Using social robots to study abnormal social development

Social robots recognize and respond to human social cues with appropriate behaviors. Social robots, and the technology used in their construction, can be unique tools in the study of abnormal social development. Autism is a pervasive developmental disorder that is characterized by social and communicative impairments. Based on three years of integration and immersion with a clinical research group which performs more than 130 diagnostic evaluations of children for autism per year, this paper discusses how social robots will make an impact on the ways in which we diagnose, treat, and understand autism

State-dependent changes of connectivity patterns and functional brain network topology in Autism Spectrum Disorder

Anatomical and functional brain studies have converged to the hypothesis that Autism Spectrum Disorders (ASD) are associated with atypical connectivity. Using a modified resting-state paradigm to drive subjects' attention, we provide evidence of a very marked interaction between ASD brain functional connectivity and cognitive state. We show that functional connectivity changes in opposite ways in ASD and typicals as attention shifts from external world towards one's body generated information. Furthermore, ASD subject alter more markedly than typicals their connectivity across cognitive states. Using differences in brain connectivity across conditions, we classified ASD subjects at a performance around 80% while classification based on the connectivity patterns in any given cognitive state were close to chance. Connectivity between the Anterior Insula and dorsal-anterior Cingulate Cortex showed the highest classification accuracy and its strength increased with ASD severity. These results pave the path for diagnosis of mental pathologies based on functional brain networks obtained from a library of mental states

Infant social attention: an endophenotype of ASD-related traits?

Background: As a neurodevelopmental disorder, symptoms of ASD likely emerge from a complex interaction between pre-existing genetic vulnerabilities and the child’s environment. One way to understand causal paths to ASD is to identify dimensional ASD-related traits that vary in the general population and that predispose individuals with other risk factors towards ASD. Moving beyond behavioral traits to explore underlying neurocognitive processes may further constrain the underlying genetics. Endophenotypes are quantitative, heritable, trait-related differences that are generally assessed with laboratory-based methods, can be identified in the general population, and may be more closely tied to particular causal chains that have a more restricted set of genetic roots. The most fruitful endophenotypes may be those observed in infancy, prior to the emergence of behavioral symptoms that they are hypothesized to cause. Social motivation is an ASD-related trait that is highly heritable. In this study, we investigate whether infant endophenotypes of social attention relate to familial risk for lower social motivation in the general population. Methods: We examined whether infant social attention (measured using habituation, EEG power and event-related potential tasks previously used in infants/toddlers with ASD) varies quantitatively with parental social motivation in 117 6-month-old and 106 12-month-old typically developing infants assessed cross-sectionally. To assess heritable aspects of social motivation, primary caregiver biological parents completed two self-report measures of social avoidance and discomfort that have shown high heritability in previous work. Results: Parents with higher social discomfort and avoidance had infants who showed shorter looks to faces but not objects; reduced theta power during naturalistic social attention; and smaller P400 responses to faces versus objects. Conclusion: Early reductions in social attention are continuously related to lower parental social motivation. Alterations in social attention may be infant endophenotypes of social motivation traits related to ASD

Developmental and sex modulated neurological alterations in autism spectrum disorder

Autism Spectrum Disorder (ASD) was first described in 1943 by Dr. Leo Kranner in a case study published in The Nervous Child. It is a neurodevelopment disorder, with a range of clinical symptoms. According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), used by clinicians to diagnose mental disorders, a child needs to have persistent social deficits, language impairments, and repetitive behaviors, that cannot be explained by neurological damage or intellectual disability. It is known that children diagnosed with ASD are often are developmentally delayed therefore alterations in the typical developmental trajectory should be a major factor in consideration when studying ASD. As of 2016, 1 in 68 children in the USA is diagnosed with ASD, of those diagnosed young males are four times more likely to be diagnosed than their female peers. Although genetic and behavioral theories exist to explain these differences, the cause for the disparity is still unknown. This Dissertation presents a unique opportunity to understand the intersection of altered neurodevelopment and the alarming sex disparities in patients with ASD from a neuroimaging perspective. The hypothesis is that there exist differences due to development and sex in with ASD. Access to ABIDE (Autism Brain Imaging Data Exchange), a open source large scale data sharing consortium of functional and anatomical MR data. Analyzing MR data for alterations due to ASD, developmental trajectory, and sex as well as the intersection of these factors. Theses modulations are observed in three Project Aims that employ various analytical approaches: (1) Structural Morphology, (2) Resting-state Functional Connectivity, and (3) Graph Theory. The major findings lie at the interaction of these three factors; developmental stage-by-diagnosis-by-sex. Structural Morphological Analyses of anatomical data show differences in cortical thickness, on the left rostral middle frontal gyrus and surface area in along the sensory motor strip, of the left paracentral gyrus and right precentral gyrus. Resting-state Functional Connectivity analyzed in multiple data driven approaches, and altered resting state connectivity patterns between the left frontal parietal network and the left parahippcampal gyrus are reported. The regions found in the Morphological Analyses are used as seeds for a priori connectivity analysis, connectivity between the left rostral middle frontal cortex and bilateral superior temporal gyrus as well as the right precentral gyrus and right middle frontal gyrus and left inferior frontal gyrus are described. Finally using Graph Theory analysis, which quantifies a whole brain connectivity matrix to calculate metrics such as path length, cluster coefficient, local efficiency, and betweeness centrality all of which are altered by the interaction of all three factors. The last investigation is an attempt to correlate the behavioral assessments, conducted by clinicians with theses neuroimaging findings to determine if there exist a relationship between them. Significant interaction effects of sex and development on ASD diagnosis are observed. The goal of the Study is to provide more information on the disorder that is by nature highly heterogeneous in symptomatology. Studying these interactions, may be key to better understand a disorder that was introduced into the medical literature 75 years ago

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

Meta-Analysis of Diagnostic Accuracy of M-CHAT by Categorical Rank of Clinical Diagnosis

This study is a systematic review and meta-analysis of published Modified Checklist for Autism in Toddlers (M-CHAT) screening studies. The purpose of this study was to determine if the quality of clinical reference outcomes across M-CHAT screening studies relates to reported diagnostic accuracy metrics. PRISMA guidelines were implemented and 3631 records were collected from 3 main databases: EBSCO, Web of Science, and PROQUEST. 22 studies with 24 sample sets then had their psychometric data directly reported and were ranked using an adaptation framework for ranking the quality of clinical reference standard categories. The data was analyzed using a random effects bivariate Reitsma model and meta-regression. The relationship was explored between clinical reference standard criteria and the diagnostic accuracy measures of Sensitivity, Specificity and the Diagnostic Odds Ratio. The results indicate that there is not a linear relationship between increases in reference standard quality and decreases in diagnostic accuracy metrics, but clinical reference standard categories may have an impact on reported accuracy. In the future, researchers should consider the quality reference standards when conducting diagnostic accuracy studies of autism screening tools

Disruption to control network function correlates with altered dynamic connectivity in the wider autism spectrum.

Autism is a common developmental condition with a wide, variable range of co-occurring neuropsychiatric symptoms. Contrasting with most extant studies, we explored whole-brain functional organization at multiple levels simultaneously in a large subject group reflecting autism's clinical diversity, and present the first network-based analysis of transient brain states, or dynamic connectivity, in autism. Disruption to inter-network and inter-system connectivity, rather than within individual networks, predominated. We identified coupling disruption in the anterior-posterior default mode axis, and among specific control networks specialized for task start cues and the maintenance of domain-independent task positive status, specifically between the right fronto-parietal and cingulo-opercular networks and default mode network subsystems. These appear to propagate downstream in autism, with significantly dampened subject oscillations between brain states, and dynamic connectivity configuration differences. Our account proposes specific motifs that may provide candidates for neuroimaging biomarkers within heterogeneous clinical populations in this diverse condition