1,987 publications from this institution
Current pathophysiological theories of schizophrenia highlight the role of altered brain connectivity. This dysconnectivity could manifest 1) anatomically, through structural changes of association fibers at the cellular level, and/or 2) functionally, through aberrant control of synaptic plasticity at the synaptic level. In this article, we review the evidence for these theories, focusing on the modulation of synaptic plasticity. In particular, we discuss how dysconnectivity, observed between brain regions in schizophrenic patients, could result from abnormal modulation of N-methyl-d-aspartate (NMDA)-dependent plasticity by other neurotransmitter systems. We focus on the implication of the dysconnection hypothesis for functional imaging at the systems level. In particular, we review recent advances in measuring plasticity in the human brain using functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) that can be used to address dysconnectivity in schizophrenia. Promising experimental paradigms include perceptual and reinforcement learning. We describe how theoretical and causal models of brain responses might contribute to a mechanistic understanding of synaptic plasticity in schizophrenia.
No abstract is provided for this article.
This issue's front section features a special Review: The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields. I was asked to write this Editorial on behalf of the editors and coauthors of the article, as an introduction— an introduction that we hope will serve as an aperitif for the article itself. The Review was solicited by Rolf Kotter (PLoS Computational Biology Reviews Editor) at one of the annual Brain Connectivity Workshops (http://www.hirnforschung.net/bcw), which we cofounded many years ago. These are highly interactive, discussion-orientated meetings that focus on the latest advances in modelling functional integration and coupling in the brain. They naturally attract a broad range of computational neuroscientists, neurophysiologists, neuroanatomists, and cognitive scientists. That year, we seemed drawn to computational neurobiology and the models of neuronal dynamics that were being used to understand interactions in the brain. After a couple of days, it became clear that we were all using exactly the same rhetoric in reference to completely different things. Terms such as mean-field approximations, mass-action, neural-mass models, neural-field models, density-dynamics, etc., were used with exuberance but did not really help us to communicate. As a result, Rolf challenged us to define and synthesise these perspectives in a coherent and pragmatic way; the response to that challenge is the article in this issue of PLoS Computational Biology by Deco, Jirsa, Robinson, Breakspear, and Friston, which took more than two years to prepare. The difficulties became evident quickly when we started to integrate our respective contributions. These were far from coherent and disclosed some fundamental differences in the perspectives adopted on modelling distributed brain activity at a statistical level. These differences ranged from the semantic (e.g., what does “mass” mean in neural-mass models) to the sublime (e.g., some of us clearly found it difficult, if not heartbreaking, to give up our favourite variables to ensure notational consistency). This, in part, reflects the fact that the coauthors are all autonomous and senior scientists in their own fields, working on different continents, and emerging from very distinct intellectual backgrounds. Even the way in which these models were being used differed markedly among the contributors. After numerous iterations and cross-revision, the end product started to attain a degree of coherence (although some might argue there is still room for improvement). The basic issue our synthesis tries to address is how different models, used to simulate and predict observed brain dynamics, can be traced back to their common fundaments. We then try to illustrate the diversity of applications that can be entertained with these models. The basis of these models rests on modelling, not on the behaviour of individual nerve cells or neurons, but on the probability density over ensembles or populations of similar neurons. The Fokker-Planck formalism becomes central here and can be harnessed using neuronal models that are cast in terms of differential equations, with or without discrete behaviours (e.g., neuronal spiking or firing). From the density dynamics afforded by the Fokker-Planck equation, we then pursue various simplifications and special cases. An important example is when the density becomes a point-mass over the expected states of a population. These are referred to as neural-mass models and predominate in the computational neuroscience literature. A key generalisation of these neural-mass models is to neural-field models, where the location of the mass or expected state of a population becomes a function of both time and position on the brain's cortical surface or subcortical structures. These models generate all sorts of interesting and neuronally plausible patterns and self-organising phenomena, which can be inferred through invasive or non-invasive electrophysiological recordings of real brains. The applications of these models are essentially twofold; some authors use them to understand the basic principles of neuronal dynamics and implicit computations; for example, understanding dynamics in terms of nonlinear mechanisms such as bifurcations, understanding perceptual categorisation in terms of multistability, or identifying the domains of parameter–space that support commonly observed spatiotemporal patterns of activity. Other authors use these models as forward or generative models, whose parameters can be optimised to reproduce an observed dataset. This is known as model inversion and allows one to estimate important biophysical constants and parameters from empirical data. In this context, one can also explore model spaces and use data to adjudicate among various neural-mass or field models. We have chosen a few key examples that highlight the necessary role of density-dynamics and mean-field approximations in computational neuroscience. Participating in this Review has been an enormously enlightening experience; at the same time it was exhausting and something I will think twice about before engaging in again. This is not meant to be a brief orientation to the field but more an attempt to provide a reference framework for people to understand their own contributions, in relation to others. It is a Review that I look forward to giving to my students; although I suspect they will take more than a weekend to digest it.
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.
In the complex scenes of everyday life, our brains must select from among many competing inputs for perceptual synthesis - so that only the most relevant are fully processed and irrelevant (distracting) information is suppressed. At the same time, we must remain responsive to salient events outside our current focus of attention - and balancing these two processing modes is a fundamental task our brain constantly needs to solve. This Research Topic examines how attentional control is guided by sensory predictions, prior knowledge, reward, task sets, and emotional factors. Moreover, the neural signatures of these mechanisms are investigated in Original Research Articles or summarized in Review, Perspective and Hypothesis & Theory Articles. Findings from a wide range of state-of-the-art complementary neuroscientific methods such as fMRI, M/EEG, TMS, and ALE-based meta-analysis are presented.The collection of papers of this Research Topic provides an overview over our current knowledge in the field and also presents novel stimulating hypotheses on how attention is controlled in the human brain. It moreover bridges the gap to other disciplines such as decision-making and social and affective neuroscience.
Being confident in whether a stimulus is present or absent (a detection judgment) is qualitatively distinct from being confident in the identity of that stimulus (a discrimination judgment). In particular, in detection, evidence can only be available for the presence, not the absence, of a target object. This asymmetry suggests that higher-order cognitive and neural processes may be required for confidence in detection, and more specifically, in judgments about absence. In a within-subject, pre-registered and performance-matched fMRI design, we observed quadratic confidence effects in frontopolar cortex for detection but not discrimination. Furthermore, in the right temporoparietal junction, confidence effects were enhanced for judgments of target absence compared to judgments of target presence. We interpret these findings as reflecting qualitative differences between the neural basis of metacognitive evaluation of detection and discrimination, potentially in line with counterfactual or higher-order models of confidence formation in detection.
This paper describes a path integral formulation of the free energy principle. The ensuing account expresses the paths or trajectories that a particle takes as it evolves over time. The main results are a method or principle of least action that can be used to emulate the behaviour of particles in open exchange with their external milieu. Particles are defined by a particular partition, in which internal states are individuated from external states by active and sensory blanket states. The variational principle at hand allows one to interpret internal dynamics—of certain kinds of particles—as inferring external states that are hidden behind blanket states. We consider different kinds of particles, and to what extent they can be imbued with an elementary form of inference or sentience. Specifically, we consider the distinction between dissipative and conservative particles, inert and active particles and, finally, ordinary and strange particles. Strange particles can be described as inferring their own actions, endowing them with apparent autonomy or agency. In short—of the kinds of particles afforded by a particular partition—strange kinds may be apt for describing sentient behaviour.
