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Background: The subthalamic nucleus (STN) is a common target for deep brain stimulation (DBS) in Parkinson’s disease (PD) and is believed to serve a role in sensorimotor integration. In addition to therapeutic neuromodulation, DBS facilitates the recording of local-field potentials (LFPs) in order to further understand the neurophysiological basis of disease. The capacity to wirelessly transmit these signals in real time has overcome the obstacle of externalization of electrodes during LFP recordings. Objective: Using the G102RS device (PINS Medical, China), we investigated the LFP changes in response to mechanical pain stimulation to further elucidate the representation of pain sensation in the basal ganglia. Methods: LFPs from 2 patients who had undergone bilateral STN-DBS were wirelessly recorded during no stimulation, low-frequency stimulation (60 and 90 Hz), and high-frequency stimulation (130 and 150 Hz) while introducing painful and nonpainful stimuli. Power spectral analysis was conducted to compare the changes in β frequency (13–30 Hz) during each stimulus. Results: During painful stimuli, STN power spectra (n = 4) revealed a significant increase in β activity compared to non-painful and no-stimulus epochs. Both low- and high-frequency stimulation produced a significant decrease in pain-related β activity. Conclusion: These 2 cases have demonstrated the potential for acute noxious stimuli to exacerbate pathologic β oscillatory activity in the STN. Our findings represent novel evidence of the neurophysiologic representation of pain in the STN of PD patients.

Understanding the functional dynamics of neural oscillations in the sensory thalamus is essential for elucidating the perception and modulation of neuropathic pain. Local field potentials were recorded from the sensory thalamus of twelve neuropathic pain patients. Single and combinational neural states were defined by the activity state of a single or paired oscillations. Relationships between the duration or occurrence rate of neural state and pre-operative pain level or pain relief induced by deep brain stimulation were evaluated. Results showed that the occurrence rate of the single neural state of low-beta oscillation was significantly correlated with pain relief. The duration and occurrence rate of combinational neural states of the paired low-beta with delta, theta, alpha, high-beta or low-gamma oscillations were more significantly correlated with pain relief than the single neural states. Moreover, these significant combinational neural states formed a local oscillatory network with low-beta oscillation as a key node. The results also showed correlations between measures of combinational neural states and subjective pain level as well. The duration of combinational neural states of paired alpha with delta or theta oscillations and the occurrence rate of neural states of the paired delta with low-beta or low-gamma oscillations were significantly correlated with pre-operative pain level. In conclusion, this study revealed that the integration of oscillations and the functional dynamics of neural states were differentially involved in modulation and perception of neuropathic pain. The functional dynamics could be biomarkers for developing neural state-dependent deep brain stimulation for neuropathic pain.

Purpose Dysautonomia can be a debilitating feature of Parkinson disease (PD). Pedunculopontine nucleus (PPN) stimulation may improve gait disorders in PD, and may also result in changes in autonomic performance. Methods To determine whether pedunculopontine nucleus stimulation improves cardiovascular responses to autonomic challenges of postural tilt and Valsalva manoeuver, eight patients with pedunculopontine nucleus deep brain stimulation were recruited to the study; two were excluded for technical reasons during testing. Participants underwent head up tilt and Valsalva manoeuver with stimulation turned ON and OFF. Continuous blood pressure and ECG waveforms were recorded during these tests. In a single patient, local field potential activity was recorded from the implanted electrode during tilt. Results The fall in systolic blood pressure after tilt was significantly smaller with stimulation ON (mean − 8.3% versus − 17.2%, p = 0.044). Valsalva ratio increased with stimulation from median 1.15 OFF to 1.20 ON (p = 0.028). Baroreflex sensitivity increased during Valsalva compared to rest with stimulation ON versus OFF (p = 0.028). The increase in baroreflex sensitivity correlated significantly with the mean depth of PPN stimulating electrode contacts. This accounted for 89% of its variance (r = 0.943, p = 0.005). Conclusion PPN stimulation can modulate the cardiovascular system in patients with PD. In this study, it reduced the postural fall in systolic blood pressure during head-up tilt and improved the cardiovascular response during Valsalva, presumably by altering the neural control of baroreflex activation.

The pedunculopontine nucleus (PPN) is a reticular collection of neurons at the junction of the midbrain and pons, playing an important role in modulating posture and locomotion. Deep brain stimulation of the PPN has been proposed as an emerging treatment for patients with Parkinson's disease (PD) or multiple system atrophy (MSA) who have gait-related atypical parkinsonian syndromes. In this study, we investigated PPN activities during gait to better understand its functional role in locomotion. Specifically, we investigated whether PPN activity is rhythmically modulated by gait cycles during locomotion. PPN local field potential (LFP) activities were recorded from PD or MSA patients with gait difficulties during stepping in place or free walking. Simultaneous measurements from force plates or accelerometers were used to determine the phase within each gait cycle at each time point. Our results showed that activities in the alpha and beta frequency bands in the PPN LFPs were rhythmically modulated by the gait phase within gait cycles, with a higher modulation index when the stepping rhythm was more regular. Meanwhile, the PPN–cortical coherence was most prominent in the alpha band. Both gait phase-related modulation in the alpha/beta power and the PPN–cortical coherence in the alpha frequency band were spatially specific to the PPN and did not extend to surrounding regions. These results suggest that alternating PPN modulation may support gait control. Whether enhancing alternating PPN modulation by stimulating in an alternating fashion could positively affect gait control remains to be tested. SIGNIFICANCE STATEMENT The therapeutic efficacy of pedunculopontine nucleus (PPN) deep brain stimulation (DBS) and the extent to which it can improve quality of life are still inconclusive. Understanding how PPN activity is modulated by stepping or walking may offer insight into how to improve the efficacy of PPN DBS in ameliorating gait difficulties. Our study shows that PPN alpha and beta activity was modulated by the gait phase, and that this was most pronounced when the stepping rhythm was regular. It remains to be tested whether enhancing alternating PPN modulation by stimulating in an alternating fashion could positively affect gait control.

Lateral habenula is believed to encode negative motivational stimuli and plays key roles in the pathophysiology of psychiatric disorders. However, how habenula activities are modulated during the perception and processing of emotional information is still poorly understood. We recorded local field potentials from bilateral habenula areas with simultaneous cortical magnetoencephalography in nine patients with psychiatric disorders during an emotional picture viewing task. Oscillatory activity in the theta/alpha band (5-10 Hz) within the habenula and prefrontal cortical regions, as well as the coupling between these structures, are increased during the perception and processing of negative emotional stimuli compared to positive emotional stimuli. The evoked increase in theta/alpha band synchronization in the frontal cortex-habenula network correlated with the emotional valence not the arousal score of the stimuli. These results provide direct evidence for increased theta/alpha synchrony within the habenula area and prefrontal cortex-habenula network in the perception of negative emotion in human participants.

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.

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.

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.

Objectives The nucleus accumbens (NAc) is known to regulate the motivation and underlie addictive behaviors, and the anterior limb of the internal capsule (ALIC) is involved in several psychiatric disorders. Our study aimed to explore the functions of NAc and ALIC electrophysiologically. Methods The local field potentials (LFPs) of the NAc and ALIC were recorded from 7 heroin addicts treated with deep brain stimulation. Correlation analysis was made between LFP powers in various frequency bands and the subjects’ neuropsychological test scores; coherence was calculated for the LFPs in NAc and ALIC. Results Both the NAc and ALIC exhibited prominent theta and alpha frequency band activity in the LFP power spectra. Additionally, a distinct beta band peak was detected in the power spectra of ALIC LFPs, which may represent the activity of striatal bridge cells. There was a significant negative correlation between the power of the theta frequency band of ALIC LFPs and visual analogue scale (VAS) scores indicative of cravings (Spearman’s ρ = −0.758, P = 0.002), and a significant positive correlation was found between the power of the alpha frequency band of NAc LFPs and subjects’ scores on the Hamilton depression inventory (ρ = 0.727, P = 0.005). LFPs of the NAc and ALIC exhibited higher coherence values in the theta and alpha frequency bands. Conclusions The results suggest that theta power in the ALIC/dorsal striatum and alpha power in the NAc may be associated with drug cravings and depressive symptoms, respectively, in heroin addicts. For these subjects, the neural activities in the dorsal and ventral striatum were mainly coordinated within the low-frequency band. Significance The study illustrates the neurophysiologic characteristics of heroin addiction and its comorbidities, providing a potential theoretical basis for optimizing deep brain stimulation (DBS) therapy.

Parkinson's 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.

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.

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.

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.

Nonlinear interaction between neural oscillations may be a biomarker for closed-loop deep brain stimulation (DBS) in Parkinson's disease. Bispectral analysis is an effective tool to detect quadratic phase coupling. In this paper we applied bispectral analysis to local field potentials (LFPs) recorded from Parkinsonism patients' subthalamic nucleus (STN) during tremor and resting states. The significance of the coupling was statistically tested using bispectrum estimation with surrogate data approaches. Strong phase couplings were found in both states, whereas the coupling frequencies varied. There are bispectral peaks around 3–6Hz in both states and the peak amplitudes in tremor state are twice larger than those in resting state, while the bispectra in resting state shows peaks in narrow frequency bands of 7–9Hz, 10–12Hz and 15–17Hz. The measure of these nonlinear interactions between neural oscillations of local field potentials can be used to distinguish pathological states in Parkinson's disease and could contribute to the development of intelligent deep brain stimulation strategies.