Disrupting abnormal neuronal oscillations with adaptive delayed feedback control
Curation statements for this article:-
Curated by eLife
eLife assessment
Large populations of neurons are capable of entering pathological synchronous oscillations under a variety of conditions and work over many decades has found ways to disrupt such oscillations using stimulation in both open loop and closed loop configurations. This study adds useful results and methodology to this line of research, by providing solid evidence that delayed feedback control via electrical stimulation can, under certain conditions, terminate network level oscillations in cultured cortical neurons. The study provides analyses and simulation results that shed light on why some networks respond to such feedback control while others do not.
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (eLife)
- Neuroscience (eLife)
Abstract
Closed-loop neuronal stimulation has a strong therapeutic potential for neurological disorders such as Parkinson’s disease. However, at the moment, standard stimulation protocols rely on continuous open-loop stimulation and the design of adaptive controllers is an active field of research. Delayed feedback control (DFC), a popular method used to control chaotic systems, has been proposed as a closed-loop technique for desynchronisation of neuronal populations but, so far, was only tested in computational studies. We implement DFC for the first time in neuronal populations and access its efficacy in disrupting unwanted neuronal oscillations. To analyse in detail the performance of this activity control algorithm, we used specialised in vitro platforms with high spatiotemporal monitoring/stimulating capabilities. We show that the conventional DFC in fact worsens the neuronal population oscillatory behaviour, which was never reported before. Conversely, we present an improved control algorithm, adaptive DFC (aDFC), which monitors the ongoing oscillation periodicity and self-tunes accordingly. aDFC effectively disrupts collective neuronal oscillations restoring a more physiological state. Overall, these results support aDFC as a better candidate for therapeutic closed-loop brain stimulation.
Article activity feed
-
-
Author Response
Reviewer #1 (Public Review):
In this manuscript, the Authors implement a delayed feedback control method and use it for the first time in biological neuronal networks. They extend a well-established computational theory and expand it into the biological realm. With this, they obtain novel evidence, never considered before, that showcases the difference between simulated neuronal networks and biological ones. Furthermore, they optimize the DFC method to achieve optimal results in the control of cell excitability in the content of biological neuronal networks, taking advantage of a closed-loop stimulation setup that, by itself, is not trivial to build and operate and that will certainly have a positive impact the fields of cellular and network electrophysiology.
Regarding the results, it would be very constructive if …
Author Response
Reviewer #1 (Public Review):
In this manuscript, the Authors implement a delayed feedback control method and use it for the first time in biological neuronal networks. They extend a well-established computational theory and expand it into the biological realm. With this, they obtain novel evidence, never considered before, that showcases the difference between simulated neuronal networks and biological ones. Furthermore, they optimize the DFC method to achieve optimal results in the control of cell excitability in the content of biological neuronal networks, taking advantage of a closed-loop stimulation setup that, by itself, is not trivial to build and operate and that will certainly have a positive impact the fields of cellular and network electrophysiology.
Regarding the results, it would be very constructive if the Authors could share the code for the quasi-real-time interface with the Multichannel Systems software (current and older hardware versions), as this represents likely a bottleneck preventing more researchers to implement such an experimental paradigm.
On the data focusing on the effects of the DFC algorithms on neuronal behavior, the evidence is very compelling, although more care should be devoted to the statistical analyses, since some of the applied statistical tests are not appropriate. In a more biological sense, further discussion and clarification of the experimental details would improve this manuscript, making it more accessible and clearer for researchers across disciplines (i.e., ranging from computational to experimental Neuroscience) and increasing the impact of this research.
In summary, this work represents a necessary bridge between recent advances in computational neuroscience and the biological implementation of neuronal control mechanisms.
Regarding sharing the control code, our application for closed-loop stimulation using aDFC, DFC and Poisson is now available in GitHub (https://github.com/NCN-Lab/aDFC). This was, in fact, our initial intention following the reviewing process. With this application, the user can run the developed algorithms with the MEA2100-256 System from Multi Channel Systems MCS GmbH.
Same with the data. The dataset with the spike data from all experiments is also now publicly available in Zenodo. The data can be found in https://doi.org/10.5281/zenodo.10138446.
Regarding the improvements in the statistical analysis, the tests are now performed following Reviewer #1 suggestions. Important to emphasize that this did not change the results/ conclusions of the work.
-
eLife assessment
Large populations of neurons are capable of entering pathological synchronous oscillations under a variety of conditions and work over many decades has found ways to disrupt such oscillations using stimulation in both open loop and closed loop configurations. This study adds useful results and methodology to this line of research, by providing solid evidence that delayed feedback control via electrical stimulation can, under certain conditions, terminate network level oscillations in cultured cortical neurons. The study provides analyses and simulation results that shed light on why some networks respond to such feedback control while others do not.
-
Reviewer #1 (Public Review):
In this manuscript, the Authors implement a delayed feedback control method and use it for the first time in biological neuronal networks. They extend a well-established computational theory and expand it into the biological realm. With this, they obtain novel evidence, never considered before, that showcases the difference between simulated neuronal networks and biological ones. Furthermore, they optimize the DFC method to achieve optimal results in the control of cell excitability in the content of biological neuronal networks, taking advantage of a closed-loop stimulation setup that, by itself, is not trivial to build and operate and that will certainly have a positive impact the fields of cellular and network electrophysiology.
Regarding the results, it would be very constructive if the Authors could …
Reviewer #1 (Public Review):
In this manuscript, the Authors implement a delayed feedback control method and use it for the first time in biological neuronal networks. They extend a well-established computational theory and expand it into the biological realm. With this, they obtain novel evidence, never considered before, that showcases the difference between simulated neuronal networks and biological ones. Furthermore, they optimize the DFC method to achieve optimal results in the control of cell excitability in the content of biological neuronal networks, taking advantage of a closed-loop stimulation setup that, by itself, is not trivial to build and operate and that will certainly have a positive impact the fields of cellular and network electrophysiology.
Regarding the results, it would be very constructive if the Authors could share the code for the quasi-real-time interface with the Multichannel Systems software (current and older hardware versions), as this represents likely a bottleneck preventing more researchers to implement such an experimental paradigm.
On the data focusing on the effects of the DFC algorithms on neuronal behavior, the evidence is very compelling, although more care should be devoted to the statistical analyses, since some of the applied statistical tests are not appropriate. In a more biological sense, further discussion and clarification of the experimental details would improve this manuscript, making it more accessible and clearer for researchers across disciplines (i.e., ranging from computational to experimental Neuroscience) and increasing the impact of this research.
In summary, this work represents a necessary bridge between recent advances in computational neuroscience and the biological implementation of neuronal control mechanisms.
-
Reviewer #2 (Public Review):
This study applies a new neuromodulation algorithm, adaptive delayed feedback control (aDFC) to in vitro and in silico neuron populations to demonstrate its effectiveness at desynchronizing synchronous neural population activity. The study compares aDFC to other neuromodulation approaches such as non-adaptive DFC and random stimulation and demonstrates that in a subset of controllable networks, aDFC succeeds in reducing overall synchrony in the neural population. Further, when characterizing population firing bouts as asynchronous versus synchronous, aDFC increased the fraction of time that the neural population was in the asynchronous versus synchronous state (albeit in one network). Overall, this study is an impressive combination of computational and experimental work that details a promising new adaptive …
Reviewer #2 (Public Review):
This study applies a new neuromodulation algorithm, adaptive delayed feedback control (aDFC) to in vitro and in silico neuron populations to demonstrate its effectiveness at desynchronizing synchronous neural population activity. The study compares aDFC to other neuromodulation approaches such as non-adaptive DFC and random stimulation and demonstrates that in a subset of controllable networks, aDFC succeeds in reducing overall synchrony in the neural population. Further, when characterizing population firing bouts as asynchronous versus synchronous, aDFC increased the fraction of time that the neural population was in the asynchronous versus synchronous state (albeit in one network). Overall, this study is an impressive combination of computational and experimental work that details a promising new adaptive neuromodulation algorithm that may be relevant for neurological disorders where excessive synchronous brain activity is currently treated with conventional open-loop DBS.
Strengths: The authors build on existing work that has suggested DFC may be a viable algorithm for desynchronizing hyper-synchronous neural populations. They demonstrate by performing in vivo experiments that, contrary to the suggestions of previous work, DFC exacerbates oscillatory intensity. As a result, they develop a new adaptive DFC (aDFC) that updates the estimate of the population's periodicity, enabling superior desynchronization of the population. Further, aDFC enables more population spiking activity that is not just a response to the stimulation (Fig. S3), potentially making the approach conducive to reducing excessive synchronization while also being permissive to neural encoding.
Another innovation of this study is developing a framework for detecting which neural populations are controllable vs. uncontrollable, i.e. consistently responsive to stimulation vs. not consistently responsive. The authors find that populations with intermediate levels of synchrony and firing rate are controllable, whereas populations outside this regime are uncontrollable. These findings are substantiated with a neural network model, where a controllable regime is also detected. The controllable subspace in the in vivo networks and in silico networks also appear to roughly correspond (intermediate synchrony and firing rates) though a direct comparison is not made.
Finally, not only do the authors find that aDFC reduces synchrony, they further identify extended periods of time when the network is in an asynchronous state and find that aDFC can extend the amount of time that the network spends in this state. While these results are compelling, there is only a single network that is able to demonstrate this effect so it is unclear how general a property this is.
Overall, the study presents a novel closed loop neuromodulation algorithm and presents compelling data demonstrating that the algorithm reduces synchrony in in vitro and in silico neural populations.
Weaknesses: The authors point out Parkinson's disease, essential tremor, epilepsy, and dystonia as the neurological disorders that suffer from excessive neural synchronization. In two of these disorders the frequency of the neural synchronization is ~15-30 Hz (Parkinson's disease) and ~5-7 Hz (essential tremor). These frequencies are well above the ~1 Hz synchronization frequency observed in the in vitro population. While this study exhibits a nice proof of principle, how readily it would extend to populations that exhibit higher synchronization frequencies is unclear.
In addition, the study relies on computing population spiking activity of neurons. Current closed-loop neuromodulation devices are outfitted with large electrodes that can sense local field potentials. The impact of this study would have been higher and more readily translatable if the authors could have detected neural population synchronization using local field potential features.
Finally, since the authors were seeking to develop a closed-loop neuromodulation solution that exhibited an improvement over existing open-loop solutions, it would have strengthened the findings and relevance of this study to have done comparisons between aDFC and high frequency open-loop stimulation (~100-120 Hz). Without this comparison it is difficult to know how aDFC may differ from existing therapeutics.
-
-