Efficient implementation of a real-time estimation system for thalamocortical hidden Parkinsonian properties

Yang, Shuangming; Deng, Bin; Wang, Jiang; Li, Yong; Liu, Chen; Fietkiewicz, Chris; Loparo, Kenneth A.

doi:10.1038/srep40152

Cited by 26 publications

(4 citation statements)

References 53 publications

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“…The possibility of determining parameters of cerebral dynamics in a non-invasive manner would allow us to study, for instance, the origins of the variability in EEG recordings. It would also enable exploring automatic biometric-based user recognition systems [63] and, through single-patient characterization, tracking the changes in brain dynamics due to aging [64,65], and monitoring the evolution of diseases [66]. The possibility of tracking the evolution of brain states during motor imagery-control [67] or task-switching control [68] is also open.…”

Section: Discussionmentioning

confidence: 99%

Extracranial Estimation of Neural Mass Model Parameters Using the Unscented Kalman Filter

Escuain-Poole

García-Ojalvo

Pons

2018

Front. Appl. Math. Stat.

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Data assimilation, defined as the fusion of data with preexisting knowledge, is particularly suited to elucidating underlying phenomena from noisy/insufficient observations. Although this approach has been widely used in diverse fields, only recently have efforts been directed to problems in neuroscience, using mainly intracranial data and thus limiting its applicability to invasive measurements involving electrode implants. Here we intend to apply data assimilation to non-invasive electroencephalography (EEG) measurements to infer brain states and their characteristics. For this purpose, we use Kalman filtering to combine synthetic EEG data with a coupled neural-mass model together with Ary's model of the head, which projects intracranial signals onto the scalp. Our results show that using several extracranial electrodes allows to successfully estimate the state and parameters of the neural masses and their interactions, whereas one single electrode provides only a very partial and insufficient view of the system. The superiority of using multiple extracranial electrodes over using only one, be it intra-or extracranial, is shown over a wide variety of dynamical behaviours. Our results show potential towards future clinical applications of the method. Author SummaryTo completely understand brain function, we will need to integrate experimental information into a consistent theoretical framework. Invasive techniques as EcoG recordings, together with models that describe the brain at the mesoscale, provide valuable information about the brain state and its dynamical evolution when combined with techniques coming from control theory, such as the Kalman filter. This method, which is specifically designed to deal with systems with noisy or imperfect data, combines experimental data with theoretical models assuming Bayesian inference. So far, implementations of the Kalman filter have not been suited for non-invasive measures like EEG. Here we attempt to overcome this situation by introducing a model of the head that allows to transfer the intracranial signals produced by a mesoscopic model to the scalp in the form of EEG recordings. Our results show the advantages of using multichannel EEG recordings, which are extended in space and allow to discriminate signals produced by the interaction of coupled columns. The extension of the Kalman method presented here can be expected to expand the applicability of the technique to all situations where EEG 1/23 arXiv:1708.05282v1 [q-bio.NC] 1 Aug 2017 recordings are used, including the routine monitoring of illnesses or rehabilitation tasks, brain-computer interface protocols, and transcranial stimulation.

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Section: Discussionmentioning

confidence: 99%

Extracranial Estimation of Neural Mass Model Parameters Using the Unscented Kalman Filter

Escuain-Poole

García-Ojalvo

Pons

2018

Front. Appl. Math. Stat.

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“…Generally speaking, computational models can verify the proposed hypotheses, reflect individual differences through the internal structure and parameters of the model, and link the basic neural circuits with brain functions, thereby providing new tools for understanding neurological diseases. Since the specific characteristics of the model depend on the neuron, it is necessary to fit the computational model to the electrophysiological record 4 .…”

Section: Introductionmentioning

confidence: 99%

“…Hertäg used standard f-I curves obtained by in vitro electrophysiologists to fit the adaptive integrate-and-fire neuron model and the adaptive exponential integrate-and-fire model 9 . Ghosh et al implemented an adaptive exponential integrate-and-fire model on the NEURON simulation platform, estimated its parameters to describe the reference neuron, and verified the initiation dynamics of the model 4 . Pozzorini proposed a protocol combined with automatic patch clamp recording technology, which can efficiently transform a large amount of in vitro electrophysiological data into a spiking neuron model 10 .…”

Section: Introductionmentioning

confidence: 99%

Fitting of TC model according to key parameters affecting Parkinson's state based on improved particle swarm optimization algorithm

Yuan

2022

Sci Rep

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Biophysical models contain a large number of parameters, while the spiking characteristics of neurons are related to a few key parameters. For thalamic neurons, relay reliability is an important characteristic that affects Parkinson's state. This paper proposes a method to fit key parameters of the model based on the spiking characteristics of neurons, and improves the traditional particle swarm optimization algorithm. That is, a nonlinear concave function and a Logistic chaotic mapping are combined to adjust the inertia weight of particles to avoid the particle falling into a local optimum in the search process or appearing premature convergence. In this paper, three parameters that play an important role in Parkinson's state of the thalamic cell model are selected and fitted by the improved particle swarm optimization algorithm. Using the fitted parameters to reconstruct the neuron model can predict the spiking trajectories well, which verifies the effectiveness of the fitting method. By comparing the fitting results with other particle swarm optimization algorithms, it is shown that the proposed particle swarm optimization algorithm can better avoid local optima and converge to the optimal values quickly.

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“…The emulation of entire brain regions via NCS is of paramount importance to identify which parameters play a role in the pathogenesis of neurological disorders. In this regard, the mechanisms of emergence of the Parkinsonian state were explored by building a model of both the basal ganglia [5] and the thalamus [6] on a field programmable gate array (FPGA) core. In these two FPGA models, the design of the specialpurpose applications started from the theoretical study of the biological structural organization at the network level [5].…”

Section: Introductionmentioning

confidence: 99%

A CMOS-based neuromorphic device for seizure detection from LFP signals

Ronchini¹,

Zamani²,

Huynh³

et al. 2021

J. Phys. D: Appl. Phys.

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Recent research provides examples of neuromorphic systems applied to process biological signals or to interface biological tissues. Usually, in such contexts, the neuromorphic system is used for automatic anomaly detection. The automation of the long-term monitoring of biological signals holds promise for lightening the burden placed on clinicians. At the same time, the adoption of such devices potentially allows processing to be performed locally, without the need to transfer data to an external processor. In turn, on-site signal analysis makes closed-loop intervention feasible, to correct the source of the anomalies. So far, the common approach has been to deploy the network of spiking neurons either on multi-core neuromorphic platforms or on programmable units (field programmable gate array). However, if the aim is to develop wearable or even chronically implantable devices, it is imperative to move in the direction of embedded solutions, tailored and optimized for the specific application. To this end, the present study proposes a neuromorphic device implemented in CMOS technology for the detection of epileptic seizures (ictal events) from local field potential (LFP) signals. The LFP data have been acquired by a multi-electrode array in a slice of mouse hippocampus-cortex. The system includes an analog-to-event converter (AEC) encoding the recorded signals into trains of spikes, and a small spiking neural network (SNN) of 2 × 1 neurons with online biological-plausible learning. The AEC yields two spike-trains: UP spikes that account for the positive slope signal and DOWN spikes that correspond to the negative slope signal. The synapses among the input and output layers are plastic and follow the spike-timing-dependent plasticity rule. Early results show that the SNN module is able to detect ictal events with a delay of 64.98 ± 30.92 ms, consuming < 50 pW. The layout of the entire system occupies 528 µm × 278 µm.

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Efficient implementation of a real-time estimation system for thalamocortical hidden Parkinsonian properties

Cited by 26 publications

References 53 publications

Extracranial Estimation of Neural Mass Model Parameters Using the Unscented Kalman Filter

Extracranial Estimation of Neural Mass Model Parameters Using the Unscented Kalman Filter

Fitting of TC model according to key parameters affecting Parkinson's state based on improved particle swarm optimization algorithm

A CMOS-based neuromorphic device for seizure detection from LFP signals

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