The Prechtl General Movement Assessment (GMA) has become a cornerstone assessment in early identification of cerebral palsy (CP), particularly during the fidgety movement period at 3–5 months of age. Additionally, assessment of motor repertoire, such as antigravity movements and postural patterns, which form the Motor Optimality Score (MOS), may provide insight into an infant’s later motor function. This study aimed to identify early specific markers for ambulation, gross motor function (using the Gross Motor Function Classification System, GMFCS), topography (unilateral, bilateral), and type (spastic, dyskinetic, ataxic, and hypotonic) of CP in a large worldwide cohort of 468 infants. We found that 95% of children with CP did not have fidgety movements, with 100% having non-optimal MOS. GMFCS level was strongly correlated to MOS. An MOS > 14 was most likely associated with GMFCS outcomes I or II, whereas GMFCS outcomes IV or V were hardly ever associated with an MOS > 8. A number of different movement patterns were associated with more severe functional impairment (GMFCS III–V), including atypical arching and persistent cramped-synchronized movements. Asymmetrical segmental movements were strongly associated with unilateral CP. Circular arm movements were associated with dyskinetic CP. This study demonstrated that use of the MOS contributes to understanding later CP prognosis, including early markers for type and severity.

Key PointsQuestionIs there an association between general movement assessment results and neurodevelopment in infants with vertical Zika virus exposure?FindingsIn this cohort study of 444 children, including 111 prenatally exposed to acute maternal illness with rash during the Zika epidemic, general movement assessment was associated with neurodevelopment at age 12 months (94% negative predictive value, 78% positive predictive value, 70% sensitivity, 96% specificity, and 91% accuracy). The Motor Optimality Score was 23 in children with normal development, 12 in children with adverse outcomes, and 5 in children with microcephaly, a significant difference.MeaningGeneral movement assessment is a helpful tool in the evaluation of neurodevelopment in Zika virus–exposed children.

IMPORTANCEThe number of children who were born to mothers with Zika virus (ZIKV) infection during pregnancy but who did not have apparent disability at birth is large, warranting the study of the risk for neurodevelopmental impairment in this population without congenital Zika syndrome (CZS).OBJECTIVE To investigate whether infants without CZS but who were exposed to ZIKV in utero have normal neurodevelopmental outcomes until 18 months of age. DESIGN, SETTING, AND PARTICIPANTSThis cohort study prospectively enrolled a group of pregnant women with ZIKV in Atlántico Department, Colombia, and in Washington, DC. With this cohort, we performed a longitudinal study of infant neurodevelopment. Infants born between August 1, 2016, and November 30, 2017, were included if they were live born, had normal fetal brain findings on magnetic resonance imaging and ultrasonography, were normocephalic at birth, and had normal examination results without clinical evidence of CZS. Seventy-seven infants born in Colombia, but 0 infants born in the United States, met the inclusion criteria.EXPOSURES Prenatal ZIKV exposure. MAIN OUTCOMES AND MEASURES Infant development was assessed by the Warner InitialDevelopmental Evaluation of Adaptive and Functional Skills (WIDEA) and the Alberta Infant Motor Scale (AIMS) at 1 or 2 time points between 4 and 18 months of age. The WIDEA and AIMS scores were converted to z scores compared with normative samples. Longitudinal mixed-effects regression models based on bootstrap resampling methods estimated scores over time, accounting for gestational age at maternal ZIKV infection and infant age at assessment. Results were presented as slope coefficients with 2-tailed P values based on z statistics that tested whether the coefficient differed from 0 (no change). RESULTSOf the 77 Colombian infants included in this cohort study, 70 (91%) had no CZS and underwent neurodevelopmental assessments. Forty infants (57%) were evaluated between 4 and 8 months of age at a median (interquartile range [IQR]) age of 5.9 (5.3-6.5) months, and 60 (86%) underwent assessment between 9 and 18 months of age at a median (IQR) age of 13.0 (11.2-16.4) months. The WIDEA total score (coefficients: age = -0.227 vs age 2 = 0.006; P < .003) and self-care domain score (coefficients: age = -0.238 vs age 2 = 0.01; P < .008) showed curvilinear associations with age. Other domain scores showed linear declines with increasing age based on coefficients for communication (-0.036; P = .001), social cognition (-0.10; P < .001), and mobility (-0.14; P < .001). The AIMS scores were similar to the normative sample over time (95% CI, -0.107 to 0.037; P = .34). Nineteen of 57 infants (33%) who underwent postnatal cranial ultrasonography had a nonspecific, mild finding. No difference was found in the decline of WIDEA z scores between infants with and those without cranial ultrasonography findings except for a complex interactive relationship involving the social cognition domain (P < .049). The AIMS z scores were lower in infants with nonspecific cra...

Background: Early identification of cerebral palsy (CP) during infancy will provide opportunities for early therapies and treatments. The aim of the present study was to present a novel machine-learning model, the Computer-based Infant Movement Assessment (CIMA) model, for clinically feasible early CP prediction based on infant video recordings. Methods: The CIMA model was designed to assess the proportion (%) of CP risk-related movements using a time-frequency decomposition of the movement trajectories of the infant's body parts. The CIMA model was developed and tested on video recordings from a cohort of 377 high-risk infants at 9-15 weeks corrected age to predict CP status and motor function (ambulatory vs. non-ambulatory) at mean 3.7 years age. The performance of the model was compared with results of the general movement 2 of 17 assessment (GMA) and neonatal imaging. Results: The CIMA model had sensitivity (92.7%) and specificity (81.6%), which was comparable to observational GMA or neonatal cerebral imaging for the prediction of CP. Infants later found to have non-ambulatory CP had significantly more CP risk-related movements (median: 92.8%, p = 0.02) compared with those with ambulatory CP (median: 72.7%). Conclusion: The CIMA model may be a clinically feasible alternative to observational GMA.

BACKGROUND AND PURPOSE:Very preterm infants (birth weight, Ͻ1500 g) are at increased risk of cognitive and motor impairment, including cerebral palsy. These adverse neurodevelopmental outcomes are associated with white matter abnormalities on MR imaging at term-equivalent age. Cerebral palsy has been predicted by analysis of spontaneous movements in the infant termed "General Movement Assessment." The goal of this study was to determine the utility of General Movement Assessment in predicting adverse cognitive, language, and motor outcomes in very preterm infants and to identify brain imaging markers associated with both adverse outcomes and aberrant general movements.

Infants who have graduated from a neonatal intensive care unit require close follow-up because they have a greater chance of experiencing later motor and cognitive difficulties; however, these difficulties are often challenging to identify at an early age. The General Movement Assessment is a low-cost and highly reliable tool that can indicate abnormal neurological development in young high-risk infants, but it has not yet been widely implemented in the United States. In this review, we discuss the literature about the use of the General Movement Assessment in high-risk infants and how to implement the tool in a clinical setting. [Pediatr Ann. 2018;47(4):e159-e164.].

Background: Early prediction of cerebral palsy (CP) using the General Movement Assessment (GMA) during the fidgety movements (FM) period has been recommended as standard of care in high-risk infants. The aim of this study was to determine the accuracy of GMA, alone or in combination with neonatal imaging, in predicting cerebral palsy (CP). Methods: Infants with increased risk of perinatal brain injury were prospectively enrolled from 2009–2014 in this multi-center, observational study. FM were classified by two certified GMA observers blinded to the clinical history. Abnormal GMA was defined as absent or sporadic FM. CP-status was determined by clinicians unaware of GMA results. Results: Of 450 infants enrolled, 405 had scorable video and follow-up data until at least 18–24 months. CP was confirmed in 42 (10.4%) children at mean age 3 years 1 month. Sensitivity, specificity, positive and negative predictive values, and accuracy of absent/sporadic FM for CP were 76.2, 82.4, 33.3, 96.8, and 81.7%, respectively. Only three (8.1%) of 37 infants with sporadic FM developed CP. The highest accuracy (95.3%) was achieved by a combination of absent FM and abnormal neonatal imaging. Conclusion: In infants with a broad range of neonatal risk factors, accuracy of early CP prediction was lower for GMA than previously reported but increased when combined with neonatal imaging. Sporadic FM did not predict CP in this study.

Key Points Question What is the external validity of a deep learning–based method to predict cerebral palsy (CP) based on infants’ spontaneous movements at 9 to 18 weeks’ corrected age? Findings In this prognostic study of 557 infants with a high risk of perinatal brain injury, a deep learning–based method for early prediction of CP had sensitivity of 71%, specificity of 94%, positive predictive value of 68%, and negative predictive value of 95%. Prognosis of CP based on the deep learning–based method was associated with later functional level and CP subtype in children with CP. Meaning This study’s findings suggest that deep learning–based assessments could support early detection of CP in infants at high risk.