Sound categories are represented as distributed patterns in the human auditory cortex

Unveiling the development of human voice perception: Neurobiological mechanisms and pathophysiology

The human voice is a critical stimulus for the auditory system that promotes social connection, informs the listener about identity and emotion, and acts as the carrier for spoken language. Research on voice processing in adults has informed our understanding of the unique status of the human voice in the mature auditory cortex and provided potential explanations for mechanisms that underly voice selectivity and identity processing. There is evidence that voice perception undergoes developmental change starting in infancy and extending through early adolescence. While even young infants recognize the voice of their mother, there is an apparent protracted course of development to reach adult-like selectivity for human voice over other sound categories and recognition of other talkers by voice. Gaps in the literature do not allow for an exact mapping of this trajectory or an adequate description of how voice processing and its neural underpinnings abilities evolve. This review provides a comprehensive account of developmental voice processing research published to date and discusses how this evidence fits with and contributes to current theoretical models proposed in the adult literature. We discuss how factors such as cognitive development, neural plasticity, perceptual narrowing, and language acquisition may contribute to the development of voice processing and its investigation in children. We also review evidence of voice processing abilities in premature birth, autism spectrum disorder, and phonagnosia to examine where and how deviations from the typical trajectory of development may manifest.

Intermediate acoustic-to-semantic representations link behavioral and neural responses to natural sounds

Recognizing sounds implicates the cerebral transformation of input waveforms into semantic representations. Although past research identified the superior temporal gyrus (STG) as a crucial cortical region, the computational fingerprint of these cerebral transformations remains poorly characterized. Here, we exploit a model comparison framework and contrasted the ability of acoustic, semantic (continuous and categorical) and sound-to-event deep neural network representation models to predict perceived sound dissimilarity and 7 T human auditory cortex functional magnetic resonance imaging responses. We confirm that spectrotemporal modulations predict early auditory cortex (Heschl's gyrus) responses, and that auditory dimensions (for example, loudness, periodicity) predict STG responses and perceived dissimilarity. Sound-to-event deep neural networks predict Heschl's gyrus responses similar to acoustic models but, notably, they outperform all competing models at predicting both STG responses and perceived dissimilarity. Our findings indicate that STG entails intermediate acoustic-to-semantic sound representations that neither acoustic nor semantic models can account for. These representations are compositional in nature and relevant to behavior.

A modality-independent proto-organization of human multisensory areas

The processing of multisensory information is based upon the capacity of brain regions, such as the superior temporal cortex, to combine information across modalities. However, it is still unclear whether the representation of coherent auditory and visual events requires any prior audiovisual experience to develop and function. Here we measured brain synchronization during the presentation of an audiovisual, audio-only or video-only version of the same narrative in distinct groups of sensory-deprived (congenitally blind and deaf) and typically developed individuals. Intersubject correlation analysis revealed that the superior temporal cortex was synchronized across auditory and visual conditions, even in sensory-deprived individuals who lack any audiovisual experience. This synchronization was primarily mediated by low-level perceptual features, and relied on a similar modality-independent topographical organization of slow temporal dynamics. The human superior temporal cortex is naturally endowed with a functional scaffolding to yield a common representation across multisensory events.

Decoding subject's own name in the primary auditory cortex

Current studies have shown that perception of subject's own name (SON) involves multiple multimodal brain regions, while activities in unimodal sensory regions (i.e., primary auditory cortex) and their interaction with multimodal regions during the self-processing remain unclear. To answer this, we combined multivariate pattern analysis and dynamic causal modelling analysis to explore the regional activation pattern and inter-region effective connection during the perception of SON. We found that SON and other names could be decoded from the activation pattern in the primary auditory cortex. In addition, we found an excitatory effect of SON on connections from the anterior insula/inferior frontal gyrus to the primary auditory cortex, and to the temporoparietal junction. Our findings extended the current knowledge of self-processing by showing that primary auditory cortex could discriminate SON from other names. Furthermore, our findings highlighted the importance of influence of the insula on the primary auditory cortex during self-processing.

Global connectivity fingerprints predict the domain generality of multiple-demand regions

A set of distributed cognitive control networks are known to contribute to diverse cognitive demands, yet it is unclear how these networks gain this domain-general capacity. We hypothesized that this capacity is largely due to the particular organization of the human brain's intrinsic network architecture. Specifically, we tested the possibility that each brain region's domain generality is reflected in its level of global (hub-like) intrinsic connectivity as well as its particular global connectivity pattern ("connectivity fingerprint"). Consistent with prior work, we found that cognitive control networks exhibited domain generality as they represented diverse task context information covering sensory, motor response, and logic rule domains. Supporting our hypothesis, we found that the level of global intrinsic connectivity (estimated with resting-state functional magnetic resonance imaging [fMRI]) was correlated with domain generality during tasks. Further, using a novel information fingerprint mapping approach, we found that each cognitive control region's unique rule response profile("information fingerprint") could be predicted based on its unique intrinsic connectivity fingerprint and the information content in regions outside cognitive control networks. Together, these results suggest that the human brain's intrinsic network architecture supports its ability to represent diverse cognitive task information largely via the location of multiple-demand regions within the brain's global network organization.

Neural responses in human superior temporal cortex support coding of voice representations

The ability to recognize abstract features of voice during auditory perception is an intricate feat of human audition. For the listener, this occurs in near-automatic fashion to seamlessly extract complex cues from a highly variable auditory signal. Voice perception depends on specialized regions of auditory cortex, including superior temporal gyrus (STG) and superior temporal sulcus (STS). However, the nature of voice encoding at the cortical level remains poorly understood. We leverage intracerebral recordings across human auditory cortex during presentation of voice and nonvoice acoustic stimuli to examine voice encoding at the cortical level in 8 patient-participants undergoing epilepsy surgery evaluation. We show that voice selectivity increases along the auditory hierarchy from supratemporal plane (STP) to the STG and STS. Results show accurate decoding of vocalizations from human auditory cortical activity even in the complete absence of linguistic content. These findings show an early, less-selective temporal window of neural activity in the STG and STS followed by a sustained, strongly voice-selective window. Encoding models demonstrate divergence in the encoding of acoustic features along the auditory hierarchy, wherein STG/STS responses are best explained by voice category and acoustics, as opposed to acoustic features of voice stimuli alone. This is in contrast to neural activity recorded from STP, in which responses were accounted for by acoustic features. These findings support a model of voice perception that engages categorical encoding mechanisms within STG and STS to facilitate feature extraction.

Voice perception occurs via specialized networks in higher order auditory cortex, but how voice features are encoded remains a central unanswered question. Using human intracerebral recordings of auditory cortex, this study provides evidence for categorical encoding of voice.

Using high spatial resolution fMRI to understand representation in the auditory network

Following rapid methodological advances, ultra-high field (UHF) functional and anatomical magnetic resonance imaging (MRI) has been repeatedly and successfully used for the investigation of the human auditory system in recent years. Here, we review this work and argue that UHF MRI is uniquely suited to shed light on how sounds are represented throughout the network of auditory brain regions. That is, the provided gain in spatial resolution at UHF can be used to study the functional role of the small subcortical auditory processing stages and details of cortical processing. Further, by combining high spatial resolution with the versatility of MRI contrasts, UHF MRI has the potential to localize the primary auditory cortex in individual hemispheres. This is a prerequisite to study how sound representation in higher-level auditory cortex evolves from that in early (primary) auditory cortex. Finally, the access to independent signals across auditory cortical depths, as afforded by UHF, may reveal the computations that underlie the emergence of an abstract, categorical sound representation based on low-level acoustic feature processing. Efforts on these research topics are underway. Here we discuss promises as well as challenges that come with studying these research questions using UHF MRI, and provide a future outlook.

Cochlea to categories: The spatiotemporal dynamics of semantic auditory representations

How does the auditory system categorize natural sounds? Here we apply multimodal neuroimaging to illustrate the progression from acoustic to semantically dominated representations. Combining magnetoencephalographic (MEG) and functional magnetic resonance imaging (fMRI) scans of observers listening to naturalistic sounds, we found superior temporal responses beginning ∼55 ms post-stimulus onset, spreading to extratemporal cortices by ∼100 ms. Early regions were distinguished less by onset/peak latency than by functional properties and overall temporal response profiles. Early acoustically-dominated representations trended systematically toward category dominance over time (after ∼200 ms) and space (beyond primary cortex). Semantic category representation was spatially specific: Vocalizations were preferentially distinguished in frontotemporal voice-selective regions and the fusiform; scenes and objects were distinguished in parahippocampal and medial place areas. Our results are consistent with real-world events coded via an extended auditory processing hierarchy, in which acoustic representations rapidly enter multiple streams specialized by category, including areas typically considered visual cortex.

Fast Periodic Auditory Stimulation Reveals a Robust Categorical Response to Voices in the Human Brain

Voices are arguably among the most relevant sounds in humans' everyday life, and several studies have suggested the existence of voice-selective regions in the human brain. Despite two decades of research, defining the human brain regions supporting voice recognition remains challenging. Moreover, whether neural selectivity to voices is merely driven by acoustic properties specific to human voices (e.g., spectrogram, harmonicity), or whether it also reflects a higher-level categorization response is still under debate. Here, we objectively measured rapid automatic categorization responses to human voices with fast periodic auditory stimulation (FPAS) combined with electroencephalography (EEG). Participants were tested with stimulation sequences containing heterogeneous non-vocal sounds from different categories presented at 4 Hz (i.e., four stimuli/s), with vocal sounds appearing every three stimuli (1.333 Hz). A few minutes of stimulation are sufficient to elicit robust 1.333 Hz voice-selective focal brain responses over superior temporal regions of individual participants. This response is virtually absent for sequences using frequency-scrambled sounds, but is clearly observed when voices are presented among sounds from musical instruments matched for pitch and harmonicity-to-noise ratio (HNR). Overall, our FPAS paradigm demonstrates that the human brain seamlessly categorizes human voices when compared with other sounds including musical instruments' sounds matched for low level acoustic features and that voice-selective responses are at least partially independent from low-level acoustic features, making it a powerful and versatile tool to understand human auditory categorization in general.

Auditory Agnosia for Environmental Sounds in Alzheimer's Disease: Not Hearing and Not Listening?

Auditory agnosia for environmental sounds (AES) is an example of central auditory dysfunction. It is presumed to be independent of language deficits and in presence of normal hearing. We undertook a detailed neuropsychological assessment including environmental sound naming and recognition in 34 clinically mild Alzheimer's disease (AD) patients and 29 age-matched healthy control subjects. In patients with AD, audiometry was performed to assess the impact on test performance, and in normal controls the Hearing Handicap Inventory for the Elderly - Screening Version to exclude more than mild hearing loss. We adapted a validated environmental sound battery and found near perfect scores in controls. We found that environmental sound agnosia is common in mild AD. We found a statistically significant difference in mean pure tone audiometry in the best ear between patients with and those patients without naming deficits of 11.3 dB (p = 0.010) and of 14.7 dB (p = 0.000) between those with and without recognition deficits. Statistical significance remained after correcting for age, aphasia, Mini-Mental State Examination score, and working memory. Slight and moderate peripheral hearing loss increases the odds ratio of recognition deficits by 13.75 (confidence interval 2.3-81.5) compared to normal hearing patients. We did not find evidence for different forms of AES. This work suggests that an interaction between peripheral hearing loss and AD pathology produces problems with environmental sound recognition. It confirms that the relationship between hearing and dementia is complex but also suggests that interventions to prevent and treat hearing loss could have an effect on AD in its clinical expression.

Decoding Multiple Sound-Categories in the Auditory Cortex by Neural Networks: An fNIRS Study

This study aims to decode the hemodynamic responses (HRs) evoked by multiple sound-categories using functional near-infrared spectroscopy (fNIRS). The six different sounds were given as stimuli (English, non-English, annoying, nature, music, and gunshot). The oxy-hemoglobin (HbO) concentration changes are measured in both hemispheres of the auditory cortex while 18 healthy subjects listen to 10-s blocks of six sound-categories. Long short-term memory (LSTM) networks were used as a classifier. The classification accuracy was 20.38 ± 4.63% with six class classification. Though LSTM networks' performance was a little higher than chance levels, it is noteworthy that we could classify the data subject-wise without feature selections.

Chinese-English bilinguals show linguistic-perceptual links in the brain associating short spoken phrases with corresponding real-world natural action sounds by semantic category

Higher cognitive functions such as linguistic comprehension must ultimately relate to perceptual systems in the brain, though how and why this forms remains unclear. Different brain networks that mediate perception when hearing real-world natural sounds has recently been proposed to respect a taxonomic model of acoustic-semantic categories. Using functional magnetic resonance imaging (fMRI) with Chinese/English bilingual listeners, the present study explored whether reception of short spoken phrases, in both Chinese (Mandarin) and English, describing corresponding sound-producing events would engage overlapping brain regions at a semantic category level. The results revealed a double-dissociation of cortical regions that were preferential for representing knowledge of human versus environmental action events, whether conveyed through natural sounds or the corresponding spoken phrases depicted by either language. These findings of cortical hubs exhibiting linguistic-perceptual knowledge links at a semantic category level should help to advance neurocomputational models of the neurodevelopment of language systems.

Correspondence of categorical and feature-based representations of music in the human brain

INTRODUCTION

Humans tend to categorize auditory stimuli into discrete classes, such as animal species, language, musical instrument, and music genre. Of these, music genre is a frequently used dimension of human music preference and is determined based on the categorization of complex auditory stimuli. Neuroimaging studies have reported that the superior temporal gyrus (STG) is involved in response to general music-related features. However, there is considerable uncertainty over how discrete music categories are represented in the brain and which acoustic features are more suited for explaining such representations.

METHODS

We used a total of 540 music clips to examine comprehensive cortical representations and the functional organization of music genre categories. For this purpose, we applied a voxel-wise modeling approach to music-evoked brain activity measured using functional magnetic resonance imaging. In addition, we introduced a novel technique for feature-brain similarity analysis and assessed how discrete music categories are represented based on the cortical response pattern to acoustic features.

RESULTS

Our findings indicated distinct cortical organizations for different music genres in the bilateral STG, and they revealed representational relationships between different music genres. On comparing different acoustic feature models, we found that these representations of music genres could be explained largely by a biologically plausible spectro-temporal modulation-transfer function model.

CONCLUSION

Our findings have elucidated the quantitative representation of music genres in the human cortex, indicating the possibility of modeling this categorization of complex auditory stimuli based on brain activity.

Multivoxel codes for representing and integrating acoustic features in human cortex

Using fMRI and multivariate pattern analysis, we determined whether spectral and temporal acoustic features are represented by independent or integrated multivoxel codes in human cortex. Listeners heard band-pass noise varying in frequency (spectral) and amplitude-modulation (AM) rate (temporal) features. In the superior temporal plane, changes in multivoxel activity due to frequency were largely invariant with respect to AM rate (and vice versa), consistent with an independent representation. In contrast, in posterior parietal cortex, multivoxel representation was exclusively integrated and tuned to specific conjunctions of frequency and AM features (albeit weakly). Direct between-region comparisons show that whereas independent coding of frequency weakened with increasing levels of the hierarchy, such a progression for AM and integrated coding was less fine-grained and only evident in the higher hierarchical levels from non-core to parietal cortex (with AM coding weakening and integrated coding strengthening). Our findings support the notion that primary auditory cortex can represent spectral and temporal acoustic features in an independent fashion and suggest a role for parietal cortex in feature integration and the structuring of sensory input.

The time course of auditory recognition measured with rapid sequences of short natural sounds

Human listeners are able to recognize accurately an impressive range of complex sounds, such as musical instruments or voices. The underlying mechanisms are still poorly understood. Here, we aimed to characterize the processing time needed to recognize a natural sound. To do so, by analogy with the “rapid visual sequential presentation paradigm”, we embedded short target sounds within rapid sequences of distractor sounds. The core hypothesis is that any correct report of the target implies that sufficient processing for recognition had been completed before the time of occurrence of the subsequent distractor sound. We conducted four behavioral experiments using short natural sounds (voices and instruments) as targets or distractors. We report the effects on performance, as measured by the fastest presentation rate for recognition, of sound duration, number of sounds in a sequence, the relative pitch between target and distractors and target position in the sequence. Results showed a very rapid auditory recognition of natural sounds in all cases. Targets could be recognized at rates up to 30 sounds per second. In addition, the best performance was observed for voices in sequences of instruments. These results give new insights about the remarkable efficiency of timbre processing in humans, using an original behavioral paradigm to provide strong constraints on future neural models of sound recognition.

Auditory Categorization of Man-Made Sounds Versus Natural Sounds by Means of MEG Functional Brain Connectivity

Previous neuroimaging studies have shown that sounds can be discriminated due to living-related or man-made-related characteristics and involve different brain regions. However, these studies have mainly provided source space analyses, which offer simple maps of activated brain regions but do not explain how regions of a distributed system are functionally organized under a specific task. In the present study, we aimed to further examine the functional connectivity of the auditory processing pathway across different categories of non-speech sounds in healthy adults, by means of MEG. Our analyses demonstrated significant activation and interconnection differences between living and man-made object sounds, in the prefrontal areas, anterior-superior temporal gyrus (aSTG), posterior cingulate cortex (PCC), and supramarginal gyrus (SMG), occurring within 80–120 ms post-stimulus interval. Current findings replicated previous ones, in that other regions beyond the auditory cortex are involved during auditory processing. According to the functional connectivity analysis, differential brain networks across the categories exist, which proposes that sound category discrimination processing relies on distinct cortical networks, a notion that has been strongly argued in the literature also in relation to the visual system.

The Rapid Emergence of Auditory Object Representations in Cortex Reflect Central Acoustic Attributes

Human listeners are bombarded by acoustic information that the brain rapidly organizes into coherent percepts of objects and events in the environment, which aids speech and music perception. The efficiency of auditory object recognition belies the critical constraint that acoustic stimuli necessarily require time to unfold. Using magnetoencephalography, we studied the time course of the neural processes that transform dynamic acoustic information into auditory object representations. Participants listened to a diverse set of 36 tokens comprising everyday sounds from a typical human environment. Multivariate pattern analysis was used to decode the sound tokens from the magnetoencephalographic recordings. We show that sound tokens can be decoded from brain activity beginning 90 msec after stimulus onset with peak decoding performance occurring at 155 msec poststimulus onset. Decoding performance was primarily driven by differences between category representations (e.g., environmental vs. instrument sounds), although within-category decoding was better than chance. Representational similarity analysis revealed that these emerging neural representations were related to harmonic and spectrotemporal differences among the stimuli, which correspond to canonical acoustic features processed by the auditory pathway. Our findings begin to link the processing of physical sound properties with the perception of auditory objects and events in cortex.

Plasticity in auditory categorization is supported by differential engagement of the auditory-linguistic network

To construct our perceptual world, the brain categorizes variable sensory cues into behaviorally-relevant groupings. Categorical representations are apparent within a distributed fronto-temporo-parietal brain network but how this neural circuitry is shaped by experience remains undefined. Here, we asked whether speech and music categories might be formed within different auditory-linguistic brain regions depending on listeners' auditory expertise. We recorded EEG in highly skilled (musicians) vs. less experienced (nonmusicians) perceivers as they rapidly categorized speech and musical sounds. Musicians showed perceptual enhancements across domains, yet source EEG data revealed a double dissociation in the neurobiological mechanisms supporting categorization between groups. Whereas musicians coded categories in primary auditory cortex (PAC), nonmusicians recruited non-auditory regions (e.g., inferior frontal gyrus, IFG) to generate category-level information. Functional connectivity confirmed nonmusicians' increased left IFG involvement reflects stronger routing of signal from PAC directed to IFG, presumably because sensory coding is insufficient to construct categories in less experienced listeners. Our findings establish auditory experience modulates specific engagement and inter-regional communication in the auditory-linguistic network supporting categorical perception. Whereas early canonical PAC representations are sufficient to generate categories in highly trained ears, less experienced perceivers broadcast information downstream to higher-order linguistic brain areas (IFG) to construct abstract sound labels.

Separable neural representations of sound sources: Speaker identity and musical timbre

Human listeners can quickly and easily recognize different sound sources (objects and events) in their environment. Understanding how this impressive ability is accomplished can improve signal processing and machine intelligence applications along with assistive listening technologies. However, it is not clear how the brain represents the many sounds that humans can recognize (such as speech and music) at the level of individual sources, categories and acoustic features. To examine the cortical organization of these representations, we used patterns of fMRI responses to decode 1) four individual speakers and instruments from one another (separately, within each category), 2) the superordinate category labels associated with each stimulus (speech or instrument), and 3) a set of simple synthesized sounds that could be differentiated entirely on their acoustic features. Data were collected using an interleaved silent steady state sequence to increase the temporal signal-to-noise ratio, and mitigate issues with auditory stimulus presentation in fMRI. Largely separable clusters of voxels in the temporal lobes supported the decoding of individual speakers and instruments from other stimuli in the same category. Decoding the superordinate category of each sound was more accurate and involved a larger portion of the temporal lobes. However, these clusters all overlapped with areas that could decode simple, acoustically separable stimuli. Thus, individual sound sources from different sound categories are represented in separate regions of the temporal lobes that are situated within regions implicated in more general acoustic processes. These results bridge an important gap in our understanding of cortical representations of sounds and their acoustics.

Neural responses to natural and model-matched stimuli reveal distinct computations in primary and nonprimary auditory cortex

A central goal of sensory neuroscience is to construct models that can explain neural responses to natural stimuli. As a consequence, sensory models are often tested by comparing neural responses to natural stimuli with model responses to those stimuli. One challenge is that distinct model features are often correlated across natural stimuli, and thus model features can predict neural responses even if they do not in fact drive them. Here, we propose a simple alternative for testing a sensory model: we synthesize a stimulus that yields the same model response as each of a set of natural stimuli, and test whether the natural and "model-matched" stimuli elicit the same neural responses. We used this approach to test whether a common model of auditory cortex-in which spectrogram-like peripheral input is processed by linear spectrotemporal filters-can explain fMRI responses in humans to natural sounds. Prior studies have that shown that this model has good predictive power throughout auditory cortex, but this finding could reflect feature correlations in natural stimuli. We observed that fMRI responses to natural and model-matched stimuli were nearly equivalent in primary auditory cortex (PAC) but that nonprimary regions, including those selective for music or speech, showed highly divergent responses to the two sound sets. This dissociation between primary and nonprimary regions was less clear from model predictions due to the influence of feature correlations across natural stimuli. Our results provide a signature of hierarchical organization in human auditory cortex, and suggest that nonprimary regions compute higher-order stimulus properties that are not well captured by traditional models. Our methodology enables stronger tests of sensory models and could be broadly applied in other domains.

Decoding sound categories based on whole-brain functional connectivity patterns

2Sound decoding is important for patients with sensory loss, such as the blind. Previous studies on sound categorization were conducted by estimating brain activity using univariate analysis or voxel-wise multivariate decoding methods and suggested some regions were sensitive to auditory categories. It is proposed that feedback connections between brain areas may facilitate auditory object selection. Therefore, it is important to explore whether functional connectivity among regions can be used to decode sound category. In this study, we constructed whole-brain functional connectivity patterns when subjects perceived four different sound categories and combined them with multivariate pattern classification analysis for sound decoding. The categorical discriminative networks and regions were determined based on the weight maps. Results showed that a high accuracy in multi-category classification was obtained based on the whole-brain functional connectivity patterns and the results were verified by different preprocessing parameters. Insight into the category discriminative functional networks showed that contributive connections crossed the left and right brain, and ranged from primary regions to high-level cognitive regions, which provide new evidence for the distributed representation of auditory object. Further analysis of brain regions in the discriminative networks showed that superior temporal gyrus and Heschl's gyrus significantly contributed to discriminating sound categories. Together, the findings reveal that functional connectivity based multivariate classification method provides rich information for auditory category decoding. The successful decoding results implicate the interactive properties of the distributed brain areas in auditory sound representation.

Sparse Ordinal Logistic Regression and Its Application to Brain Decoding

Brain decoding with multivariate classification and regression has provided a powerful framework for characterizing information encoded in population neural activity. Classification and regression models are respectively used to predict discrete and continuous variables of interest. However, cognitive and behavioral parameters that we wish to decode are often ordinal variables whose values are discrete but ordered, such as subjective ratings. To date, there is no established method of predicting ordinal variables in brain decoding. In this study, we present a new algorithm, sparse ordinal logistic regression (SOLR), that combines ordinal logistic regression with Bayesian sparse weight estimation. We found that, in both simulation and analyses using real functional magnetic resonance imaging (fMRI) data, SOLR outperformed ordinal logistic regression with non-sparse regularization, indicating that sparseness leads to better decoding performance. SOLR also outperformed classification and linear regression models with the same type of sparseness, indicating the advantage of the modeling tailored to ordinal outputs. Our results suggest that SOLR provides a principled and effective method of decoding ordinal variables.

Connecting Deep Neural Networks to Physical, Perceptual, and Electrophysiological Auditory Signals

Deep neural networks have been recently shown to capture intricate information transformation of signals from the sensory profiles to semantic representations that facilitate recognition or discrimination of complex stimuli. In this vein, convolutional neural networks (CNNs) have been used very successfully in image and audio classification. Designed to imitate the hierarchical structure of the nervous system, CNNs reflect activation with increasing degrees of complexity that transform the incoming signal onto object-level representations. In this work, we employ a CNN trained for large-scale audio object classification to gain insights about the contribution of various audio representations that guide sound perception. The analysis contrasts activation of different layers of a CNN with acoustic features extracted directly from the scenes, perceptual salience obtained from behavioral responses of human listeners, as well as neural oscillations recorded by electroencephalography (EEG) in response to the same natural scenes. All three measures are tightly linked quantities believed to guide percepts of salience and object formation when listening to complex scenes. The results paint a picture of the intricate interplay between low-level and object-level representations in guiding auditory salience that is very much dependent on context and sound category.

Task-Modulated Cortical Representations of Natural Sound Source Categories

In everyday sound environments, we recognize sound sources and events by attending to relevant aspects of an acoustic input. Evidence about the cortical mechanisms involved in extracting relevant category information from natural sounds is, however, limited to speech. Here, we used functional MRI to measure cortical response patterns while human listeners categorized real-world sounds created by objects of different solid materials (glass, metal, wood) manipulated by different sound-producing actions (striking, rattling, dropping). In different sessions, subjects had to identify either material or action categories in the same sound stimuli. The sound-producing action and the material of the sound source could be decoded from multivoxel activity patterns in auditory cortex, including Heschl's gyrus and planum temporale. Importantly, decoding success depended on task relevance and category discriminability. Action categories were more accurately decoded in auditory cortex when subjects identified action information. Conversely, the material of the same sound sources was decoded with higher accuracy in the inferior frontal cortex during material identification. Representational similarity analyses indicated that both early and higher-order auditory cortex selectively enhanced spectrotemporal features relevant to the target category. Together, the results indicate a cortical selection mechanism that favors task-relevant information in the processing of nonvocal sound categories.

Keeping track of sound objects in space: The contribution of early-stage auditory areas

The influential dual-stream model of auditory processing stipulates that information pertaining to the meaning and to the position of a given sound object is processed in parallel along two distinct pathways, the ventral and dorsal auditory streams. Functional independence of the two processing pathways is well documented by conscious experience of patients with focal hemispheric lesions. On the other hand there is growing evidence that the meaning and the position of a sound are combined early in the processing pathway, possibly already at the level of early-stage auditory areas. Here, we investigated how early auditory areas integrate sound object meaning and space (simulated by interaural time differences) using a repetition suppression fMRI paradigm at 7 T. Subjects listen passively to environmental sounds presented in blocks of repetitions of the same sound object (same category) or different sounds objects (different categories), perceived either in the left or right space (no change within block) or shifted left-to-right or right-to-left halfway in the block (change within block). Environmental sounds activated bilaterally the superior temporal gyrus, middle temporal gyrus, inferior frontal gyrus, and right precentral cortex. Repetitions suppression effects were measured within bilateral early-stage auditory areas in the lateral portion of the Heschl's gyrus and posterior superior temporal plane. Left lateral early-stages areas showed significant effects for position and change, interactions Category x Initial Position and Category x Change in Position, while right lateral areas showed main effect of category and interaction Category x Change in Position. The combined evidence from our study and from previous studies speaks in favour of a position-linked representation of sound objects, which is independent from semantic encoding within the ventral stream and from spatial encoding within the dorsal stream. We argue for a third auditory stream, which has its origin in lateral belt areas and tracks sound objects across space.

The Encoding of Sound Source Elevation in the Human Auditory Cortex

Spatial hearing is a crucial capacity of the auditory system. While the encoding of horizontal sound direction has been extensively studied, very little is known about the representation of vertical sound direction in the auditory cortex. Using high-resolution fMRI, we measured voxelwise sound elevation tuning curves in human auditory cortex and show that sound elevation is represented by broad tuning functions preferring lower elevations as well as secondary narrow tuning functions preferring individual elevation directions. We changed the ear shape of participants (male and female) with silicone molds for several days. This manipulation reduced or abolished the ability to discriminate sound elevation and flattened cortical tuning curves. Tuning curves recovered their original shape as participants adapted to the modified ears and regained elevation perception over time. These findings suggest that the elevation tuning observed in low-level auditory cortex did not arise from the physical features of the stimuli but is contingent on experience with spectral cues and covaries with the change in perception. One explanation for this observation may be that the tuning in low-level auditory cortex underlies the subjective perception of sound elevation.SIGNIFICANCE STATEMENT This study addresses two fundamental questions about the brain representation of sensory stimuli: how the vertical spatial axis of auditory space is represented in the auditory cortex and whether low-level sensory cortex represents physical stimulus features or subjective perceptual attributes. Using high-resolution fMRI, we show that vertical sound direction is represented by broad tuning functions preferring lower elevations as well as secondary narrow tuning functions preferring individual elevation directions. In addition, we demonstrate that the shape of these tuning functions is contingent on experience with spectral cues and covaries with the change in perception, which may indicate that the tuning functions in low-level auditory cortex underlie the perceived elevation of a sound source.

Frequency-Selective Attention in Auditory Scenes Recruits Frequency Representations Throughout Human Superior Temporal Cortex

A sound of interest may be tracked amid other salient sounds by focusing attention on its characteristic features including its frequency. Functional magnetic resonance imaging findings have indicated that frequency representations in human primary auditory cortex (AC) contribute to this feat. However, attentional modulations were examined at relatively low spatial and spectral resolutions, and frequency-selective contributions outside the primary AC could not be established. To address these issues, we compared blood oxygenation level-dependent (BOLD) responses in the superior temporal cortex of human listeners while they identified single frequencies versus listened selectively for various frequencies within a multifrequency scene. Using best-frequency mapping, we observed that the detailed spatial layout of attention-induced BOLD response enhancements in primary AC follows the tonotopy of stimulus-driven frequency representations-analogous to the "spotlight" of attention enhancing visuospatial representations in retinotopic visual cortex. Moreover, using an algorithm trained to discriminate stimulus-driven frequency representations, we could successfully decode the focus of frequency-selective attention from listeners' BOLD response patterns in nonprimary AC. Our results indicate that the human brain facilitates selective listening to a frequency of interest in a scene by reinforcing the fine-grained activity pattern throughout the entire superior temporal cortex that would be evoked if that frequency was present alone.

Cortical processing of pitch: Model-based encoding and decoding of auditory fMRI responses to real-life sounds

Pitch is a perceptual attribute related to the fundamental frequency (or periodicity) of a sound. So far, the cortical processing of pitch has been investigated mostly using synthetic sounds. However, the complex harmonic structure of natural sounds may require different mechanisms for the extraction and analysis of pitch. This study investigated the neural representation of pitch in human auditory cortex using model-based encoding and decoding analyses of high field (7 T) functional magnetic resonance imaging (fMRI) data collected while participants listened to a wide range of real-life sounds. Specifically, we modeled the fMRI responses as a function of the sounds' perceived pitch height and salience (related to the fundamental frequency and the harmonic structure respectively), which we estimated with a computational algorithm of pitch extraction (de Cheveigné and Kawahara, 2002). First, using single-voxel fMRI encoding, we identified a pitch-coding region in the antero-lateral Heschl's gyrus (HG) and adjacent superior temporal gyrus (STG). In these regions, the pitch representation model combining height and salience predicted the fMRI responses comparatively better than other models of acoustic processing and, in the right hemisphere, better than pitch representations based on height/salience alone. Second, we assessed with model-based decoding that multi-voxel response patterns of the identified regions are more informative of perceived pitch than the remainder of the auditory cortex. Further multivariate analyses showed that complementing a multi-resolution spectro-temporal sound representation with pitch produces a small but significant improvement to the decoding of complex sounds from fMRI response patterns. In sum, this work extends model-based fMRI encoding and decoding methods - previously employed to examine the representation and processing of acoustic sound features in the human auditory system - to the representation and processing of a relevant perceptual attribute such as pitch. Taken together, the results of our model-based encoding and decoding analyses indicated that the pitch of complex real life sounds is extracted and processed in lateral HG/STG regions, at locations consistent with those indicated in several previous fMRI studies using synthetic sounds. Within these regions, pitch-related sound representations reflect the modulatory combination of height and the salience of the pitch percept.

Encoding of natural timbre dimensions in human auditory cortex

Timbre, or sound quality, is a crucial but poorly understood dimension of auditory perception that is important in describing speech, music, and environmental sounds. The present study investigates the cortical representation of different timbral dimensions. Encoding models have typically incorporated the physical characteristics of sounds as features when attempting to understand their neural representation with functional MRI. Here we test an encoding model that is based on five subjectively derived dimensions of timbre to predict cortical responses to natural orchestral sounds. Results show that this timbre model can outperform other models based on spectral characteristics, and can perform as well as a complex joint spectrotemporal modulation model. In cortical regions at the medial border of Heschl's gyrus, bilaterally, and regions at its posterior adjacency in the right hemisphere, the timbre model outperforms even the complex joint spectrotemporal modulation model. These findings suggest that the responses of cortical neuronal populations in auditory cortex may reflect the encoding of perceptual timbre dimensions.

Voice selectivity in the temporal voice area despite matched low-level acoustic cues

In human listeners, the temporal voice areas (TVAs) are regions of the superior temporal gyrus and sulcus that respond more to vocal sounds than a range of nonvocal control sounds, including scrambled voices, environmental noises, and animal cries. One interpretation of the TVA’s selectivity is based on low-level acoustic cues: compared to control sounds, vocal sounds may have stronger harmonic content or greater spectrotemporal complexity. Here, we show that the right TVA remains selective to the human voice even when accounting for a variety of acoustical cues. Using fMRI, single vowel stimuli were contrasted with single notes of musical instruments with balanced harmonic-to-noise ratios and pitches. We also used “auditory chimeras”, which preserved subsets of acoustical features of the vocal sounds. The right TVA was preferentially activated only for the natural human voice. In particular, the TVA did not respond more to artificial chimeras preserving the exact spectral profile of voices. Additional acoustic measures, including temporal modulations and spectral complexity, could not account for the increased activation. These observations rule out simple acoustical cues as a basis for voice selectivity in the TVAs.

The impact of ultra-high field MRI on cognitive and computational neuroimaging

The ability to measure functional brain responses non-invasively with ultra high field MRI (7 T and above) represents a unique opportunity in advancing our understanding of the human brain. Compared to lower fields (3 T and below), ultra high field MRI has an increased sensitivity, which can be used to acquire functional images with greater spatial resolution, and greater specificity of the blood oxygen level dependent (BOLD) signal to the underlying neuronal responses. Together, increased resolution and specificity enable investigating brain functions at a submillimeter scale, which so far could only be done with invasive techniques. At this mesoscopic spatial scale, perception, cognition and behavior can be probed at the level of fundamental units of neural computations, such as cortical columns, cortical layers, and subcortical nuclei. This represents a unique and distinctive advantage that differentiates ultra high from lower field imaging and that can foster a tighter link between fMRI and computational modeling of neural networks. So far, functional brain mapping at submillimeter scale has focused on the processing of sensory information and on well-known systems for which extensive information is available from invasive recordings in animals. It remains an open challenge to extend this methodology to uniquely human functions and, more generally, to systems for which animal models may be problematic. To succeed, the possibility to acquire high-resolution functional data with large spatial coverage, the availability of computational models of neural processing as well as accurate biophysical modeling of neurovascular coupling at mesoscopic scale all appear necessary.

Hearing emotional sounds: category representation in the human amygdala

Previous studies have shown that the amygdala is more involved in processing animate categories, such as humans and animals, than inanimate objects, but little is known regarding whether this animate advantage applies to auditory stimuli. To address this issue, we performed a functional Magnetic Resonance Imaging (fMRI) study with emotion and category as factors, in which subjects heard sounds from different categories (i.e., humans, animals, and objects) in negative and neutral dimensions. Emotional levels and semantic familiarity were matched across categories. The results showed that the amygdala responded more to human vocalization than to animal vocalization and sounds of inanimate objects in both negative and neutral valences, and more to animal sounds than to objects in neural condition. In addition, the amygdala, together with the insula and the right superior temporal sulcus, further distinguished human voices from animal sounds. These data indicated that the amygdala is prepared to respond to animate sources, especially human vocalizations in auditory modality.

Distributed neural signatures of natural audiovisual speech and music in the human auditory cortex

During a conversation or when listening to music, auditory and visual information are combined automatically into audiovisual objects. However, it is still poorly understood how specific type of visual information shapes neural processing of sounds in lifelike stimulus environments. Here we applied multi-voxel pattern analysis to investigate how naturally matching visual input modulates supratemporal cortex activity during processing of naturalistic acoustic speech, singing and instrumental music. Bayesian logistic regression classifiers with sparsity-promoting priors were trained to predict whether the stimulus was audiovisual or auditory, and whether it contained piano playing, speech, or singing. The predictive performances of the classifiers were tested by leaving one participant at a time for testing and training the model using the remaining 15 participants. The signature patterns associated with unimodal auditory stimuli encompassed distributed locations mostly in the middle and superior temporal gyrus (STG/MTG). A pattern regression analysis, based on a continuous acoustic model, revealed that activity in some of these MTG and STG areas were associated with acoustic features present in speech and music stimuli. Concurrent visual stimulus modulated activity in bilateral MTG (speech), lateral aspect of right anterior STG (singing), and bilateral parietal opercular cortex (piano). Our results suggest that specific supratemporal brain areas are involved in processing complex natural speech, singing, and piano playing, and other brain areas located in anterior (facial speech) and posterior (music-related hand actions) supratemporal cortex are influenced by related visual information. Those anterior and posterior supratemporal areas have been linked to stimulus identification and sensory-motor integration, respectively.

Attention Modulates the Auditory Cortical Processing of Spatial and Category Cues in Naturalistic Auditory Scenes

This combined fMRI and MEG study investigated brain activations during listening and attending to natural auditory scenes. We first recorded, using in-ear microphones, vocal non-speech sounds, and environmental sounds that were mixed to construct auditory scenes containing two concurrent sound streams. During the brain measurements, subjects attended to one of the streams while spatial acoustic information of the scene was either preserved (stereophonic sounds) or removed (monophonic sounds). Compared to monophonic sounds, stereophonic sounds evoked larger blood-oxygenation-level-dependent (BOLD) fMRI responses in the bilateral posterior superior temporal areas, independent of which stimulus attribute the subject was attending to. This finding is consistent with the functional role of these regions in the (automatic) processing of auditory spatial cues. Additionally, significant differences in the cortical activation patterns depending on the target of attention were observed. Bilateral planum temporale and inferior frontal gyrus were preferentially activated when attending to stereophonic environmental sounds, whereas when subjects attended to stereophonic voice sounds, the BOLD responses were larger at the bilateral middle superior temporal gyrus and sulcus, previously reported to show voice sensitivity. In contrast, the time-resolved MEG responses were stronger for mono- than stereophonic sounds in the bilateral auditory cortices at ~360 ms after the stimulus onset when attending to the voice excerpts within the combined sounds. The observed effects suggest that during the segregation of auditory objects from the auditory background, spatial sound cues together with other relevant temporal and spectral cues are processed in an attention-dependent manner at the cortical locations generally involved in sound recognition. More synchronous neuronal activation during monophonic than stereophonic sound processing, as well as (local) neuronal inhibitory mechanisms in the auditory cortex, may explain the simultaneous increase of BOLD responses and decrease of MEG responses. These findings highlight the complimentary role of electrophysiological and hemodynamic measures in addressing brain processing of complex stimuli.

Alpha power indexes task-related networks on large and small scales: A multimodal ECoG study in humans and a non-human primate

Performing different tasks, such as generating motor movements or processing sensory input, requires the recruitment of specific networks of neuronal populations. Previous studies suggested that power variations in the alpha band (8-12Hz) may implement such recruitment of task-specific populations by increasing cortical excitability in task-related areas while inhibiting population-level cortical activity in task-unrelated areas (Klimesch et al., 2007; Jensen and Mazaheri, 2010). However, the precise temporal and spatial relationships between the modulatory function implemented by alpha oscillations and population-level cortical activity remained undefined. Furthermore, while several studies suggested that alpha power indexes task-related populations across large and spatially separated cortical areas, it was largely unclear whether alpha power also differentially indexes smaller networks of task-related neuronal populations. Here we addressed these questions by investigating the temporal and spatial relationships of electrocorticographic (ECoG) power modulations in the alpha band and in the broadband gamma range (70-170Hz, indexing population-level activity) during auditory and motor tasks in five human subjects and one macaque monkey. In line with previous research, our results confirm that broadband gamma power accurately tracks task-related behavior and that alpha power decreases in task-related areas. More importantly, they demonstrate that alpha power suppression lags population-level activity in auditory areas during the auditory task, but precedes it in motor areas during the motor task. This suppression of alpha power in task-related areas was accompanied by an increase in areas not related to the task. In addition, we show for the first time that these differential modulations of alpha power could be observed not only across widely distributed systems (e.g., motor vs. auditory system), but also within the auditory system. Specifically, alpha power was suppressed in the locations within the auditory system that most robustly responded to particular sound stimuli. Altogether, our results provide experimental evidence for a mechanism that preferentially recruits task-related neuronal populations by increasing cortical excitability in task-related cortical areas and decreasing cortical excitability in task-unrelated areas. This mechanism is implemented by variations in alpha power and is common to humans and the non-human primate under study. These results contribute to an increasingly refined understanding of the mechanisms underlying the selection of the specific neuronal populations required for task execution.

A Bayesian probit model with spatially varying coefficients for brain decoding using fMRI data

Recent advances in human neuroimaging have shown that it is possible to accurately decode how the brain perceives information based only on non-invasive functional magnetic resonance imaging measurements of brain activity. Two commonly used statistical approaches, namely, univariate analysis and multivariate pattern analysis often lead to distinct patterns of selected voxels. One current debate in brain decoding concerns whether the brain's representation of sound categories is localized or distributed. We hypothesize that the distributed pattern of voxels selected by most multivariate pattern analysis models can be an artifact due to the spatial correlation among voxels. Here, we propose a Bayesian spatially varying coefficient model, where the spatial correlation is modeled through the variance-covariance matrix of the model coefficients. Combined with a proposed region selection strategy, we demonstrate that our approach is effective in identifying the truly localized patterns of the voxels while maintaining robustness to discover truly distributed pattern. In addition, we show that localized or clustered patterns can be artificially identified as distributed if without proper usage of the spatial correlation information in fMRI data. Copyright © 2016 John Wiley & Sons, Ltd.

Distinct Cortical Pathways for Music and Speech Revealed by Hypothesis-Free Voxel Decomposition

The organization of human auditory cortex remains unresolved, due in part to the small stimulus sets common to fMRI studies and the overlap of neural populations within voxels. To address these challenges, we measured fMRI responses to 165 natural sounds and inferred canonical response profiles ("components") whose weighted combinations explained voxel responses throughout auditory cortex. This analysis revealed six components, each with interpretable response characteristics despite being unconstrained by prior functional hypotheses. Four components embodied selectivity for particular acoustic features (frequency, spectrotemporal modulation, pitch). Two others exhibited pronounced selectivity for music and speech, respectively, and were not explainable by standard acoustic features. Anatomically, music and speech selectivity concentrated in distinct regions of non-primary auditory cortex. However, music selectivity was weak in raw voxel responses, and its detection required a decomposition method. Voxel decomposition identifies primary dimensions of response variation across natural sounds, revealing distinct cortical pathways for music and speech.

Auditory Sketches: Very Sparse Representations of Sounds Are Still Recognizable

Sounds in our environment like voices, animal calls or musical instruments are easily recognized by human listeners. Understanding the key features underlying this robust sound recognition is an important question in auditory science. Here, we studied the recognition by human listeners of new classes of sounds: acoustic and auditory sketches, sounds that are severely impoverished but still recognizable. Starting from a time-frequency representation, a sketch is obtained by keeping only sparse elements of the original signal, here, by means of a simple peak-picking algorithm. Two time-frequency representations were compared: a biologically grounded one, the auditory spectrogram, which simulates peripheral auditory filtering, and a simple acoustic spectrogram, based on a Fourier transform. Three degrees of sparsity were also investigated. Listeners were asked to recognize the category to which a sketch sound belongs: singing voices, bird calls, musical instruments, and vehicle engine noises. Results showed that, with the exception of voice sounds, very sparse representations of sounds (10 features, or energy peaks, per second) could be recognized above chance. No clear differences could be observed between the acoustic and the auditory sketches. For the voice sounds, however, a completely different pattern of results emerged, with at-chance or even below-chance recognition performances, suggesting that the important features of the voice, whatever they are, were removed by the sketch process. Overall, these perceptual results were well correlated with a model of auditory distances, based on spectro-temporal excitation patterns (STEPs). This study confirms the potential of these new classes of sounds, acoustic and auditory sketches, to study sound recognition.

Population Code Dynamics in Categorical Perception

Categorical perception is a ubiquitous function in sensory information processing, and is reported to have important influences on the recognition of presented and/or memorized stimuli. However, such complex interactions among categorical perception and other aspects of sensory processing have not been explained well in a unified manner. Here, we propose a recurrent neural network model to process categorical information of stimuli, which approximately realizes a hierarchical Bayesian estimation on stimuli. The model accounts for a wide variety of neurophysiological and cognitive phenomena in a consistent framework. In particular, the reported complexity of categorical effects, including (i) task-dependent modulation of neural response, (ii) clustering of neural population representation, (iii) temporal evolution of perceptual color memory, and (iv) a non-uniform discrimination threshold, are explained as different aspects of a single model. Moreover, we directly examine key model behaviors in the monkey visual cortex by analyzing neural population dynamics during categorization and discrimination of color stimuli. We find that the categorical task causes temporally-evolving biases in the neuronal population representations toward the focal colors, which supports the proposed model. These results suggest that categorical perception can be achieved by recurrent neural dynamics that approximates optimal probabilistic inference in the changing environment.

Decoding four different sound-categories in the auditory cortex using functional near-infrared spectroscopy

The ability of the auditory cortex in the brain to distinguish different sounds is important in daily life. This study investigated whether activations in the auditory cortex caused by different sounds can be distinguished using functional near-infrared spectroscopy (fNIRS). The hemodynamic responses (HRs) in both hemispheres using fNIRS were measured in 18 subjects while exposing them to four sound categories (English-speech, non-English-speech, annoying sounds, and nature sounds). As features for classifying the different signals, the mean, slope, and skewness of the oxy-hemoglobin (HbO) signal were used. With regard to the language-related stimuli, the HRs evoked by understandable speech (English) were observed in a broader brain region than were those evoked by non-English speech. Also, the magnitudes of the HbO signals evoked by English-speech were higher than those of non-English speech. The ratio of the peak values of non-English and English speech was 72.5%. Also, the brain region evoked by annoying sounds was wider than that by nature sounds. However, the signal strength for nature sounds was stronger than that for annoying sounds. Finally, for brain-computer interface (BCI) purposes, the linear discriminant analysis (LDA) and support vector machine (SVM) classifiers were applied to the four sound categories. The overall classification performance for the left hemisphere was higher than that for the right hemisphere. Therefore, for decoding of auditory commands, the left hemisphere is recommended. Also, in two-class classification, the annoying vs. nature sounds comparison provides a higher classification accuracy than the English vs. non-English speech comparison. Finally, LDA performs better than SVM.

Identifying environmental sounds: a multimodal mapping study

Our environment is full of auditory events such as warnings or hazards, and their correct recognition is essential. We explored environmental sounds (ES) recognition in a series of studies. In study 1 we performed an Activation Likelihood Estimation (ALE) meta-analysis of neuroimaging experiments addressing ES processing to delineate the network of areas consistently involved in ES processing. Areas consistently activated in the ALE meta-analysis were the STG/MTG, insula/rolandic operculum, parahippocampal gyrus and inferior frontal gyrus bilaterally. Some of these areas truly reflect ES processing, whereas others are related to design choices, e.g., type of task, type of control condition, type of stimulus. In study 2 we report on 7 neurosurgical patients with lesions involving the areas which were found to be activated by the ALE meta-analysis. We tested their ES recognition abilities and found an impairment of ES recognition. These results indicate that deficits of ES recognition do not exclusively reflect lesions to the right or to the left hemisphere but both hemispheres are involved. The most frequently lesioned area is the hippocampus/insula/STG. We made sure that any impairment in ES recognition would not be related to language problems, but reflect impaired ES processing. In study 3 we carried out an fMRI study on patients (vs. healthy controls) to investigate how the areas involved in ES might be functionally deregulated because of a lesion. The fMRI evidenced that controls activated the right IFG, the STG bilaterally and the left insula. We applied a multimodal mapping approach and found that, although the meta-analysis showed that part of the left and right STG/MTG activation during ES processing might in part be related to design choices, this area was one of the most frequently lesioned areas in our patients, thus highlighting its causal role in ES processing. We found that the ROIs we drew on the two clusters of activation found in the left and in the right STG overlapped with the lesions of at least 4 out of the 7 patients' lesions, indicating that the lack of STG activation found for patients is related to brain damage and is crucial for explaining the ES deficit.

A topographical organization for action representation in the human brain

How the human brain represents distinct motor features into a unique finalized action still remains undefined. Previous models proposed the distinct features of a motor act to be hierarchically organized in separated, but functionally interconnected, cortical areas. Here, we hypothesized that distinct patterns across a wide expanse of cortex may actually subserve a topographically organized coding of different categories of actions that represents, at a higher cognitive level and independently from the distinct motor features, the action and its final aim as a whole. Using functional magnetic resonance imaging and pattern classification approaches on the neural responses of 14 right-handed individuals passively watching short movies of hand-performed tool-mediated, transitive, and meaningful intransitive actions, we were able to discriminate with a high accuracy and characterize the category-specific response patterns. Actions are distinctively coded in distributed and overlapping neural responses within an action-selective network, comprising frontal, parietal, lateral occipital and ventrotemporal regions. This functional organization, that we named action topography, subserves a higher-level and more abstract representation of finalized actions and has the capacity to provide unique representations for multiple categories of actions.

Multivariate sensitivity to voice during auditory categorization

Past neuroimaging studies have documented discrete regions of human temporal cortex that are more strongly activated by conspecific voice sounds than by nonvoice sounds. However, the mechanisms underlying this voice sensitivity remain unclear. In the present functional MRI study, we took a novel approach to examining voice sensitivity, in which we applied a signal detection paradigm to the assessment of multivariate pattern classification among several living and nonliving categories of auditory stimuli. Within this framework, voice sensitivity can be interpreted as a distinct neural representation of brain activity that correctly distinguishes human vocalizations from other auditory object categories. Across a series of auditory categorization tests, we found that bilateral superior and middle temporal cortex consistently exhibited robust sensitivity to human vocal sounds. Although the strongest categorization was in distinguishing human voice from other categories, subsets of these regions were also able to distinguish reliably between nonhuman categories, suggesting a general role in auditory object categorization. Our findings complement the current evidence of cortical sensitivity to human vocal sounds by revealing that the greatest sensitivity during categorization tasks is devoted to distinguishing voice from nonvoice categories within human temporal cortex.