A symmetric multivariate leakage correction for MEG connectomes

AbstractAmbiguities in the source reconstruction of magnetoencephalographic (MEG) measurements can cause spurious correlations between estimated source time-courses. In this paper, we propose a symmetric orthogonalisation method to correct for these artificial correlations between a set of multiple regions of interest (ROIs). This process enables the straightforward application of network modelling methods, including partial correlation or multivariate autoregressive modelling, to infer connectomes, or functional networks, from the corrected ROIs. Here, we apply the correction to simulated MEG recordings of simple networks and to a resting-state dataset collected from eight subjects, before computing the partial correlations between power envelopes of the corrected ROItime-courses. We show accurate reconstruction of our simulated networks, and in the analysis of real MEGresting-state connectivity, we find dense bilateral connections within the motor and visual networks, together with longer-range direct fronto-parietal connections

A magnetoencephalography study of functional brain connectivity in childhood, adolescence and adulthood

Functional brain networks are interconnected brain regions that flexibly coordinate their activity to support cognitive demands (Fair et al., 2009). Functional brain connectivity describes a statistical dependency between the activities recorded at spatially distinct brain regions (Friston, 2009). Changes in the pattern of connections and level of activation in functional brain networks are thought to occur across development (Taylor, Donner, & Pang, 2012) but the nature of these changes and their relationship to cognitive development have yet to be delineated clearly. This thesis seeks to deepen our understanding of the development of functional brain connectivity across the age range 9-25 years. We used magnetoencephalography in conjunction with canonical correlation analysis to explore functional connectivity via amplitude-amplitude envelope correlations in 110 datasets (39 working memory, 33 relevance modulation (attention processing) and 38 resting state). At the core of this thesis, we have presented novel findings that show non-linear functional connectivity changes across development, with an increase from childhood (age 9-12) to late adolescence (age 17-20) followed by a reduction into young adulthood (age 21-25), resembling an inverted-U-shaped trajectory at least in the females included in this study. Whilst there are subtle yet statistically significant differences in how the functional connectivity profile from 1-100 Hz is modulated by different factors, the overall pattern of functional connectivity development appears to be remarkably consistent across cognitive demands and networks. Critically, this work is the first example of such findings and suggests that functional brain networks supporting higher order cognitive function are not alone in undergoing functional development; sensory networks that reach structural maturity early on in life also undergo functional development from age 9 to 25

Value generalization in human avoidance learning.

Generalization during aversive decision-making allows us to avoid a broad range of potential threats following experience with a limited set of exemplars. However, over-generalization, resulting in excessive and inappropriate avoidance, has been implicated in a variety of psychological disorders. Here, we use reinforcement learning modelling to dissect out different contributions to the generalization of instrumental avoidance in two groups of human volunteers (N = 26, N = 482). We found that generalization of avoidance could be parsed into perceptual and value-based processes, and further, that value-based generalization could be subdivided into that relating to aversive and neutral feedback - with corresponding circuits including primary sensory cortex, anterior insula, amygdala and ventromedial prefrontal cortex. Further, generalization from aversive, but not neutral, feedback was associated with self-reported anxiety and intrusive thoughts. These results reveal a set of distinct mechanisms that mediate generalization in avoidance learning, and show how specific individual differences within them can yield anxiety.Wellcome, Arthritis Research U

A magnetoencephalography study of functional brain connectivity in childhood, adolescence and adulthood

Functional brain networks are interconnected brain regions that flexibly coordinate their activity to support cognitive demands (Fair et al., 2009). Functional brain connectivity describes a statistical dependency between the activities recorded at spatially distinct brain regions (Friston, 2009). Changes in the pattern of connections and level of activation in functional brain networks are thought to occur across development (Taylor, Donner, & Pang, 2012) but the nature of these changes and their relationship to cognitive development have yet to be delineated clearly. This thesis seeks to deepen our understanding of the development of functional brain connectivity across the age range 9-25 years. We used magnetoencephalography in conjunction with canonical correlation analysis to explore functional connectivity via amplitude-amplitude envelope correlations in 110 datasets (39 working memory, 33 relevance modulation (attention processing) and 38 resting state). At the core of this thesis, we have presented novel findings that show non-linear functional connectivity changes across development, with an increase from childhood (age 9-12) to late adolescence (age 17-20) followed by a reduction into young adulthood (age 21-25), resembling an inverted-U-shaped trajectory at least in the females included in this study. Whilst there are subtle yet statistically significant differences in how the functional connectivity profile from 1-100 Hz is modulated by different factors, the overall pattern of functional connectivity development appears to be remarkably consistent across cognitive demands and networks. Critically, this work is the first example of such findings and suggests that functional brain networks supporting higher order cognitive function are not alone in undergoing functional development; sensory networks that reach structural maturity early on in life also undergo functional development from age 9 to 25

Identification of continuous-time models for nonlinear dynamic systems from discrete data

A new iOFR-MF (iterative orthogonal forward regression--modulating function) algorithm is proposed to identify continuous-time models from noisy data by combining the MF method and the iOFR algorithm. In the new method, a set of candidate terms, which describe different dynamic relationships among the system states or between the input and output, are first constructed. These terms are then modulated using the MF method to generate the data matrix. The iOFR algorithm is next applied to build the relationships between these modulated terms, which include detecting the model structure and estimating the associated parameters. The relationships between the original variables are finally recovered from the model of the modulated terms. Both nonlinear state-space models and a class of higher order nonlinear input–output models are considered. The new direct method is compared with the traditional finite difference method and results show that the new method performs much better than the finite difference method. The new method works well even when the measurements are severely corrupted by noise. The selection of appropriate MFs is also discussed

A deep neural network for molecular wave functions in quasi-atomic minimal basis representation

The emergence of machine learning methods in quantum chemistry provides new methods to revisit an old problem: Can the predictive accuracy of electronic structure calculations be decoupled from their numerical bottlenecks? Previous attempts to answer this question have, among other methods, given rise to semi-empirical quantum chemistry in minimal basis representation. We present an adaptation of the recently proposed SchNet for Orbitals (SchNOrb) deep convolutional neural network model [K. T. Schütt et al., Nat. Commun. 10, 5024 (2019)] for electronic wave functions in an optimized quasi-atomic minimal basis representation. For five organic molecules ranging from 5 to 13 heavy atoms, the model accurately predicts molecular orbital energies and wave functions and provides access to derived properties for chemical bonding analysis. Particularly for larger molecules, the model outperforms the original atomic-orbital-based SchNOrb method in terms of accuracy and scaling. We conclude by discussing the future potential of this approach in quantum chemical workflows

Image database retrieval using neural networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The broad objective of this work has been to achieve retrieval of images from large unconstrained databases using image content. The problem is typified by the need to locate a target image within a database where no numerical indexing terms exist. Here, retrieval is based on important features within in an image and uses sample images or user sketches to specify a query. A typical query might be framed as "Find all images similar to this one", for example. The aim of this work has been to show how neural networks can provide a practical, flexible and robust solution to this problem. A neural network is basically an adaptive information filter which can be used to extract the salient characteristics of a data set during a training phase. The transformation learnt by the network can map the images into compact indices which support very rapid fuzzy matching of images across the database. This learning process optimises the performance of the code with respect to the contents of the database. We assess the applicability of several neural network architectures and learning rules for a practical coding scheme and investigate how the system parameters affect the performance of the system. We introduce a novel learning law which has a number of advantages over existing paradigms. In-depth mathematical analysis and extensive empirical tests are used to corroborate the arguments presented throughout. This thesis aims to show the nature of the image retrieval problem, how current research trends attempt to tackle it and how neural networks can offer us a real alternative to conventional approaches