Multivariate sensitivity to voice during auditory categorization

Dog and human neural sensitivity to voicelikeness: A comparative fMRI study

Voice-sensitivity in the auditory cortex of a range of mammals has been proposed to be determined primarily by tuning to conspecific auditory stimuli, but recent human findings indicate a role for a more general tuning to voicelikeness. Vocal emotional valence, a central characteristic of vocalisations, has been linked to the same basic acoustic parameters across species. Comparative neuroimaging revealed that during voice perception, such acoustic parameters modulate emotional valence-sensitivity in auditory cortical regions in both family dogs and humans. To explore the role of voicelikeness in auditory emotional valence-sensitivity across species, here we constructed artificial emotional sounds in two sound categories: voice-like vs. sine-wave sounds, parametrically modulating two main acoustic parameters, f0 and call length. We hypothesised that if mammalian auditory systems are characterised by a general tuning to voicelikeness, voice-like sounds will be processed preferentially, and acoustic parameters for voice-like sounds will be processed differently than for sine-wave sounds - both in dogs and humans. We found cortical areas in both species that responded stronger to voice-like than to sine-wave stimuli, while there were no regions responding stronger to sine-wave sounds in either species. Additionally, we found that in bilateral primary and emotional valence-sensitive auditory regions of both species, the processing of voice-like and sine-wave sounds are modulated by f0 in opposite ways. These results reveal functional similarities between evolutionarily distant mammals for processing voicelikeness and its effect on processing basic acoustic cues of vocal emotions.

Brain mechanism of unfamiliar and familiar voice processing: an activation likelihood estimation meta-analysis

Interpersonal communication through vocal information is very important for human society. During verbal interactions, our vocal cord vibrations convey important information regarding voice identity, which allows us to decide how to respond to speakers (e.g., neither greeting a stranger too warmly or speaking too coldly to a friend). Numerous neural studies have shown that identifying familiar and unfamiliar voices may rely on different neural bases. However, the mechanism underlying voice identification of individuals of varying familiarity has not been determined due to vague definitions, confusion of terms, and differences in task design. To address this issue, the present study first categorized three kinds of voice identity processing (perception, recognition and identification) from speakers with different degrees of familiarity. We defined voice identity perception as passively listening to a voice or determining if the voice was human, voice identity recognition as determining if the sound heard was acoustically familiar, and voice identity identification as ascertaining whether a voice is associated with a name or face. Of these, voice identity perception involves processing unfamiliar voices, and voice identity recognition and identification involves processing familiar voices. According to these three definitions, we performed activation likelihood estimation (ALE) on 32 studies and revealed different brain mechanisms underlying processing of unfamiliar and familiar voice identities. The results were as follows: (1) familiar voice recognition/identification was supported by a network involving most regions in the temporal lobe, some regions in the frontal lobe, subcortical structures and regions around the marginal lobes; (2) the bilateral superior temporal gyrus was recruited for voice identity perception of an unfamiliar voice; (3) voice identity recognition/identification of familiar voices was more likely to activate the right frontal lobe than voice identity perception of unfamiliar voices, while voice identity perception of an unfamiliar voice was more likely to activate the bilateral temporal lobe and left frontal lobe; and (4) the bilateral superior temporal gyrus served as a shared neural basis of unfamiliar voice identity perception and familiar voice identity recognition/identification. In general, the results of the current study address gaps in the literature, provide clear definitions of concepts, and indicate brain mechanisms for subsequent investigations.

Distinct functional levels of human voice processing in the auditory cortex

Voice signaling is integral to human communication, and a cortical voice area seemed to support the discrimination of voices from other auditory objects. This large cortical voice area in the auditory cortex (AC) was suggested to process voices selectively, but its functional differentiation remained elusive. We used neuroimaging while humans processed voices and nonvoice sounds, and artificial sounds that mimicked certain voice sound features. First and surprisingly, specific auditory cortical voice processing beyond basic acoustic sound analyses is only supported by a very small portion of the originally described voice area in higher-order AC located centrally in superior Te3. Second, besides this core voice processing area, large parts of the remaining voice area in low- and higher-order AC only accessorily process voices and might primarily pick up nonspecific psychoacoustic differences between voices and nonvoices. Third, a specific subfield of low-order AC seems to specifically decode acoustic sound features that are relevant but not exclusive for voice detection. Taken together, the previously defined voice area might have been overestimated since cortical support for human voice processing seems rather restricted. Cortical voice processing also seems to be functionally more diverse and embedded in broader functional principles of the human auditory system.

FMRI-based identity classification accuracy in left temporal and frontal regions predicts speaker recognition performance

Speaker recognition is characterized by considerable inter-individual variability with poorly understood neural bases. This study was aimed at (1) clarifying the cerebral correlates of speaker recognition in humans, in particular the involvement of prefrontal areas, using multi voxel pattern analysis (MVPA) applied to fMRI data from a relatively large group of participants, and (2) at investigating the relationship across participants between fMRI-based classification and the group's variable behavioural performance at the speaker recognition task. A cohort of subjects (N = 40, 28 females) selected to present a wide distribution of voice recognition abilities underwent an fMRI speaker identification task during which they were asked to recognize three previously learned speakers with finger button presses. The results showed that speaker identity could be significantly decoded based on fMRI patterns in voice-sensitive regions including bilateral temporal voice areas (TVAs) along the superior temporal sulcus/gyrus but also in bilateral parietal and left inferior frontal regions. Furthermore, fMRI-based classification accuracy showed a significant correlation with individual behavioural performance in left anterior STG/STS and left inferior frontal gyrus. These results highlight the role of both temporal and extra-temporal regions in performing a speaker identity recognition task with motor responses.

What and where in the auditory systems of sighted and early blind individuals: Evidence from representational similarity analysis

Separated ventral and dorsal streams in auditory system have been proposed to process sound identification and localization respectively. Despite the popularity of the dual-pathway model, it remains controversial how much independence two neural pathways enjoy and whether visual experiences can influence the distinct cortical organizational scheme. In this study, representational similarity analysis (RSA) was used to explore the functional roles of distinct cortical regions that lay within either the ventral or dorsal auditory streams of sighted and early blind (EB) participants. We found functionally segregated auditory networks in both sighted and EB groups where anterior superior temporal gyrus (aSTG) and inferior frontal junction (IFJ) were more related to the sound identification, while posterior superior temporal gyrus (pSTG) and inferior parietal lobe (IPL) preferred the sound localization. The findings indicated visual experiences may not have an influence on this functional dissociation and the cortex of the human brain may be organized as task-specific and modality-independent strategies. Meanwhile, partial overlap of spatial and non-spatial auditory information processing was observed, illustrating the existence of interaction between the two auditory streams. Furthermore, we investigated the effect of visual experiences on the neural bases of auditory perception and observed the cortical reorganization in EB participants in whom middle occipital gyrus was recruited to process auditory information. Our findings examined the distinct cortical networks that abstractly encoded sound identification and localization, and confirmed the existence of interaction from the multivariate perspective. Furthermore, the results suggested visual experience might not impact the functional specialization of auditory regions.

Neural processes of vocal social perception: Dog-human comparative fMRI studies

In this review we focus on the exciting new opportunities in comparative neuroscience to study neural processes of vocal social perception by comparing dog and human neural activity using fMRI methods. The dog is a relatively new addition to this research area; however, it has a large potential to become a standard species in such investigations. Although there has been great interest in the emergence of human language abilities, in case of fMRI methods, most research to date focused on homologue comparisons within Primates. By belonging to a very different clade of mammalian evolution, dogs could give such research agendas a more general mammalian foundation. In addition, broadening the scope of investigations into vocal communication in general can also deepen our understanding of human vocal skills. Being selected for and living in an anthropogenic environment, research with dogs may also be informative about the way in which human non-linguistic and linguistic signals are represented in a mammalian brain without skills for language production.

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.

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.

Encoding the local connectivity patterns of fMRI for cognitive task and state classification

In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher vectors (FV), vector of locally aggregated descriptors (VLAD) and bag-of-words (BoW) methods. We first obtain local descriptors, called mesh arc descriptors (MADs) from fMRI data, by forming local meshes around anatomical regions, and estimating their relationship within a neighborhood. Then, we extract a dictionary of relationships, called brain connectivity dictionary by fitting a generative Gaussian mixture model (GMM) to a set of MADs, and selecting codewords at the mean of each component of the mixture. Codewords represent connectivity patterns among anatomical regions. We also encode MADs by VLAD and BoW methods using k-Means clustering. We classify cognitive tasks using the Human Connectome Project (HCP) task fMRI dataset and cognitive states using the Emotional Memory Retrieval (EMR). We train support vector machines (SVMs) using the encoded MADs. Results demonstrate that, FV encoding of MADs can be successfully employed for classification of cognitive tasks, and outperform VLAD and BoW representations. Moreover, we identify the significant Gaussians in mixture models by computing energy of their corresponding FV parts, and analyze their effect on classification accuracy. Finally, we suggest a new method to visualize the codewords of the learned brain connectivity dictionary.

The time course of sound category identification: Insights from acoustic features

Humans have an impressive, automatic capacity for identifying and organizing sounds in their environment. However, little is known about the timescales that sound identification functions on, or the acoustic features that listeners use to identify auditory objects. To better understand the temporal and acoustic dynamics of sound category identification, two go/no-go perceptual gating studies were conducted. Participants heard speech, musical instrument, and human-environmental sounds ranging from 12.5 to 200 ms in duration. Listeners could reliably identify sound categories with just 25 ms of duration. In experiment 1, participants' performance on instrument sounds showed a distinct processing advantage at shorter durations. Experiment 2 revealed that this advantage was largely dependent on regularities in instrument onset characteristics relative to the spectrotemporal complexity of environmental sounds and speech. Models of participant responses indicated that listeners used spectral, temporal, noise, and pitch cues in the task. Aspects of spectral centroid were associated with responses for all categories, while noisiness and spectral flatness were associated with environmental and instrument responses, respectively. Responses for speech and environmental sounds were also associated with spectral features that varied over time. Experiment 2 indicated that variability in fundamental frequency was useful in identifying steady state speech and instrument stimuli.

Neural bases of action abstraction

There has been recent debate over whether actions are processed primarily by means of motor simulation or cognitive semantics. The current study investigated how abstract action concepts are processed in the brain, independent of the format in which they are presented. Eighteen healthy adult participants viewed different actions (e.g., diving, boxing) in the form of verbs and schematic action pictograms while functional magnetic resonance imaging (fMRI) was collected. We predicted that sensorimotor and semantic brain regions would show similar patterns of neural activity for different instances of the same action (e.g., diving pictogram and the word 'diving'). A representational similarity analysis revealed posterior temporal and sensorimotor regions where specific action concepts were encoded, independent of the format of presentation. These results reveal the neural instantiations of abstract action concepts, and demonstrate that both sensorimotor and semantic systems are involved in processing actions.