11 research outputs found

    Health-Related Quality of Life after Pediatric Traumatic Brain Injury: A Quantitative Comparison between Children’s and Parents’ Perspectives of the QOLIBRI-KID/ADO Questionnaire

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    Pediatric health-related quality of life (HRQoL) as a measure of subjective wellbeing and functioning has received increasing attention over the past decade. HRQoL in children and adolescents following pediatric traumatic brain injury (pTBI) has been poorly studied, and performing adequate measurements in this population is challenging. This study compares child/adolescent and parent reports of HRQoL following pTBI using the newly developed Quality of Life after Brain Injury in Children and Adolescents (QOLIBRI-KID/ADO) questionnaire. Three hundred dyads of 8–17-year-old children/adolescents and their parents were included in the study. The parent–child agreement, estimated using intraclass correlation coefficients and Cohen’s κ, displayed poor to moderate concordance. Approximately two-fifths of parents (39.3%) tended to report lower HRQoL for their children/adolescents on the total QOLIBRI-KID/ADO score. At the same time, about one-fifth (21.3%) reported higher HRQoL Total scores for their children/adolescents. The best agreement for parents rating adolescents (aged 13–17 years) was found in terms of the Total score and the Cognition and Self scale scores. To date, parent-reported HRQoL has been the preferred choice in pediatric research after TBI. However, with a parent–child disagreement of approximately 60%, our results highlight the importance of considering self-reports for children/adolescents capable of answering or completing the HRQoL measures

    ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries

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    This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

    Neuroimaging-based classification of PTSD using data-driven computational approaches: a multisite big data study from the ENIGMA-PGC PTSD consortium

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    Background: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. Methods: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. Results: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for D-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. Conclusion: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.Stress-related psychiatric disorders across the life spa

    Nichtinvasive Bestimmung des intrakraniellen Drucks MR-basierte Untersuchung bei Kindern mit Hydrozephalus

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    Der intrakranielle Druck („intracranial pressure“ – ICP) ist ein entscheidender Parameter bei der Diagnostik, Therapie und Verlaufsbeurteilung von Patienten mit Hydrozephalus.Derzeit gibt es keine radiologische Standardmethode, um den intrakraniellen Druck quantitativ zu bestimmen. Methoden zur invasiven und nichtinvasiven Einschätzung des ICP werden diskutiert und die Anwendung eines MR-basierten Verfahrens (MR-ICP) bei Patienten mit Hydrozephalus vorgestellt.Der MR-ICP wird nichtinvasiv aus der intrakraniellen Volumenänderung, die während eines Herzzyklus entsteht, und dem kraniozervikalen Druckgradienten des Liquorflusses berechnet.Fünfzehn Patienten mit Hydrozephalus, davon 6 (2,5–14,61 Jahre alt; Mittelwert 7,4 Jahre) mit Verdacht auf erhöhten ICP, und 9 ohne klinische Zeichen eines erhöhten ICP (2,1–15,9 Jahre alt; Mittelwert 9,8 Jahre) wurden an einem 3-T-MRT mittels Phasenkontrastangiographie untersucht. Der mediane MR-ICP der Patienten mit akuter Symptomatik lag bei 24,5 mmHg (25%-Perzentile 20,4 mmHg; 75%-Perzentile 44,5 mmHg). Der mediane MR-ICP der Patienten ohne akute Symptomatik war 9,8 mmHg (25%-Perzentile 8,6 mmHg; 75%-Perzentile 11,4 mmHg). Der Gruppenunterschied war signifikant (p <0,001; Mann-Whitney-U-Test).Der MR-ICP ist eine vielversprechende, nichtinvasive Methode zur Einschätzung des ICP.Weitere Studien zur Evaluation der Anwendung bei verschiedenen klinischen Fragestellungen sind notwendig.The intracranial pressure (ICP) is a crucially important parameter for diagnostic and therapeutic decision-making in patients with hydrocephalus.So far there is no standard method to non-invasively assess the ICP. Various approaches to obtain the ICP semi-invasively or non-invasively are discussed and the clinical application of a magnetic resonance imaging (MRI)-based method to estimate ICP (MR-ICP) is demonstrated in a group of pediatric patients with hydrocephalus.Arterial inflow, venous drainage and craniospinal cerebrospinal fluid (CSF) flow were quantified using phase-contrast imaging to derive the MR-ICP.A total of 15 patients with hydrocephalus (n=9 treated with shunt placement or ventriculostomy) underwent MRI on a 3 T scanner applying retrospectively-gated cine phase contrast sequences. Of the patients six had clinical symptoms indicating increased ICP (age 2.5–14.61 years, mean 7.4 years) and nine patients had no clinical signs of elevated ICP (age 2.1–15.9 years; mean 9.8 years; all treated with shunt or ventriculostomy). Median MR-ICP in symptomatic patients was 24.5 mmHg (25th percentile 20.4 mmHg; 75th percentile 44.6 mmHg). Median MR-ICP in patients without acute signs of increased ICP was 9.8 mmHg (25th percentile 8.6 mmHg; 75th percentile 11.4 mmHg). Group differences were significant (p < 0.001; Mann-Whitney U-test).The MR-ICP technique is a promising non-invasive tool for estimating ICP.Further studies in larger patient cohorts are warranted to investigate its application in children with hydrocephalus
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