The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Karl J Friston - One of the best experts on this subject based on the ideXlab platform.
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adiabatic Dynamic Causal Modelling
NeuroImage, 2021Co-Authors: Amirhossein Jafarian, Peter Zeidman, Robert C Wykes, Matthew C Walker, Karl J FristonAbstract:Abstract This technical note introduces adiabatic Dynamic Causal Modelling, a method for inferring slow changes in biophysical parameters that control fluctuations of fast neuronal states. The application domain we have in mind is inferring slow changes in variables (e.g., extracellular ion concentrations or synaptic efficacy) that underlie phase transitions in brain activity (e.g., paroxysmal seizure activity). The scheme is efficient and yet retains a biophysical interpretation, in virtue of being based on established neural mass models that are equipped with a slow Dynamic on the parameters (such as synaptic rate constants or effective connectivity). In brief, we use an adiabatic approximation to summarise fast fluctuations in hidden neuronal states (and their expression in sensors) in terms of their second order statistics; namely, their complex cross spectra. This allows one to specify and compare models of slowly changing parameters (using Bayesian model reduction) that generate a sequence of empirical cross spectra of electrophysiological recordings. Crucially, we use the slow fluctuations in the spectral power of neuronal activity as empirical priors on changes in synaptic parameters. This introduces a circular Causality, in which synaptic parameters underwrite fast neuronal activity that, in turn, induces activity-dependent plasticity in synaptic parameters. In this foundational paper, we describe the underlying model, establish its face validity using simulations and provide an illustrative application to a chemoconvulsant animal model of seizure activity.
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Dynamic Causal Modelling of mitigated epidemiological outcomes
arXiv: Populations and Evolution, 2020Co-Authors: Karl J Friston, Guillaume Flandin, Adeel RaziAbstract:This technical report describes the rationale and technical details for the Dynamic Causal Modelling of mitigated epidemiological outcomes based upon a variety of timeseries data. It details the structure of the underlying convolution or generative model (at the time of writing on 6-Nov-20). This report is intended for use as a reference that accompanies the predictions in following dashboard: this https URL
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The neurophysiological architecture of semantic dementia: spectral Dynamic Causal Modelling of a neurodegenerative proteinopathy
Scientific Reports, 2020Co-Authors: Elia Benhamou, Karl J Friston, Charles R. Marshall, Lucy L. Russell, Chris J. D. Hardy, Rebecca L. Bond, Harri Sivasathiaseelan, Caroline V. Greaves, Jonathan D. Rohrer, Jason D. WarrenAbstract:The selective destruction of large-scale brain networks by pathogenic protein spread is a ubiquitous theme in neurodegenerative disease. Characterising the circuit architecture of these diseases could illuminate both their pathophysiology and the computational architecture of the cognitive processes they target. However, this is challenging using standard neuroimaging techniques. Here we addressed this issue using a novel technique—spectral Dynamic Causal Modelling—that estimates the effective connectivity between brain regions from resting-state fMRI data. We studied patients with semantic dementia—the paradigmatic disorder of the brain system mediating world knowledge—relative to healthy older individuals. We assessed how the effective connectivity of the semantic appraisal network targeted by this disease was modulated by pathogenic protein deposition and by two key phenotypic factors, semantic impairment and behavioural disinhibition. The presence of pathogenic protein in SD weakened the normal inhibitory self-coupling of network hubs in both antero-mesial temporal lobes, with development of an abnormal excitatory fronto-temporal projection in the left cerebral hemisphere. Semantic impairment and social disinhibition were linked to a similar but more extensive profile of abnormally attenuated inhibitory self-coupling within temporal lobe regions and excitatory projections between temporal and inferior frontal regions. Our findings demonstrate that population-level Dynamic Causal Modelling can disclose a core pathophysiological feature of proteinopathic network architecture—attenuation of inhibitory connectivity—and the key elements of distributed neuronal processing that underwrite semantic memory.
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effective immunity and second waves a Dynamic Causal Modelling study
Wellcome Open Research, 2020Co-Authors: Karl J Friston, Peter Zeidman, Jean Daunizeau, Adeel Razi, Guillaume Flandin, Thomas Parr, Oliver J Hulme, Alexander J Billig, Vladimir LitvakAbstract:This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak; namely, the rate at which effective immunity is lost following the first wave of the pandemic. This is a crucial epidemiological parameter that speaks to both the consequences of relaxing lockdown and the propensity for a second wave of infections. Using a Dynamic Causal model of reported cases and deaths from multiple countries, we evaluated the evidence models of progressively longer periods of immunity. The results speak to an effective population immunity of about three months that, under the model, defers any second wave for approximately six months in most countries. This may have implications for the window of opportunity for tracking and tracing, as well as for developing vaccination programmes, and other therapeutic interventions.
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Dynamic Causal Modelling of immune heterogeneity
arXiv: Quantitative Methods, 2020Co-Authors: Thomas Parr, Peter Zeidman, R. J. Moran, Alexander J Billig, Anjali Bhat, Aimee Goel, Karl J FristonAbstract:An interesting inference drawn by some Covid-19 epidemiological models is that there exists a proportion of the population who are not susceptible to infection -- even at the start of the current pandemic. This paper introduces a model of the immune response to a virus. This is based upon the same sort of mean-field Dynamics as used in epidemiology. However, in place of the location, clinical status, and other attributes of people in an epidemiological model, we consider the state of a virus, B and T-lymphocytes, and the antibodies they generate. Our aim is to formalise some key hypotheses as to the mechanism of resistance. We present a series of simple simulations illustrating changes to the Dynamics of the immune response under these hypotheses. These include attenuated viral cell entry, pre-existing cross-reactive humoral (antibody-mediated) immunity, and enhanced T-cell dependent immunity. Finally, we illustrate the potential application of this sort of model by illustrating variational inversion (using simulated data) of this model to illustrate its use in testing hypotheses. In principle, this furnishes a fast and efficient immunological assay--based on sequential serology--that provides a (i) quantitative measure of latent immunological responses and (ii) a Bayes optimal classification of the different kinds of immunological response (c.f., glucose tolerance tests used to test for insulin resistance). This may be especially useful in assessing SARS-CoV-2 vaccines.
Jean Daunizeau - One of the best experts on this subject based on the ideXlab platform.
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effective immunity and second waves a Dynamic Causal Modelling study
Wellcome Open Research, 2020Co-Authors: Karl J Friston, Peter Zeidman, Jean Daunizeau, Adeel Razi, Guillaume Flandin, Thomas Parr, Oliver J Hulme, Alexander J Billig, Vladimir LitvakAbstract:This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak; namely, the rate at which effective immunity is lost following the first wave of the pandemic. This is a crucial epidemiological parameter that speaks to both the consequences of relaxing lockdown and the propensity for a second wave of infections. Using a Dynamic Causal model of reported cases and deaths from multiple countries, we evaluated the evidence models of progressively longer periods of immunity. The results speak to an effective population immunity of about three months that, under the model, defers any second wave for approximately six months in most countries. This may have implications for the window of opportunity for tracking and tracing, as well as for developing vaccination programmes, and other therapeutic interventions.
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Dynamic Causal Modelling of covid 19
Wellcome Open Research, 2020Co-Authors: Karl J Friston, Peter Zeidman, Jean Daunizeau, Adeel Razi, Guillaume Flandin, Thomas Parr, Oliver J Hulme, Alexander J Billig, Vladimir LitvakAbstract:This technical report describes a Dynamic Causal model of the spread of coronavirus through a population. The model is based upon ensemble or population Dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this Modelling is applied to epidemiological populations-to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a Modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self-organised mitigation process.
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testing and tracking in the uk a Dynamic Causal Modelling study
Wellcome Open Research, 2020Co-Authors: Karl J Friston, Peter Zeidman, Jean Daunizeau, Adeel Razi, Guillaume Flandin, Thomas Parr, Oliver J Hulme, Alexander J Billig, Vladimir LitvakAbstract:By equipping a previously reported Dynamic Causal Modelling of COVID-19 with an isolation state, we were able to model the effects of self-isolation consequent on testing and tracking. Specifically, we included a quarantine or isolation state occupied by people who believe they might be infected but are asymptomatic—and could only leave if they test negative. We recovered maximum posteriori estimates of the model parameters using time series of new cases, daily deaths, and tests for the UK. These parameters were used to simulate the trajectory of the outbreak in the UK over an 18-month period. Several clear-cut conclusions emerged from these simulations. For example, under plausible (graded) relaxations of social distancing, a rebound of infections is highly unlikely. The emergence of a second wave depends almost exclusively on the rate at which we lose immunity, inherited from the first wave. There exists no testing strategy that can attenuate mortality rates, other than by deferring or delaying a second wave. A testing and tracking policy—implemented at the present time—will defer any second wave beyond a time horizon of 18 months. Crucially, this deferment is within current testing capabilities (requiring an efficacy of tracing and tracking of about 20% of asymptomatic infected cases, with 50,000 tests per day). These conclusions are based upon a Dynamic Causal model for which we provide some construct and face validation—using a comparative analysis of the United Kingdom and Germany, supplemented with recent serological studies.
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tracking and tracing in the uk a Dynamic Causal Modelling study
arXiv: Quantitative Methods, 2020Co-Authors: Karl J Friston, Peter Zeidman, Jean Daunizeau, Vladimir Litvak, Adeel Razi, Guillaume Flandin, Thomas Parr, Oliver J Hulme, Alexander J Billig, Cathy J PriceAbstract:By equipping a previously reported Dynamic Causal model of COVID-19 with an isolation state, we modelled the effects of self-isolation consequent on tracking and tracing. Specifically, we included a quarantine or isolation state occupied by people who believe they might be infected but are asymptomatic, and only leave if they test negative. We recovered maximum posteriori estimates of the model parameters using time series of new cases, daily deaths, and tests for the UK. These parameters were used to simulate the trajectory of the outbreak in the UK over an 18-month period. Several clear-cut conclusions emerged from these simulations. For example, under plausible (graded) relaxations of social distancing, a rebound of infections within weeks is unlikely. The emergence of a later second wave depends almost exclusively on the rate at which we lose immunity, inherited from the first wave. There exists no testing strategy that can attenuate mortality rates, other than by deferring or delaying a second wave. A sufficiently powerful tracking and tracing policy--implemented at the time of writing (10th May 2020)--will defer any second wave beyond a time horizon of 18 months. Crucially, this deferment is within current testing capabilities (requiring an efficacy of tracing and tracking of about 20% of asymptomatic infected cases, with less than 50,000 tests per day). These conclusions are based upon a Dynamic Causal model for which we provide some construct and face validation, using a comparative analysis of the United Kingdom and Germany, supplemented with recent serological studies.
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effective immunity and second waves a Dynamic Causal Modelling study version 1 peer review awaiting peer review
Wellcome Open Research, 2020Co-Authors: K Friston, Peter Zeidman, Jean Daunizeau, Vladimir Litvak, Adeel Razi, Guillaume Flandin, Thomas Parr, Oliver J Hulme, Alexander J Billig, C J PriceAbstract:This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak;namely, the rate at which effective immunity is lost following the first wave of the pandemic This is a crucial epidemiological parameter that speaks to both the consequences of relaxing lockdown and the propensity for a second wave of infections Using a Dynamic Causal model of reported cases and deaths from multiple countries, we evaluated the evidence models of progressively longer periods of immunity The results speak to an effective population immunity of about three months that, under the model, defers any second wave for approximately six months in most countries This may have implications for the window of opportunity for tracking and tracing, as well as for developing vaccination programmes, and other therapeutic interventions
Vladimir Litvak - One of the best experts on this subject based on the ideXlab platform.
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effective immunity and second waves a Dynamic Causal Modelling study
Wellcome Open Research, 2020Co-Authors: Karl J Friston, Peter Zeidman, Jean Daunizeau, Adeel Razi, Guillaume Flandin, Thomas Parr, Oliver J Hulme, Alexander J Billig, Vladimir LitvakAbstract:This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak; namely, the rate at which effective immunity is lost following the first wave of the pandemic. This is a crucial epidemiological parameter that speaks to both the consequences of relaxing lockdown and the propensity for a second wave of infections. Using a Dynamic Causal model of reported cases and deaths from multiple countries, we evaluated the evidence models of progressively longer periods of immunity. The results speak to an effective population immunity of about three months that, under the model, defers any second wave for approximately six months in most countries. This may have implications for the window of opportunity for tracking and tracing, as well as for developing vaccination programmes, and other therapeutic interventions.
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Dynamic Causal Modelling of covid 19
Wellcome Open Research, 2020Co-Authors: Karl J Friston, Peter Zeidman, Jean Daunizeau, Adeel Razi, Guillaume Flandin, Thomas Parr, Oliver J Hulme, Alexander J Billig, Vladimir LitvakAbstract:This technical report describes a Dynamic Causal model of the spread of coronavirus through a population. The model is based upon ensemble or population Dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this Modelling is applied to epidemiological populations-to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a Modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self-organised mitigation process.
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testing and tracking in the uk a Dynamic Causal Modelling study
Wellcome Open Research, 2020Co-Authors: Karl J Friston, Peter Zeidman, Jean Daunizeau, Adeel Razi, Guillaume Flandin, Thomas Parr, Oliver J Hulme, Alexander J Billig, Vladimir LitvakAbstract:By equipping a previously reported Dynamic Causal Modelling of COVID-19 with an isolation state, we were able to model the effects of self-isolation consequent on testing and tracking. Specifically, we included a quarantine or isolation state occupied by people who believe they might be infected but are asymptomatic—and could only leave if they test negative. We recovered maximum posteriori estimates of the model parameters using time series of new cases, daily deaths, and tests for the UK. These parameters were used to simulate the trajectory of the outbreak in the UK over an 18-month period. Several clear-cut conclusions emerged from these simulations. For example, under plausible (graded) relaxations of social distancing, a rebound of infections is highly unlikely. The emergence of a second wave depends almost exclusively on the rate at which we lose immunity, inherited from the first wave. There exists no testing strategy that can attenuate mortality rates, other than by deferring or delaying a second wave. A testing and tracking policy—implemented at the present time—will defer any second wave beyond a time horizon of 18 months. Crucially, this deferment is within current testing capabilities (requiring an efficacy of tracing and tracking of about 20% of asymptomatic infected cases, with 50,000 tests per day). These conclusions are based upon a Dynamic Causal model for which we provide some construct and face validation—using a comparative analysis of the United Kingdom and Germany, supplemented with recent serological studies.
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cortical beta oscillations reflect the contextual gating of visual action feedback
bioRxiv, 2020Co-Authors: Jakub Limanowski, Vladimir Litvak, Karl J FristonAbstract:In sensorimotor integration, the brain needs to decide how its predictions should accommodate novel evidence by 9gating9 sensory data depending on the current context. Here, we examined the oscillatory correlates of this process using magnetoencephalography (MEG). We used virtual reality to decouple visual (virtual) and proprioceptive (real) hand postures during a task requiring matching either modality9s grasping movements to a target oscillation. Thus, we rendered visual information either task-relevant or a (to-be-ignored) distractor. Under visuo-proprioceptive incongruence, occipital beta power decreased relative to congruence when vision was task-relevant but increased when it had to be ignored. Dynamic Causal Modelling (DCM) revealed that this interaction was best explained by diametrical, task-dependent changes in visual gain. These results suggest a crucial role for beta oscillations in sensorimotor integration; particularly, in the contextual gating (i.e., gain or precision control) of visual vs proprioceptive action feedback, depending on concurrent behavioral demands.
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tracking and tracing in the uk a Dynamic Causal Modelling study
arXiv: Quantitative Methods, 2020Co-Authors: Karl J Friston, Peter Zeidman, Jean Daunizeau, Vladimir Litvak, Adeel Razi, Guillaume Flandin, Thomas Parr, Oliver J Hulme, Alexander J Billig, Cathy J PriceAbstract:By equipping a previously reported Dynamic Causal model of COVID-19 with an isolation state, we modelled the effects of self-isolation consequent on tracking and tracing. Specifically, we included a quarantine or isolation state occupied by people who believe they might be infected but are asymptomatic, and only leave if they test negative. We recovered maximum posteriori estimates of the model parameters using time series of new cases, daily deaths, and tests for the UK. These parameters were used to simulate the trajectory of the outbreak in the UK over an 18-month period. Several clear-cut conclusions emerged from these simulations. For example, under plausible (graded) relaxations of social distancing, a rebound of infections within weeks is unlikely. The emergence of a later second wave depends almost exclusively on the rate at which we lose immunity, inherited from the first wave. There exists no testing strategy that can attenuate mortality rates, other than by deferring or delaying a second wave. A sufficiently powerful tracking and tracing policy--implemented at the time of writing (10th May 2020)--will defer any second wave beyond a time horizon of 18 months. Crucially, this deferment is within current testing capabilities (requiring an efficacy of tracing and tracking of about 20% of asymptomatic infected cases, with less than 50,000 tests per day). These conclusions are based upon a Dynamic Causal model for which we provide some construct and face validation, using a comparative analysis of the United Kingdom and Germany, supplemented with recent serological studies.
Peter Zeidman - One of the best experts on this subject based on the ideXlab platform.
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adiabatic Dynamic Causal Modelling
NeuroImage, 2021Co-Authors: Amirhossein Jafarian, Peter Zeidman, Robert C Wykes, Matthew C Walker, Karl J FristonAbstract:Abstract This technical note introduces adiabatic Dynamic Causal Modelling, a method for inferring slow changes in biophysical parameters that control fluctuations of fast neuronal states. The application domain we have in mind is inferring slow changes in variables (e.g., extracellular ion concentrations or synaptic efficacy) that underlie phase transitions in brain activity (e.g., paroxysmal seizure activity). The scheme is efficient and yet retains a biophysical interpretation, in virtue of being based on established neural mass models that are equipped with a slow Dynamic on the parameters (such as synaptic rate constants or effective connectivity). In brief, we use an adiabatic approximation to summarise fast fluctuations in hidden neuronal states (and their expression in sensors) in terms of their second order statistics; namely, their complex cross spectra. This allows one to specify and compare models of slowly changing parameters (using Bayesian model reduction) that generate a sequence of empirical cross spectra of electrophysiological recordings. Crucially, we use the slow fluctuations in the spectral power of neuronal activity as empirical priors on changes in synaptic parameters. This introduces a circular Causality, in which synaptic parameters underwrite fast neuronal activity that, in turn, induces activity-dependent plasticity in synaptic parameters. In this foundational paper, we describe the underlying model, establish its face validity using simulations and provide an illustrative application to a chemoconvulsant animal model of seizure activity.
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effective immunity and second waves a Dynamic Causal Modelling study
Wellcome Open Research, 2020Co-Authors: Karl J Friston, Peter Zeidman, Jean Daunizeau, Adeel Razi, Guillaume Flandin, Thomas Parr, Oliver J Hulme, Alexander J Billig, Vladimir LitvakAbstract:This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak; namely, the rate at which effective immunity is lost following the first wave of the pandemic. This is a crucial epidemiological parameter that speaks to both the consequences of relaxing lockdown and the propensity for a second wave of infections. Using a Dynamic Causal model of reported cases and deaths from multiple countries, we evaluated the evidence models of progressively longer periods of immunity. The results speak to an effective population immunity of about three months that, under the model, defers any second wave for approximately six months in most countries. This may have implications for the window of opportunity for tracking and tracing, as well as for developing vaccination programmes, and other therapeutic interventions.
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Dynamic Causal Modelling of immune heterogeneity
arXiv: Quantitative Methods, 2020Co-Authors: Thomas Parr, Peter Zeidman, R. J. Moran, Alexander J Billig, Anjali Bhat, Aimee Goel, Karl J FristonAbstract:An interesting inference drawn by some Covid-19 epidemiological models is that there exists a proportion of the population who are not susceptible to infection -- even at the start of the current pandemic. This paper introduces a model of the immune response to a virus. This is based upon the same sort of mean-field Dynamics as used in epidemiology. However, in place of the location, clinical status, and other attributes of people in an epidemiological model, we consider the state of a virus, B and T-lymphocytes, and the antibodies they generate. Our aim is to formalise some key hypotheses as to the mechanism of resistance. We present a series of simple simulations illustrating changes to the Dynamics of the immune response under these hypotheses. These include attenuated viral cell entry, pre-existing cross-reactive humoral (antibody-mediated) immunity, and enhanced T-cell dependent immunity. Finally, we illustrate the potential application of this sort of model by illustrating variational inversion (using simulated data) of this model to illustrate its use in testing hypotheses. In principle, this furnishes a fast and efficient immunological assay--based on sequential serology--that provides a (i) quantitative measure of latent immunological responses and (ii) a Bayes optimal classification of the different kinds of immunological response (c.f., glucose tolerance tests used to test for insulin resistance). This may be especially useful in assessing SARS-CoV-2 vaccines.
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predicting ovulation from brain connectivity Dynamic Causal Modelling of the menstrual cycle
bioRxiv, 2020Co-Authors: Esmeralda Hidalgolopez, Peter Zeidman, Adeel Razi, Tianni Harris, Belinda PletzerAbstract:Abstract Longitudinal menstrual cycle research allows the assessment of sex hormones effects on brain organization in a natural framework. Here, we used spectral Dynamic Causal Modelling (spDCM) in a triple network model consisting of the default mode, salience and executive central networks (DMN, SN, and ECN), in order to address the changes in effective connectivity across the menstrual cycle. Sixty healthy young women were scanned three times (menses, pre-ovulatory and luteal phase) and spDCM was estimated for a total of 174 scans. Group level analysis using Parametric empirical Bayes showed lateralized and anterior-posterior changes in connectivity patterns depending on the cycle phase and related to the endogenous hormonal milieu. Right before ovulation the left insula recruited the frontoparietal network, while the right middle frontal gyrus decreased its connectivity to the precuneus. In exchange, the precuneus engaged bilateral angular gyrus, decoupling the DMN into anterior/posterior parts. During the luteal phase, bilateral insula engaged to each other decreasing the connectivity to parietal ECN, which in turn engaged the posterior DMN. Remarkably, the specific cycle phase in which a woman was in could be predicted by the connections that showed the strongest changes. These findings further corroborate the plasticity of the female brain in response to acute hormone fluctuations and have important implications for understanding the neuroendocrine interactions underlying cognitive changes along the menstrual cycle.
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Dynamic Causal Modelling of covid 19
Wellcome Open Research, 2020Co-Authors: Karl J Friston, Peter Zeidman, Jean Daunizeau, Adeel Razi, Guillaume Flandin, Thomas Parr, Oliver J Hulme, Alexander J Billig, Vladimir LitvakAbstract:This technical report describes a Dynamic Causal model of the spread of coronavirus through a population. The model is based upon ensemble or population Dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this Modelling is applied to epidemiological populations-to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a Modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self-organised mitigation process.
Adeel Razi - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Causal Modelling of mitigated epidemiological outcomes
arXiv: Populations and Evolution, 2020Co-Authors: Karl J Friston, Guillaume Flandin, Adeel RaziAbstract:This technical report describes the rationale and technical details for the Dynamic Causal Modelling of mitigated epidemiological outcomes based upon a variety of timeseries data. It details the structure of the underlying convolution or generative model (at the time of writing on 6-Nov-20). This report is intended for use as a reference that accompanies the predictions in following dashboard: this https URL
-
effective immunity and second waves a Dynamic Causal Modelling study
Wellcome Open Research, 2020Co-Authors: Karl J Friston, Peter Zeidman, Jean Daunizeau, Adeel Razi, Guillaume Flandin, Thomas Parr, Oliver J Hulme, Alexander J Billig, Vladimir LitvakAbstract:This technical report addresses a pressing issue in the trajectory of the coronavirus outbreak; namely, the rate at which effective immunity is lost following the first wave of the pandemic. This is a crucial epidemiological parameter that speaks to both the consequences of relaxing lockdown and the propensity for a second wave of infections. Using a Dynamic Causal model of reported cases and deaths from multiple countries, we evaluated the evidence models of progressively longer periods of immunity. The results speak to an effective population immunity of about three months that, under the model, defers any second wave for approximately six months in most countries. This may have implications for the window of opportunity for tracking and tracing, as well as for developing vaccination programmes, and other therapeutic interventions.
-
predicting ovulation from brain connectivity Dynamic Causal Modelling of the menstrual cycle
bioRxiv, 2020Co-Authors: Esmeralda Hidalgolopez, Peter Zeidman, Adeel Razi, Tianni Harris, Belinda PletzerAbstract:Abstract Longitudinal menstrual cycle research allows the assessment of sex hormones effects on brain organization in a natural framework. Here, we used spectral Dynamic Causal Modelling (spDCM) in a triple network model consisting of the default mode, salience and executive central networks (DMN, SN, and ECN), in order to address the changes in effective connectivity across the menstrual cycle. Sixty healthy young women were scanned three times (menses, pre-ovulatory and luteal phase) and spDCM was estimated for a total of 174 scans. Group level analysis using Parametric empirical Bayes showed lateralized and anterior-posterior changes in connectivity patterns depending on the cycle phase and related to the endogenous hormonal milieu. Right before ovulation the left insula recruited the frontoparietal network, while the right middle frontal gyrus decreased its connectivity to the precuneus. In exchange, the precuneus engaged bilateral angular gyrus, decoupling the DMN into anterior/posterior parts. During the luteal phase, bilateral insula engaged to each other decreasing the connectivity to parietal ECN, which in turn engaged the posterior DMN. Remarkably, the specific cycle phase in which a woman was in could be predicted by the connections that showed the strongest changes. These findings further corroborate the plasticity of the female brain in response to acute hormone fluctuations and have important implications for understanding the neuroendocrine interactions underlying cognitive changes along the menstrual cycle.
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Dynamic Causal Modelling of covid 19
Wellcome Open Research, 2020Co-Authors: Karl J Friston, Peter Zeidman, Jean Daunizeau, Adeel Razi, Guillaume Flandin, Thomas Parr, Oliver J Hulme, Alexander J Billig, Vladimir LitvakAbstract:This technical report describes a Dynamic Causal model of the spread of coronavirus through a population. The model is based upon ensemble or population Dynamics that generate outcomes, like new cases and deaths over time. The purpose of this model is to quantify the uncertainty that attends predictions of relevant outcomes. By assuming suitable conditional dependencies, one can model the effects of interventions (e.g., social distancing) and differences among populations (e.g., herd immunity) to predict what might happen in different circumstances. Technically, this model leverages state-of-the-art variational (Bayesian) model inversion and comparison procedures, originally developed to characterise the responses of neuronal ensembles to perturbations. Here, this Modelling is applied to epidemiological populations-to illustrate the kind of inferences that are supported and how the model per se can be optimised given timeseries data. Although the purpose of this paper is to describe a Modelling protocol, the results illustrate some interesting perspectives on the current pandemic; for example, the nonlinear effects of herd immunity that speak to a self-organised mitigation process.
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testing and tracking in the uk a Dynamic Causal Modelling study
Wellcome Open Research, 2020Co-Authors: Karl J Friston, Peter Zeidman, Jean Daunizeau, Adeel Razi, Guillaume Flandin, Thomas Parr, Oliver J Hulme, Alexander J Billig, Vladimir LitvakAbstract:By equipping a previously reported Dynamic Causal Modelling of COVID-19 with an isolation state, we were able to model the effects of self-isolation consequent on testing and tracking. Specifically, we included a quarantine or isolation state occupied by people who believe they might be infected but are asymptomatic—and could only leave if they test negative. We recovered maximum posteriori estimates of the model parameters using time series of new cases, daily deaths, and tests for the UK. These parameters were used to simulate the trajectory of the outbreak in the UK over an 18-month period. Several clear-cut conclusions emerged from these simulations. For example, under plausible (graded) relaxations of social distancing, a rebound of infections is highly unlikely. The emergence of a second wave depends almost exclusively on the rate at which we lose immunity, inherited from the first wave. There exists no testing strategy that can attenuate mortality rates, other than by deferring or delaying a second wave. A testing and tracking policy—implemented at the present time—will defer any second wave beyond a time horizon of 18 months. Crucially, this deferment is within current testing capabilities (requiring an efficacy of tracing and tracking of about 20% of asymptomatic infected cases, with 50,000 tests per day). These conclusions are based upon a Dynamic Causal model for which we provide some construct and face validation—using a comparative analysis of the United Kingdom and Germany, supplemented with recent serological studies.
