Úrsula Pérez-Ramírez

Computer-aided detection of brain metastases using a three-dimensional template-based matching algorithm.

The purpose of this work was to develop an algorithm for detecting brain metastases in magnetic resonance imaging (MRI), emphasizing the reduction of false positives. Firstly, three-dimensional templates were cross-correlated with the brain volume. Afterwards, each lesion candidate was segmented in the three orthogonal views as a previous step to remove elongated structures such as blood vessels. In a database containing 19 patients and 62 brain metastases, detection algorithm showed a sensitivity of 93.55%. After applying the method for false positive reduction, encouraging results were obtained: false positive rate per slice decreased from 0.64 to 0.15 and only one metastasis was removed, leading to a sensitivity of 91.94%.

Finding influential nodes for integration in brain networks using optimal percolation theory

Global integration of information in the brain results from complex interactions of segregated brain networks. Identifying the most influential neuronal populations that efficiently bind these networks is a fundamental problem of systems neuroscience. Here, we apply optimal percolation theory and pharmacogenetic interventions in vivo to predict and subsequently target nodes that are essential for global integration of a memory network in rodents. The theory predicts that integration in the memory network is mediated by a set of low-degree nodes located in the nucleus accumbens. This result is confirmed with pharmacogenetic inactivation of the nucleus accumbens, which eliminates the formation of the memory network, while inactivations of other brain areas leave the network intact. Thus, optimal percolation theory predicts essential nodes in brain networks. This could be used to identify targets of interventions to modulate brain function.Complex networks can be used to model brain networks. Here the authors identify the essential nodes in a model of a brain network and then validate these predictions by means of in vivo pharmacogenetic interventions. They find that the nucleus accumbens is a central region for brain integration.

Brain metastases detection on MR by means of three-dimensional tumor-appearance template matching

To develop and evaluate a method for an automatic detection of brain metastases in MR images.

Micro-computed tomography image-based evaluation of 3D anisotropy degree of polymer scaffolds

Anisotropy is one of the most meaningful determinants of biomechanical behaviour. This study employs micro-computed tomography (μCT) and image techniques for analysing the anisotropy of regenerative medicine polymer scaffolds. For this purpose, three three-dimensional anisotropy evaluation image methods were used: ellipsoid of inertia (EI), mean intercept length (MIL) and tensor scale (t-scale). These were applied to three patterns (a sphere, a cube and a right prism) and to two polymer scaffold topologies (cylindrical orthogonal pore mesh and spherical pores). For the patterns, the three methods provided good results. Regarding the scaffolds, EI mistook both topologies (0.0158, [ − 0.5683; 0.6001]; mean difference and 95% confidence interval), and MIL showed no significant differences (0.3509, [0.0656; 0.6362]). T-scale is the preferable method because it gave the best capability (0.3441, [0.1779; 0.5102]) to differentiate both topologies. This methodology results in the development of non-destructive tools to engineer biomimetic scaffolds, incorporating anisotropy as a fundamental property to be mimicked from the original tissue and permitting its assessment by means of μCT image analysis.

Publisher Correction: Finding influential nodes for integration in brain networks using optimal percolation theory

The original version of this Article contained an error in the last sentence of the first paragraph of the Introduction, which incorrectly read ‘Correlation of brain activity is typically measured using functional magnetic resonance imaging (fMRI), and the correlation structure is often referred to as “fu’. The correct version states ‘referred to as “functional connectivity”2–6’ in place of ‘referred to as “fu’. This has been corrected in both the PDF and HTML versions of the Article.

Brain functional connectivity alterations in a rat model of excessive alcohol drinking: A resting-state network analysis

Alcohol use disorders (AUD) are a major public health concern. Understanding the brain network alterations is of the utmost importance to diagnose and develop treatment strategies. Employing resting-state functional magnetic resonance imaging, we have performed a longitudinal study in a rat model of chronic excessive alcohol consumption, to identify functional alterations in brain networks triggered by alcohol drinking. Two time points were considered: 1) before alcohol consumption (control condition) and 2) after 30 days of alcohol drinking (alcohol condition). We first identified nine resting-state networks with group independent component analysis. Afterwards, dual regression was applied to obtain subject-specific time courses and spatial maps. L2-regularized partial correlation analysis between pairs of networks showed that functional connectivity (FC) between the retrosplenial-visual and striatal networks decreases due to alcohol consumption, whereas FC between the prefrontal-cingulate and striatal networks increases. Analysis of subject-specific spatial maps revealed FC decreases within networks after alcohol drinking, including the striatal, motor-parietal, prefrontal-cingulate, retrosplenial-visual and left motor-parietal networks. Overall, our results unveil a generalized decrease in brain FC induced by alcohol drinking in genetically predisposed animals, even after a relatively short period of exposure (1 month). The only exception to this hypo-connectivity state is the functional association between the striatal and prefrontal-cingulate networks, which increases after drinking, supporting evidence in human alcoholics.

Re: “Prediction of skeletal-related events in patients with non-small cell lung cancer”—use of Spine Instability Neoplastic Score (SINS)

We congratulate Dr. Aiba et al. on their paper, in which the Spinal Instability Neoplastic Score (SINS) was used to assess skeletal-related events (SRE) in patients with non-small cell lung cancer (NSCLC) [1]. In their paper, they mention the fact that only one orthopedic surgeon reviewed all the images as a potential limitation of the study. They note that, in clinical practice, images are assessed by different specialists, and they suggest that interobserver agreement when using the SINS is likely to be lower when images are assessed by other specialists. The authors also advocate for bone metastasis boards in order to facilitate multi-disciplinary collaboration, as a means to improve management of spinal metastases. We would like to note the following: