1,987 publications from this institution
We present a didactic introduction to spectral Dynamic Causal Modelling (DCM), a Bayesian state-space modelling approach used to infer effective connectivity from non-invasive neuroimaging data. Spectral DCM is currently the most widely applied DCM variant for resting-state functional MRI analysis. Our aim is to explain its technical foundations to an audience with limited expertise in state-space modelling and spectral data analysis. Particular attention will be paid to cross-spectral density, which is the most distinctive feature of spectral DCM and is closely related to functional connectivity, as measured by (zero-lag) Pearson correlations. In fact, the model parameters estimated by spectral DCM are those that best reproduce the cross-correlations between all measurements--at all time lags--including the zero-lag correlations that are usually interpreted as functional connectivity. We derive the functional connectivity matrix from the model equations and show how changing a single effective connectivity parameter can affect all pairwise correlations. To complicate matters, the pairs of brain regions showing the largest changes in functional connectivity do not necessarily coincide with those presenting the largest changes in effective connectivity. We discuss the implications and conclude with a comprehensive summary of the assumptions and limitations of spectral DCM.
Biehl et al. (2021) present some interesting observations on an early formulation of the free energy principle. We use these observations to scaffold a discussion of the technical arguments that underwrite the free energy principle. This discussion focuses on solenoidal coupling between various (subsets of) states in sparsely coupled systems that possess a Markov blanket—and the distinction between exact and approximate Bayesian inference, implied by the ensuing Bayesian mechanics.
Theoretical arguments and empirical evidence in neuroscience suggests that organisms represent or model their environment by minimizing a variational free-energy bound on the surprise associated with sensory signals from the environment. In this paper, we study phase transitions in coupled dissipative dynamical systems (complex Ginzburg-Landau equations) under a variety of coupling conditions to model the exchange of a system (agent) with its environment. We show that arbitrary coupling between sensory signals and the internal state of a system -- or those between its action and external (environmental) states -- do not guarantee synchronous dynamics between external and internal states: the spatial structure and the temporal dynamics of sensory signals and action (that comprise the system's Markov blanket) have to be pruned to produce synchrony. This synchrony is necessary for an agent to infer environmental states -- a pre-requisite for survival. Therefore, such sentient dynamics, relies primarily on approximate synchronization between the agent and its niche.
In the context of consciousness studies, a key challenge is how to rigorously conceptualise first-person phenomenological descriptions of lived experience and their relation to third-person empirical measurements of the activity or dynamics of the brain and body. Since the 1990s, there has been a coordinated effort to explicitly combine first-person phenomenological methods, generating qualitative data, with neuroscientific techniques used to describe and quantify brain activity under the banner of "neurophenomenology". Here, we take on this challenge and develop an approach to neurophenomenology from a mathematical perspective. We harness recent advances in theoretical neuroscience and the physics of cognitive systems to mathematically conceptualise first-person experience and its correspondence with neural and behavioural dynamics. Throughout, we make the operating assumption that the content of first-person experience can be formalised as (or related to) a belief (i.e. a probability distribution) that encodes an organism's best guesses about the state of its external and internal world (e.g. body or brain) as well as its uncertainty. We mathematically characterise phenomenology, bringing to light a tool-set to quantify individual phenomenological differences and develop several hypotheses including on the metabolic cost of phenomenology and on the subjective experience of time. We conceptualise the form of the generative passages between first- and third-person descriptions, and the mathematical apparatus that mutually constrains them, as well as future research directions. In summary, we formalise and characterise first-person subjective experience and its correspondence with third-person empirical measurements of brain and body, offering hypotheses for quantifying various aspects of phenomenology to be tested in future work.
This paper revisits the dynamic causal modelling of fMRI timeseries by replacing the usual (Taylor) approximation to neuronal dynamics with a neural mass model of the canonical microcircuit. This provides a generative or dynamic causal model of laminar specific responses that can generate haemodynamic and electrophysiological measurements. In principle, this allows the fusion of haemodynamic and (event related or induced) electrophysiological responses. Furthermore, it enables Bayesian model comparison of competing hypotheses about physiologically plausible synaptic effects; for example, does attentional modulation act on superficial or deep pyramidal cells – or both? In this technical note, we describe the resulting dynamic causal model and provide an illustrative application to the attention to visual motion dataset used in previous papers. Our focus here is on how to answer long-standing questions in fMRI; for example, do haemodynamic responses reflect extrinsic (afferent) input from distant cortical regions, or do they reflect intrinsic (recurrent) neuronal activity? To what extent do inhibitory interneurons contribute to neurovascular coupling? What is the relationship between haemodynamic responses and the frequency of induced neuronal activity? This paper does not pretend to answer these questions; rather it shows how they can be addressed using neural mass models of fMRI timeseries.
ABSTRACT Brain function and its failures arise from dynamical patterns of neuronal activity shaped by synaptic neurotransmission. Both neurotransmitter receptor expression and neuronal population dynamics show a remarkable regional variability across the human cortex. We leverage this functional specialisation to characterise the relationship between receptor architectonics and electrophysiological signals. Using dynamic causal modelling (DCM), we fitted neural mass models to a normative set of intracranial EEG data. Subsequently, Bayesian model comparison helped to evaluate whether models improved when equipped with constraints on synaptic connectivity, based on regional neurotransmitter receptor densities. The results show that dynamic causal models generated region‐specific intracranial EEG spectra accurately. Incorporating prior information on normative receptor distributions further improved model evidence, indicating that regional variation in receptor density explains variations in synaptic connectivity and ensuing cortical population dynamics. The output is a cortical atlas of neurobiologically informed intracortical synaptic connectivity parameters. These can serve as empirical priors in future, patient‐specific models. In summary, we show that molecular cortical characteristics—that is, receptor densities—enrich and inform generative, biophysically plausible models of coupled neuronal populations. This work helps to explain regional variations in human electrophysiology, provides a methodological foundation to integrate multimodal data, and serves as a normative resource for future DCM studies of electrophysiology.
This paper describes a neural field model for local (mesoscopic) dynamics on the cortical surface. Our focus is on sparse intrinsic connections that are characteristic of real cortical microcircuits. This sparsity is modelled with radial connectivity functions or kernels with non-central peaks. The ensuing analysis allows one to generate or predict spectral responses to known exogenous input or random fluctuations. Here, we characterise the effect of different connectivity architectures (the range, dispersion and propagation speed of intrinsic or lateral connections) and synaptic gains on spatiotemporal dynamics. Specifically, we look at spectral responses to random fluctuations and examine the ability of synaptic gain and connectivity parameters to induce Turing instabilities. We find that although the spatial deployment and speed of lateral connections can have a profound affect on the behaviour of spatial modes over different scales, only synaptic gain is capable of producing phase-transitions. We discuss the implications of these findings for the use of neural fields as generative models in dynamic causal modeling (DCM).
Abstract We present a computational model that elucidates the interplay between inflammation, serotonin levels, and brain activity. The model delineates how inflammation impacts extracellular serotonin, while cerebral activity reciprocally influences serotonin concentration. Understanding the reciprocal interplay between the immune system and brain dynamics is important, as unabated inflammation can lead to relapsing depression. The model predicts dynamics within the prefrontal cortex (PFC) and subcallosal cingulate cortex (SCC), mirroring patterns observed in depressive conditions. It also accommodates pharmaceutical interventions that encompass anti-inflammatory and antidepressant agents, concurrently evaluating their efficacy with regard to the severity of depressive symptoms.
Within a theoretical framework of enactive allostasis, we explore active inference strategies for minimizing surprise to achieve resilience in dynamic environments. While individual differences and extrinsic protective factors traditionally account for variability in resilience trajectories following stressor exposure, the enactive model emphasizes the importance of the physical and social environment, specifically the “enactive niche,” which is both shaped by and impacts organisms living in it, accounting for variable success in allostatic prediction and accommodation. Enactive allostasis infers or predicts states of the world to minimize surprise and maintain regulation after surprise, i.e., resilience. Action policies are selected in accordance with the inferred state of a dynamic environment; those actions concurrently shape one’s environment, buffering against current and potential stressors. Through such inferential construction, multiple potential solutions exist for achieving stability within one’s enactive niche. Spanning a range of adaptive resilience strategies, we propose four phenotypes—fragile, durable, resilient, and pro-entropic (PE)—each characterized by a constellation of genetic, epigenetic, developmental, experiential, and environmental factors. Biological regulatory outcomes range from allostatic (over)load in the fragile and durable phenotypes, to allostatic recovery in resilience, and theoretically to increasing allostatic accommodation or “growth” in the proposed PE phenotype. Awareness distinguishes phenotypes by minimizing allostatically demanding surprise and engenders the cognitive and behavioral flexibility empirically associated with resilience. We further propose a role for awareness in proactively shaping one’s enactive niche to further minimize surprise. We conclude by exploring the mechanisms of phenotypic plasticity which may bolster individual resilience.
We present an approach to characterizing the differences among event-related hemodynamic responses in functional magnetic resonance imaging that are evoked by different sorts of stimuli. This approach is predicated on a linear convolution model and standard inferential statistics as employed by statistical parametric mapping. In particular we model evoked responses, and their differences, in terms of basis functions of the peri-stimulus time. This facilitates a characterization of the temporal response profiles that has a high effective temporal resolution relative to the repetition time. To demonstrate the technique we examined differential responses to visually presented words that had been seen prior to scanning or that were novel. The form of these differences involved both the magnitude and the latency of the response components. In this paper we focus on bilateral ventrolateral prefrontal responses that show deactivations for previously seen words and activations for novel words.
