Specific characteristics of spontaneous movements in preterm infants at term age are associated with developmental delays at age 3 years

Barcoding, linear and nonlinear analysis of full-day leg movements in infants with typical development and infants at risk of developmental disabilities: Cross-sectional study

Traditional methods do not capture the multidimensional domains and dynamic nature of infant behavioral patterns. We aim to compare full-day, in-home leg movement data between infants with typical development (TD) and infants at risk of developmental disabilities (AR) using barcoding and nonlinear analysis. Eleven infants with TD (2-10 months) and nine infants AR (adjusted age: 2-14 months) wore a sensor on each ankle for 7 days. We calculated the standard deviation for linear variability and sample entropy (SampEn) of leg acceleration and angular velocity for nonlinear variability. Movements were also categorized into 16 barcoding states, and we calculated the SampEn and proportions of the barcoding. All variables were compared between the two groups using independent-samples t-test or Mann-Whitney U test. The AR group had larger linear variability compared to the TD group. SampEn was lower in the AR group compared to TD group for both acceleration and angular velocity. Two barcoding states' proportions were significantly different between the two groups. The results showed that nonlinear analysis and barcoding could be used to identify the difference of dynamic multidimensional movement patterns between infants AR and infants with TD. This information may help early diagnosis of developmental disabilities in the future.

Review of methods for conducting speech research with minimally verbal individuals with autism spectrum disorder

The purpose of this paper was to review best-practice methods of collecting and analyzing speech production data from minimally verbal autistic speakers. Data on speech production data in minimally verbal individuals are valuable for a variety of purposes, including phenotyping, clinical assessment, and treatment monitoring. Both perceptual ("by ear") and acoustic analyses of speech can reveal subtle improvements as a result of therapy that may not be apparent when correct/incorrect judgments are used. Key considerations for collecting and analyzing speech production data from this population are reviewed. The definition of "minimally verbal" that is chosen will vary depending on the specific hypotheses investigated, as will the stimuli to be collected and the task(s) used to elicit them. Perceptual judgments are ecologically valid but subject to known sources of bias; therefore, training and reliability procedures for perceptual analyses are addressed, including guidelines on how to select vocalizations for inclusion or exclusion. Factors to consider when recording and acoustically analyzing speech are also briefly discussed. In summary, the tasks, stimuli, training methods, analysis type(s), and level of detail that yield the most reliable data to answer the question should be selected. It is possible to obtain rich high-quality data even from speakers with very little speech output. This information is useful not only for research but also for clinical decision-making and progress monitoring.

Automated Movement Analysis to Predict Cerebral Palsy in Very Preterm Infants: An Ambispective Cohort Study

The General Movements Assessment requires extensive training. As an alternative, a novel automated movement analysis was developed and validated in preterm infants. Infants < 31 weeks’ gestational age or birthweight ≤ 1500 g evaluated at 3−5 months using the general movements assessment were included in this ambispective cohort study. The C-statistic, sensitivity, specificity, positive predictive value, and negative predictive value were calculated for a predictive model. A total of 252 participants were included. The median gestational age and birthweight were 274/7 weeks (range 256/7−292/7 weeks) and 960 g (range 769−1215 g), respectively. There were 29 cases of cerebral palsy (11.5%) at 18−24 months, the majority of which (n = 22) were from the retrospective cohort. Mean velocity in the vertical direction, median, standard deviation, and minimum quantity of motion constituted the multivariable model used to predict cerebral palsy. Sensitivity, specificity, positive, and negative predictive values were 55%, 80%, 26%, and 93%, respectively. C-statistic indicated good fit (C = 0.74). A cluster of four variables describing quantity of motion and variability of motion was able to predict cerebral palsy with high specificity and negative predictive value. This technology may be useful for screening purposes in very preterm infants; although, the technology likely requires further validation in preterm and high-risk term populations.

Deep learning-based quantitative analyses of spontaneous movements and their association with early neurological development in preterm infants

This study aimed to develop quantitative assessments of spontaneous movements in high-risk preterm infants based on a deep learning algorithm. Video images of spontaneous movements were recorded in very preterm infants at the term-equivalent age. The Hammersmith Infant Neurological Examination (HINE) was performed in infants at 4 months of corrected age. Joint positional data were extracted using a pretrained pose-estimation model. Complexity and similarity indices of joint angle and angular velocity in terms of sample entropy and Pearson correlation coefficient were compared between the infants with HINE < 60 and ≥ 60. Video images of spontaneous movements were recorded in 65 preterm infants at term-equivalent age. Complexity indices of joint angles and angular velocities differed between the infants with HINE < 60 and ≥ 60 and correlated positively with HINE scores in most of the joints at the upper and lower extremities (p < 0.05). Similarity indices between each joint angle or joint angular velocity did not differ between the two groups in most of the joints at the upper and lower extremities. Quantitative assessments of spontaneous movements in preterm infants are feasible using a deep learning algorithm and sample entropy. The results indicated that complexity indices of joint movements at both the upper and lower extremities can be potential candidates for detecting developmental outcomes in preterm infants.

Video-Based Automatic Baby Motion Analysis for Early Neurological Disorder Diagnosis: State of the Art and Future Directions

Neurodevelopmental disorders (NDD) are impairments of the growth and development of the brain and/or central nervous system. In the light of clinical findings on early diagnosis of NDD and prompted by recent advances in hardware and software technologies, several researchers tried to introduce automatic systems to analyse the baby's movement, even in cribs. Traditional technologies for automatic baby motion analysis leverage contact sensors. Alternatively, remotely acquired video data (e.g., RGB or depth) can be used, with or without active/passive markers positioned on the body. Markerless approaches are easier to set up and maintain (without any human intervention) and they work well on non-collaborative users, making them the most suitable technologies for clinical applications involving children. On the other hand, they require complex computational strategies for extracting knowledge from data, and then, they strongly depend on advances in computer vision and machine learning, which are among the most expanding areas of research. As a consequence, also markerless video-based analysis of movements in children for NDD has been rapidly expanding but, to the best of our knowledge, there is not yet a survey paper providing a broad overview of how recent scientific developments impacted it. This paper tries to fill this gap and it lists specifically designed data acquisition tools and publicly available datasets as well. Besides, it gives a glimpse of the most promising techniques in computer vision, machine learning and pattern recognition which could be profitably exploited for children motion analysis in videos.

Cognitive flexibility in 12-month-old preterm and term infants is associated with neurobehavioural development in 18-month-olds

There is growing evidence that preterm children are at an increased risk of poor executive functioning, which underlies behavioural and attention problems. Previous studies have suggested that early cognitive flexibility is a possible predictor of later executive function; however, how it develops in infancy and relates to the later neurobehavioural outcomes is still unclear in the preterm population. Here, we conducted a longitudinal study to investigate oculomotor response shifting in 27 preterm and 25 term infants at 12 months and its relationship with general cognitive development and effortful control, which is a temperamental aspect closely associated with executive function, at 18 months. We found that moderate to late preterm and term infants significantly inhibited previously rewarded look responses, while very preterm infants did not show significant inhibition of perseverative looking at 12 months. Moreover, lower inhibition of perseverative looking was significantly associated with lower general cognitive development and attentional shifting at 18 months. These findings suggest that the early atypical patterns of oculomotor response shifting may be a behavioural marker for predicting a higher risk of negative neurobehavioural outcomes, including attention-related problems in preterm children.

Infant's Behaviour Checklist for low birth weight infants and later neurodevelopmental outcome

Assessment of the characteristics of spontaneous movements and behaviour in early infancy helps in estimating developmental outcomes. We introduced the Infant Behaviour Checklist (IBC) and examined the relationship between the behavioural characteristics of low-birth-weight infants and neurodevelopmental outcomes at 6 years of age. The behavioural characteristics during the neonatal (36-43 weeks, adjusted) and early infancy periods (49-60 weeks, adjusted) were assessed in very-low-birth-weight infants. The IBC includes 44 common behaviours. We assessed the appearance of individual behavioural characteristics at each period according to the neurodevelopmental outcome. Of the 143 infants assessed during the neonatal period, 89 had typical development (TD), 30 had intellectual disability (ID), and 24 had autism spectrum disorder (ASD). In 78 infants assessed during early infancy, 40, 21, and 17 had TD, ID, and ASD, respectively. The frequency of appearance of three behaviour-related items was significantly lower in the ID group than in the TD group. The frequency of appearance of three posture- and behaviour-related items was significantly lower, while that of two posture-related items was significantly higher, in the ASD group than in the TD group. Behavioural assessment using the IBC may provide promising clues when considering early intervention for low-birth-weight infants.

Early Moves: a protocol for a population-based prospective cohort study to establish general movements as an early biomarker of cognitive impairment in infants

INTRODUCTION

The current diagnostic pathways for cognitive impairment rarely identify babies at risk before 2 years of age. Very early detection and timely targeted intervention has potential to improve outcomes for these children and support them to reach their full life potential. Early Moves aims to identify early biomarkers, including general movements (GMs), for babies at risk of cognitive impairment, allowing early intervention within critical developmental windows to enable these children to have the best possible start to life.

METHOD AND ANALYSIS

Early Moves is a double-masked prospective cohort study that will recruit 3000 term and preterm babies from a secondary care setting. Early Moves will determine the diagnostic value of abnormal GMs (at writhing and fidgety age) for mild, moderate and severe cognitive delay at 2 years measured by the Bayley-4. Parents will use the Baby Moves smartphone application to video their babies' GMs. Trained GMs assessors will be masked to any risk factors and assessors of the primary outcome will be masked to the GMs result. Automated scoring of GMs will be developed through applying machine-based learning to the data and the predictive value for an abnormal GM will be investigated. Screening algorithms for identification of children at risk of cognitive impairment, using the GM assessment (GMA), and routinely collected social and environmental profile data will be developed to allow more accurate prediction of cognitive outcome at 2 years. A cost evaluation for GMA implementation in preparation for national implementation will be undertaken including exploring the relationship between cognitive status and healthcare utilisation, medical costs, health-related quality of life and caregiver burden.

ETHICS AND DISSEMINATION

Ethics approval has been granted by the Medical Research Ethics Committee of Joondalup Health Services and the Health Service Human Research Ethics Committee (1902) of Curtin University (HRE2019-0739).

TRIAL REGISTRATION NUMBER

ACTRN12619001422112.

General Movement Assessment Predicts Neuro-Developmental Outcome in Very Low Birth Weight Infants at Two Years - A Five-Year Observational Study

OBJECTIVE

To assess the value of general movements (GMs) in predicting the neurodevelopmental outcome using Bayley Scale of Infant Development III (BSID-III) at two years of age in very low birth weight (VLBW) infants.

METHODS

This is a five-year observational study (January 2012-June 2017). Two hundred twenty-seven VLBW infants were assessed in the neurodevelopmental clinic between Jan 2012 and June 2017. Of these 137 infants had GMs assessments at 3 mo post term (first visit to the clinic).

RESULTS

Absence of fidgety movements (FMs) at 3 mo post term had high specificity and negative predictive value for moderate to severe neurodevelopmental outcome in motor, cognition and language domains (composite score less than 70 in the Bayley III scales of Infant development scores). At 3 mo post term, absent FMs had high sensitivity, specificity and negative predictive value (NPV) for cerebral palsy (CP).

CONCLUSIONS

GMs assessment at 3 mo post term could be considered as an important screening tool for early identification of VLBW infants who are at risk of neurodevelopmental impairment/cerebral palsy.

Computer Vision to Automatically Assess Infant Neuromotor Risk

An infant's risk of developing neuromotor impairment is primarily assessed through visual examination by specialized clinicians. Therefore, many infants at risk for impairment go undetected, particularly in under-resourced environments. There is thus a need to develop automated, clinical assessments based on quantitative measures from widely-available sources, such as videos recorded on a mobile device. Here, we automatically extract body poses and movement kinematics from the videos of at-risk infants (N = 19). For each infant, we calculate how much they deviate from a group of healthy infants (N = 85 online videos) using a Naïve Gaussian Bayesian Surprise metric. After pre-registering our Bayesian Surprise calculations, we find that infants who are at high risk for impairments deviate considerably from the healthy group. Our simple method, provided as an open-source toolkit, thus shows promise as the basis for an automated and low-cost assessment of risk based on video recordings.

How Many Days are Necessary to Represent Typical Daily Leg Movement Behavior for Infants at Risk of Developmental Disabilities?

BACKGROUND

Movement characteristics can differentiate between infants at risk and infants with typical development. However, it is unknown how many days are needed to accurately represent typical daily behavior for infants at risk of developmental disabilities when using wearable sensors. To consider the balance between participant burden and the amount of data collected and optimizing the efficiency of data collection, our study determined (1) how many days were necessary to represent typical movement behavior for infants at risk of developmental disabilities and (2) whether movement behavior was different on weekend days and weekdays.

METHODS

We used Opal wearable sensors to collect at least 5 days of 11 infants' leg movement data. The standard (average of 5 days) was compared with four methods (average of the first 1/2/3/4 days) using the Bland-Altman plots and the Spearman correlation coefficient. We also compared the data from the average of 2 weekend days to the average of the first 2 weekdays for 8 infants.

RESULTS

The Spearman correlation coefficient comparing the average of the first 2 days of data and the standards were all above 0.7. The absolute differences between them were all below 10% of the standards. The Bland-Altman plots showed more than 90% of the data points comparing the average of 2 days and the standards fell into the limit of agreement for each variable. The absolute difference between weekend days and weekdays for the leg movement rate, duration, average acceleration, and peak acceleration was 15.2%, 1.7%, 6.8% and 6.3% of the corresponding standard, respectively.

CONCLUSION

Our results suggest 2 days is the optimal amount of data to represent typical daily leg movement behavior of infants at risk of developmental disabilities while minimizing participant burden. Further, leg movement behavior did not differ distinctly across weekend days and weekdays. These results provide supportive evidence for an efficient amount of data collections when using wearable sensors to evaluate movement behavior in infants at risk of developmental disabilities.

AI Approaches Towards Prechtl's Assessment of General Movements: A Systematic Literature Review

General movements (GMs) are spontaneous movements of infants up to five months post-term involving the whole body varying in sequence, speed, and amplitude. The assessment of GMs has shown its importance for identifying infants at risk for neuromotor deficits, especially for the detection of cerebral palsy. As the assessment is based on videos of the infant that are rated by trained professionals, the method is time-consuming and expensive. Therefore, approaches based on Artificial Intelligence have gained significantly increased attention in the last years. In this article, we systematically analyze and discuss the main design features of all existing technological approaches seeking to transfer the Prechtl's assessment of general movements from an individual visual perception to computer-based analysis. After identifying their shared shortcomings, we explain the methodological reasons for their limited practical performance and classification rates. As a conclusion of our literature study, we conceptually propose a methodological solution to the defined problem based on the groundbreaking innovation in the area of Deep Learning.

Subplate Neurons as an Organizer of Mammalian Neocortical Development

Subplate neurons (SpNs) are one of the earliest born and matured neurons in the developing cerebral cortex and play an important role in the early development of the neocortex. It has been known that SpNs have an essential role in thalamocortical axon (TCA) pathfinding and the establishment of the first neural circuit from the thalamus towards cortical layer IV. In addition to this function, it has recently been revealed in mouse corticogenesis that SpNs play an important role in the regulation of radial neuronal migration during the mid-embryonic stage. Moreover, accumulating studies throw light on the possible roles of SpNs in adult brain functions and also their involvement in psychiatric or other neurological disorders. As SpNs are unique to mammals, they may have contributed to the evolution of the mammalian neocortex by efficiently organizing cortical formation during the limited embryonic period of corticogenesis. By increasing our knowledge of the functions of SpNs, we will clarify how SpNs act as an organizer of mammalian neocortical formation.

The shape of disposable diaper affects spontaneous movements of lower limbs in young infants

This study examined the characteristics of young infants' lower limb spontaneous movements based on differences in shape of diapers. Twenty-seven healthy infants (103 ± 16.3 days old) were enrolled in this study. We measured the spontaneous movements of their lower limbs in four conditions (Naked, wearing Normal type diapers, wearing Type A diapers, and wearing Type B diapers). The Normal diaper has a wider waist belt than the Type A diaper, and the Type B diaper has a narrower crotch area than the Type A diaper. We observed them in seven indices (the velocity of lower limb movements, the trajectory area of knee movement in the sagittal plane and the frontal plane, the distance between both knees and between side of abdomen and knee, and correlation of velocities between side of abdomen and knee and between left and right ankles). The results showed that the velocity of the lower limb movements in the Naked condition was higher than when wearing Normal diapers. The value for the trajectory area of knee movement in sagittal plane, which reflects the range of lower leg lifting movements and closeness of such movements to the trunk, for the Type B diaper condition was higher than that for the Normal diaper condition. This result indicates that the shape of the diaper affects the spontaneous movements of the lower limbs of young infants.

Early motor signs of autism spectrum disorder in spontaneous position and movement of the head

We examined the characteristics of spontaneous movements at 9-20 weeks postterm age in very low birth-weight infants who later developed autism spectrum disorder (ASD). We analyzed video recordings of spontaneous movements of 39 children who had no clinical issues [typically developing (TD) group], 21 children who showed developmental delay, and 14 children who were diagnosed with ASD (ASD group) at 6 years of age. Head position in each video frame was classified by visual inspection. The percentage of midline head position (PMHP) and number of changes in head position were calculated. Spontaneous limb movements were quantified using six indices. The values of PMHP were significantly lower in the ASD group than in the TD group. The lower PMHP during early infancy is associated with later development of ASD. Poorer performance in maintaining midline position of the head at this period may distinguish infants who later develop ASD from those who show TD.

Fundamental Frequency Variation of Neonatal Spontaneous Crying Predicts Language Acquisition in Preterm and Term Infants

Spontaneous cries of infants exhibit rich melodic features (i.e., time variation of fundamental frequency [F0 ]) even during the neonatal period, and the development of these characteristics might provide an essential base for later expressive prosody in language. However, little is known about the melodic features of spontaneous cries in preterm infants, who have a higher risk of later language-related problems. Thus, the present study investigated how preterm birth influenced melodic features of spontaneous crying at term-equivalent age as well as how these melodic features related to language outcomes at 18 months of corrected age in preterm and term infants. At term, moderate-to-late preterm (MLP) infants showed spontaneous cries with significantly higher F0 variation and melody complexity than term infants, while there were no significant differences between very preterm (VP) and term infants. Furthermore, larger F0 variation within cry series at term was significantly related to better language and cognitive outcomes, particularly expressive language skills, at 18 months. On the other hand, no other melodic features at term predicted any developmental outcomes at 18 months. The present results suggest that the additional postnatal vocal experience of MLP preterm infants increased F0 variation and the complexity of spontaneous cries at term. Additionally, the increases in F0 variation may partly reflect the development of voluntary vocal control, which, in turn, contributes to expressive language in infancy.

Inflexible neurobiological signatures precede atypical development in infants at high risk for autism

Variability in neurobiological signatures is ubiquitous in early life but the link to adverse developmental milestones in humans is unknown. We examined how levels of signal and noise in movement signatures during the 1st year of life constrain early development in 71 healthy typically developing infants, either at High or Low familial Risk (HR or LR, respectively) for developing Autism Spectrum Disorders (ASD). Delays in early learning developmental trajectories in HR infants (validated in an analysis of 1,445 infants from representative infant-sibling studies) were predicted by worse stochastic patterns in their spontaneous head movements as early as 1-2 months after birth, relative to HR infants who showed more rapid developmental progress, as well as relative to all LR infants. While LR 1-2 mo-old infants' movements were significantly different during a language listening task compared to during sleep, HR infants' movements were more similar during both conditions, a striking lack of diversity that reveals context-inflexible experience of ambient information. Contrary to expectation, it is not the level of variability per se that is particularly detrimental in early life. Rather, inflexible sensorimotor systems and/or atypical transition between behavioral states may interfere with the establishment of capacity to extract structure and important cues from sensory input at birth, preceding and contributing to an atypical brain developmental trajectory in toddlerhood.

Frequency-based features for early cerebral palsy prediction

In this paper we aim at predicting cerebral palsy, the most serious and lifelong motor function disorder in children, at an early age by analysing infants' motion data. An essential step for doing so is to extract informative features with high class separability. We propose a set of features derived from frequency analysis of the motion data. Then, we evaluate the practicality of our features on one of the richest data sets collected to study this disease. In this data set, the motion data are extracted from both electromagnetic sensors as well as video camera. The proposed features are used for classifying both data sets. Using these features, we manage to achieve promising classification performance. Classification accuracy of 91% for the sensor data and 88% for the video-derived data show not only the advantage of employing these features for predicting cerebral palsy, but also that replacing electromagnetic sensors with a video camera is feasible.

Video-based early cerebral palsy prediction using motion segmentation

Analysing distinct motion patterns that occur during infancy can be a way through early prediction of cerebral palsy. This analysis can only be performed by well-trained expert clinicians, and hence can not be widespread, specially in poor countries. In order to decrease the need for experts, computer-based methods can be applied. If individual motions of different body parts are available, these methods could achieve more accurate results with better clinical insight. Thus far, motion capture systems or the like were needed in order to provide such data. However, these systems not only need laboratory and experts to set up the experiment, but they could be intrusive for the infant's motions. In this paper we build up our prediction method on a solution based on a single video camera, that is far less intrusive and a lot cheaper. First, the motions of different body parts are separated, then, motion features are extracted and used to classify infants to healthy or affected. Our experimental results show that visually obtained motion data allows cerebral palsy detection as accurate as state-of-the-art electromagnetic sensor data.

Posture and movement in very preterm infants at term age in and outside the nest

OBJECTIVE

The objective of this study is to evaluate the use of nests on general movements (GM) and posture in very preterm infants at term age.

METHOD

Seventeen high-risk preterm infants-less than 30 weeks of gestation (GA)-underwent a video recording, lying in supine position, with or without nest. Posture, GM quality, and movements made around the child's midline, as well as abrupt movements and frozen postures-in extension or flexion of the four limbs-were analyzed.

RESULTS

Nest did not modify quality of GM. Children significantly adopted a curled-up position. The nest system was associated with an increase in movements toward or across the midline, as well as reduction of the hyperextension posture and head rotation movements. Frozen postures in flexion or extension, as well as abrupt movements of the four limbs, were reduced but not significantly.

CONCLUSIONS

Nest helps very preterm infants to adopt semi-flexed posture and facilitates movements across the midline and reduces movements of spine hyperextension, without GM global quality modifications.

Characterization and intervention for upper extremity exploration & reaching behaviors in infancy

This article aims to: 1) highlight general exploration, reaching, and object exploration behaviors as key activities of daily living in infancy, 2) describe how knowledge of early warning signs for these behaviors may improve early assessment, and 3) discuss interventions that may advance performance of these behaviors. Early intervention should focus on improving performance of these behaviors because: a) these early, interrelated upper extremity behaviors serve an integral role in global learning and development in infancy, b) among at-risk populations, differences have been observed in the quantity and quality of performance of these behaviors and, in many cases, these differences are associated with related perceptual-motor and cognitive delays. This article highlights how early assessment and intervention can target these key early behaviors in populations at risk for upper extremity disabilities, such as those born preterm, with Down syndrome, brachial plexus palsy, or arthrogryposis multiplex congentia.

Movement recognition technology as a method of assessing spontaneous general movements in high risk infants

Preterm birth is associated with increased risks of neurological and motor impairments such as cerebral palsy. The risks are highest in those born at the lowest gestations. Early identification of those most at risk is challenging meaning that a critical window of opportunity to improve outcomes through therapy-based interventions may be missed. Clinically, the assessment of spontaneous general movements is an important tool, which can be used for the prediction of movement impairments in high risk infants. Movement recognition aims to capture and analyze relevant limb movements through computerized approaches focusing on continuous, objective, and quantitative assessment. Different methods of recording and analyzing infant movements have recently been explored in high risk infants. These range from camera-based solutions to body-worn miniaturized movement sensors used to record continuous time-series data that represent the dynamics of limb movements. Various machine learning methods have been developed and applied to the analysis of the recorded movement data. This analysis has focused on the detection and classification of atypical spontaneous general movements. This article aims to identify recent translational studies using movement recognition technology as a method of assessing movement in high risk infants. The application of this technology within pediatric practice represents a growing area of inter-disciplinary collaboration, which may lead to a greater understanding of the development of the nervous system in infants at high risk of motor impairment.