Nous utilisons des témoins pour analyser le trafic et l’utilisation de notre site web, afin de personnaliser votre expérience. Vous pouvez désactiver ces technologies à tout moment, mais cela peut restreindre certaines fonctionnalités du site. Consultez notre Politique de protection de la vie privée pour en savoir plus.
Paramètre des cookies
Vous pouvez activer et désactiver les types de cookies que vous souhaitez accepter. Cependant certains choix que vous ferez pourraient affecter les services proposés sur nos sites (ex : suggestions, annonces personnalisées, etc.).
Cookies essentiels
Ces cookies sont nécessaires au fonctionnement du site et ne peuvent être désactivés. (Toujours actif)
Cookies analyse
Acceptez-vous l'utilisation de cookies pour mesurer l'audience de nos sites ?
Multimedia Player
Acceptez-vous l'utilisation de cookies pour afficher et vous permettre de regarder les contenus vidéo hébergés par nos partenaires (YouTube, etc.) ?
Publications
Imbalanced social-communicative and restricted repetitive behavior subtypes of autism spectrum disorder exhibit different neural circuitry
Jumana Sara Tobias Carsten Michael Daniel Claudia Yvette Bhismadev Chris Ineke Daisy Flavio Jessica Vincent Pilar David Lindsay Hannah Joerg Rosemary J. Xavier Liogier David J. René Andre Maarten Nico Bethany Laurence Bob Gahan Antonio M. Barbara Amber N. V. Jessica Roberto Antonia San José Emily Roberto Heike Jack Steve C. R. Caroline Marcel P. Ahmad
Background: The initial injury burden from incident TBI is significantly amplified by recurrent TBI (rTBI). Unfortunately, research assessin… (voir plus)g the accuracy to conduct rTBI surveillance is not available. Accurate surveillance information on recurrent injuries is needed to justify the allocation of resources to rTBI prevention and to conduct high quality epidemiological research on interventions that mitigate this injury burden. This study evaluates the accuracy of administrative health data (AHD) surveillance case definitions for rTBI and estimates the 1-year rTBI incidence adjusted for measurement error. Methods: A 25% random sample of AHD for Montreal residents from 2000 to 2014 was used in this study. Four widely used TBI surveillance case definitions, based on the International Classification of Disease and on radiological exams of the head, were applied to ascertain suspected rTBI cases. Bayesian latent class models were used to estimate the accuracy of each case definition and the 1-year rTBI measurement-error-adjusted incidence without relying on a gold standard rTBI definition that does not exist, across children (18 years), adults (18-64 years), and elderly (> =65 years). Results: The adjusted 1-year rTBI incidence was 4.48 (95% CrI 3.42, 6.20) per 100 person-years across all age groups, as opposed to a crude estimate of 8.03 (95% CrI 7.86, 8.21) per 100 person-years. Patients with higher severity index TBI had a significantly higher incidence of rTBI compared to patients with lower severity index TBI. The case definition that identified patients undergoing a radiological examination of the head in the context of any traumatic injury was the most sensitive across children [0.46 (95% CrI 0.33, 0.61)], adults [0.79 (95% CrI 0.64, 0.94)], and elderly [0.87 (95% CrI 0.78, 0.95)]. The most specific case definition was the discharge abstract database in children [0.99 (95% CrI 0.99, 1.00)], and emergency room visits claims in adults/elderly [0.99 (95% CrI 0.99, 0.99)]. Median time to rTBI was the shortest in adults (75 days) and the longest in children (120 days). Conclusion: Conducting accurate surveillance and valid epidemiological research for rTBI using AHD is feasible when measurement error is accounted for.
There are many clinical contexts which require accurate detection and segmentation of all focal pathologies (e.g. lesions, tumours) in patie… (voir plus)nt images. In cases where there are a mix of small and large lesions, standard binary cross entropy loss will result in better segmentation of large lesions at the expense of missing small ones. Adjusting the operating point to accurately detect all lesions generally leads to oversegmentation of large lesions. In this work, we propose a novel reweighing strategy to eliminate this performance gap, increasing small pathology detection performance while maintaining segmentation accuracy. We show that our reweighing strategy vastly outperforms competing strategies based on experiments on a large scale, multi-scanner, multi-center dataset of Multiple Sclerosis patient images.
Restless legs syndrome (RLS) in pregnancy is a common disorder with a multifactorial etiology. A neurological and obstetrical cohort of 308 … (voir plus)postpartum women was screened for RLS within 1 to 6 days of childbirth and 12 weeks postpartum. Of the 308 young mothers, 57 (prevalence rate 19%) were identified as having been affected by RLS symptoms in the recently completed pregnancy. Structural and functional MRI was obtained from 25 of these 57 participants. A multivariate two-window algorithm was employed to systematically chart the relationship between brain structures and phenotypical predictors of RLS. A decreased volume of the parietal, orbitofrontal and frontal areas shortly after delivery was found to be linked to persistent RLS symptoms up to 12 weeks postpartum, the symptoms' severity and intensity in the most recent pregnancy, and a history of RLS in previous pregnancies. The same negative relationship was observed between brain volume and not being married, not receiving any iron supplement and higher numbers of stressful life events. High cortisol levels, being married and receiving iron supplements, on the other hand, were found to be associated with increased volumes in the bilateral striatum. Investigating RLS symptoms in pregnancy within a brain-phenotype framework may help shed light on the heterogeneity of the condition.
The pregnancy-related restless legs syndrome (RLS) is thought to have a multifactorial etiology. However, the reason behind the manifestatio… (voir plus)n of RLS during pregnancy remains largely elusive. A neurological and obstetrical cohort of 308 postpartum women was screened for RLS symptoms twice: 1 to 6 days (T0) and 12 weeks postpartum (T1). 57 participants were identified as affected by pregnancy-associated RLS. The clinical and anamnestic indicators of the condition were assessed by a pattern-learning classifier trained to predict the RLS status. Structural MRI was obtained from 25 of the 57 participants with RLS history in pregnancy. In this sample, a multivariate two-window algorithm was employed to systematically chart the relationship between brain structures and phenotypical predictors. The RLS prevalence rate in our sample was 19% (n=57), with the women suffering from RLS being older, more often unmarried, affected by gestational diabetes and having been more exposed to stressful life events. A history of RLS and the severity and frequency of repetitive compulsive movements were found to be the strongest predictors of RLS manifestation. In the RLS group, high cortisol levels, being married and receiving iron supplements were found to be associated with increased volumes in the bilateral striatum. Investigating pregnancy-related RLS in a frame of brain phenotype modes may help shed light on the heterogeneity of the condition.