Search

Aim: This study aimed to (1) quantify attention and executive functioning in chil-dren with developmental coordination disorder (DCD), (2) assess whether some chil-dren with DCD are more likely to show attention difficulties, and (3) characterizebrain correlates of motor and attention deficits.Method: Fifty-three children (36 with DCD and 17 without) aged 8 to 10 years un-derwent T1-weighted and diffusion-weighted magnetic resonance imaging, andstandardized attention and motor assessments. Parents completed questionnaires ofexecutive functioning and symptoms of inattention and hyperactivity. We assessedregional cortical thickness and surface area, and cerebellar, callosal, and primarymotor tract structure.Results: Analyses of covariance and one-sample t-tests identified impaired atten-tion, non-motor processing speed, and executive functioning in children with DCD,yet partial Spearman's rank correlation coefficients revealed these were unrelatedto one another or the type or severity of the motor deficit. Robust regression analy-ses revealed that cortical morphology in the posterior cingulate was associated withboth gross motor skills and inattentive symptoms in children with DCD, while grossmotor skills were also associated with left corticospinal tract (CST) morphology.Interpretation: Children with DCD may benefit from routine attention and hyper-activity assessments. Alterations in the posterior cingulate and CST may be linkedto impaired forward modelling during movements in children with DCD. Overall,alterations in these regions may explain the high rate of non-motor impairments inchildren with DCD.

Gadolinium-based contrast agents (GBCA) form the cornerstone of current primary brain tumor MRI protocols at all stages of the patient journey. Though an imperfect of tumor grade, GBCA is repeatedly used for diagnosis and monitoring. In practice, however, radiologists will encounter situations where GBCA injection is unwanted or of doubtful benefit. Reducing GBCA administration could improve the patient burden of (repeated) imaging, especially in vulnerable patient groups such as children, minimize risks of putative side effects, as well as benefit costs, logistics, and the environmental footprint. This review presents standard imaging strategies to reduce GBCA exposure for pediatric and adult patients with primary brain tumors, including the effect of prolonging follow-up intervals and omitting contrast-enhanced sequences. The potential of novel pulse sequences, such as arterial spin labeling, and upcoming artificial intelligence methods, e.g., to generate synthetic post-contrast images, promise to replace GBCA-dependent approaches. To attain a concise summary of the knowledge in the field, gliomas and meningiomas as typical representatives of primary brain tumors are the review’s focus. Special attention is paid to study quality and real-life applicability.

BACKGROUND: Radiomics is the process of converting radiological images into high-dimensional data that may be used to create machine learning models capable of predicting clinical outcomes, such as disease progression, treatment response and survival. Pediatric central nervous system (CNS) tumors differ from adult CNS tumors in terms of their tissue morphology, molecular subtype and textural features. We set out to appraise the current impact of this technology in clinical pediatric neuro-oncology practice. OBJECTIVES: The aims of the study were to assess radiomics' current impact and potential utility in pediatric neuro-oncology practice; to evaluate the accuracy of radiomics-based machine learning models and compare this to the current standard which is stereotactic brain biopsy; and finally, to identify the current limitations of radiomics applications in pediatric neuro-oncology. MATERIALS AND METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards, a systematic review of the literature was carried out with protocol number CRD42022372485 in the prospective register of systematic reviews (PROSPERO). We performed a systematic literature search via PubMed, Embase, Web of Science and Google Scholar. Studies involving CNS tumors, studies that utilized radiomics and studies involving pediatric patients (age<18 years) were included. Several parameters were collected including imaging modality, sample size, image segmentation technique, machine learning model used, tumor type, radiomics utility, model accuracy, radiomics quality score and reported limitations. RESULTS: The study included a total of 17 articles that underwent full-text review, after excluding duplicates, conference abstracts and studies that did not meet the inclusion criteria. The most commonly used machine learning models were support vector machines (n=7) and random forests (n=6), with an area under the curve (AUC) range of 0.60-0.94. The included s

Neuroimaging represents an important step in the evaluation of pediatric epilepsy. The crucial role of brain imaging in the diagnosis, follow-up and presurgical assessment of patients with epilepsy is noted and has to be familiar to all neuroradiologists and trainees approaching pediatric brain imaging. Morphological qualitative imaging shows the majority of cerebral lesions/alterations underlying focal epilepsy and can highlight some features which are useful in the differential diagnosis of the different types of epilepsy. Recent advances in MRI acquisitions including diffusion-weighted imaging (DWI), post-acquisition image processing techniques, and quantification of imaging data are increasing the accuracy of lesion detection during the last decades. Functional MRI (fMRI) can be really useful and helps to identify cortical eloquent areas that are essential for language, motor function, and memory, and diffusion tensor imaging (DTI) can reveal white matter tracts that are vital for these functions, thus reducing the risk of epilepsy surgery causing new morbidities. Also positron emission tomography (PET), single photon emission computed tomography (SPECT), simultaneous electroencephalogram (EEG) and fMRI, and electrical and magnetic source imaging can be used to assess the exact localization of epileptic foci and help in the design of intracranial EEG recording strategies. The main role of these “hybrid” techniques is to obtain quantitative and qualitative informations, a necessary step to evaluate and demonstrate the complex relationship between abnormal structural and functional data and to manage a “patient-tailored” surgical approach in epileptic patients.

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide. The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance. Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted 'gold-standard' subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%. This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in

Data on fetal MRI in L1 syndrome are scarce with relevant implications for parental counseling and surgical planning. We identified two fetal MR imaging patterns in 10 fetuses harboring L1CAM mutations: the first, observed in 9 fetuses was characterized by callosal anomalies, diencephalosynapsis, and a distinct brainstem malformation with diencephalic-mesencephalic junction dysplasia and brainstem kinking. Cerebellar vermis hypoplasia, aqueductal stenosis, obstructive hydrocephalus, and pontine hypoplasia were variably associated. The second pattern observed in one fetus was characterized by callosal dysgenesis, reduced white matter, and pontine hypoplasia. The identification of these features should alert clinicians to offer a prenatal L1CAM testing.Copyright © 2021 The Authors. Annals of Clinical and Translational Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association

Data on fetal MRI in L1 syndrome are scarce with relevant implications for parental counseling and surgical planning. We identified two fetal MR imaging patterns in 10 fetuses harboring L1CAM mutations: the first, observed in 9 fetuses was characterized by callosal anomalies, diencephalosynapsis, and a distinct brainstem malformation with diencephalic-mesencephalic junction dysplasia and brainstem kinking. Cerebellar vermis hypoplasia, aqueductal stenosis, obstructive hydrocephalus, and pontine hypoplasia were variably associated. The second pattern observed in one fetus was characterized by callosal dysgenesis, reduced white matter, and pontine hypoplasia. The identification of these features should alert clinicians to offer a prenatal L1CAM testing.

Malformations of cortical development are a group of rare disorders commonly manifesting with developmental delay, cerebral palsy or seizures. The neurological outcome is extremely variable depending on the type, extent and severity of the malformation and the involved genetic pathways of brain development. Neuroimaging plays an essential role in the diagnosis of these malformations, but several issues regarding malformations of cortical development definitions and classification remain unclear. The purpose of this consensus statement is to provide standardized malformations of cortical development terminology and classification for neuroradiological pattern interpretation. A committee of international experts in paediatric neuroradiology prepared systematic literature reviews and formulated neuroimaging recommendations in collaboration with geneticists, paediatric neurologists and pathologists during consensus meetings in the context of the European Network Neuro-MIG initiative on Brain Malformations (https://www.neuro-mig.org/). Malformations of cortical development neuroimaging features and practical recommendations are provided to aid both expert and non-expert radiologists and neurologists who may encounter patients with malformations of cortical development in their practice, with the aim of improving malformations of cortical development diagnosis and imaging interpretation worldwide.

The original version of this article unfortunately contained a referencing omission. Figure 11 is reused from the original publication of Figure 10 of Gunny and Lin [1].Copyright © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

The interpretation of cerebral venous pathologies in paediatric practice is challenging as there are several normal anatomical variants, and the pathologies are diverse, involving the venous system through direct and indirect mechanisms. This paper aims to provide a comprehensive review of these entities, as their awareness can avoid potential diagnostic pitfalls. We also propose a practical classification system of paediatric cerebral venous pathologies, which will enable more accurate reporting of the neuroimaging findings, as relevant to the underlying pathogenesis of these conditions. The proposed classification system comprises of the following main groups: arterio-venous shunting-related disorders, primary venous malformations and veno-occlusive disorders. A multimodal imaging approach has been included in the relevant subsections, with a brief overview of the modality-specific pitfalls that can also limit interpretation of the neuroimaging. The article also summarises the current literature and international practices in terms of management options and outcomes in specific disease entities.Copyright © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Malformations of cortical development are a group of rare disorders commonly manifesting with developmental delay, cerebral palsy or seizures. The neurological outcome is extremely variable depending on the type, extent and severity of the malformation and the involved genetic pathways of brain development. Neuroimaging plays an essential role in the diagnosis of these malformations, but several issues regarding malformations of cortical development definitions and classification remain unclear. The purpose of this consensus statement is to provide standardized malformations of cortical development terminology and classification for neuroradiological pattern interpretation. A committee of international experts in paediatric neuroradiology prepared systematic literature reviews and formulated neuroimaging recommendations in collaboration with geneticists, paediatric neurologists and pathologists during consensus meetings in the context of the European Network Neuro-MIG initiative on Brain Malformations (https://www.neuro-mig.org/). Malformations of cortical development neuroimaging features and practical recommendations are provided to aid both expert and non-expert radiologists and neurologists who may encounter patients with malformations of cortical development in their practice, with the aim of improving malformations of cortical development diagnosis and imaging interpretation worldwide.