467 citations · 1.1k across the 17 of their papers we have counts for
19 papers
SPM 25: open source neuroimaging analysis software
Tim M. Tierney, Nicholas A. Alexander, Nicole Labra Avila +17
Statistical Parametric Mapping (SPM) is an integrated set of methods for testing hypotheses about the brain's structure and function, using data from imaging devices. These methods…
Dynamic Causal Models of Time-Varying Connectivity
Johan Medrano, Karl J. Friston, Peter Zeidman
This paper introduces a novel approach for modelling time-varying connectivity in neuroimaging data, focusing on the slow fluctuations in synaptic efficacy that mediate neuronal dy…
BSD: a Bayesian framework for parametric models of neural spectra
Johan Medrano, Nicholas A. Alexander, Robert A. Seymour +1
The analysis of neural power spectra plays a crucial role in understanding brain function and dysfunction. While recent efforts have led to the development of methods for decomposi…
Effective and anatomical connectivity of the dorso-central insula during the processing of action forms
G. Di Cesare, P. Zeidman, G. Lombardi +2
In both human and monkeys the observation and execution of actions produced the activation of a network consisting of parietal and frontal areas. Although this network is involved…
Linking fast and slow: the case for generative models
Johan Medrano, Karl J. Friston, Peter Zeidman
A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent…
Dynamic causal modelling of immune heterogeneity
Thomas Parr, Anjali Bhat, Peter Zeidman +4
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…
Parametric dynamic causal modelling
Amirhossein Jafarian, Peter Zeidman, Rob. C Wykes +2
This technical note introduces parametric dynamic causal modelling, a method for inferring slow changes in biophysical parameters that control fluctuations of fast neuronal states.…
Effective immunity and second waves: a dynamic causal modelling study
Karl J. Friston, Thomas Parr, Peter Zeidman +11
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…
Tracking and tracing in the UK: a dynamic causal modelling study
Karl J. Friston, Thomas Parr, Peter Zeidman +9
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. Specifica…
Second waves, social distancing, and the spread of COVID-19 across America
Karl J. Friston, Thomas Parr, Peter Zeidman +9
We recently described a dynamic causal model of a COVID-19 outbreak within a single region. Here, we combine several of these (epidemic) models to create a (pandemic) model of vira…
Dynamic causal modelling of COVID-19
Karl J. Friston, Thomas Parr, Peter Zeidman +9
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 outc…
Bayesian fusion and multimodal DCM for EEG and fMRI
Huilin Wei, Amirhossein Jafarian, Peter Zeidman +4
This paper asks whether integrating multimodal EEG and fMRI data offers a better characterisation of functional brain architectures than either modality alone. This evaluation rest…
Structure Learning in Coupled Dynamical Systems and Dynamic Causal Modelling
Amirhossein Jafarian, Peter Zeidman, Vladimir Litvak +1
Identifying a coupled dynamical system out of many plausible candidates, each of which could serve as the underlying generator of some observed measurements, is a profoundly ill po…
Neurovascular coupling: insights from multi-modal dynamic causal modelling of fMRI and MEG
Amirhossein Jafarian, Vladimir Litvak, Hayriye Cagnan +2
This technical note presents a framework for investigating the underlying mechanisms of neurovascular coupling in the human brain using multi-modal magnetoencephalography (MEG) and…
A tutorial on group effective connectivity analysis, part 2: second level analysis with PEB
Peter Zeidman, Amirhossein Jafarian, Mohamed L. Seghier +4
This tutorial provides a worked example of using Dynamic Causal Modelling (DCM) and Parametric Empirical Bayes (PEB) to characterise inter-subject variability in neural circuitry (…
A tutorial on group effective connectivity analysis, part 1: first level analysis with DCM for fMRI
Peter Zeidman, Amirhossein Jafarian, Nadège Corbin +4
Dynamic Causal Modelling (DCM) is the predominant method for inferring effective connectivity from neuroimaging data. In the 15 years since its introduction, the neural models and…
Optimising data for modelling neuronal responses
Peter Zeidman, Samira M Kazan, Nick Todd +3
In this technical note, we address an unresolved challenge in neuroimaging statistics: how to determine which of several datasets is the best for inferring neuronal responses. Comp…
Bayesian model reduction
Karl Friston, Thomas Parr, Peter Zeidman
This paper reviews recent developments in statistical structure learning; namely, Bayesian model reduction. Bayesian model reduction is a method for rapidly computing the evidence…
Bayesian Population Receptive Field Modelling
Peter Zeidman, Edward Harry Silson, Dietrich Samuel Schwarzkopf +2
We introduce a probabilistic (Bayesian) framework and associated software toolbox for mapping population receptive fields (pRFs) based on fMRI data. This generic approach is intend…