Arthur Leblois

Competition between Feedback Loops Underlies Normal and Pathological Dynamics in the Basal Ganglia

Experiments performed in normal animals suggest that the basal ganglia (BG) are crucial in motor program selection. BG are also involved in movement disorders. In particular, BG neuronal activity in parkinsonian animals and patients is more oscillatory and more synchronous than in normal individuals. We propose a new model for the function and dysfunction of the motor part of BG. We hypothesize that the striatum, the subthalamic nucleus, the internal pallidum (GPi), the thalamus, and the cortex are involved in closed feedback loops. The direct (cortex–striatum–GPi–thalamus–cortex) and the hyperdirect loops (cortex–subthalamic nucleus–GPi–thalamus–cortex), which have different polarities, play a key role in the model. We show that the competition between these two loops provides the BG–cortex system with the ability to perform motor program selection. Under the assumption that dopamine potentiates corticostriatal synaptic transmission, we demonstrate that, in our model, moderate dopamine depletion leads to a complete loss of action selection ability. High depletion can lead to synchronous oscillations. These modifications of the network dynamical state stem from an imbalance between the feedback in the direct and hyperdirect loops when dopamine is depleted. Our model predicts that the loss of selection ability occurs before the appearance of oscillations, suggesting that Parkinsons disease motor impairments are not directly related to abnormal oscillatory activity. Another major prediction of our model is that synchronous oscillations driven by the hyperdirect loop appear in BG after inactivation of the striatum.

Temporal and spatial alterations in GPi neuronal encoding might contribute to slow down movement in Parkinsonian monkeys

Although widely investigated, the exact relationship between changes in basal ganglia neuronal activity and parkinsonian symptoms has not yet been deciphered. It has been proposed that bradykinesia (motor slowness) is related either to a modification of the activity of the globus pallidus internalis (GPi), the main output structure, or to a loss of spatial selectivity of the extrapyramidal motor system. Here we investigate the relationship between movement initiation and GPi activity in parkinsonian non‐human primates. We compare neuronal encoding of movement in the normal and pathological conditions. After dopamine depletion, we observe an increased number of neurons responding to movement, with a less specific somato‐sensory receptive field and a disruption of the selection mechanism. Moreover, the temporal order of the response of GPi neurons in parkinsonian animals is reversed. Indeed, whereas muscle activity and movement are delayed in parkinsonian animals, GPi neuronal responses to movement occur earlier and are prolonged, compared with normal conditions. Parkinsonian bradykinesia could thus result from an impairment of both temporal and spatial specificity of the GPi response to movement.

Interaction between cognitive and motor cortico-basal ganglia loops during decision making: a computational study

In a previous modeling study, Leblois et al. (2006) demonstrated an action selection mechanism in cortico-basal ganglia loops based on competition between the positive feedback, direct pathway through the striatum and the negative feedback, hyperdirect pathway through the subthalamic nucleus. The present study investigates how multiple level action selection could be performed by the basal ganglia. To do this, the model is extended in a manner consistent with known anatomy and electrophysiology in three main areas. First, two-level decision making has been incorporated, with a cognitive level selecting based on cue shape and a motor level selecting based on cue position. We show that the decision made at the cognitive level can be used to bias the decision at the motor level. We then demonstrate that, for accurate transmission of information between decision-making levels, low excitability of striatal projection neurons is necessary, a generally observed electrophysiological finding. Second, instead of providing a biasing signal between cue choices as an external input to the network, we show that the action selection process can be driven by reasonable levels of noise. Finally, we incorporate dopamine modulated learning at corticostriatal synapses. As learning progresses, the action selection becomes based on learned visual cue values and is not interfered with by the noise that was necessary before learning.

Subthalamic deep brain stimulation increases pallidal firing rate and regularity

While high-frequency stimulation of the subthalamic nucleus (STN-HFS) is highly effective in the treatment of Parkinsons disease (PD), the mechanisms underlying its therapeutic action remain unclear. Here, we report changes of single-neuron pallidal activity during STN-HFS in a parkinsonian patient. STN-HFS increased firing rate in both segments of the pallidum. Neurons displayed time-locked responses to stimulation pulses, with an early excitation followed by inhibition and late excitation. Finally, pallidal neurons fired more regularly during STN-HFS. The time-locked responses and increased firing regularity may override abnormally patterned pallidal activity, and thereby significantly contribute to the clinical efficacy of STN-HFS in PD.

A long journey into reproducible computational neuroscience

Computational neuroscience is a powerful ally in our quest to understand the brain. Even the most simple model can shed light on the role of this or that structure and propose new hypothesis concerning the overall brain organization. However, any model in Science is doomed to be proved wrong or incomplete and replaced by a more accurate one. In the meantime, for such replacement to happen, we have first to make sure that models are actually reproducible such that they can be tested, evaluated, criticized and ultimately modified, replaced or even rejected. This is where the shoe pinches. If we cannot reproduce a model in the first place, were doomed to re-invent the wheel again and again, preventing us from building an incremental computational knowledge of the brain.

Deep brain stimulation changes basal ganglia output nuclei firing pattern in the dystonic hamster

Dystonia is a heterogeneous syndrome of movement disorders characterized by involuntary muscle contractions leading to abnormal movements and postures. While medical treatment is often ineffective, deep brain stimulation (DBS) of the internal pallidum improves dystonia. Here, we studied the impact of DBS in the entopeduncular nucleus (EP), the rodent equivalent of the human globus pallidus internus, on basal ganglia output in the dt(sz)-hamster, a well-characterized model of dystonia by extracellular recordings. Previous work has shown that EP-DBS improves dystonic symptoms in dt(sz)-hamsters. We report that EP-DBS changes firing pattern in the EP, most neurons switching to a less regular firing pattern during DBS. In contrast, EP-DBS did not change the average firing rate of EP neurons. EP neurons display multiphasic responses to each stimulation impulse, likely underlying the disruption of their firing rhythm. Finally, neurons in the substantia nigra pars reticulata display similar responses to EP-DBS, supporting the idea that EP-DBS affects basal ganglia output activity through the activation of common afferent fibers.

Interference and shaping in sensorimotor adaptations with rewards.

When a perturbation is applied in a sensorimotor transformation task, subjects can adapt and maintain performance by either relying on sensory feedback, or, in the absence of such feedback, on information provided by rewards. For example, in a classical rotation task where movement endpoints must be rotated to reach a fixed target, human subjects can successfully adapt their reaching movements solely on the basis of binary rewards, although this proves much more difficult than with visual feedback. Here, we investigate such a reward-driven sensorimotor adaptation process in a minimal computational model of the task. The key assumption of the model is that synaptic plasticity is gated by the reward. We study how the learning dynamics depend on the target size, the movement variability, the rotation angle and the number of targets. We show that when the movement is perturbed for multiple targets, the adaptation process for the different targets can interfere destructively or constructively depending on the similarities between the sensory stimuli (the targets) and the overlap in their neuronal representations. Destructive interferences can result in a drastic slowdown of the adaptation. As a result of interference, the time to adapt varies non-linearly with the number of targets. Our analysis shows that these interferences are weaker if the reward varies smoothly with the subjects performance instead of being binary. We demonstrate how shaping the reward or shaping the task can accelerate the adaptation dramatically by reducing the destructive interferences. We argue that experimentally investigating the dynamics of reward-driven sensorimotor adaptation for more than one sensory stimulus can shed light on the underlying learning rules.

A canonical neural mechanism for behavioral variability

The ability to generate variable movements is essential for learning and adjusting complex behaviours. This variability has been linked to the temporal irregularity of neuronal activity in the central nervous system. However, how neuronal irregularity actually translates into behavioural variability is unclear. Here we combine modelling, electrophysiological and behavioural studies to address this issue. We demonstrate that a model circuit comprising topographically organized and strongly recurrent neural networks can autonomously generate irregular motor behaviours. Simultaneous recordings of neurons in singing finches reveal that neural correlations increase across the circuit driving song variability, in agreement with the model predictions. Analysing behavioural data, we find remarkable similarities in the babbling statistics of 5–6-month-old human infants and juveniles from three songbird species and show that our model naturally accounts for these ‘universal statistics.

A subcortical circuit linking the cerebellum to the basal ganglia engaged in vocal learning

Speech is a complex sensorimotor skill, and vocal learning involves both the basal ganglia and the cerebellum. These subcortical structures interact indirectly through their respective loops with thalamo-cortical and brainstem networks, and directly via subcortical pathways, but the role of their interaction during sensorimotor learning remains undetermined. While songbirds and their song-dedicated basal ganglia-thalamo-cortical circuitry offer a unique opportunity to study subcortical circuits involved in vocal learning, the cerebellar contribution to avian song learning remains unknown. We demonstrate that the cerebellum provides a strong input to the song-related basal ganglia nucleus in zebra finches. Cerebellar signals are transmitted to the basal ganglia via a disynaptic connection through the thalamus and then conveyed to their cortical target and to the premotor nucleus controlling song production. Finally, cerebellar lesions impair juvenile song learning, opening new opportunities to investigate how subcortical interactions between the cerebellum and basal ganglia contribute to sensorimotor learning.