From local counterfactuals to global feature importance: efficient, robust, and model-agnostic explanations for brain connectivity networks

Background: Explainable artificial intelligence (XAI) is a technology that can enhance trust in mental state classifications by providing explanations for the reasoning behind artificial intelligence (AI) models outputs, especially for high-dimensional and highly-correlated brain signals. Feature importance and counterfactual explanations are two common approaches to generate these explanations, but both have drawbacks. While feature importance methods, such as shapley additive explanations (SHAP), can be computationally expensive and sensitive to feature correlation, counterfactual explanations only explain a single outcome instead of the entire model. Methods: To overcome these limitations, we propose a new procedure for computing global feature importance that involves aggregating local counterfactual explanations. This approach is specifically tailored to fMRI signals and is based on the hypothesis that instances close to the decision boundary and their counterfactuals mainly differ in the features identified as most important for the downstream classification task. We refer to this proposed feature importance measure as Boundary Crossing Solo Ratio (BoCSoR), since it quantifies the frequency with which a change in each feature in isolation leads to a change in classification outcome, i.e., the crossing of the model's decision boundary. Results and conclusions: Experimental results on synthetic data and real publicly available fMRI data from the Human Connect project show that the proposed BoCSoR measure is more robust to feature correlation and less computationally expensive than state-of-the-art methods. Additionally, it is equally effective in providing an explanation for the behavior of any AI model for brain signals. These properties are crucial for medical decision support systems, where many different features are often extracted from the same physiological measures and a gold standard is absent. Consequently, computing feature importance may become computationally expensive, and there may be a high probability of mutual correlation among features, leading to unreliable results from state-of-the-art XAI methods

Effects of bariatric and metabolic surgical procedures on dyslipidemia: a retrospective, observational analysis.

Aim: Obesity and co-existing metabolic comorbidities are associated with increased cardiovascular (CV) morbidity and mortality risks, generally clustered to risk factors such as dyslipidemia. The aim of this study was to evaluate the lipid profile changes in subjects with severe obesity undergoing different procedures of bariatric and metabolic surgery (BMS), sleeve gastrectomy (SG), and Roux-en-Y gastric bypass (RYGB) in a real-world, clinical setting. Methods: A single-center, retrospective, observational clinical study was performed enrolling patients undergoing BMS. The primary outcome was the change in total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL) cholesterol, and triglycerides. Results: In total, 123 patients were enrolled (males 25.2% and females 74.8%) with a mean age of 48.2 ± 7.9 years and a mean BMI of 47.0 ± 9.1 kg/m2. All patients were evaluated until 16.9 ± 8.1 months after surgery. Total and HDL cholesterol did not change after surgery, while a significant reduction in triglyceride levels was recorded. Moreover, a rapid decline of both LDL and non-HDL cholesterol among follow-up visits was observed. In particular, significant inverse correlations were found between total cholesterol, LDL cholesterol, non-HDL cholesterol, and triglycerides and the number of months elapsed after bariatric surgery. Similarly, a direct correlation was found considering HDL cholesterol. Moreover, total cholesterol, LDL cholesterol, non-HDL cholesterol, and triglycerides significantly changed among visits after RYGB, while no changes were observed in the SG group. Finally, considering lipid-lowering therapies, the improvement in lipid asset was detected only in non-treated patients. Conclusion: This study corroborates the knowledge of the improvement in lipid profile with BMS in clinical practice. Together with sustained weight loss, the BMS approach efficiently corrects dyslipidemia, contributing to decreasing the CV risk

Using machine learning to map the European Cleantech sector

This paper is the introductory chapter of a series of analyses that will result from the CLEU1 project, a collaboration between the universities of Politecnico di Torino, Politecnico di Milano and Università degli Studi di Bologna. The project focuses on Cleantech, an industry sector that develops and deploys sustainable and environmentally friendly solutions for various target applications. It aims to: i) analyse the actions that are undertaken by European Cleantech firms to engage in transformative climate and innovation actions to align with the European Green Deal-inspired policies; ii) examine the association of environmental innovation and the number of new investments made by venture capital (VC) investors in Cleantech companies on environmental indicators; iii) analyse the enabling factors for the development of European Cleantech firms, with a focus on EU-level and country-level targeted policies and regulations and the different sources of financing; iv) analyse the extent to which the implementation of policies and regulations affect both the propensity of cleantech firms to seek external equity financing and the equity offering by VC funds

Topological structure and dynamics of three-dimensional active nematics.

Topological structures are effective descriptors of the nonequilibrium dynamics of diverse many-body systems. For example, motile, point-like topological defects capture the salient features of two-dimensional active liquid crystals composed of energy-consuming anisotropic units. We dispersed force-generating microtubule bundles in a passive colloidal liquid crystal to form a three-dimensional active nematic. Light-sheet microscopy revealed the temporal evolution of the millimeter-scale structure of these active nematics with single-bundle resolution. The primary topological excitations are extended, charge-neutral disclination loops that undergo complex dynamics and recombination events. Our work suggests a framework for analyzing the nonequilibrium dynamics of bulk anisotropic systems as diverse as driven complex fluids, active metamaterials, biological tissues, and collections of robots or organisms

Data from numerical simulations of topological structure and dynamics of three-dimensional active nematics

The data in this dataset are the result of numerical simulations aimed at understanding the topology and dynamics of active nematics. In the manuscript a comparison between numerical and experimental results is also presented

Data from numerical simulations of topological structure and dynamics of three-dimensional active nematics

The data in this dataset are the result of numerical simulations aimed at understanding the topology and dynamics of active nematics. In the manuscript a comparison between numerical and experimental results is also presented

Repository for: "Pattern formation by turbulent cascades"

This repository contains underlying data, figure scripts and production codes, corresponding to the article "Pattern formation by turbulent cascades", X.M. de Wit, M. Fruchart, T. Khain, F. Toschi, V. Vitelli, 2024, Nature. (https://doi.org/10.1038/s41586-024-07074-z

Pattern formation by turbulent cascades

Fully developed turbulence is a universal and scale-invariant chaotic state characterized by an energy cascade from large to small scales at which the cascade is eventually arrested by dissipation. Here we show how to harness these seemingly structureless turbulent cascades to generate patterns. Pattern formation entails a process of wavelength selection, which can usually be traced to the linear instability of a homogeneous state. By contrast, the mechanism we propose here is fully nonlinear. It is triggered by the non-dissipative arrest of turbulent cascades: energy piles up at an intermediate scale, which is neither the system size nor the smallest scales at which energy is usually dissipated. Using a combination of theory and large-scale simulations, we show that the tunable wavelength of these cascade-induced patterns can be set by a non-dissipative transport coefficient called odd viscosity, ubiquitous in chiral fluids ranging from bioactive to quantum systems. Odd viscosity, which acts as a scaledependent Coriolis-like force, leads to a two-dimensionalization of the flow at small scales, in contrast with rotating fluids in which a two-dimensionalization occurs at large scales. Apart from odd viscosity fluids, we discuss how cascade-induced patterns can arise in natural systems, including atmospheric flows, stellar plasma such as the solar wind, or the pulverization and coagulation of objects or droplets in which mass rather than energy cascades