Construction of embedded fMRI resting-state functional connectivity networks using manifold learning

We construct embedded functional connectivity networks (FCN) from benchmark resting-state functional magnetic resonance imaging (rsfMRI) data acquired from patients with schizophrenia and healthy controls based on linear and nonlinear manifold learning algorithms, namely, Multidimensional Scaling, Isometric Feature Mapping, Diffusion Maps, Locally Linear Embedding and kernel PCA. Furthermore, based on key global graph-theoretic properties of the embedded FCN, we compare their classification potential using machine learning. We also assess the performance of two metrics that are widely used for the construction of FCN from fMRI, namely the Euclidean distance and the cross correlation metric. We show that diffusion maps with the cross correlation metric outperform the other combinations

Numerical Bifurcation Analysis of PDEs From Lattice Boltzmann Model Simulations: a Parsimonious Machine Learning Approach

We address a three-tier data-driven approach for the numerical solution of the inverse problem in Partial Differential Equations (PDEs) and for their numerical bifurcation analysis from spatio-temporal data produced by Lattice Boltzmann model simulations using machine learning. In the first step, we exploit manifold learning and in particular parsimonious Diffusion Maps using leave-one-out cross-validation (LOOCV) to both identify the intrinsic dimension of the manifold where the emergent dynamics evolve and for feature selection over the parameter space. In the second step, based on the selected features, we learn the right-hand-side of the effective PDEs using two machine learning schemes, namely shallow Feedforward Neural Networks (FNNs) with two hidden layers and single-layer Random Projection Networks (RPNNs), which basis functions are constructed using an appropriate random sampling approach. Finally, based on the learned black-box PDE model, we construct the corresponding bifurcation diagram, thus exploiting the numerical bifurcation analysis toolkit. For our illustrations, we implemented the proposed method to perform numerical bifurcation analysis of the 1D FitzHugh-Nagumo PDEs from data generated by D1Q3 Lattice Boltzmann simulations. The proposed method was quite effective in terms of numerical accuracy regarding the construction of the coarse-scale bifurcation diagram. Furthermore, the proposed RPNN scheme was ∼ 20 to 30 times less costly regarding the training phase than the traditional shallow FNNs, thus arising as a promising alternative to deep learning for the data-driven numerical solution of the inverse problem for high-dimensional PDEs

ISOMAP and machine learning algorithms for the construction of embedded functional connectivity networks of anatomically separated brain regions fromresting state fMRI data of patients with Schizophrenia

We construct Functional Connectivity Networks (FCN) from resting state fMRI (rsfMRI) recordings towards the classification of brain activity between healthy and schizophrenic subjects using a publicly available dataset (the COBRE dataset) of 145 subjects (74 healthy controls and 71 schizophrenic subjects). First, we match the anatomy of the brain of each individual to the Desikan- Killiany brain atlas. Then, we use the conventional approach of correlating the parcellated time series to construct FCN and ISOMAP, a nonlinear manifold learning algorithm to produce low-dimensional embeddings of the correlation matrices. For the classification analysis, we computed five key local graph-theoretic measures of the FCN and used the LASSO and Random Forest (RF) algorithms for feature selection. For the classification we used standard linear Support Vector Machines. The classification performance is tested by a double cross-validation scheme [consisting of an outer and an inner loop of “Leave one out” cross-validation (LOOCV)]. The standard cross-correlation methodology produced a classification rate of 73.1%, while ISOMAP resulted in 79.3%, thus providing a simpler model with a smaller number of features as chosen from LASSO and RF, namely the participation coefficient of the right thalamus and the strength of the right lingual gyrus

Diffusion with random distribution of static traps

The random walk problem is studied in two and three dimensions in the presence of a random distribution of static traps. An efficient Monte Carlo method, based on a mapping onto a polymer model, is used to measure the survival probability P(c,t) as a function of the trap concentration c and the time t. Theoretical arguments are presented, based on earlier work of Donsker and Varadhan and of Rosenstock, why in two dimensions one expects a data collapse if -ln[P(c,t)]/ln(t) is plotted as a function of (lambda t)^{1/2}/ln(t) (with lambda=-ln(1-c)), whereas in three dimensions one expects a data collapse if -t^{-1/3}ln[P(c,t)] is plotted as a function of t^{2/3}lambda. These arguments are supported by the Monte Carlo results. Both data collapses show a clear crossover from the early-time Rosenstock behavior to Donsker-Varadhan behavior at long times.Comment: 4 pages, 6 figure

Accuracy of single progesterone test to predict early pregnancy outcome in women with pain or bleeding: Meta-analysis of cohort studies

Objective To determine the accuracy with which a single progesterone measurement in early pregnancy discriminates between viable and non-viable pregnancy. Design Systematic review and meta-analysis of diagnostic accuracy studies. Data sources Medline, Embase, CINAHL, Web of Science, ProQuest, Conference Proceedings Citation Index, and the Cochrane Library from inception until April 2012, plus reference lists of relevant studies. Study selection Studies were selected on the basis of participants (women with spontaneous pregnancy of less than 14 weeks of gestation); test (single serum progesterone measurement); outcome (viable intrauterine pregnancy, miscarriage, or ectopic pregnancy) diagnosed on the basis of combinations of pregnancy test, ultrasound scan, laparoscopy, and histological examination; design (cohort studies of test accuracy); and sufficient data being reported. Results 26 cohort studies, including 9436 pregnant women, were included, consisting of 7 studies in women with symptoms and inconclusive ultrasound assessment and 19 studies in women with symptoms alone. Among women with symptoms and inconclusive ultrasound assessments, the progesterone test (5 studies with 1998 participants and cut-off values from 3.2 to 6 ng/mL) predicted a non-viable pregnancy with pooled sensitivity of 74.6% (95% confidence interval 50.6% to 89.4%), specificity of 98.4% (90.9% to 99.7%), positive likelihood ratio of 45 (7.1 to 289), and negative likelihood ratio of 0.26 (0.12 to 0.57). The median prevalence of a non-viable pregnancy was 73.2%, and the probability of a non-viable pregnancy was raised to 99.2% if the progesterone was low. For women with symptoms alone, the progesterone test had a higher specificity when a threshold of 10 ng/mL was used (9 studies with 4689 participants) and predicted a non-viable pregnancy with pooled sensitivity of 66.5% (53.6% to 77.4%), specificity of 96.3% (91.1% to 98.5%), positive likelihood ratio of 18 (7.2 to 45), and negative likelihood ratio of 0.35 (0.24 to 0.50). The probability of a non-viable pregnancy was raised from 62.9% to 96.8%. Conclusion A single progesterone measurement for women in early pregnancy presenting with bleeding or pain and inconclusive ultrasound assessments can rule out a viable pregnancy.Jorine Verhaegen, Ioannis D Gallos, Norah M van Mello, Mohamed Abdel-Aziz, Yemisi Takwoingi, Hoda Harb, Jonathan J Deeks, Ben W J Mol, Arri Coomarasam

Formative research to design an implementation strategy for a postpartum hemorrhage initial response treatment bundle (E-MOTIVE): study protocol

BACKGROUND: Postpartum hemorrhage (PPH) is the leading cause of maternal death worldwide. When PPH occurs, early identification of bleeding and prompt management using evidence-based guidelines, can avert most PPH-related severe morbidities and deaths. However, adherence to the World Health Organization recommended practices remains a critical challenge. A potential solution to inefficient and inconsistent implementation of evidence-based practices is the application of a ‘clinical care bundle’ for PPH management. A clinical care bundle is a set of discrete, evidence-based interventions, administered concurrently, or in rapid succession, to every eligible person, along with teamwork, communication, and cooperation. Once triggered, all bundle components must be delivered. The E-MOTIVE project aims to improve the detection and first response management of PPH through the implementation of the “E-MOTIVE” bundle, which consists of (1) Early PPH detection using a calibrated drape, (2) uterine Massage, (3) Oxytocic drugs, (4) Tranexamic acid, (5) Intra Venous fluids, and (6) genital tract Examination and escalation when necessary. The objective of this paper is to describe the protocol for the formative phase of the E-MOTIVE project, which aims to design an implementation strategy to support the uptake of this bundle into practice. METHODS: We will use behavior change and implementation science frameworks [e.g. capability, opportunity, motivation and behavior (COM-B) and theoretical domains framework (TDF)] to guide data collection and analysis, in Kenya, Nigeria, South Africa, Sri Lanka, and Tanzania. There are four methodological components: qualitative interviews; surveys; systematic reviews; and design workshops. We will triangulate findings across data sources, participant groups, and countries to explore factors influencing current PPH detection and management, and potentially influencing E-MOTIVE bundle implementation. We will use these findings to develop potential strategies to improve implementation, which will be discussed and agreed with key stakeholders from each country in intervention design workshops. DISCUSSION: This formative protocol outlines our strategy for the systematic development of the E-MOTIVE implementation strategy. This focus on implementation considers what it would take to support roll-out and implementation of the E-MOTIVE bundle. Our approach therefore aims to maximize internal validity in the trial alongside future scalability, and implementation of the E-MOTIVE bundle in routine practice, if proven to be effective. TRIAL REGISTRATION: ClinicalTrials.gov: NCT0434166

Miscarriage matters: the epidemiological, physical, psychological, and economic costs of early pregnancy loss

Miscarriage is generally defined as the loss of a pregnancy before viability. An estimated 23 million miscarriages occur every year worldwide, translating to 44 pregnancy losses each minute. The pooled risk of miscarriage is 15·3% (95% CI 12·5–18·7%) of all recognised pregnancies. The population prevalence of women who have had one miscarriage is 10·8% (10·3–11·4%), two miscarriages is 1·9% (1·8–2·1%), and three or more miscarriages is 0·7% (0·5–0·8%). Risk factors for miscarriage include very young or older female age (younger than 20 years and older than 35 years), older male age (older than 40 years), very low or very high body-mass index, Black ethnicity, previous miscarriages, smoking, alcohol, stress, working night shifts, air pollution, and exposure to pesticides. The consequences of miscarriage are both physical, such as bleeding or infection, and psychological. Psychological consequences include increases in the risk of anxiety, depression, post-traumatic stress disorder, and suicide. Miscarriage, and especially recurrent miscarriage, is also a sentinel risk marker for obstetric complications, including preterm birth, fetal growth restriction, placental abruption, and stillbirth in future pregnancies, and a predictor of longer-term health problems, such as cardiovascular disease and venous thromboembolism. The costs of miscarriage affect individuals, health-care systems, and society. The short-term national economic cost of miscarriage is estimated to be £471 million per year in the UK. As recurrent miscarriage is a sentinel marker for various obstetric risks in future pregnancies, women should receive care in preconception and obstetric clinics specialising in patients at high risk. As psychological morbidity is common after pregnancy loss, effective screening instruments and treatment options for mental health consequences of miscarriage need to be available. We recommend that miscarriage data are gathered and reported to facilitate comparison of rates among countries, to accelerate research, and to improve patient care and policy development

First-passage times in complex scale-invariant media

How long does it take a random walker to reach a given target point? This quantity, known as a first passage time (FPT), has led to a growing number of theoretical investigations over the last decade1. The importance of FPTs originates from the crucial role played by first encounter properties in various real situations, including transport in disordered media, neuron firing dynamics, spreading of diseases or target search processes. Most methods to determine the FPT properties in confining domains have been limited to effective 1D geometries, or for space dimensions larger than one only to homogeneous media1. Here we propose a general theory which allows one to accurately evaluate the mean FPT (MFPT) in complex media. Remarkably, this analytical approach provides a universal scaling dependence of the MFPT on both the volume of the confining domain and the source-target distance. This analysis is applicable to a broad range of stochastic processes characterized by length scale invariant properties. Our theoretical predictions are confirmed by numerical simulations for several emblematic models of disordered media, fractals, anomalous diffusion and scale free networks.Comment: Submitted version. Supplementary Informations available on Nature websit

The role of dimensionality in neuronal network dynamics

The research leading to these results has received funding from the European Union’s Seventh Framework Programme under grant agreement FP7 ICT 2011 – 284553 (Acronym: Si-CODE), the NEUROSCAFFOLDS Project n. 604263, the National Natural Science Foundation of China (Grant number: 51361130033) and the Ministry of Science and Technology of China (973 Grant number: 2014CB965003)

Ensemble approach for generalized network dismantling

Finding a set of nodes in a network, whose removal fragments the network below some target size at minimal cost is called network dismantling problem and it belongs to the NP-hard computational class. In this paper, we explore the (generalized) network dismantling problem by exploring the spectral approximation with the variant of the power-iteration method. In particular, we explore the network dismantling solution landscape by creating the ensemble of possible solutions from different initial conditions and a different number of iterations of the spectral approximation.Comment: 11 Pages, 4 Figures, 4 Table