This paper is about inferring or discovering the functional architecture of distributed systems using Dynamic Causal Modelling (DCM). We describe a scheme that recovers the (dynamic) Bayesian dependency graph (connections in a network) using observed network activity. This network discovery uses Bayesian model selection to identify the sparsity structure (absence of edges or connections) in a graph that best explains observed time-series. The implicit adjacency matrix specifies the form of the network (e.g., cyclic or acyclic) and its graph-theoretical attributes (e.g., degree distribution). The scheme is illustrated using functional magnetic resonance imaging (fMRI) time series to discover functional brain networks. Crucially, it can be applied to experimentally evoked responses (activation studies) or endogenous activity in task-free (resting state) fMRI studies. Unlike conventional approaches to network discovery, DCM permits the analysis of directed and cyclic graphs. Furthermore, it eschews (implausible) Markovian assumptions about the serial independence of random fluctuations. The scheme furnishes a network description of distributed activity in the brain that is optimal in the sense of having the greatest conditional probability, relative to other networks. The networks are characterised in terms of their connectivity or adjacency matrices and conditional distributions over the directed (and reciprocal) effective connectivity between connected nodes or regions. We envisage that this approach will provide a useful complement to current analyses of functional connectivity for both activation and resting-state studies.
This technical report revisits the analysis of family-wise error rates in statistical parametric mapping - using random field theory - reported in (Eklund et al., 2015). Contrary to the understandable spin that these sorts of analyses attract, a review of their results suggests that they endorse the use of parametric assumptions - and random field theory - in the analysis of functional neuroimaging data. We briefly rehearse the advantages parametric analyses offer over nonparametric alternatives and then unpack the implications of (Eklund et al., 2015) for parametric procedures.
Abstract Integrating costs and benefits is crucial for optimal decision-making. While much is known about decisions that involve outcome-related costs (e.g., delay, risk), many of our choices are attached to actions and require an evaluation of the associated motor costs. Yet how the brain incorporates motor costs into choices remains largely unclear. We used human functional magnetic resonance imaging during choices involving monetary reward and physical effort to identify brain regions that serve as a choice comparator for effort-reward trade-offs. By independently varying both options' effort and reward levels, we were able to identify the neural signature of a comparator mechanism. A network involving supplementary motor area (SMA) and the caudal portion of dorsal anterior cingulate cortex (dACC) encoded the difference in reward (positively) and effort levels (negatively) between chosen and unchosen choice options. We next modelled effort-discounted subjective values using a novel behavioural model. This revealed that the same network of regions involving dACC and SMA encoded the difference between the chosen and unchosen options' subjective values, and that activity was best described using a concave model of effort-discounting. In addition, this signal reflected how precisely value determined participants' choices. By contrast, separate signals in SMA and ventro-medial PFC (vmPFC) correlated with participants' tendency to avoid effort and seek reward, respectively. This suggests that the critical neural signature of decision-making for choices involving motor costs is found in human cingulate cortex and not vmPFC as typically reported for outcome-based choice. Furthermore, distinct frontal circuits ‘drive’ behaviour towards reward-maximization and effort-minimization. Significance Statement The neural processes that govern the trade-off between expected benefits and motor costs remain largely unknown. This is striking because energetic requirements play an integral role in our day-to-day choices and instrumental behaviour, and a diminished willingness to exert effort is a characteristic feature of a range of neurological disorders. We use a new behavioural characterization of how humans trade-off reward-maximization with effort-minimization to examine the neural signatures that underpin such choices, using BOLD MRI neuroimaging data. We find the critical neural signature of decision-making, a signal that reflects the comparison of value between choice options, in human cingulate cortex, whereas two distinct brain circuits ‘drive’ behaviour towards reward-maximization or effort-minimization.
The brain can be regarded as an ensemble of connected dynamical systems and as such conforms to some simple principles relating the inputs and outputs of its constituent parts. The ensuing implications, for the way we think about, and measure, neuronal interactions, can be quite profound. These range from 1) implications for which aspects of neuronal activity are important to measure and how to characterize coupling among neuronal populations; 2) implication for understanding the emergence of dynamic receptive fields and functionally specialized brain architectures; and 3) teleological implications pertaining to the genesis of dynamic instability and complexity, which is necessary for adaptive self-organization. This review focuses on the first set of implications by looking at neuronal interactions, coupling, and implicit neuronal codes from a dynamical perspective. By considering the brain in this light, one can show that a sufficient description of neuronal activity must comprise activity at the current time and its recent history. This history constitutes a neuronal transient. Such transients represent an essential metric of neuronal interactions and, implicitly, a code employed in the functional integration of brain systems. The nature of transients, expressed conjointly in different neuronal populations, reflects the underlying coupling among brain systems. A complete description of this coupling, or effective connectivity, can be expressed in terms of generalized convolution kernels (Volterra kernels) that embody high-order or nonlinear interactions. This coupling may be synchronous, and possibly oscillatory, or asynchronous. A critical distinction between synchronous and asynchronous coupling is that the former is essentially linear and the latter is nonlinear. The nonlinear nature of asynchronous coupling enables the rich, context-sensitive interactions that characterize real brain dynamics, suggesting that it plays an important role in functional integration.
This paper applies the theory of active inference—a Bayesian theory of cognition—to provide a multi-scale description of science understood as a distributed, evidence-seeking process. This allows us to naturalise the scientific process in terms of dynamics that can be read as inference or Bayesian belief updating, i.e., processes that maximize the evidence for a generative model of the sensed and measured world. Our goal is to ground a computational framework that elucidates how individual cognitive processes contribute to the emergence of the scientific system, and reciprocally, how collective values, practices and technological advancements guide individual scientific cognition. Epistemologically, it also addresses some key questions, e.g., is science a special? And in what ways is scientific pursuit an existential imperative for all beings? Pragmatically, it proposes a way of simulating the practice of science, which may have a foundational role in the next generation of augmented intelligence systems.
This is the dataset accompanying the paper "Hierarchical generative modelling for autonomous robots" by Yuan et al.
How do we navigate a deeply structured world? Why are you reading this sentence first – and did you actually look at the fifth word? This review offers some answers by appealing to active inference based on deep temporal models. It builds on previous formulations of active inference to simulate behavioural and electrophysiological responses under hierarchical generative models of state transitions. Inverting these models corresponds to sequential inference, such that the state at any hierarchical level entails a sequence of transitions in the level below. The deep temporal aspect of these models means that evidence is accumulated over nested time scales, enabling inferences about narratives (i.e., temporal scenes). We illustrate this behaviour with Bayesian belief updating – and neuronal process theories – to simulate the epistemic foraging seen in reading. These simulations reproduce perisaccadic delay period activity and local field potentials seen empirically. Finally, we exploit the deep structure of these models to simulate responses to local (e.g., font type) and global (e.g., semantic) violations; reproducing mismatch negativity and P300 responses respectively.
