On the modelling of seizure dynamics.

This scientific commentary refers to ‘On the nature of seizure dynamics’, by V. Jirsa et al. (doi:10.1093/brain/awu133)

Focusing Brain Therapeutic Interventions in Space and Time for Parkinson’s Disease

The last decade has seen major progress at all levels of neuroscience, from genes and molecules up to integrated systems-level models of brain function. In particular, there have been advances in the understanding of cell-type-specific contributions to function, together with a clearer account of how these contributions are coordinated from moment to moment to organise behavior. A major current endeavor is to leverage this knowledge to develop new therapeutic approaches. In Parkinson’s disease, there are a number of promising emerging treatments. Here, we will highlight three ambitious novel therapeutic approaches for this condition, each robustly driven by primary neuroscience. Pharmacogenetics genetically re-engineers neurons to produce neurotrophins that are neuroprotective to vulnerable dopaminergic cells or to directly replace dopamine through enzyme transduction. Deep brain stimulation (DBS) is undergoing a transformation, with adaptive DBS controlled by neural signals resulting in better motor outcomes and significant reductions in overall stimulation that could reduce side effects. Finally, optogenetics presents the opportunity to achieve cell-type-specific control with a high temporal specification on a large enough scale to effectively repair network-level dysfunction

Propagation of beta/gamma rhythms in the cortico-basal ganglia circuits of the Parkinsonian rat

Much of the motor impairment associated with Parkinson’s disease is thought to arise from pathological activity in the networks formed by the basal ganglia (BG) and motor cortex. To evaluate several hypotheses proposed to explain the emergence of pathological oscillations in Parkinsonism, we investigated changes to the directed connectivity in BG networks following dopamine depletion. We recorded local field potentials (LFPs) in the cortex and basal ganglia of rats rendered Parkinsonian by injection of 6-hydroxydopamine (6-OHDA) and in dopamine-intact controls. We performed systematic analyses of the networks using a novel tool for estimation of directed interactions (Non-Parametric Directionality, NPD). Additionally, we used a ‘conditioned’ version of the NPD analysis which reveals the dependence of the correlation between two signals upon a third reference signal. We find evidence of the dopamine dependency of both low beta (14-20 Hz) and high beta/low gamma (20-40 Hz) directed interactions within the network. Notably, 6-OHDA lesions were associated with enhancement of the cortical “hyper-direct” connection to the subthalamic nucleus (STN) and its feedback to the cortex and striatum. We find that pathological beta synchronization resulting from 6-OHDA lesioning is widely distributed across the network and cannot be located to any individual structure. Further, we provide evidence that high beta/gamma oscillations propagate through the striatum in a pathway that is independent of STN. Rhythms at high beta/gamma show susceptibility to conditioning that indicates a hierarchical organization when compared to low beta. These results further inform our understanding of the substrates for pathological rhythms in salient brain networks in Parkinsonism

Thalamocortical dynamics underlying spontaneous transitions in beta power in Parkinsonism

Parkinson's disease (PD) is a neurodegenerative condition in which aberrant oscillatory synchronization of neuronal activity at beta frequencies (15-35 Hz) across the cortico-basal ganglia-thalamocortical circuit is associated with debilitating motor symptoms, such as bradykinesia and rigidity. Mounting evidence suggests that the magnitude of beta synchrony in the parkinsonian state fluctuates over time, but the mechanisms by which thalamocortical circuitry regulates the dynamic properties of cortical beta in PD are poorly understood. Using the recently developed generic Dynamic Causal Modelling (DCM) framework, we recursively optimized a set of plausible models of the thalamocortical circuit (n = 144) to infer the neural mechanisms that best explain the transitions between low and high beta power states observed in recordings of field potentials made in the motor cortex of anesthetized Parkinsonian rats. Bayesian model comparison suggests that upregulation of cortical rhythmic activity in the beta-frequency band results from changes in the coupling strength both between and within the thalamus and motor cortex. Specifically, our model indicates that high levels of cortical beta synchrony are mainly achieved by a delayed (extrinsic) input from thalamic relay cells to deep pyramidal cells and a fast (intrinsic) input from middle pyramidal cells to superficial pyramidal cells. From a clinical perspective, our study provides insights into potential therapeutic strategies that could be utilized to modulate the network mechanisms responsible for the enhancement of cortical beta in PD. Specifically, we speculate that cortical stimulation aimed to reduce the enhanced excitatory inputs to either the superficial or deep pyramidal cells could be a potential non-invasive therapeutic strategy for PD

Inference of Brain Networks with Approximate Bayesian Computation– assessing face validity with an example application in Parkinsonism

This paper describes and validates a novel framework using the Approximate Bayesian Computation (ABC) algorithm for parameter estimation and model selection in models of mesoscale brain network activity. We provide a proof of principle, first pass validation of this framework using a set of neural mass models of the cortico-basal ganglia thalamic circuit inverted upon spectral features from experimental in vivo recordings. This optimization scheme relaxes an assumption of fixed-form posteriors (i.e. the Laplace approximation) taken in previous approaches to inverse modelling of spectral features. This enables the exploration of model dynamics beyond that approximated from local linearity assumptions and so fit to explicit, numerical solutions of the underlying non-linear system of equations. In this first paper, we establish a face validation of the optimization procedures in terms of: (i) the ability to approximate posterior densities over parameters that are plausible given the known causes of the data; (ii) the ability of the model comparison procedures to yield posterior model probabilities that can identify the model structure known to generate the data; and (iii) the robustness of these procedures to local minima in the face of different starting conditions. Finally, as an illustrative application we show (iv) that model comparison can yield plausible conclusions given the known neurobiology of the cortico-basal ganglia-thalamic circuit in Parkinsonism. These results lay the groundwork for future studies utilizing highly nonlinear or brittle models that can explain time dependent dynamics, such as oscillatory bursts, in terms of the underlying neural circuits