EEG Resting-State Brain Topological Reorganization as a Function of Age

Resting state connectivity has been increasingly studied to investigate the effects of aging on the brain. A reduced organization in the communication between brain areas was demonstrated b y combining a variety of different imaging technologies (fMRI, EEG, and MEG) and graph theory. In this paper, we propose a methodology to get new insights into resting state connectivity and its variations with age, by combining advanced techniques of effective connectivity estimation, graph theoretical approach, and classification by SVM method. We analyzed high density EEG signal srecordedatrestfrom71healthysubjects(age:20–63years). Weighted and directed connectivity was computed by means of Partial Directed Coherence based on a General Linear Kalman filter approach. To keep the information collected by the estimator, weighted and directed graph indices were extracted from the resulting networks. A relation between brain network properties and age of the subject was found, indicating a tendency of the network to randomly organize increasing with age. This result is also confirmed dividing the whole population into two subgroups according to the age (young and middle-aged adults): significant differences exist in terms of network organization measures. Classification of the subjects by means of such indices returns an accuracy greater than 80

Neural Networks and Connectivity among Brain Regions

As is widely understood, brain functioning depends on the interaction among several neural populations, which are linked via complex connectivity circuits and work together (in antagonistic or synergistic ways) to exchange information, synchronize their activity, adapt plastically to external stimuli or internal requirements, and more generally to participate in solving multifaceted cognitive tasks [...]

Definition of Neurophysiological Indices to Describe and Quantify the Cortical Plasticity Induced by Neuro-Rehabilitation

The general objective of the PhD project was to develop a methodology for the definition and analysis of neurophysiological indices able to provide a stable and reliable measure of changes induced by a rehabilitative intervention in the brain activity and organization, with the aim to: i) provide a neurophysiological description of the modifications subtending a functional recovery; ii) allow the evaluation of the effects of rehabilitation treatments in terms of brain reorganization; iii) describe specific properties in the brain general organization to be correlated with the outcome of the intervention, with possible prognostic/decision support value. For this purpose, the research activity was focused on the development of an approach for the extraction of neurophysiological indices from non-invasive estimation of the cerebral activity and connectivity based on electroencephalographic recordings. Brain activity and its changes in time were investigated at three different interconnected levels: spectral analysis, connectivity estimation and graph theory. For each of these, the state of the art methods were evaluated and methodological advancements were proposed on the basis of open problems presented by the nature of the data and by the clinical problem. Experimental data were acquired from 56 stroke patients subjected to a rehabilitative intervention based on Motor Imagery (MI). A subgroup of randomly selected patients were trained in the MI task with the support of Brain Computer Interface. New spectral and functional indices were defined and computed from EEG recorded during the execution of specific tasks (e.g. motor execution), but also from resting state brain activity, to capture both specific and general brain functional modifications. The application of the developed methods allowed to return a proof of the nature, quality and properties of the brain description and quantitative indices that can be derived from data easily recordable from a wide range of patients

MiR-182-3p targets TRF2 and impairs tumor growth of triple-negative breast cancer

Target therapy; Telomeres; Triple-negative breast cancerTerapia dirigida; Telómeros; Cáncer de mama triple negativoTeràpia dirigida; Telòmers; Càncer de mama triple negatiuThe telomeric repeat-binding factor 2 (TRF2) is a telomere-capping protein that plays a key role in the maintenance of telomere structure and function. It is highly expressed in different cancer types, and it contributes to cancer progression. To date, anti-cancer strategies to target TRF2 remain a challenge. Here, we developed a miRNA-based approach to reduce TRF2 expression. By performing a high-throughput luciferase screening of 54 candidate miRNAs, we identified miR-182-3p as a specific and efficient post-transcriptional regulator of TRF2. Ectopic expression of miR-182-3p drastically reduced TRF2 protein levels in a panel of telomerase- or alternative lengthening of telomeres (ALT)-positive cancer cell lines. Moreover, miR-182-3p induced DNA damage at telomeric and pericentromeric sites, eventually leading to strong apoptosis activation. We also observed that treatment with lipid nanoparticles (LNPs) containing miR-182-3p impaired tumor growth in triple-negative breast cancer (TNBC) models, including patient-derived tumor xenografts (PDTXs), without affecting mouse survival or tissue function. Finally, LNPs-miR-182-3p were able to cross the blood–brain barrier and reduce intracranial tumors representing a possible therapeutic option for metastatic brain lesions.The research leading to these results has been funded by Italian Association for Cancer Research (AIRC # 21579) and Ministry of Health (CO-2019-12369662) to AB. This work was financially supported by Ministry of Health Ricerca Corrente 2022 and intramural grant-in-aid to EP. RD, LP and EP were supported by AIRC fellowships

Protein-Protein Interaction Prediction via Graph Signal Processing

This paper tackles the problem of predicting the protein-protein interactions that arise in all living systems. Inference of protein-protein interactions is of paramount importance for understanding fundamental biological phenomena, including cross-species protein-protein interactions, such as those causing the 2020-21 pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Furthermore, it is relevant also for applications such as drug repurposing, where a known authorized drug is applied to novel diseases. On the other hand, a large fraction of existing protein interactions are not known, and their experimental measurement is resource consuming. To this purpose, we adopt a Graph Signal Processing based approach modeling the protein-protein interaction (PPI) network (a.k.a. the interactome) as a graph and some connectivity related node features as a signal on the graph. We then leverage the signal on graph features to infer links between graph nodes, corresponding to interactions between proteins. Specifically, we develop a Markovian model of the signal on graph that enables the representation of connectivity properties of the nodes, and exploit it to derive an algorithm to infer the graph edges. Performance assessment by several metrics recognized in the literature proves that the proposed approach, named GRAph signal processing Based PPI prediction (GRABP), effectively captures underlying biologically grounded properties of the PPI network

Networks as Biomarkers: Uses and Purposes

Networks-based approaches are often used to analyze gene expression data or protein–protein interactions but are not usually applied to study the relationships between different biomarkers. Given the clinical need for more comprehensive and integrative biomarkers that can help to identify personalized therapies, the integration of biomarkers of different natures is an emerging trend in the literature. Network analysis can be used to analyze the relationships between different features of a disease; nodes can be disease-related phenotypes, gene expression, mutational events, protein quantification, imaging-derived features and more. Since different biomarkers can exert causal effects between them, describing such interrelationships can be used to better understand the underlying mechanisms of complex diseases. Networks as biomarkers are not yet commonly used, despite being proven to lead to interesting results. Here, we discuss in which ways they have been used to provide novel insights into disease susceptibility, disease development and severity