Vocal Identity Recognition in Autism Spectrum Disorder

Acquisition Time for Resting-State HbO/Hb Coupling Measured by Functional Near-Infrared Spectroscopy in Assessing Autism

Functional near-infrared spectroscopy was used to record spontaneous hemodynamic fluctuations form the bilateral temporal lobes in 25 children with autism spectrum disorder (ASD) and 22 typically developing (TD) children. The coupling between oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (Hb) was calculated by Pearson correlation coefficient, showing significant difference between ASD and TD, thus the coupling could be a characteristic feature for ASD. To evaluate the discrimination ability of the feature obtained in different acquisition times, the receiver operating characteristic curve (ROC) was constructed and the area under curve (AUC) was calculated. The results showed AUC > 0.8 when the time duration was longer than 1.5 min, but longer than 4 min, AUC value (~0.87) hardly varied, implying the maximal discrimination ability reached. This study demonstrated the coupling could be one of characteristic features for ASD even acquired in a short measurement time.

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.

Are sleep disturbances a cause or consequence of autism spectrum disorder?

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by core symptoms such as atypical social communication, stereotyped behaviors, and restricted interests. One of the comorbid symptoms of individuals with ASD is sleep disturbance. There are two major hypotheses regarding the neural mechanism underlying ASD, i.e., the excitation/inhibition (E/I) imbalance and the altered neuroplasticity hypotheses. However, the pathology of ASD remains unclear due to inconsistent research results. This paper argues that sleep is a confounding factor, thus, must be considered when examining the pathology of ASD because sleep plays an important role in modulating the E/I balance and neuroplasticity in the human brain. Investigation of the E/I balance and neuroplasticity during sleep might enhance our understanding of the neural mechanisms of ASD. It may also lead to the development of neurobiologically informed interventions to supplement existing psychosocial therapies.

Exploring Motives and Perceived Barriers for Voice Modification: The Views of Transgender and Gender-Diverse Voice Clients

PURPOSE

To date, transgender and gender-diverse voice clients' perceptions and individual goals have been missing in discussions and research on gender-affirming voice therapy. Little is, therefore, known about the client's expectations of therapy outcomes and how these are met by treatments developed from views of vocal gender as perceived by cisgender persons. This study aimed to explore clients' individual motives and perceived barriers to undertaking gender-affirming voice therapy.

METHOD

Individual, semistructured interviews with 15 transgender and gender-diverse voice clients considering voice therapy were conducted and explored using qualitative content analysis.

RESULTS

Three themes were identified during the analysis of the participants' narratives. In the first theme, "the incongruent voice setting the rules," the contribution of the voice on the experienced gender dysphoria is put in focus. The second theme, "to reach a voice of my own choice," centers around anticipated personal gains using a modified voice. The third theme, "a voice out of reach," relates to worries and restricting factors for not being able to reach one's set goals for voice modification.

CONCLUSIONS

The interviews clearly indicate a need for a person-centered voice therapy that starts from the individuals' expressed motives for modifying the voice yet also are affirmative of anticipated difficulties related to voice modification. We recommend that these themes should form the basis of the pretherapy joint discussion between the voice client and the speech-language pathologist to ensure therapy goals that are realistic and relevant to the client.

The Jena Voice Learning and Memory Test (JVLMT): A standardized tool for assessing the ability to learn and recognize voices

The ability to recognize someone's voice spans a broad spectrum with phonagnosia on the low end and super-recognition at the high end. Yet there is no standardized test to measure an individual's ability of learning and recognizing newly learned voices with samples of speech-like phonetic variability. We have developed the Jena Voice Learning and Memory Test (JVLMT), a 22-min test based on item response theory and applicable across languages. The JVLMT consists of three phases in which participants (1) become familiarized with eight speakers, (2) revise the learned voices, and (3) perform a 3AFC recognition task, using pseudo-sentences devoid of semantic content. Acoustic (dis)similarity analyses were used to create items with various levels of difficulty. Test scores are based on 22 items which had been selected and validated based on two online studies with 232 and 454 participants, respectively. Mean accuracy in the JVLMT is 0.51 (SD = .18) with an empirical (marginal) reliability of 0.66. Correlational analyses showed high and moderate convergent validity with the Bangor Voice Matching Test (BVMT) and Glasgow Voice Memory Test (GVMT), respectively, and high discriminant validity with a digit span test. Four participants with potential super recognition abilities and seven participants with potential phonagnosia were identified who performed at least 2 SDs above or below the mean, respectively. The JVLMT is a promising research and diagnostic screening tool to detect both impairments in voice recognition and super-recognition abilities.

A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder

Autism Spectrum Disorder (ASD), according to DSM-5 in the American Psychiatric Association, is a neurodevelopmental disorder that includes deficits of social communication and social interaction with the presence of restricted and repetitive behaviors. Children with ASD have difficulties in joint attention and social reciprocity, using non-verbal and verbal behavior for communication. Due to these deficits, children with autism are often socially isolated. Researchers have emphasized the importance of early identification and early intervention to improve the level of functioning in language, communication, and well-being of children with autism. However, due to limited local assessment tools to diagnose these children, limited speech-language therapy services in rural areas, etc., these children do not get the rehabilitation they need until they get into compulsory schooling at the age of seven years old. Hence, efficient approaches towards early identification and intervention through speedy diagnostic procedures for ASD are required. In recent years, advanced technologies like machine learning have been used to analyze and investigate ASD to improve diagnostic accuracy, time, and quality without complexity. These machine learning methods include artificial neural networks, support vector machines, a priori algorithms, and decision trees, most of which have been applied to datasets connected with autism to construct predictive models. Meanwhile, the selection of features remains an essential task before developing a predictive model for ASD classification. This review mainly investigates and analyzes up-to-date studies on machine learning methods for feature selection and classification of ASD. We recommend methods to enhance machine learning's speedy execution for processing complex data for conceptualization and implementation in ASD diagnostic research. This study can significantly benefit future research in autism using a machine learning approach for feature selection, classification, and processing imbalanced data.

Intranasal oxytocin modulates brain responses to voice-identity recognition in typically developing individuals, but not in ASD

Faces and voices are prominent cues for person-identity recognition. Face recognition behavior and associated brain responses can be enhanced by intranasal administration of oxytocin. It is unknown whether oxytocin can also augment voice-identity recognition mechanisms. To find it out is particularly relevant for individuals who have difficulties recognizing voice identity such as individuals diagnosed with autism spectrum disorder (ASD). We conducted a combined behavioral and functional magnetic resonance imaging (fMRI) study to investigate voice-identity recognition following intranasal administration of oxytocin or placebo in a group of adults diagnosed with ASD (full-scale intelligence quotient > 85) and pairwise-matched typically developing (TD) controls. A single dose of 24 IU oxytocin was administered in a randomized, double-blind, placebo-controlled and cross-over design. In the control group, but not in the ASD group, administration of oxytocin compared to placebo increased responses to recognition of voice identity in contrast to speech in the right posterior superior temporal sulcus/gyrus (pSTS/G) - a region implicated in the perceptual analysis of voice-identity information. In the ASD group, the right pSTS/G responses were positively correlated with voice-identity recognition accuracy in the oxytocin condition, but not in the placebo condition. Oxytocin did not improve voice-identity recognition performance at the group level. The ASD compared to the control group had lower right pSTS/G responses to voice-identity recognition. Since ASD is known to have atypical pSTS/G, the results indicate that the potential of intranasal oxytocin to enhance mechanisms for voice-identity recognition might be variable and dependent on the functional integrity of this brain region.

Selection and classification of gene expression in autism disorder: Use of a combination of statistical filters and a GBPSO-SVM algorithm

In this work, gene expression in autism spectrum disorder (ASD) is analyzed with the goal of selecting the most attributed genes and performing classification. The objective was achieved by utilizing a combination of various statistical filters and a wrapper-based geometric binary particle swarm optimization-support vector machine (GBPSO-SVM) algorithm. The utilization of different filters was accentuated by incorporating a mean and median ratio criterion to remove very similar genes. The results showed that the most discriminative genes that were identified in the first and last selection steps included the presence of a repetitive gene (CAPS2), which was assigned as the gene most highly related to ASD risk. The merged gene subset that was selected by the GBPSO-SVM algorithm was able to enhance the classification accuracy.

Autistic Traits are Linked to Individual Differences in Familiar Voice Identification

Autistic traits vary across the general population, and are linked with face recognition ability. Here we investigated potential links between autistic traits and voice recognition ability for personally familiar voices in a group of 30 listeners (15 female, 16-19 years) from the same local school. Autistic traits (particularly those related to communication and social interaction) were negatively correlated with voice recognition, such that more autistic traits were associated with fewer familiar voices identified and less ability to discriminate familiar from unfamiliar voices. In addition, our results suggest enhanced accessibility of personal semantic information in women compared to men. Overall, this study establishes a detailed pattern of relationships between voice identification performance and autistic traits in the general population.

Voice identity processing in autism spectrum disorder

People with autism spectrum disorder (ASD) have difficulties in identifying another person by face and voice. This might contribute considerably to the development of social cognition and interaction difficulties. The characteristics of the voice recognition deficit in ASD are unknown. Here, we used a comprehensive behavioral test battery to systematically investigate voice processing in high-functioning ASD (n = 16) and typically developed pair-wise matched controls (n = 16). The ASD group had particular difficulties with discriminating, learning, and recognizing unfamiliar voices, while recognizing famous voices was relatively intact. Tests on acoustic processing abilities showed that the ASD group had a specific deficit in vocal pitch perception that was dissociable from otherwise intact acoustic processing (i.e., musical pitch, musical, and vocal timbre perception). Our results allow a characterization of the voice recognition deficit in ASD: The findings indicate that in high-functioning ASD, the difficulty to recognize voices is particularly pronounced for learning novel voices and the recognition of unfamiliar peoples' voices. This pattern might be indicative of difficulties with integrating the acoustic characteristics of the voice into a coherent percept-a function that has been previously associated with voice-selective regions in the posterior superior temporal sulcus/gyrus of the human brain. Autism Res 2017, 10: 155-168. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

Deficits in voice and multisensory processing in patients with Prader-Willi syndrome

Prader-Willi syndrome (PWS) is a rare neurodevelopmental and genetic disorder that is characterized by various expression of endocrine, cognitive and behavioral problems, among which a true obsession for food and a deficit of satiety that leads to hyperphagia and severe obesity. Neuropsychological studies have reported that PWS display altered social interactions with a specific weakness in interpreting social information and in responding to them, a symptom closed to that observed in autism spectrum disorders (ASD). Based on the hypothesis that atypical multisensory integration such as face and voice interactions would contribute in PWS to social impairment we investigate the abilities of PWS to process communication signals including the human voice. Patients with PWS recruited from the national reference center for PWS performed a simple detection task of stimuli presented in an uni-o or bimodal condition, as well as a voice discrimination task. Compared to control typically developing (TD) individuals, PWS present a specific deficit in discriminating human voices from environmental sounds. Further, PWS present a much lower multisensory benefits with an absence of violation of the race model indicating that multisensory information do not converge and interact prior to the initiation of the behavioral response. All the deficits observed in PWS were stronger for the subgroup of patients suffering from Uniparental Disomy, a population known to be more sensitive to ASD. Altogether, our study suggests that the deficits in social behavior observed in PWS derive at least partly from an impairment in deciphering the social information carried by voice signals, face signals, and the combination of both. In addition, our work is in agreement with the brain imaging studies revealing an alteration in PWS of the "social brain network" including the STS region involved in processing human voices.