Motor movement for autism spectrum disorder (ASD) detection

Many children with autistic spectrum disorders have unusual reactions to certain sensory stimuli. These reactions vary along a hyper- to hypo-responsivity continuum. For example, some children overreact to weak sensory input, but others do not respond negatively to even strong stimuli. It is typically assumed that this deviant responsivity is linked to sensitivity, although the particular stage of sensory processing affected is not known. Psychophysical vibrotactile thresholds of six male children (age: 8–12) who were diagnosed to have autistic spectrum disorders and six normal male children (age: 7–11) were measured by using a two-alternative forced-choice task. The tactile stimuli were sinusoidal displacements and they were applied on the terminal phalanx of the left middle finger of each subject. By using a forward-masking paradigm, 40- and 250-Hz thresholds of the Pacinian tactile channel and 40-Hz threshold of the Non-Pacinian I tactile channel were determined. There was no significant difference between the thresholds of autistic and normal children, and the autistic children had the same detection and masking mechanisms as the normal children. The sensory responsivity of each subject was tested by clinical questionnaires, which showed again no difference between the two subject groups. Furthermore, no significant correlations could be found between the questionnaire data and the psychophysical thresholds. However, there was a high correlation between the data from the tactile and emotional subsets of the questionnaires. These results support the hypothesis that the hyper- and hypo-responsivity to touch, which is sometimes observed in autistic spectrum disorders, is not a perceptual sensory problem, but may probably be emotional in origin.

This study examined autistic children's social behavior, affect, and use of gaze during naturalistic interactions with their mothers. Sixteen autistic children, 30 to 70 months of age, and 16 normal children, matched on receptive language, participated. Children and their mothers were videotaped during three situations: a free-play period, a more structured period during which communicative demand was made on the child, and a face-to-face interaction. In all three situations, autistic and normal children did not differ in the frequency or duration of gaze at mother's face. In the one condition (face-to-face interaction) during which affective expressions were coded, autistic and normal children also were not found to differ significantly in the frequency or duration of smiles displayed, and neither group displayed frowns. However, autistic children were much less likely than normal children to combine their smiles with eye contact in a single act that conveyed communicative intent. Autistic and normal children were not found to differ in the percentages of smiles they displayed to social versus nonsocial events. However, when autistic children's responses to mother's smiles specifically were examined, it was found that they were much less likely to smile in response to mother's smiles than were normal children. Finally, it was found that mothers of autistic children displayed fewer smiles and were less likely to smile in response to their children's smiles, when compared with mothers of normal children. These findings suggest that the autistic child's unusual affective behavior may negatively affect the behavior of others.

Background: Mitochondrial dysfunction and autistic spectrum disorders (ASDs) are closely related with each other. It has also been mitochondrial dysfunction causes impairment in cellular function which may lead to lack of social communications, language deficits and abnormal energy metabolism in autistic spectrum disorder. These are associated with laboratory evidence of lowered mitochondrial function. Objective: To observe the mitochondrial dysfunction and assess serum lactate and CK to in children with autistic spectrum disorder. Methods: This case-control study was conducted in the Department of Physiology of Bangladesh Medical University (BMU), Shahbag, Dhaka from January, 2013 to December, 2013. For this study a total number of 100 male children with age range 3-8 years were randomly selected, among which 50 were normal children and 50 were diagnosed autistic children. The autistic children were selected from the Parent’s Forum, Directorate Generals of Health Service (DOHS), Mohakhali, Dhaka and normal children were selected from some normal school. Serum lactate and creatine kinase (CK) were estimated in all children by standard laboratory method. For statistical analysis independent sample‘t’ test were done as applicable. Result: The mean of both the measured biochemical parameters were found significantly higher (p<0.001) in autistic spectrum disorder children. In addition, elevated levels of serum lactate and CK were found in 94% and 32% of autistic children respectively. Conclusion: The result of this study revealed that mitochondrial dysfunction may occur in children with autistic spectrum disorder. The severity of the autistic spectrum disorder is directly related to the biochemical abnormality for mitochondrial dysfunction. EWMCJ Vol. 13, No. 2, July 2025: 121-124

Autism spectrum disorder (ASD) is a developmental disability that involves persistent challenges in social interaction, communication and behaviour. The purpose of this study is to apply a machine learning approach to differentiate between autistic and normal children and to evaluate the performance of different classifiers in the detection of autism disorder. Heart Rate Variability (HRV) analysis is one of the strategies used for ASD detection by assessing the autonomic nervous system (ANS), which serves as a biomarker for the autism phenotype. HRV can be derived from the photoplethysmogram (PPG). Logistic Regression, Linear Discriminant Analysis and a Cubic Support Vector Machine (SVM) were chosen to evaluate the performance of HRV features in differentiating between normal and autistic children. Three different combinations of features were selected out of 19 features in total. From the results, Logistic Regression was the best classifier to differentiate between autistic and normal children in a colour stimulus test with 100% accuracy, while Linear Discriminant Analysis was best suited in the baseline test with 90% accuracy. In conclusion, the machine learning approach could be an alternative method of making an early diagnosis of ASD in the near future.

In a replication and extension of earlier studies by Hermelin & O'Connor, language recoding abilities in autistic, retarded and normal children matched for mental age and digit span, were compared in a verbal recall task. Random word lists, sentences, and anomalous sentences, eight or 12 items in length (for high and low memory span subgroups) were presented and the number of words recalled from each type of input was scored. All low span children recalled sentences better than random lists with normal children superior to retarded and autistic children and the latter group poorer than the retarded group. Autistic children showed a recency effect with both types of input. There were no group differences amongst high span children and sentences were again better recalled than random lists. In Expt II sentences were better recalled than anomalous sentences, with autistic and retarded children equivalent in performance and poorer than normal children. Although low span autistic children were clearly deficient in recall of sentence material when compared with the two control groups, the effect of conditions showed that they were able to use structure to improve recall. Since high span autistic children did not perform differently from controls it is suggested that results from this kind of study may not be generalizable, and that claims for a specific coding deficit in autistic children need further substantiation.

This study was designed to assess the effectiveness of using prompts (extra "guiding" stimuli) for teaching normal and autistic children. One group of normal children was pretrained on a color discrimination. Later, the colors were used as prompts (presented simultaneously with new training stimuli) to teach four new discriminations. Another group of normal children was trained on the same discriminations with a trial-and-error procedure (i.e., no prompting). A third group consisted of autistic children who were trained on these discriminations using the prompt procedure. Analyses of the results showed the following. (1) The trial-and-error group of normal children acquired more discriminations than the prompt group of normal children. (2) A comparison of the two prompt groups showed that the autistics failed to transfer from the prompt cue to the training cue more often than the normal children; rather, the autistics generally continued responding to the faded color cue. (3) Autistic and normal children who failed to acquire the discriminations when trained with a prompt procedure did acquire these discriminations when no prompt was used. That is, the results suggest that the presentation of an extra guiding stimulus was detrimental to the acquisition of training discriminations for all subjects, and particularly so far autistic children. Therefore, the common practice of providing extra guiding stimuli in proportion to the severity of the learning disorder may actually be harmful to the learning of new skills. Implications of these results for future research are discussed.

Autism spectrum disorder (ASD) is a developmental disorder characterized by persistent deficits in social communication and interaction. Speech is an important form of social communication. Prosody (e.g. vocal pitch, rhythm, etc.), one aspect of the speech signal, is crucial for ensuring information about the emotionality, excitability, and intent of the speaker, is accurately expressed. The objective of this study was to gain a better understanding of how auditory information is used to regulate speech prosody in autistic and non-autistic children, while exploring the relationship between the prosodic control of speech and social competence. Eighty autistic (M = 8.48 years, SD = 2.55) and non-autistic (M = 7.36 years, SD = 2.51) participants produced vocalizations while exposed to unaltered and frequency altered auditory feedback. The parent-report Multidimensional Social Competence Scale was used to assess social competence, while the Autism-Spectrum Quotient and the Autism Spectrum Rating Scales were used to assess autism characteristics. Results indicate that vocal response magnitudes and vocal variability were similar across autistic and non-autistic children. However, autistic children produced significantly faster responses to the auditory feedback manipulation. Hierarchical multiple regressions indicated that these faster responses were significantly associated with poorer parent-rated social competence and higher autism characteristics. These findings suggest that prosodic speech production differences are present in at least a subgroup of autistic children. These results represent a key step in understanding how atypicalities in the mechanisms supporting speech production may manifest in social-communication deficits, as well as broader social competence, and vice versa. Autism Res 2020, 13: 1880-1892. © 2020 International Society for Autism Research and Wiley Periodicals LLC LAY SUMMARY: In this study, autistic and non-autistic children produced vowel sounds while listening to themselves through headphones. When the children heard their vocal pitch shifted upward or downward, they compensated by shifting their vocal pitch in the opposite direction. Interestingly, autistic children were faster to correct for the perceived vowel sound changes than their typically developing peers. Faster responses in the children with ASD were linked to poorer ratings of their social abilities by their parent. These results suggest that autistic and non-autistic children show differences in how quickly they control their speech, and these differences may be related to the social challenges experienced by autistic children.

Dermatoglyphic comparisons were made among 32 autistic children aged from 4-10 to 18-11; sex-, age-, and IQ-matched retarded children; and sex- and age-matched normal children. Significant differences were found between the autistic and normal children for distribution of dermal patterns and ridge line disruption, but no significant differences were found for the total mean ridge counts or mean ridge count rankings. Apart from the right hand of the autistic children, there were no unusual scores for digital dispersion ratios. Autistic and retarded children differed only in their distribution of dermal patterns, with the autistic children apparently intermediate between retarded and normal groups. These results indicate that conclusions of unique congenital disturbance in the etiology of autism inferred from different dermatoglyphics may be premature, and that dermatoglyphics may be ineffective in delineating autistic children from other atypical populations.

Introduction: Executive Functioning (EF) has been studied separately in both normal and Autistic children but there are no specific studies on the comparative analysis of strength and weaknesses of executive functioning among children with Autism Spectrum Disorder (ASD) and normal children.
 Objective: To evaluate and compare the strength and weakness of executive functioning (EF) among children with autism spectrum disorder and normal children.
 Materials & Methods: A comparative cross-sectional survey was conducted through purposive sampling from July 2018 to February 2019 involving parents of normal school going children and diagnosed Autistic children. Children aged 3 to 8 years old with ASD (n=96) and normally developed children (n=96) were compared on a battery of Executive Functioning (EF) tasks in both groups. Data were analyzed by SPSS version 21 for descriptive statistics; comparisons were done by Independent Samples T Test, keeping p≤0.05 as significant.
 Results: There was a male preponderance among the autistic children (67 versus 45 males in normal children). The most represented ages were 4-4.11 years and 7-8 years. Tests of Executive Function showed significant decline in all the abilities (p<0.05), except in Time Management (p=0.21).
 Conclusion: Children with Autism Spectrum Disorder show major deficits in Executive Functioning when compared to normal children.

The aim of the present study was to search for a sensorimotor marker (i.e., visuopostural tuning) that could be correlated with the severity of motor impairments in children with autistic spectrum disorders. Given that autistic children were previously reported to be posturally hyporeactive to visually perceived environmental motion in comparison with normal control children (Gepner et al., 1995), we sought to determine whether children with Asperger syndrome (AS) would share the same postural hyporeactivity to visual motion. Three autistic children with mild to severe motor impairments, three AS children with soft motor signs, and nine normal control children were tested for overall postural instability and postural reactivity to environmental motion. Results indicate, first, that overall postural instability is significantly reduced in autistic children compared with both AS and normal children. Second, although postural oscillations in the fore-aft axis become more attuned to the oscillation frequency of an immersive dynamic visual display as visual speed is increased, in both control and AS subjects, this is not the case in autistic children. Despite the small number of subjects tested in this study, our data confirm the existence of a visuopostural detuning in autistic children. Third, they argue for a correlation between visuopostural tuning and severity of motor signs in children with autistic spectrum disorders. Finally, they suggest a differentiation between children with autism and children with AS with regard to postural reactivity to fast visual motion. Neurophysiological implications of these results are discussed. In particular, a visuocerebellar pathway deficit hypothesis in autism is proposed.

To compare the national prevalence of meeting physical activity, screen time, and sleep guidelines between autistic and nonautistic children and identify factors associated with meeting these guidelines. Prevalences for each health-determining behavior were estimated using the 2022 National Survey of Children's Health, using national guidelines. Complex survey-weighted logistic regression, adjusted for demographic covariates, was used to measure associations between autism and meeting each guideline, and to identify potential child, family, community, and policy-level determinants of each behavior among autistic children. The prevalence of meeting all three guidelines was low among autistic and nonautistic children across age groups. Physical activity guidelines were met at similarly low rates among autistic and nonautistic children; however, autistic children of all age groups were less likely to meet screen time guidelines, and those in the 3-5 and 6-11years age groups were less likely to meet sleep guidelines. Moderate/severe autism, irregular bedtime, low parental education, and lacking a medical home were associated with lower likelihood of meeting sleep guidelines. Irregular bedtime and high income were associated with lower likelihood of meeting physical activity guidelines. Autistic children meet guidelines for physical activity, screen time, and sleep at a low prevalence and less than their nonautistic peers. Clinicians should develop individualized plans to facilitate adherence to guidelines among autistic children. Interventions should address modifiable factors, including bedtime regularity and access to medical homes. Further research and policy efforts should be made to improve adherence to guidelines among autistic children and subsequently reduce health disparities.

In order to examine the cranial CT of autistic children and investigate the etiological significance of CT scan findings, the CT of the brain was surveyed in 24 children with early infantile autism (3 to 17 years with a mean age of 7.6), and 179 children with the normal CT despite their medical histories such as headaches or febrile convulsions. According to their ages, the autistic and normal children were divided into the following three groups: Group I (age ranging from 3 to 5), Group II (age: 6 to 9) and Group III (age: 10 to 17). There was no significant difference between the bifrontal CVI of the autistic children and that of the normal children. However, in Group III, the bifrontal CVI of the autistic children was significantly higher than that of the normal children. There was no significant difference between the bicaudate CVI of the autistic children and that of the normal children. However, in Groups I and II, the bicaudate CVI of the autistic children was significantly lower than that of the normal children. The maximum widths of the third ventricle showed no significant difference between the autistic and normal children. However, in Groups II and III, those of the autistic children were wider than those of the normal children. In the autistic children, as the age increases, the difference becomes significantly wider. A positive correlation was observed between the width of the third ventricle and ages of the autistic children. An examination of the right-left ratio of maximum transverse diameter of the brain showed that there was no significant difference between the autistic and normal children. The above-mentioned results (1)-4)) might suggest a progressive disorder of the brain structure surrounding the third ventricle or lateral ventricles in the autistic children.

This study tests the hypothesis that the development of normal and autistic children differs only in rate and asymptote. A total of 195 normal children between 1 and 5 years of age, 160 normal children between 3 months and 24 months of age, and 41 autistic children between 5 and 11 years of age were evaluated on the eight psychological variables constituting the Behavioral Rating Instrument for Autistic and other Atypical Children (BRIAAC). While many similarities were found, there were a sufficient number of differences to justify DMS-III's statement that certain autistic behaviors are not normal at any stage of development. Differences were particularly prominent when the development of normal infants was compared with that of severely disturbed autistic children. In general, the issue of whether deviant development differs only quantitatively from normal development can best be decided on the basis of developmental data and by utilizing instruments that sample all the major characteristics of both populations.

Objective To test the attachment quality of autism children between 2～6 years with attach-ment Q-Sort,and to understand the difference from normal children. Methods Use the AQS to test the parents of 167 normal children and 55 autism children,which can evaluate the children' attachment types. Results The rate of security attachment in normal children was 68.3% ,and the rate of unsure attachment was 31.7%. The rate of security attachment in autism children was 29.1% ,and the rate of unsure attachment was 70.9% ;and the rates of security attachment in normal and autism children were different significantly(X2=26.16, P<0.01). Attachment quality was not associated with sex both in normal and autism children and neither age was. Conclusions Most of the normal children have the security attachment,and the autism children have the unsure attachment. Neither age nor sex is associated with attachment quality both normal children and autism children. Key words: Attachment Q Sort; Children; Autism; Cross-sectional study

Previous research links resting frontal gamma power to key developmental outcomes in young neurotypical (NT) children and infants at risk for language impairment. However, it remains unclear whether gamma power is specifically associated with language or with more general cognitive abilities among young children diagnosedwith autism spectrum disorder (ASD). The current study evaluates differences in resting frontal gamma power between young autistic and NT children and tests whether gamma power is uniquely associated with individual differences in expressive language, receptive language and non-verbal cognitive abilities in autistic and NT children. Participants included 48 autistic children and 58 age- and sex-matched NT children (ages 22-60months). Baseline electroencephalography (EEG) recordings were acquired from each participant. Children also completed the Mullen Scales of Early Learning (MSEL). We found thatfrontal gamma power at rest did not differ between autistic and NT children. Among autistic children, reduced frontal gamma power was significantly associated with both higher expressive language skills and higher non-verbal cognitive skills, controlling for age and sex. The interaction between frontal gamma power and diagnostic status no longer explained unique variance in expressive language skills after controlling for variance associated with non-verbal cognitive skills across autistic and NT children. Together, these findings suggest that reduced gamma power is associated with both better expressive language and non-verbal cognitive skills among young autistic children. Moreover, associations between high frequency neural activity and cognition are not specific to verbal abilities but reflect neural mechanisms associated with general higher-order cognitive abilities in ASD.