Generation mechanisms and species-specific properties of parkinsonian beta oscillations in the basal ganglia
Nicollin, E.; Deffains, M.; Mallet, N. P.; Leblois, A.
Show abstract
The basal ganglia (BG), thalamus and cortex form the BG-thalamo-cortical (BGTC) network, which is essential for voluntary movement and centrally involved in Parkinson's disease (PD). In both patients and animal models, BG neuronal activity exhibits exaggerated oscillatory synchronization in the beta frequencies (13-30 Hz). Theoretical studies have proposed multiple network mechanisms for the generation of these abnormal beta oscillations. However, key properties, such as frequency and power distribution across BG nuclei, vary substantially among patients and between animal models (rodents vs. non-human primates). This variability complicates direct comparisons between theoretical models and experimental data, and questions whether distinct neuronal mechanisms may underlie beta oscillations across species. In an experimentally constrained BGTC network model with species-specific parameters (synaptic, neuronal, and network properties), we evaluate the features of abnormal beta oscillatory activity generated by different mechanisms. The network's negative feedback loops serve as potential sources of spontaneous oscillations. Using rodent- or primate-constrained models, we compare the spectral properties of oscillatory activity across network populations for each loop and derive the expected phase relationships between populations to align predictions with existing rodent data and propose testable hypotheses for primates. We also demonstrate how oscillation frequency can be modulated when multiple generation mechanisms interact as coupled oscillators in the full network. Our results, combined with observed cross-species beta oscillations characteristics, suggest that abnormal beta oscillations likely arise from distinct mechanisms in rodents and primates.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Temporal evolution of beta bursts in the parkinsonian cortico-basal ganglia network 95%
- Deep brain stimulation in the subthalamic nucleus for Parkinson's disease can restore dynamics of striatal networks 94%
- Stochastic resonance and bifurcations in a heterogeneous neuronal population explain intrinsic oscillatory patterns in entorhinal cortical stellate cells 91%
Similar papers in this journal
- Uncoupling the roles of firing rates and spike bursts in shaping the STN-GPe beta band oscillations. 94%
- A neurocomputational theory of action regulation predicts motor behavior in neurotypical individuals and patients with Parkinsons disease 94%
- State Dependency of Beta Oscillations in the Cortico-Basal-Ganglia Circuit and their Neuromodulation under Phase Locked Inputs 93%
Similar papers in this journal
- Basal ganglia contributions during the learning of a visuomotor rotation: effect of dopamine, deep brain stimulation and reinforcement 93%
- Basal ganglia and cortical control of thalamic rebound spikes 93%
- Computational modelling of novelty detection in the mismatch negativity protocols and its impairments in schizophrenia 90%
Similar papers in this journal
- Response of neuronal populations to phase-locked stimulation: model-based predictions and validation 92%
- Trial-to-trial variability of spiking delay activity in prefrontal cortex constrains burst-coding models of working memory 92%
- Optimal inhibitory-to-excitatory ratio governs slow and fast oscillations for enhanced neural communication 91%
Similar papers in this journal
- Information transmission in a constitutively active GABAergic synapse 94%
- Robust dynamical invariants in sequential neural activity 93%
- Influence Of Energy Deficiency On The Molecular Processes Of Substantia Nigra Pars Compacta Cell For Understanding Parkinsonian Neurodegeneration: A Comprehensive Biophysical Computational Model 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.