1. Clouse JR, Wood-Nartker J, Rice FA. {{Designing Beyond the Americans With Disabilities Act (ADA): Creating an Autism-Friendly Vocational Center}}. {Herd};2019 (Dec 30):1937586719888502.
The Americans with Disabilities Act (ADA) has been effective in establishing building standards that create accessible spaces for people with physical impairments. These guidelines have not addressed the needs of people with mental, emotional, and/or developmental disabilities. With the increase in autism diagnosis, designers/architects need to expand their planning to include more universal solutions. The purpose is to demonstrate ways of designing beyond ADA to address needs of people with autism spectrum disorder (ASD). To design effectively, designers/architects must identify sensory issues that influence these children in establishing a regulatory state enabling effective interaction with neurotypical peers. Design is also important for teachers, therapists, and parents of children with ASD to enable more successful interactions. If the environment is overstimulating for a child with ASD, then a parent/caregiver/therapist will struggle to achieve their goals. Mostafa recommended seven design criteria known as ASPECTSS: Acoustics, Spatial sequencing, Escape spaces, Compartmentalization, Transition spaces, Sensory zoning, and Safety, when designing for people with ASD. These classifications lay the groundwork for the established guidelines. As designers/architects, we have a responsibility to create inclusive environments. To help, the authors highlighted a vocational center showing one plan that meets ADA guidelines and another that illustrates additional environmental features addressing the needs of people with ASD. These criteria originated from evidence-based solutions derived from a literature review and personal interview. These recommendations demonstrate that sensitivity to the needs of people with autism creates a solution that is better for all people.
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2. Mash LE, Keehn B, Linke AC, Liu T, Helm JL, Haist F, Townsend J, Muller RA. {{Atypical relationships between spontaneous EEG and fMRI activity in autism}}. {Brain Connect};2019 (Dec 30)
Autism spectrum disorders (ASDs) have been linked to atypical communication among distributed brain networks. However, despite decades of research, the exact nature of differences between typically developing (TD) individuals and those with ASDs remains unclear. ASDs have been widely studied using resting state neuroimaging methods, including both functional MRI (fMRI) and electroencephalography (EEG). However, little is known about how fMRI and EEG measures of spontaneous brain activity are related in ASDs. In the current study, two cohorts of children and adolescents underwent resting-state EEG (n = 38 per group) or fMRI (n = 66 ASD, 57 TD), with a subset of individuals in both the EEG and fMRI cohorts (n = 17 per group). In the EEG cohort, occipito-parietal EEG alpha power was found to be reduced in ASDs. In the fMRI cohort, blood oxygen level-dependent (BOLD) power was regionally increased in right temporal regions and there was widespread overconnectivity between thalamus and cortical regions in the ASD group relative to the TD group. Finally, multimodal analyses found that while TD children showed consistently positive relationships between EEG alpha power and regional BOLD power, these associations were weak or negative in ASDs. These findings suggest atypical links between alpha rhythms and regional BOLD activity in ASDs, possibly implicating neural substrates and processes that coordinate thalamocortical regulation of the alpha rhythm.
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3. Tang L, Mostafa S, Liao B, Wu FX. {{A network clustering based feature selection strategy for classifying autism spectrum disorder}}. {BMC Med Genomics};2019 (Dec 30);12(Suppl 7):153.
BACKGROUND: Advanced non-invasive neuroimaging techniques offer new approaches to study functions and structures of human brains. Whole-brain functional networks obtained from resting state functional magnetic resonance imaging has been widely used to study brain diseases like autism spectrum disorder (ASD). Auto-classification of ASD has become an important issue. Existing classification methods for ASD are based on features extracted from the whole-brain functional networks, which may be not discriminant enough for good performance. METHODS: In this study, we propose a network clustering based feature selection strategy for classifying ASD. In our proposed method, we first apply symmetric non-negative matrix factorization to divide brain networks into four modules. Then we extract features from one of four modules called default mode network (DMN) and use them to train several classifiers for ASD classification. RESULTS: The computational experiments show that our proposed method achieves better performances than those trained with features extracted from the whole brain network. CONCLUSION: It is a good strategy to train the classifiers for ASD based on features from the default mode subnetwork.
