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Several challenges prevent extracting knowledge from biomedical resources, including data heterogeneity and the difficulty to obtain and collaborate on data and annotations by medical doctors. Therefore, flexibility in their representation and interconnection is required; it is also essential to be able to interact easily with such data. In recent years, semantic tools have been developed: semantic wikis are collections of wiki pages that can be annotated with properties and so combine flexibility and expressiveness, two desirable aspects when modeling databases, especially in the dynamic biomedical domain. However, semantics and collaborative analysis of biomedical data is still an unsolved challenge. The aim of this work is to create a tool for easing the design and the setup of semantic databases and to give the possibility to enrich them with biostatistical applications. As a side effect, this will also make them reproducible, fostering their application by other research groups. A command-line software has been developed for creating all structures required by Semantic MediaWiki. Besides, a way to expose statistical analyses as R Shiny applications in the interface is provided, along with a facility to export Prolog predicates for reasoning with external tools. The developed software allowed to create a set of biomedical databases for the Neuroscience Department of the University of Padova in a more automated way. They can be extended with additional qualitative and statistical analyses of data, including for instance regressions, geographical distribution of diseases, and clustering. The software is released as open source-code and published under the GPL-3 license at https://github.com/mfalda/tsv2swm.

Additional file 1. Supplementary material.

Supplemental material, sj-pdf-1-jcb-10.1177_0271678X231184365 for A new framework for metabolic connectivity mapping using bolus [18F]FDG PET and kinetic modeling by Tommaso Volpi, Giulia Vallini, Erica Silvestri, Mattia De Francisci, Tony Durbin, Maurizio Corbetta, John J Lee, Andrei G Vlassenko, Manu S Goyal and Alessandra Bertoldo in Journal of Cerebral Blood Flow & Metabolism

Supplemental material, sj-pdf-1-jcb-10.1177_0271678X231184365 for A new framework for metabolic connectivity mapping using bolus [18F]FDG PET and kinetic modeling by Tommaso Volpi, Giulia Vallini, Erica Silvestri, Mattia De Francisci, Tony Durbin, Maurizio Corbetta, John J Lee, Andrei G Vlassenko, Manu S Goyal and Alessandra Bertoldo in Journal of Cerebral Blood Flow & Metabolism

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

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.

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.

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.

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

International audience; Abstract In recent years, the field of functional neuroimaging has moved away from a pure localisationist approach of isolated functional brain regions to a more integrated view of these regions within functional networks. However, the methods used to investigate functional networks rely on local signals in grey matter and are limited in identifying anatomical circuitries supporting the interaction between brain regions. Mapping the brain circuits mediating the functional signal between brain regions would propel our understanding of the brain’s functional signatures and dysfunctions. We developed a method to unravel the relationship between brain circuits and functions: The Functionnectome. The Functionnectome combines the functional signal from fMRI with white matter circuits’ anatomy to unlock and chart the first maps of functional white matter. To showcase this method’s versatility, we provide the first functional white matter maps revealing the joint contribution of connected areas to motor, working memory, and language functions. The Functionnectome comes with an open-source companion software and opens new avenues into studying functional networks by applying the method to already existing datasets and beyond task fMRI.

Functional magnetic resonance imaging (fMRI) has been widely employed to study stroke pathophysiology. In particular, analyses of fMRI signals at rest were directed at quantifying the impact of stroke on spatial features of brain networks. However, brain networks have intrinsic time features that were, so far, disregarded in these analyses. In consequence, standard fMRI analysis failed to capture temporal imbalance resulting from stroke lesions, hence restricting their ability to reveal the interdependent pathological changes in structural and temporal network features following stroke. Here, we longitudinally analyzed hemodynamic-informed transient activity in a large cohort of stroke patients ( n = 103) to assess spatial and temporal changes of brain networks after stroke. Metrics extracted from the hemodynamic-informed transient activity were replicable within-and between-individuals in healthy participants, hence supporting their robustness and their clinical applicability. While large-scale spatial patterns of brain networks were preserved after stroke, their durations were altered, with stroke subjects exhibiting a varied pattern of longer and shorter network activations compared to healthy individuals. Specifically, patients showed a longer duration in the lateral precentral gyrus and anterior cingulum, and a shorter duration in the occipital lobe and in the cerebellum. These temporal alterations were associated with white matter damage in projection and association pathways. Furthermore, they were tied to deficits in specific behavioral domains as restoration of healthy brain dynamics paralleled recovery of cognitive functions (attention, language and spatial memory), but was not significantly correlated to motor recovery. These findings underscore the critical importance of network temporal properties in dissecting the pathophysiology of brain changes after stroke, thus shedding new light on the clinical potential of time-resolved methods for fMRI analysis.