Publications

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-posed problem that commonly arises when modelling real-world phenomena. In this review, we detail a set of statistical procedures for inferring the structure of nonlinear coupled dynamical systems (structure learning), which has proved useful in neuroscience research. A key focus here is the comparison of competing models of network architectures-and implicit coupling functions-in terms of their Bayesian model evidence. These methods are collectively referred to as dynamic causal modelling. We focus on a relatively new approach that is proving remarkably useful, namely Bayesian model reduction, which enables rapid evaluation and comparison of models that differ in their network architecture. We illustrate the usefulness of these techniques through modelling neurovascular coupling (cellular pathways linking neuronal and vascular systems), whose function is an active focus of research in neurobiology and the imaging of coupled neuronal systems. This article is part of the theme issue 'Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences'.

Induced oscillatory activity in the auditory cortex peaks at around 40 Hz in humans. Using regional cerebral blood flow and positron emission tomography we previously confirmed frequency-selective cortical responses to 40-Hz tones in auditory primary cortices and concomitant bilateral activation of the cerebellar hemispheres. In this study, using functional magnetic resonance imaging (fMRI) we estimated the influence of 40-Hz auditory stimulation on the coupling between auditory cortex and superior temporal sulcus (STS) and Crus II, using a dynamic causal model of the interactions between medial geniculate nuclei, auditory superior temporal gyrus (STG)/STS, and the cerebellar Crus II auditory region. Specifically, we tested the hypothesis that 40-Hz-selective responses in the cerebellar Crus II auditory region could be explained by frequency-specific enabling of interactions in the auditory cortico-cerebellar-thalamic loop. Our model comparison results suggest that input from auditory STG/STS to cerebellum is enhanced selectively at gamma-band frequencies around 40 Hz.

Abstract The influence of the global BOLD signal on resting state functional connectivity in fMRI data remains a topic of debate, with little consensus. In this study, we assessed the effects of global signal regression (GSR) on effective connectivity within and between resting-state networks – as estimated with dynamic causal modelling (DCM) for resting state fMRI (rsfMRI). DCM incorporates a forward (generative) model that quantifies the contribution of different types of noise (including global measurement noise), effective connectivity, and (neuro)vascular processes to functional connectivity measurements. DCM analyses were applied to two different designs; namely, longitudinal and cross-sectional designs. In the modelling of longitudinal designs, we included four extensive longitudinal resting state fMRI datasets with a total number of 20 subjects. In the analysis of cross-sectional designs, we used rsfMRI data from 361 subjects from the Human Connectome Project. We hypothesized that (1) GSR would have no discernible impact on effective connectivity estimated with DCM, and (2) GSR would be reflected in the parameters representing global measurement noise. Additionally, we performed comparative analyses of the informative value of data with and without GSR. Our results showed negligible to small effects of GSR on connectivity within small (separately estimated) RSNs. For between-network connectivity, we found two important effects: the effect of GSR on between-network connectivity (averaged over all connections) was negligible to small, while the effect of GSR on individual connections was non-negligible. Contrary to our expectations, we found either no effect (in the longitudinal designs) or a non-specific (cross-sectional design) effect of GSR on parameters representing (global) measurement noise. Data without GSR were found to be more informative than data with GSR; however, in small resting state networks the precision of posterior estimates was greater using data after GSR. In conclusion, GSR is a minor concern in DCM studies; however, individual between-network connections (as opposed to average between-network connectivity) and noise parameters should be interpreted quantitatively with some caution. The Kullback-Leibler divergence of the posterior from the prior, together with the precision of posterior estimates, might offer a useful measure to assess the appropriateness of GSR, when nuancing data features in resting state fMRI.

Artificial intelligence (AI) is increasingly proposed as a solution to environmental sustainability challenges, with applications aimed at optimizing resource utilization and energy consumption. However, AI technologies also have significant negative environmental impacts. This duality underscores the need to critically evaluate AI's role in sustainable practices. One example of AI's application in sustainability is the Occupant Controlled Smart Thermostat (OCST). These systems optimize indoor temperature management by responding to dynamic signals, such as energy price fluctuations, which reflect power grid stress. Accordingly, regulatory frameworks have mandated performance standards for such technologies to ensure effective demand responsiveness. While OCSTs are effective in managing energy demand through predefined norms like price signals, their current designs often fail to accommodate the complex interplay of conflicting priorities, such as user comfort and grid optimization, particularly in uncertain climatic conditions. For instance, extreme weather events can amplify energy demands and user needs, necessitating a more context sensitive approach. This adaptability requires OCSTs to dynamically shift between multiple normative constraints (i.e., norms), such as prioritizing userdefined temperature settings over price-based energy restrictions when contextually appropriate. In this paper, we propose an innovative approach that combines the theory of active inference from theoretical neuroscience and robotics with a rulebook formalism to enhance the decision-making capabilities of autonomous AI agents. Using simulation studies, we demonstrate how these AI agents can resolve conflicts among norms under environmental uncertainty. A minimal use case is presented, where an OCST must decide whether to warm a room based on two conflicting rules: a “price” rule that restricts energy use above a cost threshold and a “need” rule that prioritizes maintaining the user's desired temperature. Our findings illustrate the potential for advanced AI-driven OCST systems to navigate conflicting norms, enabling more resilient and user-centered solutions to sustainable energy challenges.

During resting-state EEG recordings, alpha activity is more prominent over the posterior cortex in eyes-closed (EC) conditions compared to eyes-open (EO). In this study, we characterized the difference in spectra between EO and EC conditions using dynamic causal modelling. Specifically, we investigated the role of intrinsic and extrinsic connectivity—within the visual cortex—in generating EC-EO alpha power differences over posterior electrodes. The primary visual cortex (V1) and the bilateral middle temporal visual areas (V5) were equipped with bidirectional extrinsic connections using a canonical microcircuit. The states of four intrinsically coupled subpopulations—within each occipital source—were also modelled. Using Bayesian model selection, we tested whether modulations of the intrinsic connections in V1, V5 or extrinsic connections (or a combination thereof) provided the best evidence for the data. In addition, using parametric empirical Bayes (PEB), we estimated group averages under the winning model. Bayesian model selection showed that the winning model contained both extrinsic connectivity modulations, as well as intrinsic connectivity modulations in all sources. The PEB analysis revealed increased extrinsic connectivity during EC. Overall, we found a reduction in the inhibitory intrinsic connections during EC. The results suggest that the intrinsic modulations in V5 played the most important role in producing EC-EO alpha differences, suggesting an intrinsic disinhibition in higher order visual cortex, during EC resting state.

EDITORIAL article Front. Artif. Intell., 07 June 2021Sec. Medicine and Public Health https://doi.org/10.3389/frai.2021.710179

The definition of a brain state remains elusive, with varying interpretations across different sub-fields of neuroscience—from the level of wakefulness in anaesthesia, to activity of individual neurons, voltage in EEG, and blood flow in fMRI. This lack of consensus presents a significant challenge to the development of accurate models of neural dynamics. However, at the foundation of dynamical systems theory lies a definition of what constitutes the ’state’ of a system—i.e., a specification of the system’s future. Here, we propose to adopt this definition to establish brain states in neuroimaging timeseries by applying Dynamic Causal Modelling (DCM) to low-dimensional embedding of resting and task condition fMRI data. We find that ~90% of subjects in resting conditions are better described by first-order models, whereas ~55% of subjects in task conditions are better described by second-order models. Our work calls into question the status quo of using first-order equations almost exclusively within computational neuroscience and provides a new way of establishing brain states, as well as their associated phase space representations, in neuroimaging datasets.