Examining Microstructural White Matter Differences between Children with Typical and Those with Delayed Recovery Two Weeks Post-Concussion

Examining post-concussion white matter change in a pediatric sample

Diffusion-Weight Imaging (DWI) is increasingly used to explore a range of outcomes in pediatric concussion, particularly the neurobiological underpinnings of symptom recovery. However, the DWI findings within the broader pediatric concussion literature are mixed, which can largely be explained by methodological heterogeneity. To address some of these limitations, the aim of the present study was to utilize internationally- recognized criteria for concussion and a consistent imaging timepoint to conduct a comprehensive, multi-parametric survey of white matter microstructure after concussion. Forty-three children presenting with concussion to the emergency department of a tertiary level pediatric hospital underwent neuroimaging and were classified as either normally recovering (n = 27), or delayed recovering (n = 14) based on their post-concussion symptoms at 2 weeks post-injury.We combined multiple DWI metrics across four modeling approaches using Linked Independent Component Analysis (LICA) to extract several independent patterns of covariation in tissue microstructure present in the study cohort. Our analysis did not identify significant differences between the symptomatic and asymptomatic groups and no component significantly predicted delayed recovery. If white matter microstructure changes are implicated in delayed recovery from concussion, these findings, alongside previous work, suggest that current diffusion techniques are insufficient to detect those changes at this time.

Multicompartmental models and diffusion abnormalities in paediatric mild traumatic brain injury

The underlying pathophysiology of paediatric mild traumatic brain injury and the time-course for biological recovery remains widely debated, with clinical care principally informed by subjective self-report. Similarly, clinical evidence indicates that adolescence is a risk factor for prolonged recovery, but the impact of age-at-injury on biomarkers has not been determined in large, homogeneous samples. The current study collected diffusion MRI data in consecutively recruited patients (n = 203; 8-18 years old) and age and sex-matched healthy controls (n = 170) in a prospective cohort design. Patients were evaluated subacutely (1-11 days post-injury) as well as at 4 months post-injury (early chronic phase). Healthy participants were evaluated at similar times to control for neurodevelopment and practice effects. Clinical findings indicated persistent symptoms at 4 months for a significant minority of patients (22%), along with residual executive dysfunction and verbal memory deficits. Results indicated increased fractional anisotropy and reduced mean diffusivity for patients, with abnormalities persisting up to 4 months post-injury. Multicompartmental geometric models indicated that estimates of intracellular volume fractions were increased in patients, whereas estimates of free water fractions were decreased. Critically, unique areas of white matter pathology (increased free water fractions or increased neurite dispersion) were observed when standard assumptions regarding parallel diffusivity were altered in multicompartmental models to be more biologically plausible. Cross-validation analyses indicated that some diffusion findings were more reproducible when ∼70% of the total sample (142 patients, 119 controls) were used in analyses, highlighting the need for large-sample sizes to detect abnormalities. Supervised machine learning approaches (random forests) indicated that diffusion abnormalities increased overall diagnostic accuracy (patients versus controls) by ∼10% after controlling for current clinical gold standards, with each diffusion metric accounting for only a few unique percentage points. In summary, current results suggest that novel multicompartmental models are more sensitive to paediatric mild traumatic brain injury pathology, and that this sensitivity is increased when using parameters that more accurately reflect diffusion in healthy tissue. Results also indicate that diffusion data may be insufficient to achieve a high degree of objective diagnostic accuracy in patients when used in isolation, which is to be expected given known heterogeneities in pathophysiology, mechanism of injury and even criteria for diagnoses. Finally, current results indicate ongoing clinical and physiological recovery at 4 months post-injury.

No Evidence of a Difference in Susceptibility-Weighted Imaging Lesion Burden or Functional Network Connectivity between Children with Typical and Delayed Recovery Two Weeks Post-Concussion

Susceptibility weighted imaging (SWI) and resting state functional magnetic resonance imaging have been highlighted as two novel neuroimaging modalities that have been underutilized when attempting to predict whether a child with concussion will recover normally or have a delayed recovery course. This study aimed to investigate whether there was a difference between children who recover normally from a concussion and children with delayed recovery in terms of SWI lesion burden and resting state network makeup. Forty-one children who presented to the emergency department of a tertiary level pediatric hospital with concussion participated in this study as a part of a larger prospective, longitudinal observational cohort study into concussion assessment and recovery. Children underwent neuroimaging 2 weeks post-injury and were classified as either normally recovering (n = 27), or delayed recovering (n = 14) based on their post-concussion symptoms at 2 weeks post-injury. No participants showed lesions detected using SWI; therefore, no group differences could be assessed. No between-group resting state network differences were uncovered using dual regression analysis. These findings, alongside previously published work, suggest that potential causes of delayed recovery from concussion may not be found using current neuroimaging paradigms.

Injury Biomechanics of a Child’s Head: Problems, Challenges and Possibilities with a New aHEAD Finite Element Model

Traumatic brain injury (TBI) is a major public health problem among children. The predominant causes of TBI in young children are motor vehicle accidents, firearm incidents, falls, and child abuse. The limitation of in vivo studies on the human brain has made the finite element modelling an important tool to study brain injury. Numerical models based on the finite element approach can provide valuable data on biomechanics of brain tissues and help explain many pathological conditions. This work reviews the existing numerical models of a child’s head. However, the existing literature is very limited in reporting proper geometric representation of a small child’s head. Therefore, an advanced 2-year-old child’s head model, named aHEAD 2yo (aHEAD: advanced Head models for safety Enhancement And medical Development), has been developed, which advances the state-of-the-art. The model is one of the first published in the literature, which entirely consists of hexahedral elements for three-dimensional (3D) structures of the head, such as the cerebellum, skull, and cerebrum with detailed geometry of gyri and sulci. It includes cerebrospinal fluid as Smoothed Particle Hydrodynamics (SPH) and a detailed model of pressurized bringing veins. Moreover, the presented review of the literature showed that material models for children are now one of the major limitations. There is also no unambiguous opinion as to the use of separate materials for gray and white matter. Thus, this work examines the impact of various material models for the brain on the biomechanical response of the brain tissues during the mechanical loading described by Hardy et al. The study compares the inhomogeneous models with the separation of gray and white matter against the homogeneous models, i.e., without the gray/white matter separation. The developed model along with its verification aims to establish a further benchmark in finite element head modelling for children and can potentially provide new insights into injury mechanisms.