Publications

In this chapter, we focus on psychological and brain perspectives on the experience of coincidence. We first introduce the topic of the experience of coincidence in general. In the second section, we outline several psychological mechanisms that underlie the experience of coincidence in humans, such as cognitive biases, the role of context and the role of individual differences. In the third and final section we formulate the phenomenon of coincidence in the light of the unifying brain account of predictive coding, while arguing that the notion of coincidence provides a wonderful example of a construct that connects the Bayesian brain to folk psychology and philosophy.

Background – predicting the future UK Covid-19 epidemic provides a baseline of a vaccine only mitigation policy from which to judge additional public health interventions.Methods - a Dynamic Causal Model (DCM) is used to compare previous predictions with actual estimates over a 12-month period. It is also used to predict the future trends in infections, long-Covid, hospital admissions and deaths over the next 12 months.Findings – the October 2022 predictions made in October 2021underestimated cases by 43%, deaths by 24%, hospital admissions by 31% and overestimated post covid syndrome lasting more than 12 weeks by 94%. Plausible reasons for the disparities are the reduction in non-pharmaceutical interventions and the arrival of the Omicron variants. The predictions for October 2023 indicate a large wave of infections with significant health and economic consequences such as 120,000 additional Covid-19 related deaths, 800,000 hospital admissions and 3.5 million Post Covid-19 Syndrome lasting more than 12 weeks.Interpretation - the 12-month projection in 2021 underestimated the scale of the subsequent wave of infections. The 2023 projection could well underestimate the size of the next wave of Covid-19 infection. Vaccination alone will not control the epidemic. The Covid-19 epidemic is not over.Funding: KF is supported by Wellcome through core funding to the Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology [203147].Declaration of Interest: None to declare.

Abstract Local changes in cerebral hemodynamics are observed within a few hundred milliseconds of changes in neural activity. If hemodynamic responses are mediated by passive diffusion of a spatial signal (from the site of neural activity to the microvessels) then the dynamics of the response suggest a lower limit on the signal's apparent diffusion and elimination. The aim of this work was to estimate these limits and narrow the field of possible candidate substances. A simple biophysical simulation was used to examine how the time course of concentration changes in a spatial signal, at the site of action (microvessels), depends on key diffusion parameters (source geometry, apparent diffusion and elimination half‐life). The simulations suggested (1) that the rise in signal concentration is mostly a function of source geometry and diffusion. Conversely falls in concentration depend on elimination and (2) even when sources are very sparsely distributed Nitric Oxide would have a sufficiently fast diffusion and elimination to signal the early components of activity‐dependent hemodynamic response by passive diffusion. © 1995 Wiley‐Liss, Inc.

The articles in this special issue provide a rich and thoughtful perspective on the brain as an inference machine. They illuminate key aspects of the internal or generative models the brain might use for perception. Furthermore, they explore the implications for a sense of agency and the nature of false inference in neuropsychiatric syndromes. In this review, I try to gather together some of the themes that emerge in this special issue and use them to illustrate how far one can take the notion of predictive coding in understanding behaviour and agency.

Thisarticle has been written in response to Dr. Fred L. Bookstein's article entitled ‘“Voxel-Based Morphometry” Should Not Be Used with Imperfectly Registered Images’ in this issue of NeuroImage. We will address three main issues: (i) Dr. Bookstein appears to have misunderstood the objective of voxel-based morphometry (VBM) and the nature of the continuum we referred to. (ii) We agree with him when he states that findings from VBM can pertain to systematic registration errors during spatial normalization. (iii) His argument about voxelwise tests on smooth data holds in the absence of error variance, but is of no consequence when using actual data. We first review the tenets of VBM, paying particular attention to the relationship between VBM and tensor-based morphometry. The last two sections of this response deal with the specific concerns raised by Dr. Bookstein.

Abstract The remarkable resilience of cognitive functions to focal brain damage suggests that multiple degenerate neuronal systems can sustain the same function either via similar mechanisms or by implementing different cognitive strategies. In degenerate functional neuroanatomy, multiple degenerate neuronal systems might be present in a single brain where they are either co‐activated or remain latent during task performance. In degeneracy over subjects, a particular function may be sustained by only one neuronal system within a subject, but by different systems over subjects. Degeneracy over subjects might have arisen from (ab)normal variation in neurodevelopmental trajectories or long‐term plastic changes following structural lesions. We discuss how degenerate neuronal systems can be revealed using (1) intersubject variability, (2) multiple lesion studies and (3) an iterative approach integrating information from lesion and functional imaging studies.

Tsuda examines the potential contribution of nonlinear dynamical systems, with many degrees of freedom, to understanding brain function. We offer suggestions concerning symmetry and transients to strengthen the physiological motivation and theoretical consistency of this novel research direction: Symmetry plays a fundamental role, theoretically and in relation to real brains. We also highlight a distinction between chaotic “transience” and “itineracy.”