Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area

A Functional Data Method for Causal Dynamic Network Modeling of Task-Related fMRI

Functional MRI (fMRI) is a popular approach to investigate brain connections and activations when human subjects perform tasks. Because fMRI measures the indirect and convoluted signals of brain activities at a lower temporal resolution, complex differential equation modeling methods (e.g., Dynamic Causal Modeling) are usually employed to infer the neuronal processes and to fit the resulting fMRI signals. However, this modeling strategy is computationally expensive and remains to be mostly a confirmatory or hypothesis-driven approach. One major statistical challenge here is to infer, in a data-driven fashion, the underlying differential equation models from fMRI data. In this paper, we propose a causal dynamic network (CDN) method to estimate brain activations and connections simultaneously. Our method links the observed fMRI data with the latent neuronal states modeled by an ordinary differential equation (ODE) model. Using the basis function expansion approach in functional data analysis, we develop an optimization-based criterion that combines data-fitting errors and ODE fitting errors. We also develop and implement a block coordinate-descent algorithm to compute the ODE parameters efficiently. We illustrate the numerical advantages of our approach using data from realistic simulations and two task-related fMRI experiments. Compared with various effective connectivity methods, our method achieves higher estimation accuracy while improving the computational speed by from tens to thousands of times. Though our method is developed for task-related fMRI, we also demonstrate the potential applicability of our method (with a simple modification) to resting-state fMRI, by analyzing both simulated and real data from medium-sized networks

Analyzing the Policy Implications of Subsidies, Causality Structures, and Pre-Determined Demand on Consumer Food Acquisitions

The overall objective of this dissertation is to contribute to a better understanding of consumer food acquisitions by considering policies to promote dietary fiber intake, modeling consumer food acquisitions as a complex system, and by determining the effects of pre-determined demand and regularity conditions on policy analysis. To achieve these objectives, three related empirical investigations of consumer food acquisitions are conducted. The first paper conducts a panel regression on nine per-capita fiber intake categories taken from purchases of a variety of food to uncover socioeconomic, demographic and government food policy related factors on the per capita intake of dietary fiber in the United States. Although consumer response to 2010 Dietary Guidelines for Americans in terms of increased intake of dietary fiber showed mixed results, a proposed 20% subsidy on prices of fruits and vegetables showed some promising results concerning increasing fiber intake in the U.S. diet. The second uses individual and household attributes, characteristics of the local food environment, the individual’s dietary pattern, prices, health outcomes, and policy variables jointly to estimate a complex graphical causality structure. The resulting directed acyclic graph shows a number of complicated relationship among these variables. Concerning the paths between poverty, race and food insecurity, we find a number of paths. Thus, policymakers that want to reduce the problems associated with food insecurity need a full picture of the complex interactions among all these variables. In addition, we find variables associated with the Supplemental Nutrition Assistance Program participation and food insecurity to be not strictly endogenous. Obesity was found to be strictly endogenous. The objective of the third paper is to examine the affect that ignoring pre-determined demand and theoretical regularity conditions will have on consumer food demand. To accomplish this we used the Almost Ideal Demand System because of its wide use in applied policy research. A major result from this study is that elasticities calculated under the presence of pre-commitments are more elastic relative to those calculated without. The result from a proposed subsidy further reinforces the importance of accounting for pre-commitments. In terms of satisfying regularity conditions, the AIDS with pre-commitments performs slightly better. One further important result from this study is not only the need to account for pre-commitments, but also the need to account for the timing of a consumer’s pre-commitments, since pre-committed quantities could vary over time

Individual Differences in Skill Development: Toward a Causal Explanation

Describing and explaining individual differences in skill development is a fundamental component of educational research. However, given the highly interdisciplinary nature of the field and the various theoretical and methodological approaches involved, studies on skill development often fail to provide a coherent and cumulative body of research. In this three-study dissertation, I discuss foundational conceptual and methodological issues in skill development, and show how different approaches can be integrated in a principled and cumulative fashion. The first study presents a general framework, referred to under the label of “academic mobility”, for describing the development of educational inequalities using student learning outcomes. In this study, I discuss ways of operationalizing the concept of educational inequality, and measure academic mobility at a national level using five mobility metrics. While the first study is descriptive in nature, the second and third study intend to shed some light into why individual differences might arise. The second study presents an approach for establishing the explanatory relevance of different predictors based on distal and proximal considerations. For this purpose, I implement several causal search algorithms and find that, consistent with my research hypotheses, previous achievement and executive functions are proximal mechanisms of both reading and math achievement. Finally, in the third paper I describe the relationship between executive functions and academic achievement by implementing several within-person methodological strategies