Abstract

Closed-loop paradigms provide us with the opportunity to optimize stimulation protocols for perturbation of pathological oscillatory activity in brain-related disorders. In this vein, spiking activity of motor cortex neurons and beta activity of local field potentials in the subthalamic nucleus have both been used independently of each other as neuronal signals to trigger deep brain stimulation for alleviating Parkinsonism. These approaches were superior to the standard continuous high-frequency stimulation protocols used in daily practice. However, they achieved their effects by bursts of stimulation that were applied at high-frequency as well, i.e., independent of the phase information in the stimulated region. In this context, we propose that, by timing stimulation pulses relative to the ongoing oscillation, an alternative approach, namely the targeted perturbation of pathological rhythms, could be obtained. In this modeling study, we first captured the underlying dynamics of neuronal oscillations in the human subthalamic nucleus by phased coupled neuronal oscillators. We then quantified the nature of the interaction between these coupled oscillators by obtaining a physiologically informed phase response curve from local field potentials. Reconstruction of the phase response curve predicted the sensitivity of the phase oscillator to external stimuli, revealing phase intervals that optimally maximized the degree of perturbation. We conclude that our specifically timed intervention based on the coupled oscillator concept will enable us to identify personalized ways of delivering stimulation pulses in closed-loop paradigms triggered by the phase of pathological oscillations. This will pave the way for novel physiological insights and substantial clinical benefits. In addition, this precisely phased modulation may be capable of modifying the effective interactions between oscillators in an entirely new manner.

Highlights

  • Brain neuromodulation by deep brain stimulation (DBS) is a recognized form of treatment for several neurological and neuropsychiatric disorders such as severe Parkinson’s disease (PD) (Schuepbach et al, 2013), dystonia (Vidailhet et al, 2005), and essential tremor (Deuschl et al, 2011)

  • The recording of local field potentials (LFP) revealed that the most upper electrode contact of the quadripolar lead has the highest spectral power in the beta-frequency band (15–30 Hz, Figure 2A), indicating that it is located in the sensorimotor part of the subthalamic nucleus (STN) (Figure 2A) (Holdefer et al, 2010; Yoshida et al, 2010; Zaidel et al, 2010; Novak et al, 2011; Deffains et al, 2014)

  • Using this electrode contact as a reference, the neighboring contact revealed the most prominent phase synchronization when applying the Weighted Phase Lag Index (WPLI). This phase coherence showed a significant synchronization in the frequency range between 23–27 and 17–22 Hz for P1 and P2, respectively (Figure 2B), indicating that there is a pathological increase of functional connectivity in the sensorimotor part of the STN

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Summary

Introduction

Brain neuromodulation by deep brain stimulation (DBS) is a recognized form of treatment for several neurological and neuropsychiatric disorders such as severe Parkinson’s disease (PD) (Schuepbach et al, 2013), dystonia (Vidailhet et al, 2005), and essential tremor (Deuschl et al, 2011). Closed-loop paradigms modulating the stimulation parameters on the basis of online recorded physiological markers provide us with the opportunity to adjust stimulation protocols and improve therapeutic efficacy. In this regard, the first studies in both non-human primates (Rosin et al, 2011) and Parkinsonian patients (Little et al, 2013) addressed the current limitations by applying stimulation in an adaptive manner only when specific physiological markers were detected. Despite being superior to the standard stimulation protocols, these closed-loop approaches achieved their effects by bursts of stimulation applied at high frequency independent of the phase information in the stimulated region (Rosin et al, 2011; Little et al, 2013)

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