Papers 5
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Santoro, A., Neri, M., Poetto, S., Orsenigo, D., Diano, M., Gatica, M., Petri, G.
We present the first large-scale comparison of higher-order interaction (HOI) metrics — spanning information theory and topology — applied to resting-state and task fMRI data from 100 Human Connectome Project subjects. We identify three distinct classes of HOI metrics: redundant, synergistic, and topological, with the latter bridging the two. Despite their differences, all metrics align with the brain's unimodal-to-transmodal hierarchy and, in some cases, with receptor architecture. HOI metrics outperform classical functional connectivity in fingerprinting and are more predictive of behavior, positioning them as key tools for linking brain architecture and cognition.
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Gardinazzi, Y., Gonzaléz March, R., Kalahasti, S., Montaño Ramirez, A., Neri, M., Nguyen, C., Palermo, G., Weis, E., Ledebur, K., Dervić, E.
Comorbidity networks, which capture disease-disease co-occurrence usually based on electronic health records, reveal structured patterns in how diseases cluster and progress across individuals. However, how these networks evolve across different age groups and how this evolution relates to properties like disease prevalence and mortality remains understudied. To address these issues, we used publicly available comorbidity networks extracted from a comprehensive dataset of 45 million Austrian hospital stays from 1997 to 2014, covering 8.9 million patients. These networks grow and become denser with age. We identified groups of diseases that exhibit similar patterns of structural centrality throughout the lifespan, revealing three dominant age-related components with peaks in early childhood, midlife, and late life. To uncover the drivers of this structural change, we examined the relationship between prevalence and degree. This allowed us to identify conditions that were disproportionately connected to other diseases. Using betweenness centrality in combination with mortality data, we further identified high-mortality bridging diseases. Several diseases show high connectivity relative to their prevalence, such as iron deficiency anemia (D50) in children, nicotine dependence (F17), and lipoprotein metabolism disorders (E78) in adults. We also highlight structurally central diseases with high mortality that emerge at different life stages, including cancers (C group), liver cirrhosis (K74), subarachnoid hemorrhage (I60), and chronic kidney disease (N18). These findings underscore the importance of targeting age-specific, network-central conditions with high mortality for prevention and integrated care.
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Neri, M., Brovelli, A., Castro, S., Fraisopi, F., Gatica, M., Herzog, R., Mindlin, I., Mediano, P., Petri, G., Bor, D., Rosas, F., Tramacere, A., Estarellas, M.
In recent decades, neuroscience has advanced with increasingly sophisticated strategies for recording and analyzing brain activity, enabling detailed investigations into the roles of functional units, such as individual neurons, brain regions, and their interactions. Recently, new strategies for the investigation of cognitive functions regard the study of higher-order interactions---that is, the interactions involving more than two brain regions or neurons. While methods focusing on individual units and their interactions at various levels offer valuable and often complementary insights, each approach comes with its own set of limitations. In this context, a conceptual map to categorize and locate diverse strategies could be crucial to orient researchers and guide future research directions. To this end, we define the spectrum of orders of interaction, namely a framework that categorizes the interactions among neurons or brain regions based on the number of elements involved in these interactions. We use a simulation of a toy model and a few case studies to demonstrate the utility and the challenges of the exploration of the spectrum. We conclude by proposing future research directions aimed at enhancing our understanding of brain function and cognition through a more nuanced methodological framework.
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Robiglio, T., Neri, M., Coppes, D., Agostinelli, C., Battiston, F., Lucas, M., Petri, G.
The interplay between causal mechanisms and emerging collective behaviors is a central aspect of the understanding, control, and prediction of complex networked systems. Here we study this interplay in the context of higher-order mechanisms and behaviors in two representative models: a simplicial Ising model and a simplicial social contagion model. In both systems, we find that group (higher-order) interactions show emergent synergistic (higher-order) behavior. The emergent synergy appears only at the group level and depends in a complex non-linear way on the tradeoff between the strengths of the low- and higher-order mechanisms, and is invisible to low-order behavioral observables. Finally, we present a simple method to detect higher-order mechanisms by using this signature.
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Combrisson, E., Basanisi, R., Neri, M., Auzias, G., Petri, G., Marinazzo, D., Panzeri, S., Brovelli, A.
This study combines information decomposition theory with MEG to explore cortico-cortical functional interactions in goal-directed learning. Results show that 'information gain' is encoded through synergistic and higher-order interactions in cortical circuits, particularly involving prefrontal regions, suggesting a distributed neural mechanism for rational decision-making.