Insights into Parkinson's disease from mean-field modeling of brain electrical activity

Abstract

Event Abstract Back to Event Insights into Parkinson's disease from mean-field modeling of brain electrical activity Parkinson's disease is characterized by the gradual degeneration of the substantia nigra pars compacta and associated areas, which decreases dopaminergic input mainly to the striatum. In addition to cognitive and mood disturbances, this results in motor symptoms such as difficulty initiating movements, slowness of movement, and resting tremor of the extremities. Electrophysiological correlates of Parkinson's disease include altered average neuronal firing rates and responses to transient stimuli, and synchronized oscillations around 5 Hz and 20 Hz in the basal ganglia, thalamus, and cortex. Furthermore, the alpha peak in electroencephalographic (EEG) spectra shifts to lower frequencies, and relative low-frequency power increases. There is a wealth of empirical data on neuronal activity in Parkinson's disease, whereas quantitative models are relatively sparse. Existing models have not allowed a consensus to be reached on the substrates and mechanisms responsible for parkinsonian oscillations and changes in average firing rates. Moreover, as far as we are aware, no previous quantitative model has addressed changes in EEG spectra in parkinsonian patients. We present a physiologically-based model of the basal ganglia-thalamocortical system, which describes fluctuating average firing rates within each component. Key connections between cortex, thalamus, and basal ganglia are taken into account, including direct and indirect pathways through two populations of striatal neurons, primarily expressing either the D1 or D2 type of dopamine receptor, and a hyperdirect pathway from cortex to the subthalamic nucleus (STN). Based on empirical findings, we consider various possible effects of dopamine loss. Of these, two are mutually exclusive: (i) decreased corticostriatal connection strengths and striatal firing thresholds to approximate a reduced signal-to-noise ratio in the striatum; and (ii) increased cortico-D2 and decreased cortico-D1 connection strengths, representing differential modulation of the direct and indirect pathways. Either (i) or (ii) can be combined with (iii) weakened lateral inhibition in the external pallidum (GPe); (iv) weaker intracortical excitation and especially inhibition resulting from mesocortical dopamine loss; and/or (v) reduced GPe and STN firing thresholds, and a stronger D2-GPe projection. We find that a combination of options (ii)-(v) produces changes in average firing rates and responses to transient stimuli that accord with experiments, and increased synchronization throughout the basal ganglia-thalamocortical system. Furthermore, the strengthened indirect pathway leads to enhanced oscillations around 5 Hz. Particularly strong subcircuits may sustain limit cycles, for which phase relationships between nuclei are consistent with empirical observations. Approximately 20 Hz oscillations are found to have a corticothalamic origin, but can spread to the basal ganglia when the indirect pathway becomes strong. Changes in model EEG spectra corresponding to the combination of (ii)-(v) agree with empirical results, including increased relative theta power, decreased relative alpha power, and lower dominant frequencies in the alpha range. The fact that a single set of parameter changes around the healthy state produces all the above electrophysiological effects suggests that our model captures many features of the parkinsonian brain in a realistic manner. Conference: Computational and systems neuroscience 2009, Salt Lake City, UT, United States, 26 Feb - 3 Mar, 2009. Presentation Type: Poster Presentation Topic: Poster Presentations Citation: (2009). Insights into Parkinson's disease from mean-field modeling of brain electrical activity. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.040 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 30 Jan 2009; Published Online: 30 Jan 2009. Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Google Google Scholar PubMed Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.