Functional magnetic resonance imaging (fMRI) during performance of a hidden figures task (HFT) was used to compare differences in brain function in children diagnosed with autism disorder (AD) ...compared to children with attention-deficit/hyperactivity disorder (ADHD) and typical controls (TC). Overall greater functional MRI activity was observed in the two control groups compared to children with AD. Laterality differences were also evident, with AD subjects preferentially showing activity in the right medial temporal region while controls tended to activate the left medial temporal cortex. Reduced fMRI activity was observed in the parietal, ventral-temporal and hippocampal regions in the AD group, suggesting differences in the way that children with AD process the HFT.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, ODKLJ, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, VSZLJ, ZAGLJ
An autism susceptibility locus (AUTS1, MIM#608636) has been identified in chromosome 7q31. NrCAM is a candidate gene for AUTS1 because it is expressed in the brain and encodes a receptor involved in ...nervous system development. Polymorphisms in NrCAM have been reported to be associated with autism susceptibility and with substance abuse, implicating NrCAM in reward circuitry. Self-stimulatory, perseverative behavior in autism might be due to defects in reward circuitry. In addition, models of drug addiction have also borrowed from models of obsessive-compulsive behavior designed to reduce anxiety. Thus, our goals were to replicate previous associations of NrCAM with autism, making use of a large cohort, and to clarify whether NrCAM was associated with a specific endophenotype of autism in the repetitive behaviors and stereotyped interests domains.
We genotyped six NrCAM single nucleotide polymorphisms in 352 families and we tested for association between these polymorphisms and autism in the entire cohort and in two subsets, one with severe obsessive-compulsive behaviors and one with pronounced self-stimulatory behaviors.
We found no association between single nucleotide polymorphisms of NrCAM and autism in our large cohort, or in the severe obsessive-compulsive behavior and self-stimulatory behavior subsets. However, we observed a significant overtransmission (21 transmitted vs 6 nontransmitted, chi2=12.054, P=0.0005) of the haplotype G-G-A-G-C-A of rs722519-rs1269622-rs405945-rs6958498-rs401433-rs439587 in the severe obsessive-compulsive behavior subset, likely driven by the G-C haplotype of rs6958498-rs401433, which itself showed significant overtransmission (31 transmitted vs 13 nontransmitted, chi2=8.844, P=0.003).
Overtransmission of particular haplotypes of NrCAM, that may relate to the expression level of NrCAM in the brain, appeared to be associated with autism in the severe obsessive-compulsive behavior subset.
Conditions that include pervasive developmental disorder, autism, childhood disintegrative disorder, and Asperger's syndrome are all included under the umbrella term of autism spectrum disorder. Even ...today, some people still refer to "Asperger's syndrome," which is generally considered a lesser form of autism spectrum condition. Early childhood is when autism spectrum disorder usually first manifests itself, and it eventually causes problems with social integration, academic achievement, and professional functioning in society. To develop a method using as Inception V3 algorithm to easily determine the presence of the disease. Video-based classification is used to detect autism spectrum disorder. To help in the effective diagnosis of disease with higher accuracy. A minority of children seem to experience typical development in their first year of life but undergo a phase of regression between 18 and 24 months of age, during which they exhibit the symptoms of autism. This study utilizes a Deep Learning (DL) algorithm such as Inception V3 for the early detection of a disorder. Datasets, comprising videos, have been assembled for identifying autism spectrum disorder. A deep learning algorithm is applied to train the videos of children, resulting in the creation of a model file. When an input image is provided for disease prediction, the model effectively identifies the presence of the disorder. Therefore, the proposed system provides an effective solution to predict the presence of autism spectrum disorder in a more efficient way. The findings show that the LSTM classification model achieved 93% accuracy. Thus, this research work helps in the effective diagnosis of autism spectrum disorder with higher accuracy than existing models.
In this groundbreaking study, we introduce a novel methodology for the early identification of autism disorder, a crucial step towards more effective intervention and support. Our approach, known as ...the Artificial Intelligence Assisted Hybrid Learning Scheme (AIHLS), capitalizes on the synergy between cutting-edge transfer learning techniques, specifically Xception, and the XGBoost Classifier. By combining the strengths of these advanced technologies, our method offers a promising avenue for the early detection of Autism Disorder in its nascent stages. When Xception's feature extraction capabilities are combined with the predictive prowess of XGBoost Classifier, the result is a potent framework capable of addressing complex problems in various domains. This combination offers the advantage of capturing fine-grained image features through Xception and subsequently leveraging XGBoost's ability to make accurate predictions based on these features. This significant advancement has the capacity to transform the landscape of autism diagnosis, offering the promise of earlier and more precise assessments. Such advancements hold the potential to enhance the overall quality of life for individuals on the autism spectrum as well as their families. In comparison to conventional models, the proposed model has demonstrated an outstanding accuracy rate of 97.11%, underscoring its remarkable performance.
Neurological disorders are very complex and difficult to diagnose. Electric signals are primary means of communication between brain cells, and are constantly active, even during deep sleep. ...Electroencephalogram (EEG) is a brain signal, which is used to measure electrical activity in the brain. To activate neurons, metal disk covered by electrodes is used which attach to the scalp and used as EEG recording for identification of any disorder. The EEG signals are useful for early detection of neurological disorders such as Epileptic seizure, Alzheimer, schizophrenia, autism spectrum disorder (ASD) and many more. Analyzing EEG data sets using classification techniques of machine learning or deep learning can bring meaningful insights on neurological disorder diagnostics. This paper reviews recent advancements in predicting neurological disorders such as Epilepsy, Alzheimer's, Autism, Schizophrenia and Parkinson's disorder and the promising effects and pitfalls. We have identified the best suiting classifier for each of the mentioned diseases and the accuracy achieved so far using EEG data. We implemented prediction of four common neurological disorders, namely autism, epilepsy, Parkinson's disease, and schizophrenia, from EEG data. We have received accuracy of 79.3% accuracy for predicting ASD, 63.3% with dataset of schizophrenia, 86.1% with PD and 94% with epilepsy disorder dataset.
The use of a different protocol to assess the same aspects of the Functional Communication Profile (FCP) may contribute to a faster and less expensive determination of individual profiles of ...abilities and inabilities. The purpose of this study was to verify the applicability of a checklist to replace the aforementioned complete protocol as a way to facilitate clinical and therapeutic follow-up processes.
The participants in this study were 50 children aged from 3 to 12 years, with diagnoses within the autism spectrum who were receiving specialized speech-language therapy for at least six months. The participants were filmed while interacting with the speech-language pathologist, and the data were transcribed to the FCP protocol. After the recording and prior to the transcription, the speech-language pathologists were asked to answer the checklist of Communicative Functions.
All answers on the checklist and on the FCP were compared. The results indicated that there were statistical differences in nine of the 20 communicative functions, and in nine of the 50 children. These results suggest that the checklist is efficient to describe a group of children but not to characterize them individually. Therefore, it is possible to identify differences in the communicative profile but not to specify the frequency with which each function occurs.
The checklist can be used as a tool in the therapeutic follow-up processes of children with autism spectrum disorders, but it does not replace the complete FCP protocol.
A complex disorder called ASD (Autism Spectrum Disorder) is characterised by persistent problems with social communication, narrow interests, and repetitive behaviour. ASD sufferers may behave, ...communicate, interact, and learn in different ways from the average person. The majority of the time, their appearance does not set them apart from others. ASD sufferers may be highly talented in many different areas. For instance, some people with ASD may be highly communicative while others are nonverbal. While some people with ASD need significant daily support, others are able to live and work independently. Parents and caretakers can find early signs of this condition before a child becomes one year old. However, by the time the child is two or three years old, the symptoms are typically more severe. Here artificial intelligence techniques and data science techniques can be used. This paper presents a novel approach for detecting ASD from relevant observations in the patients. Differences in results between the two types of neural networks used have been shown as well.
Autism is a common developmental disorder affecting brain function, that retard the joint attention of autistic children. The current study aimed to study the improvement of the joint attention of ...autistic children through the use of a various activities program. The total study sample included (46) autistic children whose ages varied from (5-9) years. The final sample of the study included (12) autistic children. The participants were divided into two groups, experimental and control groups, each of (6) children. The researcher used the Stanford-Binet scale intelligence scale, the Autistic Children Scale, the Joint Attention Scale, and a program of various activities. The results revealed improving the joint attention skills of autistic participants. the various activities program (social, motor, cultural, and artistic activities) contributed to improve the joint attention skills of children with autism disorder. The results of this work can generally influence how researchers design their studies and provide an example of how children with autism are involved in various activities, whether at home, school, or treatment centers.
The aim of this study was to investigate the effect of 8 weeks of proprioceptive training on motor coordination in children with autism spectrum disorder in Shiraz schools. The method was ...quasi-experimental and a pretest/posttest design with a control group. 16 participants (5 to 12 years old, 8.62 ± 2.21) were randomly homogenized in experimental and control groups according to the results of Bruininks-Oseretsky subtests. Experimental subjects individually performed proprioceptive training in 24 sessions while the control group individually performed the similar number of sessions of regular occupational therapy. After the completion of the training course, posttest was conducted for both groups. Results demonstrated a significant difference between the two groups in all subscales of eye, hand and bimanual coordination (P≤0.05). Regarding the evaluations in this study and the significance of motor coordination due to changes in processing and sensory-motor systems, it can be concluded that proprioceptive training improved motor coordination in children with autism.
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