A Longitudinal Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis

In this paper we propose a novel method for the segmentation of longitudinal brain MRI scans of patients suffering from Multiple Sclerosis. The method builds upon an existing cross-sectional method for simultaneous whole-brain and lesion segmentation, introducing subject-specific latent variables to encourage temporal consistency between longitudinal scans. It is very generally applicable, as it does not make any prior assumptions on the scanner, the MRI protocol, or the number and timing of longitudinal follow-up scans. Preliminary experiments on three longitudinal datasets indicate that the proposed method produces more reliable segmentations and detects disease effects better than the cross-sectional method it is based upon

Sparse reduced-rank regression for imaging genetics studies: models and applications

We present a novel statistical technique; the sparse reduced rank regression (sRRR) model which is a strategy for multivariate modelling of high-dimensional imaging responses and genetic predictors. By adopting penalisation techniques, the model is able to enforce sparsity in the regression coefficients, identifying subsets of genetic markers that best explain the variability observed in subsets of the phenotypes. To properly exploit the rich structure present in each of the imaging and genetics domains, we additionally propose the use of several structured penalties within the sRRR model. Using simulation procedures that accurately reflect realistic imaging genetics data, we present detailed evaluations of the sRRR method in comparison with the more traditional univariate linear modelling approach. In all settings considered, we show that sRRR possesses better power to detect the deleterious genetic variants. Moreover, using a simple genetic model, we demonstrate the potential benefits, in terms of statistical power, of carrying out voxel-wise searches as opposed to extracting averages over regions of interest in the brain. Since this entails the use of phenotypic vectors of enormous dimensionality, we suggest the use of a sparse classification model as a de-noising step, prior to the imaging genetics study. Finally, we present the application of a data re-sampling technique within the sRRR model for model selection. Using this approach we are able to rank the genetic markers in order of importance of association to the phenotypes, and similarly rank the phenotypes in order of importance to the genetic markers. In the very end, we illustrate the application perspective of the proposed statistical models in three real imaging genetics datasets and highlight some potential associations

Phases of systematic brain processing differentially relate to cognitive constructs of attention and executive function in typically-developing children: a latent variable analysis

2017 Summer.Includes bibliographical references.The series of studies presented in this dissertation examines the complex interrelationships between brain measures, cognitive abilities, and simple behaviors in typically-developing children. Much recent research has been dedicated to understanding the interaction between neural processing and behaviors across development. However, the field continues to rely on simplistic statistical approaches (e.g., correlations, t tests, ANOVAs), which 1) are unable to simultaneously examine multiple interrelationships among variables of interest, and 2) are easily confounded by sources of measurement error. The result is weak relationships between brain and behavioral measures. In this series of studies, we progressively demonstrate how more sophisticated statistical approaches, namely structural equation modeling (SEM) techniques, can be utilized in order to improve researchers' ability to detect brain-behavior relationships in children. All three of the present studies utilize event-related potential (ERP) and behavioral data collected from a sample of typically-developing children ages of 7- to 13-years-old during two separate sessions. In Study 1, we explore the interrelationships between the E-wave component of an ERP, two trait behavioral measures of attentional processing, and simple reaction time (RT) measures during the ERP task. Whereas simple bivariate correlations indicated that the E-wave and RT only shared 7.9 – 9.6% of their variance, a latent variable approach using E-wave and trait attention measures successfully predicted 47.7% of the variance in RT. However, the predictive coefficient from brain-to-behavior was still weak (β = .23), suggesting that there may be neural influences in addition to the E-wave that contribute to the variance in RT. Thus, in Study 2 we elaborated on this model and explored whether the full time-course of an averaged ERP could be conceptualized as a sequence of phases that represents stimulus-to-response decision-making processes. Specifically, we tested a latent variable path model in which one ERP component predicted the next in chronological order, with the full stream of neural processing ultimately predicting RT during the task (N1 → P2 → N2 → P3 → E-wave → RT). Age served as a control variable on each phase of processing and on RT. Results indicated strong predictive relationships from one component to the next (β's = .59 - .86), with the full stream of processing significantly predicting RT (β = .45). The model was fully-mediated, underscoring the importance of the full time-course of the ERP for understanding behaviors during the task. In addition, there were significant age effects on the N2, P3, and RT latent variables (β =.28, -.48, & -.42 respectively). Given the nature of path analyses, the findings suggested that "age" was likely a multifaceted construct representing maturation within multiple domains of cognitive or motor functioning. Study 3 explored the differential relationships between two developmentally-sensitive cognitive constructs and each of the phases of neural processing, effectively replacing "age" with more substantive definitions of maturational effects in the model. The two cognitive constructs captured aspects of attention and executive function processing. Indeed, the findings indicated that each phase of neural processing was differentially influenced by each of the two cognitive constructs. The data suggested that children with better, more matured abilities within a specific cognitive domain tended to have smaller amplitude ERP components from the N1 through the P3, and larger amplitude E-wave components. Conceptually, children with more matured cognitive abilities were able to process the ERP task more efficiently (or with less effort), and engaged in greater anticipatory processing leading to the task behavior when compared to children with less matured cognitive abilities. Of note, the full model did still significantly predict RT during the task, and to a much greater extent than was found in Study 2 (β = .92). The series of investigations in this dissertation demonstrate the utility of SEM approaches for understanding brain-behavior relationships in typically-developing children. Namely, the studies showed that 1) latent variable approaches are helpful in reducing measurement error in ERP and behavioral data, which may impede the detection of brain-behavior relationships when using more simplistic statistical approaches; 2) conceptualizing the full time-course of an ERP preceding a task behavior is not only helpful, but necessary to successfully predict behaviors; and 3) we can further elucidate unique influences of maturation on neural processing within multiple cognitive domains when we embrace advanced statistical approaches like SEM. Implications of the findings and import to the field are discussed in the final chapter

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

Group integrative dynamic factor models for inter- and intra-subject brain networks

This work introduces a novel framework for dynamic factor model-based data integration of multiple subjects, called GRoup Integrative DYnamic factor models (GRIDY). The framework facilitates the determination of inter-subject differences between two pre-labeled groups by considering a combination of group spatial information and individual temporal dependence. Furthermore, it enables the identification of intra-subject differences over time by employing different model configurations for each subject. Methodologically, the framework combines a novel principal angle-based rank selection algorithm and a non-iterative integrative analysis framework. Inspired by simultaneous component analysis, this approach also reconstructs identifiable latent factor series with flexible covariance structures. The performance of the framework is evaluated through simulations conducted under various scenarios and the analysis of resting-state functional MRI data collected from multiple subjects in both the Autism Spectrum Disorder group and the control group

An automated bedside measure for monitoring neonatal cortical activity: a supervised deep learning-based electroencephalogram classifier with external cohort validation

Peer reviewe

Individual variation in patterns of task focused, and detailed, thought are uniquely associated within the architecture of the medial temporal lobe

Understanding the neural processes that support different patterns of ongoing thought is an important goal of contemporary cognitive neuroscience. Early accounts assumed the default mode network (DMN) was especially important for conscious attention to task-irrelevant/personally relevant material. However, simple task-negative accounts of the DMN are incompatible with more recent evidence that neural patterns within the system can be related to ongoing processing during active task states. To better characterize the contribution of the DMN to ongoing thought, we conducted a cross-sectional analysis of the relationship between the structural organisation of the brain, as indexed by cortical thickness, and patterns of experience, identified using experience sampling in the cognitive laboratory. In a sample of 181 healthy individuals (mean age 20 years, 117 females) we identified an association between cortical thickness in the anterior parahippocampus and patterns of task focused thought, as well as an adjacent posterior region in which cortical thickness was associated with experiences with higher levels of subjective detail. Both regions fell within regions of medial temporal lobe associated with the DMN, yet varied in their functional connectivity: the time series of signals in the ‘on-task’ region were more correlated with systems important for external task-relevant processing (as determined by meta-analysis) including the dorsal and ventral attention, and fronto-parietal networks. In contrast, connectivity within the region linked to subjective ‘detail’ was more correlated with the medial core of the DMN (posterior cingulate and the medial pre-frontal cortex) and regions of primary visual cortex. These results provide cross-sectional evidence that confirms a role of the DMN in how detailed experiences are and so provide further evidence that the role of this system in experience is not simply task-irrelevant. Our results also highlight processes within the medial temporal lobe, and their interactions with other regions of cortex as important in determining multiple aspects of how human cognition unfolds