Academic literature on the topic 'Motor, autism spectrum disorder, early detection, fetal'

Atypical motor patterns are potential early markers and predictors of later diagnosis of Autism Spectrum Disorder (ASD). This study aimed to investigate the early motor trajectories of infants at high-risk (HR) of ASD through MOVIDEA, a semi-automatic software developed to analyze 2D and 3D videos and provide objective kinematic features of their movements. MOVIDEA was developed within the Italian Network for early detection of Autism Spectrum Disorder (NIDA Network), which is currently coordinating the most extensive surveillance program for infants at risk for neurodevelopmental disorders (NDDs). MOVIDEA was applied to video recordings of 53 low-risk (LR; siblings of typically developing children) and 50 HR infants’ spontaneous movements collected at 10 days and 6, 12, 18, and 24 weeks. Participants were grouped based on their clinical outcome (18 HR received an NDD diagnosis, 32 HR and 53 LR were typically developing). Results revealed that early developmental trajectories of specific motor parameters were different in HR infants later diagnosed with NDDs from those of infants developing typically. Since MOVIDEA was useful in the association of quantitative measures with specific early motor patterns, it should be applied to the early detection of ASD/NDD markers.

Previous studies on autism have shown a lack of motor anticipation in children and adults with autism. As part of a programme of research into early detection of autism, we focussed on an everyday situation: spoon-feeding. We hypothesize that an anticipation deficit may be found very early on by observing whether the baby opens his or her mouth in anticipation of the spoon’s approach. The study is based on a retrospective analysis from family home movies. Observation of infants later diagnosed with autism or an autism spectrum disorder (ASD) (n = 13) and infants with typical development (n = 14) between 4 and 6 months old show that the autism/ASD group has an early anticipation deficit.

Autism spectrum disorder (ASD) is associated with abnormal brain development during fetal life. Overall, increasing evidence indicates an important role of epigenetic dysfunction in ASD. The placenta is critical to and produces neurotransmitters that regulate fetal brain development. We hypothesized that placental DNA methylation changes are a feature of the fetal development of the autistic brain and importantly could help to elucidate the early pathogenesis and prediction of these disorders. Genome-wide methylation using placental tissue from the full-term autistic disorder subtype was performed using the Illumina 450K array. The study consisted of 14 cases and 10 control subjects. Significantly epigenetically altered CpG loci (FDR p-value <0.05) in autism were identified. Ingenuity Pathway Analysis (IPA) was further used to identify molecular pathways that were over-represented (epigenetically dysregulated) in autism. Six Artificial Intelligence (AI) algorithms including Deep Learning (DL) to determine the predictive accuracy of CpG markers for autism detection. We identified 9655 CpGs differentially methylated in autism. Among them, 2802 CpGs were inter- or non-genic and 6853 intragenic. The latter involved 4129 genes. AI analysis of differentially methylated loci appeared highly accurate for autism detection. DL yielded an AUC (95% CI) of 1.00 (1.00–1.00) for autism detection using intra- or intergenic markers by themselves or combined. The biological functional enrichment showed, four significant functions that were affected in autism: quantity of synapse, microtubule dynamics, neuritogenesis, and abnormal morphology of neurons. In this preliminary study, significant placental DNA methylation changes. AI had high accuracy for the prediction of subsequent autism development in newborns. Finally, biologically functional relevant gene pathways were identified that may play a significant role in early fetal neurodevelopmental influences on later cognition and social behavior.

Autism appears to be a neuro developmental disorder that is visible in the early years. It is a wide-spectrum disorder that indicates that the severity and symptoms can vary from person to person. The Centre for Disease Control found that one in 68 was diagnosed with autism spectrum disorder with increasing numbers in every year. Detection of autism in adults is a cumbersome procedure because in adults, many symptoms can blend with some other mental health, motor impairment disorders so misinterpretation of actual diseases can in turn lead to a terrible life without proper diagnosis and effective treatment mechanisms. Machine learning is a powerful computer tool that supports different application domains Learning complex relationships or patterns from large datasets to draw accurate conclusions. Disease assessment can be done with predictive health data analysis and more appropriate treatment mechanisms that are now a hot area of research. Supervised learning is an important step of Machine learning which uses a rule-based approach by examining empirical data sets to build accurate predictive models. In this paper, decision tree, random forest, SVM, neural networks algorithms are applied on autism spectrum data which have been collected from UCI repository. The results of decision tree, random forest, SVM, neural networks algorithms on autism dataset are presented in this paper in an efficient manner. Analysis performed over these accurate results which will be useful to make right decisions in predicting autism spectrum disorder (ASD) at early stages. Thus, early autism intervention using machine learning techniques opens up a new way for autistic individuals to develop the potential to lead a better life by improving their behavioural and emotional skills.

Autism spectrum disorder (ASD) is a developmental disability in early childhood. Early identification and intervention in children with ASD are essential for children and their families. This study aimed to identify the earliest signs of ASD. Using a large cohort including data from 104,062 fetal records in the Japan Environment and Children’s Study, we examined the Ages and Stages Questionnaires® (ASQ-3TM) scores of children with and without ASD. The ASQ-3 comprises five domains: communication, gross motor, fine motor, problem solving, and personal-social. The ASQ-3 scores were obtained at ages 6 months, 1 year, and 3 years. There were 64,501 children with available ASQ-3 data. The number of children diagnosed with ASD was 188 (0.29%) at 3 years of age. The highest relative risk (RR) for any domain below the monitoring score at 6 months was in the communication (RR 1.90, 95% CI 1.29–2.78, p = 0.0041), followed by fine motor (RR 1.50, 95% CI 1.28–1.76, p < 0.0001) domain. A low ASQ-3 score in the communication domain at 6 months was related to an ASD diagnosis at 3 years of age. The ASQ-3 score at 6 months can contribute to the early identification of and intervention for ASD.

Purpose: Delays and motor deficiencies in Autism Spectrum Disorder (ASD) are extremely common and often announce the appearance of widespread atypical development. However, they are not properly emphasized in the diagnostic or assessment criteria for ASD. Thus, our paper provides a literature review on the motor evaluation of stereotyped movements of ASD individuals in relation to early diagnosis, treatment and possible interventions. Methodology: Computerized searches (Pubmed) and manual searches were performed to identify the most relevant studies. We used the following keywords and search terms: autism spectrum disorder, autism, ASD, motor skills, motor disorders, stereotyped movement, assessment, evaluation, measurement, diagnosis, detection. Identified studies were screened by abstracts and conclusions for relevance. The criteria for the inclusion in the review were as follows: peer-reviewed studies; published after 2010; English-language. The following exclusion criteria were applied: published before 2010; participants diagnosed with other developmental disorders than ASD; studies focusing on participants with ASD that did not evaluate motor skills and development. Findings: Early diagnosis of ASD is essential to develop effective interventions and mitigate the ASD main symptoms. Our results show that objective and quantitative measures of motor function should be considered as a priority for future research on the subject. Unique contribution to theory, practice and policy: Specific motor and movement deviations such as stereotyped movements must be evaluated using high technology devices to promote the early diagnosis of children with ASD. Thus, motor deficits should be considered to effectively diagnose ASD. We highly recommend evaluating movement using quantitative methods to assess significant gaps in motor function of ASD individuals.

Autism spectrum disorder (ASD), a neurodevelopmental condition affecting approximately 1 in 44 children in North America, is thought to be a connectivity disorder. Valproic acid (VPA) is a multi-target drug widely used to treat epilepsy. It is also a toxic teratogen as well as a histone deacetylase inhibitor, and fetal exposure to VPA increases the risk of ASD. While the VPA model has been well-characterized for behavioral and neuronal deficits including hyperconnectivity, microglia, the principal immune cells of CNS that regulate dendrite and synapse formation during early brain development, have not been well-characterized and may provide potential hints regarding the etiology of this disorder. Therefore, in this study, we determined the effect of prenatal exposure to VPA on microglial numbers during early postnatal brain development. We found that prenatal exposure to VPA causes a significant reduction in the number of microglia in the primary motor cortex (PMC) during early postnatal brain development, particularly at postnatal day 6 (P6) and postnatal day 10 (P10) in male mice. The early microglial reduction in the VPA model coincides with active cortical synaptogenesis and is significant because it may potentially play a role in mediating impaired connectivity in ASD.

Children with autism spectrum disorder (ASD) not only have communication and social difficulties, but also exhibit poor balance and motor control ability, which frequently affect daily activities. Effective balance and motor control rely on the integration of somatosensory, visual, and vestibular inputs. Although reports of balance dysfunction in ASD have been documented, comprehensive studies of balance and vestibular function in children with ASD are scarce. In this study, we retrospectively reviewed 36 pediatric patients diagnosed with ASD who underwent balance/vestibular laboratory testing in our speciality clinic. Results from sensory organization test (SOT) or modified clinical test for sensory integration of balance (mCTSIB) found that out of 15 patients, 80% had abnormal findings. Of the children who successfully completed each vestibular test, abnormal responses were observed in 12 (80%) sensory organization tests, 5 (24%) vestibular evoked myogenic potential (VEMP), 22 (66%) videonystagmography (VNG), and 11 (32%) sinusoidal rotary chair tests. These results indicate that balance and vestibular testing may be of diagnostic value for clinicians and providers as an aid in early detection, intervention, and the development of appropriate management and therapies for this patient population. Increased awareness of this topic is warranted to promote better clinical management of this special group of patients and improve their quality of life.

In spite of the huge progress in the treatment of diabetes mellitus, we are still in the situation that both pregestational (PGDM) and gestational diabetes (GDM) impose an additional risk to the embryo, fetus, and course of pregnancy. PGDM may increase the rate of congenital malformations, especially cardiac, nervous system, musculoskeletal system, and limbs. PGDM may interfere with fetal growth, often causing macrosomia, but in the presence of severe maternal complications, especially nephropathy, it may inhibit fetal growth. PGDM may also induce a variety of perinatal complications such as stillbirth and perinatal death, cardiomyopathy, respiratory morbidity, and perinatal asphyxia. GDM that generally develops in the second half of pregnancy induces similar but generally less severe complications. Their severity is higher with earlier onset of GDM and inversely correlated with the degree of glycemic control. Early initiation of GDM might even cause some increase in the rate of congenital malformations. Both PGDM and GDM may cause various motor and behavioral neurodevelopmental problems, including an increased incidence of attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). Most complications are reduced in incidence and severity with the improvement in diabetic control. Mechanisms of diabetic-induced damage in pregnancy are related to maternal and fetal hyperglycemia, enhanced oxidative stress, epigenetic changes, and other, less defined, pathogenic mechanisms.

IntroductionNeurodevelopmental disorders (NDD), including cerebral palsy (CP), autism spectrum disorder (ASD) and foetal alcohol spectrum disorder (FASD), are characterised by impaired development of the early central nervous system, impacting cognitive and/or physical function. Early detection of NDD enables infants to be fast-tracked to early intervention services, optimising outcomes. Aboriginal and Torres Strait Islander infants may experience early life factors increasing their risk of neurodevelopmental vulnerability, which persist into later childhood, further compounding the health inequities experienced by First Nations peoples in Australia. The LEAP-CP prospective cohort study will investigate the efficacy of early screening programmes, implemented in Queensland, Australia to earlier identify Aboriginal and Torres Strait Islander infants who are ‘at risk’ of adverse neurodevelopmental outcomes (NDO) or NDD. Diagnostic accuracy and feasibility of early detection tools for identifying infants ‘at risk’ of a later diagnosis of adverse NDO or NDD will be determined.Methods and analysisAboriginal and/or Torres Strait Islander infants born in Queensland, Australia (birth years 2020–2022) will be invited to participate. Infants aged <9 months corrected age (CA) will undergo screening using the (1) General Movements Assessment (GMA); (2) Hammersmith Infant Neurological Examination (HINE); (3) Rapid Neurodevelopmental Assessment (RNDA) and (4) Ages and Stages Questionnaire-Aboriginal adaptation (ASQ-TRAK). Developmental outcomes at 12 months CA will be determined for: (1) neurological (HINE); (2) motor (Peabody Developmental Motor Scales 2); (3) cognitive and communication (Bayley Scales of Infant Development III); (4) functional capabilities (Paediatric Evaluation of Disability Inventory-Computer Adaptive Test) and (5) behaviour (Infant Toddler Social and Emotional Assessment). Infants will be classified as typically developing or ‘at risk’ of an adverse NDO and/or specific NDD based on symptomology using developmental and diagnostic outcomes for (1) CP (2) ASD and (3) FASD. The effects of perinatal, social and environmental factors, caregiver mental health and clinical neuroimaging on NDOs will be investigated.Ethics and disseminationEthics approval has been granted by appropriate Queensland ethics committees; Far North Queensland Health Research Ethics Committee (HREC/2019/QCH/50533 (Sep ver 2)-1370), the Townsville HHS Human Research Ethics Committee (HREC/QTHS/56008), the University of Queensland Medical Research Ethics Committee (2020000185/HREC/2019/QCH/50533) and the Children’s Health Queensland HHS Human Research Ethics Committee (HREC/20/QCHQ/63906) with governance and support from local First Nations communities. Findings from this study will be disseminated via peer-reviewed publications and conference presentations.Trial registration numberACTRN12619000969167.

Several evidences showed atypical gross and fine motor functions in infants and children with Autism Spectrum Disorder (ASD). For this reason, motor impairments or abnormalities should be investigated as potential early signs of the disorder and correlated to the severity of its core symptoms. Thus, the early detection of motor abnormalities may be potentially useful to diagnose later social impairments.  Main aim of the present PhD project is to identify early predictors of ASD through the investigation of antenatal and postnatal motor development in fetuses and infants at low- and high-risk for ASD. The underlying hypothesis is that assessment of motor performances may be effective in predicting abnormal outcomes in infants at risk for neurological development. To this aim, two specific experiments have been performed: Experiment 1. Analysis of early motor repertoire in infants at low and high risk for ASD. Experiment 2. Analysis of fetal movements in pregnancies at low and high risk for ASD through ultrasound (US) techniques. All the experimental activities have been performed at the Istituto Superiore di Sanità (ISS) within the Network for early detection of autism spectrum disorders (NIDA) and the European project “Brainview – fetal ultrasound screening for neurodevelopmental disorders in normal and high-risk pregnancies” Marie Sklodowska – Curie actions, Innovative Training Networks (ETN), H2020 –MSCA- ETN-2014. During the Ph.D., I collaborated with Prof. Andrea Guzzetta and his staff at the Stella Maris Foundation on the analysis of infant’s spontaneous movements and with Dr. Maria Bulgheroni (Ab. Acus company) and her staff of bio-engineers on the development and implementation of a software for the kinematic analysis of infant’s movements. To investigate antenatal neurobehaviours of fetuses at risk for ASD, I have collaborated with Dr. Laura Iaconianni, head gynecologist of the “Ultrasound Diagnostic Centre Eco.B.I.” in Rome. Given the importance of further exploring the early motor trajectories in infants with ASD, this study had the overall purpose to collect longitudinal data on motor development of infants at high risk for ASD. The present work has several strengths and gave light to novel findings. First, data from the first experimental study supported the importance of carefully exploring the developmental trajectories of the spontaneous movements in the first 5-6 months of life of infants at high-risk for ASD since potentially predictive of later social impairments. Second, the development of the MOVIDEA software provided the possibility of detecting spontaneous movements for future application in clinical settings. Finally, the standard operative procedures developed to collect and analyze fetal movements and basal biometrical data during 2D and 4D ultrasound recording allow to evaluate possible indicators of an adequate fetal health during the gynecological examination of pregnant women during the first and second trimester.  In conclusion, the present work defined motor prenatal and postnatal trajectories to detect early signs of ASD in at-risk populations. In fact, given the well-established link between motor development and social competencies, it is possible to use this protocol as screenings in clinical settings to identify children at risk for neurodevelopmental disorders early in life and provide them and their families adequate care, services and interventions. Even if the low number of high risk with ASD prevents us from any consideration regarding the comparison between groups and the detection of early markers of ASD, the current protocols and techniques may be considered valuable tools to investigate motor developmental trajectories in infants.

Autism spectrum disorder (ASD) is one of the common disorders in brain. Early detection of ASD improves the overall mental health, which is very important for the future of the child. ASD affects social coordination, emotions, and motor activity of an individual. This is due to the difficulties in getting self-evaluation results and expressive experiences. In the first case study in this chapter, an efficient method to automatically detect the expressive states of individuals with the help of physiological signals is explored. In the second case study of the chapter, the authors explore breast cancer prediction using SMO and IBK. Breast cancer is the second leading cause of cancer deaths in women worldwide and occurs in nearly one out of eight. In this proposed system, the tumor is the feature that is used to identify the breast cancer presence in women. Tumors are basically of two types (i.e., benign or malignant). In order to provide appropriate treatment to the patients, symptoms must be studied properly, and an automatic prediction system is required that will classify the tumor into benign or malignant using SMO and IBK.

Autism spectrum disorder (ASD) is one of the common disorders in brain. Early detection of ASD improves the overall mental health, which is very important for the future of the child. ASD affects social coordination, emotions, and motor activity of an individual. This is due to the difficulties in getting self-evaluation results and expressive experiences. In the first case study in this chapter, an efficient method to automatically detect the expressive states of individuals with the help of physiological signals is explored. In the second case study of the chapter, the authors explore breast cancer prediction using SMO and IBK. Breast cancer is the second leading cause of cancer deaths in women worldwide and occurs in nearly one out of eight. In this proposed system, the tumor is the feature that is used to identify the breast cancer presence in women. Tumors are basically of two types (i.e., benign or malignant). In order to provide appropriate treatment to the patients, symptoms must be studied properly, and an automatic prediction system is required that will classify the tumor into benign or malignant using SMO and IBK.