Hallmarks of neurodegenerative diseases.

Decades of research have identified genetic factors and biochemical pathways involved in neurodegenerative diseases (NDDs). We present evidence for the following eight hallmarks of NDD: pathological protein aggregation, synaptic and neuronal network dysfunction, aberrant proteostasis, cytoskeletal abnormalities, altered energy homeostasis, DNA and RNA defects, inflammation, and neuronal cell death. We describe the hallmarks, their biomarkers, and their interactions as a framework to study NDDs using a holistic approach. The framework can serve as a basis for defining pathogenic mechanisms, categorizing different NDDs based on their primary hallmarks, stratifying patients within a specific NDD, and designing multi-targeted, personalized therapies to effectively halt NDDs.

RTN4/NoGo-receptor binding to BAI adhesion-GPCRs regulates neuronal development.

RTN4-binding proteins were widely studied as "NoGo" receptors, but their physiological interactors and roles remain elusive. Similarly, BAI adhesion-GPCRs were associated with numerous activities, but their ligands and functions remain unclear. Using unbiased approaches, we observed an unexpected convergence: RTN4 receptors are high-affinity ligands for BAI adhesion-GPCRs. A single thrombospondin type 1-repeat (TSR) domain of BAIs binds to the leucine-rich repeat domain of all three RTN4-receptor isoforms with nanomolar affinity. In the 1.65 Å crystal structure of the BAI1/RTN4-receptor complex, C-mannosylation of tryptophan and O-fucosylation of threonine in the BAI TSR-domains creates a RTN4-receptor/BAI interface shaped by unusual glycoconjugates that enables high-affinity interactions. In human neurons, RTN4 receptors regulate dendritic arborization, axonal elongation, and synapse formation by differential binding to glial versus neuronal BAIs, thereby controlling neural network activity. Thus, BAI binding to RTN4/NoGo receptors represents a receptor-ligand axis that, enabled by rare post-translational modifications, controls development of synaptic circuits.

A Neuro-hormonal Circuit for Paternal Behavior Controlled by a Hypothalamic Network Oscillation.

Parental behavior is pervasive throughout the animal kingdom and essential for species survival. However, the relative contribution of the father to offspring care differs markedly across animals, even between related species. The mechanisms that organize and control paternal behavior remain poorly understood. Using Sprague-Dawley rats and C57BL/6 mice, two species at opposite ends of the paternal spectrum, we identified that distinct electrical oscillation patterns in neuroendocrine dopamine neurons link to a chain of low dopamine release, high circulating prolactin, prolactin receptor-dependent activation of medial preoptic area galanin neurons, and paternal care behavior in male mice. In rats, the same parameters exhibit inverse profiles. Optogenetic manipulation of these rhythms in mice dramatically shifted serum prolactin and paternal behavior, whereas injecting prolactin into non-paternal rat sires triggered expression of parental care. These findings identify a frequency-tuned brain-endocrine-brain circuit that can act as a gain control system determining a species' parental strategy.

Practical guide for preparation, computational reconstruction and analysis of 3D human neuronal networks in control and ischaemic conditions.

To obtain commensurate numerical data of neuronal network morphology in vitro, network analysis needs to follow consistent guidelines. Important factors in successful analysis are sample uniformity, suitability of the analysis method for extracting relevant data and the use of established metrics. However, for the analysis of 3D neuronal cultures, there is little coherence in the analysis methods and metrics used in different studies. Here, we present a framework for the analysis of neuronal networks in 3D. First, we selected a hydrogel that supported the growth of human pluripotent stem cell-derived cortical neurons. Second, we tested and compared two software programs for tracing multi-neuron images in three dimensions and optimized a workflow for neuronal analysis using software that was considered highly suitable for this purpose. Third, as a proof of concept, we exposed 3D neuronal networks to oxygen-glucose deprivation- and ionomycin-induced damage and showed morphological differences between the damaged networks and control samples utilizing the proposed analysis workflow. With the optimized workflow, we present a protocol for preparing, challenging, imaging and analysing 3D human neuronal cultures.

Enhanced responses to inflammatory cytokine interleukin-6 in micropatterned networks of cultured cortical neurons.

Cortical neurons in dissociated cultures are an indispensable model system for pharmacological research that provides insights into chemical responses in well-defined environments. However, cortical neurons plated on homogeneous substrates develop an unstructured network that exhibits excessively synchronized activity, which occasionally masks the consequences induced by external substances. Here, we show that hyperactivity and excessive synchrony in cultured cortical networks can be effectively suppressed by growing neurons in microfluidic devices. These devices feature a hierarchically modular design that resembles the in vivo network. We focused on interleukin-6, a pro-inflammatory cytokine, and assessed its acute and chronic effects. Fluorescence calcium imaging of spontaneous neural activity for up to 20 days of culture showed detectable modulation of collective activity events and neural correlation in micropatterned neurons, which was not apparent in neurons cultured on homogeneous substrates. Our results indicate that engineered neuronal networks provide a unique platform for detecting and understanding the fundamental effects of biochemical compounds on neuronal networks.

The angiotensin II receptors type 1 and 2 modulate astrocytes and their crosstalk with microglia and neurons in an in vitro model of ischemic stroke.

BACKGROUND: Astrocytes are the most abundant cell type of the central nervous system and are fundamentally involved in homeostasis, neuroprotection, and synaptic plasticity. This regulatory function of astrocytes on their neighboring cells in the healthy brain is subject of current research. In the ischemic brain we assume disease specific differences in astrocytic acting. The renin-angiotensin-aldosterone system regulates arterial blood pressure through endothelial cells and perivascular musculature. Moreover, astrocytes express angiotensin II type 1 and 2 receptors. However, their role in astrocytic function has not yet been fully elucidated. We hypothesized that the angiotensin II receptors impact astrocyte function as revealed in an in vitro system mimicking cerebral ischemia. Astrocytes derived from neonatal wistar rats were exposed to telmisartan (angiotensin II type 1 receptor-blocker) or PD123319 (angiotensin II type 2 receptor-blocker) under normal conditions (control) or deprivation from oxygen and glucose. Conditioned medium (CM) of astrocytes was harvested to elucidate astrocyte-mediated indirect effects on microglia and cortical neurons. RESULT: The blockade of angiotensin II type 1 receptor by telmisartan increased the survival of astrocytes during ischemic conditions in vitro without affecting their proliferation rate or disturbing their expression of S100A10, a marker of activation. The inhibition of the angiotensin II type 2 receptor pathway by PD123319 resulted in both increased expression of S100A10 and proliferation rate. The CM of telmisartan-treated astrocytes reduced the expression of pro-inflammatory mediators with simultaneous increase of anti-inflammatory markers in microglia. Increased neuronal activity was observed after treatment of neurons with CM of telmisartan- as well as PD123319-stimulated astrocytes. CONCLUSION: Data show that angiotensin II receptors have functional relevance for astrocytes that differs in healthy and ischemic conditions and effects surrounding microglia and neuronal activity via secretory signals. Above that, this work emphasizes the strong interference of the different cells in the CNS and that targeting astrocytes might serve as a therapeutic strategy to influence the acting of glia-neuronal network in de- and regenerative context.

Neuronal circuits overcome imbalance in excitation and inhibition by adjusting connection numbers.

The interplay between excitation and inhibition is crucial for neuronal circuitry in the brain. Inhibitory cell fractions in the neocortex and hippocampus are typically maintained at 15 to 30%, which is assumed to be important for stable dynamics. We have studied systematically the role of precisely controlled excitatory/inhibitory (E/I) cellular ratios on network activity using mice hippocampal cultures. Surprisingly, networks with varying E/I ratios maintain stable bursting dynamics. Interburst intervals remain constant for most ratios, except in the extremes of 0 to 10% and 90 to 100% inhibitory cells. Single-cell recordings and modeling suggest that networks adapt to chronic alterations of E/I compositions by balancing E/I connectivity. Gradual blockade of inhibition substantiates the agreement between the model and experiment and defines its limits. Combining measurements of population and single-cell activity with theoretical modeling, we provide a clearer picture of how E/I balance is preserved and where it fails in living neuronal networks.

Artificial-intelligence-based decision support tools for the differential diagnosis of colitis.

BACKGROUND: Whereas Artificial Intelligence (AI) based tools have recently been introduced in the field of gastroenterology, application in inflammatory bowel disease (IBD) is in its infancies. We established AI-based algorithms to distinguish IBD from infectious and ischemic colitis using endoscopic images and clinical data. METHODS: First, we trained and tested a Convolutional Neural Network (CNN) using 1796 real-world images from 494 patients, presenting with three diseases (IBD [n = 212], ischemic colitis [n = 157], and infectious colitis [n = 125]). Moreover, we evaluated a Gradient Boosted Decision Trees (GBDT) algorithm using five clinical parameters as well as a hybrid approach (CNN + GBDT). Patients and images were randomly split into two completely independent datasets. The proposed approaches were benchmarked against each other and three expert endoscopists on the test set. RESULTS: For the image-based CNN, the GBDT algorithm and the hybrid approach global accuracies were .709, .792, and .766, respectively. Positive predictive values were .602, .702, and .657. Global areas under the receiver operating characteristics (ROC) and precision recall (PR) curves were .727/.585, .888/.823, and .838/.733, respectively. Global accuracy did not differ between CNN and endoscopists (.721), but the clinical parameter-based GBDT algorithm outperformed CNN and expert image classification. CONCLUSIONS: Decision support systems exclusively based on endoscopic image analysis for the differential diagnosis of colitis, representing a complex clinical challenge, seem not yet to be ready for primetime and more diverse image datasets may be necessary to improve performance in future development. The clinical value of the proposed clinical parameters algorithm should be evaluated in prospective cohorts.

One seasonal clock fits all?

Adaptation of physiology and behavior to seasonal changes in the environment are for many organisms essential for survival. Most of our knowledge about the underlying mechanisms comes from research on photoperiodic regulation of reproduction in plants, insects and mammals. However, even humans, who mostly live in environments with minimal seasonal influences, show annual rhythms in physiology (e.g., immune activity, brain function), behavior (e.g., sleep-wake cycles) and disease prevalence (e.g., infectious diseases). As seasonal variations in environmental conditions may be drastically altered due to climate change, the understanding of the mechanisms underlying seasonal adaptation of physiology and behavior becomes even more relevant. While many species have developed specific solutions for dedicated tasks of photoperiodic regulation, we find a number of common principles and mechanisms when comparing insect and mammalian systems: (1) the circadian system contributes to photoperiodic regulation; (2) similar signaling molecules (VIP and PDF) are used for transferring information from the circadian system to the neuroendocrine system controlling the photoperiodic response; (3) the hormone melatonin participates in seasonal adaptation in insects as well as mammals; and (4) changes in photoperiod affect neurotransmitter function in both animal groups. The few examples of overlap elaborated in this perspective article, as well as the discussion on relevance for humans, should be seen as encouragement to unravel the machinery of seasonal adaptation in a multitude of organisms.

Junctional instability in neuroepithelium and network hyperexcitability in a focal cortical dysplasia human model.

Focal cortical dysplasia is a highly epileptogenic cortical malformation with few treatment options. Here, we generated human cortical organoids from patients with focal cortical dysplasia type II. Using this human model, we mimicked some focal cortical dysplasia hallmarks, such as impaired cell proliferation, the presence of dysmorphic neurons and balloon cells, and neuronal network hyperexcitability. Furthermore, we observed alterations in the adherens junctions zonula occludens-1 and partitioning defective 3, reduced polarization of the actin cytoskeleton, and fewer synaptic puncta. Focal cortical dysplasia cortical organoids showed downregulation of the small GTPase RHOA, a finding that was confirmed in brain tissue resected from these patients. Functionally, both spontaneous and optogenetically-evoked electrical activity revealed hyperexcitability and enhanced network connectivity in focal cortical dysplasia organoids. Taken together, our findings suggest a ventricular zone instability in tissue cohesion of neuroepithelial cells, leading to a maturational arrest of progenitors or newborn neurons, which may predispose to cellular and functional immaturity and compromise the formation of neural networks in focal cortical dysplasia.

Low-Cost Optimized U-Net Model with GMM Automatic Labeling Used in Forest Semantic Segmentation.

Currently, Convolutional Neural Networks (CNN) are widely used for processing and analyzing image or video data, and an essential part of state-of-the-art studies rely on training different CNN architectures. They have broad applications, such as image classification, semantic segmentation, or face recognition. Regardless of the application, one of the important factors influencing network performance is the use of a reliable, well-labeled dataset in the training stage. Most of the time, especially if we talk about semantic classification, labeling is time and resource-consuming and must be done manually by a human operator. This article proposes an automatic label generation method based on the Gaussian mixture model (GMM) unsupervised clustering technique. The other main contribution of this paper is the optimization of the hyperparameters of the traditional U-Net model to achieve a balance between high performance and the least complex structure for implementing a low-cost system. The results showed that the proposed method decreased the resources needed, computation time, and model complexity while maintaining accuracy. Our methods have been tested in a deforestation monitoring application by successfully identifying forests in aerial imagery.

Exploring the Pathological Effect of Aß42 Oligomers on Neural Networks in Primary Cortical Neuron Culture.

Alzheimer's disease (AD) is a multifactorial disorder that affects cognitive functioning, behavior, and neuronal properties. The neuronal dysfunction is primarily responsible for cognitive decline in AD patients, with many causal factors including plaque accumulation of Aß42. Neural hyperactivity induced by Aß42 deposition causes abnormalities in neural networks, leading to alterations in synaptic activity and interneuron dysfunction. Even though neuroimaging techniques elucidated the underlying mechanism of neural connectivity, precise understanding at the cellular level is still elusive. Previous multielectrode array studies have examined the neuronal network modulation in in vitro cultures revealing the relevance of ion channels and the chemical modulators in the presence of Aß42. In this study, we investigated neuronal connectivity and dynamic changes using a high-density multielectrode array, particularly looking at network-wide parameter changes over time. By comparing the neuronal network between normal and Aß42treated neuronal cultures, it was possible to discover the direct pathological effect of the Aß42 oligomer altering the network characteristics. The detrimental effects of the Aß42 oligomer included not only a decline in spike activation but also a qualitative impairment in neural connectivity as well as a disorientation of dispersibility. As a result, this will improve our understanding of how neural networks are modified during AD progression.

Pathobiology of Cognitive Impairment in Parkinson Disease: Challenges and Outlooks.

Cognitive impairment (CI) is a characteristic non-motor feature of Parkinson disease (PD) that poses a severe burden on the patients and caregivers, yet relatively little is known about its pathobiology. Cognitive deficits are evident throughout the course of PD, with around 25% of subtle cognitive decline and mild CI (MCI) at the time of diagnosis and up to 83% of patients developing dementia after 20 years. The heterogeneity of cognitive phenotypes suggests that a common neuropathological process, characterized by progressive degeneration of the dopaminergic striatonigral system and of many other neuronal systems, results not only in structural deficits but also extensive changes of functional neuronal network activities and neurotransmitter dysfunctions. Modern neuroimaging studies revealed multilocular cortical and subcortical atrophies and alterations in intrinsic neuronal connectivities. The decreased functional connectivity (FC) of the default mode network (DMN) in the bilateral prefrontal cortex is affected already before the development of clinical CI and in the absence of structural changes. Longitudinal cognitive decline is associated with frontostriatal and limbic affections, white matter microlesions and changes between multiple functional neuronal networks, including thalamo-insular, frontoparietal and attention networks, the cholinergic forebrain and the noradrenergic system. Superimposed Alzheimer-related (and other concomitant) pathologies due to interactions between α-synuclein, tau-protein and ß-amyloid contribute to dementia pathogenesis in both PD and dementia with Lewy bodies (DLB). To further elucidate the interaction of the pathomechanisms responsible for CI in PD, well-designed longitudinal clinico-pathological studies are warranted that are supported by fluid and sophisticated imaging biomarkers as a basis for better early diagnosis and future disease-modifying therapies.

Adaptation-induced plasticity in the sensory cortex.

For medical and fundamental reasons, we need to understand adult brain plasticity at several levels: structural, physiological, and behavioral. Historically, brain plasticity has been mostly investigated by weakening or removing sensory inputs. The visual system has been extensively used because diminishing visual inputs, i.e., visual deprivation-induced plasticity, permits more tractable findings. The present review is centered on the reverse strategy, by imposing a novel stimulus, i.e., adaptation-induced plasticity. Adaptation refers to the constant (milliseconds to hours) presentation of a nonoptimal stimulus (adapter) within the receptive field (RF, spatial area that modulates neuronal firing) of the neuron under observation. We specifically focus on how adaptation impacts the tuning of visual neurons with other associated properties. After adaptation, visual cortical neurons respond robustly to the adapter (before adaptation it typically evokes feeble responses) by developing alternate tuning curves that outlast the adaptation time. Here, with dendritic structure as foundation, we synthesize a push-pull mechanism of development and acquisition of novel tuning curves following adaptation. We then explain how these changes apply at the global level across different brain regions and species with a short description of underlying neurochemical changes. Finally, we discuss physiopathological consequences and conclude with some gaps and questions that need to be addressed to further comprehend such neuroplasticity.

The Discovery of a LEMD2-Associated Nuclear Envelopathy with Early Progeroid Appearance Suggests Advanced Applications for AI-Driven Facial Phenotyping.

Over a relatively short period of time, the clinical geneticist's "toolbox" has been expanded by machine-learning algorithms for image analysis, which can be applied to the task of syndrome identification on the basis of facial photographs, but these technologies harbor potential beyond the recognition of established phenotypes. Here, we comprehensively characterized two individuals with a hitherto unknown genetic disorder caused by the same de novo mutation in LEMD2 (c.1436C>T;p.Ser479Phe), the gene which encodes the nuclear envelope protein LEM domain-containing protein 2 (LEMD2). Despite different ages and ethnic backgrounds, both individuals share a progeria-like facial phenotype and a distinct combination of physical and neurologic anomalies, such as growth retardation; hypoplastic jaws crowded with multiple supernumerary, yet unerupted, teeth; and cerebellar intention tremor. Immunofluorescence analyses of patient fibroblasts revealed mutation-induced disturbance of nuclear architecture, recapitulating previously published data in LEMD2-deficient cell lines, and additional experiments suggested mislocalization of mutant LEMD2 protein within the nuclear lamina. Computational analysis of facial features with two different deep neural networks showed phenotypic proximity to other nuclear envelopathies. One of the algorithms, when trained to recognize syndromic similarity (rather than specific syndromes) in an unsupervised approach, clustered both individuals closely together, providing hypothesis-free hints for a common genetic etiology. We show that a recurrent de novo mutation in LEMD2 causes a nuclear envelopathy whose prognosis in adolescence is relatively good in comparison to that of classical Hutchinson-Gilford progeria syndrome, and we suggest that the application of artificial intelligence to the analysis of patient images can facilitate the discovery of new genetic disorders.

Loss of LAMP5 interneurons drives neuronal network dysfunction in Alzheimer's disease.

In Alzheimer's disease (AD), where amyloid-ß (Aß) and tau deposits in the brain, hyperexcitation of neuronal networks is an underlying disease mechanism, but its cause remains unclear. Here, we used the Collaborative Cross (CC) forward genetics mouse platform to identify modifier genes of neuronal hyperexcitation. We found LAMP5 as a novel regulator of hyperexcitation in mice, critical for the survival of distinct interneuron populations. Interestingly, synaptic LAMP5 was lost in AD brains and LAMP5 interneurons degenerated in different AD mouse models. Genetic reduction of LAMP5 augmented functional deficits and neuronal network hypersynchronicity in both Aß- and tau-driven AD mouse models. To this end, our work defines the first specific function of LAMP5 interneurons in neuronal network hyperexcitation in AD and dementia with tau pathology.

Mechanism of carbachol-induced 40 Hz gamma oscillations and the effects of NMDA activation on oscillatory dynamics in a model of the CA3 subfield of the hippocampus.

Gamma oscillations are a prominent feature of various neural systems, including the CA3 subfield of the hippocampus. In CA3, in vitro carbachol application induces ∼40 Hz gamma oscillations in the network of glutamatergic excitatory pyramidal neurons (PNs) and local GABAergic inhibitory neurons (INs). Activation of NMDA receptors within CA3 leads to an increase in the frequency of carbachol-induced oscillations to ∼60 Hz, a broadening of the distribution of individual oscillation cycle frequencies, and a decrease in the time lag between PN and IN spike bursts. In this work, we develop a biophysical integrate-and-fire model of the CA3 subfield, we show that the dynamics of our model are in concordance with physiological observations, and we provide computational support for the hypothesis that the 'E-I' mechanism is responsible for the emergence of ∼40 Hz gamma oscillations in the absence of NMDA activation. We then incorporate NMDA receptors into our CA3 model, and we show that our model exhibits the increase in gamma oscillation frequency, broadening of the cycle frequency distribution, and decrease in the time lag between PN and IN spike bursts observed experimentally. Remarkably, we find an inverse relationship in our model between the net NMDA current delivered to PNs and INs in an oscillation cycle and cycle frequency. Furthermore, we find a disparate effect of NMDA receptors on PNs versus INs - we show that NMDA receptors on INs tend to increase oscillation frequency, while NMDA receptors on PNs tend to slightly decrease or not affect oscillation frequency. We find that these observations can be explained if NMDA activity above a threshold level causes a shift in the mechanism underlying gamma oscillations; in the absence of NMDA receptors, the 'E-I' mechanism is primarily responsible for the generation of gamma oscillations (at 40 Hz), while when NMDA receptors are active, the mechanism of gamma oscillations shifts to the 'I-I' mechanism, and we argue that within the 'I-I' regime (which displays a higher baseline oscillation frequency of ∼60 Hz), slight changes in the level of NMDA activity are inversely related to cycle frequency.

Microcirculatory alterations in critically ill COVID-19 patients analyzed using artificial intelligence.

BACKGROUND: The sublingual microcirculation presumably exhibits disease-specific changes in function and morphology. Algorithm-based quantification of functional microcirculatory hemodynamic variables in handheld vital microscopy (HVM) has recently allowed identification of hemodynamic alterations in the microcirculation associated with COVID-19. In the present study we hypothesized that supervised deep machine learning could be used to identify previously unknown microcirculatory alterations, and combination with algorithmically quantified functional variables increases the model's performance to differentiate critically ill COVID-19 patients from healthy volunteers. METHODS: Four international, multi-central cohorts of critically ill COVID-19 patients and healthy volunteers (n = 59/n = 40) were used for neuronal network training and internal validation, alongside quantification of functional microcirculatory hemodynamic variables. Independent verification of the models was performed in a second cohort (n = 25/n = 33). RESULTS: Six thousand ninety-two image sequences in 157 individuals were included. Bootstrapped internal validation yielded AUROC(CI) for detection of COVID-19 status of 0.75 (0.69-0.79), 0.74 (0.69-0.79) and 0.84 (0.80-0.89) for the algorithm-based, deep learning-based and combined models. Individual model performance in external validation was 0.73 (0.71-0.76) and 0.61 (0.58-0.63). Combined neuronal network and algorithm-based identification yielded the highest externally validated AUROC of 0.75 (0.73-0.78) (P < 0.0001 versus internal validation and individual models). CONCLUSIONS: We successfully trained a deep learning-based model to differentiate critically ill COVID-19 patients from heathy volunteers in sublingual HVM image sequences. Internally validated, deep learning was superior to the algorithmic approach. However, combining the deep learning method with an algorithm-based approach to quantify the functional state of the microcirculation markedly increased the sensitivity and specificity as compared to either approach alone, and enabled successful external validation of the identification of the presence of microcirculatory alterations associated with COVID-19 status.

Bursting hierarchy in an adaptive exponential integrate-and-fire network synchronization.

Neuronal network synchronization has received wide interest. In the present manuscript, we study the influence of initial membrane potentials together with network topology on bursting synchronization, in particular the sequential order of stabilized bursting among neurons. We find a hierarchical phenomenon on their bursting order. With a focus on situations where network coupling advances spiking times of neurons, we grade neurons into different layers. Together with the neuronal network structure, we construct directed graphs to indicate bursting propagation between different layers. More explicitly, neurons in upper layers burst earlier than those in lower layers. More interestingly, we find that among the same layer, bursting order of neurons is mainly associated with the number of neurons they connected to the upper layer; more stimuli lead to earlier bursting. Receiving effectively the same stimuli from the upper layer, we observe neurons with fewer connections would burst earlier.

Deep brain stimulation for movement disorder treatment: exploring frequency-dependent efficacy in a computational network model.

A large-scale computational model of the basal ganglia network and thalamus is proposed to describe movement disorders and treatment effects of deep brain stimulation (DBS). The model of this complex network considers three areas of the basal ganglia region: the subthalamic nucleus (STN) as target area of DBS, the globus pallidus, both pars externa and pars interna (GPe-GPi), and the thalamus. Parkinsonian conditions are simulated by assuming reduced dopaminergic input and corresponding pronounced inhibitory or disinhibited projections to GPe and GPi. Macroscopic quantities are derived which correlate closely to thalamic responses and hence motor programme fidelity. It can be demonstrated that depending on different levels of striatal projections to the GPe and GPi, the dynamics of these macroscopic quantities (synchronisation index, mean synaptic activity and response efficacy) switch from normal to Parkinsonian conditions. Simulating DBS of the STN affects the dynamics of the entire network, increasing the thalamic activity to levels close to normal, while differing from both normal and Parkinsonian dynamics. Using the mentioned macroscopic quantities, the model proposes optimal DBS frequency ranges above 130 Hz.