Skin conductance response patterns of face processing in children with autism spectrum disorder

Threat Responsivity Predicts Posttraumatic Stress Disorder Hyperarousal Symptoms in Children after Hurricane Florence

Following a traumatic event, posttraumatic stress disorder (PTSD) symptoms are common. Considerable research has identified a relationship between physiological responses during fear learning and PTSD. Adults with PTSD display atypical physiological responses, such as increased skin conductance responses (SCR) to threatening cues during fear learning (Orr et al., 2000). However, little research has examined these responses in childhood when fear learning first emerges. We hypothesized that greater threat responsivity in early acquisition during fear conditioning before Hurricane Florence would predict PTSD symptoms in a sample of young children following the hurricane. The final sample included 58 children in North Carolina who completed fear learning before Hurricane Florence-a potentially traumatic event. After the hurricane, we assessed severity of hurricane impact and PTSD symptoms. We found that threat responsivity as measured by differential SCR during fear learning before the hurricane predicted PTSD hyperarousal symptoms and that hurricane impact predicted PTSD symptoms following the disaster. This exploratory work suggests that prospective associations between threat responsivity and PTSD symptoms observed in adulthood may be replicated in early childhood. Results are discussed in the context of the current COVID-19 crisis.

Fast and Accurate Diagnosis of Autism (FADA): a novel hierarchical fuzzy system based autism detection tool

The main aim of this research work was to develop and validate a novel graphical user interface based hierarchical fuzzy autism detection tool named as "Fast and Accurate Diagnosis of Autism" for the diagnosis of autism disorder quickly and accurately and in addition, this tool also highlights the highly impaired area in each participant. Two groups of children had been participated in this study which includes autism group (N = 40) and normal group (N = 40). The hierarchical fuzzy expert system had been developed using IF-Then rules based on the experiences of the specialists and both the groups were tested on the designed system. It had been validated that the designed system was easily discriminating between the autistic participants and normal participants with an accuracy of 99%. Moreover, the results of the designed system were compared with Childhood Autism Rating Scale; also the tool was clearly highlighting the most impaired area in each participant. It had also been seen that the designed system has a sensitivity of 98.2% and specificity of 99.2%. It can be said that the designed tool can be used by doctors to diagnose autism along with its severity levels and to highlight the highly impaired area accurately and in no time.