Joint Estimation of Multiple Graphical Models from High Dimensional Time Series

In this manuscript we consider the problem of jointly estimating multiple graphical models in high dimensions. We assume that the data are collected from n subjects, each of which consists of T possibly dependent observations. The graphical models of subjects vary, but are assumed to change smoothly corresponding to a measure of closeness between subjects. We propose a kernel based method for jointly estimating all graphical models. Theoretically, under a double asymptotic framework, where both (T,n) and the dimension d can increase, we provide the explicit rate of convergence in parameter estimation. It characterizes the strength one can borrow across different individuals and impact of data dependence on parameter estimation. Empirically, experiments on both synthetic and real resting state functional magnetic resonance imaging (rs-fMRI) data illustrate the effectiveness of the proposed method.Comment: 40 page

Network analysis of multivariate data in psychological science

Stress and Psychopatholog

Multi-view machine learning methods to uncover brain-behaviour associations

The heterogeneity of neurological and mental disorders has been a key confound in disease understanding and treatment outcome prediction, as the study of patient populations typically includes multiple subgroups that do not align with the diagnostic categories. The aim of this thesis is to investigate and extend classical multivariate methods, such as Canonical Correlation Analysis (CCA), and latent variable models, e.g., Group Factor Analysis (GFA), to uncover associations between brain and behaviour that may characterize patient populations and subgroups of patients. In the first contribution of this thesis, we applied CCA to investigate brain-behaviour associations in a sample of healthy and depressed adolescents and young adults. We found two positive-negative brain-behaviour modes of covariation, capturing externalisation/ internalisation symptoms and well-being/distress. In the second contribution of the thesis, I applied sparse CCA to the same dataset to present a regularised approach to investigate brain-behaviour associations in high dimensional datasets. Here, I compared two approaches to optimise the regularisation parameters of sparse CCA and showed that the choice of the optimisation strategy might have an impact on the results. In the third contribution, I extended the GFA model to mitigate some limitations of CCA, such as handling missing data. I applied the extended GFA model to investigate links between high dimensional brain imaging and non-imaging data from the Human Connectome Project, and predict non-imaging measures from brain functional connectivity. The results were consistent between complete and incomplete data, and replicated previously reported findings. In the final contribution of this thesis, I proposed two extensions of GFA to uncover brain behaviour associations that characterize subgroups of subjects in an unsupervised and supervised way, as well as explore within-group variability at the individual level. These extensions were demonstrated using a dataset of patients with genetic frontotemporal dementia. In summary, this thesis presents multi-view methods that can be used to deepen our understanding about the latent dimensions of disease in mental/neurological disorders and potentially enable patient stratification

Brain network mechanisms in learning behavior

The study of learning has been a central focus of psychology and neuroscience since their inception. Cognitive neuroscience’s traditional approach to understanding learn-ing has been to decompose it into discrete cognitive processes with separable and localized underlying neural systems. While this focus on modular cognitive functions for individual brain areas has led to considerable progress, there is increasing evidence that much of learn-ing behavior relies on overlapping cognitive and neural systems, which may be harder to disentangle than previously envisioned. This is not surprising, as the processes underlying learning must involve widespread integration of information from sensory, affective, and motor sources. The standard tools of cognitive neuroscience limit our ability to describe processes that rely on widespread coordination of brain activity. To understand learning, it will be necessary to characterize dynamic co-activation at the circuit level. In this dissertation, I present three studies that seek to describe the roles of distrib-uted brain networks in learning. I begin by giving an overview of our current understand-ing of multiple forms of learning, describing the neural and computational mechanisms thought to underlie incremental feedback-based learning and flexible episodic memory. I will focus in particular on the difficulties in separating these processes at the cognitive level and in localizing them to individual regions at the neural level. I will then describe recent findings that have begun to characterize the brain’s large-scale network structure, emphasiz-ing the potential roles that distributed networks could play in understanding learning and cognition more generally. I will end the introduction by reviewing current attempts to char-acterize the dynamics of large-scale brain networks, which will be essential for providing a mechanistic link to learning behavior. Chapter 2 is a study demonstrating that intrinsic connectivity between the hippo-campus and the ventromedial prefrontal cortex, as well as between these regions and dis-tributed brain networks, is related to individual differences in the transfer of learning on a sensory preconditioning task. The hippocampus and ventromedial prefrontal cortex have both been shown to be involved in this type of learning, and this study represents an early attempt to link connectivity between individual regions and broader networks to learning processes. Chapter 3 is a study that takes advantage of recent developments in mathematical modeling of temporal networks to demonstrate a relationship between large-scale network dynamics and reinforcement learning within individuals. This study shows that the flexibil-ity of network connectivity in the striatum is related to learning performance over time, as well as to individual differences in parameters estimated from computational models of re-inforcement learning. Notably, connectivity between the striatum and visual as well as or-bitofrontal regions increased over the course of the task, which is consistent with an inte-grative role for the region in learning value-based associations. Network flexibility in a dis-tinct set of regions is associated with episodic memory for object images presented during the learning task. Chapter 4 examines the role of dopamine, a neurotransmitter strongly linked to val-ue updating in reinforcement learning, in the dynamic network changes occurring during learning. Patients with Parkinson’s disease, who experience a loss of dopaminergic neu-rons in the substantia nigra, performed a reversal-learning task while undergoing functional magnetic resonance imaging. Patients were scanned on and off of a dopamine precursor medication (levodopa) in a within-subject design in order to examine the impact of dopa-mine on brain network dynamics during learning. The reversal provided an experimental manipulation of dynamic connectivity, and patients on medication showed greater modula-tion of striatal-cortical connectivity. Similar results were found in a number of regions re-ceiving midbrain projections including the prefrontal cortex and medial temporal lobe. This study indicates that dopamine inputs from the midbrain modulate large-scale network dy-namics during learning, providing a direct link between reinforcement learning theories of value updating and network neuroscience accounts of dynamic connectivity. Together, these results indicate that large-scale networks play a critical role in multi-ple forms of learning behavior. Each highlights the potential importance of understanding dynamic routing and integration of information across large-scale circuits for our concep-tion of learning and other cognitive processes. Understanding the when, where, and how of this information flow in the brain may provide an alternative or compliment to traditional theories of distinct learning systems. These studies also illustrate challenges in integrating this perspective with established theories in cognitive neuroscience. Chapter 5 will situate the studies in a broader discussion of how brain activity relates to cognition in general, while pointing out current roadblocks and potential ways forward for a cognitive network neuroscience of learning

Towards Understanding the Role of Environmental Risk Factors in Psychosis and Beyond: A Data-Driven Network Approach

Psychotic disorders impose high burden on both the affected individual and society. Despite extensive research efforts in recent decades, their etiology remains poorly understood, hindering progress in prevention and treatment. Two distinct developments in the field may represent ways forward: First, there is a growing recognition of the importance of several potentially malleable environmental risk factors, such as childhood trauma, stressful life events, or cannabis use, in the onset, progression, and maintenance of psychotic disorders. Second, the ubiquitous common cause model of psychotic disorders is increasingly challenged by alternative conceptualizations of mental disorders, such as the network approach to psychopathology. In the common cause model, symptoms are viewed as mere effects of a common cause (the disorder itself, e.g., ‘schizophrenia’), i.e., symptoms covary because of their joint dependence on an assumed latent disorder entity. This traditional view also assumes that environmental factors influence symptoms via the disorder entity. In contrast, the network approach to psychopathology views mental disorders as networks of directly interacting symptoms and other components, such as environmental risk factors. Patterns of covariation between symptoms and other components are assumed to reflect meaningful relationships and become the focus of analysis. Building upon these developments, this thesis proposes a network approach to disentangle potential pathways by which environmental risk factors increase the risk for psychotic disorders. Specifically, the five presented papers focus on individual symptoms and their associations with common environmental risk factors of psychotic disorders. Network structures were generated from empirical data by estimating unique pairwise relationships, i.e., the associations between any two variables that remain after controlling for all other variables under consideration; primarily in the form of undirected pairwise Markov random fields. The first paper built upon evidence for an affective pathway from childhood trauma to psychosis and demonstrated that a similar pathway applied to exposure to recent stressful life events in at-risk and recent onset psychosis patients. Specifically, results showed that burden of recent life events did not link to positive and negative psychotic symptoms directly, but only indirectly, via symptoms of general psychopathology, such as depression, guilt, and anxiety. The second paper zoomed into the proposed affective pathway via increased stress reactivity through which childhood trauma is thought to contribute to the liability for psychopathology at large, including psychotic disorders. The findings provide a detailed characterization of putative psychological stress processes underlying distinct types of childhood trauma in the general population: childhood trauma reflecting deprivation (i.e., neglect) was exclusively associated with stressful experiences representing low perceived self-efficacy, whereas childhood trauma reflecting threat (i.e., abuse) was specifically associated with stressful experiences reflecting perceived helplessness. The third paper then addressed another important risk factor for psychotic disorders, cannabis use. The results suggest that characteristics of cannabis use in the general population may contribute differentially to the risk for certain psychotic experiences and affective symptoms: Network associations were particularly pronounced between increased frequency of cannabis use and certain delusional experiences, i.e., persecutory delusions and thought broadcasting, on the one hand, and earlier onset of cannabis use and visual hallucinatory experiences and irritability, on the other. The fourth paper investigated which environmental and demographic factors explained heterogeneity in symptom networks of psychosis to highlight potential etiological divergence in risk for psychosis in the general population. Results point to distinct sex-specific etiological mechanisms contributing to psychosis risk: In women, an affective pathway to psychosis may have distinct importance, especially after interpersonal trauma. In men, an ethnic minority background was associated with strong interconnections between individual psychotic experiences, which has been linked to poor outcomes in previous research. The fifth and final paper presented the protocol for an experience sampling study in the help-seeking population of the Early Recognition Center for Mental Disorders of the University Hospital Cologne. A central goal in this project will be to elucidate how personalized symptom networks derived from intensive longitudinal data differ as a function of environmental exposure. In sum, findings from this thesis illustrate that environmental risk factors increase psychosis risk through diverse, potentially sex-specific pathways that often involve affective psychopathology. This confirms the notion that the etiology of psychosis is complex and best understood from a broad, transdiagnostic perspective. The results presented are also relevant for clinical practice as they pave the way for a better selection of appropriate interventions and treatments. In particular, this thesis highlights affective disturbances and negative beliefs as potential intervention targets in the affective pathway to psychosis, especially following trauma and stressful life events. In perspective, the use of personalized network approaches may improve the ability to tailor therapeutic strategies based on the dynamics of a patient’s symptoms and environmental risk factors as captured in daily life. Recently proposed multilayered network approaches have potential to further advance our understanding of psychosis etiology by linking psychological and biological levels of analysis

DECONVOLUTION AND NETWORK CONSTRUCTION BY SINGLE CELL RNA SEQUENCING DATA

In this dissertation, we develop three novel analytic approaches for scRNA-seq data. In the first project, we aim to utilize scRNA-seq data to efficiently deconvolute bulk RNA-seq data. Deconvolution of bulk RNA-seq data by scRNA-seq data benefits from the high resolution in the characterization of transcriptomic heterogeneity from single-cells while enjoying higher statistical testing power with lower cost provided by bulk samples. Specifically, we propose an ENSEMBLE method SCDC (scRNA-seq DeConvolution), which integrates deconvolution results derived from multiple reference datasets, implicitly addressing the well-known batch effects. SCDC is benchmarked against existing methods and illustrated by the application to a human pancreatic islet dataset and a mouse mammary gland dataset.In the second project, to better understand gene regulatory networks under different but related conditions with single-cell resolution, we propose to construct Joint Gene Networks with scRNA-seq data (JGNsc) using the Gaussian graphical models (GGMs) framework. The sparsity feature of scRNA-seq data hinders the direct application of the popular GGMs. To facilitate the use of GGMs, JGNsc first proposes a hybrid imputation procedure that combines a Bayesian zero-inflated Poisson model with an iterative low-rank matrix completion step to efficiently impute zeros resulted from technical artifacts. JGNsc then transforms the imputed data via a nonparanormal transformation, based on which joint GGMs are constructed. We demonstrate JGNsc and assess its performance using synthetic data and two cancer clinical studies of medulloblastoma and glioblastoma.In the third project, for scRNA-seq data with continuous or ambiguous cell states, we develop a covariance-based change point detection (CPD) procedure to infer the discrete subgroups by utilizing the continuous pseudotime of single-cells. Little research suggests whether and how well the existing multivariate CPD methods work for scRNA-seq data. Hence, popular existing methods are benchmarked and evaluated in the simulation study and are shown to be powered for detecting mean but not covariance changes. To detect covariance changes, we propose the algorithm covcpd, which partitions single-cell samples into homogeneous network groups by utilizing a covariance equality testing statistic. covcpd is evaluated by simulation and is further illustrated through a mouse embryonic dataset and a human embryonic stem-cell dataset.Doctor of Philosoph