Estimating effective connectivity in linear brain network models

Contemporary neuroscience has embraced network science to study the complex and self-organized structure of the human brain; one of the main outstanding issues is that of inferring from measure data, chiefly functional Magnetic Resonance Imaging (fMRI), the so-called effective connectivity in brain networks, that is the existing interactions among neuronal populations. This inverse problem is complicated by the fact that the BOLD (Blood Oxygenation Level Dependent) signal measured by fMRI represent a dynamic and nonlinear transformation (the hemodynamic response) of neuronal activity. In this paper, we consider resting state (rs) fMRI data; building upon a linear population model of the BOLD signal and a stochastic linear DCM model, the model parameters are estimated through an EM-type iterative procedure, which alternately estimates the neuronal activity by means of the Rauch-Tung-Striebel (RTS) smoother, updates the connections among neuronal states and refines the parameters of the hemodynamic model; sparsity in the interconnection structure is favoured using an iteratively reweighting scheme. Experimental results using rs-fMRI data are shown demonstrating the effectiveness of our approach and comparison with state of the art routines (SPM12 toolbox) is provided

The role of noise modeling in the estimation of resting-state brain effective connectivity

Causal relations among neuronal populations of the brain are studied through the so-called effective connectivity (EC) network. The latter is estimated from EEG or fMRI measurements, by inverting a generative model of the corresponding data. It is clear that the goodness of the estimated network heavily depends on the underlying modeling assumptions. In this present paper we consider the EC estimation problem using fMRI data in resting-state condition. Specifically, we investigate on how to model endogenous fluctuations driving the neuronal activity

Sparse DCM for whole-brain effective connectivity from resting-state fMRI data

Contemporary neuroscience has embraced network science and dynamical systems to study the complex and self-organized structure of the human brain. Despite the developments in non-invasive neuroimaging techniques, a full understanding of the directed interactions in whole brain networks, referred to as effective connectivity, as well as their role in the emergent brain dynamics is still lacking. The main reason is that estimating brain connectivity requires solving a formidable large-scale inverse problem from indirect and noisy measurements. Building on the dynamic causal modelling framework, the present study offers a novel method for estimating whole-brain effective connectivity from resting-state functional magnetic resonance data. To this purpose sparse estimation methods are adapted to infer the parameters of our novel model, which is based on a linearized, region-specific haemodynamic response function. The resulting algorithm, referred to as sparse DCM, is shown to compare favorably with state-of-the art methods when tested on both synthetic and real data. We also provide a graph-theoretical analysis on the whole-brain effective connectivity estimated using data from a cohort of healthy individuals, which reveals properties such as asymmetry in the connectivity structure as well as the different roles of brain areas in favoring segregation or integration

PRDA: An R package for Prospective and Retrospective Design Analysis

The paper describes the PRDA package available at https://cran.r-project.org/web/packages/PRDA/ . PRDA is an R package performing prospective or retrospective design analysis (see Gelman & Carlin, 2014 and Altoè et al., 2020) to evaluate inferential risks (i.e., power, Type M error, and Type S error) in a study considering Pearson’s correlation between two variables or mean comparisons (one-sample, paired, two-sample, andWelch’st-test). Prospective Design Analysis is performed in the planning stage of a study to define the required sample size to obtain a given level of power. Retrospective Design Analysis, instead, is performed when the data have already been collected to evaluate the inferential risks associated with the study. PRDA, additionally, offers the possibility to conduct a prospective/retroprospective design analysis taking into account for the uncertainty about the hypothetical value of effect size. In fact, hypothetical effect size can be defined as a single value according to previous results in the literature or experts indications, or by specifying a distribution of plausible values

Improving the evaluation of eyewitness evidence in legal decision‐making:Testing an active versus passive teaching aid

Judges, jurors and other triers of fact often rely upon eyewitness evidence in criminal trials, but eyewitness memory is not always accurate and can sometimes be contaminated. The I-I-Eye is an evidence-based teaching aid designed to improve the evaluation of eyewitness evidence in legal settings. We aimed to further test the I-I-Eye and examine whether adding an active component to this teaching aid improves its effectiveness. Two experiments (N = 324 and N = 322) were conducted using a 2 (case strength: weak vs. strong) by 3 (teaching aid condition: control vs. passive vs. active) between-subjects design. Results of both experiments showed that the I-I-Eye can help jurors recognize strong eyewitness cases, although it was not particularly effective when the evidence was weak. It was also found that the active component did not further improve sensitivity. We discuss whether teaching aids such as the I-I-Eye may assist decision-makers in the evaluation of eyewitness evidence, while highlighting some of its main limitations found in our results

Performance of Circulating Placental Growth Factor as A Screening Marker for Diagnosis of Ovarian Endometriosis: A Pilot Study

The aim of this study is to compare the circulating placental growth factor (PlGF) concentration in women with and without endometrioma to verify the performance of this marker to diagnose the disease

Boosting promotes advantageous risk-taking

Abstract: Due to the prevalence and importance of choices with uncertain outcomes, it is essential to establish what interventions improve risky decision-making, how they work, and for whom. Two types of low-intensity behavioural interventions are promising candidates: nudges and boosts. Nudges guide people to better decisions by altering how a choice is presented, without restricting any options or modifying the underlying payoff matrix. Boosts, on the other hand, teach people decision strategies that focus their attention on key aspects of the choice, which allows them to make more informed decisions. A recent study compared these two types of interventions and found that boosts worked better for risky choices aimed at maximising gains, whereas nudges worked best for choices aimed at minimising losses. Though intriguing, these findings could not be easily interpreted because of a limitation in the items used. Here we replicate that study, with an extended item set. We find that boosts work by promoting risk-taking when it is beneficial, whereas nudges have a consistent (lesser) impact, regardless of whether risk-taking is beneficial or not. These results suggest that researchers and policymakers should consider the base rate risk propensity of the target population when designing decision-support systems

Where Morphological and Molecular Classifications Meet: The Role of p53 Immunohistochemistry in the Prognosis of Low-Risk Endometrial Carcinoma (GLAMOUR Study)

Simple Summary There is a lack of literature on the role of molecular classification in patients with morphological low-risk EC. We aimed to evaluate the incidence and prognostic role of p53 mutations in this specific subgroup of patients. Our findings show that 4.9% of low-risk EC are p53abn; the OR for the recurrence of p53abn versus p53wt patients was 5.23-CI 95% 0.98-27.95, p = 0.053. No difference in OS was observed between the two groups. Recurrences were mostly local and occur two years after diagnosis. Our data might serve as a valuable tool for clinicians' everyday practice, but larger prospective studies are urgently needed.Abstract No prospective study has validated molecular classification to guide adjuvant treatment in endometrial cancer (EC), and not even retrospective data are present for patients with morphological low-risk EC. We conducted a retrospective, multicenter, observational study including 370 patients with low-risk endometrioid EC to evaluate the incidence and prognostic role of p53 abnormal expression (p53abn) in this specific subgroup. Among 370 patients, 18 had abnormal expressions of p53 (4.9%). In 13 out of 370 patients (3.6%), recurrences were observed and two were p53abn. When adjusting for median follow-up time, the odds ratio (OR) for recurrence among those with p53abn versus p53 wild type (p53wt) was 5.23-CI 95% 0.98-27.95, p = 0.053. The most common site of recurrence was the vaginal cuff (46.2%). One recurrence occurred within the first year of follow-up, and the patient exhibited p53abn. Both 1-year and 2-year DFS rates were 94.4% and 100% in the p53abn and p53wt groups, respectively. One patient died from the disease and comprised p53wt. No difference in OS was registered between the two groups; the median OS was 21.9 months (16.4-30.1). Larger multicenter studies are needed to tailor the treatment of low-risk EC patients with p53abn. Performing molecular classification on all EC patients might be cost-effective, and despite the limits of our relatively small sample, p53abn patients seem to be at greater risk of recurrence, especially locally and after two years since diagnosis

Aspectos epidemiológicos do Câncer Infantojuvenil em Porto Velho-RO no período de 2018 a 2020 / Epidemiological aspects of childhood cancer in Porto Velho-RO from 2018 to 2020

A presente pesquisa tem como objetivo determinar a prevalência do câncer infantojuvenil no Município de Porto Velho-RO no período de 2018 a 2020. Para tanto, utilizou-se método descritivo de dados quantitativos que teve como base de dados o Departamento de Informática do Sistema Único de Saúde (DATASUS). Verificou-se 389 casos registrados entre o período de 2018 a 2020, sendo 31 casos diagnosticados em 2018, 196 em 2019 e 162 em 2020. Foi possível observar que o câncer mais predominante na faixa etária de 0 a 5 anos é a leucemia linfóide, em contrapartida o carcinoma in situ de pele foi o tipo de câncer mais frequente no grupo etário entre 15 a 19 anos. Sendo assim, conclui-se que a incidência dos mais variados tipos de tumores na população pediátrica é diferente em relação à faixa etária e ao sexo, tornando seu perfil epidemiológico bastante diversificado. O câncer infantojuvenil no município de Porto Velho-RO apresenta informações epidemiológicas específicas a respeito da maior frequência do tipo de câncer, maior prevalente e a distribuição do gênero mais acometido. Com essas informações, este estudo abre uma perspectiva para a realização de novos trabalhos apresentando uma descrição da situação epidemiológica atualizada e da atenção a esta patologia, evidenciando o fato de haver poucos estudos e pesquisas em relação ao tema nas demais regiões do estado, sendo, portanto, de extrema relevância