Paediatric motor phenotypes in early-onset ataxia, developmental coordination disorder, and central hypotonia

Identifying and Evaluating Young Children with Developmental Central Hypotonia: An Overview of Systematic Reviews and Tools

Children with developmental central hypotonia have reduced muscle tone secondary to non-progressive damage to the brain or brainstem. Children may have transient delays, mild or global functional impairments, and the lack of a clear understanding of this diagnosis makes evaluating appropriate interventions challenging. This overview aimed to systematically describe the best available evidence for tools to identify and evaluate children with developmental central hypotonia aged 2 months to 6 years. A systematic review of systematic reviews or syntheses was conducted with electronic searches in PubMed, Medline, CINAHL, Scopus, Cochrane Database of Systematic Reviews, Google Scholar, and PEDro and supplemented with hand-searching. Methodological quality and risk-of-bias were evaluated, and included reviews and tools were compared and contrasted. Three systematic reviews, an evidence-based clinical assessment algorithm, three measurement protocols, and two additional measurement tools were identified. For children aged 2 months to 2 years, the Hammersmith Infant Neurological Examination has the strongest measurement properties and contains a subset of items that may be useful for quantifying the severity of hypotonia. For children aged 2-6 years, a clinical algorithm and individual tools provide guidance. Further research is required to develop and validate all evaluative tools for children with developmental central hypotonia.

Pathogenetic Insights into Developmental Coordination Disorder Reveal Substantial Overlap with Movement Disorders

Developmental Coordination Disorder (DCD) is a neurodevelopmental condition characterized by non-progressive central motor impairments. Mild movement disorder features have been observed in DCD. Until now, the etiology of DCD has been unclear. Recent studies suggested a genetic substrate in some patients with DCD, but comprehensive knowledge about associated genes and underlying pathogenetic mechanisms is still lacking. In this study, we first identified genes described in the literature in patients with a diagnosis of DCD according to the official diagnostic criteria. Second, we exposed the underlying pathogenetic mechanisms of DCD, by investigating tissue- and temporal gene expression patterns and brain-specific biological mechanisms. Third, we explored putative shared pathogenetic mechanisms between DCD and frequent movement disorders with a known genetic component, including ataxia, chorea, dystonia, and myoclonus. We identified 12 genes associated with DCD in the literature, which are ubiquitously expressed in the central nervous system throughout brain development. These genes are involved in cellular processes, neural signaling, and nervous system development. There was a remarkable overlap (62%) in pathogenetic mechanisms between DCD-associated genes and genes linked with movement disorders. Our findings suggest that some patients might have a genetic etiology of DCD, which could be considered part of a pathogenetic movement disorder spectrum.

Instrumented Gait Classification Using Meaningful Features in Patients with Impaired Coordination

Early onset ataxia (EOA) and developmental coordination disorder (DCD) both affect cerebellar functioning in children, making the clinical distinction challenging. We here aim to derive meaningful features from quantitative SARA-gait data (i.e., the gait test of the scale for the assessment and rating of ataxia (SARA)) to classify EOA and DCD patients and typically developing (CTRL) children with better explainability than previous classification approaches. We collected data from 18 EOA, 14 DCD and 29 CTRL children, while executing both SARA gait tests. Inertial measurement units were used to acquire movement data, and a gait model was employed to derive meaningful features. We used a random forest classifier on 36 extracted features, leave-one-out-cross-validation and a synthetic oversampling technique to distinguish between the three groups. Classification accuracy, probabilities of classification and feature relevance were obtained. The mean classification accuracy was 62.9% for EOA, 85.5% for DCD and 94.5% for CTRL participants. Overall, the random forest algorithm correctly classified 82.0% of the participants, which was slightly better than clinical assessment (73.0%). The classification resulted in a mean precision of 0.78, mean recall of 0.70 and mean F1 score of 0.74. The most relevant features were related to the range of the hip flexion-extension angle for gait, and to movement variability for tandem gait. Our results suggest that classification, employing features representing different aspects of movement during gait and tandem gait, may provide an insightful tool for the differential diagnoses of EOA, DCD and typically developing children.

2D Gait Skeleton Data Normalization for Quantitative Assessment of Movement Disorders from Freehand Single Camera Video Recordings

Overlapping phenotypic features between Early Onset Ataxia (EOA) and Developmental Coordination Disorder (DCD) can complicate the clinical distinction of these disorders. Clinical rating scales are a common way to quantify movement disorders but in children these scales also rely on the observer's assessment and interpretation. Despite the introduction of inertial measurement units for objective and more precise evaluation, special hardware is still required, restricting their widespread application. Gait video recordings of movement disorder patients are frequently captured in routine clinical settings, but there is presently no suitable quantitative analysis method for these recordings. Owing to advancements in computer vision technology, deep learning pose estimation techniques may soon be ready for convenient and low-cost clinical usage. This study presents a framework based on 2D video recording in the coronal plane and pose estimation for the quantitative assessment of gait in movement disorders. To allow the calculation of distance-based features, seven different methods to normalize 2D skeleton keypoint data derived from pose estimation using deep neural networks applied to freehand video recording of gait were evaluated. In our experiments, 15 children (five EOA, five DCD and five healthy controls) were asked to walk naturally while being videotaped by a single camera in 1280 × 720 resolution at 25 frames per second. The high likelihood of the prediction of keypoint locations (mean = 0.889, standard deviation = 0.02) demonstrates the potential for distance-based features derived from routine video recordings to assist in the clinical evaluation of movement in EOA and DCD. By comparison of mean absolute angle error and mean variance of distance, the normalization methods using the Euclidean (2D) distance of left shoulder and right hip, or the average distance from left shoulder to right hip and from right shoulder to left hip were found to better perform for deriving distance-based features and further quantitative assessment of movement disorders.

Developmental neurobiology of cerebellar and Basal Ganglia connections

BACKGROUND

The high prevalence of mixed phenotypes of Early Onset Ataxia (EOA) with comorbid dystonia has shifted the pathogenetic concept from the cerebellum towards the interconnected cerebellar motor network. This paper on EOA with comorbid dystonia (EOA-dystonia) explores the conceptual relationship between the motor phenotype and the cortico-basal-ganglia-ponto-cerebellar network.

METHODS

In EOA-dystonia, we reviewed anatomic-, genetic- and biochemical-studies on the comorbidity between ataxia and dystonia.

RESULTS

In a clinical EOA cohort, the prevalence of dystonia was over 60%. Both human and animal studies converge on the underlying role for the cortico-basal-ganglia-ponto-cerebellar network. Genetic -clinical and -in silico network studies reveal underlying biological pathways for energy production and neural signal transduction.

CONCLUSIONS

EOA-dystonia phenotypes are attributable to the cortico-basal-ganglia-ponto-cerebellar network, instead of to the cerebellum, alone. The underlying anatomic and pathogenetic pathways have clinical implications for our understanding of the heterogeneous phenotype, neuro-metabolic and genetic testing and potentially also for new treatment strategies, including neuro-modulation.

Applicability of quantitative oculomotor and SARA assessment in children

BACKGROUND

In clinical practice, eye movements can provide an early diagnostic marker for early onset ataxia (EOA). However, quantitative oculomotor assessment is not included in the most frequently used and age-validated ataxia rating scale in children, the Scale for the Assessment and Rating of Ataxia (SARA). We aimed to investigate the applicability of semi-quantitative eye movement assessment by the International Cooperative Ataxia Rating Scale (ICARS_OCM) and Ocular Motion Score (OMS_7-10) complementary to SARA measurements in children.

METHODS

In 52 typically developing children (aged 4-16 years; n = 4 per year of age), three independent assessors scored saccadic eye movements and ocular pursuit according to the ICARS_OCM and matching parameters from the OMS_7-10. For ICARS_OCM, we determined 1) construct validity for coordinated eye movements by correlation with OMS_7-10, ICARS_{EYE-HAND-COORDINATION} and SARA subscale scores, 2) agreement percentage and inter-rater agreement (Fleiss Kappa) and 3) age-dependency.

RESULTS

Spearman's rank correlations of ICARS_OCM with OMS_7-10 and ICARS- and SARA subscales were moderate to fair (all p < .001). Inter-rater agreement of ICARS-_OCM was 80.8%; (Fleiss Kappa: 0.411). ICARS_OCM scores revealed a similar exponentially decreasing association with age as the other SARA (sub)scores, reaching a plateau at 10 years of age.

INTERPRETATION

ICARS_OCM has a valid construct for the measurement of coordinated eye movement performance and is reliably assessable in children. ICARS_OCM reveals a similar age-dependent relationship as the other ataxia subscales, reflecting the physiological maturation of the cerebellum. In children, these data may implicate that ICARS_OCM can reliably contribute to coordination assessment, complementary to the SARA subscales.

Early Onset Ataxia with Comorbid Dystonia: Clinical, Anatomical and Biological Pathway Analysis Expose Shared Pathophysiology

In degenerative adult onset ataxia (AOA), dystonic comorbidity is attributed to one disease continuum. However, in early adult onset ataxia (EOA), the prevalence and pathogenesis of dystonic comorbidity (EOAD⁺), are still unclear. In 80 EOA-patients, we determined the EOAD⁺-prevalence in association with MRI-abnormalities. Subsequently, we explored underlying biological pathways by genetic network and functional enrichment analysis. We checked pathway-outcomes in specific EOAD⁺-genotypes by comparing results with non-specifically (in-silico-determined) shared genes in up-to-date EOA, AOA and dystonia gene panels (that could concurrently cause ataxia and dystonia). In the majority (65%) of EOA-patients, mild EOAD⁺-features concurred with extra-cerebellar MRI abnormalities (at pons and/or basal-ganglia and/or thalamus (p = 0.001)). Genetic network and functional enrichment analysis in EOAD⁺-genotypes indicated an association with organelle- and cellular-component organization (important for energy production and signal transduction). In non-specifically, in-silico-determined shared EOA, AOA and dystonia genes, pathways were enriched for Krebs-cycle and fatty acid/lipid-metabolic processes. In frequently occurring EOAD⁺-phenotypes, clinical, anatomical and biological pathway analyses reveal shared pathophysiology between ataxia and dystonia, associated with cellular energy metabolism and network signal transduction. Insight in the underlying pathophysiology of heterogeneous EOAD⁺-phenotype-genotype relationships supports the rationale for testing with complete, up-to-date movement disorder gene lists, instead of single EOA gene-panels.