Yongzhi Huang

Characteristics of local field potentials correlate with pain relief by deep brain stimulation

OBJECTIVE To investigate the link between neuronal activity recorded from the sensory thalamus and periventricular gray/periaqueductal gray (PVAG) and pain relief by deep brain stimulation (DBS). METHODS Local field potentials (LFPs) were recorded from the sensory thalamus and PVAG post-operatively from ten patients with neuropathic pain. The LFPs were quantified using spectral and time-frequency analysis, the relationship between the LFPs and pain relief was quantified with nonlinear correlation analysis. RESULTS The theta oscillations of both sensory thalamus and PVAG correlated inversely with pain relief. The high beta oscillations in the sensory thalamus and the alpha oscillations in the PVAG correlated positively with pain relief. Moreover, the ratio of high-power duration to low-power duration of theta band activity in the sensory thalamus and PVAG correlated inversely with pain relief. The duration ratio at the high beta band in the sensory thalamus correlated positively with pain relief. CONCLUSIONS Our results reveal distinct neuronal oscillations at the theta, alpha, and beta frequencies correlating with pain relief by DBS. SIGNIFICANCE The study provides quantitative measures for predicting the outcomes of neuropathic pain relief by DBS as well as potential biomarkers for developing adaptive stimulation strategies.

Measuring complex behaviors of local oscillatory networks in deep brain local field potentials.

BACKGROUND Multiple oscillations emerging from the same neuronal substrate serve to construct a local oscillatory network. The network usually exhibits complex behaviors of rhythmic, balancing and coupling between the oscillations, and the quantification of these behaviors would provide valuable insight into organization of the local network related to brain states. NEW METHOD An integrated approach to quantify rhythmic, balancing and coupling neural behaviors based upon power spectral analysis, power ratio analysis and cross-frequency power coupling analysis was presented. Deep brain local field potentials (LFPs) were recorded from the thalamus of patients with neuropathic pain and dystonic tremor. t-Test was applied to assess the difference between the two patient groups. RESULTS The rhythmic behavior measured by power spectral analysis showed significant power spectrum difference in the high beta band between the two patient groups. The balancing behavior measured by power ratio analysis showed significant power ratio differences at high beta band to 8-20 Hz, and 30-40 Hz to high beta band between the patient groups. The coupling behavior measured by cross-frequency power coupling analysis showed power coupling differences at (theta band, high beta band) and (45-55 Hz, 70-80 Hz) between the patient groups. COMPARISON WITH EXISTING METHOD The study provides a strategy for studying the brain states in a multi-dimensional behavior space and a framework to screen quantitative characteristics for biomarkers related to diseases or nuclei. CONCLUSIONS The work provides a comprehensive approach for understanding the complex behaviors of deep brain LFPs and identifying quantitative biomarkers for brain states related to diseases or nuclei.

Oscillatory neural representations in the sensory thalamus predict neuropathic pain relief by deep brain stimulation

OBJECTIVE Understanding the function of sensory thalamic neural activity is essential for developing and improving interventions for neuropathic pain. However, there is a lack of investigation of the relationship between sensory thalamic oscillations and pain relief in patients with neuropathic pain. This study aims to identify the oscillatory neural characteristics correlated with pain relief induced by deep brain stimulation (DBS), and develop a quantitative model to predict pain relief by integrating characteristic measures of the neural oscillations. APPROACH Measures of sensory thalamic local field potentials (LFPs) in thirteen patients with neuropathic pain were screened in three dimensional feature space according to the rhythm, balancing, and coupling neural behaviours, and correlated with pain relief. An integrated approach based on principal component analysis (PCA) and multiple regression analysis is proposed to integrate the multiple measures and provide a predictive model. MAIN RESULTS This study reveals distinct thalamic rhythms of theta, alpha, high beta and high gamma oscillations correlating with pain relief. The balancing and coupling measures between these neural oscillations were also significantly correlated with pain relief. SIGNIFICANCE The study enriches the series research on the function of thalamic neural oscillations in neuropathic pain and relief, and provides a quantitative approach for predicting pain relief by DBS using thalamic neural oscillations.

Decoding voluntary movements and postural tremor based on thalamic LFPs for closed-loop stimulation for essential tremor

High frequency deep brain stimulation (DBS) targeting motor thalamus is an effective therapy for essential tremor (ET). However, conventional continuous stimulation may deliver unnecessary current to the brain since tremor mainly affects voluntary movements and sustained postures in ET. We recorded LFPs from the motor thalamus, surface electromyographic (EMG) signals and/or behavioural measurements in seven ET patients during temporary lead externalization after the first surgery for DBS when they performed different voluntary upper limb movements and in nine more patients during the surgery, when they were asked to lift their arms to trigger postural tremor. We show that both voluntary movements and postural tremor can be decoded based on features extracted from thalamic LFPs using a machine learning based binary classifier. This information can be used to close the loop for DBS so that stimulation could be delivered on demand, without the need for peripheral sensors or additional invasive electrodes.

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

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.

Direct neurophysiological evidence for a role of the human anterior cingulate cortex in central command.

INTRODUCTION The role of the anterior cingulate cortex (ACC) is still controversial. The ACC has been implicated in such diverse functions as cognition, arousal and emotion in addition to motor and autonomic control. Therefore the ACC is the ideal candidate to orchestrate cardiovascular performance in anticipation of perceived skeletal activity. The aim of this experiment was to investigate whether the ACC forms part of the neural network of central command whereby cardiovascular performance is governed by a top-down mechanism. METHODS & RESULTS Direct local field potential (LFP) recordings were made using intraparenchymal electrodes in six human ACCs to measure changes in neuronal activity during performance of a motor task in which anticipation of exercise was uncoupled from skeletal activity itself. Parallel cardiovascular arousal was indexed by electrocardiographic changes in heart rate. During anticipation of exercise, ACC LFP power within the 25-60 Hz frequency band increased significantly by 21% compared to rest (from 62.7 μV2/Hz (±SE 4.94) to 76.0μV2/Hz (±SE 7.24); p = 0.004). This 25-60 Hz activity increase correlated with a simultaneous heart rate increase during anticipation (Pearsons r = 0.417, p = 0.016). CONCLUSIONS/SIGNIFICANCE We provide the first invasive electrophysiological evidence to support the role of the ACC in both motor preparation and the top-down control of cardiovascular function in exercise. This further implicates the ACC in the bodys response to the outside world and its possible involvement in such extreme responses as emotional syncope and hyperventilation. In addition we describe the frequency at which the neuronal ACC populations perform these tasks in the human.

The dynamic response of neural activity to deep brain stimulation

Parkinsons disease (PD) is a progressive, neurodegenerative disorder, characterized by hallmark motor symptoms. Deep brain stimulation (DBS) has been used to treat advanced PD successfully. Previous studies have found that the DBS also has an effect on the electrophysiological activity of the deep brain nucleus while alleviate the PD symptoms. Here, in an attempt to gain a greater understanding of dynamic response of neural activity during subthalamic nucleus (STN) DBS for PD, local filed potentials (LFPs) were recorded from the STN during closed-loop DBS. The time frequency analysis methods short-time Fourier transform and continuous wavelet transform were used to detect the dynamic change of LFPs and the related factors which affect the length of stimulation time. The results suggest that both alpha activity and beta activity are dynamic change with electric stimulation. The delay time of DBS inhibit beta activity is about 160 ms. These results also demonstrated that the length of stimulation time are associated with the baseline amplitude, the average amplitude and the peak amplitude of beta activity. Studying the response of neural activity to electrical stimulation can reveal the electrophysiological mechanisms of DBS. Furthermore, it can improve the treatment of closed-loop DBS for PD and promote the development of intelligent neural modulation.

Dynamic synchronization state identification

In the sensory thalamus and periventricular gray/periaqueductal gray (PVAG) nucleus, the synchronization level of multiple frequency band oscillations of local field potentials (LFPs) have been shown to be associated with chronic pain perception and modulation. In this study, a state identification approach was generated to dynamically identify the synchronization state of neural oscillation. In this approach, a pattern extraction model was created to characterize the patterning of the neural oscillations based on wavelet packet transform. The value of wavelet packet coefficients represents the synchronization level of pattern. And then a state discrimination model was designed to distinguish the synchronization state and de-synchronization state of pattern based on calculating a suitable threshold and discrimination strategies. By using the sensory thalamus and PVAG LFPs of neuropathic pain and simulation signals, the parameters of the approach were optimized for theta pattern (6–9Hz) and alpha pattern (9–12hz) identification respectively. Finally, the mean best performance of identifying the theta pattern states from 300s simulation signals achieved 91% sensitivity and 86% specificity, and achieved 80% sensitivity and 88% specificity for alpha pattern state identification. Then this approach was applied to the sensory thalamus and PVAG LFPs, and was able to identify the synchronization state of theta and alpha pattern. This study provides a reliable approach to dynamically identify the synchronization level of pattern of neuropathic pain disease through optimizing the parameters. Based on this approach, a real-time monitoring of the pain state and an adaptive treatment regimen can be achieved.

Characteristics of thalamic local field potentials in patients with disorders of consciousness.

A functioning thalamus is essential for treatment of patients with disorders of consciousness (DOC) using deep brain stimulation (DBS). This work aims to identify the potential biomarkers related to consciousness from the thalamic deep brain local field potentials (LFPs) in DOC patients. The frequency features of central thalamic LFPs were characterized with spectral analysis. The features were further compared to those of LFPs from the ventroposterior lateral nucleus of the thalamus (VPL) in patients with pain. There are several distinct characteristics of thalamic LFPs found in patients with DOC. The most important feature is the oscillation around 10Hz which could be relevant to the existence of residual consciousness, whereas high power below 8Hz seemed to be associated with loss of consciousness. The invasive deep brain recording tool opens a unique way to explore the brain function in consciousness, awareness and alertness and clarify the potential mechanisms of thalamic stimulation in DOC.