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Plasticity after stroke is a complex phenomenon. Functional reorganization occurs not only in the perilesional tissue but throughout the brain. However, the local connection mechanisms generating such global network changes remain largely unknown. To address this question, time must be considered as a formal variable of the problem rather than a simple repeated observation. Here, we hypothesized that the presence of temporal connection motifs, such as the formation of temporal triangles (T) and edges (E) over time, would explain large-scale brain reorganization after stroke. To test our hypothesis, we adopted a statistical framework based on temporal exponential random graph models (tERGMs), where the aforementioned temporal motifs were implemented as parameters and adapted to capture global network changes after stroke. We first validated the performance on synthetic time-varying networks as compared to standard static approaches. Then, using real functional brain networks, we showed that estimates of tERGM parameters were sufficient to reproduce brain network changes from 2 weeks to 1 year after stroke. These temporal connection signatures, reflecting within-hemisphere segregation (T) and between hemisphere integration (E), were associated with patients' future behaviour. In particular, interhemispheric temporal edges significantly correlated with the chronic language and visual outcome in subcortical and cortical stroke, respectively. Our results indicate the importance of time-varying connection properties when modelling dynamic complex systems and provide fresh insights into modelling of brain network mechanisms after stroke.

Each panel corresponds to a model parameter as described in the main text: A) edges, B) temporal edges, C) temporal triangles, D) stability. MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. In each panel the left side plot shows the MCMC sample statistics while the right plot shows the distribution of the difference between the observed and simulated values. MCMC sample statistics are varying randomly around the observed values at each step (so the chain is ���mixing��� well) and the difference between the observed and simulated values of the sample statistics have a roughly bell-shaped distribution, centered at 0. The sawtooth pattern visible on the degree term deviation plot is due to the combination of discrete values and small range in the statistics, so this is an inherent property of the statistic, not a problem with the fit (Hunter et al., 2008). As a side note, the same values were obtained for the model parameters when using MPLE instead of MCMCLE.

Static graph metrics are defined by removing any reference to the past networks. This only affects the definition of edges between components (E) and triangles within components (T), according to the following equations

tERGMs are fitted over the first two time visits covering the acute phase of stroke, i.e. 2 weeks and 3 months. The corresponding parameter values ��E (interhemispheric temporal edges) and ��T (intrahemispheric temporal triangles) �������� corrected for experimental nuisance variables - are correlated with the multidomain behavioral scores in the chronic phase, i.e. 1 year. In panel A), Spearman correlation coefficients are reported along with their p-values between parentheses. 95% confidence intervals via bootstrap with replacement sampling are reported next to significant correlations. Panel B) shows the R2 values of the linear models used to explain patient's behavior along with the p-values of the independent variables, ie tERGM parameters, lesion size and age. Panel C) shows the R2 values of the linear models used to predict the behavior in hold-out patients, along with the p-values associated with Pearson's correlation between predicted and actual behavioral scores. In all panels, one asterisk denotes a statistical significance of p

S1A - Validation of temporal exponential graph models (tERGMs) in the case of relaxed temporal triangles on synthetic networks. A) Sequence of connectivity matrices representing a time-varying synthetic network. Unweighted and undirected networks are generated by a model that reproduces the large-scale reconfiguration changes after stroke. Interhemispheric integration and within-hemisphere segregation are obtained by allowing the formation of temporal edges E between blocks and temporal triangles T within blocks. Blocks (i.e. red squares) represent here two different systems, eg, the hemispheres (Material and methods). B) Cumulative counts of E and T between consecutive time steps for the synthetic temporal network. C) Prediction performance: area under the curve for the receiver operating characteristic (AUR, red curve) and precision recall (AUP, blue curve) of the out-of-sample link prediction in the networks simulated by the tERGM. Different curves correspond to different time steps. D) Validation: modularity Q values for i) the actual time-varying synthetic network (black line), ii) the ones simulated by the tERGM (green line), and iii) those simulated by a static version of the tERGM where the graph metrics were only accounting for the presence - not the formation - of edges and triangles (gray line).; S1B - Validation of tERGMs in the case of homogeneity violation. A) Sequence of connectivity matrices representing a time-varying synthetic network. The parameters to generate these networks were the same used for Fig. S3A. The only difference is that here the formation of temporal edges E and temporal triangles T are allowed only in the top block. Blocks (i.e. red squares) would represent here two different systems (eg, hemispheres). B) Cumulative counts of E and T between consecutive time steps for the synthetic temporal network. Because the rewiring process is now only taking place in one block the counts are lower compared to Fig. S3A; As a consequence, the corresponding parameter also become lower (��_T=0.05 , ��_E=0.05). C) Prediction performance: area under the curve for the receiver operating characteristic (AUR, red curve) and precision recall (AUP, blue curve) of the out-of-sample link prediction in the networks simulated by the tERGM. Notice that the overall performance is higher here as compared to Fig. 2C. This is explained by the fact that the stability parameter introduced in the tERGM (see Eq. 5) is able to explicitly capture the homogeneity violation of the rewiring process. D) Validation: modularity Q values for i) the actual time-varying synthetic network (black line), ii) the ones simulated by the tERGM (green line), and iii) those simulated by a static version of the tERGM where the graph metrics were only accounting for the presence - not the formation - of edges and triangles (gray line).

Temporal triangles T are measured within both the cortical hemispheres. Notice that for temporal edges E this is irrelevant as the networks are symmetric. Spearman correlation coefficients R are reported along with their p-values.

A) Goodness-of-fit (Stroke and Controls). Left panel shows the values for the area under the receiving operating characteristic curve (AUC); right panel shows the values for precision-recall curve (AUP). Errorbar plots show mean and standard deviations values of each group. B) Wilcoxon tests (Stroke - Controls). P-values are in negative logarithmic scale for the sake of illustration (ie the higher the more significant). Dashed lines correspond to 0.05 significance level. C) Spearman correlations (Stroke). P-values are in negative logarithmic scale for the sake of illustration (the higher the more significant). Dashed lines correspond to 0.05 significance level.

Tables show the Spearman correlation coefficients and the associated p-value between parentheses. Asterisks denote significant associations (pText S1). Panel C) shows the correlations obtained when predicting the behavior at visit 3 using the relative difference of the network modularity between visit 2 and visit 1. The reported correlation with the language score was not significant after adjusting for age and lesion size. Network modularity was computed using the Newman algorithm following the same procedure as described in Siegel et al, 2008, Re-emergence of modular brain networks in stroke recovery. Cortex, 101, 44���59

Two observation windows were considered covering acute and chronic phases, i.e, from 2 weeks to 3 months and from 2 weeks to 1 year. Link prediction performance is assessed by generating new network samples from the fitted tERGMs for each subject and time step. The performance is measured by the area under the precision-recall and receiver operating characteristic curves, i.e., AUP and AUR. The grand-average of the mean values across time are reported for each group -i.e., sucbortical/cortical/healthy- along with their standard deviations.

Network science has been extensively developed to characterize structural properties of complex systems, including brain networks inferred from neuroimaging data. As a result of the inference process, networks estimated from experimentally obtained biological data, represent one instance of a larger number of realizations with similar intrinsic topology. A modelling approach is therefore needed to support statistical inference on the bottom-up local connectivity mechanisms influencing the formation of the estimated brain networks. Here, we adopted a statistical model based on exponential random graphs (ERGM) to reproduce brain networks, or connectomes, estimated by spectral coherence between high-density electroencephalographic (EEG) signals. ERGMs are made up by different local graph metrics, whereas the parameters weight the respective contribution in explaining the observed network. We validated this approach in a dataset of N = 108 healthy subjects during eyes-open (EO) and eyes-closed (EC) resting-state conditions. Results showed that the tendency to form triangles and stars, reflecting clustering and node centrality, better explained the global properties of the EEG connectomes as compared to other combinations of graph metrics. In particular, the synthetic networks generated by this model configuration replicated the characteristic differences found in the real brain networks, with EO eliciting significantly higher segregation in the alpha frequency band (8–13 Hz) as compared to EC. Furthermore, the fitted ERGM parameter values provided complementary information showing that clustering connections are significantly more represented from EC to EO in the alpha range, but also in the beta band (14–29 Hz), which is known to play a crucial role in cortical processing of visual input and externally oriented attention. Taken together, these findings support the current view of the brain functional segregation and integration in terms of modules and hubs, and provide a statistical approach to extract new information on the (re)organizational mechanisms in healthy and diseased brains.

Network science has been extensively developed to characterize structural properties of complex systems, including brain networks inferred from neuroimaging data. As a result of the inference process, networks estimated from experimentally obtained biological data, represent one instance of a larger number of realizations with similar intrinsic topology. A modelling approach is therefore needed to support statistical inference on the bottom-up local connectivity mechanisms influencing the formation of the estimated brain networks. Here, we adopted a statistical model based on exponential random graphs (ERGM) to reproduce brain networks, or connectomes, estimated by spectral coherence between high-density electroencephalographic (EEG) signals. ERGMs are made up by different local graph metrics, whereas the parameters weight the respective contribution in explaining the observed network. We validated this approach in a dataset of N = 108 healthy subjects during eyes-open (EO) and eyes-closed (EC) resting-state conditions. Results showed that the tendency to form triangles and stars, reflecting clustering and node centrality, better explained the global properties of the EEG connectomes as compared to other combinations of graph metrics. In particular, the synthetic networks generated by this model configuration replicated the characteristic differences found in the real brain networks, with EO eliciting significantly higher segregation in the alpha frequency band (8–13 Hz) as compared to EC. Furthermore, the fitted ERGM parameter values provided complementary information showing that clustering connections are significantly more represented from EC to EO in the alpha range, but also in the beta band (14–29 Hz), which is known to play a crucial role in cortical processing of visual input and externally oriented attention. Taken together, these findings support the current view of the brain functional segregation and integration in terms of modules and hubs, and provide a statistical approach to extract new information on the (re)organizational mechanisms in healthy and diseased brains.

Network science has been extensively developed to characterize structural properties of complex systems, including brain networks inferred from neuroimaging data. As a result of the inference process, networks estimated from experimentally obtained biological data, represent one instance of a larger number of realizations with similar intrinsic topology. A modelling approach is therefore needed to support statistical inference on the bottom-up local connectivity mechanisms influencing the formation of the estimated brain networks. Here, we adopted a statistical model based on exponential random graphs (ERGM) to reproduce brain networks, or connectomes, estimated by spectral coherence between high-density electroencephalographic (EEG) signals. ERGMs are made up by different local graph metrics, whereas the parameters weight the respective contribution in explaining the observed network. We validated this approach in a dataset of N = 108 healthy subjects during eyes-open (EO) and eyes-closed (EC) resting-state conditions. Results showed that the tendency to form triangles and stars, reflecting clustering and node centrality, better explained the global properties of the EEG connectomes as compared to other combinations of graph metrics. In particular, the synthetic networks generated by this model configuration replicated the characteristic differences found in the real brain networks, with EO eliciting significantly higher segregation in the alpha frequency band (8–13 Hz) as compared to EC. Furthermore, the fitted ERGM parameter values provided complementary information showing that clustering connections are significantly more represented from EC to EO in the alpha range, but also in the beta band (14–29 Hz), which is known to play a crucial role in cortical processing of visual input and externally oriented attention. Taken together, these findings support the current view of the brain functional segregation and integration in terms of modules and hubs, and provide a statistical approach to extract new information on the (re)organizational mechanisms in healthy and diseased brains.

El estudio de la incidencia y del tipo de serología positiva que provoca el rechazo de los donadores de hemo componentes, constituye una pieza fundamental en el proceso de aseguramiento de calidad en un Banco de Sangre. Basado en esta necesidad, se evaluaron los distintos motivos de rechazo de personas donantes de sangre, haciendo énfasis en el descarte por serología positiva. De los cuatro mil individuos que se atendieron en promedio en la última década, se rechazaron cerca de un 20%, siendo las serologías positivas las responsables de una cuarta parte de estos rechazos. La mayor problemática ha pasado de ser las pruebas positivas de Chagas y VDRL, a los distintos marcadores de hepatitis B y C, especialmente los anticuerpos anti-HBcore. En el período 2003-2004, los portadores crónicos de hepatitis B constituyeron la mayoría de exámenes positivos, con una incidencia del 1.8%. Si se comparan los datos obtenidos en el presente reporte con estudios similares realizados en países como Estados Unidos y Argentina, encontramos una baja incidencia de serologías positivas.

En un periodo de ocho meses, se aislaron 48 cepas de Enterococcus spp. las que se emplearon para realizar una comparación entre la susceptibilidad realizada usando el sistema automatizado Vitek de la casa BioMerieux y el método manual E-test de la casa ABBiodisk. Los resultados obtenidos muestran una buena corelación entre ambos métodos, por lo que se sugiere utilizar el sistema Vitek en forma rutinaria y recurrir al método Etest en casos de duda o como confirmación de resistencia a la hora de escoger la mejor opción terapéutica.