Compensation of Hyperexcitability with Simulation-Based Inference
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Curated by eLife
eLife Assessment
This study introduces a valuable simulation-based inference (SBI) framework to identify degenerate compensatory mechanisms that stabilize network activity despite neuronal hyperexcitability, a feature common to many brain disorders. By estimating posterior distributions of network parameters, the authors highlight factors such as threshold potential and interneuron-to-principal cell connectivity as key compensators for increased intrinsic excitability and interneuron loss. While the approach is promising and could become a key tool for probing network degeneracy, the study is currently incomplete. To fully realize its potential, the framework requires improved scalability and more rigorous cross-validation.
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Abstract
Abstract
The activity of healthy neuronal networks is tightly regulated, and a shift towards hyperexcitability can cause various problems, such as epilepsies, memory deficits, and motor disorders. Numerous cellular, synaptic, and intrinsic mechanisms of hyperexcitability and compensatory mechanisms to restore healthy activity have been proposed. However, quantifying multiple compensatory mechanisms and their dependence on specific pathophysiological mechanisms has proven challenging, even in computational models. We use simulation-based inference to quantify the interactions of compensatory mechanisms in a spiking neuronal network model. Various parameters of the model can compensate for changes in other parameters to maintain baseline activity, and we rank them by their compensatory potential. Furthermore, specific causes of hyperexcitability - interneuron loss, excitatory recurrent synapses, and principal cell depolarization - have distinct compensatory mechanisms that can restore normal excitability. Our results show that spiking neuronal network simulators could provide the quantitative foundation for targeting pathophysiological network mechanisms with precise interventions.
Article activity feed
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eLife Assessment
This study introduces a valuable simulation-based inference (SBI) framework to identify degenerate compensatory mechanisms that stabilize network activity despite neuronal hyperexcitability, a feature common to many brain disorders. By estimating posterior distributions of network parameters, the authors highlight factors such as threshold potential and interneuron-to-principal cell connectivity as key compensators for increased intrinsic excitability and interneuron loss. While the approach is promising and could become a key tool for probing network degeneracy, the study is currently incomplete. To fully realize its potential, the framework requires improved scalability and more rigorous cross-validation.
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Joint Public Review:
Summary:
This manuscript couples a 32-parameter model with simulation-based inference (SBI) to identify parameter changes that can compensate for three canonical hyperexcitability perturbations (interneuron loss, recurrent-excitatory sprouting, and intrinsic depolarisation). The study demonstrates a careful implementation of SBI and offers a practical ranking of "compensatory levers" that could, in principle, guide therapeutic strategies for epilepsy and related network disorders.
Strengths:
(1) By analysing three mechanistically distinct hyper-excitable regimes within the same modelling and inference framework, the work reveals how different perturbations require different compensatory interventions.
(2) The authors adopt posterior estimation to systematically rank the efficiency of different mechanisms in …
Joint Public Review:
Summary:
This manuscript couples a 32-parameter model with simulation-based inference (SBI) to identify parameter changes that can compensate for three canonical hyperexcitability perturbations (interneuron loss, recurrent-excitatory sprouting, and intrinsic depolarisation). The study demonstrates a careful implementation of SBI and offers a practical ranking of "compensatory levers" that could, in principle, guide therapeutic strategies for epilepsy and related network disorders.
Strengths:
(1) By analysing three mechanistically distinct hyper-excitable regimes within the same modelling and inference framework, the work reveals how different perturbations require different compensatory interventions.
(2) The authors adopt posterior estimation to systematically rank the efficiency of different mechanisms in balancing hyperexcitability.
(3) Code and data are available.
Weaknesses:
(1) A highly dense presentation of the simulated models and undefined symbols makes it hard for readers outside the modelling community to follow the biological message. An illustration of the models, accompanied by some explanations and references to the main equations and parameters discussed in this paper, would make the first section much more straightforward.
(2) This methodology appears to be a brute-force approach, requiring millions of simulations to tune 32 parameters in a network of 500-700 cells. It isn't scalable. Moreover, the authors did not use cross-validation, which, with a relatively low increase in computational cost, would provide a quantitative measure as to how well it generalizes; this combination raises doubts about both scalability and reliability.
(3) Several parameters remain so broadly distributed after fitting that the model cannot say with confidence which specific changes matter. Therefore, presenting them as "compensatory levers" is somewhat questionable.
(4) Every conclusion is drawn from simulated data; without testing the predictions on recordings, we have no evidence that the proposed interventions would work in real neural tissue. Because today we cannot diagnose which of the three modelled pathological regimes is actually present in vivo, the paper's recommendations cannot yet be used to guide therapy.
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