Biology subjects

Duchet, B.

Publications and source records attributed to Duchet, B..

13 recordsLinked to original sources

bioRxiv2026

Evidence for traveling beta waves in the parkinsonian subthalamic nucleus

Beta oscillations (13-35 Hz) in the subthalamic nucleus (STN) are a hallmark of Parkinsons disease (PD), yet little is known about their spatial dynamics. Since traveling waves (spatially propagating oscillations) have been observed in cortical and subcortical structures at different frequencies, we hypothesized that beta oscillations propagate as traveling waves in the STN, likely originating from the motor region of the nucleus. We leveraged multi-contact local field potential recordings from PD patients to estimate pairwise delays between contacts, and asked whether these delays could be accounted for by a simple spherical traveling wave model. Using statistical controls to safeguard against overfitting, we found evidence for spherical traveling beta waves in 11 out of 20 hemispheres, on average during 16% of the recording duration. We further demonstrated that sources are most likely located in the dorsal portion of the motor STN at the group level, and observed a positive correlation between propagation speed and residual motor impairment. Our results suggest that parkinsonian STN beta activity can organise into mesoscopic traveling waves, which has important implications for the pathophysiology of PD and the improvement of sensing-guided DBS.

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bioRxiv2025

Dithering suppresses half-harmonic neural synchronisation to photic stimulation in humans

While entraining neural rhythms using brain stimulation has been suggested as a therapeutic mechanism to normalise brain activity in conditions such as depression, chronic pain, or Alzheimers disease, periodic stimulation can also inadvertently entrain brain rhythms at sub- and superharmonics of the stimulation frequency, which could lead to deleterious effects. Slightly jittering stimulation pulses (called "dithering") was previously proposed on the basis of mathematical modelling to selectively entrain a target neural rhythm while avoiding harmonic entrainment. In this study, we investigated the potential of dithering in humans. Using photic stimulation (light flicker) and EEG recordings in healthy participants, we showed that dithering suppresses half-harmonic synchronisation relative to perfectly periodic flicker, and more so than synchronisation at the stimulation frequency. This was also the case for a periodic condition with reduced stimulation amplitude, as predicted by theory. Furthermore, we demonstrated using synthetic data and modelling that the half-harmonic responses observed in participants cannot be explained by the super-position of evoked responses (even when modulated at the half-harmonic frequency), and are better matched by a minimal oscillator model. Our findings are consistent with half-harmonic EEG synchronisation in response to photic stimulation predom-inantly reflecting half-harmonic entrainment rather than the summation of evoked responses, and with dithering being an effective strategy to suppress subharmonic entrainment.

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bioRxiv2024

Response of neuronal populations to phase-locked stimulation: model-based predictions and validation

BackgroundModulation of neuronal oscillations holds promise for the treatment of neurological disorders. Nonetheless, stimulating neuronal populations in a continuous open-loop manner can lead to side effects and suboptimal efficiency. Closed-loop strategies such as phase-locked stimulation aim to address these shortcomings by offering a more targeted modulation. While theories have been developed to understand the neural response to stimulation, their predictions have not been thoroughly tested using experimental data. ObjectiveWe aimed to test the predictions of a mathematical model regarding the response of neuronal populations to phase-locked stimulation. MethodsUsing a coupled oscillator model, we expanded on two key predictions describing the response to stimulation as a function of the phase and amplitude of ongoing neural activity. To investigate these predictions, we analyzed electrocorticogram (ECoG) recordings from a previously conducted study in Parkinsonian rats, and extracted the corresponding phase and response curves. ResultsWe demonstrated that the amplitude response to stimulation is strongly correlated to the derivative of the phase response ({rho} > 0.8) in all animals except one, thereby validating a key model prediction. The second prediction postulated that the stimulation becomes ineffective when the network synchrony is high, a trend that appeared missing in the data. Our analysis explained this discrepancy by showing that the neural populations in Parkinsonian rats did not reach the level of synchrony for which the theory would predict ineffective stimulation. ConclusionsOur results highlight the potential of fine-tuning stimulation paradigms informed by mathematical models that consider both the ongoing phase and amplitude of the targeted neural oscillation.

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bioRxiv2024

Evoked Resonant Neural Activity Long-Term Dynamics can be Reproduced by a Computational Model with Vesicle Depletion

Subthalamic deep brain stimulation (DBS) robustly generates high-frequency oscillations known as evoked resonant neural activity (ERNA). Recently the importance of ERNA has been demonstrated through its ability to predict the optimal DBS contact in the subthalamic nucleus in patients with Parkinsons disease. However, the underlying mechanisms of ERNA are not well understood, and previous modelling efforts have not managed to reproduce the wealth of published data describing the dynamics of ERNA. Here, we therefore aim to present a minimal model capable of reproducing the characteristics of the slow ERNA dynamics published to date. We make biophysically-motivated modifications to the Kuramoto model and fit its parameters to the slow dynamics of ERNA obtained from data. We further validate the model against experimental data from Parkinsons disease patients by simulating variable stimulation and medication states, as well as the response of individual neurons. Our results demonstrate that it is possible to reproduce the slow dynamics of ERNA with a single neuronal population, and, crucially, with vesicle depletion as the key mechanism behind the ERNA frequency decay. We provide a series of predictions from the model that could be the subject of future studies for further validation. Author SummaryERNA is a high amplitude response to stimulation of deep brain structures, with a frequency over twice that of the frequency of stimulation. While the underlying mechanisms of ERNA are still unclear, recent findings have demonstrated its importance as the best indicator of which stimulation contact to select for DBS therapy in patients with Parkinsons disease. Previous modelling studies of ERNA focus on the immediate responses to stimulation (<200ms) and rely on interconnected neural structures and delays. Our work shows that the long-term (on the scale of one or more seconds) ERNA response to continuous stimulation can be modelled using a single neural structure. The proposed model also captures the long-term frequency and amplitude characteristics of ERNA with variable stimulation and medication paradigms. The key features of the model, in particular the depletion of vesicles carrying neurotransmitters between neurons by high-frequency stimulation, may provide insights into the underlying mechanisms of ERNA and inform future investigations into this neural response.

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bioRxiv2024

How to design optimal brain stimulation to modulate phase-amplitude coupling?

ObjectivePhase-amplitude coupling (PAC), the coupling of the amplitude of a faster brain rhythm to the phase of a slower brain rhythm, plays a significant role in brain activity and has been implicated in various neurological disorders. For example, in Parkinsons disease, PAC between the beta (13-30 Hz) and gamma (50-200 Hz) rhythms in the motor cortex is exaggerated, while in Alzheimers disease, PAC between the theta (4-8 Hz) and gamma rhythms is diminished. Modulating PAC (i.e. reducing or enhancing PAC) using brain stimulation could therefore open new therapeutic avenues. However, while it has been previously reported that phase-locked stimulation can increase PAC, it is unclear what the optimal stimulation strategy to modulate PAC might be. Here, we provide a theoretical framework to narrow down the experimental optimisation of stimulation aimed at modulating PAC, which would otherwise rely on trial and error. ApproachWe make analytical predictions using a Stuart-Landau model, and confirm these predictions in a more realistic model of coupled neural populations. Main resultsOur framework specifies the critical Fourier coefficients of the stimulation waveform which should be tuned to optimally modulate PAC. Depending on the characteristics of the amplitude response curve of the fast population, these components may include the slow frequency, the fast frequency, combinations of these, as well as their harmonics. We also show that the optimal balance of energy between these Fourier components depends on the relative strength of the endogenous slow and fast rhythms, and that the alignment of fast components with the fast rhythm should change throughout the slow cycle. Furthermore, we identify the conditions requiring to phase-lock stimulation to the fast and/or slow rhythms. SignificanceTogether, our theoretical framework lays the foundation for guiding the development of innovative and more effective brain stimulation aimed at modulating PAC for therapeutic benefit.

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bioRxiv2022

How to entrain a selected neuronal rhythm but not others: open-loop dithered brain stimulation for selective entrainment

While brain stimulation therapies such as deep brain stimulation for Parkinsons disease can be effective, they have yet to reach their full potential across neurological disorders. Entraining neuronal rhythms using rhythmic brain stimulation has been suggested as a new therapeutic mechanism to restore neurotypical behavior in conditions such as chronic pain, depression, and Alzheimers disease. However, theoretical and experimental evidence indicate that brain stimulation can also entrain neuronal rhythms at sub- and super-harmonics, far from the stimulation frequency. Crucially, these counterintuitive effects can be harmful to patients, for example by triggering debilitating involuntary movements in Parkinsons disease. We therefore propose a principled approach to selectively promote rhythms close to the stimulation frequency, while avoiding potential harmful effects by preventing entrainment at sub- and super-harmonics. Our open-loop approach to selective entrainment, dithered stimulation, consists in adding white noise to the stimulation period. We theoretically establish the ability of dithered stimulation to selectively entrain a given brain rhythm, and verify its efficacy in simulations of uncoupled neural oscillators, and networks of coupled neural oscillators. Furthermore, we show that dithered stimulation can be implemented in neurostimulators with limited capabilities by toggling within a finite set of stimulation frequencies. Likely implementable across a variety of existing brain stimulation devices, dithering-based selective entrainment has potential to enable new brain stimulation therapies, as well as new neuroscientific research exploiting its ability to modulate higher-order entrainment.

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bioRxiv2022

Mean-field approximations with adaptive coupling for networks with spike-timing-dependent plasticity

Understanding the effect of spike-timing-dependent plasticity (STDP) is key to elucidate how neural networks change over long timescales and to design interventions aimed at modulating such networks in neurological disorders. However, progress is restricted by the significant computational cost associated with simulating neural network models with STDP, and by the lack of low-dimensional description that could provide analytical insights. Phase-difference-dependent plasticity (PDDP) rules approximate STDP in phase oscillator networks, which prescribe synaptic changes based on phase differences of neuron pairs rather than differences in spike timing. Here we construct mean-field approximations for phase oscillator networks with STDP to describe part of the phase space for this very high dimensional system. We first show that single-harmonic PDDP rules can approximate a simple form of symmetric STDP, while multi-harmonic rules are required to accurately approximate causal STDP. We then derive exact expressions for the evolution of the average PDDP coupling weight in terms of network synchrony. For adaptive networks of Kuramoto oscillators that form clusters, we formulate a family of low-dimensional descriptions based on the mean field dynamics of each cluster and average coupling weights between and within clusters. Finally, we show that such a two-cluster mean-field model can be fitted to synthetic data to provide a low-dimensional approximation of a full adaptive network with symmetric STDP. Our framework represents a step towards a low-dimensional description of adaptive networks with STDP, and could for example inform the development of new therapies aimed at maximizing the long-lasting effects of brain stimulation.

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bioRxiv2022

When do bursts matter in the motor cortex? Investigating changes in the intermittencies of beta rhythms associated with movement states.

Time series of brain activity recorded from different anatomical regions and in different behavioural states and pathologies can be summarised by the power spectrum. Recently, attention has shifted to characterising the properties of changing temporal dynamics in rhythmic neural activity. Here, we present evidence from electrocorticography recordings made from the motor cortex to show that, dependent on the specific motor context, the statistics of temporal transients in beta frequency (14-30 Hz) rhythms (i.e., bursts) can significantly add to the description of states such rest, movement preparation, movement execution, and movement imagery. We show that the statistics of burst duration and amplitude can significantly improve the classification of motor states and that burst features reflect nonlinearities not detectable in the power spectrum, with states increasing in order of nonlinearity from movement execution to movement preparation to rest. Further, we provide mechanistic explanations for these features by fitting models of the motor cortical microcircuit to the empirical data and investigate how dynamical instabilities interact with noise to generate burst dynamics. Finally, we examine how beta bursting in motor cortex may influence the integration of exogenous inputs to the cortex and suggest that properties of spontaneous activity cannot be reliably used to infer the response of the cortex to external inputs. These findings have significance for the classification of motor states, for instance in novel brain-computer interfaces. Critically, we increase the understanding of how transient brain rhythms may contribute to cortical processing, which in turn, may inform novel approaches for its modulation with brain stimulation.

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bioRxiv2022

Sub-harmonic Entrainment of Cortical Gamma Oscillations to Deep Brain Stimulation in Parkinson's Disease: Predictions and Validation of a Patient-Specific Non-Linear Model

ObjectivesThe exact mechanisms of deep brain stimulation (DBS) are still an active area of investigation, in spite of its clinical successes. This is due in part to the lack of understanding of the effects of stimulation on neuronal rhythms. Entrainment of brain oscillations has been hypothesised as a potential mechanism of neuromodulation. A better understanding of entrainment might further inform existing methods of continuous DBS, and help refine algorithms for adaptive methods. The purpose of this study is to develop and test a theoretical framework to predict entrainment of cortical rhythms to DBS across a wide range of stimulation parameters. Materials and MethodsWe fit a model of interacting neural populations to selected features characterising PD patients off-stimulation finely-tuned gamma rhythm recorded through electrocorticography. Using the fitted models, we predict basal ganglia DBS parameters that would result in 1:2 entrainment, a special case of sub-harmonic entrainment observed in patients and predicted by theory. ResultsWe show that the neural circuit models fitted to patient data exhibit 1:2 entrainment when stimulation is provided across a range of stimulation parameters. Furthermore, we verify key features of the region of 1:2 entrainment in the stimulation frequency/amplitude space with follow-up recordings from the same patients, such as the loss of 1:2 entrainment above certain stimulation amplitudes. ConclusionOur results reveal that continuous, constant frequency DBS in patients may lead to nonlinear patterns of neuronal entrainment across stimulation parameters, and that these responses can be predicted by modelling. Should entrainment prove to be an important mechanism of therapeutic stimulation, our modelling framework may reduce the parameter space that clinicians must consider when programming devices for optimal benefit.

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bioRxiv2020

Optimal Closed-loop Deep Brain Stimulation with Multi-Contact Electrodes

Deep brain stimulation (DBS) is a well-established treatment option for a variety of neurological disorders, including Parkinsons disease (PD) and essential tremor (ET). It is widely believed that the efficacy, efficiency and side-effects of the treatment can be improved by stimulating closed-loop, according to the symptoms of a patient. Multi-contact electrodes powered by independent current sources are a recent development in DBS technology which allow for greater precision when targeting one or more pathological regions but, in order to realise the potential of such systems, algorithms must be developed to deal with their increased complexity. This motivates the need to understand how applying DBS to multiple regions (or neural populations) can affect the efficacy and efficiency of the treatment. On the basis of a theoretical model, our paper aims to address the question of how to best apply DBS to multiple neural populations to maximally desynchronise brain activity. Using a coupled oscillator model, we derive analytical expressions which predict how the symptom severity should change as a result of applying stimulation. On the basis of these expressions we derive an algorithm describing when the stimulation should be delivered to individual contacts. Remarkably, these expressions also allow us to determine the conditions for when stimulation using information from individual contacts is likely to be advantageous. Using numerical simulation, we demonstrate that our methods have the potential to be both more effective and efficient than existing methods found in the literature.

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bioRxiv2020

Average beta burst duration profiles provide a signature of dynamical changes between the ON and OFF medication states in Parkinson's disease

Parkinsons disease motor symptoms are associated with an increase in subthalamic nucleus beta band oscillatory power. However, these oscillations are phasic, and there is a growing body of evidence suggesting that beta burst duration may be of critical importance to motor symptoms. This makes insights into the dynamics of beta bursting generation valuable, in particular to refine closed-loop deep brain stimulation in Parkinsons disease. In this study, we ask the question "Can average burst duration reveal how dynamics change between the ON and OFF medication states?". Our analysis of local field potentials from the subthalamic nucleus demonstrates using linear surrogates that the system generating beta oscillations is more likely to act in a non-linear regime OFF medication and that the change in a non-linearity measure is correlated with motor impairment. In addition, we pinpoint the simplest dynamical changes that could be responsible for changes in the temporal patterning of beta oscillations between medication states by fitting to data biologically inspired models, and simpler beta envelope models. Finally, we show that the non-linearity can be directly extracted from average burst duration profiles under the assumption of constant noise in envelope models. This reveals that average burst duration profiles provide a window into burst dynamics, which may underlie the success of burst duration as a biomarker. In summary, we demonstrate a relationship between average burst duration profiles, dynamics of the system generating beta oscillations, and motor impairment, which puts us in a better position to understand the pathology and improve therapies such as deep brain stimulation. Author summaryIn Parkinsons disease, motor impairment is associated with abnormal oscillatory activity of neurons in deep motor regions of the brain. These oscillations come in the shape of bursts, and the duration of these bursts has recently been shown to be of importance to motor symptoms. To better understand the disease and refine therapies, we relate the duration of these bursts to properties of the system generating them in the pathological state and in a proxy of the healthy state. The data suggest that the system generating bursts involves more complexity in the pathological state, and we show that a measure of this complexity is associated with motor impairment. We propose biologically inspired models and simpler models that can generate the burst patterns observed in the pathological and healthy state. The models confirm what was observed in data, and tell us how burst generation mechanisms could differ in the disease. Finally, we identify a mathematical link allowing us to infer properties of the burst generating system from burst duration measurements in patient recordings. This sheds some light on the significance of burst duration as a marker of pathology.

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bioRxiv2019

Phase dependence of response curves to stimulation and their relationship: from a Wilson-Cowan model to essential tremor patient data

Essential tremor manifests predominantly as a tremor of the upper limbs. One therapy option is high-frequency deep brain stimulation, which continuously delivers electrical stimulation to the ventral intermediate nucleus of the thalamus at about 130 Hz. Investigators have been looking at stimulating less, chiefly to reduce side effects. One strategy, phase-locked deep brain stimulation, consists of stimulating according to the phase of the tremor, once per period. In this study, we aim to reproduce the phase dependent effects of stimulation seen in patient data with a biologically inspired Wilson-Cowan model. To this end, we first analyse patient data, and conclude that half of the datasets have response curves that are better described by sinusoidal curves than by straight lines, while an effect of phase cannot be consistently identified in the remaining half. Using the Hilbert phase we derive analytical expressions for phase and amplitude responses to phase-dependent stimulation and study their relationship in the linearisation of a stable focus model, a simplification of the Wilson-Cowan model in the stable focus regime. Analytical results provide a good approximation for response curves observed in patients with consistent significance. Additionally, we fitted the full non-linear Wilson-Cowan model to these patients, and we show that the model can fit in each case to the dynamics of patient tremor as well as the phase response curve, and the best fits are found to be stable foci for each patients (tied best fit in one instance). The model provides satisfactory prediction of how patient tremor will react to phase-locked stimulation by predicting patient amplitude response curves although they were not explicitly fitted. This can be partially explained by the relationship between the response curves in the model being compatible with what is found in the data. We also note that the non-linear Wilson-Cowan model is able to describe response to stimulation more precisely than the linearisation.

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bioRxiv2018

Predicting the effects of deep brain stimulation using a reduced coupled oscillator model

Deep brain stimulation (DBS) is known to be an effective treatment for a variety of neurological disorders, including Parkinsons disease and essential tremor (ET). At present, it involves administering a train of pulses with constant frequency via electrodes implanted into the brain. New closed-loop approaches involve delivering stimulation according to the ongoing symptoms or brain activity and have the potential to provide improvements in terms of efficiency, efficacy and reduction of side effects. The success of closed-loop DBS depends on being able to devise a stimulation strategy that minimizes oscillations in neural activity associated with symptoms of motor disorders. A useful stepping stone towards this is to construct a mathematical model, which can describe how the brain oscillations should change when stimulation is applied at a particular state of the system. Our work focuses on the use of coupled oscillators to represent neurons in areas generating pathological oscillations. Using a reduced form of the Kuramoto model, we analyse how a patient should respond to stimulation when neural oscillations have a given phase and amplitude. We predict that, provided certain conditions are satisfied, the best stimulation strategy should be phase specific but also that stimulation should have a greater effect if applied when the amplitude of brain oscillations is lower. We compare this surprising prediction with data obtained from ET patients. In light of our predictions, we also propose a new hybrid strategy which effectively combines two of the strategies found in the literature, namely phase-locked and adaptive DBS.\n\nAuthor summaryDeep brain stimulation (DBS) involves delivering electrical impulses to target sites within the brain and is a proven therapy for a variety of neurological disorders. Closed loop DBS is a promising new approach where stimulation is applied according to the state of a patient. Crucial to the success of this approach is being able to predict how a patient should respond to stimulation. Our work focusses on DBS as applied to patients with essential tremor (ET). On the basis of a theoretical model, which describes neurons as oscillators that respond to stimulation and have a certain tendency to synchronize, we provide predictions for how a patient should respond when stimulation is applied at a particular phase and amplitude of the ongoing tremor oscillations. Previous experimental studies of closed loop DBS provided stimulation either on the basis of ongoing phase or amplitude of pathological oscillations. Our study suggests how both of these measurements can be used to control stimulation. As part of this work, we also look for evidence for our theories in experimental data and find our predictions to be satisfied in one patient. The insights obtained from this work should lead to a better understanding of how to optimise closed loop DBS strategies.

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