@article{dedcd3dda4734143b41b8475f14b6324,
title = "The role of coupling connections in a model of the cortico-basal ganglia-thalamocortical neural loop for the generation of beta oscillations",
abstract = "Excessive neural synchronization in the cortico-basal ganglia-thalamocortical circuits in the beta ( [Formula presented]) frequency range (12–35 Hz) is closely associated with dopamine depletion in Parkinson's disease (PD) and correlated with movement impairments, but the neural basis remains unclear. In this work, we establish a double-oscillator neural mass model for the cortico-basal ganglia-thalamocortical closed-loop system and explore the impacts of dopamine depletion induced changes in coupling connections within or between the two oscillators on neural activities within the loop. Spectral analysis of the neural mass activities revealed that the power and frequency of their principal components are greatly dependent on the coupling strengths between nuclei. We found that the increased intra-coupling in the basal ganglia-thalamic (BG-Th) oscillator contributes to increased oscillations in the lower [Formula presented] frequency band (12–25 Hz), while increased intra-coupling in the cortical oscillator mainly contributes to increased oscillations in the upper [Formula presented] frequency band (26–35 Hz). Interestingly, pathological upper [Formula presented] oscillations in the cortical oscillator may be another origin of the lower [Formula presented] oscillations in the BG-Th oscillator, in addition to increased intra-coupling strength within the BG-Th network. Lower [Formula presented] oscillations in the BG-Th oscillator can also change the dominant oscillation frequency of a cortical nucleus from the upper to the lower [Formula presented] band. Thus, this work may pave the way towards revealing a possible neural basis underlying the Parkinsonian state.",
keywords = "Beta oscillations, Cortico-basal ganglia-thalamocortical circuits, Coupling strength, Double-oscillator system, Neural mass model",
author = "Chen Liu and Changsong ZHOU and Jiang Wang and Chris Fietkiewicz and Loparo, {Kenneth A.}",
note = "Funding Information: This work was supported by the National Natural Science Foundation of China (Grant Nos. 61701336 and 61871287 ), the Natural Science Foundation of Tianjin, China (Grant No. 17JCQNJC00800 ) and the funding of Hong Kong Scholars Programs (Grant No. XJ2016006 ) and partially supported by Hong Kong Baptist University (HKBU) Strategic Development Fund, the Hong Kong RGC ( HKBU 12200217 ). This research was conducted using the resources of the High Performance Cluster Computing Center, Hong Kong Baptist University, which receives funding from RGC, University Grant Committee of the HKSAR and HKBU. The authors also gratefully acknowledged the financial support provided by Opening Foundation of Key Laboratory of Opto-technology and Intelligent Control (Lanzhou Jiaotong University), Ministry of Education ( KFKT 2018–5 ) and the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. Funding Information: This work was supported by the National Natural Science Foundation of China (Grant Nos. 61701336 and 61871287), the Natural Science Foundation of Tianjin, China (Grant No.17JCQNJC00800) and the funding of Hong Kong Scholars Programs (Grant No. XJ2016006) and partially supported by Hong Kong Baptist University (HKBU) Strategic Development Fund, the Hong Kong RGC (HKBU 12200217). This research was conducted using the resources of the High Performance Cluster Computing Center, Hong Kong Baptist University, which receives funding from RGC, University Grant Committee of the HKSAR and HKBU. The authors also gratefully acknowledged the financial support provided by Opening Foundation of Key Laboratory of Opto-technology and Intelligent Control (Lanzhou Jiaotong University), Ministry of Education (KFKT 2018?5) and the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.",
year = "2020",
month = mar,
doi = "10.1016/j.neunet.2019.12.021",
language = "English",
volume = "123",
pages = "381--392",
journal = "Neural Networks",
issn = "0893-6080",
publisher = "Elsevier Ltd.",
}