Rosa Q. So

Effective Deep Brain Stimulation Suppresses Low-Frequency Network Oscillations in the Basal Ganglia by Regularizing Neural Firing Patterns

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for the motor symptoms of Parkinsons disease (PD). The effects of DBS depend strongly on stimulation frequency: high frequencies (>90 Hz) improve motor symptoms, while low frequencies (<50 Hz) are either ineffective or exacerbate symptoms. The neuronal basis for these frequency-dependent effects of DBS is unclear. The effects of different frequencies of STN-DBS on behavior and single-unit neuronal activity in the basal ganglia were studied in the unilateral 6-hydroxydopamine lesioned rat model of PD. Only high-frequency DBS reversed motor symptoms, and the effectiveness of DBS depended strongly on stimulation frequency in a manner reminiscent of its clinical effects in persons with PD. Quantification of single-unit activity in the globus pallidus externa (GPe) and substantia nigra reticulata (SNr) revealed that high-frequency DBS, but not low-frequency DBS, reduced pathological low-frequency oscillations (∼9 Hz) and entrained neurons to fire at the stimulation frequency. Similarly, the coherence between simultaneously recorded pairs of neurons within and across GPe and SNr shifted from the pathological low-frequency band to the stimulation frequency during high-frequency DBS, but not during low-frequency DBS. The changes in firing patterns in basal ganglia neurons were not correlated with changes in firing rate. These results indicate that high-frequency DBS is more effective than low-frequency DBS, not as a result of changes in firing rate, but rather due to its ability to replace pathological low-frequency network oscillations with a regularized pattern of neuronal firing.

Relative contributions of local cell and passing fiber activation and silencing to changes in thalamic fidelity during deep brain stimulation and lesioning: a computational modeling study

Deep brain stimulation (DBS) and lesioning are two surgical techniques used in the treatment of advanced Parkinson’s disease (PD) in patients whose symptoms are not well controlled by drugs, or who experience dyskinesias as a side effect of medications. Although these treatments have been widely practiced, the mechanisms behind DBS and lesioning are still not well understood. The subthalamic nucleus (STN) and globus pallidus pars interna (GPi) are two common targets for both DBS and lesioning. Previous studies have indicated that DBS not only affects local cells within the target, but also passing axons within neighboring regions. Using a computational model of the basal ganglia-thalamic network, we studied the relative contributions of activation and silencing of local cells (LCs) and fibers of passage (FOPs) to changes in the accuracy of information transmission through the thalamus (thalamic fidelity), which is correlated with the effectiveness of DBS. Activation of both LCs and FOPs during STN and GPi-DBS were beneficial to the outcome of stimulation. During STN and GPi lesioning, effects of silencing LCs and FOPs were different between the two types of lesioning. For STN lesioning, silencing GPi FOPs mainly contributed to its effectiveness, while silencing only STN LCs did not improve thalamic fidelity. In contrast, silencing both GPi LCs and GPe FOPs during GPi lesioning contributed to improvements in thalamic fidelity. Thus, two distinct mechanisms produced comparable improvements in thalamic function: driving the output of the basal ganglia to produce tonic inhibition and silencing the output of the basal ganglia to produce tonic disinhibition. These results show the importance of considering effects of activating or silencing fibers passing close to the nucleus when deciding upon a target location for DBS or lesioning.

Optimized temporal pattern of brain stimulation designed by computational evolution

Computational evolution produces an improved pattern of brain stimulation to treat Parkinson’s disease in animals and patients. Building better DBS with evolution In deep brain stimulation (DBS), electrodes inserted deep within a patients’ brain deliver electric pulses that can alleviate symptoms of Parkinson’s disease or tremor or even of diseases such as obsessive-compulsive disorder. Yet, the stimuli are usually standard, with uniform frequencies and amplitudes. To design better stimuli, Brocker et al. built a model of Parkinson’s disease and used computational evolution to find a new temporal stimulation pattern. Application of this evolution-derived stimulation pattern in animal models and Parkinson’s disease patients verified that it was just as effective as the standard stimulation pattern but required significantly less overall energy. Because one drawback of DBS is the need for frequent risky surgeries to change the power source, less drain on the battery means healthier patients. Brain stimulation is a promising therapy for several neurological disorders, including Parkinson’s disease. Stimulation parameters are selected empirically and are limited to the frequency and intensity of stimulation. We varied the temporal pattern of deep brain stimulation to ameliorate symptoms in a parkinsonian animal model and in humans with Parkinson’s disease. We used model-based computational evolution to optimize the stimulation pattern. The optimized pattern produced symptom relief comparable to that from standard high-frequency stimulation (a constant rate of 130 or 185 Hz) and outperformed frequency-matched standard stimulation in a parkinsonian rat model and in patients. Both optimized and standard high-frequency stimulation suppressed abnormal oscillatory activity in the basal ganglia of rats and humans. The results illustrate the utility of model-based computational evolution of temporal patterns to increase the efficiency of brain stimulation in treating Parkinson’s disease and thereby reduce the energy required for successful treatment below that of current brain stimulation paradigms.

Characterizing Effects of Subthalamic Nucleus Deep Brain Stimulation on Methamphetamine-Induced Circling Behavior in Hemi-Parkinsonian Rats

The unilateral 6-hydroxydopamine (6-OHDA) lesioned rat model is frequently used to study the effects of subthalamic nucleus (STN) deep brain stimulation (DBS) for the treatment of Parkinsons disease. However, systematic knowledge of the effects of DBS parameters on behavior in this animal model is lacking. The goal of this study was to characterize the effects of DBS on methamphetamine-induced circling in the unilateral 6-OHDA lesioned rat. DBS parameters tested include stimulation amplitude, stimulation frequency, methamphetamine dose, stimulation polarity, and anatomical location of the electrode. When an appropriate stimulation amplitude and dose of methamphetamine were applied, high-frequency stimulation (>; 130 Hz), but not low frequency stimulation (<; 10 Hz), reversed the bias in ipsilateral circling without inhibiting movement. This characteristic frequency tuning profile was only generated when at least one electrode used during bipolar stimulation was located within the STN. No difference was found between bipolar stimulation and monopolar stimulation when the most effective electrode contact was selected, indicating that monopolar stimulation could be used in future experiments. Methamphetamine-induced circling is a simple, reliable, and sensitive behavioral test and holds potential for high-throughput study of the effects of STN DBS in unilaterally lesioned rats.

Neuromodulation for Epilepsy

Several palliative neuromodulation treatment modalities are currently available for adjunctive use in the treatment of medically intractable epilepsy. Over the past decades, a variety of different central and peripheral nervous system sites have been identified, clinically and experimentally, as potential targets for chronic, nonresponsive therapeutic neurostimulation. Currently, the main modalities in clinical use, from most invasive to least invasive, are anterior thalamus deep brain stimulation, vagus nerve stimulation, and trigeminal nerve stimulation. Significant reductions in seizure frequency have been demonstrated in clinical trials using each of these neuromodulation therapies.

Failure to suppress low-frequency neuronal oscillatory activity underlies the reduced effectiveness of random patterns of deep brain stimulation

Subthalamic nucleus (STN) deep brain stimulation (DBS) is an established treatment for the motor symptoms of Parkinsons disease (PD). However, the mechanisms of action of DBS are unknown. Random temporal patterns of DBS are less effective than regular DBS, but the neuronal basis for this dependence on temporal pattern of stimulation is unclear. Using a rat model of PD, we quantified the changes in behavior and single-unit activity in globus pallidus externa and substantia nigra pars reticulata during high-frequency STN DBS with different degrees of irregularity. Although all stimulus trains had the same average rate, 130-Hz regular DBS more effectively reversed motor symptoms, including circling and akinesia, than 130-Hz irregular DBS. A mixture of excitatory and inhibitory neuronal responses was present during all stimulation patterns, and mean firing rate did not change during DBS. Low-frequency (7-10 Hz) oscillations of single-unit firing times present in hemiparkinsonian rats were suppressed by regular DBS, and neuronal firing patterns were entrained to 130 Hz. Irregular patterns of DBS less effectively suppressed 7- to 10-Hz oscillations and did not regularize firing patterns. Random DBS resulted in a larger proportion of neuron pairs with increased coherence at 7-10 Hz compared with regular 130-Hz DBS, which suggested that long pauses (interpulse interval >50 ms) during random DBS facilitated abnormal low-frequency oscillations in the basal ganglia. These results suggest that the efficacy of high-frequency DBS stems from its ability to regularize patterns of neuronal firing and thereby suppress abnormal oscillatory neural activity within the basal ganglia.

On the asynchronously continuous control of mobile robot movement by motor cortical spiking activity.

This paper presents an asynchronously intracortical brain-computer interface (BCI) which allows the subject to continuously drive a mobile robot. This system has a great implication for disabled patients to move around. By carefully designing a multiclass support vector machine (SVM), the subjects self-paced instantaneous movement intents are continuously decoded to control the mobile robot. In particular, we studied the stability of the neural representation of the movement directions. Experimental results on the nonhuman primate showed that the overt movement directions were stably represented in ensemble of recorded units, and our SVM classifier could successfully decode such movements continuously along the desired movement path. However, the neural representation of the stop state for the self-paced control was not stably represented and could drift.

Independent Mobility Achieved through a Wireless Brain-Machine Interface

Individuals with tetraplegia lack independent mobility, making them highly dependent on others to move from one place to another. Here, we describe how two macaques were able to use a wireless integrated system to control a robotic platform, over which they were sitting, to achieve independent mobility using the neuronal activity in their motor cortices. The activity of populations of single neurons was recorded using multiple electrode arrays implanted in the arm region of primary motor cortex, and decoded to achieve brain control of the platform. We found that free-running brain control of the platform (which was not equipped with any machine intelligence) was fast and accurate, resembling the performance achieved using joystick control. The decoding algorithms can be trained in the absence of joystick movements, as would be required for use by tetraplegic individuals, demonstrating that the non-human primate model is a good pre-clinical model for developing such a cortically-controlled movement prosthetic. Interestingly, we found that the response properties of some neurons differed greatly depending on the mode of control (joystick or brain control), suggesting different roles for these neurons in encoding movement intention and movement execution. These results demonstrate that independent mobility can be achieved without first training on prescribed motor movements, opening the door for the implementation of this technology in persons with tetraplegia.

Prediction and detection of seizures from simultaneous thalamic and scalp electroencephalography recordings

OBJECTIVE The authors explored the feasibility of seizure detection and prediction using signals recorded from the anterior thalamic nucleus, a major target for deep brain stimulation (DBS) in the treatment of epilepsy. METHODS Using data from 5 patients (13 seizures in total), the authors performed a feasibility study and analyzed the performance of a seizure prediction and detection algorithm applied to simultaneously acquired scalp and thalamic electroencephalography (EEG). The thalamic signal was obtained from DBS electrodes. The applied algorithm used the similarity index as a nonlinear measure for seizure identification, with patient-specific channel and threshold selection. Receiver operating characteristic (ROC) curves were calculated using data from all patients and channels to compare the performance between DBS and EEG recordings. RESULTS Thalamic DBS recordings were associated with a mean prediction rate of 84%, detection rate of 97%, and false-alarm rate of 0.79/hr. In comparison, scalp EEG recordings were associated with a mean prediction rate of 71%, detection rate of 100%, and false-alarm rate of 1.01/hr. From the ROC curves, when considering all channels, DBS outperformed EEG for both detection and prediction of seizures. CONCLUSIONS This is the first study to compare automated seizure detection and prediction from simultaneous thalamic and scalp EEG recordings. The authors have demonstrated that signals recorded from DBS leads are more robust than EEG recordings and can be used to predict and detect seizures. These results indicate feasibility for future designs of closed-loop anterior nucleus DBS systems for the treatment of epilepsy.

EC-PC spike detection for high performance brain-computer interface.

Spike detection is often the first step in neural signal processing. It has profound effects on subsequent steps down the signal processing pipeline. Most existing spike detection algorithms require manual setting of detection threshold, which is very inconvenient for long-term neural interface. Furthermore, these algorithms are usually only evaluated using simulated dataset. Few studies are devoted to evaluating how different spike detection algorithms affect decoding performance in brain-computer interface. We have proposed a new spike detection algorithm called “exponential component - power component” (EC-PC) that offers fully automatic unsupervised spike detection. In this study, we compared the performance of a motor decoding task when different spike detection algorithms were used. EC-PC is shown to produce a higher decoding accuracy compared with other existing algorithms. Our results suggest that EC-PC can help improve motor decoding performance of brain-computer interface.