Publications

Functional near-infrared spectroscopy (fNIRS) is an emerging technique for measuring changes in cerebral hemoglobin concentration via optical absorption changes. Although there is great interest in using fNIRS to study brain connectivity, current methods are unable to infer the directionality of neuronal connections. In this paper, we apply Dynamic Causal Modelling (DCM) to fNIRS data. Specifically, we present a generative model of how observed fNIRS data are caused by interactions among hidden neuronal states. Inversion of this generative model, using an established Bayesian framework (variational Laplace), then enables inference about changes in directed connectivity at the neuronal level. Using experimental data acquired during motor imagery and motor execution tasks, we show that directed (i.e., effective) connectivity from the supplementary motor area to the primary motor cortex is negatively modulated by motor imagery, and this suppressive influence causes reduced activity in the primary motor cortex during motor imagery. These results are consistent with findings of previous functional magnetic resonance imaging (fMRI) studies, suggesting that the proposed method enables one to infer directed interactions in the brain mediated by neuronal dynamics from measurements of optical density changes.

Dynamic causal modelling (DCM) was originally proposed as a hypothesis driven procedure in which a small number of neurobiologically motivated models are compared. Model comparison in this context usually proceeds by individually fitting each model to data and then approximating the corresponding model evidence with a free energy bound. However, a recent trend has emerged for comparing very large numbers of models in a more exploratory manner. This led Friston and Penny (2011) to propose a post-hoc approximation to the model evidence, which is computed by optimising only the largest (full) model of a set of models. The evidence for any (reduced) submodel is then obtained using a generalisation of the Savage-Dickey density ratio (Dickey, 1971). The benefit of this post-hoc approach is a huge reduction in the computational time required for model fitting. This is because only a single model is fitted to data, allowing a potentially huge model space to be searched relatively quickly. In this paper, we explore the relationship between the free energy bound and post-hoc approximations to the model evidence in the context of deterministic (bilinear) dynamic causal models (DCMs) for functional magnetic resonance imaging data.

In a recent paper, Rosenberg et al. (1) present a compelling explanation for the perceptual symptoms of autism in terms of a failure of divisive normalization. In divisive normalization the output of individual neurons is scaled (or divided) by the combined activity of the neural population in which they are embedded, and thus local visual context provides a means of gain control—a volume dial—for stimulus-evoked responses. However, to properly understand the wider mechanistic implications, beyond local inhibition or gain in the visual cortex, one has to posit a biologically plausible instantiation of context-sensitive neural responses across multiple timescales and hierarchical levels …

The present study was undertaken to determine the apparent value for the volume of distribution of water to be used in the dynamic/integral technique for generating functional CBF images. A value of 0.86 resulted in only a minor loss of accuracy compared to the more accurate (but time-consuming) dynamic only analysis, which incorporated the regionally fitted estimates of the volume of distribution of water. In contrast to the traditionally used in vitro value of 0.95, the value of 0.86 allows for the inclusion of a significant part of the washout phase in the integral analysis, thereby producing statistically improved CBF images.

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: https://www.fil.ion.ucl.ac.uk/spm/covid-19/dashboard

The vertebrate brain must balance internally generated predictions with constraints of environmental affordances. This balance constitutes a fundamental principle of neural organization that underwrites cortical computation. Using the prosomeric model of the neuraxis, we show how dorsalizing and ventralizing morphogenetic gradients specify excitatory and inhibitory lineages during development, establishing the functional architecture of active affordance. These developmental asymmetries are elaborated through telencephalic expansion, pallial-subpallial integration, and laminar differentiation of the neocortex, as described by the structural model. We demonstrate that motor control emerges within a sensory-predictive architecture due to the alar origin of the telencephalon and that increasing excitatory-inhibitory complementarity within the mammalian neocortex enables selective, context-sensitive action. Subpallial and diencephalic systems provide inhibitory governance over cortical action tendencies, supporting policy evaluation and selection in the framework of active inference. At the base of this hierarchy, the hypothalamus integrates homeostatic and allostatic signals to bias the landscape of affordances, shaping the likelihood of action policies. Together, these findings establish active affordance as a developmental and evolutionary framework linking prosomeric neurodevelopment, cortical architecture, subcortical control, and adaptive behavior. Active inference is thereby situated as the mature cortical expression of a conserved biological solution to acting in an uncertain world.

If one formulates Helmholtz's ideas about perception in terms of modern-day theories one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics it can be shown that the problems of inferring what cause our sensory inputs and learning causal regularities in the sensorium can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on Empirical Bayes and hierarchical models of how sensory information is generated. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of the brain's organisation and responses. In this paper, we suggest that these perceptual processes are just one emergent property of systems that conform to a free-energy principle. The free-energy considered here represents a bound on the surprise inherent in any exchange with the environment, under expectations encoded by its state or configuration. A system can minimise free-energy by changing its configuration to change the way it samples the environment, or to change its expectations. These changes correspond to action and perception, respectively, and lead to an adaptive exchange with the environment that is characteristic of biological systems. This treatment implies that the system's state and structure encode an implicit and probabilistic model of the environment. We will look at models entailed by the brain and how minimisation of free-energy can explain its dynamics and structure.

A technique is described for estimating the position of the intercommisural line (AC–PC line) directly from landmarks on positron emission tomographic (PET) images, namely the ventral aspects of the anterior and posterior corpus callosum, the thalamus, and occipital pole. The relationship of this estimate to the true AC–PC line, fitted through the centres of the anterior and posterior commissures, showed minimal vertical and angular displacement when measured on magnetic resonance imaging (MRI) scans. Using regression analysis, the ease and reliability of fitting to these points was found to be high. This directly derived AC–PC line estimate was validated in terms of the assumptions used in the method of Fox et al. The ratio of distance between the AC–PC line and a line passing through the base of the inion (GI line) to total brain height was 0.21, as predicted. The technique has been further validated by localizing focal activation of the sensorimotor cortex. The technique is discussed in terms of absolute limits to localization of structures in the brain using noninvasive tomographic techniques in general and PET in particular.

We aim to investigate the spatial experience of patients with chronic scotomas caused by lesion to early visual cortex, including primary visual cortex (V1) and adjacent extrastriate visual areas. These experiments are conducted as part of an adversarial collaboration testing contrasting theories of consciousness: Integrated Information Theory (IIT) and two Predictive Processing accounts, Active Inference (AI) and Neurorepresentationalism (NREP). The central question is whether lesions to early visual cortex alter the experienced extent of visual space itself, or instead primarily disrupt stimulus content within an otherwise preserved visual space. To address this, we use paradigms in which patients estimate distances or spatial extents that either span a scotomatous region or fall entirely within intact visual field locations. Psychometric functions relating perceived and physical extent are modeled to estimate shifts in the point of subjective equality (PSE). According to IIT, lesions to early visual cortex, including V1 and occipital exstrastriate cortex, should lead to systematic reductions in perceived spatial extent across the scotoma (negative PSE shifts), reflecting a contraction of experienced space. In contrast, Predictive Processing accounts posit that higher-level predictive mechanisms preserve spatial structure despite loss of early input, predicting little or no systematic contraction (with NREP allowing limited context-dependent effects). By quantifying distortions in perceived spatial extent, this protocol aims to distinguish between these competing theoretical predictions.