The impact of aging on human brain network target controllability

Understanding how few distributed areas can steer large-scale brain activity is a fundamental question that has practical implications, which range from inducing specific patterns of behavior to counteracting disease. Recent endeavors based on network controllability provided fresh insights into the potential ability of single regions to influence whole brain dynamics through the underlying structural connectome. However, controlling the entire brain activity is often unfeasible and might not always be necessary. The question whether single areas can control specific target subsystems remains crucial, albeit still poorly explored. Furthermore, the structure of the brain network exhibits progressive changes across the lifespan, but little is known about the possible consequences in the controllability properties. To address these questions, we adopted a novel target controllability approach that quantifies the centrality of brain nodes in controlling specific target anatomo-functional systems. We then studied such target control centrality in human connectomes obtained from healthy individuals aged from 5 to 85. Main results showed that the sensorimotor system has a high influencing capacity, but it is difficult for other areas to influence it. Furthermore, we reported that target control centrality varies with age and that temporal-parietal regions, whose cortical thinning is crucial in dementia-related diseases, exhibit lower values in older people. By simulating targeted attacks, such as those 19 occurring in focal stroke, we showed that the ipsilesional hemisphere is the most affected one regardless of the damaged area. Notably, such degradation in target control centrality was more evident in younger people, thus supporting early-vulnerability hypotheses after stroke

Target controllability in genetic networks of macrophage activation

International audienceMacrophage cells play an important role in the Multiple Sclerosis (MS) disease. They are known to participate both to the degen-erative process, myelin destruction, and to the regenerative one, coordinating remyelination. We focused on two activation statesof the macropaghes: ‘alert’, which senses the environment, and ‘pro-inflammatory’, which is entitled of the cell defense againstexternal agents. The correct genetic activation of macrophage phenotypes permits a correct remyelinating response [1], thus thepossibility to steer it towards an healthy state while acting on a limited number of genes (drivers) would be greatly advantageous.We modeled macrophage activation as a network (Figure 1.a), whereNnodes correspond to genes involved in inflammationand directed links correspond to significant influences (inhibition or activation) as retrieved from macrophages.com [2] activationpathways. To enhance interpretation, genes were assigned to four different categories, according to their position inside a cell.We assumed a linear time invariant dynamics and modeled our problem in a target controllability framework [3]. Each gene istested as a driver and target nodes are the 19 genes for which the difference in gene expression between the two states of the macr-pohage cells is most significant(p<0.05)for patients and controls (Figure 1.b). Since computing the rank of the controllabilitymatrix is ill-conditioned for a large network, it was not possible to test all target nodes at the same time. We computed thetargetcontrol centralityas the number of target nodes that can be controlled from a driver node, when the target are chosen as follows:•Step 0: the target set contains the first target node, the target control centrality is zero.•Step 1: build the subgraph to apply the Kalman criterion, nodes accessible from the driver that can reach the target set.•Step 2: check target controllability.–If the configuration is not controllable, discard the last target added to the target set and test the successive one;–else, include the successive target in the target set and increase by one the target control centrality.•Repeat steps 1 and 2 until the last target node is tested.Results showed that the driver nodesIRF7,PRKCDandSTAT1, known to be involved in the MS disease [4] and [5], can controlup to 7 target nodes. Our work is a preliminary step towards the identification of the genes influencing the inflammatory process ofmacrophages, which is a crucial mechanism in the MS’ diseas

Network-based brain computer interfaces: principles and applications

Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of applications from exoskeleton control to neurofeedback (NFB) rehabilitation. In general, BCI usability critically depends on the ability to comprehensively characterize brain functioning and correctly identify the user s mental state. To this end, much of the efforts have focused on improving the classification algorithms taking into account localized brain activities as input features. Despite considerable improvement BCI performance is still unstable and, as a matter of fact, current features represent oversimplified descriptors of brain functioning. In the last decade, growing evidence has shown that the brain works as a networked system composed of multiple specialized and spatially distributed areas that dynamically integrate information. While more complex, looking at how remote brain regions functionally interact represents a grounded alternative to better describe brain functioning. Thanks to recent advances in network science, i.e. a modern field that draws on graph theory, statistical mechanics, data mining and inferential modelling, scientists have now powerful means to characterize complex brain networks derived from neuroimaging data. Notably, summary features can be extracted from these networks to quantitatively measure specific organizational properties across a variety of topological scales. In this topical review, we aim to provide the state-of-the-art supporting the development of a network theoretic approach as a promising tool for understanding BCIs and improve usability

Target controllability in genetic networks of macrophage activation

International audienceMacrophage cells play an important role in the Multiple Sclerosis (MS) disease. They are known to participate both to the degen-erative process, myelin destruction, and to the regenerative one, coordinating remyelination. We focused on two activation statesof the macropaghes: ‘alert’, which senses the environment, and ‘pro-inflammatory’, which is entitled of the cell defense againstexternal agents. The correct genetic activation of macrophage phenotypes permits a correct remyelinating response [1], thus thepossibility to steer it towards an healthy state while acting on a limited number of genes (drivers) would be greatly advantageous.We modeled macrophage activation as a network (Figure 1.a), whereNnodes correspond to genes involved in inflammationand directed links correspond to significant influences (inhibition or activation) as retrieved from macrophages.com [2] activationpathways. To enhance interpretation, genes were assigned to four different categories, according to their position inside a cell.We assumed a linear time invariant dynamics and modeled our problem in a target controllability framework [3]. Each gene istested as a driver and target nodes are the 19 genes for which the difference in gene expression between the two states of the macr-pohage cells is most significant(p<0.05)for patients and controls (Figure 1.b). Since computing the rank of the controllabilitymatrix is ill-conditioned for a large network, it was not possible to test all target nodes at the same time. We computed thetargetcontrol centralityas the number of target nodes that can be controlled from a driver node, when the target are chosen as follows:•Step 0: the target set contains the first target node, the target control centrality is zero.•Step 1: build the subgraph to apply the Kalman criterion, nodes accessible from the driver that can reach the target set.•Step 2: check target controllability.–If the configuration is not controllable, discard the last target added to the target set and test the successive one;–else, include the successive target in the target set and increase by one the target control centrality.•Repeat steps 1 and 2 until the last target node is tested.Results showed that the driver nodesIRF7,PRKCDandSTAT1, known to be involved in the MS disease [4] and [5], can controlup to 7 target nodes. Our work is a preliminary step towards the identification of the genes influencing the inflammatory process ofmacrophages, which is a crucial mechanism in the MS’ diseas

Predicting the Progression of Mild Cognitive Impairment Using Machine Learning: A Systematic and Quantitative Review

Context. Automatically predicting if a subject with Mild Cognitive Impairment (MCI) is going to progress to Alzheimer's disease (AD) dementia in the coming years is a relevant question regarding clinical practice and trial inclusion alike. A large number of articles have been published, with a wide range of algorithms, input variables, data sets and experimental designs. It is unclear which of these factors are determinant for the prediction, and affect the predictive performance that can be expected in clinical practice. We performed a systematic review of studies focusing on the automatic prediction of the progression of MCI to AD dementia. We systematically and statistically studied the influence of different factors on predictive performance. Method. The review included 172 articles, 93 of which were published after 2014. 234 experiments were extracted from these articles. For each of them, we reported the used data set, the feature types (defining 10 categories), the algorithm type (defining 12 categories), performance and potential methodological issues. The impact of the features and algorithm on the performance was evaluated using t-tests on the coefficients of mixed effect linear regressions. Results. We found that using cognitive, fluorodeoxyglucose-positron emission tomog-raphy or potentially electroencephalography and magnetoencephalography variables significantly improves predictive performance compared to not including them (p=0.046, 0.009 and 0.003 respectively), whereas including T1 magnetic resonance imaging, amyloid positron emission tomography or cerebrospinal fluid AD biomarkers does not show a significant effect. On the other hand, the algorithm used in the method does not have a significant impact on performance. We identified several methodological issues. Major issues, found in 23.5% of studies, include the absence of a test set, or its use for feature selection or parameter tuning. Other issues, found in 15.0% of studies, pertain to the usability of the method in clinical practice. We also highlight that short-term predictions are likely not to be better than predicting that subjects stay stable over time. Finally, we highlight possible biases in publications that tend not to publish methods with poor performance on large data sets, which may be censored as negative results. Conclusion. Using machine learning to predict MCI to AD dementia progression is a promising and dynamic field. Among the most predictive modalities, cognitive scores are the cheapest and less invasive, as compared to imaging. The good performance they offer question the wide use of imaging for predicting diagnosis evolution, and call for further exploring fine cognitive assessments. Issues identified in the studies highlight the importance of establishing good practices and guidelines for the use of machine learning as a decision support system in clinical practice

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

Noeuds pilotes dans les réseaux biologiques

The possibility of using mathematical tools to describe and influence complex interconneced systems is getting more and more attainable. Methods based on network controllability to identify the nodes able to impact the state of a whole system are nowadays increasingly studied. However, the problem has a high combinatorial and numerical complexity because of the huge number of a priori equivalent solutions. There has recently been a growing interest in finding the minimum number of inputs to control the whole or a part of the system, and in evaluating the ability of a single node in steering this process. However, specific problems have drawn less attention. In some biological settings it may be required to act on a single node, and it may be of interest to affect only a well-defined subset of the units, a target set. This leads to a single input target control problem, where we can exploit biological constraints to study the relative importance of different driver nodes. This dissertation aims to apply controllability theory to biological networks in an original way, to understand what insight mathematical controllability theory can bring to biological networks, and to study the importance of different driver nodes in controlling a target set. We develop a heuristic that we call step-wise target controllability, which measures the centrality of a driver node as the number of targets it can control and provides a controllable configuration of targets. We show that this method is efficient for sparse directed networks. Our method represents a practical answer to use to our advantage the complexity of the control problem, exploiting existing biological knowledge.La possibilité d’appliquer la théorie mathématique du contrôle pour influencer les systèmes biologiques devient de plus en plus réalisable. Des méthodes pour identifier les nœuds capables d’avoir un impact sur l’état de tout un système sont disponibles. Pourtant, le problème a une complexité combinatoire et numérique élevée en raison du grand nombre de solutions équivalentes a priori. Il y a eu récemment un intérêt croissant pour trouver le nombre minimum d’entrées pour contrôler tout ou partie du système, et pour évaluer la capacité d’un seul nœud à piloter ce système. Cependant, des problèmes spécifiques ont moins attiré l’attention. Dans certains contextes biologiques, il peut être nécessaire d’agir sur un seul nœud, et il peut être intéressant de ne modifier qu’un sous-ensemble bien défini d’unités, un ensemble cible. Cela conduit à un problème de contrôle de la cible d’entrée unique, où nous pouvons exploiter les contraintes biologiques pour étudier l’importance relative des différents nœuds pilotes. Cette thèse vise à appliquer la théorie du contrôle aux réseaux biologiques de manière originale, pour comprendre ce que la théorie mathématique du contrôle peut apporter aux réseaux biologiques et pour étudier l’importance des différents nœuds pilotes dans le contrôle d’un ensemble cible. Nous développons une heuristique que nous appelons step-wise target controllability, qui mesure la centralité d’un nœud pilote en tant que nombre de cibles qu’il peut contrôler et fournit une configuration contrôlable de cibles. Notre méthode représente une réponse pratique pour utiliser la complexité du problème de contrôle, en exploitant les connaissances biologiques existantes

A step-wise criterion to determine target control centrality in the aging brain network

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Modeling COVID-19 in school settings to evaluate prevention and control protocols

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Impact of contact data resolution on the evaluation of interventions in mathematical models of infectious diseases

International audienceComputational models offer a unique setting to test strategies to mitigate the spread of infectious diseases, providing useful insights to applied public health. To be actionable, models need to be informed by data, which can be available at different levels of detail. While high-resolution data describing contacts between individuals are increasingly available, data gathering remains challenging, especially during a health emergency. Many models thus use synthetic data or coarse information to evaluate intervention protocols. Here, we evaluate how the representation of contact data might affect the impact of various strategies in models, in the realm of COVID-19 transmission in educational and work contexts. Starting from high-resolution contact data, we use detailed to coarse data representations to inform a model of SARS-CoV-2 transmission and simulate different mitigation strategies. We find that coarse data representations estimate a lower risk of superspreading events. However, the rankings of protocols according to their efficiency or cost remain coherent across representations, ensuring the consistency of model findings to inform public health advice. Caution should be taken, however, on the quantitative estimations of those benefits and costs triggering the adoption of protocols, as these may depend on data representation