The impact of bilateral subthalamic deep brain stimulation on long-latency event-related potentials

Low frequency subthalamic stimulation and event-related potentials in Parkinson disease

BACKGROUND

High frequency (130 Hz) subthalamic Deep-Brain-Stimulation (STN-DBS) optimally improves cardinal motor symptoms in Parkinson disease (PD). Low stimulation frequencies (60-80 Hz) improve axial symptoms in some patients and, according to preliminary evidences, may also have a beneficial effect on the cognitive component of motor planning.

OBJECTIVE

To analyze the configuration of the P300 component of cortical event-related auditory potentials (ERPs), a reliable index of attentive cognitive functions, at different stimulation frequencies in STN-DBS in PD patients.

METHODS

12 PD patients underwent ERPs recordings using a standard oddball auditory paradigm with STN-DBS at 60 Hz, 80 Hz, 130 Hz, and OFF-stimulation, applied in a randomized double-blind sequence. ERPs analysis considered the peak amplitude and latency of the P300 components at midline electrode positions (Fz, Cz, Pz).

RESULTS

P300 latency over Cz and Pz electrodes significantly increased with STN-DBS at 130 Hz compared to OFF-stimulation. P300 latency was also significantly increased, though to a lesser degree, over Pz electrode with stimulation at 80 Hz. No significant P300 latency modifications were detected at 60 Hz stimulation compared to OFF-stimulation condition. P300 amplitude did not change significantly for any of the stimulation conditions tested.

CONCLUSIONS

Low frequency STN-DBS is associated with minor modifications of P300 latency compared to conventional stimulation at 130 Hz, possibly suggesting that 60 and 80 Hz may have less interference with attentive and cognitive processes in PD patients.

Biomarkers and neuromodulation techniques in substance use disorders

Addictive disorders are a severe health concern. Conventional therapies have just moderate success and the probability of relapse after treatment remains high. Brain stimulation techniques, such as transcranial Direct Current Stimulation (tDCS) and Deep Brain Stimulation (DBS), have been shown to be effective in reducing subjectively rated substance craving. However, there are few objective and measurable parameters that reflect neural mechanisms of addictive disorders and relapse. Key electrophysiological features that characterize substance related changes in neural processing are Event-Related Potentials (ERP). These high temporal resolution measurements of brain activity are able to identify neurocognitive correlates of addictive behaviours. Moreover, ERP have shown utility as biomarkers to predict treatment outcome and relapse probability. A future direction for the treatment of addiction might include neural interfaces able to detect addiction-related neurophysiological parameters and deploy neuromodulation adapted to the identified pathological features in a closed-loop fashion. Such systems may go beyond electrical recording and stimulation to employ sensing and neuromodulation in the pharmacological domain as well as advanced signal analysis and machine learning algorithms. In this review, we describe the state-of-the-art in the treatment of addictive disorders with electrical brain stimulation and its effect on addiction-related neurophysiological markers. We discuss advanced signal processing approaches and multi-modal neural interfaces as building blocks in future bioelectronics systems for treatment of addictive disorders.

Removing deep brain stimulation artifacts from the electroencephalogram: Issues, recommendations and an open-source toolbox

A major question for deep brain stimulation (DBS) research is understanding how DBS of one target area modulates activity in different parts of the brain. EEG gives privileged access to brain dynamics, but its use with implanted patients is limited since DBS adds significant high-amplitude electrical artifacts that can completely obscure neural activity measured using EEG. Here, we systematically review and discuss the methods available for removing DBS artifacts. These include simple techniques such as oversampling, antialiasing analog filtering and digital low-pass filtering, which are necessary but typically not sufficient to fully remove DBS artifacts when each is used in isolation. We also cover more advanced methods, including techniques tracking outliers in the frequency-domain, which can be effective, but are rarely used. The reason for that is twofold: First, it requires advanced skills in signal processing since no user friendly tool for removing DBS artifacts is currently available. Second, it involves fine-tuning to avoid over-aggressive filtering. We highlight an open-source toolbox incorporating most artifact removal methods, allowing users to combine different strategies.

Electroencephalographic read-outs of the modulation of cortical network activity by deep brain stimulation

Deep brain stimulation (DBS), a reversible and adjustable treatment for neurological and psychiatric refractory disorders, consists in delivering electrical currents to neuronal populations located in subcortical structures. The targets of DBS are spatially restricted, but connect to many parts of the brain, including the cortex, which might explain the observed clinical benefits in terms of symptomatology. The DBS mechanisms of action at a large scale are however poorly understood, which has motivated several groups to recently conduct many research programs to monitor cortical responses to DBS. Here we review the knowledge gathered from the use of electroencephalography (EEG) in patients treated by DBS. We first focus on the methodology to record and process EEG signals concurrently to DBS. In the second part of the review, we address the clinical and scientific benefits brought by EEG/DBS studies so far.

Network effects of deep brain stimulation

The ability to differentially alter specific brain functions via deep brain stimulation (DBS) represents a monumental advance in clinical neuroscience, as well as within medicine as a whole. Despite the efficacy of DBS in the treatment of movement disorders, for which it is often the gold-standard therapy when medical management becomes inadequate, the mechanisms through which DBS in various brain targets produces therapeutic effects is still not well understood. This limited knowledge is a barrier to improving efficacy and reducing side effects in clinical brain stimulation. A field of study related to assessing the network effects of DBS is gradually emerging that promises to reveal aspects of the underlying pathophysiology of various brain disorders and their response to DBS that will be critical to advancing the field. This review summarizes the nascent literature related to network effects of DBS measured by cerebral blood flow and metabolic imaging, functional imaging, and electrophysiology (scalp and intracranial electroencephalography and magnetoencephalography) in order to establish a framework for future studies.