P084 Gamma Oscillation in the Basal Ganglia: Interplay Between Local Inhibition and Beta Synchronization

Federico Fattorini*1,2, Mahboubeh Ahmadipour1,2, Enrico Cataldo3, Alberto Mazzoni1,2, Nicolò Meneghetti1,2


1The Biorobotics Institute, Scuola Superiore Sant’Anna, Pisa, Italy
2Department of Excellence for Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy

3Department of Physics, University of Pisa, Pisa, Italy



*Email:federico.fattorini@santannapisa.it
Introduction

Basal ganglia (BG) gamma oscillations (30-100 Hz) have been proposed as valuable biomarkers for guiding adaptive deep brain stimulation in Parkinson’s disease (PD) [1], offering a reliable alternative to beta oscillations (10-30 Hz). However, the origins of gamma oscillations in these structures remain poorly understood. Using a validated spiking network model of the BG [2], we identified striatal and pallidal sources of gamma oscillations. We found that their generation relied on self-inhibitory feedback within these populations and was strongly influenced by interactions with pathological beta oscillations. Our findings provide new insights into the generation of BG gamma oscillations and their role in PD pathology.


Methods
The BG model (Fig. 1A) included approximately 14000 neurons divided into 6 populations: D1 and D2 medium spiny neurons, fast-spiking neurons, prototypic (GPe-TI) and arkypallidal populations of external globus pallidus and subthalamic nucleus. We utilized non-linear integrate-and-fire neurons, using population-specific parameters. The transition from healthy to Parkinsonian conditions was simulated with a dopamine depletion parameter that increased the input to D2. The origins of gamma oscillations were explored by selectively disconnecting model projections and isolating nuclei that exhibited gamma activity. Interactions with pathological beta oscillations were analyzed by studying phase-frequency and phase-amplitude coupling.
Results
We identified two distinct gamma oscillations in our model (Fig. 1B): high-frequency (≈100 Hz) gamma in GPe-TI and slower (≈70 Hz) ones in D2 medium spiny neurons. While GPe-TI gamma oscillations were prominent in healthy and pathological states, D2 oscillations emerged under dopamine-depleted conditions. Both rhythms required self-inhibition within the corresponding nuclei to be generated. However, this mechanism alone could not account for all gamma dynamics. Beta oscillations, generated by the model under pathological conditions, affected GPe-TI gamma frequency via phase-frequency coupling and amplified D2 gamma activity through phase-amplitude coupling. Both interactions were mediated by beta-induced modulation of spiking activity.

Discussion
By employing a computational model of the BG, we offered a comprehensive explanation of gamma rhythmogenesis in these structures, identifying two sources: D2 and GPe-TI. Our results were consistent with experimental findings from both rat [3] and human local field potentials [4] and aligned with the results of other computational models [5]. We also clarified how these rhythms were generated through self-inhibition within these nuclei and how they interacted with pathological beta synchronization. Our insights into the mechanism behind gamma generation in BG represent a crucial step toward advancing our understanding of PD and improving their potential as biomarkers for adaptive deep brain stimulation.





Figure 1. A) Computational model of the basal ganglia: FSN (striatal spiking interneurons), D1/D2 (medium spiny neurons with D1 and D2 dopamine receptors), GPe-TA/TI (arkypallidal/prototypic populations of the globus pallidus externa), and STN (subthalamic nucleus). B) Power spectral densities (PSDs) of GPe-TI (top) and D2 (bottom) activities under healthy and Parkinsonian (PD) conditions.
Acknowledgements
This work was supported by the Italian Ministry of Research, in the context of the project NRRP “Fit4MedRob-Fit for Medical Robotics” Grant (# PNC0000007).
References



1.https://doi.org/10.1038/s41591-024-03196-z

2.https://doi.org/10.1371/journal.pcbi.1010645

3.https://doi.org/10.1111/cns.14241

4.https://doi.org/10.1016/j.expneurol.2012.07.005


5.https://doi.org/10.1523/JNEUROSCI.0419-23.2023