typically developing children
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2022 ◽  
Mary Beth Nebel ◽  
Daniel Lidstone ◽  
Liwei Wang ◽  
David Benkeser ◽  
Stewart H Mostofsky ◽  

The exclusion of high-motion participants can reduce the impact of motion in functional Magnetic Resonance Imaging (fMRI) data. However, the exclusion of high-motion participants may change the distribution of clinically relevant variables in the study sample, and the resulting sample may not be representative of the population. Our goals are two-fold: 1) to document the biases introduced by common motion exclusion practices in functional connectivity research and 2) to introduce a framework to address these biases by treating excluded scans as a missing data problem. We use a study of autism spectrum disorder to illustrate the problem and the potential solution. We aggregated data from 545 children (8-13 years old) who participated in resting-state fMRI studies at Kennedy Krieger Institute (173 autistic and 372 typically developing) between 2007 and 2020. We found that autistic children were more likely to be excluded than typically developing children, with 29.1% and 16.1% of autistic and typically developing children excluded, respectively, using a lenient criterion and 80.8% and 59.8% with a stricter criterion. The resulting sample of autistic children with usable data tended to be older, have milder social deficits, better motor control, and higher intellectual ability than the original sample. These measures were also related to functional connectivity strength among children with usable data. This suggests that the generalizability of previous studies reporting naïve analyses (i.e., based only on participants with usable data) may be limited by the selection of older children with less severe clinical profiles because these children are better able to remain still during an rs-fMRI scan. We adapt doubly robust targeted minimum loss based estimation with an ensemble of machine learning algorithms to address these data losses and the resulting biases. The proposed approach selects more edges that differ in functional connectivity between autistic and typically developing children than the naïve approach, supporting this as a promising solution to improve the study of heterogeneous populations in which motion is common.

2022 ◽  
Vol 12 (1) ◽  
pp. 94
K. K. Mujeeb Rahman ◽  
M. Monica Subashini

Autism spectrum disorder (ASD) is a complicated neurological developmental disorder that manifests itself in a variety of ways. The child diagnosed with ASD and their parents’ daily lives can be dramatically improved with early diagnosis and appropriate medical intervention. The applicability of static features extracted from autistic children’s face photographs as a biomarker to distinguish them from typically developing children is investigated in this study paper. We used five pre-trained CNN models: MobileNet, Xception, EfficientNetB0, EfficientNetB1, and EfficientNetB2 as feature extractors and a DNN model as a binary classifier to identify autism in children accurately. We used a publicly available dataset to train the suggested models, which consisted of face pictures of children diagnosed with autism and controls classed as autistic and non-autistic. The Xception model outperformed the others, with an AUC of 96.63%, a sensitivity of 88.46%, and an NPV of 88%. EfficientNetB0 produced a consistent prediction score of 59% for autistic and non-autistic groups with a 95% confidence level.

2022 ◽  
Vol 38 (1) ◽  
Afaf Hamdy Khalil ◽  
Ahmed Mohamed Zayed ◽  
Ayman Amer ◽  
Hemmat Baz

Abstract Background The current study aimed at constructing an Arabic-language questionnaire to investigate the association of the severity of ADHD with children’s degree of exposure to multimedia per day and the age of starting the engagement, and the effect of different multimedia programs on the attention, language, and socio-behavioral aspects in children presented with attention deficit hyperactivity disorder (ADHD). The present study was conducted on 69 children who attended the Phoniatric Unit at Mansoura University Hospitals and were divided into 2 groups: 30 normal typically developing children as a control group and 39 children with ADHD as the study group. The study group was subdivided into 3 subgroups according to ADHD severity; each subgroup consisted of 13 children. Results The time at which the child started to be exposed to multimedia showed no significant differences among ADHD subgroups as all of the cases started before the age of 2 years. Kids with mild ADHD had a significant increase in watching children’s programs, cartoons, rhymes, and commercials than the other two higher grades (moderate and severe) of ADHD. Conclusion The constructed Arabic questionnaire proved to be reliable and a valid tool that examined the relationship between multimedia usage and ADHD.

2022 ◽  
Vol 14 (1) ◽  
Irène Pittet ◽  
Nada Kojovic ◽  
Martina Franchini ◽  
Marie Schaer

Abstract Background Imitation skills play a crucial role in social cognitive development from early childhood. Many studies have shown a deficit in imitation skills in children with autism spectrum disorders (ASD). Little is known about the development of imitation behaviors in children with ASD. This study aims to measure the trajectories of early imitation skills in preschoolers with ASD and how these skills impact other areas of early development. Methods For this purpose, we assessed imitation, language, and cognition skills in 177 children with ASD and 43 typically developing children (TD) aged 2 to 5 years old, 126 of which were followed longitudinally, yielding a total of 396 time points. Results Our results confirmed the presence of an early imitation deficit in toddlers with ASD compared to TD children. The study of the trajectories showed that these difficulties were marked at the age of 2 years and gradually decreased until the age of 5 years old. Imitation skills were strongly linked with cognitive and language skills and level of symptoms in our ASD group at baseline. Moreover, the imitation skills at baseline were predictive of the language gains a year later in our ASD group. Using a data-driven clustering method, we delineated different developmental trajectories of imitation skills within the ASD group. Conclusions The clinical implications of the findings are discussed, particularly the impact of an early imitation deficit on other areas of competence of the young child.

2021 ◽  
Vol 26 (4) ◽  
pp. 785-796
Kyungmin Park ◽  
Hyojin Yoon

Objectives: The purpose of this study was to investigate reading comprehension monitoring including three types of error detection (lexical inconsistency, internal inconsistency, external inconsistency) and correction with expository discourse in school-aged children with attention deficit hyperactivity disorder (ADHD).Methods: Nineteen ADHD children with vocabulary delay, 17 ADHD children without vocabulary delay, and 20 typically developing children students from third, fourth, and fifth grades participated in the study. In order to assess comprehension monitoring; expository discourses contained three different types of errors. Comprehension monitoring tasks were presented in the following order: First, children were asked to find out errors in two expository texts of comparison and causation. After finding out errors, children were asked to change the appropriate words verbally.Results: ADHD children with vocabulary delay did show difficultly in reading comprehension monitoring tasks when compared to age-matched typically developing children and ADHD children without language impairment. Internal inconsistency was the most difficult error to identify and correct, and lexical inconsistency was the easiest error for all three groups.Conclusion: The result proposed that even children with ADHD who have no difficulty in basic language and reading skills were likely to have difficulty properly using reading comprehension monitoring, which is closely related to working memory and executive functions. The poor comprehension monitoring skills would negatively influence effective reading comprehension.

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