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We propose a permutation-based method for testing a large collection of hypotheses simultaneously. Our method provides lower bounds for the number of true discoveries in any selected subset of hypotheses. These bounds are simultaneously valid with high confidence. The methodology is particularly useful in functional Magnetic Resonance Imaging cluster analysis, where it provides a confidence statement on the percentage of truly activated voxels within clusters of voxels, avoiding the well-known spatial specificity paradox. We offer a user-friendly tool to estimate the percentage of true discoveries for each cluster while controlling the family-wise error rate for multiple testing and taking into account that the cluster was chosen in a data-driven way. The method adapts to the spatial correlation structure that characterizes functional Magnetic Resonance Imaging data, gaining power over parametric approaches.

Identifying cerebral cortex layers is crucial for comparative studies of the cytoarchitecture aiming at providing insights into the relations between brain structure and function across species. The absence of extensive annotated datasets typically limits the adoption of machine learning approaches, leading to the manual delineation of cortical layers by neuroanatomists. We introduce a self-supervised approach to detect layers in 2D Nissl-stained histological slices of the cerebral cortex. It starts with the segmentation of individual cells and the creation of an attributed cell-graph. A self-supervised graph convolutional network generates cell embeddings that encode morphological and structural traits of the cellular environment and are exploited by a community detection algorithm for the final layering. Our method, the first self-supervised of its kind with no spatial transcriptomics data involved, holds the potential to accelerate cytoarchitecture analyses, sidestepping annotation needs and advancing cross-species investigation.

Deep learning has proven to be more effective than other methods in medical image analysis, including the seemingly simple but challenging task of segmenting individual cells, an essential step for many biological studies. Comparative neuroanatomy studies are an example where the instance segmentation of neuronal cells is crucial for cytoarchitecture characterization. This paper presents an end-to-end framework to automatically segment single neuronal cells in Nissl-stained histological images of the brain, thus aiming to enable solid morphological and structural analyses for the investigation of changes in the brain cytoarchitecture. A U-Net-like architecture with an EfficientNet as the encoder and two decoding branches is exploited to regress four color gradient maps and classify pixels into contours between touching cells, cell bodies, or background. The decoding branches are connected through attention gates to share relevant features, and their outputs are combined to return the instance segmentation of the cells. The method was tested on images of the cerebral cortex and cerebellum, outperforming other recent deep-learning-based approaches for the instance segmentation of cells.

In comparative neuroanatomy, the characterization of brain cytoarchitecture is critical to a better understanding of brain structure and function, as it helps to distill information on the development, evolution, and distinctive features of different populations. The automatic segmentation of individual brain cells is a primary prerequisite and yet remains challenging. A new method (MR-NOM) was developed for the instance segmentation of cells in Nissl-stained histological images of the brain. MR-NOM exploits a multi-scale approach to deliberately over-segment the cells into superpixels and subsequently merge them via a classifier based on shape, structure, and intensity features. The method was tested on images of the cerebral cortex, proving successful in dealing with cells of varying characteristics that partially touch or overlap, showing better performance than two state-of-the-art methods.

Between the years 2015 and 2019, members of the Horizon 2020-funded Innovative Training Network named "AMVA4NewPhysics" studied the customization and application of advanced multivariate analysis methods and statistical learning tools to high-energy physics problems, as well as developed entirely new ones. Many of those methods were successfully used to improve the sensitivity of data analyses performed by the ATLAS and CMS experiments at the CERN Large Hadron Collider; several others, still in the testing phase, promise to further improve the precision of measurements of fundamental physics parameters and the reach of searches for new phenomena. In this paper, the most relevant new tools, among those studied and developed, are presented along with the evaluation of their performances., Comment: 101 pages, 21 figures, submitted to Elsevier. [v2]: Updated to published version (in 'Reviews in Physics')

Between the years 2015 and 2019, members of the Horizon 2020-funded Innovative Training Network named "AMVA4NewPhysics" studied the customization and application of advanced multivariate analysis methods and statistical learning tools to high-energy physics problems, as well as developed entirely new ones. Many of those methods were successfully used to improve the sensitivity of data analyses performed by the ATLAS and CMS experiments at the CERN Large Hadron Collider; several others, still in the testing phase, promise to further improve the precision of measurements of fundamental physics parameters and the reach of searches for new phenomena. In this paper, the most relevant new tools, among those studied and developed, are presented along with the evaluation of their performances., Comment: 101 pages, 21 figures, submitted to Elsevier. [v2]: Updated to published version (in 'Reviews in Physics')

Between the years 2015 and 2019, members of the Horizon 2020-funded Innovative Training Network named "AMVA4NewPhysics" studied the customization and application of advanced multivariate analysis methods and statistical learning tools to high-energy physics problems, as well as developed entirely new ones. Many of those methods were successfully used to improve the sensitivity of data analyses performed by the ATLAS and CMS experiments at the CERN Large Hadron Collider; several others, still in the testing phase, promise to further improve the precision of measurements of fundamental physics parameters and the reach of searches for new phenomena. In this paper, the most relevant new tools, among those studied and developed, are presented along with the evaluation of their performances., Comment: 101 pages, 21 figures, submitted to Elsevier. [v2]: Updated to published version (in 'Reviews in Physics')

Permutation testing in linear models, where the number of nuisance coefficients is smaller than the sample size, is a well-studied topic. The common approach of such tests is to permute residuals after regressing on the nuisance covariates. Permutation-based tests are valuable in particular because they can be highly robust to violations of the standard linear model, such as non-normality and heteroscedasticity. Moreover, in some cases they can be combined with existing, powerful permutation-based multiple testing methods. Here, we propose permutation tests for models where the number of nuisance coefficients exceeds the sample size. The performance of the novel tests is investigated with simulations. In a wide range of simulation scenarios our proposed permutation methods provided appropriate type I error rate control, unlike some competing tests, while having good power.

The massive diffusion of social media fosters disintermediation and changes the way users are informed, the way they process reality, and the way they engage in public debate. The cognitive layer of users and the related social dynamics define the nature and the dimension of informational threats. Users show the tendency to interact with information adhering to their preferred narrative and to ignore dissenting information. Confirmation bias seems to account for users decisions about consuming and spreading content; and, at the same time, aggregation of favored information within those communities reinforces group polarization. In this work, the authors address the problem of (mis)information operations with a holistic and integrated approach. Cognitive weakness induced by this new information environment are considered. Moreover, (mis)information operations, with particular reference to the Italian context, are considered; and the fact that the phenomenon is more complex than expected is highlighted. The paper concludes by providing an integrated research roadmap accounting for the possible future technological developments., Comment: The paper first appeared in Volume 18, Issue 3 of the Journal of Information Warfare

The most prevalent approach to activation localization in neuroimaging is to identify brain regions as contiguous supra-threshold clusters, check their significance using random field theory, and correct for the multiple clusters being tested. Besides recent criticism on the validity of the random field assumption, a spatial specificity paradox remains: the larger the detected cluster, the less we know about the location of activation within that cluster. This is because cluster inference implies “there exists at least one voxel with an evoked response in the cluster”, and not that “all the voxels in the cluster have an evoked response”. Inference on voxels within selected clusters is considered bad practice, due to the voxel-wise false positive rate inflation associated with this circular inference. Here, we propose a remedy to the spatial specificity paradox. By applying recent results from the multiple testing statistical literature, we are able to quantify the proportion of truly active voxels within selected clusters, an approach we call All-Resolutions Inference (ARI). If this proportion is high, the paradox vanishes. If it is low, we can further “drill down” from the cluster level to sub-regions, and even to individual voxels, in order to pinpoint the origin of the activation. In fact, ARI allows inference on the proportion of activation in all voxel sets, no matter how large or small, however these have been selected, all from the same data. We use two fMRI datasets to demonstrate the non-triviality of the spatial specificity paradox, and its resolution using ARI. We verify that the endless circularity permitted by ARI does not render its estimates overly conservative using both simulation, and a data split.

Permutation methods are very useful in several scientic elds. They have the advantage of making fewer assumptions about the data and of providing more reliable inferential results. They are also particularly useful in case of high-dimensional problems since they easily account for dependence between tests, thereby allowing for more powerful multiplicity control procedures. Indeed, Westfall and Young's min-p procedure often improves on the Holm procedure by providing more rejections. The advantage of being able to make fewer assumptions about the process generating the data unfortunately involves an inherent limitation in the way a process can be modeled (e.g. through multiple linear models). In this work, we propose a permutation (and rotation) method which allows the inference in the multivariate linear model even in the presence of covariates (i.e. nuisance parameters, i.e. confounders). Also, the method allows for the immediate application of the min-p procedure. We make clear how permutations are a particular case of rotations of the data. Permutation tests are exact, while rotation tests retain exactness under multiple-multivariate linear model with normal errors. When errors are not normal, the rotation tests are weakly exchangeable (i.e. approximated and asymptotically exact). A real application to genetic data is presented and discussed.

A test for the fixed effect in mixed-models is proposed. It is based on permutation strategy and is exact. The testing approach presented is very general and the class of model covered is very broad. Multivariate responses with different type of variables (e.g. continuous, categorical and ranks) are usually tested with separated models and the overall test are usually reached trough Bonferroni-like combinations, i.e. without taking in account the joint distribution of the tests statistics. On the contrary in this approach the joint distribution is immediately obtained and the dependence among tests is taken in account in the overall test.

We show a novel approach for k-FWER control which does not involve any correction, but only testing the hypotheses along a (possibly datadriven) order until a suitable number of p-values are found above the uncorrected α level. p-values can arise from any linear model in a parametric or non parametric setting. The approach is not only very simple and computationally light, but also the data-driven order enhances power when the sample size is small (and also when k and/or m is large). We illustrate the method on an original study about gene discovery in multiple sclerosis, in which were involved a small number of couples of twins, discordant by disease.

We introduce a new test procedure for multiple hypothesis testing based on the permutation space of the sum of test-statistics (t-sum). The underlying combining function is shown to be an instance of a family to which it also belongs the well-known combining functions based on the maximum of test-statistics (t-max). After discussing the family-wise error rate and the false discovery rate, two common approaches to the control of the type I error in multiple testing, we consider two further error rates, the stochastic family error and the mean square error model fit estimator. By means of a two large set of simulations we shoe that besides controlling the family-wise error rate the weak sense, the t-sum procedure also controls the stochastic family error and could considerably outperform the t-max procedure in power and mean square error in experiments with low degrees of freedom. They are also shown several circumstances in which it fits the model better than a procedure controlling the false discovery rate and even better of simply performing a series of univariate tests, which do not control any errors. The t-sum procedure is suitable for pilot and exploratory studies in neuroimaging and in other experimental contexts in which the sample size/number of hypotheses ratio is low, the data correlation is moderate, and the proportion of false hypothesis is possibly large. We end the discussion outlining possible investigations of the more general form of combining function (weighted sum) with the aim of data-driven selection of an optimal poewer combining function.

Nel presente articolo ci si propone di studiare il fenomeno dell'abusivismo edilizio nella città di Messina negli anni 1990-2003. Sono prese in considerazione tre tipologie di violazione edilizia: Sanzioni Amministrative, Violazioni alla Legge Urbanistica, Sequestri Edilizi. L'interesse è anche focalizzato sull'analisi del fenomeno nelle quattordici circoscrizioni. La bassa numerosità del campione e la non normalità nella distribuzione dei tre fenomeni considerati non garantiscono risultati asintotici validi, pertanto si è utilizzata, in un approccio non parametrico di permutazione, la procedura di Stochastic Ordering multivariata (tre tipi di reati) e multistrato (quattordici quartieri) al fine di verificare l'esistenza di doppio ordinamento stocastico, equivalente ad un trend positivo prima di un anno di picco (1993) e negativo nei successivi.

Nel presente articolo ci si propone di studiare il fenomeno dell'abusivismo edilizio nella città di Messina negli anni 1990-2003. Sono prese in considerazione tre tipologie di violazione edilizia: Sanzioni Amministrative, Violazioni alla Legge Urbanistica, Sequestri Edilizi. L'interesse è anche focalizzato sull'analisi del fenomeno nelle quattordici circoscrizioni. La bassa numerosità del campione e la non normalità nella distribuzione dei tre fenomeni considerati non garantiscono risultati asintotici validi, pertanto si è utilizzata, in un approccio non parametrico di permutazione, la procedura di Stochastic Ordering multivariata (tre tipi di reati) e multistrato (quattordici quartieri) al fine di verificare l'esistenza di doppio ordinamento stocastico, equivalente ad un trend positivo prima di un anno di picco (1993) e negativo nei successivi.

Nel presente articolo ci si propone di studiare il fenomeno dell'abusivismo edilizio nella città di Messina negli anni 1990-2003. Sono prese in considerazione tre tipologie di violazione edilizia: Sanzioni Amministrative, Violazioni alla Legge Urbanistica, Sequestri Edilizi. L'interesse è anche focalizzato sull'analisi del fenomeno nelle quattordici circoscrizioni. La bassa numerosità del campione e la non normalità nella distribuzione dei tre fenomeni considerati non garantiscono risultati asintotici validi, pertanto si è utilizzata, in un approccio non parametrico di permutazione, la procedura di Stochastic Ordering multivariata (tre tipi di reati) e multistrato (quattordici quartieri) al fine di verificare l'esistenza di doppio ordinamento stocastico, equivalente ad un trend positivo prima di un anno di picco (1993) e negativo nei successivi.

Nel presente articolo ci si propone di studiare il fenomeno dell'abusivismo edilizio nella città di Messina negli anni 1990-2003. Sono prese in considerazione tre tipologie di violazione edilizia: Sanzioni Amministrative, Violazioni alla Legge Urbanistica, Sequestri Edilizi. L'interesse è anche focalizzato sull'analisi del fenomeno nelle quattordici circoscrizioni. La bassa numerosità del campione e la non normalità nella distribuzione dei tre fenomeni considerati non garantiscono risultati asintotici validi, pertanto si è utilizzata, in un approccio non parametrico di permutazione, la procedura di Stochastic Ordering multivariata (tre tipi di reati) e multistrato (quattordici quartieri) al fine di verificare l'esistenza di doppio ordinamento stocastico, equivalente ad un trend positivo prima di un anno di picco (1993) e negativo nei successivi.

Nel presente articolo ci si propone di studiare il fenomeno dell'abusivismo edilizio nella città di Messina negli anni 1990-2003. Sono prese in considerazione tre tipologie di violazione edilizia: Sanzioni Amministrative, Violazioni alla Legge Urbanistica, Sequestri Edilizi. L'interesse è anche focalizzato sull'analisi del fenomeno nelle quattordici circoscrizioni. La bassa numerosità del campione e la non normalità nella distribuzione dei tre fenomeni considerati non garantiscono risultati asintotici validi, pertanto si è utilizzata, in un approccio non parametrico di permutazione, la procedura di Stochastic Ordering multivariata (tre tipi di reati) e multistrato (quattordici quartieri) al fine di verificare l'esistenza di doppio ordinamento stocastico, equivalente ad un trend positivo prima di un anno di picco (1993) e negativo nei successivi.