Publications

Abstract There are considerable gaps in our understanding of the relationship between human brain activity measured at different temporal and spatial scales by intracranial electroencephalography and fMRI. By comparing individual features and summary descriptions of intracranial EEG activity we determined which best predict fMRI changes in the sensorimotor cortex in two brain states: at rest and during motor performance. We also then examine the specificity of this relationship to spatial colocalisation of the two signals. We acquired electrocorticography and fMRI simultaneously (ECoG-fMRI) in the sensorimotor cortex of 3 patients with epilepsy. During motor activity, high gamma power was the only frequency band where the electrophysiological response was colocalised with fMRI measures across all subjects. The best model of fMRI changes was its principal components, a parsimonious description of the entire ECoG spectrogram. This model performed much better than a model based on the classical frequency bands both during task and rest periods or models derived on a summary of cross spectral changes (e.g. ‘root mean squared EEG frequency’). This suggests that the region specific fMRI signal is reflected in spatially and spectrally distributed EEG activity.

This paper introduces a new paradigm that allows one to quantify the Bayesian beliefs evidenced by subjects during oculomotor pursuit. Subjects' eye tracking responses to a partially occluded sinusoidal target were recorded non-invasively and averaged. These response averages were then analysed using dynamic causal modelling (DCM). In DCM, observed responses are modelled using biologically plausible generative or forward models – usually biophysical models of neuronal activity. Our key innovation is to use a generative model based on a normative (Bayes-optimal) model of active inference to model oculomotor pursuit in terms of subjects' beliefs about how visual targets move and how their oculomotor system responds. Our aim here is to establish the face validity of the approach, by manipulating the content and precision of sensory information – and examining the ensuing changes in the subjects' implicit beliefs. These beliefs are inferred from their eye movements using the normative model. We show that on average, subjects respond to an increase in the 'noise' of target motion by increasing sensory precision in their models of the target trajectory. In other words, they attend more to the sensory attributes of a noisier stimulus. Conversely, subjects only change kinetic parameters in their model but not precision, in response to increased target speed. Using this technique one can estimate the precisions of subjects' hierarchical Bayesian beliefs about target motion. We hope to apply this paradigm to subjects with schizophrenia, whose pursuit abnormalities may result from the abnormal encoding of precision.

Simultaneous cross-scale recording using multiple imaging devices with differing spatiotemporal resolutions is increasingly common in empirical neuroscience. This type of scenario introduces a key question: to what extent are differences between the resultant timeseries due to genuine variation in neural dynamics across scales, as opposed to device-based artefacts? In this study, we address how to disentangle these effects—specifically, how to isolate measurement artefacts introduced by the observer (i.e., the device) from the true dynamics of the system (i.e., the neural system). We demonstrate that stochastic fluctuations—typically viewed as a confound—can in fact be leveraged when incorporated into generative models. Using synthetic data, we demonstrate that Bayesian model comparison more accurately distinguishes system- from observer-level differences when models include noise, compared to noise-free alternatives. We apply this technique to empirical data and show that cross-scale variation between simultaneously recorded high-frequency broadband signals from macroelectrodes and microwires in the human hippocampus is better explained by differences in observer functions than by underlying system dynamics. These findingssuggest that noise, when appropriately modelled, can enhance inference in multimodalneuroimaging by revealing the origin of cross-scale signal variation.

Abstract This paper considers neural representation through the lens of active inference, a normative framework for understanding brain function. It delves into how living organisms employ generative models to minimize the discrepancy between predictions and observations (as scored with variational free energy). The ensuing analysis suggests that the brain learns generative models to navigate the world adaptively, not (or not solely) to understand it. Different living organisms may possess an array of generative models, spanning from those that support action‐perception cycles to those that underwrite planning and imagination; namely, from explicit models that entail variables for predicting concurrent sensations, like objects, faces, or people—to action‐oriented models that predict action outcomes. It then elucidates how generative models and belief dynamics might link to neural representation and the implications of different types of generative models for understanding an agent's cognitive capabilities in relation to its ecological niche. The paper concludes with open questions regarding the evolution of generative models and the development of advanced cognitive abilities—and the gradual transition from pragmatic to detached neural representations. The analysis on offer foregrounds the diverse roles that generative models play in cognitive processes and the evolution of neural representation.

Generalised spike wave (GSW) discharges are the electroencephalographic (EEG) hallmark of absence seizures, clinically characterised by a transitory interruption of ongoing activities and impaired consciousness, occurring during states of reduced awareness. Several theories have been proposed to explain the pathophysiology of GSW discharges and the role of thalamus and cortex as generators. In this work we extend the existing theories by hypothesizing a role for the precuneus, a brain region neglected in previous works on GSW generation but already known to be linked to consciousness and awareness. We analysed fMRI data using dynamic causal modelling (DCM) to investigate the effective connectivity between precuneus, thalamus and prefrontal cortex in patients with GSW discharges.

Models of coupled oscillators are used to describe a wide variety of phenomena in neuroimaging. These models typically rest on the premise that oscillator dynamics do not evolve beyond their respective limit cycles, and hence that interactions can be described purely in terms of phase differences. Whilst mathematically convenient, the restrictive nature of phase-only models can limit their explanatory power. We therefore propose a generalisation of dynamic causal modelling that incorporates both phase and amplitude. This allows for the separate quantifications of phase and amplitude contributions to the connectivity between neural regions. We establish, using model-generated data and simulations of coupled pendula, that phase-only models perform well only under weak coupling conditions. We also show that, despite their higher complexity, phase-amplitude models can describe strongly coupled systems more effectively than their phase-only counterparts. We relate our findings to four metrics commonly used in neuroimaging: the Kuramoto order parameter, cross-correlation, phase-lag index, and spectral entropy. We find that, with the exception of spectral entropy, the phase-amplitude model is able to capture all metrics more effectively than the phase-only model. We then demonstrate, using local field potential recordings in rodents and functional magnetic resonance imaging in macaque monkeys, that amplitudes in oscillator models play an important role in describing neural dynamics in anaesthetised brain states.