Stimulating at the right time to recover network states in a model of the cortico-basal ganglia-thalamic circuit

Abstract

Synchronization of neural oscillations is thought to facilitate communication in the brain. Neurodegenerative pathologies such as Parkinson’s disease (PD) can result in synaptic reorganization of the motor circuit, leading to altered neuronal dynamics and impaired neural communication. Treatments for PD aim to restore network function via pharmacological means such as dopamine replacement, or by suppressing pathological oscillations with deep brain stimulation. We tested the hypothesis that brain stimulation can operate beyond a simple “reversible lesion” effect to augment network communication. Specifically, we examined the modulation of beta band (14–30 Hz) activity, a known biomarker of motor deficits and potential control signal for stimulation in Parkinson’s. To do this we setup a neural mass model of population activity within the cortico-basal ganglia-thalamic (CBGT) circuit with parameters that were constrained to yield spectral features comparable to those in experimental Parkinsonism. We modulated the connectivity of two major pathways known to be disrupted in PD and constructed statistical summaries of the spectra and functional connectivity of the resulting spontaneous activity. These were then used to assess the network-wide outcomes of closed-loop stimulation delivered to motor cortex and phase locked to subthalamic beta activity. Our results demonstrate that the spatial pattern of beta synchrony is dependent upon the strength of inputs to the STN. Precisely timed stimulation has the capacity to recover network states, with stimulation phase inducing activity with distinct spectral and spatial properties. These results provide a theoretical basis for the design of the next-generation brain stimulators that aim to restore neural communication in disease.

Synaptic changes in pallidostriatal circuits observed in parkinsonian model triggers abnormal beta synchrony with accurate spatio-temporal properties across the basal ganglia

“…While pallidostriatal circuits can generate pathological β oscillations showing characteristics of those observed in parkinsonian rats, we cannot exclude other circuits taking part in β generation. In particular, the large BG-thalamo-cortical loop circuits (Leblois et al, 2006; Pavlides et al, 2015), or the STN-GPe loop may interact positively with BG-generated β oscillations (Corbit et al, 2016; Ortone et al, 2022; West et al, 2022). However, experiments in parkinsonian rats clearly show that the cortex and STN are not necessary for the expression of β oscillations while in fact GPe is (De la Crompe et al, 2020).…”

“…Nevertheless, the β power in STN or pallidal local field potentials serves as an important biomarker for deep brain stimulation in PD patients (Little and Brown, 2020; Tinkhauser et al, 2020). Unlike mean firing rate changes, the generation mechanisms of pathological oscillations remain unknown and several competing models have been proposed (reviewed in (Pavlides et al, 2015; Rubin, 2017), but also see (McCarthy et al, 2011; Corbit et al, 2016; Adam et al, 2022; West et al, 2022)). From a theoretical perspective, any neuronal network incorporating negative feedback loops with delays can generate oscillations, with time constants and delays in the loop constraining the oscillations frequency (Ermentrout et al, 2001).…”

Dopamine depletion leads to pathological synchronization of distinct basal ganglia loops in the beta band

“…Most previous computational models on the onset of β oscillations (e.g., [26, 34, 67]) described how the STN-GPe loop was originating the pathological oscillations but focused only on perturbations induced by modulations of the intensity from striatal population to pallidum [29], without taking into account the pallidostriatal feedback that leads to the striatopallidal β loop. In a relevant work, Corbit and colleagues introduced a detailed small scale model of the striatopallidal loop [50] and showed how it generated β oscillations.…”

“…For instance, the dopamine depletion values in our model could both increase due to the progression of PD and decrease due to the action of dopamine agonists, but to proper model this latter aspect we have to take into account several other pathophysiological aspects [73]. Moreover, it would be interesting to simulate the action of Deep Brain stimulation as in [67,[74][75][76] also in our network to evaluate the input of different targets on suppressing the β oscillations and compare it to experimental results [77][78][79].…”

Phase-specific Deep Brain Stimulation revisited: effects of stimulation on postural and kinetic tremor

“…In sum, the efficacy of phase-specific DBS relies on how well we can continuously identify the best time to interact with the pathological brain network as different diseased states emerge. Despite being complex and computationally expensive, robust tremor control via phase-specific stimulation might become feasible via future machine learning innovations allowing for a swift sensing of the current network state and appropriate identification of, and adaption to, the optimal phase of stimulation [49,50].…”