A physiologically realistic computational model of the basal ganglia network

International audienceThe basal ganglia (BG) are a set of nuclei that process movement information: they refine and adjust simple movement actions. The BG has two major pathways: the striatum (STR)-indirect neuron pathway and the subthalamic (STN)-hyperdirect nucleus pathway. The GPe is the connecting nucleus between the two pathways. The STR inhibits the GPe and the STN excites the GPe which is divided into two types of neurons [1, 4], the prototypical and the arkypallidal. This discovery allows for a better understanding of the functioning of this neural network. We model the STN-GPeA-GPeP-STR(D2) network and study the influence of the nucleus on each other like in [2]. The neurons have been modeled as point neurons using the Hodgkin-Huxley formalism and the synapses as exponential functions. From extensive simulations performed with the SiReNe software (Neural network simulator, in french: Simulateur de Réseaux de Neurones [3]), we show that our network is in good agreement with the physiological results of [2]. This simulator is based on a hybrid method combining time-step and event-driven computations with a Runge-Kutta numerical method at inner level. GPe is mainly inhibited by GABAergic inputs ofthe STR and we study the impact of STR connectivity on GPe. We observe that the GPeP and GPeA react in opposite ways when the STR is activated, i.e. GPeP is entirely inhibited whereas the GPeA and STN are completely excited, as observed in [2]. This work aims at better understanding the synaptic connectivity scheme. This model will allow us to test hypotheses regarding the pathological rhythmogenesis in Parkinson disease, both at the cellular and connectivity levels

A computational model of GPe prototypic and arkypallidal neurons with automated parameter fitting.

International audienceParkinson’s disease is characterized by pathological oscillations in the basal ganglia. To gain insight on the origin of these oscillations, we developed a computational model of the globus pallidus (GPe). Our model consists of interconnected prototypic (GPeP) and arkypallidal (GPeA) neurons [1, 5]. We modeled GPeP and GPeA neurons as single-compartment neurons using Hodgkin-Huxley formalism. The GPeA and GPeP neurons have similar ionic currents (I_NaP, I_NaF, I_HCN, I_SK, I_Kv3, I_Ca2+, I_leak) but differ from their conductance values. We tuned the parameters automatically with a multi-objective optimization approach, a variant of the differential evolution [4, 6]. From extensive simulations performed with the SiReNe software (Neural networks simulator, in french: Simulateur de Réseaux de Neurones [3]), we show that our model of GPeP and GPeA neurons are in good agreement with the physiological results of [1], i.e. F-I curves (see Fig. 1A), Voltage-Clamp and I-V relation (see Fig. 1B,C), shape of Action Potentials. Moreover, we show that our GPeP/A neurons interconnected with GABAergic synapses exhibit activity patterns similar to those observed in vivo [2]. This work aims at better understanding the influence of these two different types of neurons in Parkinson’s disease

Cannabinoid-induced motor dysfunction via autophagy inhibition

The recreational and medical use of cannabis is largely increasing worldwide. Cannabis use, however, can cause adverse side effects, so conducting innovative studies aimed to understand and potentially reduce cannabis-evoked harms is important. Previous research conducted on cultured neural cells had supported that CNR1/CB1R (cannabinoid receptor 1), the main molecular target of cannabis, affects macroautophagy/autophagy. However, it was not known whether CNR1 controls autophagy in the brain in vivo, and, eventually, what the functional consequences of a potential CNR1-autophagy connection could be. We have now found that Δ9-tetrahydrocannabinol (THC), the major intoxicating constituent of cannabis, impairs autophagy in the mouse striatum. Administration of autophagy activators (specifically, the rapalog temsirolimus and the disaccharide trehalose) rescues THC-induced autophagy inhibition and motor dyscoordination. The combination of various genetic strategies in vivo supports the idea that CNR1 molecules located on neurons belonging to the direct (striatonigral) pathway are required for the autophagy- and motor-impairing activity of THC. By identifying autophagy as a mechanistic link between THC and motor performance, our findings may open a new conceptual view on how cannabis acts in the brain

Arkypallidal neuron: a new player in the basal ganglia circuitry.

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Long-term depression at distinct glutamatergic synapses in the basal ganglia.

International audienceLong-term adaptations of synaptic transmission are believed to be the cellular basis of information storage in the brain. In particular, long-term depression of excitatory neurotransmission has been under intense investigation since convergent lines of evidence support a crucial role for this process in learning and memory. Within the basal ganglia, a network of subcortical nuclei forming a key part of the extrapyramidal motor system, plasticity at excitatory synapses is essential to the regulation of motor, cognitive, and reward functions. The striatum, the main gateway of the basal ganglia, receives convergent excitatory inputs from cortical areas and transmits information to the network output structures and is a major site of activity-dependent plasticity. Indeed, long-term depression at cortico-striatal synapses modulates the transfer of information to basal ganglia output structures and affects voluntary movement execution. Cortico-striatal plasticity is thus considered as a cellular substrate for adaptive motor control. Downstream in this network, the subthalamic nucleus and substantia nigra nuclei also receive glutamatergic innervation from the cortex and the subthalamic nucleus, respectively. Although these connections have been less investigated, recent studies have started to unravel the molecular mechanisms that contribute to adjustments in the strength of cortico-subthalamic and subthalamo-nigral transmissions, revealing that adaptations at these synapses governing the output of the network could also contribute to motor planning and execution. Here, we review our current understanding of long-term depression mechanisms at basal ganglia glutamatergic synapses and emphasize the common and unique plastic features observed at successive levels of the network in healthy and pathological conditions

Cellular and Synaptic Dysfunctions in Parkinson’s Disease: Stepping out of the Striatum

International audienc

Properties of Neurons in External Globus Pallidus Can Support Optimal Action Selection

International audienc

Alteration of GABAergic neurotransmission in Huntington's disease

International audienc

A physiologically realistic computational model of the basal ganglia network

International audienceThe basal ganglia (BG) are a set of nuclei that process movement information: they refine and adjust simple movement actions. The BG has two major pathways: the striatum (STR)-indirect neuron pathway and the subthalamic (STN)-hyperdirect nucleus pathway. The GPe is the connecting nucleus between the two pathways. The STR inhibits the GPe and the STN excites the GPe which is divided into two types of neurons [1, 4], the prototypical and the arkypallidal. This discovery allows for a better understanding of the functioning of this neural network. We model the STN-GPeA-GPeP-STR(D2) network and study the influence of the nucleus on each other like in [2]. The neurons have been modeled as point neurons using the Hodgkin-Huxley formalism and the synapses as exponential functions. From extensive simulations performed with the SiReNe software (Neural network simulator, in french: Simulateur de Réseaux de Neurones [3]), we show that our network is in good agreement with the physiological results of [2]. This simulator is based on a hybrid method combining time-step and event-driven computations with a Runge-Kutta numerical method at inner level. GPe is mainly inhibited by GABAergic inputs ofthe STR and we study the impact of STR connectivity on GPe. We observe that the GPeP and GPeA react in opposite ways when the STR is activated, i.e. GPeP is entirely inhibited whereas the GPeA and STN are completely excited, as observed in [2]. This work aims at better understanding the synaptic connectivity scheme. This model will allow us to test hypotheses regarding the pathological rhythmogenesis in Parkinson disease, both at the cellular and connectivity levels

A computational model of GPe prototypic and arkypallidal neurons with automated parameter fitting.

International audienceParkinson’s disease is characterized by pathological oscillations in the basal ganglia. To gain insight on the origin of these oscillations, we developed a computational model of the globus pallidus (GPe). Our model consists of interconnected prototypic (GPeP) and arkypallidal (GPeA) neurons [1, 5]. We modeled GPeP and GPeA neurons as single-compartment neurons using Hodgkin-Huxley formalism. The GPeA and GPeP neurons have similar ionic currents (I_NaP, I_NaF, I_HCN, I_SK, I_Kv3, I_Ca2+, I_leak) but differ from their conductance values. We tuned the parameters automatically with a multi-objective optimization approach, a variant of the differential evolution [4, 6]. From extensive simulations performed with the SiReNe software (Neural networks simulator, in french: Simulateur de Réseaux de Neurones [3]), we show that our model of GPeP and GPeA neurons are in good agreement with the physiological results of [1], i.e. F-I curves (see Fig. 1A), Voltage-Clamp and I-V relation (see Fig. 1B,C), shape of Action Potentials. Moreover, we show that our GPeP/A neurons interconnected with GABAergic synapses exhibit activity patterns similar to those observed in vivo [2]. This work aims at better understanding the influence of these two different types of neurons in Parkinson’s disease