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Aims: To investigate the accuracy of phenotypic early-onset ataxia (EOA) recognition among developmental conditions, including developmental coordination disorder (DCD) and hypotonia of central nervous system origin, and the effect of scientifically validated EOA features on changing phenotypic consensus., Method: We included 32 children (4-17y) diagnosed with EOA (n=11), DCD (n=10), and central hypotonia (n=11). Three paediatric neurologists independently assessed videotaped motor behaviour phenotypically and quantitatively (using the Scale for Assessment and Rating of Ataxia [SARA]). We determined: (1) phenotypic interobserver agreement and phenotypic homogeneity (percentage of phenotypes with full consensus by all three observers according to the underlying diagnosis); (2) SARA (sub)score profiles; and (3) the effect of three scientifically validated EOA features on phenotypic consensus., Results: Phenotypic homogeneity occurred in 8 out of 11, 2 out of 10, and 1 out of 11 patients with EOA, DCD, and central hypotonia respectively. Homogeneous phenotypic discrimination of EOA from DCD and central hypotonia occurred in 16 out of 21 and 22 out of 22 patients respectively. Inhomogeneously discriminated EOA and DCD phenotypes (5 out of 21) revealed overlapping SARA scores with different SARA subscore profiles. After phenotypic reassessment with scientifically validated EOA features, phenotypic homogeneity changed from 16 to 18 patients., Interpretation: In contrast to complete distinction between EOA and central hypotonia, the paediatric motor phenotype did not reliably distinguish between EOA and DCD. Reassessment with scientifically validated EOA features could contribute to a higher phenotypic consensus. Early-onset ataxia (EOA) and central hypotonia motor phenotypes were reliably distinguished. EOA and developmental coordination disorder (DCD) motor phenotypes were not reliably distinguished. The EOA and DCD phenotypes have different profiles of the Scale for Assessment and Rating of Ataxia., (© 2019 The Authors. Developmental Medicine & Child Neurology published by John Wiley & Sons Ltd on behalf of Mac Keith Press.)

Background: During childhood, many conditions may impact coordination. Examples are physiological age-related development and pathological conditions, such as early onset ataxia and developmental coordination disorder. These conditions are generally diagnosed by clinical specialists. However, in absence of a gold phenotypic standard, objective reproducibility among specialists appears limited., Methods: We investigated whether quantitative analysis of an upper limb coordination task (the finger-to-nose test) could discriminate between physiological and pathological conditions impacting coordination. We used inertial measurement units to estimate movement trajectories of the participants while they executed the finger-to-nose test. We employed random forests to classify each participant in one category., Findings: On average, 87.4% of controls, 74.4% of early onset ataxia and 24.8% of developmental coordination disorder patients were correctly classified. The relatively good classification of early onset ataxia patients and controls contrasts with the poor classification of developmental coordination disorder patients., Interpretation: In absence of a gold phenotypic standard for developmental coordination disorder recognition, it remains elusive whether the finger-to-nose test in these patients represents a sufficiently accurate entity to reflect symptoms distinctive of this disorder. Based on the relatively good results in early onset ataxia patients and controls, we conclude that quantitative analysis of the finger-to-nose test can provide a reliable support tool during the assessment of phenotypic early onset ataxia., (Copyright © 2018. Published by Elsevier Ltd.)

Early-Onset Ataxia (EOA) and Developmental Coordination Disorder (DCD) are two conditions that affect coordination in children. Phenotypic identification of impaired coordination plays an important role in their diagnosis. Gait is one of the tests included in rating scales that can be used to assess motor coordination. A practical problem is that the resemblance between EOA and DCD symptoms can hamper their diagnosis. In this study we employed inertial sensors and a supervised classifier to obtain an automatic classification of the condition of participants. Data from shank and waist mounted inertial measurement units were used to extract features during gait in children diagnosed with EOA or DCD and age-matched controls. We defined a set of features from the recorded signals and we obtained the optimal features for classification using a backward sequential approach. We correctly classified 80.0%, 85.7%, and 70.0% of the control, DCD and EOA children, respectively. Overall, the automatic classifier correctly classified 78.4% of the participants, which is slightly better than the phenotypic assessment of gait by two pediatric neurologists (73.0%). These results demonstrate that automatic classification employing signals from inertial sensors obtained during gait maybe used as a support tool in the differential diagnosis of EOA and DCD. Furthermore, future extension of the classifier's test domains may help to further improve the diagnostic accuracy of pediatric coordination impairment. In this sense, this study may provide a first step towards incorporating a clinically objective and viable biomarker for identification of EOA and DCD., (Copyright © 2016 Elsevier B.V. All rights reserved.)