Global Perturbation of Initial Geometry in a Biomechanical Model of Cortical Morphogenesis

Cortical folding pattern is a main characteristic of the geometry of the human brain which is formed by gyri (ridges) and sulci (grooves). Several biological hypotheses have suggested different mechanisms that attempt to explain the development of cortical folding and its abnormal evolutions. Based on these hypotheses, biomechanical models of cortical folding have been proposed. In this work, we compare biomechanical simulations for several initial conditions by using an adaptive spherical parameterization approach. Our approach allows us to study and explore one of the most potential sources of reproducible cortical folding pattern: the specification of initial geometry of the brain.Comment: 4 pages 2 columns (IEEE style), 41st EMB Conferenc

Etude du cortex sensori-moteur en Imagerie par Résonance Magnétique Fonctionnelle : du sujet sain à l'enfant avec paralysie cérébrale

Cerebral palsy (CP) is a non-progressive injury to the developing central nervous system resulting in permanent disorders of the development of movement and posture, causing activity limitation. Therefore physical and rehabilitation medicine has a particular interest in the study of organization and reorganization of the sensorimotor cortex following early brain injury in order to propose new methods for motor rehabilitation. We first showed that motor cortex could be analyzed in functional magnetic resonance imaging (fMRI) using action-observation and passive movement tasks. We then demonstrated in patients with unilateral CP that resting state analysis could study functional connectivity in sensorimotor system. Moreover, our work showed that observing hand movement produced, in CP patients, large bilateral activations in temporo-parieto-fronto-occipital network, comprising most of the nodes of the well described action-observation network. For either side, observing hand movements recruits the primary motor cortex, contralateral to the viewed hand, as would be expected in healthy persons. In addition, we showed that the combination of observation of congruent hand movement simultaneously to passive movement of the paretic hand recruits more motor areas, giving neuronal substrate to propose video-guided passive movement of paretic hand in CP rehabilitation. Finally we present the perinatal stroke as a well suited model to analyze the postlesional neural plasticity notably the "mal-adaptive" plasticity.La paralysie cérébrale (PC) est définie comme un trouble du mouvement secondaire à une lésion cérébrale précoce non progressive. C'est la première cause de handicap moteur de l'enfant. La médecine physique et de réadaptation a donc un intérêt particulier à l'étude du contrôle centrale de la motricité (motricité-cérébrale) aussi bien en termes d'organisation que de réorganisation après lésion, et ce, afin de proposer de nouvelles méthodes de rééducation motrice. Après avoir montré, chez le sujet sain, que la motricité-cérébrale pouvait être étudiée, en imagerie par résonance magnétique fonctionnelle (IRMf), par l'observation du mouvement et le mouvement passif, nous démontrons que l'IRMf " de repos " peut étudier la connectivité fonctionnelle du réseau sensori-moteur chez l'enfant avec PC. D'autre part, dans cette population, l'observation d'une main parétique active le réseau sensori-moteur comme attendu chez le sujet sain, avec une prédominance d'activation dans le cortex moteur primaire lésé. Aussi, lorsque l'on combine cette tâche d'observation du mouvement à un mouvement passif congruent de la main parétique, on observe une activation plus importante des aires sensori-motrices, notamment des aires de " haut niveau ". Cette tâche de mouvement vidéo-guidé nous semble pouvoir constituer une tâche de rééducation motrice dont l'efficience sera à tester ultérieurement. Enfin, nous exposons l'accident vasculaire néonatal comme modèle d'étude de la plasticité postlésionnelle, notamment pour différencier la plasticité " mal-adaptative " de la plasticité efficiente

GNN-based structural information to improve DNN-based basal ganglia segmentation in children following early brain lesion

International audienceAnalyzing the basal ganglia following an early brain lesion is crucial due to their noteworthy role in sensory–motor functions. However, the segmentation of these subcortical structures on MRI is challenging in children and is further complicated by the presence of a lesion. Although current deep neural networks (DNN) perform well in segmenting subcortical brain structures in healthy brains, they lack robustness when faced with lesion variability, leading to structural inconsistencies. Given the established spatial organization of the basal ganglia, we propose enhancing the DNN-based segmentation through post-processing with a graph neural network (GNN). The GNN conducts node classification on graphs encoding both class probabilities and spatial information regarding the regions segmented by the DNN. In this study, we focus on neonatal arterial ischemic stroke (NAIS) in children. The approach is evaluated on both healthy children and children after NAIS using three DNN backbones: U-Net, UNETr, and MSGSE-Net. The results show an improvement in segmentation performance, with an increase in the median Dice score by up to 4% and a reduction in the median Hausdorff distance (HD) by up to 93% for healthy children (from 36.45 to 2.57) and up to 91% for children suffering from NAIS (from 40.64 to 3.50). The performance of the method is compared with atlas-based methods. Severe cases of neonatal stroke result in a decline in performance in the injured hemisphere, without negatively affecting the segmentation of the contra-injured hemisphere. Furthermore, the approach demonstrates resilience to small training datasets, a widespread challenge in the medical field, particularly in pediatrics and for rare pathologies

Detecting cerebral palsy in neonatal stroke children: GNN-based detection considering the structural organization of basal ganglia

International audienceAs a long-term consequence of neonatal arterial ischaemic stroke (NAIS), the presence of cerebral palsy (CP) depends on the structural integrity of brain areas, especially of basal ganglia. Yet, it remains challenging to establish an early diagnosis of CP from a conventional structural MRI. In this study, we introduce a graph neural network-based classification for the recognition of NAIS children and mainly for the detection of children with CP among the NAIS ones. From the structural MRI of 68 children aged 7 years old and their corresponding segmentation of basal ganglia, we construct graphs where nodes represent structures, carrying on node and edge attributes structural information (volumes, distances). The classification accuracy achieved by the proposed method is of 86% for the detection of NAIS and of 89% for the detection of CP among neonatal stroke children

U-Net 3D par patchs et régularisation spatiale avec CRF pour l'extraction de cerveau sur IRM de porcelet

International audienceDu fait de ses similarités morphologiques avec le cerveau de l’enfant, le porcelet est un modèle prisé. Il est notamment utilisé dans l’étude de la paralysie cérébrale afin de quantifier l’impact de la lésion précoce sur le neurodéveloppement en particulier sur des IRM que nous considérons dans ces travaux. Pour disposer d’une évaluation objective, il est nécessaire de développer des algorithmes automatiques de segmentation des différentes structures et tissus . L’étape préliminaire cruciale consiste à extraire le cerveau. Nous proposons ici une méthode d’extraction entièrement automatique du cerveau du porcelet combinant un réseau U-Net 3D par patchs et l’utilisation de champs aléatoires conditionnels (CRF) pour la régularisation spatiale finale. Notre méthode est entraînée et évaluée à partir de 15 IRM T1 de porcelets âgés de 15 jours et comparée à l’outil BET, standard pour le cerveau humain. L’influence de la taille et de la distribution des patchs utilisés lors de l’apprentissage profond est étudiée tout comme les bénéfices de la régularisation spatiale. Les premiers résultats montrent que notre approche est prometteuse en terme de DICE moyen (0.954) et de distance d’Hausdorff (7.73), surpassant les performances de BET (DICE de 0.754 et distance d’Hausdorff de 24.31

Hand function after neonatal stroke: a graph model based on basal ganglia and thalami structure

International audienceIntroduction: Neonatal arterial ischemic stroke (NAIS) is a common model to study the impact of a unilateral early brain insult on developmental brain plasticity and the appearance of long-term outcomes. Motor difficulties that may arise are typically related to poor function of the affected (contra-lesioned) hand, but surprisingly also of the ipsilesional hand. Although many longitudinal studies after NAIS have shown that predicting the occurrence of gross motor difficulties is easier, accurately predicting hand motor function (for both hands) from morphometric MRI remains complicated. The hypothesis of an association between the structural organization of the basal ganglia (BG) and thalamus with hand motor function seems intuitive, given their key role in sensorimotor function. Neuroimaging studies have frequently investigated these structures to evaluate the correlation between their volumes and motor function following early brain injury. However, the results have been controversial. We hypothesize the involvement of other structural parameters.Method: The study involves 35 children (mean age 7.3 years, SD 0.4) with middle cerebral artery NAIS who underwent a structural T1-weighted 3D MRI and clinical examination to assess manual dexterity using the Box and Blocks Test (BBT). Graphs are used to represent high-level structural information of the BG and thalami (volumes, elongations, distances) measured from the MRI. A graph neural network (GNN) is proposed to predict children’s hand motor function through a graph regression. To reduce the impact of external factors on motor function (such as behavior and cognition), we calculate a BBT score ratio for each child and hand.Results: The results indicate a significant correlation between the score ratios predicted by our method and the actual score ratios of both hands (p < 0.05), together with a relatively high accuracy of prediction (mean L1 distance < 0.03). The structural information seems to have a different influence on each hand’s motor function. The affected hand’s motor function is more correlated with the volume, while the ‘unaffected’ hand function is more correlated with the elongation of the structures. Experiments emphasize the importance of considering the whole macrostructural organization of the basal ganglia and thalami networks, rather than the volume alone, to predict hand motor function.Conclusion: There is a significant correlation between the structural characteristics of the basal ganglia/thalami and motor function in both hands. These results support the use of MRI macrostructural features of the basal ganglia and thalamus as an early biomarker for predicting motor function in both hands after early brain injury

Hand function after neonatal stroke: A graph model based on basal ganglia and thalami structure

Introduction: Neonatal arterial ischemic stroke (NAIS) is a common model to study the impact of a unilateral early brain insult on developmental brain plasticity and the appearance of long-term outcomes. Motor difficulties that may arise are typically related to poor function of the affected (contra-lesioned) hand, but surprisingly also of the ipsilesional hand. Although many longitudinal studies after NAIS have shown that predicting the occurrence of gross motor difficulties is easier, accurately predicting hand motor function (for both hands) from morphometric MRI remains complicated. The hypothesis of an association between the structural organization of the basal ganglia (BG) and thalamus with hand motor function seems intuitive given their key role in sensorimotor function. Neuroimaging studies have frequently investigated these structures to evaluate the correlation between their volumes and motor function following early brain injury. However, the results have been controversial. We hypothesize the involvement of other structural parameters. Method: The study involves 35 children (mean age 7.3 years, SD 0.4) with middle cerebral artery NAIS who underwent a structural T1-weighted 3D MRI and clinical examination to assess manual dexterity using the Box and Blocks Test (BBT). Graphs are used to represent high-level structural information of the BG and thalami (volumes, elongations, distances) measured from the MRI. A graph neural network (GNN) is proposed to predict children’s hand motor function through a graph regression. To reduce the impact of external factors on motor function (such as behavior and cognition), we calculate a BBT score ratio for each child and hand. Results: The results indicate a significant correlation between the score ratios predicted by our method and the actual score ratios of both hands (p < 0.05), together with a relatively high accuracy of prediction (mean L1 distance < 0.03). The structural information seems to have a different influence on each hand’s motor function. The affected hand’s motor function is more correlated with the volume, while the ‘unaffected’ hand function is more correlated with the elongation of the structures. Experiments emphasize the importance of considering the whole macrostructural organization of the basal ganglia and thalami networks, rather than the volume alone, to predict hand motor function. Conclusion: There is a significant correlation between the structural characteristics of the basal ganglia/thalami and motor function in both hands. These results support the use of MRI macrostructural features of the basal ganglia and thalamus as an early biomarker for predicting motor function in both hands after early brain injury

Système de vision « slow-motion » à bas coût pour l'activité physique : tests préliminaires autour de l'analyse de la marche

International audienceLa marche est généralement évaluée par une observation simple, mais de plus en plus de bilans cliniques sont filmés pour ensuite être analysés. Grâce à l'utilisation de la caméra ultra-rapide Miro Phantom C110, nous avons étudié la marche dans le plan sagittal de 10 sujets sains. L'approche a consisté à détecter des marqueurs placés au niveau de repère anatomique précis sur le membre inférieur, d’en extraire les coordonnées en 2D pour calculer différents paramètres de la marche. Les objectifs de ce projet sont de vérifier l’utilisation d’une caméra ultra-rapide pour déterminer des paramètres cinétiques et cinématiques de la marche ainsi que d’analyser l’intérêt de la haute fréquence d’acquisition sur la détection d’évènement précis au cours du cycle de marche

Système de vision « slow-motion » à bas coût pour l'activité physique : tests préliminaires autour de l'analyse de la marche

International audienceLa marche est généralement évaluée par une observation simple, mais de plus en plus de bilans cliniques sont filmés pour ensuite être analysés. Grâce à l'utilisation de la caméra ultra-rapide Miro Phantom C110, nous avons étudié la marche dans le plan sagittal de 10 sujets sains. L'approche a consisté à détecter des marqueurs placés au niveau de repère anatomique précis sur le membre inférieur, d’en extraire les coordonnées en 2D pour calculer différents paramètres de la marche. Les objectifs de ce projet sont de vérifier l’utilisation d’une caméra ultra-rapide pour déterminer des paramètres cinétiques et cinématiques de la marche ainsi que d’analyser l’intérêt de la haute fréquence d’acquisition sur la détection d’évènement précis au cours du cycle de marche