Prediction of Gait Impairment in Toddlers Born Preterm From Near-Term Brain Microstructure Assessed With DTI, Using Exhaustive Feature Selection and Cross-Validation

Corpus callosum long-term biometry in very preterm children related to cognitive and motor outcomes

BACKGROUND

The corpus callosum (CC) is suggested as an indirect biomarker of white matter volume, which is often affected in preterm birth. However, diagnosing mild white matter injury is challenging.

METHODS

We studied 124 children born preterm (mean age: 8.4 ± 1.1 years), using MRI to assess CC measurements and cognitive/motor outcomes based on the Wechsler Intelligence Scale for Children-V (WPPSI-V) and Movement Assessment Battery for Children-2 (MABC-2).

RESULTS

Children with normal outcomes exhibited greater height (10.2 ± 2.1 mm vs. 9.4 ± 2.3 mm; p = 0.01) and fractional anisotropy at splenium (895[680-1000] vs 860.5[342-1000]) and total CC length (69.1 ± 4.8 mm vs. 67.3 ± 5.1 mm; p = 0.02) compared to those with adverse outcomes. All measured CC areas were smaller in the adverse outcome group. Models incorporating posterior CC measurements demonstrated the highest specificity (83.3% Sp, AUC: 0.65) for predicting neurological outcomes. CC length and splenium height were the only linear measurements associated with manual dexterity and total MABC-2 score while both the latter and genu were related with Full-Scale Intelligence Quotient.

CONCLUSIONS

CC biometry in children born very preterm at school-age is associated with outcomes and exhibits a specific subregion alteration pattern. The posterior CC may serve as an important neurodevelopmental biomarker in very preterm infants.

IMPACT

The corpus callosum has the potential to serve as a reliable and easily measurable biomarker of white matter integrity in very preterm children. Estimating diffuse white matter injury in preterm infants using conventional MRI sequences is not always conclusive. The biometry of the posterior part of the corpus callosum is associated with cognitive and certain motor outcomes at school age in children born very preterm. Length and splenium measurements seem to serve as reliable biomarkers for assessing neurological outcomes in this population.

Machine Learning Prediction Models for Neurodevelopmental Outcome After Preterm Birth: A Scoping Review and New Machine Learning Evaluation Framework

BACKGROUND AND OBJECTIVES

Outcome prediction of preterm birth is important for neonatal care, yet prediction performance using conventional statistical models remains insufficient. Machine learning has a high potential for complex outcome prediction. In this scoping review, we provide an overview of the current applications of machine learning models in the prediction of neurodevelopmental outcomes in preterm infants, assess the quality of the developed models, and provide guidance for future application of machine learning models to predict neurodevelopmental outcomes of preterm infants.

METHODS

A systematic search was performed using PubMed. Studies were included if they reported on neurodevelopmental outcome prediction in preterm infants using predictors from the neonatal period and applying machine learning techniques. Data extraction and quality assessment were independently performed by 2 reviewers.

RESULTS

Fourteen studies were included, focusing mainly on very or extreme preterm infants, predicting neurodevelopmental outcome before age 3 years, and mostly assessing outcomes using the Bayley Scales of Infant Development. Predictors were most often based on MRI. The most prevalent machine learning techniques included linear regression and neural networks. None of the studies met all newly developed quality assessment criteria. Studies least prone to inflated performance showed promising results, with areas under the curve up to 0.86 for classification and R2 values up to 91% in continuous prediction. A limitation was that only 1 data source was used for the literature search.

CONCLUSIONS

Studies least prone to inflated prediction results are the most promising. The provided evaluation framework may contribute to improved quality of future machine learning models.

Three decades of gait index development: A comparative review of clinical and research gait indices

BACKGROUND

A wide variety of indices have been developed to quantify gait performance markers and associate them with their respective pathologies. Indices scores have enabled better decisions regarding patient treatments and allowed for optimized monitoring of the evolution of their condition. The extensive range of human gait indices presented over the last 30 years is evaluated and summarized in this narrative literature review exploring their application in clinical and research environments.

METHODS

The analysis will explore historical and modern gait indices, focusing on the clinical efficacy with respect to their proposed pathology, age range, and associated parameter limits. Features, methods, and clinically acceptable errors are discussed while simultaneously assessing indices advantages and disadvantages. This review analyses all indices published between 1994 and February 2021 identified using the Medline, PubMed, ScienceDirect, CINAHL, EMBASE, and Google Scholar databases.

FINDINGS

A total of 30 indices were identified as noteworthy for clinical and research purposes and another 137 works were included for discussion. The indices were divided in three major groups: observational (13), instrumented (16) and hybrid (1). The instrumented indices were further sub-divided in six groups, namely kinematic- (4), spatiotemporal- (5), kinetic- (2), kinematic- and kinetic- (2), electromyographic- (1) and Inertial Measurement Unit-based indices (2).

INTERPRETATION

This work is one of the first reviews to summarize observational and instrumented gait indices, exploring their applicability in research and clinical contexts. The aim of this review is to assist members of these communities with the selection of the proper index for the group in analysis.

The Effects of Preterm Birth on Musculoskeletal Health-Related Disorders

Preterm birth is associated with various diseases and conditions which demand multidisciplinary medical care. Approximately 10% of all neonates are born prematurely with an increasing survival rate in almost all Western countries. This ongoing, yet desirable trend is creating new challenges for sufficient medical treatment regimens, which should be upheld throughout the patients’ lives. Orthopedic surgeons are focused on musculoskeletal disorders and the improvement of patients’ ability to cope with the challenges of everyday life. The most common conditions associated with preterm birth are cerebral palsy and a dysregulation of the calcium/phosphorus metabolism, which may lead to fractures. These diseases may vary greatly in their organic manifestation and clinical presentation. This demands multidisciplinary cooperation and parental support. Clinical management is aimed on the early enhancement of a patient’s physical, as well as neurological condition, and to prevent the development of secondary musculoskeletal disorders. In this article, we give an overview of the current literature on the most common musculoskeletal disorders associated with preterm birth and critically discuss state of the art diagnostic standards and treatment algorithms.

Delayed Maturation of the Middle Cerebellar Peduncles at Near-Term Age Predicts Abnormal Neurodevelopment in Preterm Infants

BACKGROUND

The infant brain grows quickly with elaborate microstructural development during the neonatal period. The white matter, during critical periods of development, is selectively vulnerable to altered maturation and impaired growth in very-low-birth-weight (VLBW) infants.

OBJECTIVE

To evaluate whether abnormal white matter maturation in VLBW infants is associated with poor neurodevelopmental outcomes at 18 months of corrected age.

METHODS

Between 2015 and 2017, we recruited 60 VLBW infants at 24-32 weeks of gestational age and 15 full-term controls. All participants underwent magnetic resonance imaging at near-term age and were assessed at 18 months of corrected age with the Bayley Scales of Infant and Toddler Development, Third Edition. The associations between regional white matter fractional anisotropy (FA) and mean diffusivity on diffusion tensor imaging (DTI) and developmental outcomes were explored using multivariable linear regression after correcting for gestational age, postmenstrual age at DTI scan, and maternal education level.

RESULTS

The FA values of the splenium of the corpus callosum (p = 0.032), corticospinal tract (p = 0.025), middle cerebellar peduncle (MCP) (p < 0.001), and cingulum (p = 0.043) were significantly related to cognitive scores; however, only the association corresponding to the MCP remained significant after correcting for multiple comparisons. The MCP FA (p = 0.008) was associated with motor scores after correction for multiple comparisons (p = 0.008). Cognitive impairment (area under the curve [AUC] = 0.823, 95% confidence interval [CI] = 0.722-0.911) and motor impairment (AUC = 0.776, 95% CI = 0.656-0.899) were predicted by MCP FA.

CONCLUSIONS

The FA of MCP at near-term age may predict developmental outcomes of VLBW infants at 18 months of corrected age.

The cingulum in very preterm infants relates to language and social-emotional impairment at 2 years of term-equivalent age

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Maturation of specific WM tracts in preterm individuals differs from those of term controls.

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The elastic net logistic regression model was used to identify altered white matter tracts in the preterm brain.

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The alteration of the cingulum in the preterm at near-term correlate with neurodevelopmental scores at 18–22 months of age.

Background

Relative to full-term infants, very preterm infants exhibit disrupted white matter (WM) maturation and problems related to development, including motor, cognitive, social-emotional, and receptive and expressive language processing.

Objective

The present study aimed to determine whether regional abnormalities in the WM microstructure of very preterm infants, as defined relative to those of full-term infants at a near-term age, are associated with neurodevelopmental outcomes at the age of 18–22 months.

Methods

We prospectively enrolled 89 very preterm infants (birth weight < 1500 g) and 43 normal full-term control infants born between 2016 and 2018. All infants underwent a structural brain magnetic resonance imaging scan at near-term age. The diffusion tensor imaging (DTI) metrics of the whole-brain WM tracts were extracted based on the neonatal probabilistic WM pathway. The elastic net logistic regression model was used to identify altered WM tracts in the preterm brain. We evaluated the associations between the altered WM microstructure at near-term age and motor, cognitive, social-emotional, and receptive and expressive language developments at 18–22 months of age, as measured using the Bayley Scales of Infant Development, Third Edition.

Results

We found that the elastic net logistic regression model could classify preterm and full-term neonates with an accuracy of 87.9% (corrected p < 0.008) using the DTI metrics in the pathway of interest with a 10% threshold level. The fractional anisotropy (FA) values of the body and splenium of the corpus callosum, middle cerebellar peduncle, left and right uncinate fasciculi, and right portion of the pathway between the premotor and primary motor cortices (premotor-PMC), as well as the mean axial diffusivity (AD) values of the left cingulum, were identified as contributive features for classification. Increased adjusted AD values in the left cingulum pathway were significantly correlated with language scores after false discovery rate (FDR) correction (r = 0.217, p = 0.043). The expressive language and social-emotional composite scores showed a significant positive correlation with the AD values in the left cingulum pathway (r = 0.226 [p = 0.036] and r = 0.31 [p = 0.003], respectively) after FDR correction.

Conclusion

Our approach suggests that the cingulum pathways of very preterm infants differ from those of full-term infants and significantly contribute to the prediction of the subsequent development of the language and social-emotional domains. This finding could improve our understanding of how specific neural substrates influence neurodevelopment at later ages, and individual risk prediction, thus helping to inform early intervention strategies that address developmental delay.

Structural Brain Lesions and Gait Pathology in Children With Spastic Cerebral Palsy

The interaction between brain damage and motor function is not yet fully understood in children with spastic cerebral palsy (CP). Therefore, a semi-quantitative MRI (sqMRI) scale was used to explore whether identified brain lesions related to functional abilities and gait pathology in this population. A retrospective cohort of ambulatory children with spastic CP was selected [N = 104; 52 bilateral (bCP) and 52 unilateral (uCP)]. Extent and location-specific scores were defined according to the sqMRI scale guidelines. The gross motor function classification system (GMFCS), the gait profile score (GPS), GPSs per motion plane, gait variable scores (GVS) and multiple-joint (MJ) gait patterns were related to brain lesion scores. In all groups, the global total brain scores correlated to the GPS (total: r_s = 0.404, p ≤ 0.001; bCP: r_s = 0.335, p ≤ 0.05; uCP: r_s = 0.493, p ≤ 0.001). The global total hemispheric scores correlated to the GMFCS (total: r_s = 0.392, p ≤ 0.001; bCP: r_s = 0.316, p ≤ 0.05; uCP: r_s = 0.331, p ≤ 0.05). The laterality scores of the hemispheres in the total group correlated negatively to the GMFCS level (r_s = −0.523, p ≤ 0.001) and the GVS-knee sagittal (r_s = −0.311, p ≤ 0.01). Lesion location, for the total group demonstrated positive correlations between parietal lobe involvement and the GPS (r_s = 0.321, p ≤ 0.001) and between periventricular layer damage and the GMFCS (r_s = 0.348, p ≤ 0.001). Involvement of the anterior part of the corpus callosum (CC) was associated with the GVS-hip sagittal in all groups (total: r_pb = 0.495, p ≤ 0.001; bCP: r_pb = 0.357, p ≤ 0.05; uCP: r_pb = 0.641, p ≤ 0.001). The global total hemispheric and laterality of the hemispheres scores differentiated between the minor and both the extension (p ≤ 0.001 and p ≤ 0.001) and flexion (p = 0.016 and p = 0.013, respectively) MJ patterns in the total group. Maximal periventricular involvement and CC intactness were associated with extension patterns (p ≤ 0.05 and p ≤ 0.001, respectively). Current findings demonstrated relationships between brain structure and motor function as well as pathological gait, in this cohort of children with CP. These results might facilitate the timely identification of gait pathology and, ultimately, guide individualized treatment planning of gait impairments in children with CP.