Structure Learning in Coupled Dynamical Systems and Dynamic Causal Modelling

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 (ie, hypotheses about) network architectures and implicit coupling functions in terms of their Bayesian model evidence. These methods are collectively referred to as dynamical casual modelling (DCM). We focus on a relatively new approach that is proving remarkably useful; namely, Bayesian model reduction (BMR), 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

Altered intrinsic functional connectivity in language-related brain regions in association with verbal memory performance in euthymic bipolar patients

Potential abnormalities in the structure and function of the temporal lobes have been studied much less in bipolar disorder than in schizophrenia. This may not be justified because language-related symptoms, such as pressured speech and flight of ideas, and cognitive deficits in the domain of verbal memory are amongst the hallmark of bipolar disorder (BD), and contribution of temporal lobe dysfunction is therefore likely. In the current study, we examined resting-state functional connectivity (FC) between the auditory cortex (Heschl’s gyrus [HG], planum temporale [PT]) and whole brain using seed correlation analysis in n = 21 BD euthymic patients and n = 20 matched healthy controls and associated it with verbal memory performance. In comparison to controls BD patients showed decreased functional connectivity between Heschl’s gyrus and planum temporale and the left superior and middle temporal gyrus. Additionally, fronto-temporal functional connectivity with the right inferior frontal/precentral gyrus and the insula was increased in patients. Verbal episodic memory deficits in the investigated sample of BD patients and language-related symptoms might therefore be associated with a diminished FC within the auditory/temporal gyrus and a compensatory fronto-temporal pathway

Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area

The brain: What is critical about it?

We review the recent proposal that the most fascinating brain properties are related to the fact that it always stays close to a second order phase transition. In such conditions, the collective of neuronal groups can reliably generate robust and flexible behavior, because it is known that at the critical point there is the largest abundance of metastable states to choose from. Here we review the motivation, arguments and recent results, as well as further implications of this view of the functioning brain.Comment: Proceedings of BIOCOMP2007 - Collective Dynamics: Topics on Competition and Cooperation in the Biosciences. Vietri sul Mare, Italy (2007

Emergent complex neural dynamics

A large repertoire of spatiotemporal activity patterns in the brain is the basis for adaptive behaviour. Understanding the mechanism by which the brain's hundred billion neurons and hundred trillion synapses manage to produce such a range of cortical configurations in a flexible manner remains a fundamental problem in neuroscience. One plausible solution is the involvement of universal mechanisms of emergent complex phenomena evident in dynamical systems poised near a critical point of a second-order phase transition. We review recent theoretical and empirical results supporting the notion that the brain is naturally poised near criticality, as well as its implications for better understanding of the brain