Insights from a model based study on optimizing non invasive brain electrical stimulation for Parkinson's disease.

Parkinson's Disease (PD) is a disorder in the central nervous system which includes symptoms such as tremor, rigidity, and Bradykinesia. Deep brain stimulation (DBS) is the most effective method to treat PD motor symptoms especially when the patient is not responsive to other treatments. However, its invasiveness and high risk, involving electrode implantation in the Basal Ganglia (BG), prompt recent research to emphasize non-invasive Transcranial Electrical Stimulation (TES). TES proves to be effective in treating some PD symptoms with inherent safety and no associated risks. This study explores the potential of using TES, to modify the firing pattern of cells in BG that are responsible for motor symptoms in PD. The research employs a mathematical model of the BG to examine the impact of applying TES to the brain. This is conducted using a realistic head model incorporating the Finite Element Method (FEM). According to our findings, the firing pattern associated with Parkinson's disease shifted towards a healthier firing pattern through the use of tACS. Employing an adaptive algorithm that continually monitored the behavior of BG cells (specifically, Globus Pallidus Pars externa (GPe)), we determined the optimal electrode number and placement to concentrate the current within the intended region. This resulted in a peak induced electric field of 1.9 v/m at the BG area. Our mathematical modeling together with precise finite element simulation of the brain and BG suggests that proposed method effectively mitigates Parkinsonian behavior in the BG cells. Furthermore, this approach ensures an improvement in the condition while adhering to all safety constraints associated with the current injection into the brain.

A data-driven subspace distributed fault detection strategy for linear heterogeneous multi-agent systems.

This paper presents a new data-driven subspace distributed fault detection strategy specifically designed for linear heterogeneous multi-agent systems (MASs). The proposed approach leverages the characteristics of heterogeneous MASs, where agents exhibit diverse dynamics and parameters. By utilizing subspace construction techniques, the proposed method captures the normal behavior of each agent and enables the detection of deviations that indicate the presence of faults. Unlike existing methods, the approach is completely data-driven and eliminating the need for centralized information or communication among the agents. Simulation results demonstrate the effectiveness and efficiency of the proposed approach in detecting simultaneous faults in different agents. Overall, the proposed approach represents a significant departure from existing methods and offers a powerful new tool for fault detection in heterogeneous multi-agent systems.

Subspace-based predictive control of Parkinson's disease: A model-based study.

Deep brain stimulation (DBS) of the Basal Ganglia (BG) is an effective treatment to suppress the symptoms of Parkinson's disease (PD). Using a closed-loop scheme in DBS can not only improve its therapeutic effects but it can also reduce its energy consumption and possible side effects. In this paper, a predictive closed loop control strategy is employed to suppress the PD in real-time. A linear multi-input multi-output (MIMO) state-delayed system is considered as a simplified model of the BG neuronal network relating the stimulation signals as inputs to the beta power of local field potentials as PD biomarkers. The effect of time delay in different areas of the BG is incorporated into this model and a real-time subspace-based identification is implemented to continuously model the state of the BG neuronal network and drive the predictive control strategy. Simulation results show that the proposed MIMO subspace based predictive controller can suppress PD symptoms more effectively and with less power consumption compared to the conventional open-loop DBS and a recently proposed single-input single-output closed loop controller.

Reconstruction of the input signal of the leaky integrate-and-fire neuronal model from its interspike intervals.

Extracting the input signal of a neuron by analyzing its spike output is an important step toward understanding how external information is coded into discrete events of action potentials and how this information is exchanged between different neurons in the nervous system. Most of the existing methods analyze this decoding problem in a stochastic framework and use probabilistic metrics such as maximum-likelihood method to determine the parameters of the input signal assuming a leaky and integrate-and-fire (LIF) model. In this article, the input signal of the LIF model is considered as a combination of orthogonal basis functions. The coefficients of the basis functions are found by minimizing the norm of the observed spikes and those generated by the estimated signal. This approach gives rise to the deterministic reconstruction of the input signal and results in a simple matrix identity through which the coefficients of the basis functions and therefore the neuronal stimulus can be identified. The inherent noise of the neuron is considered as an additional factor in the membrane potential and is treated as the disturbance in the reconstruction algorithm. The performance of the proposed scheme is evaluated by numerical simulations, and it is shown that input signals with different characteristics can be well recovered by this algorithm.

Velocity Selective Neural Signal Recording Using a Space-Time Electrode Array.

Extracting the activity of a particular neural fiber from the extracellular recording of a peripheral nerve is quite important from different clinical perspectives. While traditional neural recording methods are unable to provide such granularity, new signal recording and processing techniques have offered promising solutions recently. A multi-electrode cuff in conjunction with a delay and sum beamforming structure has been used to detect the activity of different fibers in a nerve based on the propagation velocity of action potentials. However, as it is shown in this paper, simple delay and sum beamforming method encounters severe selectivity problem and signal distortion especially at higher velocities. In addition to scrutinizing the performance of the delay and sum beamformer and its inherent problems, here we propose a new beamforming method, based on broadband sensor array signal processing techniques, which exhibits much better selectivity with uniform frequency-velocity response. Our simulation results show how the new method can faithfully extract individual neural fiber activities and explore potential applications which could emerge from using such technique.