Cortical information flow in Parkinson's disease: a composite network/field model
The basal ganglia play a crucial role in the execution of movements, as demonstrated by the severe motor deficits that accompany Parkinson's disease (PD). Since motor commands originate in the cortex, an important question is how the basal ganglia influence cortical information flow, and how th...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2013-04-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00039/full |
| _version_ | 1818331319741972480 |
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| author | Cliff C. Kerr Cliff C. Kerr Cliff C. Kerr Sacha J. Van Albada Samuel A. Neymotin Samuel A. Neymotin George L. Chadderdon P. A. Robinson P. A. Robinson William W. Lytton William W. Lytton |
| author_facet | Cliff C. Kerr Cliff C. Kerr Cliff C. Kerr Sacha J. Van Albada Samuel A. Neymotin Samuel A. Neymotin George L. Chadderdon P. A. Robinson P. A. Robinson William W. Lytton William W. Lytton |
| author_sort | Cliff C. Kerr |
| collection | DOAJ |
| description | The basal ganglia play a crucial role in the execution of movements, as demonstrated by the severe motor deficits that accompany Parkinson's disease (PD). Since motor commands originate in the cortex, an important question is how the basal ganglia influence cortical information flow, and how this influence becomes pathological in PD. To explore this, we developed a composite neuronal network/neural field model. The network model consisted of 4950 spiking neurons, divided into 15 excitatory and inhibitory cell populations in the thalamus and cortex. The field model consisted of the cortex, thalamus, striatum, subthalamic nucleus, and globus pallidus. Both models have been separately validated in previous work. Three field models were used: one with basal ganglia parameters based on data from healthy individuals, one based on data from individuals with PD, and one purely thalamocortical model. Spikes generated by these field models were then used to drive the network model. Compared to the network driven by the healthy model, the PD-driven network had lower firing rates, a shift in spectral power towards lower frequencies, and higher probability of bursting; each of these findings is consistent with empirical data on PD. In the healthy model, we found strong Granger causality in the beta and low gamma bands between cortical layers, but this was largely absent in the PD model. In particular, the reduction in Granger causality from the main "input" layer of the cortex (layer 4) to the main "output" layer (layer 5) was pronounced. This may account for symptoms of PD that seem to reflect deficits in information flow, such as bradykinesia. In general, these results demonstrate that the brain's large-scale oscillatory environment, represented here by the field model, strongly influences the information processing that occurs within its subnetworks. Hence, it may be preferable to drive spiking network models with physiologically realistic inputs rather than pure white noise. |
| first_indexed | 2024-12-13T13:17:58Z |
| format | Article |
| id | doaj.art-05e6c120369c4142b3bd4e2b3268198a |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-12-13T13:17:58Z |
| publishDate | 2013-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-05e6c120369c4142b3bd4e2b3268198a2022-12-21T23:44:29ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882013-04-01710.3389/fncom.2013.0003945203Cortical information flow in Parkinson's disease: a composite network/field modelCliff C. Kerr0Cliff C. Kerr1Cliff C. Kerr2Sacha J. Van Albada3Samuel A. Neymotin4Samuel A. Neymotin5George L. Chadderdon6P. A. Robinson7P. A. Robinson8William W. Lytton9William W. Lytton10SUNY Downstate Medical CenterUniversity of SydneyWestmead Millennium InstituteJülich Research Centre and JARASUNY Downstate Medical CenterYale UniversitySUNY Downstate Medical CenterUniversity of SydneyWestmead Millennium InstituteSUNY Downstate Medical CenterKings County HospitalThe basal ganglia play a crucial role in the execution of movements, as demonstrated by the severe motor deficits that accompany Parkinson's disease (PD). Since motor commands originate in the cortex, an important question is how the basal ganglia influence cortical information flow, and how this influence becomes pathological in PD. To explore this, we developed a composite neuronal network/neural field model. The network model consisted of 4950 spiking neurons, divided into 15 excitatory and inhibitory cell populations in the thalamus and cortex. The field model consisted of the cortex, thalamus, striatum, subthalamic nucleus, and globus pallidus. Both models have been separately validated in previous work. Three field models were used: one with basal ganglia parameters based on data from healthy individuals, one based on data from individuals with PD, and one purely thalamocortical model. Spikes generated by these field models were then used to drive the network model. Compared to the network driven by the healthy model, the PD-driven network had lower firing rates, a shift in spectral power towards lower frequencies, and higher probability of bursting; each of these findings is consistent with empirical data on PD. In the healthy model, we found strong Granger causality in the beta and low gamma bands between cortical layers, but this was largely absent in the PD model. In particular, the reduction in Granger causality from the main "input" layer of the cortex (layer 4) to the main "output" layer (layer 5) was pronounced. This may account for symptoms of PD that seem to reflect deficits in information flow, such as bradykinesia. In general, these results demonstrate that the brain's large-scale oscillatory environment, represented here by the field model, strongly influences the information processing that occurs within its subnetworks. Hence, it may be preferable to drive spiking network models with physiologically realistic inputs rather than pure white noise.http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00039/fullBasal GangliaThalamusCortexGranger causalityParkinsons's diseaseneural field model |
| spellingShingle | Cliff C. Kerr Cliff C. Kerr Cliff C. Kerr Sacha J. Van Albada Samuel A. Neymotin Samuel A. Neymotin George L. Chadderdon P. A. Robinson P. A. Robinson William W. Lytton William W. Lytton Cortical information flow in Parkinson's disease: a composite network/field model Frontiers in Computational Neuroscience Basal Ganglia Thalamus Cortex Granger causality Parkinsons's disease neural field model |
| title | Cortical information flow in Parkinson's disease: a composite network/field model |
| title_full | Cortical information flow in Parkinson's disease: a composite network/field model |
| title_fullStr | Cortical information flow in Parkinson's disease: a composite network/field model |
| title_full_unstemmed | Cortical information flow in Parkinson's disease: a composite network/field model |
| title_short | Cortical information flow in Parkinson's disease: a composite network/field model |
| title_sort | cortical information flow in parkinson s disease a composite network field model |
| topic | Basal Ganglia Thalamus Cortex Granger causality Parkinsons's disease neural field model |
| url | http://journal.frontiersin.org/Journal/10.3389/fncom.2013.00039/full |
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