29 research outputs found
The autonomic brain: multi-dimensional generative hierarchical modelling of the autonomic connectome
The autonomic nervous system governs the body's multifaceted internal adaptation to diverse changes in the external environment, a role more complex than is accessible to the methods — and data scales — hitherto used to illuminate its operation. Here we apply generative graphical modelling to large-scale multimodal neuroimaging data encompassing normal and abnormal states to derive a comprehensive hierarchical representation of the autonomic brain. We demonstrate that whereas conventional structural and functional maps identify regions jointly modulated by parasympathetic and sympathetic systems, only graphical analysis discriminates between them, revealing the cardinal roles of the autonomic system to be mediated by high-level distributed interactions. We provide a novel representation of the autonomic system — a multidimensional, generative network — that renders its richness tractable within future models of its function in health and disease
The autonomic brain: Multi-dimensional generative hierarchical modelling of the autonomic connectome.
The autonomic nervous system governs the body's multifaceted internal adaptation to diverse changes in the external environment, a role more complex than is accessible to the methods-and data scales-hitherto used to illuminate its operation. Here we apply generative graphical modelling to large-scale multimodal neuroimaging data encompassing normal and abnormal states to derive a comprehensive hierarchical representation of the autonomic brain. We demonstrate that whereas conventional structural and functional maps identify regions jointly modulated by parasympathetic and sympathetic systems, only graphical analysis discriminates between them, revealing the cardinal roles of the autonomic system to be mediated by high-level distributed interactions. We provide a novel representation of the autonomic system-a multidimensional, generative network-that renders its richness tractable within future models of its function in health and disease
Delineation between different components of chronic pain using dimension reduction - an ASL fMRI study in hand osteoarthritis
DK was supported by grants from GENIEUR
COST action and the ‘Sint Annadal’ Foundation
Maastricht. MAH and SW are supported
by a Medical Research Council Experimental
Medicine Challenge Grant award (MR/
N026969/1) and the NIHR Biomedical
Research Centre for Mental Health at the
South London and Maudsley NHS Trust. The
data collected for this study were part of an
academic–industrial collaboration between
King’s College London and the study sponsor,
Pfizer Global Research and Development,
UK. All data collection was performed
by King’s College London scientists only
Brain tumour genetic network signatures of survival
Tumour heterogeneity is increasingly recognized as a major obstacle to
therapeutic success across neuro-oncology. Gliomas are characterised by
distinct combinations of genetic and epigenetic alterations, resulting in
complex interactions across multiple molecular pathways. Predicting disease
evolution and prescribing individually optimal treatment requires statistical
models complex enough to capture the intricate (epi)genetic structure
underpinning oncogenesis. Here, we formalize this task as the inference of
distinct patterns of connectivity within hierarchical latent representations of
genetic networks. Evaluating multi-institutional clinical, genetic, and outcome
data from 4023 glioma patients over 14 years, across 12 countries, we employ
Bayesian generative stochastic block modelling to reveal a hierarchical network
structure of tumour genetics spanning molecularly confirmed glioblastoma, IDH-
wildtype; oligodendroglioma, IDH-mutant and 1p/19q codeleted; and astrocytoma,
IDH- mutant. Our findings illuminate the complex dependence between features
across the genetic landscape of brain tumours, and show that generative network
models reveal distinct signatures of survival with better prognostic fidelity
than current gold standard diagnostic categories.Comment: Main article: 52 pages, 1 table, 7 figures. Supplementary material:
13 pages, 11 supplementary figure
Representational ethical model calibration
Equity is widely held to be fundamental to the ethics of healthcare. In the context of clinical decision-making, it rests on the comparative fidelity of the intelligence – evidence-based or intuitive – guiding the management of each individual patient. Though brought to recent attention by the individuating power of contemporary machine learning, such epistemic equity arises in the context of any decision guidance, whether traditional or innovative. Yet no general framework for its quantification, let alone assurance, currently exists. Here we formulate epistemic equity in terms of model fidelity evaluated over learnt multidimensional representations of identity crafted to maximise the captured diversity of the population, introducing a comprehensive framework for Representational Ethical Model Calibration. We demonstrate the use of the framework on large-scale multimodal data from UK Biobank to derive diverse representations of the population, quantify model performance, and institute responsive remediation. We offer our approach as a principled solution to quantifying and assuring epistemic equity in healthcare, with applications across the research, clinical, and regulatory domains
Preliminary report: parasympathetic tone links to functional brain networks during the anticipation and experience of visceral pain
Medical Research Council project grant - Medical Research Council Grant Number - MGAB1A1
Deep forecasting of translational impact in medical research.
The value of biomedical research-a $1.7 trillion annual investment-is ultimately determined by its downstream, real-world impact, whose predictability from simple citation metrics remains unquantified. Here we sought to determine the comparative predictability of future real-world translation-as indexed by inclusion in patents, guidelines, or policy documents-from complex models of title/abstract-level content versus citations and metadata alone. We quantify predictive performance out of sample, ahead of time, across major domains, using the entire corpus of biomedical research captured by Microsoft Academic Graph from 1990-2019, encompassing 43.3 million papers. We show that citations are only moderately predictive of translational impact. In contrast, high-dimensional models of titles, abstracts, and metadata exhibit high fidelity (area under the receiver operating curve [AUROC]Â >Â 0.9), generalize across time and domain, and transfer to recognizing papers of Nobel laureates. We argue that content-based impact models are superior to conventional, citation-based measures and sustain a stronger evidence-based claim to the objective measurement of translational potential