ABSTRACT
This study investigated how emotional valence of a perceived emotional state impacted performance on the Reading the Mind in the Eyes task (RMET) in adolescents with autism spectrum disorder (ASD) and typically developing (TD) controls. Valence of items on the RMET, Adult (RMET-A) and Child (RMET-C) versions, was first classified in a survey of 113 medical students. Adolescents with ASD (N = 33) and TD adolescents (N = 30) were administered both RMET versions. Individuals with ASD made more errors than TD controls on positive and negative, but not neutral, valence items. The difference in performance was accentuated on the RMET-A compared to the RMET-C. Both emotional valence and complexity of language contribute to RMET performance in individuals with ASD.
Subject(s)
Autism Spectrum Disorder , Adult , Child , Humans , Adolescent , Autism Spectrum Disorder/psychology , Emotions , Recognition, Psychology , Surveys and Questionnaires , Task Performance and AnalysisABSTRACT
Vocal communication in animals often involves taking turns vocalizing. In humans, turn-taking is a fundamental rule in conversation. Among non-human primates, the common marmoset is known to engage in antiphonal calling using phee calls and trill calls. Calls of the trill type are the most common, yet difficult to study, because they are not very loud and uttered in conditions when animals are in close proximity to one another. Here we recorded trill calls in captive pair-housed marmosets using wearable microphones, while the animals were together with their partner or separated, but within trill call range. Trills were exchanged mainly with the partner and not with other animals in the room. Animals placed outside the home cage increased their trill call rate and uttered more trills in response to their partner compared to strangers. The fundamental frequency, F0, of trills increased when animals were placed outside the cage. Our results indicate that trill calls can be monitored using wearable audio equipment and that minor changes in social context affect trill call interactions and spectral properties of trill calls.
Subject(s)
Callithrix/psychology , Social Behavior , Vocalization, Animal/physiology , Animals , Behavior Observation Techniques/instrumentation , Female , Male , Social Environment , Wearable Electronic DevicesABSTRACT
Mutation or disruption of the SH3 and ankyrin repeat domains 3 (SHANK3) gene represents a highly penetrant, monogenic risk factor for autism spectrum disorder, and is a cause of Phelan-McDermid syndrome. Recent advances in gene editing have enabled the creation of genetically engineered non-human-primate models, which might better approximate the behavioural and neural phenotypes of autism spectrum disorder than do rodent models, and may lead to more effective treatments. Here we report CRISPR-Cas9-mediated generation of germline-transmissible mutations of SHANK3 in cynomolgus macaques (Macaca fascicularis) and their F1 offspring. Genotyping of somatic cells as well as brain biopsies confirmed mutations in the SHANK3 gene and reduced levels of SHANK3 protein in these macaques. Analysis of data from functional magnetic resonance imaging revealed altered local and global connectivity patterns that were indicative of circuit abnormalities. The founder mutants exhibited sleep disturbances, motor deficits and increased repetitive behaviours, as well as social and learning impairments. Together, these results parallel some aspects of the dysfunctions in the SHANK3 gene and circuits, as well as the behavioural phenotypes, that characterize autism spectrum disorder and Phelan-McDermid syndrome.
Subject(s)
Behavior, Animal , Brain/physiopathology , Macaca fascicularis/genetics , Macaca fascicularis/psychology , Mutation , Nerve Tissue Proteins/genetics , Neural Pathways/physiopathology , Animals , Brain/pathology , Eye Movements/genetics , Female , Germ-Line Mutation/genetics , Heredity/genetics , Interpersonal Relations , Magnetic Resonance Imaging , Male , Muscle Tonus/genetics , Neural Pathways/pathology , Sleep/genetics , Vocalization, AnimalABSTRACT
This paper introduces an end-to-end feedforward convolutional neural network that is able to reliably classify the source and type of animal calls in a noisy environment using two streams of audio data after being trained on a dataset of modest size and imperfect labels. The data consists of audio recordings from captive marmoset monkeys housed in pairs, with several other cages nearby. The network in this paper can classify both the call type and which animal made it with a single pass through a single network using raw spectrogram images as input. The network vastly increases data analysis capacity for researchers interested in studying marmoset vocalizations, and allows data collection in the home cage, in group housed animals.
