Fast reproducible identification and large-scale databasing of individual functional cognitive networks

<p>Abstract</p> <p>Background</p> <p>Although cognitive processes such as reading and calculation are associated with reproducible cerebral networks, inter-individual variability is considerable. Understanding the origins of this variability will require the elaboration of large multimodal databases compiling behavioral, anatomical, genetic and functional neuroimaging data over hundreds of subjects. With this goal in mind, we designed a simple and fast acquisition procedure based on a 5-minute functional magnetic resonance imaging (fMRI) sequence that can be run as easily and as systematically as an anatomical scan, and is therefore used in every subject undergoing fMRI in our laboratory. This protocol captures the cerebral bases of auditory and visual perception, motor actions, reading, language comprehension and mental calculation at an individual level.</p> <p>Results</p> <p>81 subjects were successfully scanned. Before describing inter-individual variability, we demonstrated in the present study the reliability of individual functional data obtained with this short protocol. Considering the anatomical variability, we then needed to correctly describe individual functional networks in a voxel-free space. We applied then non-voxel based methods that automatically extract main features of individual patterns of activation: group analyses performed on these individual data not only converge to those reported with a more conventional voxel-based random effect analysis, but also keep information concerning variance in location and degrees of activation across subjects.</p> <p>Conclusion</p> <p>This collection of individual fMRI data will help to describe the cerebral inter-subject variability of the correlates of some language, calculation and sensorimotor tasks. In association with demographic, anatomical, behavioral and genetic data, this protocol will serve as the cornerstone to establish a hybrid database of hundreds of subjects suitable to study the range and causes of variation in the cerebral bases of numerous mental processes.</p

Evolution de la production laitière au cours de la lactation : modèle de prédiction chez la vache laitière

National audienceThe objective of this study was to build an empirical model for the prediction of the evolution of milk yield during lactation for correctly fed cows without any significant disease. It was elaborated hom a set of 877 lactations coming from 5 experimental herds of the INRA, representing a broad range of situations (yield level, calving period, age, ...) which provided distinct samples for the development and the validation of the model. The final proposed model integrates the effects of lactation stage, gestation stage and season. Input variables are calving date, parity, insemination date and yield potential, The latter is estimated horn a,yield measured between weeks 2 and 8 of lactation. The final model provides a very good fit of individual production curves, even when they present an unusual shape. The quality of prediction on an individual scale is limited: 29% of lactations of the validation sample present an average variation with the model higher than 2 kg/d. By contrast, with groups of cows the prediction becomes excellent: with 20 cows the average variation with the model exceeds 1 kg/d in only 2% of the cases. This model is thus an effective tool for the prediction of a reference of production on the herd scale. Its parameters are specified in the appendixL’objectif de cette étude a été de construire un modèle empirique de prédiction de l’évolution de la production laitière au cours de la lactation chez des vaches correctement alimentées et indemnes de troubles sanitaires majeurs. Il a été élaboré à partir d’un jeu de 877 lactations provenant de 5 troupeaux expérimentaux de l’INRA, représentant une large gamme de situations (niveau de production, période de vêlage, âge …) et permettant de disposer d’échantillons distincts pour la construction et la validation du modèle. Le modèle final proposé intègre les effets du stade de lactation, du stade de gestation et de la saison. Les variables d’entrée nécessaires sont la date de vêlage, la parité, la date d’insémination fécondante et le potentiel de production. Ce dernier est estimé à partir d’une valeur de production mesurée entre les semaines 2 à 8 de lactation. Le modèle final permet un très bon ajustement des courbes de production individuelles, même lorsqu’elles présentent des formes inhabituelles. Sa qualité de prédiction à l’échelle individuelle est limitée : 29% des lactations de l’échantillon de validation présentent un écart moyen au modèle supérieur à 2 kg/j. En revanche, pour des groupes de vaches la prédiction devient excellente : sur des lots de 20 vaches, l’écart moyen au modèle ne dépasse 1 kg/j que dans 2 % des cas. Ce modèle est donc un outil efficace de prédiction d’une référence de production à l’échelle d’un troupeau. Les données nécessaires à son utilisation pratique sont précisées en annexe