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Childhood stroke occurs from birth to 18 years of age, ranks among the top ten childhood causes of death, and leaves lifelong neurological impairments. Arterial ischemic stroke in infancy and childhood occurs due to arterial occlusion in the brain, resulting in a focal lesion. Our understanding of mechanisms of injury and repair associated with focal injury in the developing brain remains rudimentary. Neuroimaging can reveal important insights into these mechanisms. In adult stroke population, multi-center neuroimaging studies are common and have accelerated the translation process leading to improvements in treatment and outcome. These studies are centered on the growing evidence that neuroimaging measures and other biomarkers (e.g., from blood and cerebrospinal fluid) can enhance our understanding of mechanisms of risk and injury and be used as complementary outcome markers. These factors have yet to be studied in pediatric stroke because most neuroimaging studies in this population have been conducted in single-centred, small cohorts. By pooling neuroimaging data across multiple sites, larger cohorts of patients can significantly boost study feasibility and power in elucidating mechanisms of brain injury, repair and outcomes. These aims are particularly relevant in pediatric stroke because of the decreased incidence rates and the lack of mechanism-targeted trials.Toward these aims, we developed the Pediatric Stroke Neuroimaging Platform (PEDSNIP) in 2015, funded by The Brain Canada Platform Support Grant, to focus on three identified neuroimaging priorities. These were: developing and harmonizing multisite clinical protocols, creating the infrastructure and methods to import, store and organize the large clinical neuroimaging dataset from multiple sites through the International Pediatric Stroke Study (IPSS), and enabling central searchability. To do this, developed a two-pronged approach that included building 1) A Clinical-MRI Data Repository (standard of care imaging) linked to clinical data and longitudinal outcomes and 2) A Research-MRI neuroimaging data set acquired through our extensive collaborative, multi-center, multidisciplinary network. This dataset was collected prospectively in eight North American centers to test the feasibility and implementation of harmonized advanced Research-MRI, with the addition of clinical information, genetic and proteomic studies, in a cohort of children presenting with acute ischemic stroke.Here we describe the process that enabled the development of PEDSNIP built to provide the infrastructure to support neuroimaging research priorities in pediatric stroke. Having built this Platform, we are now able to utilize the largest neuroimaging and clinical data pool on pediatric stroke data worldwide to conduct hypothesis-driven research. We are actively working on a bioinformatics approach to develop predictive models of risk, injury and repair and accelerate breakthrough discoveries leading to mechanism-targeted treatments that improve outcomes and minimize the burden following childhood stroke. This unique transformational resource for scientists and researchers has the potential to result in a paradigm shift in the management, outcomes and quality of life in children with stroke and their families, with far-reaching benefits for other brain conditions of people across the lifespan.

Abstract Deep learning (DL) is a widely applied mathematical modeling technique. Classically, DL models utilize large volumes of training data, which are not available in many healthcare contexts. For patients with brain tumors, non-invasive diagnosis would represent a substantial clinical advance, potentially sparing patients from the risks associated with surgical intervention on the brain. Such an approach will depend upon highly accurate models built using the limited datasets that are available. Herein, we present a novel genetic algorithm (GA) that identifies optimal architecture parameters using feature embeddings from state-of-the-art image classification networks to identify the pediatric brain tumor, adamantinomatous craniopharyngioma (ACP). We optimized classification models for preoperative Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and combined CT and MRI datasets with demonstrated test accuracies of 85.3%, 83.3%, and 87.8%, respectively. Notably, our GA improved baseline model performance by up to 38%. This work advances DL and its applications within healthcare by identifying optimized networks in small-scale data contexts. The proposed system is easily implementable and scalable for non-invasive computer-aided diagnosis, even for uncommon diseases.

Mutations in the family of genes encoding the tubulin subunits of microtubules are associated with a spectrum of human brain malformations known as tubulinopathies. How these mutations impact tubulin activity to give rise to distinct developmental consequences is poorly understood. Here we report two patients exhibiting brain malformations characteristic of tubulinopathies and heterozygous T178M missense mutations in different β-tubulin genes, TUBB2A or TUBB3. RNAseq analysis indicates that both TUBB2A and TUBB3 are expressed in the brain during development, but only TUBB2A maintains high expression in neurons into adulthood. The T178 residue is highly conserved in β-tubulins and located in the exchangeable GTP-binding pocket of β-tubulin. To determine the impact of T178M on β-tubulin function we created an analogous mutation in the β-tubulin of budding yeast and show that the substitution acts dominantly to produce kinetically stabilized microtubules that assemble and disassemble slowly, with fewer transitions between these states. In vitro experiments with purified mutant tubulin demonstrate that T178M decreases the intrinsic assembly activity of β-tubulin and forms microtubules that rarely transition to disassembly. We provide evidence that the T178M substitution disrupts GTPase-dependent conformational changes in tubulin, providing a mechanistic explanation for kinetic stabilization. Our findings demonstrate the importance of tubulin’s GTPase activity during brain development, and indicate that tubulin isotypes play different, important roles during brain development.

Introduction: We sought to validate a handheld, near-infrared spectroscopy (NIRS) device for detecting intracranial hematomas in children with head injury. Methods: Eligible patients were those 3.5 milliliters in volume and 0.2 on three successive scans. We calculated diagnostic performance measures with corresponding exact two-sided 95% Clopper-Pearson confidence intervals (CI). Hypothesis test evaluated whether predictive performance exceeded chance agreement (predictive Youden’s index > 0). Results: A total of 464 patients were enrolled and 344 met inclusion for primary data analysis: 10.5% (36/344) had evidence of a hematoma on HCT, and 4.7% (16/344) had qualifying hematomas. The handheld brain scanner demonstrated a sensitivity of 58.3% (21/36) and specificity of 67.9% (209/308) for hematomas of any size. For qualifying hematomas the scanner was designed to detect, sensitivity was 81% (13/16) and specificity was 67.4% (221/328). Predictive performance exceeded chance agreement with a predictive Youden’s index of 0.11 (95% CI, 0.10 – 0.15; P < 0.001) for all hematomas, and 0.09 (95% CI, 0.08 – 0.12; P < 0.001) for qualifying hematomas. Conclusion: The handheld brain scanner can non-invasively detect a subset of intracranial hematomas in children and may serve an adjunctive role to head-injury neuroimaging decision rules that predict the risk of clinically significant intracranial pathology after head trauma.

PurposeAdolescent idiopathic scoliosis (AIS) is a three-dimensional spinal structural deformity that occurs in otherwise normal individuals. Although curve progression and severity vary amongst individuals, AIS can lead to significant cosmetic and functional deformity. AIS etiology has been determined to be genetic, however, exact genetic and biological processes underlying this disorder remain unknown. Vestibular structure and function have potentially been related to the etiopathogenesis of AIS. Here, we aimed to characterize the anatomy of the semicircular canals (SCC) within the vestibular system through a novel approach utilizing T2-weighted magnetic resonance images (MRI).MethodsThree dimensional, MRI-based models of the SCCs were generated from AIS subjects (n = 20) and healthy control subjects (n = 19). Linear mixed models were used to compare SCC morphological measurements in the two groups. We compared side-to-side differences in the SCC measurements between groups (group*side interaction).ResultsSide-to-side differences in the lateral SCC were different between the two groups [false discovery rate adjusted p-value: 0.0107]. Orientation of right versus left lateral SCC was significantly different in the AIS group compared to the control group [mean side-to-side difference: -4.1°, 95% CI: -6.4° to -1.7°]. Overall, among subjects in the AIS group, the left lateral SCC tended to be oriented in a more horizontal position than subjects in the control group.SignificanceAsymmetry within the SCCs of the vestibular system of individuals with AIS potentially results in abnormal efferent activity to postural muscles. Consequences of this muscular activity during periods of rapid growth, which often coincides with AIS onset and progression, warrant consideration.