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Synaptic dysfunction is associated with several brain disorders, including Alzheimer's disease, Parkinson's disease (PD) and obsessive compulsive disorder (OCD). Utilizing synaptic plasticity, brain stimulation is capable of reshaping synaptic connectivity. This may pave the way for novel therapies that specifically counteract pathological synaptic connectivity. For instance, in PD, novel multichannel coordinated reset stimulation (CRS) was designed to counteract neuronal synchrony and down-regulate pathological synaptic connectivity. CRS was shown to entail long-lasting therapeutic aftereffects in PD patients and related animal models. This is in marked contrast to conventional deep brain stimulation (DBS) therapy, where PD symptoms return shortly after stimulation ceases. In the present paper, we study synaptic reshaping by periodic multichannel stimulation (PMCS) in networks of leaky integrate-and-fire (LIF) neurons with spike-timing-dependent plasticity (STDP). During PMCS, phase-shifted periodic stimulus trains are delivered to segregated neuronal subpopulations. Harnessing STDP, PMCS leads to changes of the synaptic network structure. We found that the PMCS-induced changes of the network structure depend on both the phase lags between stimuli and the shape of individual stimuli. Single-pulse stimuli and burst stimuli with low intraburst frequency down-regulate synapses between neurons receiving stimuli simultaneously. In contrast, burst stimuli with high intraburst frequency up-regulate these synapses. We derive theoretical approximations of the stimulation-induced network structure. This enables us to formulate stimulation strategies for inducing a variety of network structures. Our results provide testable hypotheses for future pre-clinical and clinical studies and suggest that periodic multichannel stimulation may be suitable for reshaping plastic neuronal networks to counteract pathological synaptic connectivity. Furthermore, we provide novel insight on how the stimulus type may affect the long-lasting outcome of conventional DBS. This may strongly impact parameter adjustment procedures for clinical DBS, which, so far, primarily focused on acute effects of stimulation. Author summary: Synaptic dysfunction accompanies several brain disorders, such as Alzheimer's, Parkinson's disease and obsessive compulsive disorder. For therapeutic purposes, stimulation is delivered to disease-related brain areas. Brain stimulation therapies that manipulate synaptic connections in disease-related brain areas may provide long-lasting symptom relief. In this computational and theoretical study, we study periodic multichannel stimulation, a stimulation technique that allows for manipulating selected synaptic populations. Stimulus trains are delivered to multiple neuronal subpopulations in order to trigger neuronal responses. Using a model network of leaky integrate-and-fire neurons with spike-timing-dependent plasticity and theoretical analysis, we show how the relative timings between and the shape of delivered stimuli can be tuned to down-regulate certain synaptic connections while up-regulating others. Single-pulse stimuli triggered precise neuronal responses and were suitable for inducing a variety of synaptic network structures. When burst stimuli were employed, tuning the intraburst frequency allowed for distinguishing between down- and up-regulation of synaptic connections within individual neuronal subpopulations. Our work provides a theoretical basis for selecting suitable stimulation parameters for inducing long-lasting therapeutic effects in patients suffering from neurological disorders. [ABSTRACT FROM AUTHOR]

Abnormally strong neural synchronization may impair brain function, as observed in several brain disorders. We computationally study how neuronal dynamics, synaptic weights, and network structure co-emerge, in particular, during (de)synchronization processes and how they are affected by external perturbation. To investigate the impact of different types of plasticity mechanisms, we combine a network of excitatory integrate-and-fire neurons with different synaptic weight and/or structural plasticity mechanisms: (i) only spike-timing-dependent plasticity (STDP), (ii) only homeostatic structural plasticity (hSP), i.e., without weight-dependent pruning and without STDP, (iii) a combination of STDP and hSP, i.e., without weight-dependent pruning, and (iv) a combination of STDP and structural plasticity (SP) that includes hSP and weight-dependent pruning. To accommodate the diverse time scales of neuronal firing, STDP, and SP, we introduce a simple stochastic SP model, enabling detailed numerical analyses. With tools from network theory, we reveal that structural reorganization may remarkably enhance the network's level of synchrony. When weaker contacts are preferentially eliminated by weight-dependent pruning, synchrony is achieved with significantly sparser connections than in randomly structured networks in the STDP-only model. In particular, the strengthening of contacts from neurons with higher natural firing rates to those with lower rates and the weakening of contacts in the opposite direction, followed by selective removal of weak contacts, allows for strong synchrony with fewer connections. This activity-led network reorganization results in the emergence of degree-frequency, degree-degree correlations, and a mixture of degree assortativity. We compare the stimulation-induced desynchronization of synchronized states in the STDP-only model (i) with the desynchronization of models (iii) and (iv). The latter require stimuli of significantly higher intensity to achieve long-term desynchronization. These findings may inform future pre-clinical and clinical studies with invasive or non-invasive stimulus modalities aiming at inducing long-lasting relief of symptoms, e.g., in Parkinson's disease. Author summary: Synaptic weight and structural plasticity of neuronal networks determine their behavior, and abnormalities therein may underlie disordered states. Studying how different plasticity mechanisms govern network dynamics, particularly during (de)synchronization processes, holds clinical importance concerning, e.g., Parkinson's disease. The marked difference between the timescales at which neuronal spiking activity (milliseconds), synaptic weight modifications (minutes-hours), and structural changes (hours-days) occur in the brain makes plastic network models computationally expensive, which may limit the scope of studies. Here, we present a leaky integrate-and-fire (LIF) neuron network model with a standard spike-timing-dependent plasticity (STDP) rule for weight plasticity and a stochastic structural plasticity (SP) method. The model is computationally efficient, allowing for detailed numerical analyses of network dynamics and structure. Combining the model with tools from network science, we show that structural reorganization resulting from SP can optimize the network for synchronization, elevating the level of synchrony while concurrently reducing overall network connections. This leads to the emergence of structural correlations between the natural firing rates of neurons and the number of their pre- and post-synaptic partners. Additionally, we demonstrate that synchronized networks that evolved with SP can be more robust against desynchronization stimulation. [ABSTRACT FROM AUTHOR]

Pathological synchronization in the basal ganglia network has been considered an important component of Parkinson’s disease pathophysiology. An established treatment for some patients with Parkinson’s disease is deep brain stimulation, in which a tonic high-frequency pulse train is delivered to target regions of the brain. In recent years, a novel neuromodulation paradigm called coordinated reset stimulation has been proposed, which aims to reverse the pathological synchrony by sequentially delivering short high-frequency bursts to distinct sub-regions of the pathologically synchronized network, with an average intra-burst interval for each sub-region corresponding to period of the pathological oscillation. It has further been proposed that the resultant desynchronization can be enhanced when stimulation is interrupted periodically, and that it is particularly beneficial to precisely tune the stimulation ON and OFF time-windows to the underlying pathological frequency. Pre-clinical and clinical studies of coordinated reset stimulation have relied on these proposals for their stimulation protocols. In this study, we present a modified ON-OFF coordinated reset stimulation paradigm called periodic flashing and study its behavior through computational modeling using the Kuramoto coupled phase oscillator model. We demonstrate that in contrast to conventional coordinated reset stimulation, the periodic flashing variation does not exhibit a need for precise turning of the ON-OFF periods to the pathological frequency, and demonstrates desynchronization for a wide range of ON and OFF periods. We provide a mechanistic explanation for the previously observed sensitivities and demonstrate that they are an artifact of the specific ON-OFF cycling paradigm used. As a practical consequence, the periodic flashing paradigm simplifies the tuning of optimal stimulation parameters by decreasing the dimension of the search space. It also suggests new, more flexible ways of delivering coordinated reset stimulation. [ABSTRACT FROM AUTHOR]

Coordinated reset neuromodulation consists of the application of consecutive brief high-frequency pulse trains through the different contacts of the stimulation electrode. In theoretical studies, by achieving unlearning of abnormal connectivity between neurons, coordinated reset neuromodulation reduces pathological synchronization, a hallmark feature of Parkinson's disease pathophysiology. Here we show that coordinated reset neuromodulation of the subthalamic nucleus has both acute and sustained long-lasting aftereffects on motor function in parkinsonian nonhuman primates. Long-lasting aftereffects were not observed with classical deep brain stimulation. These observations encourage further development of coordinated reset neuromodulation for treating motor symptoms in Parkinson disease patients. ANN NEUROL 2012;72:816-820 [ABSTRACT FROM AUTHOR]

Purpose: Subjective tinnitus is associated with pathologic enhanced neuronal synchronization. We used a model based desynchronization technique, acoustic coordinated reset (CR) neuromodulation, to specifically counteract tinnitus-related neuronal synchrony thereby inducing an unlearning of pathological synaptic connectivity and neuronal synchrony. Methods: In a prospective, randomized, single blind, placebo-controlled trial in 63 patients with chronic tonal tinnitus and up to 50 dB hearing loss we studied safety and efficacy of different doses of acoustic CR neuromodulation. We measured visual analogue scale and tinnitus questionnaire (TQ) scores and spontaneous EEG. Results: CR treatment was safe, well-tolerated and caused a significant decrease of tinnitus loudness and symptoms. Placebo treatment did not lead to any significant changes. Effects gained in 12 weeks of treatment persisted through a preplanned 4-week therapy pause and showed sustained long-term effects after 10 months of therapy: Response, i.e. a reduction of at least 6 TQ points, was obtained in 75% of patients with a mean TQ reduction of 50% among responders. CR therapy significantly lowered tinnitus frequency and reversed the tinnitus related EEG alterations. Conclusion: The CR-induced reduction of tinnitus and underlying neuronal characteristics indicates a new non-invasive therapy which might also be applicable to other conditions with neuronal hypersynchrony. [ABSTRACT FROM AUTHOR]

Background: Development of new tinnitus treatments requires prospective placebo-controlled randomized trials to prove their efficacy. The Tinnitus Questionnaire (TQ) is a validated and commonly used instrument for assessment of tinnitus severity and has been used in many clinical studies. Defining the Minimal Clinically Important Difference (MCID) for TQ changes is an important step to a better interpretation of the clinical relevance of changes observed in clinical trials. In this study we aimed to estimate the minimum change of the TQ score that could be considered clinically relevant. Methods: 757 patients with chronic tinnitus were pooled from the TRI database and the RESET study. An anchor-based approach using the Clinical Global Impression (CGI) scale and distributional approaches were used to estimate MCID. Receiver Operating Characteristic (ROC) curves were calculated to define optimal TQ change cutoffs discriminating between minimally changed and unchanged subjects. Results: The relationship between TQ change scores and CGI ratings of change was good (r = 0.52, p<0.05). Mean change scores associated with minimally better and minimally worse CGI categories were -6.65 and +2.72 respectively. According to the ROC method MCID for improvement was -5 points and for deterioration +1 points. Conclusion: Distribution and anchor-based methods yielded comparable results in identifying MCIDs. ΔTQ scores of -5 and +1 points were identified as the minimal clinically relevant change for improvement and worsening respectively. The asymmetry of the MCIDs for improvement and worsening may be related to expectation effects. [ABSTRACT FROM AUTHOR]

Tinnitus is a deafferentation-induced phantom phenomenon characterized by abnormal cerebral synchrony and connectivity. Computationally, we show that desynchronizing acoustic coordinated reset (CR) stimulation can effectively counteract both up-regulated synchrony and connectivity. CR stimulation has initially been developed for the application to electrical deep brain stimulation. We here adapt this approach to non-invasive, acoustic CR stimulation. For this, we use the tonotopic organization of the central auditory system and replace electrical stimulation bursts applied to different brain sites by acoustically delivered tones of different pitch. Based on our simulations, we propose non-invasive acoustic CR stimulation as a possible novel therapy for tinnitus. [ABSTRACT FROM AUTHOR]

Abstract: Large-amplitude oscillations of macroscopic neuronal signals, such as local field potentials and electroencephalography or magnetoencephalography signals, are commonly considered as being generated by a population of mutually synchronized neurons. In a computational study in generic networks of phase oscillators and bursting neurons, however, we show that this common belief may be wrong if the neuronal population receives an external rhythmic input. The latter may stem from another neuronal population or an external, e.g., sensory or electrical, source. In that case the population field potential may be entrained by the rhythmic input, whereas the individual neurons are phase desynchronized both mutually and with their field potential. Intriguingly, the corresponding large-amplitude oscillations of the population mean field are generated by pairwise desynchronized neurons oscillating at frequencies shifted far away from the frequency of the macroscopic field potential. [Copyright &y& Elsevier]

Recent experimental studies have shown that astrocytes respond to external stimuli with a transient increase of the intracellular calcium concentration or can exhibit self-sustained spontaneous activity. Both evoked and spontaneous astrocytic calcium oscillations are accompanied by exocytosis of glutamate caged in astrocytes leading to paroxysmal depolarization shifts (PDS) in neighboring neurons. Here, we present a simple mathematical model of the interaction between astrocytes and neurons that is able to numerically reproduce the experimental results concerning the initiation of the PDS. The timing of glutamate release from the astrocyte is studied by means of a combined modeling of a vesicle cycle and the dynamics of SNARE-proteins. The neuronal slow inward currents (SICs), induced by the astrocytic glutamate and leading to PDS, are modeled via the activation of presynaptic glutamate receptors. The dependence of the bidirectional communication between neurons and astrocytes on the concentration of glutamate transporters is analyzed, as well. Our numerical results are in line with experimental findings showing that astrocyte can induce synchronous PDSs in neighboring neurons, resulting in a transient synchronous spiking activity. [ABSTRACT FROM AUTHOR]

Abstract: We study possible anti-kindling effects of the standard high-frequency deep brain stimulation (HFDBS) and of a desynchronizing multisite coordinated reset stimulation (MCRS) theoretically in a mathematical model of the subthalamic nucleus (STN). The latter is an effective target for deep brain stimulation (DBS) in patients suffering from Parkinson’s disease (PD). Depending on the structures being activated, electrical pulses may have excitatory and/or inhibitory impact. According to our simulation results MCRS may achieve robust long-term anti-kindling (i.e., curative) effects, irrespectively, of the ratio between excitatory and inhibitory impact. This means, that during MCRS the STN unlearns its pathologic synaptic connections and reestablishes a physiological level of connectivity. In contrast, HFDBS has anti-kindling effects only if its impact is predominantly excitatory. Our results are relevant for selecting appropriate locations for DBS electrodes. In fact, even with HFDBS we may expect anti-kindling effects, provided the target is properly chosen. [Copyright &y& Elsevier]

Abstract: In a modeling study, we show that synaptic connectivity can effectively be reshaped by an appropriate modulation of neuronal dynamics. To this end, we incorporate synaptic plasticity with symmetric spike-timing characteristics into a population of bursting neurons, which are interacting via chemical synapses. Under spontaneous conditions, qualitatively different stable dynamical states may coexist. We observe states characterized either by pathological synchrony or by uncorrelated activity. Suitably designed stimulation protocols enable to shift the neuronal population from one dynamical state to another. Due to low-frequency periodic pulse train stimulation, the population learns pathologically strong interactions, as known from the kindling phenomenon. In contrast, desynchronizing stimulation, e.g., multi-site coordinated reset stimulation, enables the network to unlearn pathologically strong synaptic interactions, so that a powerful long-term anti-kindling is achieved. We demonstrate that anti-kindling can be achieved even with weak and/or short desynchronizing stimuli, which are not able to cause a complete desynchronization in the course of the stimulation. Our results show that desynchronizing stimulation may serve as a novel curative approach for the therapy of neurological diseases connected with pathological cerebral synchrony. [Copyright &y& Elsevier]

Synchronization processes may severely impair brain function, for instance, in Parkinson's disease, essential tremor or epilepsies. We present three different effectively desynchronizing stimulation techniques which have been developed with methods from nonlinear dynamics and statistical physics. These techniques exploit either stochastic phase resetting principles or complex delayed feedback mechanisms. We explain how these methods work and how they can be applied to therapeutic brain stimulation. [ABSTRACT FROM AUTHOR]

In a modeling study we show that desynchronization stimulation may have powerful anti-kindling effects. For this, we incorporate spike-timing-dependent plasticity into a generic network of coupled phase oscillators, which serves as a model network of synaptically interacting neurons. Two states may coexist under spontaneous conditions: a state of uncorrelated firing and a state of pathological synchrony. Appropriate stimulation protocols make the network learn or unlearn the pathological synaptic interactions, respectively. Low-frequency periodic pulse train stimulation causes a kindling. Permanent high-frequency stimulation, used as golden standard for deep brain stimulation in medically refractory movement disorders, basically freezes the synaptic weights. In contrast, desynchronization stimulation, e.g., by means of a multi-site coordinated reset, has powerful long-term anti-kindling effects and enables the network to unlearn pathologically strong synaptic interactions. We propose desynchronization stimulation for the therapy of movement disorders and epilepsies. [ABSTRACT FROM AUTHOR]

Numerous electroencephalography (EEG) and magnetoencephalography (MEG) studies aim at identifying the chronological order of activation of brain areas. This paper demonstrates that the timing sequence obtained with the gold standard for EEG/MEG analysis (averaging across trials) may not correlate at all with the actual transmission of a stimulus’ effect within a pathway formed by connected brain areas. This is shown by studying transmission of stimulus-locked responses in a model that shares basic features with stimulated neuronal rhythms: in two phase oscillators with bistable coupling and noise one oscillator is stimulated. The model presents a mechanism that causes a response clustering, i.e., a switching between two different responses across trials, without extinction of the averaged response (calculated over all trials). Transmission times are calculated for all trials as well as for the two clusters separately with standard averaged responses and with a stochastic phase resetting analysis. The stochastic phase resetting analysis provides reliable estimates of the transmission time. In contrast, transmission times calculated by averaging across trials correspond to the phase difference in the different stable synchronized states (when calculated for the two clusters separately) or their weighted superposition (when calculated over all trials). The standard method does not detect the time elapsing during the transmission of the stimulus’ action. The results presented here call into question many findings reported in the evoked response literature. [ABSTRACT FROM AUTHOR]

The coordinated reset of neural subpopulations is introduced as an effectively desynchronizing stimulation technique. For this, short sequences of high- frequency pulse trains are administered at different sites in a coordinated way. Desynchronization is easily maintained by performing a coordinated reset with demand-controlled timing or by periodically administering resetting high-frequency pulse trains of demand-controlled length. Unlike previously developed methods, this novel approach is robust against variations of model parameters and does not require time-consuming calibration. The novel technique is suggested to be used for demand-controlled deep brain stimulation in patients suffering from Parkinson 's disease or essential tremor. It might even be applicable to diseases with intermittently emerging synchronized neural oscillations like epilepsy. [ABSTRACT FROM AUTHOR]

Synchronization of neuronal firing is a hallmark of several neurological diseases. Recently, stimulation techniques have been developed which make it possible to desynchronize oscillatory neuronal activity in a mild and effective way, without suppressing the neurons' firing. As yet, these techniques are being used to establish demand-controlled deep brain stimulation (DBS) techniques for the therapy of movement disorders like severe Parkinson's disease or essential tremor. We here present a first conceptualization suggesting that the nucleus accumbens is a promising target for the standard, that is, permanent high-frequency, DBS in patients with severe and chronic obsessive-compulsive disorder (OCD). In addition, we explain how demand-controlled DBS techniques may be applied to the therapy of OCD in those cases that are refractory to behavioral therapies and pharmacological treatment. [ABSTRACT FROM AUTHOR]

Transient phase dynamics, synchronization, and desynchronization which are stimulus-locked (i.e., tightly time-locked to a repetitively administered stimulus) are studied in two coupled phase oscillators in the presence of noise. The presented method makes it possible to detect such processes in numerical and experimental signals. The time resolution is enormous, since it is only restricted by the sampling rate. Stochastic stimulus locking of the phases or the n:m phase difference at a particular time t relative to stimulus onset is defined by the presence of one or more prominent peaks in the cross-trial distribution of the phases or the n:m phase difference at time t relative to stimulus onset in an ensemble of poststimulus responses. The oscillators' coupling may cause a transient cross-trial response clustering of the poststimulus responses. In particular, the mechanism by which intrinsic noise induces symmetric antiphase cross-trial response clustering in coupled detuned oscillators is a stochastic resonance. Unlike the presented approach, both cross-trial averaging (where an ensemble of poststimulus responses is simply averaged) and cross-trial cross correlation (CTCC) lead to severe misinterpretations: Triggered averaging cannot distinguish a cross-trial response clustering or decorrelation from a mean amplitude decrease of the single responses. CTCC not only depends on the oscillators' phase difference but also on their phases and, thus, inevitably displays "artificial" oscillations that are not related to synchronization or desynchronization. [ABSTRACT FROM AUTHOR]

Composite stimulation techniques are presented here which are based on a soft (i.e., slow and mild) reset. They effectively desynchronize a cluster of globally coupled phase oscillators in the presence of noise. A composite stimulus contains two qualitatively different stimuli. The first stimulus is either a periodic pulse train or a smooth, sinusoidal periodic stimulus with an entraining frequency close to the cluster's natural frequency. In the course of several periods of the entrainment, the cluster's dynamics is reset (restarted), independently of its initial dynamic state. The second stimulus, a single pulse, is administered with a fixed delay after the first stimulus in order to desynchronize the cluster by hitting it in a vulnerable state. The incoherent state is unstable, and thus the desynchronized cluster starts to resynchronize. Nevertheless, resynchronization can effectively be blocked by repeatedly delivering the same composite stimulus. Previously designed stimulation techniques essentially rely on a hard (i.e., abrupt) reset. With the composite stimulation techniques based on a soft reset, an effective desynchronization can be achieved even if strong, quickly resetting stimuli are not available or not tolerated. Accordingly, the soft methods are very promising for applications in biology and medicine requiring mild stimulation. In particular, it can be applied to effectively maintain incoherency in a population of oscillatory neurons which try to synchronize their firing. Accordingly, it is explained how to use the soft techniques for (i) an improved, milder, and demand-controlled deep brain stimulation for patients with Parkinson's disease or essential tremor, and for (ii) selectively blocking gamma activity in order to manipulate visual binding. [ABSTRACT FROM AUTHOR]

Based on a stochastic phase-resetting approach, three different double-pulse stimulation techniques are presented here which make it possible to effectively desynchronize a population of phase oscillators in the presence of noise. In the three sorts of double pulses the first, stronger pulse restarts the cluster independent of its initial dynamic state. The three methods differ with respect to the mechanism through which the second, weaker pulse desynchronizes the cluster. Both first and second pulses are delivered to the same site. Because of the oscillators' global couplings in the model under consideration, the incoherent state is unstable, so that after the desynchronization the cluster tends to resynchronize. However, resynchronization is effectively blocked by repeated administration of a double pulse. The experimental application of double-pulse stimulation is explained in detail. In particular, demand-controlled deep brain double-pulse stimulation is suggested for the therapy of patients suffering from Parkinson's disease or essential tremor. [ABSTRACT FROM AUTHOR]

Rhythmic activities that alternate between coherent and incoherent phases are ubiquitous in chemical, ecological, climate, or neural systems. Despite their importance, general mechanisms for their emergence are little understood. In order to fill this gap, we present a framework for describing the emergence of recurrent synchronization in complex networks with adaptive interactions. This phenomenon is manifested at the macroscopic level by temporal episodes of coherent and incoherent dynamics that alternate recurrently. At the same time, the dynamics of the individual nodes do not change qualitatively. We identify asymmetric adaptation rules and temporal separation between the adaptation and the dynamics of individual nodes as key features for the emergence of recurrent synchronization. Our results suggest that asymmetric adaptation might be a fundamental ingredient for recurrent synchronization phenomena as seen in pattern generators, e.g., in neuronal systems. [ABSTRACT FROM AUTHOR]

The synaptic organization of the brain is constantly modified by activity-dependent synaptic plasticity. In several neurological disorders, abnormal neuronal activity and pathological synaptic connectivity may significantly impair normal brain function. Reorganization of neuronal circuits by therapeutic stimulation has the potential to restore normal brain dynamics. Increasing evidence suggests that the temporal stimulation pattern crucially determines the long-lasting therapeutic effects of stimulation. Here, we tested whether a specific pattern of brain stimulation can enable the suppression of pathologically strong inter-population synaptic connectivity through spike-timing-dependent plasticity (STDP). More specifically, we tested how introducing a time shift between stimuli delivered to two interacting populations of neurons can effectively decouple them. To that end, we first used a tractable model, i.e., two bidirectionally coupled leaky integrate-and-fire (LIF) neurons, to theoretically analyze the optimal range of stimulation frequency and time shift for decoupling. We then extended our results to two reciprocally connected neuronal populations (modules) where inter-population delayed connections were modified by STDP. As predicted by the theoretical results, appropriately time-shifted stimulation causes a decoupling of the two-module system through STDP, i.e., by unlearning pathologically strong synaptic interactions between the two populations. Based on the overall topology of the connections, the decoupling of the two modules, in turn, causes a desynchronization of the populations that outlasts the cessation of stimulation. Decoupling effects of the time-shifted stimulation can be realized by time-shifted burst stimulation as well as time-shifted continuous simulation. Our results provide insight into the further optimization of a variety of multichannel stimulation protocols aiming at a therapeutic reshaping of diseased brain networks. Author summary: To clinically advance different types of brain stimulation, e.g., deep brain stimulation or epicortical stimulation, in numerous clinical studies, typically only a few types of stimulus patterns have been delivered to different target areas in the midbrain or cortex. To further leverage the power of these clinical trials we here present a theoretical and numerical study demonstrating that the effects of brain stimulation may massively depend on variations of supposedly minor parameters. To this end, we introduce a time shift between stimulus trains delivered to two anatomically separate neuronal populations interacting through plastic synapses. Depending on the specific time shift, stimulation may be ineffective or induce pronounced changes of the connections between and, in turn, within the neuronal populations, ultimately causing a long-lasting unlearning of abnormal neuronal synchrony. To thoroughly understand the time shift-induced decoupling mechanism, we first consider a simple two-neuron motif of two leaky integrate-and-fire neurons. Intriguingly, our results obtained in the two-neuron motif are in excellent agreement with the two-population scenario, illustrating the predictive power of comparably simple models. Our results are important in the context of the control of plastic neuronal networks and provide testable hypotheses for the improvement of clinically used stimulation techniques. [ABSTRACT FROM AUTHOR]

Acoustic coordinated reset (CR) therapy based on neuromodulation and neuroplasticity principles has been proposed for the treatment of tonal tinnitus. The original therapy involved periodic delivery of randomly ordered sequences of four low-level tones centered around the frequency of a tone that matched the tinnitus pitch, fT, with fixed ratios relative to fT and delivered several hours/day over several weeks. Here we transform the original CR tone selection method to a more perceptually-relevant equivalent rectangular bandwidth (ERB) frequency scale, the ERBN-number scale. Specifically, we provide a mathematical model that enables calculation of CR tones that accounts for fT- and hearing loss-related ERB widening and ERB overlaps and gaps of CR tone alignments. Further, the model ensures symmetric CR tone alignments based on modelling studies that indicate the effect is optimal if the CR stimuli are symmetrically spaced relative to the tinnitus-related population of abnormally synchronized cortical neurons to activate the adjacent sub-populations. We also present experimentally testable ERB-based CR tone alignment strategies and explain how to use the ERB-based model in experiments, clinical studies, other types of tinnitus sound treatment such as tailor-made notch music training and limitations of our approach. [ABSTRACT FROM AUTHOR]

Adaptive deep brain stimulation (aDBS) is a closed-loop method, where high-frequency DBS is turned on and off according to a feedback signal, whereas conventional high-frequency DBS (cDBS) is delivered permanently. Using a computational model of subthalamic nucleus and external globus pallidus, we extend the concept of adaptive stimulation by adaptively controlling not only continuous, but also demand-controlled stimulation. Apart from aDBS and cDBS, we consider continuous pulsatile linear delayed feedback stimulation (cpLDF), specifically designed to induce desynchronization. Additionally, we combine adaptive on-off delivery with continuous delayed feedback modulation by introducing adaptive pulsatile linear delayed feedback stimulation (apLDF), where cpLDF is turned on and off using pre-defined amplitude thresholds. By varying the stimulation parameters of cDBS, aDBS, cpLDF, and apLDF we obtain optimal parameter ranges. We reveal a simple relation between the thresholds of the local field potential (LFP) for aDBS and apLDF, the extent of the stimulation-induced desynchronization, and the integral stimulation time required. We find that aDBS and apLDF can be more efficient in suppressing abnormal synchronization than continuous simulation. However, apLDF still remains more efficient and also causes a stronger reduction of the LFP beta burst length. Hence, adaptive on-off delivery may further improve the intrinsically demand-controlled pLDF. [ABSTRACT FROM AUTHOR]

We analyze multistability in a star-type network of phase oscillators with coupling weights governed by phase-difference-dependent plasticity. It is shown that a network with N leaves can evolve into 2 N various asymptotic states, characterized by different values of the coupling strength between the hub and the leaves. Starting from the simple case of two coupled oscillators, we develop an analytical approach based on two small parameters ε and μ , where ε is the ratio of the time scales of the phase variables and synaptic weights, and μ defines the sharpness of the plasticity boundary function. The limit μ → 0 corresponds to a hard boundary. The analytical results obtained on the model of two oscillators are generalized for multi-leaf star networks. Multistability with 2 N various asymptotic states is numerically demonstrated for one-, two-, three- and nine-leaf star-type networks. [ABSTRACT FROM AUTHOR]

Excessive neuronal synchrony is a hallmark of several neurological disorders, e.g., Parkinson's disease. An established treatment for medically refractory Parkinson's disease is high-frequency deep brain stimulation. However, it provides only acute relief, and symptoms return shortly after cessation of stimulation. A theory-based approach called coordinated reset (CR) has shown great promise in achieving long-lasting effects. During CR stimulation, phase-shifted stimuli are delivered to multiple stimulation sites to counteract neuronal synchrony. Computational studies in plastic neuronal networks reported that synaptic weights reduce during stimulation, which may cause sustained structural changes leading to stabilized desynchronized activity even after stimulation ceases. Corresponding long-lasting effects were found in recent preclinical and clinical studies. We study long-lasting desynchronization by CR stimulation in excitatory recurrent neuronal networks of integrate-and-fire neurons with spike-timing-dependent plasticity (STDP). We focus on the impact of the stimulation frequency and the number of stimulation sites on long-lasting effects. We compare theoretical predictions to simulations of plastic neuronal networks. Our results are important regarding CR calibration for two reasons. We reveal that long-lasting effects become most pronounced when stimulation parameters are adjusted to the characteristics of STDP—rather than to neuronal frequency characteristics. This is in contrast to previous studies where the CR frequency was adjusted to the dominant neuronal rhythm. In addition, we reveal a nonlinear dependence of long-lasting effects on the number of stimulation sites and the CR frequency. Intriguingly, optimal long-lasting desynchronization does not require larger numbers of stimulation sites. [ABSTRACT FROM AUTHOR]

In plastic neuronal networks, the synaptic strengths are adapted to the neuronal activity. Specifically, spike-timing-dependent plasticity (STDP) is a fundamental mechanism that modifies the synaptic strengths based on the relative timing of pre- and postsynaptic spikes, taking into account the spikes' temporal order. In many studies, propagation delays were neglected to avoid additional dynamic complexity or computational costs. So far, networks equipped with a classic STDP rule typically rule out bidirectional couplings (i.e., either loops or uncoupled states) and are, hence, not able to reproduce fundamental experimental findings. In this review paper, we consider additional features, e.g., extensions of the classic STDP rule or additional aspects like noise, in order to overcome the contradictions between theory and experiment. In addition, we review in detail recent studies showing that a classic STDP rule combined with realistic propagation patterns is able to capture relevant experimental findings. In two coupled oscillatory neurons with propagation delays, bidirectional synapses can be preserved and potentiated. This result also holds for large networks of type-II phase oscillators. In addition, not only the mean of the initial distribution of synaptic weights, but also its standard deviation crucially determines the emergent structural connectivity, i.e., the mean final synaptic weight, the number of two-neuron loops, and the symmetry of the final connectivity pattern. The latter is affected by the firing rates, where more symmetric synaptic configurations emerge at higher firing rates. Finally, we discuss these findings in the context of the computational neuroscience-based development of desynchronizing brain stimulation techniques. [ABSTRACT FROM AUTHOR]

Several brain diseases are characterized by abnormally strong neuronal synchrony. Coordinated Reset (CR) stimulation was computationally designed to specifically counteract abnormal neuronal synchronization processes by desynchronization. In the presence of spike-timing-dependent plasticity (STDP) this may lead to a decrease of synaptic excitatory weights and ultimately to an anti-kindling, i.e. unlearning of abnormal synaptic connectivity and abnormal neuronal synchrony. The long-lasting desynchronizing impact of CR stimulation has been verified in pre-clinical and clinical proof of concept studies. However, as yet it is unclear how to optimally choose the CR stimulation frequency, i.e. the repetition rate at which the CR stimuli are delivered. This work presents the first computational study on the dependence of the acute and long-term outcome on the CR stimulation frequency in neuronal networks with STDP. For this purpose, CR stimulation was applied with Rapidly Varying Sequences (RVS) as well as with Slowly Varying Sequences (SVS) in a wide range of stimulation frequencies and intensities. Our findings demonstrate that acute desynchronization, achieved during stimulation, does not necessarily lead to long-term desynchronization after cessation of stimulation. By comparing the long-term effects of the two different CR protocols, the RVS CR stimulation turned out to be more robust against variations of the stimulation frequency. However, SVS CR stimulation can obtain stronger anti-kindling effects. We revealed specific parameter ranges that are favorable for long-term desynchronization. For instance, RVS CR stimulation at weak intensities and with stimulation frequencies in the range of the neuronal firing rates turned out to be effective and robust, in particular, if no closed loop adaptation of stimulation parameters is (technically) available. From a clinical standpoint, this may be relevant in the context of both invasive as well as non-invasive CR stimulation. [ABSTRACT FROM AUTHOR]

An abstract is presented of the research paper "Long-lasting neuronal desynchronization caused by coordinated reset stimulation," by Peter A. Tass, which was presented at the Twentieth Annual Computational Neuroscience Meeting held in Sweden in July 2011.

In a mathematical model we show that the dynamical multi-stability of a network of bursting subthalamic neurons, caused by synaptic plasticity, has strong impact on the stimulus-response properties when exposed to desynchronizing coordinated reset (CR) stimulation. Such stimuli can reliably shift the network from a stable state with pathological synchrony and connectivity to a stable desynchronized state with down-regulated connectivity. Finally, the desynchronizing stimulation protocol is tested in two experimental setups: first, we studied long-lasting effects of CR stimulation in rat hippocampal slice rendered epileptic by magnesium withdrawal. We show that the CR stimulation causes a long-lasting desynchronization between hippocampal neuronal populations together with a widespread decrease of the amplitude of the epileptiform activity. In contrast, periodic stimulation control stimulation induces a long-lasting increase of both synchronization and amplitude. Second, we developed appropriate hardware enabling a clinical pilot-study applying coordinated reset stimulation through externalized DBS electrodes to patients suffering from Parkinson's disease. CR stimulation resulted in a long-lasting reduction of the symptoms depicted by clinical motor scores. These theoretical and experimental findings support the idea that desynchronizing stimulation can induce long-lasting effects in neuronal networks featuring dynamical multi-stability. [ABSTRACT FROM AUTHOR]

Transmission of stimulus-locked responses is studied in a model that shares basic features with stimulated neuronal rhythms: In two phase oscillators with bistable coupling and noise one oscillator is stimulated. The model presents a mechanism which causes a response clustering, i.e. a switching between two different responses across trials, without extinction of the averaged response (calculated over all trials). Transmission times are determined for all trials as well as for the two clusters separately with standard averaged responses. Transmission times calculated in this standard way correspond to the phase difference in the different stable synchronized states (when calculated for the two clusters separately) or their weighted superposition (when calculated over all trials). The standard method does not detect the time elapsing during the transmission of the stimulus' action. The consequences for the analysis of evoked responses are severe. [ABSTRACT FROM AUTHOR]

Desynchronizing stimulation techniques were developed to specifically counteract abnormal neuronal synchronization relevant to several neurological and psychiatric disorders. The goal of our approach is to achieve an anti-kindling, where the affected neural networks unlearn abnormal synaptic connectivity and, hence, abnormal neuronal synchrony, by means of desynchronizing stimulation, in particular, Coordinated Reset (CR) stimulation. As known from neuroscience, psychology and education, learning effects can be enhanced by means of the spacing principle, i.e. by delivering repeated stimuli spaced by pauses as opposed to delivering a massed stimulus (in a single long stimulation session). To illustrate that the spacing principle may boost the anti-kindling effect of CR neuromodulation, in this computational study we carry this approach to extremes. To this end, we deliver spaced CR neuromodulation at particularly weak intensities which render permanently delivered CR neuromodulation ineffective. Intriguingly, spaced CR neuromodulation at these particularly weak intensities effectively induces an anti-kindling. In fact, the spacing principle enables the neuronal population to successively hop from one attractor to another one, finally approaching attractors characterized by down-regulated synaptic connectivity and synchrony. Our computational results might open up novel opportunities to effectively induce sustained desynchronization at particularly weak stimulation intensities, thereby avoiding side effects, e.g., in the case of deep brain stimulation. [ABSTRACT FROM AUTHOR]

We study the dynamics of Kuramoto oscillator networks with two distinct adaptation processes, one varying the coupling strengths and the other altering the network structure. Such systems model certain networks of oscillatory neurons where the neuronal dynamics, synaptic weights, and network structure interact with and shape each other. We model synaptic weight adaptation with spike-timing-dependent plasticity (STDP) that runs on a longer time scale than neuronal spiking. Structural changes that include addition and elimination of contacts occur at yet a longer time scale than the weight adaptations. First, we study the steady-state dynamics of Kuramoto networks that are bistable and can settle in synchronized or desynchronized states. To compare the impact of adding structural plasticity, we contrast the network with only STDP to one with a combination of STDP and structural plasticity. We show that the inclusion of structural plasticity optimizes the synchronized state of a network by allowing for synchronization with fewer links than a network with STDP alone. With non-identical units in the network, the addition of structural plasticity leads to the emergence of correlations between the oscillators' natural frequencies and node degrees. In the desynchronized regime, the structural plasticity decreases the number of contacts, leading to a sparse network. In this way, adding structural plasticity strengthens both synchronized and desynchronized states of a network. Second, we use desynchronizing coordinated reset stimulation and synchronizing periodic stimulation to induce desynchronized and synchronized states, respectively. Our findings indicate that a network with a combination of STDP and structural plasticity may require stronger and longer stimulation to switch between the states than a network with STDP only. [ABSTRACT FROM AUTHOR]

Using Hodgkin–Huxley and isolated subthalamic nucleus (STN) model neurons as examples, we show that electrical high-frequency stimulation (HFS) suppresses sustained neuronal spiking. The mechanism of suppression is explained on the basis of averaged equations derived from the original neuron equations in the limit of high frequencies. We show that for frequencies considerably greater than the reciprocal of the neuron’s characteristic time scale, the result of action of HFS is defined by the ratio between the amplitude and the frequency of the stimulating signal. The effect of suppression emerges due to a stabilization of the neuron’s resting state or due to a stabilization of a low-amplitude subthreshold oscillation of its membrane potential. Intriguingly, although we neglect synaptic dynamics, neural circuity as well as contribution of glial cells, the results obtained with the isolated high-frequency stimulated STN model neuron resemble the clinically observed relations between stimulation amplitude and stimulation frequency required to suppress Parkinsonian tremor. [ABSTRACT FROM AUTHOR]

Intuitively one might expect independent noise to be a powerful tool for desynchronizing a population of synchronized neurons. We here show that, intriguingly, for oscillatory neural populations with adaptive synaptic weights governed by spike timing-dependent plasticity (STDP) the opposite is true. We found that the mean synaptic coupling in such systems increases dynamically in response to the increase of the noise intensity, and there is an optimal noise level, where the amount of synaptic coupling gets maximal in a resonance-like manner as found for the stochastic or coherence resonances, although the mechanism in our case is different. This constitutes a noise-induced self-organization of the synaptic connectivity, which effectively counteracts the desynchronizing impact of independent noise over a wide range of the noise intensity. Given the attempts to counteract neural synchrony underlying tinnitus with noisers and maskers, our results may be of clinical relevance [ABSTRACT FROM AUTHOR]

The brain operates through complex interactions in the flow of information and signal processing within neural networks. The 'wiring' of such networks, being neuronal or glial, can physically and/or functionally go rogue in various pathological states. Neuromodulation, as a multidisciplinary venture, attempts to correct such faulty nets. In this review, selected approaches and challenges in neuromodulation are discussed. The use of water-dispersible carbon nanotubes has been proven effective in the modulation of neurite outgrowth in culture and in aiding regeneration after spinal cord injury in vivo. Studying neural circuits using computational biology and analytical engineering approaches brings to light geometrical mapping of dynamics within neural networks, much needed information for stimulation interventions in medical practice. Indeed, sophisticated desynchronization approaches used for brain stimulation have been successful in coaxing 'misfiring' neuronal circuits to resume productive firing patterns in various human disorders. Devices have been developed for the real-time measurement of various neurotransmitters as well as electrical activity in the human brain during electrical deep brain stimulation. Such devices can establish the dynamics of electrochemical changes in the brain during stimulation. With increasing application of nanomaterials in devices for electrical and chemical recording and stimulating in the brain, the era of cellular, and even intracellular, precision neuromodulation will soon be upon us. [ABSTRACT FROM AUTHOR]

For a feedforward loop of oscillatory Hodgkin-Huxley neurons interacting via excitatory chemical synapses, we show that a great variety of spatiotemporal periodic firing patterns can be encoded by properly chosen communication delays and synaptic weights, which contributes to the concept of temporal coding by spikes. These patterns can be obtained by a modulation of the multiple coexisting stable in-phase synchronized states or traveling waves propagating along or against the direction of coupling. We derive explicit conditions for the network parameters allowing us to achieve a desired pattern. Interestingly, whereas the delays directly affect the time differences between spikes of interacting neurons, the synaptic weights control the phase differences. Our results show that already such a simple neural circuit may unfold an impressive spike coding capability. [ABSTRACT FROM AUTHOR]

We show that synchronization processes can effectively be controlled with nonlinear delayed feedback. We demonstrate that nonlinear delayed feedback can have a twofold impact on the collective dynamics of large ensembles of coupled oscillators: synchronizing and, mostly, desynchronizing effects. By means of a model equation for the mean field, we explore the existence and stability of the feedback-induced desynchronized states, their multistability and dynamical properties. We propose nonlinear delayed feedback stimulation for the therapy of neurological diseases characterized by abnormal synchrony. [ABSTRACT FROM AUTHOR]

When two phase oscillators interact with a time-delay, new synchronized and desynchronized regimes appear, in this way giving rise to the phenomenon of multistability. The number of coexisting stable states grows with an increase of the delay, and their frequencies quantize. We show that, while the number of synchronized solutions grows linearly with the delay and/or coupling, the set of the desynchronized solutions, i.e. those with different average frequencies of the individual oscillators, raises quadratically with increasing delay. For the synchronized states, we analyze the mutual arrangement of the basins of attraction, and conclude that the structure and size of the basins are apparently the same for each state. We discuss possible implications for desynchronizing brain stimulation techniques. [ABSTRACT FROM AUTHOR]

We present a detailed bifurcation analysis of desynchronization transitions in a system of two coupled phase oscillators with delay. The coupling between the oscillators combines a delayed self-feedback of each oscillator with an instantaneous mutual interaction. The delayed self-feedback leads to a rich variety of dynamical regimes, ranging from phase-locked and periodically modulated synchronized states to chaotic phase synchronization and desynchronization. We show that an increase of the coupling strength between oscillators may lead to a loss of synchronization. Intriguingly, the delay has a twofold influence on the oscillations: synchronizing for small and intermediate coupling strength and desynchronizing if the coupling strength exceeds a certain threshold value. We show that the desynchronization transition has the form of a crisis bifurcation of a chaotic attractor of chaotic phase synchronization. This study contributes to a better understanding of the impact of time delay on interacting oscillators. [ABSTRACT FROM AUTHOR]

A novel control method for desynchronization of strongly synchronized populations of interacting oscillators is described. We show that an ensemble's mean field, nonlinearly processed and fed back into the ensemble, causes an effective desynchronization. The method is mild, demand controlled, and robust against system and stimulation parameter variations. The desynchronization and decoupling effects of the method are illustrated by examples of one and two interacting populations of limit-cycle oscillators. We suggest our method for mild and effective deep brain stimulation in neurological diseases characterized by pathological cerebral synchronization. [ABSTRACT FROM AUTHOR]

Effective desynchronization can be exploited as a tool for probing the functional significance of synchronized neural activity underlying perceptual and cognitive processes or as a mild treatment for neurological disorders like Parkinson’s disease. In this article we show that pulse-based desynchronization techniques, originally developed for networks of globally coupled oscillators (Kuramoto model), can be adapted to networks of coupled neurons with dendritic dynamics. Compared to the Kuramoto model, the dendritic dynamics significantly alters the response of the neuron to the stimulation. Under medium stimulation amplitude a bistability of the re- sponse of a single neuron is observed. When stimulated at some initial phases, the neuron displays only modulations of its firing, whereas at other initial phases it stops oscillating entirely. Significant alterations in the duration of stimulation-induced transients are also observed. These transients endure after the end of the stimulation and cause maximal desynchronization to occur not during the stimulation, but with some delay after the stimulation has been turned off. To account for this delayed desynchronization effect, we have designed a new calibration procedure for finding the stimulation parameters that result in optimal desynchronization. We have also developed a new desynchronization technique by low frequency entrainment. The stimulation techniques originally developed for the Kuramoto model, when using the new calibration procedure, can also be applied to networks with dendritic dynamics. However, the mechanism by which desynchronization is achieved is substantially different than for the network of Kuramoto oscillators. In particular, the addition of dendritic dynamics significantly changes the timing of the stimulation required to obtain desynchronization. We propose desynchronization stimulation for experimental analysis of synchronized neural processes and for the therapy of movement disorders. [ABSTRACT FROM AUTHOR]

We present nonlinear delayed feedback stimulation as a technique for effective desynchronization. This method is intriguingly robust with respect to system and stimulation parameter variations. We demonstrate its broad applicability by applying it to different generic oscillator networks as well as to a population of bursting neurons. Nonlinear delayed feedback specifically counteracts abnormal interactions and, thus, restores the natural frequencies of the individual oscillatory units. Nevertheless, nonlinear delayed feedback enables to strongly detune the macroscopic frequency of the collective oscillation. We propose nonlinear delayed feedback stimulation for the therapy of neurological diseases characterized by abnormal synchrony. [ABSTRACT FROM AUTHOR]

The Kuramoto model of globally coupled phase oscillators is an essentially nonlinear dynamical system with a rich dynamics including synchronization and chaos. We study the Kuramoto model from the standpoint of bifurcation and chaos theory of low-dimensional dynamical systems. We find a chaotic attractor in the four-dimensional Kuramoto model and study its origin. The torus destruction scenario is one of the major mechanisms by which chaos arises. L. P. Shilnikov has made decisive contributions to its discovery. We show also that in the Kuramoto model the transition to chaos is in accordance with the torus destruction scenario. We present the general bifurcation diagram containing phase chaos, Cherry flow as well as periodic and quasiperiodic dynamics. [ABSTRACT FROM AUTHOR]

Abstract: Recently extensive work has been done towards developing methods for effective desynchronization of globally coupled phase oscillators (the Kuramoto model), by means of short stimulation pulses, or sequences of pulses. This is of great importance for the treatment of neurological disorders like Parkinson’s disease and essential tremor. As a progressive step towards the goal of being able to apply these desynchronization and phase-resetting techniques medically as a form of treatment, we demonstrate here how these ideas can be generalized and applied to a network of two-dimensional phase oscillators with inertia. This model has been previously presented as a simplification of a neuron with an axon and dendrite, and can be used to account for intrinsic transient behavior often seen experimentally. The stimulation techniques originally developed for the Kuramoto model work on a network of globally coupled inertial phase oscillators in a qualitatively similar way. In both cases desynchronization can be achieved when the stimulation causes nearby trajectories to diverge from each other. However, the mechanism by which this divergence of trajectories is achieved, is substantially different for the network of inertial oscillators. In particular, the addition of inertia results in a broad range of transient dynamics not present in the Kuramoto model. Nevertheless, the basic principles of phase resetting and desynchronization still apply. This suggests a robustness of these techniques which is of extreme importance to the medical applications. [Copyright &y& Elsevier]

Deep brain stimulation (DBS) is a neurosurgical procedure that allows targeted circuit-based neuromodulation. DBS is a standard of care in Parkinson disease, essential tremor and dystonia, and is also under active investigation for other conditions linked to pathological circuitry, including major depressive disorder and Alzheimer disease. Modern DBS systems, borrowed from the cardiac field, consist of an intracranial electrode, an extension wire and a pulse generator, and have evolved slowly over the past two decades. Advances in engineering and imaging along with an improved understanding of brain disorders are poised to reshape how DBS is viewed and delivered to patients. Breakthroughs in electrode and battery designs, stimulation paradigms, closed-loop and on-demand stimulation, and sensing technologies are expected to enhance the efficacy and tolerability of DBS. In this Review, we provide a comprehensive overview of the technical development of DBS, from its origins to its future. Understanding the evolution of DBS technology helps put the currently available systems in perspective and allows us to predict the next major technological advances and hurdles in the field. [ABSTRACT FROM AUTHOR]