The role of coupling connections in a model of the cortico-basal ganglia-thalamocortical neural loop for the generation of beta oscillations.

Excessive neural synchronization in the cortico-basal ganglia-thalamocortical circuits in the beta (ß) frequency range (12-35 Hz) is closely associated with dopamine depletion in Parkinson's disease (PD) and correlated with movement impairments, but the neural basis remains unclear. In this work, we establish a double-oscillator neural mass model for the cortico-basal ganglia-thalamocortical closed-loop system and explore the impacts of dopamine depletion induced changes in coupling connections within or between the two oscillators on neural activities within the loop. Spectral analysis of the neural mass activities revealed that the power and frequency of their principal components are greatly dependent on the coupling strengths between nuclei. We found that the increased intra-coupling in the basal ganglia-thalamic (BG-Th) oscillator contributes to increased oscillations in the lower ß frequency band (12-25 Hz), while increased intra-coupling in the cortical oscillator mainly contributes to increased oscillations in the upper ß frequency band (26-35 Hz). Interestingly, pathological upper ß oscillations in the cortical oscillator may be another origin of the lower ß oscillations in the BG-Th oscillator, in addition to increased intra-coupling strength within the BG-Th network. Lower ß oscillations in the BG-Th oscillator can also change the dominant oscillation frequency of a cortical nucleus from the upper to the lower ß band. Thus, this work may pave the way towards revealing a possible neural basis underlying the Parkinsonian state.

Application of Reinforcement Learning to Deep Brain Stimulation in a Computational Model of Parkinson's Disease.

Deep brain stimulation (DBS) has been proven to be an effective treatment to deal with the symptoms of Parkinson's disease (PD). Currently, the DBS is in an open-loop pattern with which the stimulation parameters remain constant regardless of fluctuations in the disease state, and adjustments of parameters rely mostly on trial and error of experienced clinicians. This could bring adverse effects to patients due to possible overstimulation. Thus closed-loop DBS of which stimulation parameters are automatically adjusted based on variations in the ongoing neurophysiological signals is desired. In this paper, we present a closed-loop DBS method based on reinforcement learning (RL) to regulate stimulation parameters based on a computational model. The network model consists of interconnected biophysically-based spiking neurons, and the PD state is described as distorted relay reliability of thalamus (TH). Results show that the RL-based closed-loop control strategy can effectively restore the distorted relay reliability of the TH but with less DBS energy expenditure.

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

Real-time estimation of dynamical characteristics of thalamocortical cells, such as dynamics of ion channels and membrane potentials, is useful and essential in the study of the thalamus in Parkinsonian state. However, measuring the dynamical properties of ion channels is extremely challenging experimentally and even impossible in clinical applications. This paper presents and evaluates a real-time estimation system for thalamocortical hidden properties. For the sake of efficiency, we use a field programmable gate array for strictly hardware-based computation and algorithm optimization. In the proposed system, the FPGA-based unscented Kalman filter is implemented into a conductance-based TC neuron model. Since the complexity of TC neuron model restrains its hardware implementation in parallel structure, a cost efficient model is proposed to reduce the resource cost while retaining the relevant ionic dynamics. Experimental results demonstrate the real-time capability to estimate thalamocortical hidden properties with high precision under both normal and Parkinsonian states. While it is applied to estimate the hidden properties of the thalamus and explore the mechanism of the Parkinsonian state, the proposed method can be useful in the dynamic clamp technique of the electrophysiological experiments, the neural control engineering and brain-machine interface studies.

Closed-Loop Control of Tremor-Predominant Parkinsonian State Based on Parameter Estimation.

A significant feature of Parkinson's disease (PD) is the inability of the thalamus to respond faithfully to sensorimotor information from the cerebral cortex. This may be the result of abnormal oscillations in the basal ganglia (BG). Deep brain stimulation (DBS) is regarded as an effective method to modulate these pathological brain rhythmic activities. However, the selection of DBS parameters is challenging because the mechanism is not well understood. This work proposes the design of a closed-loop control strategy to automatically adjust the parameters of a DBS waveform based on a computational model. By estimating the synaptic input from BG to the thalamic neuron model as feedback variable, we designed and compared various control algorithms to counteract the effects of pathological oscillatory inputs. We then obtained optimal DBS parameters to modulate the tremor-predominant Parkinsonian state. We showed that even a simple proportional controller provides higher fidelity of thalamic relay of sensorimotor information and lower energy expenditure, as compared with classical open-loop DBS. Integral action further enhances DBS performance. Additionally, a positive bias voltage further improves the relay ability of the thalamus with decreased stimulation energy expenditure. These findings were conducive to the development of a more effective DBS to further improve the treatment of the PD.

Integration of Principles of Systems Biology and Radiation Biology: Toward Development of in silico Models to Optimize IUdR-Mediated Radiosensitization of DNA Mismatch Repair Deficient (Damage Tolerant) Human Cancers.

Over the last 7 years, we have focused our experimental and computational research efforts on improving our understanding of the biochemical, molecular, and cellular processing of iododeoxyuridine (IUdR) and ionizing radiation (IR) induced DNA base damage by DNA mismatch repair (MMR). These coordinated research efforts, sponsored by the National Cancer Institute Integrative Cancer Biology Program (ICBP), brought together system scientists with expertise in engineering, mathematics, and complex systems theory and translational cancer researchers with expertise in radiation biology. Our overall goal was to begin to develop computational models of IUdR- and/or IR-induced base damage processing by MMR that may provide new clinical strategies to optimize IUdR-mediated radiosensitization in MMR deficient (MMR(-)) "damage tolerant" human cancers. Using multiple scales of experimental testing, ranging from purified protein systems to in vitro (cellular) and to in vivo (human tumor xenografts in athymic mice) models, we have begun to integrate and interpolate these experimental data with hybrid stochastic biochemical models of MMR damage processing and probabilistic cell cycle regulation models through a systems biology approach. In this article, we highlight the results and current status of our integration of radiation biology approaches and computational modeling to enhance IUdR-mediated radiosensitization in MMR(-) damage tolerant cancers.