Dynamic Neural State Identification in Deep Brain Local Field Potentials of Neuropathic Pain.

Huichun Luo,Shouyan Wang,Yongzhi Huang,Tipu Z Aziz,Yunpeng Zhang,Xueying Du,Alexander L Green

doi:10.3389/fnins.2018.00237

Abstract

In neuropathic pain, the neurophysiological and neuropathological function of the ventro-posterolateral nucleus of the thalamus (VPL) and the periventricular gray/periaqueductal gray area (PVAG) involves multiple frequency oscillations. Moreover, oscillations related to pain perception and modulation change dynamically over time. Fluctuations in these neural oscillations reflect the dynamic neural states of the nucleus. In this study, an approach to classifying the synchronization level was developed to dynamically identify the neural states. An oscillation extraction model based on windowed wavelet packet transform was designed to characterize the activity level of oscillations. The wavelet packet coefficients sparsely represented the activity level of theta and alpha oscillations in local field potentials (LFPs). Then, a state discrimination model was designed to calculate an adaptive threshold to determine the activity level of oscillations. Finally, the neural state was represented by the activity levels of both theta and alpha oscillations. The relationship between neural states and pain relief was further evaluated. The performance of the state identification approach achieved sensitivity and specificity beyond 80% in simulation signals. Neural states of the PVAG and VPL were dynamically identified from LFPs of neuropathic pain patients. The occurrence of neural states based on theta and alpha oscillations were correlated to the degree of pain relief by deep brain stimulation. In the PVAG LFPs, the occurrence of the state with high activity levels of theta oscillations independent of alpha and the state with low-level alpha and high-level theta oscillations were significantly correlated with pain relief by deep brain stimulation. This study provides a reliable approach to identifying the dynamic neural states in LFPs with a low signal-to-noise ratio by using sparse representation based on wavelet packet transform. Furthermore, it may advance closed-loop deep brain stimulation based on neural states integrating multiple neural oscillations.

Highlights

Deep brain local field potentials (LFPs) contain rich information regarding the function of subcortical nuclei in humans (Friston et al, 2015)
The state identification approach was developed to dynamically identify the neural state of the ventroposterolateral nucleus of the thalamus (VPL) and the periventricular gray/periaqueductal gray (PVAG) corresponding to neuropathic pain by dynamically discriminating the synchronization state of theta and alpha oscillations
In the VPL and the PVAG nuclei, the theta, alpha, beta, and gamma neural oscillations of LFPs are associated with neuropathic pain

Summary

Introduction

Deep brain local field potentials (LFPs) contain rich information regarding the function of subcortical nuclei in humans (Friston et al, 2015). LFPs exhibit oscillatory behaviors in different frequency bands Such neural oscillations are simultaneously involved in the neurophysiological and neuropathological functions of nuclei in conditions such as neuropathic pain (Ploner et al, 2017), Parkinson’s disease (Hammond et al, 2007; Oswal et al, 2013; Brittain and Brown, 2014), and dystonia (Neumann et al, 2012; Whitmer et al, 2013). The periventricular gray/periaqueductal gray (PVAG) LFPs exhibit increased power of 8–12 Hz oscillations when pain intensity increases (Green et al, 2009) The activities of both 6–9 and 10– 12 Hz oscillations are significantly related to the DBS treatment effect for neuropathic pain (Huang et al, 2016b). Subsystems may operate at different frequencies and can form local oscillatory networks in given nuclei (Priori et al, 2004)

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Conclusion