MULTIVARIATE MODELING OF COGNITIVE PERFORMANCE AND CATEGORICAL PERCEPTION FROM NEUROIMAGING DATA

State-of-the-art cognitive-neuroscience mainly uses hypothesis-driven statistical testing to characterize and model neural disorders and diseases. While such techniques have proven to be powerful in understanding diseases and disorders, they are inadequate in explaining causal relationships as well as individuality and variations. In this study, we proposed multivariate data-driven approaches for predictive modeling of cognitive events and disorders. We developed network descriptions of both structural and functional connectivities that are critical in multivariate modeling of cognitive performance (i.e., fluency, attention, and working memory) and categorical perceptions (i.e., emotion, speech perception). We also performed dynamic network analysis on brain connectivity measures to determine the role of different functional areas in relation to categorical perceptions and cognitive events. Our empirical studies of structural connectivity were performed using Diffusion Tensor Imaging (DTI). The main objective was to discover the role of structural connectivity in selecting clinically interpretable features that are consistent over a large range of model parameters in classifying cognitive performances in relation to Acute Lymphoblastic Leukemia (ALL). The proposed approach substantially improved accuracy (13% - 26%) over existing models and also selected a relevant, small subset of features that were verified by domain experts. In summary, the proposed approach produced interpretable models with better generalization.Functional connectivity is related to similar patterns of activation in different brain regions regardless of the apparent physical connectedness of the regions. The proposed data-driven approach to the source localized electroencephalogram (EEG) data includes an array of tools such as graph mining, feature selection, and multivariate analysis to determine the functional connectivity in categorical perceptions. We used the network description to correctly classify listeners behavioral responses with an accuracy over 92% on 35 participants. State-of-the-art network description of human brain assumes static connectivities. However, brain networks in relation to perception and cognition are complex and dynamic. Analysis of transient functional networks with spatiotemporal variations to understand cognitive functions remains challenging. One of the critical missing links is the lack of sophisticated methodologies in understanding dynamics neural activity patterns. We proposed a clustering-based complex dynamic network analysis on source localized EEG data to understand the commonality and differences in gender-specific emotion processing. Besides, we also adopted Bayesian nonparametric framework for segmentation neural activity with a finite number of microstates. This approach enabled us to find the default network and transient pattern of the underlying neural mechanism in relation to categorical perception. In summary, multivariate and dynamic network analysis methods developed in this dissertation to analyze structural and functional connectivities will have a far-reaching impact on computational neuroscience to identify meaningful changes in spatiotemporal brain activities

Improved dynamic connection detection power in estimated dynamic functional connectivity considering multivariate dependencies between brain regions

To estimate dynamic functional connectivity (dFC), the conventional method of sliding window correlation (SWC) suffers from poor performance of dynamic connection detection. This stems from the equal weighting of observations, suboptimal time scale, nonsparse output, and the fact that it is bivariate. To overcome these limitations, we exploited the kernel-reweighted logistic regression (KELLER) algorithm, a method that is common in genetic studies, to estimate dFC in resting state functional magnetic resonance imaging (rs-fMRI) data. KELLER can estimate dFC through estimating both spatial and temporal patterns of functional connectivity between brain regions. This paper compares the performance of the proposed KELLER method with current methods (SWC and tapered-SWC (T-SWC) with different window lengths) based on both simulated and real rs-fMRI data. Estimated dFC networks were assessed for detecting dynamically connected brain region pairs with hypothesis testing. Simulation results revealed that KELLER can detect dynamic connections with a statistical power of 87.35% compared with 70.17% and 58.54% associated with T-SWC (p-value = .001) and SWC (p-value \u3c.001), respectively. Results of these different methods applied on real rs-fMRI data were investigated for two aspects: calculating the similarity between identified mean dynamic pattern and identifying dynamic pattern in default mode network (DMN). In 68% of subjects, the results of T-SWC with window length of 100 s, among different window lengths, demonstrated the highest similarity to those of KELLER. With regards to DMN, KELLER estimated previously reported dynamic connection pairs between dorsal and ventral DMN while SWC-based method was unable to detect these dynamic connections

A Review on Dependence Measures in Exploring Brain Networks from fMRI Data

Functional magnetic resonance imaging (fMRI) technique allows us to capture activities occurring in a human brain via signals from blood flow, known as BOLD (blood oxygen level-dependent) signals. Exploring a relationship among brain regions inside human brains from fMRI data is an active and challenging research topic. Relationships or associations between brain regions are commonly referred to as brain connectivity or brain network. This connectivity can be divided into two groups, the functional connectivity which describes the statistical information among brain regions and the effective connectivity which specifies how one region interacts with others by a causal model. This survey paper provides a review on learning brain connectivities via fMRI data, mathematical definitions or dependence measures of such connectivities. These well-known measures include correlation, partial correlation, conditional independence, dynamical causal modeling, Granger causality, and structural equation modeling, which all can be translated in terms of mathematical conditions of model parameters. We also discusses about relevant estimation techniques that have been widely used in the problems of fMRI modeling. Understanding a rigorous definition on relationships in human brain allows us to interpret or compare the results in the context of learning brain network more clearly

Whole Brain Network Dynamics of Epileptic Seizures at Single Cell Resolution

Epileptic seizures are characterised by abnormal brain dynamics at multiple scales, engaging single neurons, neuronal ensembles and coarse brain regions. Key to understanding the cause of such emergent population dynamics, is capturing the collective behaviour of neuronal activity at multiple brain scales. In this thesis I make use of the larval zebrafish to capture single cell neuronal activity across the whole brain during epileptic seizures. Firstly, I make use of statistical physics methods to quantify the collective behaviour of single neuron dynamics during epileptic seizures. Here, I demonstrate a population mechanism through which single neuron dynamics organise into seizures: brain dynamics deviate from a phase transition. Secondly, I make use of single neuron network models to identify the synaptic mechanisms that actually cause this shift to occur. Here, I show that the density of neuronal connections in the network is key for driving generalised seizure dynamics. Interestingly, such changes also disrupt network response properties and flexible dynamics in brain networks, thus linking microscale neuronal changes with emergent brain dysfunction during seizures. Thirdly, I make use of non-linear causal inference methods to study the nature of the underlying neuronal interactions that enable seizures to occur. Here I show that seizures are driven by high synchrony but also by highly non-linear interactions between neurons. Interestingly, these non-linear signatures are filtered out at the macroscale, and therefore may represent a neuronal signature that could be used for microscale interventional strategies. This thesis demonstrates the utility of studying multi-scale dynamics in the larval zebrafish, to link neuronal activity at the microscale with emergent properties during seizures

Inference of high-dimensional linear models with time-varying coefficients

In part 1, we propose a pointwise inference algorithm for high-dimensional linear models with time-varying coefficients and dependent error processes. The method is based on a novel combination of the nonparametric kernel smoothing technique and a Lasso bias-corrected ridge regression estimator using a bias-variance decomposition to address non-stationarity in the model. A hypothesis testing setup with familywise error control is presented alongside synthetic data and a real application to fMRI data for Parkinson's disease. In part 2, we propose an algorithm for covariance and precision matrix estimation high-dimensional transpose-able data. The method is based on a Kronecker product approximation of the graphical lasso and the application of the alternating directions method of multipliers minimization. A simulation example is provided

Longitudinal changes in visual attention and EEG coherence: implications for the development of attention networks

Theoretical models of attention development have emphasized the role of neural networks in the emergence of attentional control towards the end of the first year of life. The current study aimed to assess the relation between neural network maturation and developing attention skills by examining changes in observed attention and EEG coherence across a short-term longitudinal study. Participants were 339 infants participating in a larger study; data was collected at 5 and 10 months of age. It was hypothesized that increases in frontal and fronto-parietal EEG coherence during this time would be positively associated with controlled attention behaviors at 10 months. Attention behaviors were coded using Video Coding System software developed by James Long Company. Results indicated that on average EEG coherence values were significantly greater at 10 months than at 5 months across multiple pairs. However, only for certain pairs were changes in EEG coherence significantly associated with changes in observed attention behaviors. Effect sizes were relatively small and do not provide strong support for the role or neural networks in developing attention skills. Results are discussed in terms of directions for future research

The neuro-computational role of uncertainty in anxiety

Anxiety disorders are the most common mental health disorders and comprise a large number of years lost to disability. The work in this thesis is oriented towards understanding anxiety using a computational approach, focusing on uncertainty estimation as a key process. Chapter 1 introduces the role of uncertainty within anxiety and motivates the subsequent experimental chapters. Chapter 2 is a review of the computational role of the amygdala in humans, a key area for uncertainty computation. Chapter 3 is an experimental chapter which aimed to address gaps in the literature highlighted in the preceding chapters, namely the link between sensory uncertainty processing and anxiety and the role of the amygdala in this process. This chapter focuses on the development of a novel computational hierarchical Bayesian model to quantify sensory uncertainty and its application to neuroimaging data, with intolerance of uncertainty relating to greater neural activation in the insula but not amygdala. Chapter 4 targets the computational mechanisms underlying the negative self-bias observed in subclinical social anxiety. Again, this chapter focuses on the development of novel computational belief-update models which explicitly model uncertainty. Here, we see that a reduced trait self-positivity underpins this negative social evaluation process. The final experimental chapter presented in Chapter 5 investigates the link between different computational mechanisms, such as uncertainty, and a range of mood and anxiety symptomatology. This study revealed cognitive, social and somatic computational profiles that share a threat bias mechanism but have distinct negative-self bias and aversive learning signatures. Contrary to expectations, none of the uncertainty measures showed any associations with anxiety symptom subtypes. Finally, chapter 6 brings together the work in this thesis and alongside limitations of the work, discusses how these experiments contribute to our understanding of anxiety and the role of uncertainty across the anxiety spectrum

Geometric Functional Data Analysis

In this thesis, we introduce a comprehensive framework for the analysis of statistical samples that are functional data with non-trivial geometry. Geometry can interplay with functional data in different forms. The most general setting considered here is that of functional data supported on random non-linear smooth manifolds. This is a situation often encountered in neuroimaging, where modern imaging modalities are now able to produce structural brain representations coupled with functional information. Practitioners have commonly approached the analysis of such data with a two step approach. In the first step the manifolds are registered to a template and in the second step the functional information is analyzed on the template ignoring the registration step. The separation of the two steps precludes studies aimed at understanding how geometric variations relate to functional variations. On the other hand, functional data analysis has mostly developed tools for simplified settings, such as one-dimensional functional samples, limiting their applicability to real data. We formulate a model which is able to jointly represent geometric and functional variations. In this setting, modeling functional information requires the formulation of models able to incorporate structural information on the geometry of the underlying domains, with the aim of mitigating the curse of dimensionality. This is achieved by adopting regularized models involving differential operator penalties. Modeling random smooth manifolds requires the formulation of models constrained to produce `sensible' shapes, e.g. not self-intersecting. This is achieved by means of diffeomorphic flows. The proposed models have been applied to real data to perform studies able to relate structural changes to functional changes, and specifically, to study associations between brain shape and cerebral cortex thickness. We can also deal with more complex functional samples, themselves constrained to lie in a non-linear subspace. This is for instance the case of covariance operators, describing brain connectivity, which are symmetric and positive semi-definite operators. Thanks to the proposed models, we are able to model connectivity as an `object' and study its variations in time or across individuals. We also consider further extensions of this framework to the inverse problems setting, which is the setting where each sample is a latent object, and only indirect measurements are available.EPSRC Centre for Doctoral Training in Analysis (Cambridge Centre for Analysis) EP/L016516/

Exploring the Neural Mechanisms of Physics Learning

This dissertation presents a series of neuroimaging investigations and achievements that strive to deepen and broaden our understanding of human problem solving and physics learning. Neuroscience conceives of dynamic relationships between behavior, experience, and brain structure and function, but how neural changes enable human learning across classroom instruction remains an open question. At the same time, physics is a challenging area of study in which introductory students regularly struggle to achieve success across university instruction. Research and initiatives in neuroeducation promise a new understanding into the interactions between biology and education, including the neural mechanisms of learning and development. These insights may be particularly useful in understanding how students learn, which is crucial for helping them succeed. Towards this end, we utilize methods in functional magnetic resonance imaging (fMRI), as informed by education theory, research, and practice, to investigate the neural mechanisms of problem solving and learning in students across semester-long University-level introductory physics learning environments. In the first study, we review and synthesize the neuroimaging problem solving literature and perform quantitative coordinate-based meta-analysis on 280 problem solving experiments to characterize the common and dissociable brain networks that underlie human problem solving across different representational contexts. Then, we describe the Understanding the Neural Mechanisms of Physics Learning project, which was designed to study functional brain changes associated with learning and problem solving in undergraduate physics students before and after a semester of introductory physics instruction. We present the development, facilitation, and data acquisition for this longitudinal data collection project. We then perform a sequence of fMRI analyses of these data and characterize the first-time observations of brain networks underlying physics problem solving in students after university physics instruction. We measure sustained and sequential brain activity and functional connectivity during physics problem solving, test brain-behavior relationships between accuracy, difficulty, strategy, and conceptualization of physics ideas, and describe differences in student physics-related brain function linked with dissociations in conceptual approach. The implications of these results to inform effective instructional practices are discussed. Then, we consider how classroom learning impacts the development of student brain function by examining changes in physics problem solving-related brain activity in students before and after they completed a semester-long Modeling Instruction physics course. Our results provide the first neurobiological evidence that physics learning environments drive the functional reorganization of large-scale brain networks in physics students. Through this collection of work, we demonstrate how neuroscience studies of learning can be grounded in educational theory and pedagogy, and provide deep insights into the neural mechanisms by which students learn physics