L-DOPA-induced dyskinesia (LID) is the most common form of hyperkinetic movement disorder resulting from altered information processing in the cortico-basal ganglia network. We here review recent advances clarifying the altered interplay between striatal output pathways in this movement disorder. We also review studies revealing structural and synaptic changes to the striatal microcircuitry and altered cortico-striatal activity dynamics in LID. We furthermore highlight the recent progress made in understanding the involvement of cerebellar and brain stem nuclei. These recent developments illustrate that dyskinesia research continues to provide key insights into cellular and circuit-level plasticity within the cortico-basal ganglia network and its interconnected brain regions.

Oscillations arise in many real-world systems and are associated with both functional and dysfunctional states. Whether a network can oscillate can be estimated if we know the strength of interaction between nodes. But in real-world networks (in particular in biological networks) it is usually not possible to know the exact connection weights. Therefore, it is important to determine the structural properties of a network necessary to generate oscillations. Here, we provide a proof that uses dynamical system theory to prove that an odd number of inhibitory nodes and strong enough connections are necessary to generate oscillations in a single cycle threshold-linear network. We illustrate these analytical results in a biologically plausible network with either firing-rate based or spiking neurons. Our work provides structural properties necessary to generate oscillations in a network. We use this knowledge to reconcile recent experimental findings about oscillations in basal ganglia with classical findings.

This in silico study predicts the impact that the single-cell neuronal morphological alterations will have on the striatal microcircuit connectivity. We find that the richness in the topological striatal motifs is significantly reduced in Parkinson's disease (PD), highlighting that just measuring the pairwise connectivity between neurons gives an incomplete description of network connectivity. Moreover, we predict how the resulting electrophysiological changes of striatal projection neuron excitability together with their reduced number of dendritic branches affect their response to the glutamatergic drive from the cortex and thalamus. We find that the effective glutamatergic drive is likely significantly increased in PD, in accordance with the hyperglutamatergic hypothesis.

The Basal Ganglia (BG) plays a pivotal role in movement-related decision-making. In Parkinson’s disease (PD) like scenario, neuronal properties and network connectivity get altered. This leads to aberrant neuronal spiking characteristics affecting the overall oscillatory dynamics of the network. Classically, the rate of change (drift) in the membrane potential and the variation (diffusion) of the same across multiple spikes are modelled using drift-diffusion framework. During active state of a subject, the movement due to behavioral responses are a result of sustained spiking of multiple neurons within a nucleus. The diversity within a nucleus leads to formation of groups of neurons having similar dynamics. However, relation between the diversity in the neuronal responses and the movement behavior are not well studied. In this paper, we proposed a novel framework to cluster neurons based on the Hierarchical Drift Diffusion Model (HDDM). Considering the sustained nature of neuronal spiking, we distinguished between active and resting states which inherently reflected the broader network states responsible for behavioral responses. We used Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to group neurons based on HDDM latent variables like drift rate. Our findings revealed discrete clusters in regions like the globus pallidus externa (GPe), globus pallidus interna (GPi), and subthalamic nucleus (STN) in the BG. Results demonstrated the well formed clusters using the latent information of HDDM which were not revealed using direct observables such as Revised Local Variation (LvR) and Instantaneous Firing Rate (IFR).

According to the efficient coding hypothesis, sensory neurons are adapted to provide maximal information about the environment, given some biophysical constraints. In early visual areas, stimulus-induced modulations of neural activity (or tunings) are predominantly single-peaked. However, periodic tuning, as exhibited by grid cells, has been linked to a significant increase in decoding performance. Does this imply that the tuning curves in early visual areas are sub-optimal? We argue that the time scale at which neurons encode information is imperative to understand the advantages of single-peaked and periodic tuning curves, respectively. Here, we show that the possibility of catastrophic (large) errors creates a trade-off between decoding time and decoding ability. We investigate how decoding time and stimulus dimensionality affect the optimal shape of tuning curves for removing catastrophic errors. In particular, we focus on the spatial periods of the tuning curves for a class of circular tuning curves. We show an overall trend for minimal decoding time to increase with increasing Fisher information, implying a trade-off between accuracy and speed. This trade-off is reinforced whenever the stimulus dimensionality is high, or there is ongoing activity. Thus, given constraints on processing speed, we present normative arguments for the existence of the single-peaked tuning organization observed in early visual areas.

Parkinson's disease (PD) is a progressive and debilitating brain disorder. Besides the characteristic movement-related symptoms, the disease also causes decline in sensory and cognitive processing. The extent of symptoms and brain-wide projections of neuromodulators such as dopamine suggest that many brain regions are simultaneously affected in PD. To characterise brain-wide disease-related changes in neuronal function, we analysed resting state magnetoencephalogram (MEG) from two groups: PD patients and healthy controls. Besides standard spectral analysis, we quantified the aperiodic components (& kappa;, & lambda;) of the neural activity by fitting a power law & kappa;/f(& lambda;) - f is the frequency, & kappa; and & lambda; are the fitting parameters-to the MEG power spectrum and studied its relationship with age and Unified Parkinson's Disease Rating Scale (UPDRS). Consistent with previous results, the most significant spectral changes were observed in the high theta/low-alpha band (7-10 Hz) in all brain regions. Furthermore, analysis of the aperiodic part of the spectrum showed that in all but frontal regions & lambda; was significantly larger in PD patients than in control subjects. Our results indicate that PD is associated with significant changes in aperiodic activity across the whole neocortex. Surprisingly, even early sensory areas showed a significantly larger & lambda; in patients than in healthy controls. Moreover, & lambda; was not affected by the Levodopa medication. Finally, & lambda; was positively correlated with patient age but not with UPDRS-III. Because & lambda; is closely associated with excitation-inhibition balance, our results propose new hypotheses about neural correlates of PD in cortical networks.

The cerebellar cortex computes sensorimotor information from many brain areas through a feedforward inhibitory (FFI) microcircuit between the input stage, the granule cell (GC) layer, and the output stage, the Purkinje cells (PCs). Although in other brain areas FFI underlies a precise excitation versus inhibition temporal correlation, recent findings in the cerebellum highlighted more complex behaviors at GC-molecular layer interneuron (MLI)-PC pathway. To dissect the temporal organization of this cerebellar FFI pathway, we combined ex vivo patch-clamp recordings of PCs in male mice with a viral-based strategy to express Channelrhodopsin2 in a subset of mossy fibers (MFs), the major excitatory inputs to GCs. We show that although light-mediated MF activation elicited pairs of excitatory and inhibitory postsynaptic currents in PCs, excitation (E) from GCs and inhibition (I) from MLIs reached PCs with a wide range of different temporal delays. However, when GCs were directly stimulated, a low variability in E/I delays was observed. Our results demonstrate that in many recordings MF stimulation recruited different groups of GCs that trigger E and/or I, and expanded PC temporal synaptic integration. Finally, using a computational model of the FFI pathway, we showed that this temporal expansion could strongly influence how PCs integrate GC inputs. Our findings show that specific E/I delays may help PCs encoding specific MF inputs.SIGNIFICANCE STATEMENT Sensorimotor information is conveyed to the cerebellar cortex by mossy fibers. Mossy fiber inputs activate granule cells that excite molecular interneurons and Purkinje cells, the sole output of the cerebellar cortex, leading to a sequence of synaptic excitation and inhibition in Purkinje cells, thus defining a feedforward inhibitory pathway. Using electrophysiological recordings, optogenetic stimulation, and mathematical modeling, we demonstrated that different groups of granule cells can elicit synaptic excitation and inhibition with various latencies onto Purkinje cells. This temporal variability controls how granule cells influence Purkinje cell discharge and may support temporal coding in the cerebellar cortex.

It is well established that midbrain dopaminergic neurons support reinforcement learning (RL) in the basal ganglia by transmitting a reward prediction error (RPE) to the striatum. In particular, different computational models and experiments have shown that a striatum-wide RPE signal can support RL over a small discrete set of actions (e.g., no/no-go, choose left/right). However, there is accumulating evidence that the basal ganglia functions not as a selector between predefined actions but rather as a dynamical system with graded, continuous outputs. To reconcile this view with RL, there is a need to explain how dopamine could support learning of continuous outputs, rather than discrete action values. Inspired by the recent observations that besides RPE, the firing rates of midbrain dopaminergic neurons correlate with motor and cognitive variables, we propose a model in which dopamine signal in the striatum carries a vector-valued error feedback signal (a loss gradient) instead of a homogeneous scalar error (a loss). We implement a local, "three-factor" corticostriatal plasticity rule involving the presynaptic firing rate, a postsynaptic factor, and the unique dopamine concentration perceived by each striatal neuron. With this learning rule, we show that such a vector-valued feedback signal results in an increased capacity to learn a multidimensional series of real-valued outputs. Crucially, we demonstrate that this plasticity rule does not require precise nigrostriatal synapses but remains compatible with experimental observations of random placement of varicosities and diffuse volume transmission of dopamine.