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

Inferring causation from time series in Earth system sciences

The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers. © 2019, The Author(s)

Three Essays on the Interrelationships Among Climate, Conflict and Economics

Conflict in a country is socially expensive and many are trying to understand what factors stimulate it in an effort to figure out ways to lessen its incidence. In this work three essays are presented on factors that drive conflict. The factors examined are: 1) the interrelationship between climate and conflict, 2) the causality between commodity prices and conflict, 3) the ways cereal demand affects and is affected by terrorism. In the first essay, we use a global dataset to econometrically explore whether the probability of conflict is affected by climate. We find that precipitation variation does have a statistically significant effect. That is, the less precipitation this year relative to the last, the more likely the country is to suffer from civil conflict. Methodologically the best predictions are obtained from a semiparametric estimation technique. In the second essay, we econometrically investigate the dynamic relationship between commodity prices and the onset of conflict in Sudan. Applying Structure Vector Autoregression (SVAR) and Linear Non-Gaussian Acyclic Model (LiNGAM), we find that wheat price is a cause of conflict events in Sudan. However, we find no feedback from conflict to commodity prices. In the third essay, we examine the extent that demand for three main cereals in Sudan (sorghum, millet, and wheat) is altered by the incidence of terrorism plus the effect of terrorism events on cereal demand. This is done by using an Almost Ideal Demand System (AIDS) and a Directed Acyclic Graph (DAG) approach. The results show terrorist attacks do cause changes in commodity demand for wheat. The DAG analysis also tentatively suggests that wheat demand is both marginally affected by and directly affecting the incidence of terrorism (conflict) in Sudan. Subsequently, we generate forecasts for the three commodities shares with the AIDS and DAG models, incorporating the effects of terrorist attacks. Examining those results independently and jointly, we find that a composite forecast of the two generates better forecasts

Three Essays on the Relationship between Economic Development and Environmental Quality

This thesis is concerned with examining the relationship between indicators of economic growth and environmental quality. During this process, the analysis explores and attempts to interlink the following theoretical and empirical frameworks: Angelsen and Kaimowitz’s theories for deforestation, the Environmental Kuznets Curve (EKC) hypothesis and the forest transition theory. Macro-level data are used to examine the implications of these frameworks. The implications of the first essay suggest that different crops have a different impact on rate of change of agricultural land use. The second analysis suggests that the results from a Directed Acyclical Graph Approach present a uni-directional causal relationship between income and pollution emissions. The third and final essay suggests that property rights structures and economic incentives appear to be the most probable explanations for the forest transition in India. The macro-level nature of the data sets employed provides information on the broad trends and patterns. For policy recommendations, a more detailed and specific analysis needs to be carried out concentrating on a certain region

Divergences for prototype-based classification and causal structure discovery:Theory and application to natural datasets

Dit proefschrift bestaat uit twee delen. In het eerste deel beschrijven we hoe de op prototypen gebaseerde classificator LVQ uitgebreid kan worden door gebruik te maken van maten uit de informatie theorie. Daarnaast vergelijken we verschillende manieren van datarepresentatie in deze LVQ configuratie, in dit geval histogrammen van foto’s, SIFT- en SURF-kenmerken. We tonen hoe hiervoor een enkele gecombineerde afstandsmaat kan worden geformuleerd, door de afzonderlijke afstandsmaten samen te nemen. In het tweede deel onderzoeken we het vinden van causale verbanden en toepassingen op problemen die uit het leven zijn gegrepen. Daarnaast verkennen we de combinatie met relevantie leren in LVQ en tonen we enkele toepassingen

Bayesian network structure learning in the presence of data noise

A Bayesian Network (BN) is a type of a probabilistic graphical model that captures conditional and marginal independencies between variables. These models are generally represented by a Directed Acyclic Graph (DAG), which is composed by nodes and arcs. The nodes represent variables and the absence of arcs represent conditional or marginal independencies. When BNs are applied to real-world problems, the structure of these models is often assumed to be causal (often referred to as a causal BN), and is often constructed from either expert knowledge and Randomised Controlled Trials (RCTs). However, these two approaches can be time-consuming and expensive, and it might not always be possible or ethical to perform RCTs. As a result, structure learning algorithms that recover graphical structures from observational data, which in turn could be used to inform causal structures, have received increasing attention over the past few decades. To be able to guarantee the correctness of a structure learnt from data, a structure learning algorithm must rely on assumptions that may not hold in practice. One such crucial and commonly used assumption is that the observed data are independently and identically sampled from the underlying distribution, such that all statistical quantities of the distribution can be recovered with no bias from the observed data when sample size goes to infinite. While such assumptions are often needed to be able to devise theoretical guarantees, the impact of violating these assumptions when working with real data tends to be overlooked. Empirical investigations show that structure learning algorithms perform considerably worse on noisy data that violate many of their theoretical assumptions, relative to how they perform on clean synthetic data that do not violate any of their data-generating assumptions. However, there has been limited research on how to deal with these problems effectively and efficiently. This thesis investigates this research direction and primarily focuses on improving structure learning in the presence of measurement error and systematic missing data, which are two of the most common types of data noise present in real data sets

Estimating networks of sustainable development goals

An increasing number of researchers and practitioners advocate for a systemic understanding of the Sustainable Development Goals (SDGs) through interdependency networks. Ironically, the burgeoning network-estimation literature seems neglected by this community. We provide an introduction to the most suitable estimation methods for SDG networks. Building a dataset with 87 development indicators in four countries over 20 years, we perform a comparative study of these methods. We find important differences in the estimated network structures as well as in synergies and trade-offs between SDGs. Finally, we provide some guidelines on the potentials and limitations of estimating SDG networks for policy advice

Discovering phase and causal dependencies on manufacturing processes

Discovering phase and causal dependencies on manufacturing processes. Keyword machine learning, causality, Industry 4.