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In the post-harvest area, a common approach to quantify the average color of fruits peel over time is the sampling of small number of points generally on its equatorial region using a colorimeter. However, when we use a colorimeter to classify an uneven-colored fruit misclassification may occur because points in the peel region may not be representative of average color of fruit. The main problem when we use this method is to determine the number of points to be sampled as well as the location of these points on the fruit\'s surface. An alternative method to evaluate measure of color is digital image analysis because it covers whole of the object surface, by using a sample of pixels taken from the image. As the colorimeter approach is faster and easier than image analysis, it may not be suitable for assessing the overall mean color of the papaya\'s peel and its performance will depend on the number of measured points and choice of sampled region. In this sense, the comparison between these approach is still necessary because we need to know if a sample on the equatorial region can reproduce a sample over the whole region, and if the colorimeter can compete with a scanner or digital camera in measuring the mean hue of papaya peel over time. Thus, we proposed a longitudinal concordance correlation (LCC) based on polynomial mixed-effects regression model to evaluate the extent of agreement among methods. The results show that ideally image analysis of whole fruit\'s region should be used to compute the mean hue and that the topography and curved surface of papaya fruit did not affect the mean hue obtained by the scanner. Since there are still no packages available to estimate the LCC in the free software environment R, we are developing a package called lcc, which provides functions for estimating the longitudinal concordance correlation (LCC) among methods based on variance components and fixed effects of polynomial mixed-effects model. Additionally, we implemented arguments in this function to estimating the longitudinal Pearson correlation (LPC), as precision measure, and longitudinal bias corrector factor (LA), as accuracy measure. Moreover, these components can be estimated using different structures for variance- covariance matrices of random effects and variance functions to model heteroscedasticity among within-group errors using or not the time as variance covariate. / No setor de pós-colheita é muito comum a utilização de colorímetros para avaliar a cor média da casca de frutos ao longo do tempo. No entanto, muitas vezes as técnicas de amostragem utilizando esse equipamento podem levar a medidas tendenciosas da média amostral. Alternativamente, a utilização de imagens digitais pode levar a um menor viés, uma vez que toda a região da casca do fruto é amostrada de forma sistemática. No entanto, ainda é necessária a comparação de ambas abordagens, pois o colorímetro tem vantagens em relação a facilidade de utilização e menor tempo para realizar a amostragem em cada fruto quando comparado a um scanner de mesa. Assim, no caso de variáveis respostas medidas em uma escala contínua, a reprodutibilidade das medidas tomadas por ambos equipamentos pode ser avaliada por meio do coeficiente de correlação de concordância. Dessa forma, para avaliar o perfil da concordância entre métodos, nós propomos uma correlação de concordância longitudinal (LCC), baseada em um modelo de regressão polinomial com efeitos mistos. Os resultados sugeriram que as técnicas por meio de imagens digitais devem ser utilizadas para a quantificação da tonalidade média de frutos. Adicionalmente, a partir do perfil de concordância estimado notamos que existe um período em que ambos os equipamentos podem ser utilizados. A performance do coeficiente de concordância longitudinal foi avaliada por meio de um estudo de simulação, o qual sugeriu que nossa metodologia é robusta a dados desbalanceados (\"dropout\") e que a probabilidade de convergência é aceitavel para uma amostra de 20 frutos e ideal para amostras a partir de 100 frutos. Uma vez que ainda não existem pacotes disponibilizados no ambiente computacional R para a estimação da correlação de concordância longitudinal, nós estamos desenvolvendo um pacote intitulado lcc, o qual será submetido ao \"Comprehensive R Archive Network\" (CRAN). Nesse pacote nós implementamos procedimentos para estimação da correlação de concordância longitudinal, da correlação de Person longitudinal e de uma medida de acurácia longitudinal. Além disso, nosso pacote foi desenvolvido para dados balanceados e desbalanceados, permitindo modelar a heteroscedasticidade entre erros dentro do grupo usando ou não o tempo como covariável, e, também, permitindo a inclusão de covariáveis no preditor linear para controlar variações sistemáticas na variável resposta.

Les affects, émotions et humeurs sont des processus complexes dont le fonctionnement précis échappe toujours à la neuroscience affective. Un récent mouvement des études IRMf s’est tourné vers la recherche d’effets aux niveaux inter- et intra-individuels en raison du manque d’applicabilité individuelle des résultats provenant de moyennes de groupes basées sur des données transversales. En particulier, la recherche intra-individuelle permet l’étude de liens directs entre l’affectivité et la connectivité chez de mêmes individus à travers le temps. De précédentes études en IRMf rapportent ce type associations chez un unique participant, notre objectif a été d’étudier les effets intra-individuels communs pour un groupe d’individus. Nous avons utilisé le jeu de données Day2day, composé de 40 à 50 sessions pour 6 participants, chaque session incluant des données d’IRMf au repos ainsi que d’auto-évaluations des états affectifs. Nous avons analysé la relation entre l’affectivité et la connectivité fonctionnelle entre des régions cérébrales précédemment liées aux émotions et affects à l’aide de régressions linéaires mixtes multivariées. Nos modèles ont isolé des patrons de connectivité communs et généralisables liés aux variations intra-individuelles de l’affectivité observées au cours de plusieurs semaines et mois. Ces modèles impliquaient particulièrement l’amygdale et l’insula. Nos résultats ouvrent la possibilité de reproduire de tels modèles sur des jeux de données plus larges ainsi qu’à évaluer l’hétérogénéité entre sujets au-delà des effets moyens. La caractérisation de tels processus neurobiologiques pourrait être d’une grande utilité en clinique comme biomarqueur transdiagnostique de l’état affectif ou potentielle cible thérapeutique. / Affects, emotions and moods are complex processes, the precise functioning of which still eludes affective neuroscience. A recent movement in fMRI has turned to research of effects at the inter- and intra-individual level in response to the lack of individual-level applicability of results from cross-sectional group mean studies. In particular, intra-individual research enables the study of direct links between affective states and underlying connectivity in individuals across time. Previous fMRI studies have described these associations in a single participant, our objective was to find shared intraindividual effects across multiple subjects. We have used the Day2day dataset, comprising 40 to 50 sessions for six participants, each session including data from resting-state fMRI scans and self-report measures of state affectivity. We have investigated the relationship between affectivity and connectivity in brain regions linked to emotions and affects using multivariate mixed linear analysis. Our models have isolated common and generalizable patterns of connectivity linked to variations in affectivity observed over multiple weeks and months. These models involved mainly the amygdala and insula. Our results incentivize the re-creation of such modelsin larger datasets, and to assess heterogeneity beyond group mean effects. The characterization of such neurobiological processes could be of great use in a clinical setting as a transdiagnostic biomarker or as a potential therapeutic target.