Mean-field modeling of the basal ganglia-thalamocortical system. II

Abstract

Neuronal correlates of Parkinson's disease (PD) include a shift to lower frequencies in the electroencephalogram (EEG) and enhanced synchronized oscillations at 3-7 and 7-30 Hz in the basal ganglia, thalamus, and cortex. This study describes the dynamics of a recent physiologically-based mean-field model of the basal ganglia-thalamocortical system, and shows how it accounts for many key electrophysiological correlates of PD. Its detailed functional connectivity comprises partially segregated direct and indirect pathways through two populations of striatal neurons, a hyperdirect pathway involving a corticosubthalamic projection, thalamostriatal feedback, and local inhibition in striatum and external pallidum (GPe). In a companion paper, realistic steady-state firing rates were obtained for the healthy state, and after dopamine loss modeled by weaker direct and stronger indirect pathways, reduced intrapallidal inhibition, lower firing thresholds of the GPe and subthalamic nucleus (STN), a stronger projection from striatum to GPe, and weaker cortical interactions. Here it is shown that oscillations around 5 and 20 Hz can arise with a strong indirect pathway, which also causes increased synchronization throughout the basal ganglia. Furthermore, increased theta power with progressive nigrostriatal degeneration is correlated with reduced alpha power and peak frequency, in agreement with empirical results. Unlike the hyperdirect pathway, the indirect pathway sustains oscillations with phase relationships that coincide with those found experimentally. Alterations in the responses of basal ganglia to transient stimuli accord with experimental observations. Reduced cortical gains due to both nigrostriatal and mesocortical dopamine loss lead to slower changes in cortical activity and may be related to bradykinesia. Finally, increased EEG power found in some studies may be partly explained by a lower effective GPe firing threshold, reduced GPe-GPe inhibition, and/or weaker intracortical connections in parkinsonian patients. Strict separation of the direct and indirect pathways is not necessary to obtain these results.

Origins and suppression of oscillations in a computational model of Parkinson’s disease

“…All simulations were run using NEURON (Hines and Carnevale, 1997, 2001). While there are other network models of the basal ganglia and PD (Holgado et al, 2010; van Albada et al, 2009; van Albada and Robinson, 2009), we chose the Hahn and McIntyre model for the physiologically realistic modeling of the neurons and their connectivity. Furthermore, the model’s output can be directly compared to physiological recordings and thus can be used to design future non-human primate experiments.…”

“…However, in order to analyze previous models the time delays were assumed equal and the system had to be linearized around a specific point (Holgado et al, 2010; van Albada et al, 2009; van Albada and Robinson, 2009). Accurate delays are important in determining the behavior of a closed-loop system and the origin of oscillations.…”

“…Previous mean-field models of the basal ganglia have been used to determine conditions under which pathological beta oscillations emerge (Holgado et al, 2010; Pasillas-Lépine, 2013; van Albada et al, 2009; van Albada and Robinson, 2009). However, in order to determine how oscillations are generated in many of these models, delays between the nuclei are constrained to be equal.…”

Neuroimaging, Neural Population Models for

“…However, if either the data speak to this detail or the need to Encyclopedia of Computational Neuroscience DOI 10.1007/978-1-4614-7320-6_70-1 # Springer Science+Business Media New York 2014 interpret the model meaningfully requires it, then we should include it. For example, it cannot be doubted that a proper description of Parkinsonism requires the inclusion of the basal ganglia and the thalamus (van Albada and Robinson 2009;van Albada et al 2009). Furthermore, in principle one would always wish to find a unifying, single model that can fit and predict multiple phenomena (Breakspear et al 2006).…”

Multi-channel neural mass modelling and analyzing

“…On the other hand, the multi-channel neural model can be used to validate the performance of neural signal processing methods and give stronger support for analyzing the real EEG data. For example, the multi-channel neural model has been used to test some hypotheses of information processing in the brain and to understand the complex coupling activities of neural oscillations from the point of view of physiology for some brain disorders, such as the mechanism of epilepsy, parkinsonism and Alzheimer's [2][3][4].…”