Autism spectrum disorders

The high prevalence of autism spectrum disorder (ASD) results in large costs to individuals, families, and society. Among diagnosed individuals, restrictive and repetitive behaviors (RRBs) correlate with functional impairments substantially impacting wellbeing but remain less studied than social and communication deficits. Brain resting-state functional connectivity (fc) measures intrinsic, potentially RRB-associated neural dynamics. Here, whole-brain (WB), and iterated seed-based (SB)fc guided by the preceding WBfc and a priori hypotheses was performed. Combined results were used to model a brain network beginning with qualitative assessment of its potential functional association with RRBs. Once rigorously defined, the network was used to inform construction of a dynamical systems model of brain activity hypothesized to correlate with RRB severity. Qualitative model behavior tracked expectations of real cortical activity in RRB presentation. Model numerical output was found to correlate with behavioral measures of RRBs to a significantly greater degree than the underlying brain connectivity values themselves did. Some summary measures of model output were also found to correlate significantly, though near threshold, with severity measures in the other two ASD core deficit domains, and particularly, far more extensively than should be expected given the underlying brain connectivity values themselves were apparently effectively wholly uncorrelated with the measures. Significant findings are: (1) dynamical modeling of brain activity can identify significant correlations with symptom manifestation that fc alone cannot; (2) dynamical modeling of brain activity could potentially increase understanding of ASD’s extensive heterogeneity across symptom domains; (3) extensive overlap between the constructed network and known RRB-implicated brain divisions was identified, with cerebellum, increasingly implicated in distributed neocortical functional differences in RRBs in humans and animal models, centrally connected to multiple such divisions; (4) further overlap is found via striatal circuitry, implicated in multiple RRB-like behaviors previously, and forming at least 1/3 of the functional basis for the network’s hypothetical relationship with RRBs; (5) ASD-associated angular gyrus, PFC, ACC overlap was found. This successful tandem application of fc, dynamical modeling, and neurocognitive network theory illustrates the need for broad theoretical approaches in illuminating ASD heterogeneity and the neurocognitive underpinnings of specific ASD presentations.

Autism spectrum disorders (ASDs) are one of the complex, pervasive, and multifactorial
neurodevelopmental conditions which affect one in 68 children. Scientific research has
proven the efficiency of using technologies to improve communication and social skills of
autistic children. The use of technological devices, such as mobile applications and
multimedia, increase the interest of autistic children to learn while playing games. This
thesis presents the re-engineering, extension, and evolution of an existing prototype
Windows-based mobile application called Ying to become an Android mobile application
which is augmented with facial and emotion recognition. This mobile app complements
different approaches of traditional therapy, such as Applied Behavior Analysis (ABA).
Ying integrates different computer-assisted technologies, including speech recognition,
audio and visual interaction, and mobile applications to enhance autistic children’s social
behavior and verbal communication skills. An evaluation of the efficacy of using Ying has
been conducted and its results are presented in the thesis.

An important area of research is emerging for adults diagnosed with autism
spectrum disorders (ASD) with respect to the challenges they face in their social
relationships. Social relationships include those that are romantic in nature such as dating, cohabitation and marriage. Researchers suggest that engaging in a healthy and happy romantic relationship has many physical and mental health benefits that contribute to improved quality of life. This study used a quasi-experimental group design to explore the effects of psychoeducation for adults with ASD. Study participants were assigned to one of two group conditions. One group received the Relationship Enhancement®(RE) program and the other received RE with supplements targeting specific social skills. Social skills, empathy and social support were measured pre and post intervention for participants both within and between groups. Significant differences were found in the assessment of all participants, in both groups, as measured by t-tests and effect sizes in the variables of social skills (t(37)=- 2.28-2.72, p=.028-.029, η2=.122) and empathy (t(37)=-2.31, p=.027, η2=.123). These differences indicate that all participants, in both groups improved in social skills and empathy from pre to post treatment. No significant differences were found among all group participants for the variable of social support. There were no significant differences between groups for the three variables measured (p>0.05). Overall, these results provide support for the use of RE and the targeted supplements to increase social skills and empathy among adults with ASD who are interested in engaging in romantic relationships.

Autism is a neurodevelopmental disorder that is characterized by deficits
involving social interaction, communication, and perception. Although there is much
research that has examined functional neural connectivity in individuals with autism, few
have conducted these studies in very young children while awake across EEG power and coherence measures. Anomalies in EEG coherence and power have been associated with deficits in executive function and mental activity. The present study examined neural activation and functional connectivity with an EEG, in children ages 3 -5, during an eyesclosed baseline period. Discrete Fourier Transform was performed on artifact-free segments of EEG data to produce power density values. In addition, coherence measurements were examined to assess functional connectivity in the alpha bandwidth during the baseline recording. Children with autism spectrum disorder (ASD)
demonstrated reduced alpha coherence in fronto-temporal regions and between right
temporal sites when compared to typically developing (TD) children. In addition, the reduction in coherence was based on ASD severity, such that high-functioning children
with ASD showed greater coherence than low-functioning children with ASD. Children
with ASD also displayed reduced power in the alpha, beta, and theta frequency
bandwidths in frontal, temporal, central, and occipital regions compared to TD children.
Interestingly, delta power differentiated children based on developmental status such that high-functioning children with ASD demonstrated the greatest delta power, followed by TD children, and then low-functioning children with ASD. Finally, TD children
demonstrated left anterior temporal EEG asymmetry in the alpha bandwidth, whereas
children with high-functioning ASD exhibited left posterior temporal EEG asymmetry
and right frontal EEG asymmetry. Thus, the results suggest that children with ASD
exhibit atypical patterns of brain activity and functional connectivity compared to their
typically developing counterparts.

Autism Spectrum Disorders (ASD) affects one in every 110 children. Medical and educational research have demonstrated that ASD children's social skills and adaptation can be much improved, provided that interventions are early and intensive enough. The advancement of computer technologies and their ubiquitous penetration in people's life make them widely available to support intensive sociocognitive rehabilitation. Additionally, computer interactions are a natural choice for people with autism who value lawful and "systematizing" tools. A number of computer-aided approaches have been developed, showing effectiveness and generalization, but little quantitative research was conducted to identify the critical factors of engaging and improving the child's interest and performance. This thesis designs an adaptive computer interaction system, called Ying, which detects learning patterns in children with ASD and explores the computer interactive possibilities. The system tailors its content based on periodic performance assessments that offer a more effective learning path for children with ASD.