Analysis of lateralization of brain dopamine function in psychosis: [18F] FDOPA PET imaging study

Abnormal dopamine brain function is a hallmark of psychotic disorders like schizophrenia, and the main pharmacological target for psychotic treatment. However, the functional organization and regulation of the dopamine system are not completely known, due to its complex topology and interplay with other neuroreceptor systems. The primary objective of this research is to comprehend the normal state of dopamine lateralization in the human brain and identify whether dopamine lateralization is altered in schizophrenia. This study considered a dataset of 136 patients with psychosis matched to 143 healthy controls acquired with the [18F] FDOPA PET imaging, a biomarker for measuring dopamine synthesis capacity in vivo in humans. For each subject, neuroimaging metrics from 41 regions of interest (ROIs) were derived using the Desikan-Killiany atlas for each brain hemisphere. For each ROI and each subject, a lateralization index (lx) was computed to compare the dopamine function between the left and right hemispheres. The same metrics were fed into the Random Forest, XGBoost, SVM, KNN, Naïve Bayes, and Logistic Regression classifier models to distinguish patients and controls by exploiting the difference with the best-performing model in brain dopamine lateralization. In normal individuals, brain dopamine is mainly lateralized in the Inferior Parietal (p=0.039) and Transverse Temporal (p=0.004) with a significant effect of age and gender. Moreover, when comparing lateralization between controls and patients, left-biased lateralization in Putamen decreases by 50%, right-biased lateralization in Accumbens decreases by 60%, and right-biased lateralization in Pallidum changes direction and shows a significant increase around 300% in Ki levels. In terms of patient classification, the best performing model was XGBoost with the metrics of 79% accuracy, 79% precision, 79% recall, and 78% f1-score on the test set. Finally, the post hoc model agnostic explainability method SHAP reported the Accumbens, Fusiform, Posterior Cingulate, Thalamus, and Pallidum as the top 5 most salient features which have a significant effect on the decision. In conclusion, healthy controls present a clear lateralization of dopamine function that can change its direction and magnitude in the case of schizophrenia. Further studies should focus to investigate the biological rationale behind these differences and their implication for the stratification of patients with psychosis.Abnormal dopamine brain function is a hallmark of psychotic disorders like schizophrenia, and the main pharmacological target for psychotic treatment. However, the functional organization and regulation of the dopamine system are not completely known, due to its complex topology and interplay with other neuroreceptor systems. The primary objective of this research is to comprehend the normal state of dopamine lateralization in the human brain and identify whether dopamine lateralization is altered in schizophrenia. This study considered a dataset of 136 patients with psychosis matched to 143 healthy controls acquired with the [18F] FDOPA PET imaging, a biomarker for measuring dopamine synthesis capacity in vivo in humans. For each subject, neuroimaging metrics from 41 regions of interest (ROIs) were derived using the Desikan-Killiany atlas for each brain hemisphere. For each ROI and each subject, a lateralization index (lx) was computed to compare the dopamine function between the left and right hemispheres. The same metrics were fed into the Random Forest, XGBoost, SVM, KNN, Naïve Bayes, and Logistic Regression classifier models to distinguish patients and controls by exploiting the difference with the best-performing model in brain dopamine lateralization. In normal individuals, brain dopamine is mainly lateralized in the Inferior Parietal (p=0.039) and Transverse Temporal (p=0.004) with a significant effect of age and gender. Moreover, when comparing lateralization between controls and patients, left-biased lateralization in Putamen decreases by 50%, right-biased lateralization in Accumbens decreases by 60%, and right-biased lateralization in Pallidum changes direction and shows a significant increase around 300% in Ki levels. In terms of patient classification, the best performing model was XGBoost with the metrics of 79% accuracy, 79% precision, 79% recall, and 78% f1-score on the test set. Finally, the post hoc model agnostic explainability method SHAP reported the Accumbens, Fusiform, Posterior Cingulate, Thalamus, and Pallidum as the top 5 most salient features which have a significant effect on the decision. In conclusion, healthy controls present a clear lateralization of dopamine function that can change its direction and magnitude in the case of schizophrenia. Further studies should focus to investigate the biological rationale behind these differences and their implication for the stratification of patients with psychosis

On the Heegaard Floer homology of Dehn surgery and unknotting number

n this thesis we generalise three theorems from the literature on Heegaard Floer homology and Dehn surgery: one by Ozsv ́ath and Szab ́o on deficiency symmetries in half-integral L -space surgeries, and two by Greene which use Donaldson’s diagonali- sation theorem as an obstruction to integral and half-integral L -space surgeries. Our generalisation is two-fold: first, we eliminate the L -space conditions, opening these techniques up for use with much more general 3-manifolds, and second, we unify the integral and half-integral surgery results into a broader theorem applicable to non- zero rational surgeries in S 3 which bound sharp, simply connected, negative-definite smooth 4-manifolds. Such 3-manifolds are quite common and include, for example, a huge number of Seifert fibred spaces. Over the course of the first three chapters, we begin by introducing background material on knots in 3-manifolds, the intersection form of a simply connected 4- manifold, Spin- and Spin c -structures on 3- and 4-manifolds, and Heegaard Floer ho- mology (including knot Floer homology). While none of the results in these chapters are original, all of them are necessary to make sense of what follows. In Chapter 4, we introduce and prove our main theorems, using arguments that are predominantly algebraic or combinatorial in nature. We then apply these new theorems to the study of unknotting number in Chapter 5, making considerable headway into the extremely difficult problem of classifying the 3-strand pretzel knots with unknotting number one. Finally, in Chapter 6, we present further applications of the main theorems, ranging from a plan of attack on the famous Seifert fibred space realisation problem to more biologically motivated problems concerning rational tangle replacement. An appendix on the implications of our theorems for DNA topology is provided at the end.Open Acces

Chiral Rings of Deconstructive [SU(n_c)]^N Quivers

Dimensional deconstruction of 5D SQCD with general n_c, n_f and k_CS gives rise to 4D N=1 gauge theories with large quivers of SU(n_c) gauge factors. We construct the chiral rings of such [SU(n_c)]^N theories, off-shell and on-shell. Our results are broadly similar to the chiral rings of single U(n_c) theories with both adjoint and fundamental matter, but there are also some noteworthy differences such as nonlocal meson-like operators where the quark and antiquark fields belong to different nodes of the quiver. And because our gauge groups are SU(n_c) rather than U(n_c), our chiral rings also contain a whole zoo of baryonic and antibaryonic operators.Comment: 93 pages, LaTeX, PSTricks macros; 1 reference added in v

Shape theory and mathematical design of a general geometric kernel through regular stratified objects

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This dissertation focuses on the mathematical design of a unified shape kernel for geometric computing, with possible applications to computer aided design (CAM) and manufacturing (CAM), solid geometric modelling, free-form modelling of curves and surfaces, feature-based modelling, finite element meshing, computer animation, etc. The generality of such a unified shape kernel grounds on a shape theory for objects in some Euclidean space. Shape does not mean herein only geometry as usual in geometric modelling, but has been extended to other contexts, e. g. topology, homotopy, convexity theory, etc. This shape theory has enabled to make a shape analysis of the current geometric kernels. Significant deficiencies have been then identified in how these geometric kernels represent shapes from different applications. This thesis concludes that it is possible to construct a general shape kernel capable of representing and manipulating general specifications of shape for objects even in higher-dimensional Euclidean spaces, regardless whether such objects are implicitly or parametrically defined, they have ‘incomplete boundaries’ or not, they are structured with more or less detail or subcomplexes, which design sequence has been followed in a modelling session, etc. For this end, the basic constituents of such a general geometric kernel, say a combinatorial data structure and respective Euler operators for n-dimensional regular stratified objects, have been introduced and discussed

Remote Sensing Applied in Forest Management to Optimize Ecosystem Services: Advances in Research

Research Highlights: the wide variety of multispectral sensors that currently exist make it possible to improve the study of forest systems and ecosystem services. Background and Objectives: this study aims to analyze the current usefulness of remote sensing in forest management and ecosystem services sciences, and to identify future lines of research on these issues worldwide during the period 1976–2019. Materials and Methods: a bibliometric technique is applied to 2066 articles published between 1976 and 2019 on these topics to find findings on scientific production and key subject areas. Results: scientific production has increased annually, so that in the last five years, 50.34% of all articles have been published. The thematic areas in which more articles were linked were environmental science, agricultural, and biological sciences, and earth and planetary sciences. Seven lines of research have been identified that generate contributions on this topic. In addition, the analysis of the relevance of the keywords has detected the ten main future directions of research. The growing worldwide trend of scientific production shows interest in developing aspects of this field of study. Conclusions: this study contributes to the academic, scientific, and institutional discussion to improve decision-making, and proposes new scenarios and uses of this technology to improve the administration and management of forest resources

Module hierarchy and centralisation in the anatomy and dynamics of human cortex

Systems neuroscience has recently unveiled numerous fundamental features of the macroscopic architecture of the human brain, the connectome, and we are beginning to understand how characteristics of brain dynamics emerge from the underlying anatomical connectivity. The current work utilises complex network analysis on a high-resolution structural connectivity of the human cortex to identify generic organisation principles, such as centralised, modular and hierarchical properties, as well as specific areas that are pivotal in shaping cortical dynamics and function. After confirming its small-world and modular architecture, we characterise the cortex’ multilevel modular hierarchy, which appears to be reasonably centralised towards the brain’s strong global structural core. The potential functional importance of the core and hub regions is assessed by various complex network metrics, such as integration measures, network vulnerability and motif spectrum analysis. Dynamics facilitated by the large-scale cortical topology is explored by simulating coupled oscillators on the anatomical connectivity. The results indicate that cortical connectivity appears to favour high dynamical complexity over high synchronizability. Taking the ability to entrain other brain regions as a proxy for the threat posed by a potential epileptic focus in a given region, we also show that epileptic foci in topologically more central areas should pose a higher epileptic threat than foci in more peripheral areas. To assess the influence of macroscopic brain anatomy in shaping global resting state dynamics on slower time scales, we compare empirically obtained functional connectivity data with data from simulating dynamics on the structural connectivity. Despite considerable micro-scale variability between the two functional connectivities, our simulations are able to approximate the profile of the empirical functional connectivity. Our results outline the combined characteristics a hierarchically modular and reasonably centralised macroscopic architecture of the human cerebral cortex, which, through these topological attributes, appears to facilitate highly complex dynamics and fundamentally shape brain function

Identifying aging-related genes in mouse hippocampus using gateway nodes

BACKGROUND: High-throughput studies continue to produce volumes of metadata representing valuable sources of information to better guide biological research. With a stronger focus on data generation, analysis models that can readily identify actual signals have not received the same level of attention. This is due in part to high levels of noise and data heterogeneity, along with a lack of sophisticated algorithms for mining useful information. Networks have emerged as a powerful tool for modeling high-throughput data because they are capable of representing not only individual biological elements but also different types of relationships en masse. Moreover, well-established graph theoretic methodology can be applied to network models to increase efficiency and speed of analysis. In this project, we propose a network model that examines temporal data from mouse hippocampus at the transcriptional level via correlation of gene expression. Using this model, we formally define the concept of “gateway” nodes, loosely defined as nodes representing genes co-expressed in multiple states. We show that the proposed network model allows us to identify target genes implicated in hippocampal aging-related processes. RESULTS: By mining gateway genes related to hippocampal aging from networks made from gene expression in young and middle-aged mice, we provide a proof-of-concept of existence and importance of gateway nodes. Additionally, these results highlight how network analysis can act as a supplement to traditional statistical analysis of differentially expressed genes. Finally, we use the gateway nodes identified by our method as well as functional databases and literature to propose new targets for study of aging in the mouse hippocampus. CONCLUSIONS: This research highlights the need for methods of temporal comparison using network models and provides a systems biology approach to extract information from correlation networks of gene expression. Our results identify a number of genes previously implicated in the aging mouse hippocampus related to synaptic plasticity and apoptosis. Additionally, this model identifies a novel set of aging genes previously uncharacterized in the hippocampus. This research can be viewed as a first-step for identifying the processes behind comparative experiments in aging that is applicable to any type of temporal multi-state network

A Hybrid Systems Model for Simple Manipulation and Self-Manipulation Systems

Rigid bodies, plastic impact, persistent contact, Coulomb friction, and massless limbs are ubiquitous simplifications introduced to reduce the complexity of mechanics models despite the obvious physical inaccuracies that each incurs individually. In concert, it is well known that the interaction of such idealized approximations can lead to conflicting and even paradoxical results. As robotics modeling moves from the consideration of isolated behaviors to the analysis of tasks requiring their composition, a mathematically tractable framework for building models that combine these simple approximations yet achieve reliable results is overdue. In this paper we present a formal hybrid dynamical system model that introduces suitably restricted compositions of these familiar abstractions with the guarantee of consistency analogous to global existence and uniqueness in classical dynamical systems. The hybrid system developed here provides a discontinuous but self-consistent approximation to the continuous (though possibly very stiff and fast) dynamics of a physical robot undergoing intermittent impacts. The modeling choices sacrifice some quantitative numerical efficiencies while maintaining qualitatively correct and analytically tractable results with consistency guarantees promoting their use in formal reasoning about mechanism, feedback control, and behavior design in robots that make and break contact with their environment. For more information: Kod*La