Disruption of transfer entropy and inter-hemispheric brain functional connectivity in patients with disorder of consciousness

Severe traumatic brain injury can lead to disorders of consciousness (DOC) characterized by deficit in conscious awareness and cognitive impairment including coma, vegetative state, minimally consciousness, and lock-in syndrome. Of crucial importance is to find objective markers that can account for the large-scale disturbances of brain function to help the diagnosis and prognosis of DOC patients and eventually the prediction of the coma outcome. Following recent studies suggesting that the functional organization of brain networks can be altered in comatose patients, this work analyzes brain functional connectivity (FC) networks obtained from resting-state functional magnetic resonance imaging (rs-fMRI). Two approaches are used to estimate the FC: the Partial Correlation (PC) and the Transfer Entropy (TE). Both the PC and the TE show significant statistical differences between the group of patients and control subjects; in brief, the inter-hemispheric PC and the intra-hemispheric TE account for such differences. Overall, these results suggest two possible rs-fMRI markers useful to design new strategies for the management and neuropsychological rehabilitation of DOC patients.Comment: 25 pages; 4 figures; 3 tables; 1 supplementary figure; 4 supplementary tables; accepted for publication in Frontiers in Neuroinformatic

Enhanced pre-frontal functional-structural networks to support postural control deficits after traumatic brain injury in a pediatric population

Traumatic brain injury (TBI) affects the structural connectivity, triggering the re-organization of structural-functional circuits in a manner that remains poorly understood. We focus here on brain networks re-organization in relation to postural control deficits after TBI. We enrolled young participants who had suffered moderate to severeTBI, comparing them to young typically developing control participants. In comparison to control participants, TBI patients (but not controls) recruited prefrontal regions to interact with two separated networks: 1) a subcortical network including part of the motor network, basal ganglia, cerebellum, hippocampus, amygdala, posterior cingulum and precuneus; and 2) a task-positive network, involving regions of the dorsal attention system together with the dorsolateral and ventrolateral prefrontal regions

Information flow between resting state networks

The resting brain dynamics self-organizes into a finite number of correlated patterns known as resting state networks (RSNs). It is well known that techniques like independent component analysis can separate the brain activity at rest to provide such RSNs, but the specific pattern of interaction between RSNs is not yet fully understood. To this aim, we propose here a novel method to compute the information flow (IF) between different RSNs from resting state magnetic resonance imaging. After haemodynamic response function blind deconvolution of all voxel signals, and under the hypothesis that RSNs define regions of interest, our method first uses principal component analysis to reduce dimensionality in each RSN to next compute IF (estimated here in terms of Transfer Entropy) between the different RSNs by systematically increasing k (the number of principal components used in the calculation). When k = 1, this method is equivalent to computing IF using the average of all voxel activities in each RSN. For k greater than one our method calculates the k-multivariate IF between the different RSNs. We find that the average IF among RSNs is dimension-dependent, increasing from k =1 (i.e., the average voxels activity) up to a maximum occurring at k =5 to finally decay to zero for k greater than 10. This suggests that a small number of components (close to 5) is sufficient to describe the IF pattern between RSNs. Our method - addressing differences in IF between RSNs for any generic data - can be used for group comparison in health or disease. To illustrate this, we have calculated the interRSNs IF in a dataset of Alzheimer's Disease (AD) to find that the most significant differences between AD and controls occurred for k =2, in addition to AD showing increased IF w.r.t. controls.Comment: 47 pages, 5 figures, 4 tables, 3 supplementary figures. Accepted for publication in Brain Connectivity in its current for

Utility of the SENIORS elderly heart failure risk model applied to the RICA registry of acute heart failure

Background: Heart failure (HF) is predominantly a disease of the elderly. Reliable risk stratification would help in the management of this population, but no model has been well evaluated in elderly HF patients in both acute and chronic settings and not being restricted by ejection fraction. To evaluate the utility of the SENIORS risk model, developed from a clinical trial of elderly patients with chronic HF, in an independent cohort (National Spanish Registry: RICA) of elderly acute HF patients. Methods: We applied the SENIORS risk model to 926 patients in RICA to estimate risk at one year of a) composite outcome of all-cause mortality or cardiovascular hospital admission and b) all-cause mortality. Results: In the RICA registry mean age was 78 years, mean ejection fraction 51% and 87% were in NYHA II and III. At one year death/CV hospitalization occurred in 31.9% and all-cause mortality in 19.5%. The risk model provided good separation of Kaplan Meier curves stratified by tertile for death/CV hospitalization and all-cause mortality. The observed versus expected rates of death/CV hospitalization in the lowest, middle and highest risk tertiles were (%) 34/24, 45/41 and 57/67, and for death 13/16, 32/38 and 44/70 respectively. C-statistic for all-cause mortality or CV hospitalization was 0.60 and for all-cause mortality 0.66. Conclusion: The SENIORS risk model was a reliable tool for relative risk stratification among acute heart failure patients in a “real world” registry, but predicted versus observed risk showed some variability. The model provides a useful basis for clinical risk prediction

VIM-Klebsiella oxytoca outbreak in a Neonatal Intensive Care Unit. This time it wasn't the drain

Objective:We describe an outbreak of VIM-carbapenemase-K. oxytoca (VIM-Kox) in a NICU Study design: Prospective Epidemiological Surveillance:            a) Systematically (weekly screening cultures) or on admission, if the patient had a history of previous colonization by VIM-Kox.            b) Clinical cultures, done if infection was suspected.            c) Other possible microorganism sources were investigated: their mothers (rectal microbiota), milk packages and preparation apparata in the lactodietary section, echocardiagram transductors, cribs, the sinks (faucets and drains), washing bowls, etc.Molecular typing was performed using the DiversiLab (bioMérieux) system on all VIM-Kox  isolated from environment or patients (one by neonate). Results:We identified 20 VIM-Kox cases, the most only presented colonization, but 4 showed infection. Three of the ten sinks (drains) in our NICU, were positive for VIM-Kox.  Another four drains harbored P.aeruginosa, S. maltophilia and/or Enterobacter sp.Nevertheless the VIM-Kox bacteria in the sinks (drains) were not the same as those in the patients, who showed three different strains Conclusions:- A VIM-Kox colonization or infection outbreak in a NICU is described.-Rather than environment, not even drains, the source of the outbreak was other patients.-The outbreak was relatively brief, as a result of the rapidness with which appropriate measures were taken and followed

Geometry shapes propagation: Assessing the presence and absence of cortical symmetries through a computational model of cortical spreading depression

Cortical spreading depression (CSD), a depolarization wave which originates in the visual cortex and travels toward the frontal lobe, has been suggested to be one neural correlate of aura migraine. To the date, little is known about the mechanisms which can trigger or stop aura migraine. Here, to shed some light on this problem and, under the hypothesis that CSD might mediate aura migraine, we aim to study different aspects favoring or disfavoring the propagation of CSD. In particular, by using a computational neuronal model distributed throughout a realistic cortical mesh, we study the role that the geometry has in shaping CSD. Our results are two-fold: first, we found significant differences in the propagation traveling patterns of CSD, both intra and inter-hemispherically, revealing important asymmetries in the propagation profile. Second, we developed methods able to identify brain regions featuring a peculiar behavior during CSD propagation. Our study reveals dynamical aspects of CSD, which, if applied to subject-specific cortical geometry, might shed some light on how to differentiate between healthy subjects and those suffering migraine

Metastable Resting State Brain Dynamics

Metastability refers to the fact that the state of a dynamical system spends a large amount of time in a restricted region of its available phase space before a transition takes place, bringing the system into another state from where it might recur into the previous one. beim Graben and Hutt (2013) suggested to use the recurrence plot (RP) technique introduced by Eckmann et al. (1987) for the segmentation of system's trajectories into metastable states using recurrence grammars. Here, we apply this recurrence structure analysis (RSA) for the first time to resting-state brain dynamics obtained from functional magnetic resonance imaging (fMRI). Brain regions are defined according to the brain hierarchical atlas (BHA) developed by Diez et al. (2015), and as a consequence, regions present high-connectivity in both structure (obtained from diffusion tensor imaging) and function (from the blood-level dependent-oxygenation—BOLD—signal). Remarkably, regions observed by Diez et al. were completely time-invariant. Here, in order to compare this static picture with the metastable systems dynamics obtained from the RSA segmentation, we determine the number of metastable states as a measure of complexity for all subjects and for region numbers varying from 3 to 100. We find RSA convergence toward an optimal segmentation of 40 metastable states for normalized BOLD signals, averaged over BHA modules. Next, we build a bistable dynamics at population level by pooling 30 subjects after Hausdorff clustering. In link with this finding, we reflect on the different modeling frameworks that can allow for such scenarios: heteroclinic dynamics, dynamics with riddled basins of attraction, multiple-timescale dynamics. Finally, we characterize the metastable states both functionally and structurally, using templates for resting state networks (RSNs) and the automated anatomical labeling (AAL) atlas, respectively.SR would like to acknowledge Ikerbasque (The Basque Foundation for Science) and moreover, this research is supported by the Basque Government through the BERC 2018-2021 program and by the Spanish State Research Agency through BCAM Severo Ochoa excellence accreditation SEV2017-0718 and through project RTI2018-093860-B- C21 funded by (AEI/FEDER, UE) and acronym MathNEURO. JC acknowledges financial support from Ikerbasque, Ministerio Economia, Industria y Competitividad (Spain) and FEDER (grant DPI2016-79874-R) and the Department of Economical Development and Infrastructure of the Basque Country (Elkartek Program, KK-2018/00032). Finally, PG acknowledges BCAM’s hospitality during a visiting fellowship in fall 2017

The UNIVERSIA/UPM OPEN COURSEWARE iniciative to share the knowledge

This paper shows the most innovative aspects of the Universia/UPM OpenCourseWare (OCW) project referred to globalization of higher education in a Latin-American environment and the sharing of knowledge. The MIT idea of offering, through Internet, the available educational resources in an open way has been spread all over the world and many Universities and Institutions have joint this initiative. Universia, Institution which gathers one of the biggest world universities net, has launched an OCW site, with the technical collaboration of the Technical University of Madrid (UPM) who is working as the main university project promoter. The OCW-Universia site has one of the greatest growth rates at present and is facing new challenges and developments which will allow its expansion as a reference within an international context

Objetivos, evolución y perspectivas del OPEN COURSEWARE de la UPM

En esta comunicación se presentan los principales objetivos, la situación actual y los retos futuros, del sitio OCW de la Universidad Politécnica de Madrid, enmarcados en el entorno de la iniciativa OCW Universia y del Consocio Mundial OCW. Durante los dos últimos cursos la UPM ha realizado un importante esfuerzo que la ha permitido situarse como la Universidad Iberoamericana con mayor cantidad de contenidos educativos en abierto y con el mayor número de accesos y descargas de este tipo de recursos. Para poder afianzar esta posición privilegiada, e impulsar y desarrollar nuevas ideas del sitio OCW-UPM son necesarios una serie de requisitos, que son detallados y analizados en esta ponenci