Cortical network effects of subthalamic deep brain stimulation in a thalamo-cortical microcircuit model

Farokhniaee, AmirAli; Lowery, Madeleine M.

doi:10.1088/1741-2552/abee50

Cited by 18 publications

(27 citation statements)

References 54 publications

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“…In terms of the NMSE of 24 varying DBS frequencies {2. 5,5,7.5,10,15,20,30,40,50,60,70,80,90,100,110,120,130,140,150,160,170,180,190,and 200Hz}, the alternative approach is significantly larger than our rate model (ANOVA, p = 8.99 × 10 -9 ). Thus, the alternative approach is not sufficient for reproducing firing rate dynamics; our rate model is the more sophisticated approach, and is efficient in detecting the firing rate mechanism consistent across various DBS frequencies.…”

Section: Supplementary Methods B -Alternative Approach To Model Insta...mentioning

confidence: 53%

“…With our rate model, we have accurately reproduced the firing rate dynamics obtained from both synthetic and experimental data. Compared with the spiking network models 44,20 , the firing rate model is much faster and more robust in implementations, because of the fewer number of equations and lower dimensionality in model parameters 45 . We also developed a concatenated-frequencies optimization method that systematically computes the optimal model parameters that are fitted across different DBS frequencies.…”

Section: Discussionmentioning

confidence: 99%

“…We implemented the simplex method in "fminsearch" 32 to find the optimal parameter set 𝛷 𝑜𝑝𝑡 = {𝜏 𝑜𝑝𝑡 , 𝑟 𝑏,𝑜𝑝𝑡 , 𝑐 𝑜𝑝𝑡 , 𝑠 𝑜𝑝𝑡 , 𝑘 𝑜𝑝𝑡 } that minimized the objective function 𝐽(𝛷) defined in (8). "fminsearch" is an automatic parameter optimization method, which is more accurate and efficient than the commonly used hand-tuning methods used for DBS models 1,4,24 .…”

Section: The Rate Model and The Parameter Optimization Methodsmentioning

confidence: 99%

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Modelling Instantaneous Firing Rate of DBS Target Neuronal Ensembles in Basal Ganglia and Thalamus

Tian

Murphy

Steiner

et al. 2022

Preprint

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Parkinson's disease (PD) is the second most common neurodegenerative disorder symptom, and is closely related to the dysfunction of the basal ganglia-thalamocortical network. Deep Brain Stimulation (DBS) is an effective treatment for suppressing PD motor symptoms; however, the underlying mechanisms of DBS remain elusive. A recent study demonstrated that different substructures of the basal ganglia and thalamus respond differently to various frequencies of DBS. Despite the capability of existing models in qualitatively interpreting experimental data, there are very few unified computational models that quantitatively capture the dynamics of the neuronal activity of varying stimulated substructures - including subthalamic nucleus (STN), substantia nigra pars reticulate (SNr) and ventralis intermedius (Vim) - across different DBS frequencies. Here, we developed a novel mathematical model to represent the firing rate of nuclei receiving DBS (microstimulation), including STN, SNr and Vim - across different DBS frequencies. In our model, the DBS pulses are filtered through a synapse model and a transfer function to formulate the firing rate variability. To consistently fit the model in varying frequencies of DBS, we developed a novel parameter optimization method based on the concatenated data from all DBS frequencies. Our model accurately reproduces the firing rates observed and calculated from both experimental and synthetic data. The optimal model parameters are consistent across different DBS frequencies, and this consistency conforms to the relatively static synaptic structures in short durations of DBS. Our model can be potentially implemented in navigating the DBS parameter space and improves DBS clinical effects.

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Section: Supplementary Methods B -Alternative Approach To Model Insta...mentioning

confidence: 53%

Section: Discussionmentioning

confidence: 99%

Section: The Rate Model and The Parameter Optimization Methodsmentioning

confidence: 99%

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Modelling Instantaneous Firing Rate of DBS Target Neuronal Ensembles in Basal Ganglia and Thalamus

Tian

Murphy

Steiner

et al. 2022

Preprint

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show abstract

“…In fact, one of the key areas in which computational models serve as an invaluable tool for developing novel therapies is that related to predicting the effects of DBS [35], [49]. Other computational models of the BG-T-C network and its relashionship with PD are presented by Farokhniaee and Lowery [21], and Fleming et al [22].…”

Section: Related Workmentioning

confidence: 99%

A Data-Driven Biophysical Computational Model of Parkinson’s Disease Based on Marmoset Monkeys

Ranieri¹,

Pimentel

Romano

et al. 2021

IEEE Access

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In this work we propose a new biophysical computational model of brain regions relevant to Parkinson's Disease (PD) based on local field potential data collected from the brain of marmoset monkeys. PD is a neurodegenerative disorder, linked to the death of dopaminergic neurons at the substantia nigra pars compacta, which affects the normal dynamics of the basal ganglia-thalamus-cortex (BG-T-C) neuronal circuit of the brain. Although there are multiple mechanisms underlying the disease, a complete description of those mechanisms and molecular pathogenesis are still missing, and there is still no cure. To address this gap, computational models that resemble neurobiological aspects found in animal models have been proposed. In our model, we performed a data-driven approach in which a set of biologically constrained parameters is optimised using differential evolution. Evolved models successfully resembled spectral signatures of local field potentials and single-neuron mean firing rates from healthy and parkinsonian marmoset brain data. This is the first computational model of PD based on simultaneous electrophysiological recordings from seven brain regions of Marmoset monkeys. Results indicate that the proposed model may facilitate the investigation of the mechanisms of PD and eventually support the development of new therapies. The DE method could also be applied to other computational neuroscience problems in which biological data is used to fit multi-scale models of brain circuits.

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“…There recently has also been the development of larger-scale cortical models which contain tens of thousands of spiking neuron models (Esser, Hill, and Tononi, 2005; Potjans and Diesmann, 2014). Previous models of the motor cortex have been limited in replicating physiological detail and mainly been focused on generating movement dynamics or responses to stimulation (Farokhniaee and Lowery, 2021; Moody and Zipser, 1998; Esser, Hill, and Tononi, 2005; Manola et al, 2007). To the authors’ knowledge, the role of network connectivity in generation of the spontaneous cortical activity in physiologically-based spiking neural network models, however, has not been explored.…”

Section: Introductionmentioning

confidence: 99%

Modelling Spontaneous Firing Activity of the Motor Cortex in a Spiking Neural Network with Random and Local Connectivity

Haggie

Besier

McMorland

2022

Preprint

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Computational models of cortical activity can provide insight into the mechanisms of higher-order processing in the human brain including planning, perception and the control of movement. Activity in the cortex is ongoing even in the absence of sensory input or discernable movements and is thought to be linked to the topology of the underlying cortical circuitry (Ringach2009). However, the connectivity and its functional role in the generation of spatio-temporal firing patterns and cortical computations are still vastly unknown. Movement of the body is a key function of the brain, with the motor cortex the main cortical area implicated in the generation of movement. We built a spiking neural network model of the motor cortex which incorporates a laminar structure and circuitry based on a previous cortical model by (Potjans2014). A local connectivity scheme was implemented to introduce more physiological plausbility to the cortex model, and the effect on the rates, distributions and irregularity of neuronal firing, was compared to the original random connectivity method and experimental data. Local connectivity increased the distribution of and overall rate of neuronal firing. It also resulted in the irregularity of firing being more similar to those observed in experimental measurements, and a reduction in the variability in power spectrum measures. The larger variability in dynamical behaviour of the local connectivity model suggests that the topological structure of the connections in neuronal population plays a significant role in firing patterns during spontaneous activity. This model aims to take steps towards replicating the macroscopic network of the motor cortex, replicating realistic firing in order to shed light on information coding in the cortex. Large scale computational models such as this one can capture how structure and function relate to observable neuronal firing behaviour, and investigates the underlying computational mechanisms of the brain.

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Cortical network effects of subthalamic deep brain stimulation in a thalamo-cortical microcircuit model

Cited by 18 publications

References 54 publications

Modelling Instantaneous Firing Rate of DBS Target Neuronal Ensembles in Basal Ganglia and Thalamus

Modelling Instantaneous Firing Rate of DBS Target Neuronal Ensembles in Basal Ganglia and Thalamus

A Data-Driven Biophysical Computational Model of Parkinson’s Disease Based on Marmoset Monkeys

Modelling Spontaneous Firing Activity of the Motor Cortex in a Spiking Neural Network with Random and Local Connectivity

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