Dynamic functional connectivity: why the controversy?

In principle, dynamic functional connectivity in fMRI is just a statistical measure. A passer-by might think it to be a specialist topic, but it continues to attract widespread attention and spark controversy. Why

Forward Stagewise Naive Bayes

The naïve Bayes approach is a simple but often satisfactory method for supervised classification. In this paper, we focus on the naïve Bayes model and propose the application of regularization techniques to learn a naïve Bayes classifier. The main contribution of the paper is a stagewise version of the selective naïve Bayes, which can be considered a regularized version of the naïve Bayes model. We call it forward stagewise naïve Bayes. For comparison’s sake, we also introduce an explicitly regularized formulation of the naïve Bayes model, where conditional independence (absence of arcs) is promoted via an L 1/L 2-group penalty on the parameters that define the conditional probability distributions. Although already published in the literature, this idea has only been applied for continuous predictors. We extend this formulation to discrete predictors and propose a modification that yields an adaptive penalization. We show that, whereas the L 1/L 2 group penalty formulation only discards irrelevant predictors, the forward stagewise naïve Bayes can discard both irrelevant and redundant predictors, which are known to be harmful for the naïve Bayes classifier. Both approaches, however, usually improve the classical naïve Bayes model’s accuracy

Learning an L1-regularized Gaussian Bayesian Network in the Equivalence Class Space

Learning the structure of a graphical model from data is a common task in a wide range of practical applications. In this paper, we focus on Gaussian Bayesian networks, i.e., on continuous data and directed acyclic graphs with a joint probability density of all variables given by a Gaussian. We propose to work in an equivalence class search space, specifically using the k-greedy equivalence search algorithm. This, combined with regularization techniques to guide the structure search, can learn sparse networks close to the one that generated the data. We provide results on some synthetic networks and on modeling the gene network of the two biological pathways regulating the biosynthesis of isoprenoids for the Arabidopsis thaliana plan

CODRESS

The present project called "Codress" is a service that seeks to cover the need for clothing, through a digital web platform that allows people to rent clothes and / or sell clothes online, mainly dresses and accessories in good condition. It is intended for the active, social and eventful woman who knows that it is not worth spending money on clothes that she will not wear more than a couple of times. Likewise, it is aimed at people, who are interested in renting and / or selling their clothes and thus obtain an extra income, the innovation of this project is that it allows you to have centralized information from different brands in one place, in addition to it will provide personalized recommendations based on your personal tastes with a recommendation algorithm.El presente proyecto denominado “Codress”, es un servicio que busca cubrir la necesidad de vestimenta, mediante una plataforma digital web que permita a las personas alquilar ropa y/o vender ropa por internet, principalmente vestidos y accesorios en buen estado. Está pensado en la mujer activa, social y llena de eventos, que sabe que no vale la pena gastar dinero en una ropa que no usará más de un par de veces. Así mismo, está orientado a personas, que tengan interés en alquilar y/o vender sus prendas y así obtener un ingreso extra, lo innovador de este proyecto es que te permite tener la información centralizada de diferentes marcas en un solo lugar, además de que el mismo brindará recomendaciones personalizadas en base a tus gustos personales con un algoritmo de recomendación

osl-dynamics, a toolbox for modeling fast dynamic brain activity

Neural activity contains rich spatiotemporal structure that corresponds to cognition. This includes oscillatory bursting and dynamic activity that span across networks of brain regions, all of which can occur on timescales of tens of milliseconds. While these processes can be accessed through brain recordings and imaging, modeling them presents methodological challenges due to their fast and transient nature. Furthermore, the exact timing and duration of interesting cognitive events are often a priori unknown. Here, we present the OHBA Software Library Dynamics Toolbox (osl-dynamics), a Python-based package that can identify and describe recurrent dynamics in functional neuroimaging data on timescales as fast as tens of milliseconds. At its core are machine learning generative models that are able to adapt to the data and learn the timing, as well as the spatial and spectral characteristics, of brain activity with few assumptions. osl-dynamics incorporates state-of-the-art approaches that can be, and have been, used to elucidate brain dynamics in a wide range of data types, including magneto/electroencephalography, functional magnetic resonance imaging, invasive local field potential recordings, and electrocorticography. It also provides novel summary measures of brain dynamics that can be used to inform our understanding of cognition, behavior, and disease. We hope osl-dynamics will further our understanding of brain function, through its ability to enhance the modeling of fast dynamic processes

Weak Interactions in Cocrystals of Isoniazid with Glycolic and Mandelic Acids

This research was funded by the Network of Excellence “Metallic Ions in Biological Systems” CTQ2017-90802-REDT [Ministerio de Economía y Competitividad (Spain) and European Regional Development Fund (EU)], and the Xunta de Galicia (Spain) [Rede de Excelencia MetalBIO ED431D 2017/01].Acknowledgments: We thank the “Centre de Tecnologies de la Informació” (CTI) at the Univeritat de les Illes Baleares for computational facilities.This work deals with the preparation of pyridine-3-carbohydrazide (isoniazid, inh) cocrystals with two -hydroxycarboxylic acids. The interaction of glycolic acid (H2ga) or d,l-mandelic acid (H2ma) resulted in the formation of cocrystals or salts of composition (inh) (H2ga) (1) and [Hinh]+[Hma]– (H2ma) (2) when reacted with isoniazid. An N0-(propan-2-ylidene)isonicotinic hydrazide hemihydrate, (pinh) 1/2(H2O) (3), was also prepared by condensation of isoniazid with acetone in the presence of glycolic acid. These prepared compounds were well characterized by elemental analysis, and spectroscopic methods, and their three-dimensional molecular structure was determined by single crystal X-ray crystallography. Hydrogen bonds involving the carboxylic acid occur consistently with the pyridine ring N atom of the isoniazid and its derivatives. The remaining hydrogen-bonding sites on the isoniazid backbone vary based on the steric influences of the derivative group. These are contrasted in each of the molecular systems. Finally, Hirshfeld surface analysis and Density-functional theory (DFT) calculations (including NCIplot and QTAIM analyses) have been performed to further characterize and rationalize the non-covalent interactions.Network of Excellence “Metallic Ions in Biological Systems” CTQ2017-90802-REDT [Ministerio de Economía y Competitividad (Spain) and European Regional Development Fund (EU)]Xunta de Galicia (Spain) [Rede de Excelencia MetalBIO ED431D 2017/01

Effective psychological therapy for PTSD changes the dynamics of specific large-scale brain networks

In posttraumatic stress disorder (PTSD), re-experiencing of the trauma is a hallmark symptom proposed to emerge from a de-contextualised trauma memory. Cognitive therapy for PTSD (CT-PTSD) addresses this de-contextualisation through different strategies. At the brain level, recent research suggests that the dynamics of specific large-scale brain networks play an essential role in both the healthy response to a threatening situation and the development of PTSD. However, very little is known about how these dynamics are altered in the disorder and rebalanced after treatment and successful recovery. Using a data-driven approach and fMRI, we detected recurring large-scale brain functional states with high temporal precision in a population of healthy trauma-exposed and PTSD participants before and after successful CT-PTSD. We estimated the total amount of time that each participant spent on each of the states while being exposed to trauma-related and neutral pictures. We found that PTSD participants spent less time on two default mode subnetworks involved in different forms of self-referential processing in contrast to PTSD participants after CT-PTSD (mtDMN+ and dmDMN+) and healthy trauma-exposed controls (only mtDMN+). Furthermore, re-experiencing severity was related to decreased time spent on the default mode subnetwork involved in contextualised retrieval of autobiographical memories, and increased time spent on the salience and visual networks. Overall, our results support the hypothesis that PTSD involves an imbalance in the dynamics of specific large-scale brain network states involved in self-referential processes and threat detection, and suggest that successful CT-PTSD might rebalance this dynamic aspect of brain function

The psychological correlates of distinct neural states occurring during wakeful rest

When unoccupied by an explicit external task, humans engage in a wide range of different types of self-generated thinking. These are often unrelated to the immediate environment and have unique psychological features. Although contemporary perspectives on ongoing thought recognise the heterogeneity of these self-generated states, we lack both a clear understanding of how to classify the specific states, and how they can be mapped empirically. In the current study, we capitalise on advances in machine learning that allow continuous neural data to be divided into a set of distinct temporally re-occurring patterns, or states. We applied this technique to a large set of resting state data in which we also acquired retrospective descriptions of the participants' experiences during the scan. We found that two of the identified states were predictive of patterns of thinking at rest. One state highlighted a pattern of neural activity commonly seen during demanding tasks, and the time individuals spent in this state was associated with descriptions of experience focused on problem solving in the future. A second state was associated with patterns of activity that are commonly seen under less demanding conditions, and the time spent in it was linked to reports of intrusive thoughts about the past. Finally, we found that these two neural states tended to fall at either end of a neural hierarchy that is thought to reflect the brain's response to cognitive demands. Together, these results demonstrate that approaches which take advantage of time-varying changes in neural function can play an important role in understanding the repertoire of self-generated states. Moreover, they establish that important features of self-generated ongoing experience are related to variation along a similar vein to those seen when the brain responds to cognitive task demands