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

Epicoccin A Ameliorates PD-like Symptoms in Zebrafish: Enhancement of PINK1/Parkin-Dependent Mitophagy and Inhibition of Excessive Oxidative Stress

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder, yet effective agents for its prevention and therapy remain highly limited. Epicoccin A, a significant secondary metabolite from Exserohilum sp., demonstrates various biological activities; however, its neuroprotective effects have not been elucidated. Here, we investigated the therapeutic potential of epicoccin A for PD by evaluating its impact on neural phenotype, reactive oxygen species (ROS) generation, and locomotor activity in PD-like zebrafish. Transcriptomic analysis and molecular docking were conducted, with key gene expressions further verified using real-time qPCR. As a result, epicoccin A notably mitigated dopaminergic neuron loss, neural vasculature deficiency, nervous system injury, ROS accumulation, locomotor impairments, and abnormal expressions of hallmark genes associated with PD and oxidative stress. Underlying mechanism investigation indicated epicoccin A may alleviate PD-like symptoms by activating PINK1/Parkin-dependent mitophagy, as evidenced by the reversal of aberrant gene expressions related to the pink1/parkin pathway and its upstream mTOR/FoxO pathway following epicoccin A co-treatments. This finding was further confirmed by the robust interactions between epicoccin A and these mitophagy regulators. Our results suggest that epicoccin A relieves PD symptoms by activating pink1/parkin-dependent mitophagy and inhibiting excessive oxidative stress, highlighting its potential as a therapeutic approach for PD.

Chaotic recurrent neural networks for brain modelling: A review

Even in the absence of external stimuli, the brain is spontaneously active. Indeed, most cortical activity is internally generated by recurrence. Both theoretical and experimental studies suggest that chaotic dynamics characterize this spontaneous activity. While the precise function of brain chaotic activity is still puzzling, we know that chaos confers many advantages. From a computational perspective, chaos enhances the complexity of network dynamics. From a behavioural point of view, chaotic activity could generate the variability required for exploration. Furthermore, information storage and transfer are maximized at the critical border between order and chaos. Despite these benefits, many computational brain models avoid incorporating spontaneous chaotic activity due to the challenges it poses for learning algorithms. In recent years, however, multiple approaches have been proposed to overcome this limitation. As a result, many different algorithms have been developed, initially within the reservoir computing paradigm. Over time, the field has evolved to increase the biological plausibility and performance of the algorithms, sometimes going beyond the reservoir computing framework. In this review article, we examine the computational benefits of chaos and the unique properties of chaotic recurrent neural networks, with a particular focus on those typically utilized in reservoir computing. We also provide a detailed analysis of the algorithms designed to train chaotic RNNs, tracing their historical evolution and highlighting key milestones in their development. Finally, we explore the applications and limitations of chaotic RNNs for brain modelling, consider their potential broader impacts beyond neuroscience, and outline promising directions for future research.

Modeling the modulation of beta oscillations in the basal ganglia by dual-target optogenetic stimulation

Optogenetic techniques provide precise control over the activity of specific neurons within the nucleus, offering more accurate regulatory effects compared to deep brain stimulation. The heterogeneity of the globus pallidus externa (GPe) has garnered wide attention, wherein significant differences in pathological changes emphasize its potential as a stimulation target with distinct mechanisms. A basal ganglia-thalamus (BG-Th) network model incorporating heterogeneous GPe is developed to explore potential optogenetic stimulation targets for treating Parkinson's disease (PD). Initially, the modulation mechanisms of single-target optogenetic stimulation on the abnormal rhythmic oscillations of BG nuclei are examined. Excitation of D1 medium spine neuron (MSN), calcium-binding protein parvalbumin (PV) GPe, and inhibition of globus pallidus interna (GPi) can effectively suppress synchronous bursting activity in GPi, while excitation of GPi promotes high-frequency discharge to disrupt beta oscillations. Furthermore, dual-target optogenetic stimulation strategies are devised to reduce energy consumption. Results show that targets with similar mechanisms exhibit additive effects, whereas targets with opposing mechanisms lead to cancellation. The underlying effective mechanisms of dual-target strategies are: enhancing the inhibitory input to GPi thus inhibiting the activity of GPi, or disrupting beta oscillations by restoring high-frequency discharges in GPi. The strategy composed of exciting D1 MSN and inhibiting GPi requires the minimum total light intensity among single-target and dual-target strategies in our simulation. Furthermore, simultaneously enhancing PV GPe and inhibiting D2 MSN achieves the greatest reduction in total energy consumption (40.8% reduction), compared to only enhancing PV GPe. The findings unveil effective circuit mechanisms of optogenetic stimulation and provide novel insights for designing precise regulatory strategies for PD.

A Model with Dopamine Depletion in Basal Ganglia and Cerebellum Predicts Changes in Thalamocortical Beta Oscillations

Parkinsonism is presented as a motor syndrome characterized by rigidity, tremors, and bradykinesia, with Parkinson's disease (PD) being the predominant cause. The discovery that those motor symptoms result from the death of dopaminergic cells in the substantia nigra led to focus most of parkinsonism research on the basal ganglia (BG). However, recent findings point to an active involvement of the cerebellum in this motor syndrome. Here, we have developed a multiscale computational model of the rodent brain's BG-cerebellar network. Simulations showed that a direct effect of dopamine depletion on the cerebellum must be taken into account to reproduce the alterations of neural activity in parkinsonism, particularly the increased beta oscillations widely reported in PD patients. Moreover, dopamine depletion indirectly impacted spike-time-dependent plasticity at the parallel fiber-Purkinje cell synapses, degrading associative motor learning as observed in parkinsonism. Overall, these results suggest a relevant involvement of cerebellum in parkinsonism associative motor symptoms.

Structural constraints on the emergence of oscillations in multi-population neural networks

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.

Synaptic Changes in Pallidostriatal Circuits Observed in the Parkinsonian Model Triggers Abnormal Beta Synchrony with Accurate Spatio-temporal Properties across the Basal Ganglia

Excessive oscillatory activity across basal ganglia (BG) nuclei in the β frequencies (12-30 Hz) is a hallmark of Parkinson's disease (PD). While the link between oscillations and symptoms remains debated, exaggerated β oscillations constitute an important biomarker for therapeutic effectiveness in PD. The neuronal mechanisms of β-oscillation generation however remain unknown. Many existing models rely on a central role of the subthalamic nucleus (STN) or cortical inputs to BG. Contrarily, neural recordings and optogenetic manipulations in normal and parkinsonian rats recently highlighted the central role of the external pallidum (GPe) in abnormal β oscillations, while showing that the integrity of STN or motor cortex is not required. Here, we evaluate the mechanisms for the generation of abnormal β oscillations in a BG network model where neuronal and synaptic time constants, connectivity, and firing rate distributions are strongly constrained by experimental data. Guided by a mean-field approach, we show in a spiking neural network that several BG sub-circuits can drive oscillations. Strong recurrent STN-GPe connections or collateral intra-GPe connections drive γ oscillations (>40 Hz), whereas strong pallidostriatal loops drive low-β (10-15 Hz) oscillations. We show that pathophysiological strengthening of striatal and pallidal synapses following dopamine depletion leads to the emergence of synchronized oscillatory activity in the mid-β range with spike-phase relationships between BG neuronal populations in-line with experiments. Furthermore, inhibition of GPe, contrary to STN, abolishes oscillations. Our modeling study uncovers the neural mechanisms underlying PD β oscillations and may thereby guide the future development of therapeutic strategies.

The onset of motor learning impairments in Parkinson's disease: a computational investigation

The basal ganglia (BG) is part of a basic feedback circuit regulating cortical function, such as voluntary movements control, via their influence on thalamocortical projections. BG disorders, namely Parkinson's disease (PD), characterized by the loss of neurons in the substantia nigra, involve the progressive loss of motor functions. At the present, PD is incurable. Converging evidences suggest the onset of PD-specific pathology prior to the appearance of classical motor signs. This latent phase of neurodegeneration in PD is of particular relevance in developing more effective therapies by intervening at the earliest stages of the disease. Therefore, a key challenge in PD research is to identify and validate markers for the preclinical and prodromal stages of the illness. We propose a mechanistic neurocomputational model of the BG at a mesoscopic scale to investigate the behavior of the simulated neural system after several degrees of lesion of the substantia nigra, with the aim of possibly evaluating which is the smallest lesion compromising motor learning. In other words, we developed a working framework for the analysis of theoretical early-stage PD. While simulations in healthy conditions confirm the key role of dopamine in learning, in pathological conditions the network predicts that there may exist abnormalities of the motor learning process, for physiological alterations in the BG, that do not yet involve the presence of symptoms typical of the clinical diagnosis.

Distributed dopaminergic signaling in the basal ganglia and its relationship to motor disability in Parkinson's disease

The degeneration of mesencephalic dopaminergic neurons that innervate the basal ganglia is responsible for the cardinal motor symptoms of Parkinson's disease (PD). It has been thought that loss of dopaminergic signaling in one basal ganglia region - the striatum - was solely responsible for the network pathophysiology causing PD motor symptoms. While our understanding of dopamine (DA)'s role in modulating striatal circuitry has deepened in recent years, it also has become clear that it acts in other regions of the basal ganglia to influence movement. Underscoring this point, examination of a new progressive mouse model of PD shows that striatal dopamine DA depletion alone is not sufficient to induce parkinsonism and that restoration of extra-striatal DA signaling attenuates parkinsonian motor deficits once they appear. This review summarizes recent advances in the effort to understand basal ganglia circuitry, its modulation by DA, and how its dysfunction drives PD motor symptoms.