European Journal of Neuroscience

Unraveling the mechanisms underlying oscillatory behavior is critical for understanding normal and pathological brain processes. Here we used electrophysiology in mouse neocortical slices and principles of nonlinear dynamics to demonstrate how an increase in the N-methyl-d-aspartic acid receptor (NMDAR) conductance can create a nonlinear whole-cell current–voltage (I–V) relationship which leads to changes in cellular stability.

Modern imaging methods have lead to the identification of disease-related alterations affecting functional connectivity. The mechanism of how such alterations in functional connectivity arise in a structured network of interacting neural populations is as yet poorly understood. We use a modeling approach to explore the way in which this can arise and to highlight the important role that local population dynamics can have in shaping emergent spatial functional connectivity patterns.

Dopamine modulates cortical circuit activity, in part, through its actions on GABAergic interneurons, including increasing the excitability of fast-spiking interneurons. Though such effects have been demonstrated in single cells, there are no studies that examine how such mechanisms may lead to the effects of dopamine at a neural network level.

Power-law behaviors in brain activity in healthy animals, in the form of neuronal avalanches, potentially benefit the computational activities of the brain, including information storage, transmission and processing. In contrast, power-law behaviors associated with seizures, in the form of epileptic quakes, potentially interfere with the brain’s computational activities.

Epileptic seizures, interictal epileptic spikes and high-frequency oscillations (HFOs) are recognized as three electrophysiological markers of epileptogenic neuronal systems. Combining experimental and computational approaches will advance our understanding of (hyper)excitability mechanisms that underlie these electrophysiological signatures.

Epileptic seizure activity manifests as complex spatio-temporal dynamics on the clinically relevant macroscopic scale. These dynamics are known to arise from spatially heterogeneous tissue, but the relationship between specific spatial abnormalities and epileptic rhythm generation is not well understood.

Focal-onset seizures have traditionally been conceptualised as having a highly circumscribed onset in an ‘abnormal’ brain region, with evolution of the seizure requiring recruitment of adjacent or connected ‘normal’ brain regions. Complementing this concept of the spatial evolution of seizures, the purpose of our present paper is to explore the evidence for the dynamic evolution of focal epilepsy using bifurcation analysis of a neural mass model, and subsequently relating these bifurcations to specific features of clinical data recordings in the time domain.

Cortical oscillations arise during behavioral and mental tasks, and all temporal oscillations have particular spatial patterns. Studying the mechanisms that generate and modulate the spatiotemporal characteristics of oscillations is important for understanding neural information processing and the signs and symptoms of dynamical diseases of the brain.

Parkinsonism has been linked with changes in neuronal firing patterns in the basal ganglia (BG) and associated areas of the thalamus and cortex. We provide an overview of these findings and discuss some efforts to use computational models to understand these relationships as well as the therapeutic effects of deep brain stimulation (DBS). In particular, several modeling studies that we consider focus on the idea that DBS works by regularizing BG outputs. For example, models show how parkinsonian basal ganglia outputs may compromise thalamocortical relay of excitatory inputs (curly brackets), while DBS- induced regularization may restore relay fidelity, and these ideas lead to predictions about the importance of particular BG outputs in the emergence of parkinsonian signs and of particular DBS properties in alleviating these signs.

A key pathology in the development of Parkinson’s disease is the occurrence of persistent beta oscillations, which are correlated with difficulty in movement initiation. We investigated the network model composed of the subthalamic nucleus (STN) and globus pallidus (GP) developed by A. Nevado Holgado et al. [(2010) Journal of Neuroscience, 30, 12340–12352], who identified the conditions under which this circuit could generate beta oscillations.

Deep brain stimulation (DBS) is a remarkably successful treatment for the motor symptoms of Parkinson’s disease. High-frequency stimulation of the subthalamic nucleus (STN) within the basal ganglia is a main clinical target, but the physiological mechanisms of therapeutic STN DBS at the cellular and network level are unclear.

We used a computational model to study the effect of deep brain stimulation on the spiking activity of a bursting subthalamic nucleus neuron, hypothesizing that the firing mode of the cell will have a major impact. Our simulations showed that the model exhibits a silent period at stimulation offset and frequency-dependent effects. Our results demonstrated a method of quantitatively assessing neuronal changes induced by DBS, to maximize therapeutic benefit and minimize unwanted side effects.

Model simulations suggest that optogenetics can be used to discover and characterize dynamical motifs responsible for cortical oscillations and identify defects in them that could underlie psychiatric illnesses. The image shows responses from a cortical network in a state characterized by two time scales, a slow intrinsic one due to pyramidal cells and a fast one due to fast GABAergic inhibition, both of which lead to resonances in response to periodic optogenetic stimulation.