from the µECoG signal. We analyzed decoding accuracy as a function of number of electrodes, spatial resolution, and time from implantation. Over a one-year period, we found significant decoding with individual electrodes that increased significantly as we decoded simultaneously more electrodes. Further, we found that the decoding for behavioral choice was better than the decoding of TCr. Finally, because the decoding accuracy of individual electrodes varied on a day-by-day basis, electrode arrays with high channel counts ensure robust decoding in the long term. SIGNIFICANCE: Our results demonstrate the utility of high-resolution and high-channel-count, chronic µECoG recording. We developed a surface electrode array that can be scaled to cover larger cortical areas without increasing the chamber footprint.

Post-decision processing in primate prefrontal cortex influences subsequent choices on an auditory decision-making task.

Perceptual decisions do not occur in isolation but instead reflect ongoing evaluation and adjustment processes that can affect future decisions. However, the neuronal substrates of these across-decision processes are not well understood, particularly for auditory decisions. We measured and manipulated the activity of choice-selective neurons in the ventrolateral prefrontal cortex (vlPFC) while monkeys made decisions about the frequency content of noisy auditory stimuli. As the decision was being formed, vlPFC activity was not modulated strongly by the task. However, after decision commitment, vlPFC population activity encoded the sensory evidence, choice, and outcome of the current trial and predicted subject-specific choice biases on the subsequent trial. Consistent with these patterns of neuronal activity, electrical microstimulation in vlPFC tended to affect the subsequent, but not current, decision. Thus, distributed post-commitment representations of graded decision-related information in prefrontal cortex can play a causal role in evaluating past decisions and biasing subsequent ones.

Neuronal phase consistency tracks dynamic changes in acoustic spectral regularity.

The brain parses the auditory environment into distinct sounds by identifying those acoustic features in the environment that have common relationships (e.g., spectral regularities) with one another and then grouping together the neuronal representations of these features. Although there is a large literature that tests how the brain tracks spectral regularities that are predictable, it is not known how the auditory system tracks spectral regularities that are not predictable and that change dynamically over time. Furthermore, the contribution of brain regions downstream of the auditory cortex to the coding of spectral regularity is unknown. Here, we addressed these two issues by recording electrocorticographic activity, while human patients listened to tone-burst sequences with dynamically varying spectral regularities, and identified potential neuronal mechanisms of the analysis of spectral regularities throughout the brain. We found that the degree of oscillatory stimulus phase consistency (PC) in multiple neuronal-frequency bands tracked spectral regularity. In particular, PC in the delta-frequency band seemed to be the best indicator of spectral regularity. We also found that these regularity representations existed in multiple regions throughout cortex. This widespread reliable modulation in PC - both in neuronal-frequency space and in cortical space - suggests that phase-based modulations may be a general mechanism for tracking regularity in the auditory system specifically and other sensory systems more generally. Our findings also support a general role for the delta-frequency band in processing the regularity of auditory stimuli.

The Contribution of Primary Auditory Cortex to Auditory Categorization in Behaving Monkeys.

The specific contribution of core auditory cortex to auditory perception -such as categorization- remains controversial. To identify a contribution of the primary auditory cortex (A1) to perception, we recorded A1 activity while monkeys reported whether a temporal sequence of tone bursts was heard as having a "small" or "large" frequency difference. We found that A1 had frequency-tuned responses that habituated, independent of frequency content, as this auditory sequence unfolded over time. We also found that A1 firing rate was modulated by the monkeys' reports of "small" and "large" frequency differences; this modulation correlated with their behavioral performance. These findings are consistent with the hypothesis that A1 contributes to the processes underlying auditory categorization.

Contribution of spiking activity in the primary auditory cortex to detection in noise.

A fundamental problem in hearing is detecting a "target" stimulus (e.g., a friend's voice) that is presented with a noisy background (e.g., the din of a crowded restaurant). Despite its importance to hearing, a relationship between spiking activity and behavioral performance during such a "detection-in-noise" task has yet to be fully elucidated. In this study, we recorded spiking activity in primary auditory cortex (A1) while rhesus monkeys detected a target stimulus that was presented with a noise background. Although some neurons were modulated, the response of the typical A1 neuron was not modulated by the stimulus- and task-related parameters of our task. In contrast, we found more robust representations of these parameters in population-level activity: small populations of neurons matched the monkeys' behavioral sensitivity. Overall, these findings are consistent with the hypothesis that the sensory evidence, which is needed to solve such detection-in-noise tasks, is represented in population-level A1 activity and may be available to be read out by downstream neurons that are involved in mediating this task.NEW & NOTEWORTHY This study examines the contribution of A1 to detecting a sound that is presented with a noisy background. We found that population-level A1 activity, but not single neurons, could provide the evidence needed to make this perceptual decision.

Functional Organization of the Ventral Auditory Pathway.

The fundamental problem in audition is determining the mechanisms required by the brain to transform an unlabelled mixture of auditory stimuli into coherent perceptual representations. This process is called auditory-scene analysis. The perceptual representations that result from auditory-scene analysis are formed through a complex interaction of perceptual grouping, attention, categorization and decision-making. Despite a great deal of scientific energy devoted to understanding these aspects of hearing, we still do not understand (1) how sound perception arises from neural activity and (2) the causal relationship between neural activity and sound perception. Here, we review the role of the "ventral" auditory pathway in sound perception. We hypothesize that, in the early parts of the auditory cortex, neural activity reflects the auditory properties of a stimulus. However, in latter parts of the auditory cortex, neurons encode the sensory evidence that forms an auditory decision and are causally involved in the decision process. Finally, in the prefrontal cortex, which receives input from the auditory cortex, neural activity reflects the actual perceptual decision. Together, these studies indicate that the ventral pathway contains hierarchical circuits that are specialized for auditory perception and scene analysis.

Recent refinements to cranial implants for rhesus macaques (Macaca mulatta).

The advent of cranial implants revolutionized primate neurophysiological research because they allow researchers to stably record neural activity from monkeys during active behavior. Cranial implants have improved over the years since their introduction, but chronic implants still increase the risk for medical complications including bacterial contamination and resultant infection, chronic inflammation, bone and tissue loss and complications related to the use of dental acrylic. These complications can lead to implant failure and early termination of study protocols. In an effort to reduce complications, we describe several refinements that have helped us improve cranial implants and the wellbeing of implanted primates.

Causal contribution of primate auditory cortex to auditory perceptual decision-making.

Auditory perceptual decisions are thought to be mediated by the ventral auditory pathway. However, the specific and causal contributions of different brain regions in this pathway, including the middle-lateral (ML) and anterolateral (AL) belt regions of the auditory cortex, to auditory decisions have not been fully identified. To identify these contributions, we recorded from and microstimulated ML and AL sites while monkeys decided whether an auditory stimulus contained more low-frequency or high-frequency tone bursts. Both ML and AL neural activity was modulated by the frequency content of the stimulus. But, only the responses of the most stimulus-sensitive AL neurons were systematically modulated by the monkeys' choices. Consistent with this observation, microstimulation of AL, but not ML, systematically biased the monkeys' behavior toward the choice associated with the preferred frequency of the stimulated site. Together, these findings suggest that AL directly and causally contributes sensory evidence to form this auditory decision.

Temporal Integration of Auditory Information Is Invariant to Temporal Grouping Cues

Auditory perception depends on the temporal structure of incoming acoustic stimuli. Here, we examined whether a temporal manipulation that affects the perceptual grouping also affects the time dependence of decisions regarding those stimuli. We designed a novel discrimination task that required human listeners to decide whether a sequence of tone bursts was increasing or decreasing in frequency. We manipulated temporal perceptual-grouping cues by changing the time interval between the tone bursts, which led to listeners hearing the sequences as a single sound for short intervals or discrete sounds for longer intervals. Despite these strong perceptual differences, this manipulation did not affect the efficiency of how auditory information was integrated over time to form a decision. Instead, the grouping manipulation affected subjects' speed-accuracy trade-offs. These results indicate that the temporal dynamics of evidence accumulation for auditory perceptual decisions can be invariant to manipulations that affect the perceptual grouping of the evidence.

Neural correlates of auditory scene analysis and perception.

The auditory system is designed to transform acoustic information from low-level sensory representations into perceptual representations. These perceptual representations are the computational result of the auditory system's ability to group and segregate spectral, spatial and temporal regularities in the acoustic environment into stable perceptual units (i.e., sounds or auditory objects). Current evidence suggests that the cortex-specifically, the ventral auditory pathway-is responsible for the computations most closely related to perceptual representations. Here, we discuss how the transformations along the ventral auditory pathway relate to auditory percepts, with special attention paid to the processing of vocalizations and categorization, and explore recent models of how these areas may carry out these computations.

Characterizing the impact of category uncertainty on human auditory categorization behavior.

Categorization is an important cognitive process. However, the correct categorization of a stimulus is often challenging because categories can have overlapping boundaries. Whereas perceptual categorization has been extensively studied in vision, the analogous phenomenon in audition has yet to be systematically explored. Here, we test whether and how human subjects learn to use category distributions and prior probabilities, as well as whether subjects employ an optimal decision strategy when making auditory-category decisions. We asked subjects to classify the frequency of a tone burst into one of two overlapping, uniform categories according to the perceived tone frequency. We systematically varied the prior probability of presenting a tone burst with a frequency originating from one versus the other category. Most subjects learned these changes in prior probabilities early in testing and used this information to influence categorization. We also measured each subject's frequency-discrimination thresholds (i.e., their sensory uncertainty levels). We tested each subject's average behavior against variations of a Bayesian model that either led to optimal or sub-optimal decision behavior (i.e. probability matching). In both predicting and fitting each subject's average behavior, we found that probability matching provided a better account of human decision behavior. The model fits confirmed that subjects were able to learn category prior probabilities and approximate forms of the category distributions. Finally, we systematically explored the potential ways that additional noise sources could influence categorization behavior. We found that an optimal decision strategy can produce probability-matching behavior if it utilized non-stationary category distributions and prior probabilities formed over a short stimulus history. Our work extends previous findings into the auditory domain and reformulates the issue of categorization in a manner that can help to interpret the results of previous research within a generative framework.