Cell Assembly Signatures Defined by Short-Term Synaptic Plasticity in Cortical Networks

The Interactions of Temporal and Sensory Representations in the Basal Ganglia

In rodents and primates, interval estimation has been associated with a complex network of cortical and subcortical structures where the dorsal striatum plays a paramount role. Diverse evidence ranging from individual neurons to population activity has demonstrated that this area hosts temporal-related neural representations that may be instrumental for the perception and production of time intervals. However, little is known about how temporal representations interact with other well-known striatal representations, such as kinematic parameters of movements or somatosensory representations. An attractive hypothesis suggests that somatosensory representations may serve as the scaffold for complex representations such as elapsed time. Alternatively, these representations may coexist as independent streams of information that could be integrated into downstream nuclei, such as the substantia nigra or the globus pallidus. In this review, we will revise the available information suggesting an instrumental role of sensory representations in the construction of temporal representations at population and single-neuron levels throughout the basal ganglia.

Conceptual framework for neuronal ensemble identification and manipulation related to behavior using calcium imaging

Significance: The identification and manipulation of spatially identified neuronal ensembles with optical methods have been recently used to prove the causal link between neuronal ensemble activity and learned behaviors. However, the standardization of a conceptual framework to identify and manipulate neuronal ensembles from calcium imaging recordings is still lacking. Aim: We propose a conceptual framework for the identification and manipulation of neuronal ensembles using simultaneous calcium imaging and two-photon optogenetics in behaving mice. Approach: We review the computational approaches that have been used to identify and manipulate neuronal ensembles with single cell resolution during behavior in different brain regions using all-optical methods. Results: We proposed three steps as a conceptual framework that could be applied to calcium imaging recordings to identify and manipulate neuronal ensembles in behaving mice: (1) transformation of calcium transients into binary arrays; (2) identification of neuronal ensembles as similar population vectors; and (3) targeting of neuronal ensemble members that significantly impact behavioral performance. Conclusions: The use of simultaneous two-photon calcium imaging and two-photon optogenetics allowed for the experimental demonstration of the causal relation of population activity and learned behaviors. The standardization of analytical tools to identify and manipulate neuronal ensembles could accelerate interventional experiments aiming to reprogram the brain in normal and pathological conditions.

Translational neuronal ensembles: Neuronal microcircuits in psychology, physiology, pharmacology and pathology

Multi-recording techniques show evidence that neurons coordinate their firing forming ensembles and that brain networks are made by connections between ensembles. While “canonical” microcircuits are composed of interconnected principal neurons and interneurons, it is not clear how they participate in recorded neuronal ensembles: “groups of neurons that show spatiotemporal co-activation”. Understanding synapses and their plasticity has become complex, making hard to consider all details to fill the gap between cellular-synaptic and circuit levels. Therefore, two assumptions became necessary: First, whatever the nature of the synapses these may be simplified by “functional connections”. Second, whatever the mechanisms to achieve synaptic potentiation or depression, the resultant synaptic weights are relatively stable. Both assumptions have experimental basis cited in this review, and tools to analyze neuronal populations are being developed based on them. Microcircuitry processing followed with multi-recording techniques show temporal sequences of neuronal ensembles resembling computational routines. These sequences can be aligned with the steps of behavioral tasks and behavior can be modified upon their manipulation, supporting the hypothesis that they are memory traces. In vitro, recordings show that these temporal sequences can be contained in isolated tissue of histological scale. Sequences found in control conditions differ from those recorded in pathological tissue obtained from animal disease models and those recorded after the actions of clinically useful drugs to treat disease states, setting the basis for new bioassays to test drugs with potential clinical use. These findings make the neuronal ensembles theoretical framework a dynamic neuroscience paradigm.

Inhibitory conductance controls place field dynamics in the hippocampus

Hippocampal place cells receive a disparate collection of excitatory and inhibitory currents that endow them with spatially selective discharges and rhythmic activity. Using a combination of in vivo intracellular and extracellular recordings with opto/chemogenetic manipulations and computational modeling, we investigate the influence of inhibitory and excitatory inputs on CA1 pyramidal cell responses. At the cell bodies, inhibition leads and is stronger than excitation across the entire theta cycle. Pyramidal neurons fire on the ascending phase of theta when released from inhibition. Computational models equipped with the observed conductances reproduce these dynamics. In these models, place field properties are favored when the increased excitation is coupled with a reduction of inhibition within the field. As predicted by our simulations, firing rate within place fields and phase locking to theta are impaired by DREADDs activation of interneurons. Our results indicate that decreased inhibitory conductance is critical for place field expression.

Valero et al. examine the influence of inhibition on place fields. They show that hippocampal neurons are dominated by inhibitory conductances during theta oscillations. A transient increase of excitation and drop of inhibition mediates place field emergence in simulations. Consistently, chemogenetic activation of interneurons deteriorates place cell properties in vivo.

Septotemporal variation in modulation of synaptic transmission, paired-pulse ratio and frequency facilitation/depression by adenosine and GABAB receptors in the rat hippocampus

Short-term synaptic plasticity represents a fundamental mechanism in neural information processing and is regulated by neuromodulators. Here, using field recordings from the CA1 region of adult rat hippocampal slices, we show that excitatory synaptic transmission is suppressed by strong but not moderate activation of adenosine A₁ receptors by 2-Chloro-N⁶-cyclopentyladenosine (CCPA) more in the dorsal than the ventral hippocampus; in contrast, both mild and strong activation of GABA_B receptors by baclofen (1 μM, 10 μM) suppress synaptic transmission more in the ventral than the dorsal hippocampus. Using a 10-pulse stimulation train of variable frequency, we found that CCPA modulates short-term synaptic plasticity independently of the suppression of synaptic transmission in both segments of the hippocampus and at stimulation frequencies greater than 10 Hz. However, specifically regarding the paired-pulse ratio (PPR) and frequency facilitation/depression (FF/D) we found significant drug action before but not after adjusting conditioning responses to control levels. Activation of GABA_BRs by baclofen suppressed synaptic transmission more in the ventral than the dorsal hippocampus. Furthermore, relatively high (10 μM) but not low (1 μM) baclofen concentration enhanced both PPR and FF in both hippocampal segments at stimulation frequencies greater than 1 Hz, independently of the suppression of synaptic transmission by baclofen. These results show that A₁Rs and GABA_BRs control synaptic transmission more effectively in the dorsal and the ventral hippocampus, respectively, and suggest that these receptors modulate PPR and FF/D at different frequency bands of afferent input, in both segments of the hippocampus.

Striatal neuronal ensembles reveal differential actions of amantadine and clozapine to ameliorate mice L-DOPA-induced dyskinesia

Amantadine and clozapine have proved to reduce abnormal involuntary movements (AIMs) in preclinical and clinical studies of L-DOPA-Induced Dyskinesias (LID). Even though both drugs decrease AIMs, they may have different action mechanisms by using different receptors and signaling profiles. Here we asked whether there are differences in how they modulate neuronal activity of multiple striatal neurons within the striatal microcircuit at histological level during the dose-peak of L-DOPA in ex-vivo brain slices obtained from dyskinetic mice. To answer this question, we used calcium imaging to record the activity of dozens of neurons of the dorsolateral striatum before and after drugs administration in vitro. We also developed an analysis framework to extract encoding insights from calcium imaging data by quantifying neuronal activity, identifying neuronal ensembles by linking neurons that coactivate using hierarchical cluster analysis and extracting network parameters using Graph Theory. The results show that while both drugs reduce LIDs scores behaviorally in a similar way, they have several different and specific actions on modulating the dyskinetic striatal microcircuit. The extracted features were highly accurate in separating amantadine and clozapine effects by means of principal components analysis (PCA) and support vector machine (SVM) algorithms. These results predict possible synergistic actions of amantadine and clozapine on the dyskinetic striatal microcircuit establishing a framework for a bioassay to test novel antidyskinetic drugs or treatments in vitro.

Identification of Pattern Completion Neurons in Neuronal Ensembles Using Probabilistic Graphical Models

Neuronal ensembles are groups of neurons with coordinated activity that could represent sensory, motor, or cognitive states. The study of how neuronal ensembles are built, recalled, and involved in the guiding of complex behaviors has been limited by the lack of experimental and analytical tools to reliably identify and manipulate neurons that have the ability to activate entire ensembles. Such pattern completion neurons have also been proposed as key elements of artificial and biological neural networks. Indeed, the relevance of pattern completion neurons is highlighted by growing evidence that targeting them can activate neuronal ensembles and trigger behavior. As a method to reliably detect pattern completion neurons, we use conditional random fields (CRFs), a type of probabilistic graphical model. We apply CRFs to identify pattern completion neurons in ensembles in experiments using in vivo two-photon calcium imaging from primary visual cortex of male mice and confirm the CRFs predictions with two-photon optogenetics. To test the broader applicability of CRFs we also analyze publicly available calcium imaging data (Allen Institute Brain Observatory dataset) and demonstrate that CRFs can reliably identify neurons that predict specific features of visual stimuli. Finally, to explore the scalability of CRFs we apply them to in silico network simulations and show that CRFs-identified pattern completion neurons have increased functional connectivity. These results demonstrate the potential of CRFs to characterize and selectively manipulate neural circuits.SIGNIFICANCE STATEMENT We describe a graph theory method to identify and optically manipulate neurons with pattern completion capability in mouse cortical circuits. Using calcium imaging and two-photon optogenetics in vivo we confirm that key neurons identified by this method can recall entire neuronal ensembles. This method could be broadly applied to manipulate neuronal ensemble activity to trigger behavior or for therapeutic applications in brain prostheses.

Scalable and accurate method for neuronal ensemble detection in spiking neural networks

We propose a novel, scalable, and accurate method for detecting neuronal ensembles from a population of spiking neurons. Our approach offers a simple yet powerful tool to study ensemble activity. It relies on clustering synchronous population activity (population vectors), allows the participation of neurons in different ensembles, has few parameters to tune and is computationally efficient. To validate the performance and generality of our method, we generated synthetic data, where we found that our method accurately detects neuronal ensembles for a wide range of simulation parameters. We found that our method outperforms current alternative methodologies. We used spike trains of retinal ganglion cells obtained from multi-electrode array recordings under a simple ON-OFF light stimulus to test our method. We found a consistent stimuli-evoked ensemble activity intermingled with spontaneously active ensembles and irregular activity. Our results suggest that the early visual system activity could be organized in distinguishable functional ensembles. We provide a Graphic User Interface, which facilitates the use of our method by the scientific community.

Long-term stability of cortical ensembles

Neuronal ensembles, coactive groups of neurons found in spontaneous and evoked cortical activity, are causally related to memories and perception, but it is still unknown how stable or flexible they are over time. We used two-photon multiplane calcium imaging to track over weeks the activity of the same pyramidal neurons in layer 2/3 of the visual cortex from awake mice and recorded their spontaneous and visually evoked responses. Less than half of the neurons remained active across any two imaging sessions. These stable neurons formed ensembles that lasted weeks, but some ensembles were also transient and appeared only in one single session. Stable ensembles preserved most of their neurons for up to 46 days, our longest imaged period, and these 'core' cells had stronger functional connectivity. Our results demonstrate that neuronal ensembles can last for weeks and could, in principle, serve as a substrate for long-lasting representation of perceptual states or memories.

Neuronal ensembles in memory processes

A neuronal ensemble represents the concomitant activity of a specific group of neurons that could encompass a broad repertoire of brain functions such as motor, perceptual, memory or cognitive states. On the other hand, a memory engram portrays the physical manifestation of memory or the changes that enable learning and retrieval. Engram studies focused for many years on finding where memories are stored as in, which cells or brain regions represent a memory trace, and disregarded the investigation of how neuronal activity patterns give rise to such memories. Recent experiments suggest that the association and reactivation of specific neuronal groups could be the main mechanism underlying the brain's ability to remember past experiences and envision future actions. Thus, the growing consensus is that the interaction between neuronal ensembles could allow sequential activity patterns to become memories and recurrent memories to compose complex behaviors. The goal of this review is to propose how the neuronal ensemble framework could be translated and useful to understand memory processes.

Dopamine D1 Receptors Regulate Spines in Striatal Direct-Pathway and Indirect-Pathway Neurons

BACKGROUND

Dopamine transmission is involved in the maintenance of the structural plasticity of direct-pathway and indirect-pathway striatal projection neurons (d-SPNs and i-SPNs, respectively). The lack of dopamine in Parkinson's disease produces synaptic remodeling in both types of SPNs, reducing the length of the dendritic arbor and spine density and increasing the intrinsic excitability. Meanwhile, the elevation of dopamine levels by levodopa recovers these alterations selectively in i-SPNs. However, little is known about the specific role of the D1 receptor (D1R) in these alterations.

METHODS

To explore the specific role of D1R in the synaptic remodeling of SPNs, we used knockout D1R mice (D1R^-/- ) and wild-type mice crossed with drd2-enhanced green fluorescent protein (eGFP) to identify d-SPNs and i-SPNs. Corticostriatal slices were used for reconstruction of the dendritic arbors after Lucifer yellow intracellular injection and for whole-cell recordings in naïve and parkinsonian mice treated with saline or levodopa.

RESULTS

The genetic inactivation of D1R reduces the length of the dendritic tree and the spine density in all SPNs, although more so in d-SPNs, which also increases their spiking. In parkinsonian D1R^-/- mice, the spine density decreases in i-SPNs, and this spine loss recovers after chronic levodopa.

CONCLUSIONS

D1R is essential for the maintenance of spine plasticity in d-SPNs but also affects i-SPNs, indicating an important crosstalk between these 2 types of neurons. © 2020 International Parkinson and Movement Disorder Society.

Playing the piano with the cortex: role of neuronal ensembles and pattern completion in perception and behavior

Neuronal ensembles, i.e. coactive groups of neurons, have been long postulated to be functional building blocks of cortical circuits and units of the neural code. Calcium imaging of neuronal populations has demonstrated the widespread existence of spontaneous and sensory-evoked ensembles in cortical circuits in vivo. The development of two-photon optical techniques to simultaneously record and activate neurons with single cell resolution ("piano" experiments) has revealed the existence of pattern completion neurons, which can trigger an entire ensemble, and demonstrated a causal relation between ensembles and behavior. We review recent results controlling visual perception with targeted holographic manipulation of cortical ensembles by stimulating pattern completion neurons. Analyzing population activity as neuronal ensembles and exploiting pattern completion could enable control of brain states in health and disease.

Controlling Visually Guided Behavior by Holographic Recalling of Cortical Ensembles

Neurons in cortical circuits are often coactivated as ensembles, yet it is unclear whether ensembles play a functional role in behavior. Some ensemble neurons have pattern completion properties, triggering the entire ensemble when activated. Using two-photon holographic optogenetics in mouse primary visual cortex, we tested whether recalling ensembles by activating pattern completion neurons alters behavioral performance in a visual task. Disruption of behaviorally relevant ensembles by activation of non-selective neurons decreased performance, whereas activation of only two pattern completion neurons from behaviorally relevant ensembles improved performance, by reliably recalling the whole ensemble. Also, inappropriate behavioral choices were evoked by the mistaken activation of behaviorally relevant ensembles. Finally, in absence of visual stimuli, optogenetic activation of two pattern completion neurons could trigger behaviorally relevant ensembles and correct behavioral responses. Our results demonstrate a causal role of neuronal ensembles in a visually guided behavior and suggest that ensembles implement internal representations of perceptual states.

Synchronized activation of striatal direct and indirect pathways underlies the behavior in unilateral dopamine-depleted mice

For more than three decades it has been known, that striatal neurons become hyperactive after the loss of dopamine input, but the involvement of dopamine (DA) D1‐ or D2‐receptor‐expressing neurons has only been demonstrated indirectly. By recording neuronal activity using fluorescent calcium indicators in D1 or D2 eGFP‐expressing mice, we showed that following dopamine depletion, both types of striatal output neurons are involved in the large increase in neuronal activity generating a characteristic cell assembly of particular neurons that dominate the pattern. When we expressed channelrhodopsin in all the output neurons, light activation in freely moving animals, caused turning like that following dopamine loss. However, if the light stimulation was patterned in pulses the animals circled in the other direction. To explore the neuronal participation during this stimulation we infected normal mice with channelrhodopsin and calcium indicator in striatal output neurons. In slices made from these animals, continuous light stimulation for 15 s induced many cells to be active together and a particular dominant group of neurons, whereas light in patterned pulses activated fewer cells in more variable groups. These results suggest that the simultaneous activity of a large dominant group of striatal output neurons is intimately associated with parkinsonian symptoms.

Modeling driver cells in developing neuronal networks

Spontaneous emergence of synchronized population activity is a characteristic feature of developing brain circuits. Recent experiments in the developing neo-cortex showed the existence of driver cells able to impact the synchronization dynamics when single-handedly stimulated. We have developed a spiking network model capable to reproduce the experimental results, thus identifying two classes of driver cells: functional hubs and low functionally connected (LC) neurons. The functional hubs arranged in a clique orchestrated the synchronization build-up, while the LC drivers were lately or not at all recruited in the synchronization process. Notwithstanding, they were able to alter the network state when stimulated by modifying the temporal activation of the functional clique or even its composition. LC drivers can lead either to higher population synchrony or even to the arrest of population dynamics, upon stimulation. Noticeably, some LC driver can display both effects depending on the received stimulus. We show that in the model the presence of inhibitory neurons together with the assumption that younger cells are more excitable and less connected is crucial for the emergence of LC drivers. These results provide a further understanding of the structural-functional mechanisms underlying synchronized firings in developing circuits possibly related to the coordinated activity of cell assemblies in the adult brain.

From Structure to Activity: Using Centrality Measures to Predict Neuronal Activity

It is clear that the topological structure of a neural network somehow determines the activity of the neurons within it. In the present work, we ask to what extent it is possible to examine the structural features of a network and learn something about its activity? Specifically, we consider how the centrality (the importance of a node in a network) of a neuron correlates with its firing rate. To investigate, we apply an array of centrality measures, including In-Degree, Closeness, Betweenness, Eigenvector, Katz, PageRank, Hyperlink-Induced Topic Search (HITS) and NeuronRank to Leaky-Integrate and Fire neural networks with different connectivity schemes. We find that Katz centrality is the best predictor of firing rate given the network structure, with almost perfect correlation in all cases studied, which include purely excitatory and excitatory–inhibitory networks, with either homogeneous connections or a small-world structure. We identify the properties of a network which will cause this correlation to hold. We argue that the reason Katz centrality correlates so highly with neuronal activity compared to other centrality measures is because it nicely captures disinhibition in neural networks. In addition, we argue that these theoretical findings are applicable to neuroscientists who apply centrality measures to functional brain networks, as well as offer a neurophysiological justification to high level cognitive models which use certain centrality measures.

Cortical stimulation relieves parkinsonian pathological activity in vitro

Previously, we have shown that chemical excitatory drives such as N-methyl-d-aspartate (NMDA) are capable of activating the striatal microcircuit exhibiting neuronal ensembles that alternate their activity producing temporal sequences. One aim of this work was to demonstrate whether similar activity could be evoked by delivering cortical stimulation. Dynamic calcium imaging allowed us to follow the activity of dozens of neurons with single-cell resolution in mus musculus brain slices. A train of electrical stimuli in the cortex evoked network activity similar to the one induced by bath application of NMDA. Previously, we have also shown that the dopamine-depleted striatal microcircuit increases its spontaneous activity generating dominant recurrent ensembles that interrupt the temporal sequences found in control microcircuits. This activity correlates with parkinsonian pathological activity. Several cortical stimulation protocols such as transcranial magnetic stimulation reduce motor signs of Parkinsonism. Here, we show that cortical stimulation in vitro temporarily eliminates the pathological activity from the dopamine-depleted striatal microcircuit by turning off some neurons that sustain this activity and recruiting new ones that allow transitions between network states, similar to the control circuit. When cortical stimulation is given in the presence of L-DOPA, parkinsonian activity is eliminated during the whole recording period. The present experimental evidence suggests that cortical stimulation such as that generated by transcranial magnetic stimulation, or otherwise, may allow reduce L-DOPA dosage.

Evidence of a Task-Independent Neural Signature in the Spectral Shape of the Electroencephalogram

Genetic and neurophysiological studies of electroencephalogram (EEG) have shown that an individual's brain activity during a given cognitive task is, to some extent, determined by their genes. In fact, the field of biometrics has successfully used this property to build systems capable of identifying users from their neural activity. These studies have always been carried out in isolated conditions, such as relaxing with eyes closed, identifying visual targets or solving mathematical operations. Here we show for the first time that the neural signature extracted from the spectral shape of the EEG is to a large extent independent of the recorded cognitive task and experimental condition. In addition, we propose to use this task-independent neural signature for more precise biometric identity verification. We present two systems: one based on real cepstrums and one based on linear predictive coefficients. We obtained verification accuracies above 89% on 4 of the 6 databases used. We anticipate this finding will create a new set of experimental possibilities within many brain research fields, such as the study of neuroplasticity, neurodegenerative diseases and brain machine interfaces, as well as the mentioned genetic, neurophysiological and biometric studies. Furthermore, the proposed biometric approach represents an important advance towards real world deployments of this new technology.

Hierarchical Chunking of Sequential Memory on Neuromorphic Architecture with Reduced Synaptic Plasticity

Chunking refers to a phenomenon whereby individuals group items together when performing a memory task to improve the performance of sequential memory. In this work, we build a bio-plausible hierarchical chunking of sequential memory (HCSM) model to explain why such improvement happens. We address this issue by linking hierarchical chunking with synaptic plasticity and neuromorphic engineering. We uncover that a chunking mechanism reduces the requirements of synaptic plasticity since it allows applying synapses with narrow dynamic range and low precision to perform a memory task. We validate a hardware version of the model through simulation, based on measured memristor behavior with narrow dynamic range in neuromorphic circuits, which reveals how chunking works and what role it plays in encoding sequential memory. Our work deepens the understanding of sequential memory and enables incorporating it for the investigation of the brain-inspired computing on neuromorphic architecture.

Resting state functional magnetic resonance imaging processing techniques in stroke studies

In recent years, there has been considerable research interest in the study of brain connectivity using the resting state functional magnetic resonance imaging (rsfMRI). Studies have explored the brain networks and connection between different brain regions. These studies have revealed interesting new findings about the brain mapping as well as important new insights in the overall organization of functional communication in the brain network. In this paper, after a general discussion of brain networks and connectivity imaging, the brain connectivity and resting state networks are described with a focus on rsfMRI imaging in stroke studies. Then, techniques for preprocessing of the rsfMRI for stroke patients are reviewed, followed by brain connectivity processing techniques. Recent research on brain connectivity using rsfMRI is reviewed with an emphasis on stroke studies. The authors hope this paper generates further interest in this emerging area of computational neuroscience with potential applications in rehabilitation of stroke patients.

Closed-loop Robots Driven by Short-Term Synaptic Plasticity: Emergent Explorative vs. Limit-Cycle Locomotion

We examine the hypothesis, that short-term synaptic plasticity (STSP) may generate self-organized motor patterns. We simulated sphere-shaped autonomous robots, within the LPZRobots simulation package, containing three weights moving along orthogonal internal rods. The position of a weight is controlled by a single neuron receiving excitatory input from the sensor, measuring its actual position, and inhibitory inputs from the other two neurons. The inhibitory connections are transiently plastic, following physiologically inspired STSP-rules. We find that a wide palette of motion patterns are generated through the interaction of STSP, robot, and environment (closed-loop configuration), including various forward meandering and circular motions, together with chaotic trajectories. The observed locomotion is robust with respect to additional interactions with obstacles. In the chaotic phase the robot is seemingly engaged in actively exploring its environment. We believe that our results constitute a concept of proof that transient synaptic plasticity, as described by STSP, may potentially be important for the generation of motor commands and for the emergence of complex locomotion patterns, adapting seamlessly also to unexpected environmental feedback. We observe spontaneous and collision induced mode switchings, finding in addition, that locomotion may follow transiently limit cycles which are otherwise unstable. Regular locomotion corresponds to stable limit cycles in the sensorimotor loop, which may be characterized in turn by arbitrary angles of propagation. This degeneracy is, in our analysis, one of the drivings for the chaotic wandering observed for selected parameter settings, which is induced by the smooth diffusion of the angle of propagation.

Pathophysiological signatures of functional connectomics in parkinsonian and dyskinetic striatal microcircuits

A challenge in neuroscience is to integrate the cellular and system levels. For instance, we still do not know how a few dozen neurons organize their activity and relations in a microcircuit or module of histological scale. By using network theory and Ca(2+) imaging with single-neuron resolution we studied the way in which striatal microcircuits of dozens of cells orchestrate their activity. In addition, control and diseased striatal tissues were compared in rats. In the control tissue, functional connectomics revealed small-world, scale-free and hierarchical network properties. These properties were lost during pathological conditions in ways that could be quantitatively analyzed. Decorticated striatal circuits disclosed that corticostriatal interactions depend on privileged connections with a set of highly connected neurons or "hubs". In the 6-OHDA model of Parkinson's disease there was a decrease in hubs number; but the ones that remained were linked to dominant network states. l-DOPA induced dyskinesia provoked a loss in the hierarchical structure of the circuit. All these conditions conferred distinct temporal sequences to circuit activity. Temporal sequences appeared as particular signatures of disease process thus bringing the possibility of a future quantitative pathophysiology at a histological scale.