Subthalamic nucleus phase-amplitude coupling correlates with motor impairment in Parkinson's disease

Low-beta versus high-beta band cortico-subcortical coherence in movement inhibition and expectation

Beta band oscillations in the sensorimotor cortex and subcortical structures, such as the subthalamic nucleus (STN) and internal pallidum (GPi), are closely linked to motor control. Recent research suggests that low-beta (14.5-23.5 Hz) and high-beta (23.5-35 Hz) cortico-STN coherence arise through distinct networks, possibly reflecting indirect and hyperdirect pathways. In this study, we sought to probe whether low- and high-beta coherence also exhibit different functional roles in facilitating and inhibiting movement. Twenty patients with Parkinson's disease who had deep brain stimulation electrodes implanted in either STN or GPi performed a classical go/nogo task while undergoing simultaneous magnetoencephalography and local field potentials recordings. Subjects' expectations were manipulated by presenting go- and nogo-trials with varying probabilities. We identified a lateral source in the sensorimotor cortex for low-beta coherence, as well as a medial source near the supplementary motor area for high-beta coherence. Task-related coherence time courses for these two sources revealed that low-beta coherence was more strongly implicated than high-beta coherence in the performance of go-trials. Accordingly, average pre-stimulus low-beta but not high-beta coherence or spectral power correlated with overall reaction time across subjects. High-beta coherence during unexpected nogo-trials was higher compared to expected nogo-trials at a relatively long latency of 3 s after stimulus presentation. Neither low- nor high-beta coherence showed a significant correlation with patients' symptom severity at baseline assessment. While low-beta cortico-subcortical coherence appears to be related to motor output, the role of high-beta coherence requires further investigation.

Current and future applications of local field potential-guided programming for Parkinson's disease with the Percept™ rechargeable neurostimulator

Deep brain stimulation (DBS) has been established as an effective neuromodulatory treatment for Parkinson's disease (PD) with motor complications or refractory tremor. Various DBS devices with unique technology platforms are commercially available and deliver continuous, open-loop stimulation. The Percept™ family of neurostimulators use BrainSense™ technology with five key features to sense local field potentials while stimulating, enabling integration of physiologic data into the routine practice of DBS programming. The newly approved Percept™ rechargeable RC implantable pulse generator offers a smaller, thinner design and reduced recharge time with prolonged recharge interval. In this review, we describe the application of local field potential sensing-based programming in PD and highlight the potential future clinical implementation of closed-loop stimulation using the Percept™ RC implantable pulse generator.

Clinico-physiological correlates of Parkinson's disease from multi-resolution basal ganglia recordings

Parkinson's disease (PD) has been associated with pathological neural activity within the basal ganglia. Herein, we analyzed resting-state single-neuron and local field potential (LFP) activities from people with PD who underwent awake deep brain stimulation surgery of the subthalamic nucleus (STN; n = 125) or globus pallidus internus (GPi; n = 44), and correlated rate-based and oscillatory features with UPDRSIII off-medication subscores. Rate-based single-neuron features did not correlate with PD symptoms. STN single-neuron and LFP low-beta (12-21 Hz) power and burst dynamics showed modest correlations with bradykinesia and rigidity severity, while STN spiketrain theta (4-8 Hz) power correlated modestly with tremor severity. GPi low- and high-beta (21-30 Hz) power and burst dynamics correlated moderately with bradykinesia and axial symptom severity. These findings suggest that elevated single-neuron and LFP oscillations may be linked to symptoms, though modest correlations imply that the pathophysiology of PD may extend beyond resting-state beta oscillations.

Exploring the electrophysiology of Parkinson's disease with magnetoencephalography and deep brain recordings

Aberrant information processing in the basal ganglia and connected cortical areas are key to many neurological movement disorders such as Parkinson's disease. Investigating the electrophysiology of this system is difficult in humans because non-invasive methods, such as electroencephalography or magnetoencephalography, have limited sensitivity to deep brain areas. Recordings from electrodes implanted for therapeutic deep brain stimulation, in contrast, provide clear deep brain signals but are not suited for studying cortical activity. Therefore, we combine magnetoencephalography and local field potential recordings from deep brain stimulation electrodes in individuals with Parkinson's disease. Here, we make these data available, inviting a broader scientific community to explore the dynamics of neural activity in the subthalamic nucleus and its functional connectivity to cortex. The dataset encompasses resting-state recordings, plus two motor tasks: static forearm extension and self-paced repetitive fist clenching. Most patients were recorded both in the medicated and the unmedicated state. Along with the raw data, we provide metadata on channels, events and scripts for pre-processing to help interested researchers get started.

Comparison of beta peak detection algorithms for data-driven deep brain stimulation programming strategies in Parkinson's disease

Oscillatory activity within the beta frequency range (13-30 Hz) serves as a Parkinson's disease biomarker for tailoring deep brain stimulation (DBS) treatments. Currently, identifying clinically relevant beta signals, specifically frequencies of peak amplitudes within the beta spectral band, is a subjective process. To inform potential strategies for objective clinical decision making, we assessed algorithms for identifying beta peaks and devised a standardized approach for both research and clinical applications. Employing a novel monopolar referencing strategy, we utilized a brain sensing device to measure beta peak power across distinct contacts along each DBS electrode implanted in the subthalamic nucleus. We then evaluated the accuracy of ten beta peak detection algorithms against a benchmark established by expert consensus. The most accurate algorithms, all sharing similar underlying algebraic dynamic peak amplitude thresholding approaches, matched the expert consensus in performance and reliably predicted the clinical stimulation parameters during follow-up visits. These findings highlight the potential of algorithmic solutions to overcome the subjective bias in beta peak identification, presenting viable options for standardizing this process. Such advancements could lead to significant improvements in the efficiency and accuracy of patient-specific DBS therapy parameterization.

Dysfunction of motor cortices in Parkinson's disease

The cerebral cortex has long been thought to be involved in the pathophysiology of motor symptoms of Parkinson's disease. The impaired cortical function is believed to be a direct and immediate effect of pathologically patterned basal ganglia output, mediated to the cerebral cortex by way of the ventral motor thalamus. However, recent studies in humans with Parkinson's disease and in animal models of the disease have provided strong evidence suggesting that the involvement of the cerebral cortex is much broader than merely serving as a passive conduit for subcortical disturbances. In the present review, we discuss Parkinson's disease-related changes in frontal cortical motor regions, focusing on neuropathology, plasticity, changes in neurotransmission, and altered network interactions. We will also examine recent studies exploring the cortical circuits as potential targets for neuromodulation to treat Parkinson's disease.

Subthalamic nucleus input-output dynamics are correlated with Parkinson's burden and treatment efficacy

The subthalamic nucleus (STN) is pivotal in basal ganglia function in health and disease. Micro-electrode recordings of >25,000 recording sites from 146 Parkinson's patients undergoing deep brain stimulation (DBS) allowed differentiation between subthalamic input, represented by local field potential (LFP), and output, reflected in spike discharge rate (SPK). As with many natural systems, STN neuronal activity exhibits power-law dynamics characterized by the exponent α. We, therefore, dissected STN data into aperiodic and periodic components using the Fitting Oscillations & One Over F (FOOOF) tool. STN LFP showed significantly higher aperiodic exponents than SPK. Additionally, SPK beta oscillations demonstrated a downward frequency shift compared to LFP. Finally, the STN aperiodic and spiking parameters explained a significant fraction of the variance of the burden and treatment efficacy of Parkinson's disease. The unique STN input-output dynamics may clarify its role in Parkinson's physiology and can be utilized in closed-loop DBS therapy.

Subthalamic nucleus synchronization between beta band local field potential and single-unit activity in Parkinson's disease

Local field potential (LFP) oscillations in the beta band (13-30 Hz) in the subthalamic nucleus (STN) of Parkinson's disease patients have been implicated in disease severity and treatment response. The relationship between single-neuron activity in the STN and regional beta power changes remains unclear. We used spike-triggered average (STA) to assess beta synchronization in STN. Beta power and STA magnitude at the beta frequency range were compared in three conditions: STN versus other subcortical structures, dorsal versus ventral STN, and high versus low beta power STN recordings. Magnitude of STA-LFP was greater within the STN compared to extra-STN structures along the trajectory path, despite no difference in percentage of the total power. Within the STN, there was a higher percent beta power in dorsal compared to ventral STN but no difference in STA-LFP magnitude. Further refining the comparison to high versus low beta peak power recordings inside the STN to evaluate if single-unit activity synchronized more strongly with beta band activity in areas of high beta power resulted in a significantly higher STA magnitude for areas of high beta power. Overall, these results suggest that STN single units strongly synchronize to beta activity, particularly units in areas of high beta power.

Neuroscience fundamentals relevant to neuromodulation: Neurobiology of deep brain stimulation in Parkinson's disease

Deep Brain Stimulation (DBS) has become a pivotal therapeutic approach for Parkinson's Disease (PD) and various neuropsychiatric conditions, impacting over 200,000 patients. Despite its widespread application, the intricate mechanisms behind DBS remain a subject of ongoing investigation. This article provides an overview of the current knowledge surrounding the local, circuit, and neurobiochemical effects of DBS, focusing on the subthalamic nucleus (STN) as a key target in PD management. The local effects of DBS, once thought to mimic a reversible lesion, now reveal a more nuanced interplay with myelinated axons, neurotransmitter release, and the surrounding microenvironment. Circuit effects illuminate the modulation of oscillatory activities within the basal ganglia and emphasize communication between the STN and the primary motor cortex. Neurobiochemical effects, encompassing changes in dopamine levels and epigenetic modifications, add further complexity to the DBS landscape. Finally, within the context of understanding the mechanisms of DBS in PD, the article highlights the controversial question of whether DBS exerts disease-modifying effects in PD. While preclinical evidence suggests neuroprotective potential, clinical trials such as EARLYSTIM face challenges in assessing long-term disease modification due to enrollment timing and methodology limitations. The discussion underscores the need for robust biomarkers and large-scale prospective trials to conclusively determine DBS's potential as a disease-modifying therapy in PD.

A Systematic Review of Neurophysiology-Based Localization Techniques Used in Deep Brain Stimulation Surgery of the Subthalamic Nucleus

OBJECTIVE

This systematic review is conducted to identify, compare, and analyze neurophysiological feature selection, extraction, and classification to provide a comprehensive reference on neurophysiology-based subthalamic nucleus (STN) localization.

MATERIALS AND METHODS

The review was carried out using the methods and guidelines of the Kitchenham systematic review and provides an in-depth analysis on methods proposed on STN localization discussed in the literature between 2000 and 2021. Three research questions were formulated, and 115 publications were identified to answer the questions.

RESULTS

The three research questions formulated are answered using the literature found on the respective topics. This review discussed the technologies used in past research, and the performance of the state-of-the-art techniques is also reviewed.

CONCLUSION

This systematic review provides a comprehensive reference on neurophysiology-based STN localization by reviewing the research questions other new researchers may also have.

Subthalamic Nucleus Deep Brain Stimulation Restores Motor and Sensorimotor Cortical Neuronal Oscillatory Activity in the Free-Moving 6-Hydroxydopamine Lesion Rat Parkinson Model

OBJECTIVES

Enhanced beta oscillations in cortical-basal ganglia (BG) thalamic circuitries have been linked to clinical symptoms of Parkinson's disease. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) reduces beta band activity in BG regions, whereas little is known about activity in cortical regions. In this study, we investigated the effect of STN DBS on the spectral power of oscillatory activity in the motor cortex (MCtx) and sensorimotor cortex (SMCtx) by recording via an electrocorticogram (ECoG) array in free-moving 6-hydroxydopamine (6-OHDA) lesioned rats and sham-lesioned controls.

MATERIALS AND METHODS

Male Sprague-Dawley rats (250-350 g) were injected either with 6-OHDA or with saline in the right medial forebrain bundle, under general anesthesia. A stimulation electrode was then implanted in the ipsilateral STN, and an ECoG array was placed subdurally above the MCtx and SMCtx areas. Six days after the second surgery, the free-moving rats were individually recorded in three conditions: 1) basal activity, 2) during STN DBS, and 3) directly after STN DBS.

RESULTS

In 6-OHDA-lesioned rats (N = 8), the relative power of theta band activity was reduced, whereas activity of broad-range beta band (12-30 Hz) along with two different subbeta bands, that is, low (12-30 Hz) and high (20-30 Hz) beta band and gamma band, was higher in MCtx and SMCtx than in sham-lesioned controls (N = 7). This was, to some extent, reverted toward control level by STN DBS during and after stimulation. No major differences were found between contacts of the electrode grid or between MCtx and SMCtx.

CONCLUSION

Loss of nigrostriatal dopamine leads to abnormal oscillatory activity in both MCtx and SMCtx, which is compensated by STN stimulation, suggesting that parkinsonism-related oscillations in the cortex and BG are linked through their anatomic connections.

Mapping Subcortico-Cortical Coupling-A Comparison of Thalamic and Subthalamic Oscillations

BACKGROUND

The ventral intermediate nucleus of the thalamus (VIM) is an effective target for deep brain stimulation in tremor patients. Despite its therapeutic importance, its oscillatory coupling to cortical areas has rarely been investigated in humans.

OBJECTIVES

The objective of this study was to identify the cortical areas coupled to the VIM in patients with essential tremor.

METHODS

We combined resting-state magnetoencephalography with local field potential recordings from the VIM of 19 essential tremor patients. Whole-brain maps of VIM-cortex coherence in several frequency bands were constructed using beamforming and compared with corresponding maps of subthalamic nucleus (STN) coherence based on data from 19 patients with Parkinson's disease. In addition, we computed spectral Granger causality.

RESULTS

The topographies of VIM-cortex and STN-cortex coherence were very similar overall but differed quantitatively. Both nuclei were coupled to the ipsilateral sensorimotor cortex in the high-beta band; to the sensorimotor cortex, brainstem, and cerebellum in the low-beta band; and to the temporal cortex, brainstem, and cerebellum in the alpha band. High-beta coherence to sensorimotor cortex was stronger for the STN (P = 0.014), whereas low-beta coherence to the brainstem was stronger for the VIM (P = 0.017). Although the STN was driven by cortical activity in the high-beta band, the VIM led the sensorimotor cortex in the alpha band.

CONCLUSIONS

Thalamo-cortical coupling is spatially and spectrally organized. The overall similar topographies of VIM-cortex and STN-cortex coherence suggest that functional connections are not necessarily unique to one subcortical structure but might reflect larger frequency-specific networks involving VIM and STN to a different degree. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

The guiding effect of local field potential during deep brain stimulation surgery for programming in Parkinson's disease patients

BACKGROUND

Parkinson's disease (PD) patients undergoing deep brain stimulation (DBS) surgery require subsequent programming, which is complex and cumbersome. The local field potential (LFP) in the deep brain is associated with motor symptom improvement. The current study aimed to identify LFP biomarkers correlated with improved motor symptoms in PD patients after DBS and verify their guiding role in postoperative programming.

METHODS

Initially, the study included 36 PD patients undergoing DBS surgery. Temporary external electrical stimulation was performed during electrode implantation, and LFP signals around the electrode contacts were collected before and after stimulation. The stimulating contact at 6 months of programming was regarded as the optimal and effective stimulating contact. The LFP signal of this contact during surgery was analyzed to identify potential LFP biomarkers. Next, we randomly assigned another 30 PD patients who had undergone DBS to physician empirical programming and LFP biomarker-guided programming groups and compared the outcomes.

RESULTS

In the first part of the study, LFP signals of electrode contacts changed after electrical stimulation. Electrical stimulation reduced gamma energy and the beta/alpha oscillation ratio. The different programming method groups were compared, indicating the superiority of beta/alpha oscillations ratio-guided programming over physician experience programming for patients' improvement rate (IR) of UPDRS-III. There were no significant differences in the IR of UPDRS-III, post-LED, IR-PDQ39, number of programmings, and the contact change rate between the gamma oscillations-guided programming and empirical programming groups.

CONCLUSION

Overall, the findings reveal that gamma oscillations and the beta/alpha oscillations ratio are potential biomarkers for programming in PD patients after DBS. Instead of relying solely on spike action potential signals from single neurons, LFP biomarkers can provide the appropriate depth for electrode placement.

Coupling between beta band and high frequency oscillations as a clinically useful biomarker for DBS

Beta hypersynchrony was recently introduced into clinical practice in Parkinson's disease (PD) to identify the best stimulation contacts and for adaptive deep brain stimulation (aDBS) sensing. However, many other oscillopathies accompany the disease, and beta power sensing may not be optimal for all patients. The aim of this work was to study the potential clinical usefulness of beta power phase-amplitude coupling (PAC) with high frequency oscillations (HFOs). Subthalamic nucleus (STN) local field potentials (LFPs) from externalized DBS electrodes were recorded and analyzed in PD patients (n = 19). Beta power and HFOs were evaluated in a resting-state condition; PAC was then studied and compared with the electrode contact positions, structural connectivity, and medication state. Beta-HFO PAC (mainly in the 200-500 Hz range) was observed in all subjects. PAC was detectable more specifically in the motor part of the STN compared to beta power and HFOs. Moreover, the presence of PAC better corresponds to the stimulation setup based on the clinical effect. PAC is also sensitive to the laterality of symptoms and dopaminergic therapy, where the greater PAC cluster reflects the more affected side and medication "off" state. Coupling between beta power and HFOs is known to be a correlate of the PD "off" state. Beta-HFO PAC seems to be more sensitive than beta power itself and could be more helpful in the selection of the best clinical stimulation contact and probably also as a potential future input signal for aDBS.

Abnormally low sensorimotor α band nonlinearity serves as an effective EEG biomarker of Parkinson's disease

Biomarkers obtained from the neurophysiological signals of patients with Parkinson's disease (PD) have objective value in assessing their motor condition for effective diagnosis, monitoring, and clinical intervention. Prominent cortical biomarkers of PD have typically been derived from various β band wave features. This study approached the topic from an alternative perspective and attempted to estimate a recently suggested measure representing α band nonlinear autocorrelative memory from a publicly available EEG dataset that involves 15 patients with earlier-stage PD (dopaminergic medication OFF and ON states) and 16 age-matched healthy controls. The cortical nonlinearity was elevated for the PD ON state compared with the OFF state for bilateral sensorimotor channels C3 and C4 (n = 26; P = 0.003). A similar statistical difference was also identified between PD OFF state and healthy subjects (n = 26; P = 0.049). Analysis over all channels revealed that the α band nonlinearity induced upon medication was constrained to sensorimotor regions. The α nonlinearity measure was compared with a well-accepted cortical biomarker of β-γ phase-amplitude coupling (PAC). They were in moderate negative correlation (r = -0.412; P = 0.036) for only healthy subjects, but not for the patients. The nonlinearity measure was found to be insusceptible to the nonstationary variations within the particular data. Our study provides further evidence that the α band nonlinearity measure can serve as a promising cortical biomarker of PD. The suggested measure can be estimated from a noninvasive low-resolution single scalp EEG channel of patients with relatively early-stage PD, who did not yet need to undergo deep brain stimulation operation.NEW & NOTEWORTHY This study suggests a nonlinearity measure that differentiates Parkinson's disease (PD) dopamine OFF-state scalp EEG data from those of dopamine ON-state patients and healthy subjects. Unlike typical PD cortical biomarkers based on β band activity, this metric shows elevation upon dopaminergic medication in the α band. We provide evidence supporting its potential as an early-stage promising PD biomarker that can be estimated from noninvasive EEG recordings with low resolution and SNR.

Wavelet-Based Bracketing, Time-Frequency Beta Burst Detection: New Insights in Parkinson's Disease

Studies have shown that beta band activity is not tonically elevated but comprises exaggerated phasic bursts of varying durations and magnitudes, for Parkinson's disease (PD) patients. Current methods for detecting beta bursts target a single frequency peak in beta band, potentially ignoring bursts in the wider beta band. In this study, we propose a new robust framework for beta burst identification across wide frequency ranges. Chronic local field potential at-rest recordings were obtained from seven PD patients implanted with Medtronic SenSight™ deep brain stimulation (DBS) electrodes. The proposed method uses wavelet decomposition to compute the time-frequency spectrum and identifies bursts spanning multiple frequency bins by thresholding, offering an additional burst measure, ∆f, that captures the width of a burst in the frequency domain. Analysis included calculating burst duration, magnitude, and ∆f and evaluating the distribution and likelihood of bursts between the low beta (13-20 Hz) and high beta (21-35 Hz). Finally, the results of the analysis were correlated to motor impairment (MDS-UPDRS III) med off scores. We found that low beta bursts with longer durations and larger width in the frequency domain (∆f) were positively correlated, while high beta bursts with longer durations and larger ∆f were negatively correlated with motor impairment. The proposed method, finding clear differences between bursting behavior in high and low beta bands, has clearly demonstrated the importance of considering wide frequency bands for beta burst behavior with implications for closed-loop DBS paradigms.

Dynamic networks of cortico-muscular interactions in sleep and neurodegenerative disorders

The brain plays central role in regulating physiological systems, including the skeleto-muscular and locomotor system. Studies of cortico-muscular coordination have primarily focused on associations between movement tasks and dynamics of specific brain waves. However, the brain-muscle functional networks of synchronous coordination among brain waves and muscle activity rhythms that underlie locomotor control remain unknown. Here we address the following fundamental questions: what are the structure and dynamics of cortico-muscular networks; whether specific brain waves are main network mediators in locomotor control; how the hierarchical network organization relates to distinct physiological states under autonomic regulation such as wake, sleep, sleep stages; and how network dynamics are altered with neurodegenerative disorders. We study the interactions between all physiologically relevant brain waves across cortical locations with distinct rhythms in leg and chin muscle activity in healthy and Parkinson's disease (PD) subjects. Utilizing Network Physiology framework and time delay stability approach, we find that 1) each physiological state is characterized by a unique network of cortico-muscular interactions with specific hierarchical organization and profile of links strength; 2) particular brain waves play role as main mediators in cortico-muscular interactions during each state; 3) PD leads to muscle-specific breakdown of cortico-muscular networks, altering the sleep-stage stratification pattern in network connectivity and links strength. In healthy subjects cortico-muscular networks exhibit a pronounced stratification with stronger links during wake and light sleep, and weaker links during REM and deep sleep. In contrast, network interactions reorganize in PD with decline in connectivity and links strength during wake and non-REM sleep, and increase during REM, leading to markedly different stratification with gradual decline in network links strength from wake to REM, light and deep sleep. Further, we find that wake and sleep stages are characterized by specific links strength profiles, which are altered with PD, indicating disruption in the synchronous activity and network communication among brain waves and muscle rhythms. Our findings demonstrate the presence of previously unrecognized functional networks and basic principles of brain control of locomotion, with potential clinical implications for novel network-based biomarkers for early detection of Parkinson's and neurodegenerative disorders, movement, and sleep disorders.

Oscillatory beta dynamics inform biomarker-driven treatment optimization for Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder characterized by loss of dopaminergic neurons and dysregulation of the basal ganglia. Cardinal motor symptoms include bradykinesia, rigidity, and tremor. Deep brain stimulation (DBS) of select subcortical nuclei is standard of care for medication-refractory PD. Conventional open-loop DBS delivers continuous stimulation with fixed parameters that do not account for a patient's dynamic activity state or medication cycle. In comparison, closed-loop DBS, or adaptive DBS (aDBS), adjusts stimulation based on biomarker feedback that correlates with clinical state. Recent work has identified several neurophysiological biomarkers in local field potential recordings from PD patients, the most promising of which are 1) elevated beta (∼13-30 Hz) power in the subthalamic nucleus (STN), 2) increased beta synchrony throughout basal ganglia-thalamocortical circuits, notably observed as coupling between the STN beta phase and cortical broadband gamma (∼50-200 Hz) amplitude, and 3) prolonged beta bursts in the STN and cortex. In this review, we highlight relevant frequency and time domain features of STN beta measured in PD patients and summarize how spectral beta power, oscillatory beta synchrony, phase-amplitude coupling, and temporal beta bursting inform PD pathology, neurosurgical targeting, and DBS therapy. We then review how STN beta dynamics inform predictive, biomarker-driven aDBS approaches for optimizing PD treatment. We therefore provide clinically useful and actionable insight that can be applied toward aDBS implementation for PD.

Cross-Frequency Coupling and Intelligent Neuromodulation

Cross-frequency coupling (CFC) reflects (nonlinear) interactions between signals of different frequencies. Evidence from both patient and healthy participant studies suggests that CFC plays an essential role in neuronal computation, interregional interaction, and disease pathophysiology. The present review discusses methodological advances and challenges in the computation of CFC with particular emphasis on potential solutions to spurious coupling, inferring intrinsic rhythms in a targeted frequency band, and causal interferences. We specifically focus on the literature exploring CFC in the context of cognition/memory tasks, sleep, and neurological disorders, such as Alzheimer's disease, epilepsy, and Parkinson's disease. Furthermore, we highlight the implication of CFC in the context and for the optimization of invasive and noninvasive neuromodulation and rehabilitation. Mainly, CFC could support advancing the understanding of the neurophysiology of cognition and motor control, serve as a biomarker for disease symptoms, and leverage the optimization of therapeutic interventions, e.g., closed-loop brain stimulation. Despite the evident advantages of CFC as an investigative and translational tool in neuroscience, further methodological improvements are required to facilitate practical and correct use in cyborg and bionic systems in the field.

Spectral Topography of the Subthalamic Nucleus to Inform Next-Generation Deep Brain Stimulation

BACKGROUND

The landscape of neurophysiological symptoms and behavioral biomarkers in basal ganglia signals for movement disorders is expanding. The clinical translation of sensing-based deep brain stimulation (DBS) also requires a thorough understanding of the anatomical organization of spectral biomarkers within the subthalamic nucleus (STN).

OBJECTIVES

The aims were to systematically investigate the spectral topography, including a wide range of sub-bands in STN local field potentials (LFP) of Parkinson's disease (PD) patients, and to evaluate its predictive performance for clinical response to DBS.

METHODS

STN-LFPs were recorded from 70 PD patients (130 hemispheres) awake and at rest using multicontact DBS electrodes. A comprehensive spatial characterization, including hot spot localization and focality estimation, was performed for multiple sub-bands (delta, theta, alpha, low-beta, high-beta, low-gamma, high-gamma, and fast-gamma (FG) as well as low- and fast high-frequency oscillations [HFO]) and compared to the clinical hot spot for rigidity response to DBS. A spectral biomarker map was established and used to predict the clinical response to DBS.

RESULTS

The STN shows a heterogeneous topographic distribution of different spectral biomarkers, with the strongest segregation in the inferior-superior axis. Relative to the superiorly localized beta hot spot, HFOs (FG, slow HFO) were localized up to 2 mm more inferiorly. Beta oscillations are spatially more spread compared to other sub-bands. Both the spatial proximity of contacts to the beta hot spot and the distance to higher-frequency hot spots were predictive for the best rigidity response to DBS.

CONCLUSIONS

The spatial segregation and properties of spectral biomarkers within the DBS target structure can additionally be informative for the implementation of next-generation sensing-based DBS. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Elevated Gaussian-modeled beta power in the cortex characterizes aging, but not Parkinson's disease

Aging is a key risk factor for the development of Parkinson's disease (PD). PD is characterized by excessive synchrony of beta oscillations (13-30 Hz) in the basal ganglia thalamo-cortical network. However, cortical beta power is not reliably elevated in individuals with PD. Here, we sought to disentangle how resting cortical beta power compares in younger controls, older controls, and individuals with PD using scalp electroencephalogram (EEG) and a novel approach for quantifying beta power. Specifically, we used a Gaussian model to determine if sensorimotor beta power distinguishes these groups. In addition, we looked at the distribution of beta power across the entire cortex. Our findings showed that Gaussian-modeled beta power does not differentiate individuals with PD (on medication) from healthy younger or older controls in sensorimotor cortex. However, beta power (and not theta or alpha) was higher in healthy older versus younger controls. This effect was most pronounced in regions near sensorimotor cortex including the frontal and parietal areas [P < 0.05, false discovery rate (FDR) corrected]. In addition, the bandwidth of the periodic beta was also higher in healthy older than young individuals in parietal regions. Finally, the aperiodic component, specifically the exponent of the signal, was higher (steeper) in younger controls than in individuals with PD in the right parietal-occipital region (P < 0.05, FDR corrected), possibly reflecting differences in neuronal spiking. Our findings suggest that cortical Gaussian beta power is possibly modulated by age and could be further explored in longitudinal studies to determine whether sensorimotor beta increases with increasing age.NEW & NOTEWORTHY Altered sensorimotor beta activity has been shown to be a feature in aging and PD. Using a novel approach, we clarify that resting sensorimotor beta power does not distinguish subjects with PD from healthy younger and older controls. However, beta power was higher in older compared with younger controls in central sensorimotor, frontal, and parietal regions. These results provide a clearer picture of sensorimotor beta power, demonstrating that it is elevated in aging but not PD.

Effects of Contralateral Deep Brain Stimulation and Levodopa on Subthalamic Nucleus Oscillatory Activity and Phase-Amplitude Coupling

BACKGROUND

The modulatory effects of medication and deep brain stimulation (DBS) on subthalamic nucleus (STN) neural activity in Parkinson's disease have been widely studied. However, effects on the contralateral side to the stimulated STN, in particular, changes in local field potential (LFP) oscillatory activity and phase-amplitude coupling (PAC), have not yet been reported.

OBJECTIVE

The aim of this study was to examine changes in STN LFP activity across a range of frequency bands and STN PAC for different combinations of DBS and medication on/off on the side contralateral to the applied stimulation.

MATERIALS AND METHODS

We examined STN LFPs that were recorded using externalized leads from eight parkinsonian patients during unilateral DBS from the side contralateral to the stimulation. LFP spectral power in alpha (5 to ∼13 Hz), low beta (13 to ∼20 Hz), high beta (20-30 Hz), and high gamma plus high-frequency oscillation (high gamma+HFO) (100-400 Hz) bands were estimated for different combinations of medication and unilateral stimulation (off/on). PAC between beta and high gamma+HFO in the STN LFPs was also investigated. The effect of the condition was examined using linear mixed models.

RESULTS

PAC in the STN LFP was reduced by DBS when compared to the baseline condition (no medication and stimulation). Medication had no significant effect on PAC. Alpha power decreased with DBS, both alone and when combined with medication. Beta power decreased with DBS, medication, and DBS and medication combined. High gamma+HFO power increased during the application of contralateral DBS and was unaltered by medication.

CONCLUSIONS

The results provide new insights into the effects of DBS and levodopa on STN LFP PAC and oscillatory activity on the side contralateral to stimulation. These may have important implications in understanding mechanisms underlying motor improvements with DBS, including changes on both contralateral and ipsilateral sides, while suggesting a possible role for contralateral sensing during unilateral DBS.

A comparison of methods to suppress electrocardiographic artifacts in local field potential recordings

OBJECTIVE

Local field potential (LFP) recordings from deep brain stimulation (DBS) electrodes are often contaminated with electrocardiographic (ECG) artifacts that hinder the detection of disease-specific electrical brain activity.

METHODS

Three ECG suppression methods were evaluated: (1) QRS interpolation of the Perceive toolbox, (2) template subtraction, and (3) singular value decomposition (SVD). LFPs were recorded with the Medtronic Percept^TM PC system in nine Parkinson's disease patients with stimulation OFF ("OFF-DBS"; anode disconnected) and ON at 0 mA ("ON-DBS 0 mA"; anode connected). Findings were verified with simulated ECG-contaminated time series.

RESULTS

ECG artifacts were present in 10 out of 18 ON-DBS 0 mA recordings. All ECG suppression methods drastically reduced artifact-induced beta band (13-35 Hz) power and at least partly recovered the beta peak and beta burst dynamics. Using external ECG recordings and lengthening artifact epoch length improved the performance of the suppression methods. Increasing epoch length, however, elevated the risk of flattening the beta peak and losing beta burst dynamics.

CONCLUSIONS

The SVD method formed the preferred trade-off between artifact cleaning and signal loss, as long as its parameter settings are adequately chosen.

SIGNIFICANCE

ECG suppression methods enable analysis of disease-specific neural activity from signals affected by ECG artifacts.

A systematic review of local field potential physiomarkers in Parkinson's disease: from clinical correlations to adaptive deep brain stimulation algorithms

Deep brain stimulation (DBS) treatment has proven effective in suppressing symptoms of rigidity, bradykinesia, and tremor in Parkinson's disease. Still, patients may suffer from disabling fluctuations in motor and non-motor symptom severity during the day. Conventional DBS treatment consists of continuous stimulation but can potentially be further optimised by adapting stimulation settings to the presence or absence of symptoms through closed-loop control. This critically relies on the use of 'physiomarkers' extracted from (neuro)physiological signals. Ideal physiomarkers for adaptive DBS (aDBS) are indicative of symptom severity, detectable in every patient, and technically suitable for implementation. In the last decades, much effort has been put into the detection of local field potential (LFP) physiomarkers and in their use in clinical practice. We conducted a research synthesis of the correlations that have been reported between LFP signal features and one or more specific PD motor symptoms. Features based on the spectral beta band (~ 13 to 30 Hz) explained ~ 17% of individual variability in bradykinesia and rigidity symptom severity. Limitations of beta band oscillations as physiomarker are discussed, and strategies for further improvement of aDBS are explored.

Profiling the low-beta characteristics of the subthalamic nucleus in early- and late-onset Parkinson's disease

Objectives

Low-beta oscillation (13-20 Hz) has rarely been studied in patients with early-onset Parkinson's disease (EOPD, age of onset ≤50 years). We aimed to explore the characteristics of low-beta oscillation in the subthalamic nucleus (STN) of patients with EOPD and investigate the differences between EOPD and late-onset Parkinson's disease (LOPD).

Methods

We enrolled 31 EOPD and 31 LOPD patients, who were matched using propensity score matching. Patients underwent bilateral STN deep brain stimulation (DBS). Local field potentials were recorded using intraoperative microelectrode recording. We analyzed the low-beta band parameters, including aperiodic/periodic components, beta burst, and phase-amplitude coupling. We compared low-beta band activity between EOPD and LOPD. Correlation analyses were performed between the low-beta parameters and clinical assessment results for each group.

Results

We found that the EOPD group had lower aperiodic parameters, including offset (p = 0.010) and exponent (p = 0.047). Low-beta burst analysis showed that EOPD patients had significantly higher average burst amplitude (p = 0.016) and longer average burst duration (p = 0.011). Furthermore, EOPD had higher proportion of long burst (500-650 ms, p = 0.008), while LOPD had higher proportion of short burst (200-350 ms, p = 0.007). There was a significant difference in phase-amplitude coupling values between low-beta phase and fast high frequency oscillation (300-460 Hz) amplitude (p = 0.019).

Conclusion

We found that low-beta activity in the STN of patients with EOPD had characteristics that varied when compared with LOPD, and provided electrophysiological evidence for different pathological mechanisms between the two types of PD. These differences need to be considered when applying adaptive DBS on patients of different ages.

Developments in the mechanistic understanding and clinical application of deep brain stimulation for Parkinson's disease

INTRODUCTION. Deep brain stimulation (DBS) is a life-changing treatment for patients with Parkinson's disease (PD) and gives the unique opportunity to directly explore how basal ganglia work. Despite the rapid technological innovation of the last years, the untapped potential of DBS is still high.

AREAS COVERED. This review summarizes the developments in the mechanistic understanding of DBS and the potential clinical applications of cutting-edge technological advances. Rather than a univocal local mechanism, DBS exerts its therapeutic effects through several multimodal mechanisms and involving both local and network-wide structures, although crucial questions remain unexplained. Nonetheless, new insights in mechanistic understanding of DBS in PD have provided solid bases for advances in preoperative selection phase, prediction of motor and non-motor outcomes, leads placement and postoperative stimulation programming.

EXPERT OPINION. DBS has not only strong evidence of clinical effectiveness in PD treatment, but technological advancements are revamping its role of neuromodulation of brain circuits and key to better understanding PD pathophysiology. In the next few years, the worldwide use of new technologies in clinical practice will provide large data to elucidate their role and to expand their applications for PD patients, providing useful insights to personalize DBS treatment and follow-up.

Subthalamic high-beta oscillation informs the outcome of deep brain stimulation in patients with Parkinson's disease

Background

The therapeutic effect of deep brain stimulation (DBS) of the subthalamic nucleus (STN) for Parkinson's disease (PD) is related to the modulation of pathological neural activities, particularly the synchronization in the β band (13–35 Hz). However, whether the local β activity in the STN region can directly predict the stimulation outcome remains unclear.

Objective

We tested the hypothesis that low-β (13–20 Hz) and/or high-β (20–35 Hz) band activities recorded from the STN region can predict DBS efficacy.

Methods

Local field potentials (LFPs) were recorded in 26 patients undergoing deep brain stimulation surgery in the subthalamic nucleus area. Recordings were made after the implantation of the DBS electrode prior to its connection to a stimulator. The maximum normalized powers in the theta (4–7 Hz), alpha (7–13 Hz), low-β (13–20 Hz), high-β (20–35 Hz), and low-γ (40–55 Hz) subbands in the postoperatively recorded LFP were correlated with the stimulation-induced improvement in contralateral tremor or bradykinesia–rigidity. The distance between the contact selected for stimulation and the contact with the maximum subband power was correlated with the stimulation efficacy. Following the identification of the potential predictors by the significant correlations, a multiple regression analysis was performed to evaluate their effect on the outcome.

Results

The maximum high-β power was positively correlated with bradykinesia–rigidity improvement (rs = 0.549, p &lt; 0.0001). The distance to the contact with maximum high-β power was negatively correlated with bradykinesia–rigidity improvement (rs = −0.452, p &lt; 0.001). No significant correlation was observed with low-β power. The maximum high-β power and the distance to the contact with maximum high-β power were both significant predictors for bradykinesia–rigidity improvement in the multiple regression analysis, explaining 37.4% of the variance altogether. Tremor improvement was not significantly correlated with any frequency.

Conclusion

High-β oscillations, but not low-β oscillations, recorded from the STN region with the DBS lead can inform stimulation-induced improvement in contralateral bradykinesia–rigidity in patients with PD. High-β oscillations can help refine electrode targeting and inform contact selection for DBS therapy.

A Novel Framework in Quantifying Oscillatory Coupling to Gait Disturbance in Parkinson's Disease

Phase-amplitude coupling (PAC) based on the uniform phase empirical mode decomposition (UPEMD) is proposed to improve the accuracy of PAC assessment. The framework is applied to investigate the mechanism and improvement measure of gait disturbance for Parkinson's disease (PD). Hβ modulation is suppressed at the time of contralateral heel strikes and rebounds when the contralateral foot rests on the ground and the ipsilateral foot is raised. Prominent PACs exist between δ and Lβ/Hβ activities. Auditory cue improves the gait; meanwhile, it enhances the Hβ modulation, and suppresses the δ-Lβ/Hβ PACs, which may rebound toward the before-cue stage afterward. Our findings suggest the proposed UPEMD-PAC is a useful framework in quantifying PAC with pre-determined frequencies, whereas the δ-Lβ/Hβ PACs in the subthalamic nucleus serve as potential biomarkers for gait disturbance in PD. Clinical Relevance- This manifests the efficacy of auditory cues on gait disturbance. The proposed framework may be useful in diagnosing the severity of motor impairment.

A Pursuit of the Degree of Nonlinearity for β Oscillations under Motor Imagery

The power of β oscillations is an essential pathological biomarker for movement disorders, parkinsonism in particular. Motor imagery training was reported to support self-regulate such β oscillations. Past studies had focused on the modulation of β oscillatory power per se, ignoring the intrinsic oscillatory characteristics-the nonlinearity of the waveform. This work applied ensemble empirical mode decomposition to decompose neural activities in multiple frequency bands without destroying the temporal characteristics of the raw signal at all scales. We explored the dynamics of the degree of nonlinearity plus the averaged power across all periods and frequency bands of interest and tested how motor imagery may or may not induce nonlinearities under various frequency bands. With motor imagery, the degree of nonlinearity for the β activity is significantly suppressed referenced to that without, of note, and the average power fails to present significant differences between segments with and without motor imagery training. Our results indicate that the degree of nonlinearity is a complementary and vital biomarker as the average power for β oscillations, thereby providing theoretical support for the possible application in motor imagery therapy. Clinical Relevance- This suggests that motor imagery can suppress irregular patterns of β oscillations for healthy, and the degree of nonlinearity is an effective feature in improving classification in training states for the MI-neurofeedback paradigm compared to that of the averaged power.

Spectral and spatial distribution of subthalamic beta peak activity in Parkinson's disease patients

Current efforts to optimize subthalamic deep brain stimulation in Parkinson's disease patients aim to harness local oscillatory activity in the beta frequency range (13-35 Hz) as a feedback-signal for demand-based adaptive stimulation paradigms. A high prevalence of beta peak activity is prerequisite for this approach to become routine clinical practice. In a large dataset of postoperative rest recordings from 106 patients we quantified occurrence and identified determinants of spectral peaks in the alpha, low and high beta bands. At least one peak in beta band occurred in 92% of patients and 84% of hemispheres off medication, irrespective of demographic parameters, clinical subtype or motor symptom severity. Distance to previously described clinical sweet spot was significantly related both to beta peak occurrence and to spectral power (rho -0.21, p 0.006), particularly in the high beta band. Electrophysiological landscapes of our cohort's dataset in normalised space showed divergent heatmaps for alpha and beta but found similar regions for low and high beta frequency bands. We discuss potential ramifications for clinicians' programming decisions. In summary, this report provides robust evidence that spectral peaks in beta frequency range can be detected in the vast majority of Parkinsonian subthalamic nuclei, increasing confidence in the broad applicability of beta-guided deep brain stimulation.

A translational perspective on pathophysiological changes of oscillatory activity in dystonia and parkinsonism

Intracerebral recordings from movement disorders patients undergoing deep brain stimulation have allowed the identification of pathophysiological patterns in oscillatory activity that correlate with symptom severity. Changes in oscillatory synchrony occur within and across brain areas, matching the classification of movement disorders as network disorders. However, the underlying mechanisms of oscillatory changes are difficult to assess in patients, as experimental interventions are technically limited and ethically problematic. This is why animal models play an important role in neurophysiological research of movement disorders. In this review, we highlight the contributions of translational research to the mechanistic understanding of pathological changes in oscillatory activity, with a focus on parkinsonism and dystonia, while addressing the limitations of current findings and proposing possible future directions.

Subthalamic low beta bursts differ in Parkinson's disease phenotypes

OBJECTIVE

Parkinson's disease (PD) patients may be categorized into tremor-dominant (TD) and postural-instability and gait disorder (PIGD) motor phenotypes, but the dynamical aspects of subthalamic nucleus local field potentials (STN-LFP) and the neural correlates of this phenotypical classification remain unclear.

METHODS

35 STN-LFP (20 PIGD and 15 TD) were investigated through continuous wavelet transform and machine-learning-based methods. The beta oscillation - the main band associated with motor impairment in PD - dynamics was characterized through beta burst parameters across phenotypes and burst intervals under specific proposed criteria for optimal burst threshold definition.

RESULTS

Low-frequency (13-22 Hz) beta burst probability was the best predictor for PD phenotypes (75% accuracy). PIGD patients presented higher average burst duration (p = 0.018), while TD patients exhibited higher burst probability (p = 0.014). Categorization into shorter and longer than 400 ms bursts led to significant interaction between burst length categories and the phenotypes (p < 0.050) as revealed by mixed-effects models. Long burst durations and short bursts probability positively correlated, respectively, with rigidity-bradykinesia (p = 0.029) and tremor (p = 0.038) scores.

CONCLUSIONS

Subthalamic low-frequency beta bursts differed between TD and PIGD phenotypes and correlated with motor symptoms.

SIGNIFICANCE

These findings improve the PD phenotypes' electrophysiological characterization and may define new criteria for adaptive deep brain stimulation.

Theta-gamma coupling as a cortical biomarker of brain-computer interface-mediated motor recovery in chronic stroke

Chronic stroke patients with upper-limb motor disabilities are now beginning to see treatment options that were not previously available. To date, the two options recently approved by the United States Food and Drug Administration include vagus nerve stimulation and brain-computer interface therapy. While the mechanisms for vagus nerve stimulation have been well defined, the mechanisms underlying brain-computer interface-driven motor rehabilitation are largely unknown. Given that cross-frequency coupling has been associated with a wide variety of higher-order functions involved in learning and memory, we hypothesized this rhythm-specific mechanism would correlate with the functional improvements effected by a brain-computer interface. This study investigated whether the motor improvements in chronic stroke patients induced with a brain-computer interface therapy are associated with alterations in phase-amplitude coupling, a type of cross-frequency coupling. Seventeen chronic hemiparetic stroke patients used a robotic hand orthosis controlled with contralesional motor cortical signals measured with EEG. Patients regularly performed a therapeutic brain-computer interface task for 12 weeks. Resting-state EEG recordings and motor function data were acquired before initiating brain-computer interface therapy and once every 4 weeks after the therapy. Changes in phase-amplitude coupling values were assessed and correlated with motor function improvements. To establish whether coupling between two different frequency bands was more functionally important than either of those rhythms alone, we calculated power spectra as well. We found that theta-gamma coupling was enhanced bilaterally at the motor areas and showed significant correlations across brain-computer interface therapy sessions. Importantly, an increase in theta-gamma coupling positively correlated with motor recovery over the course of rehabilitation. The sources of theta-gamma coupling increase following brain-computer interface therapy were mostly located in the hand regions of the primary motor cortex on the left and right cerebral hemispheres. Beta-gamma coupling decreased bilaterally at the frontal areas following the therapy, but these effects did not correlate with motor recovery. Alpha-gamma coupling was not altered by brain-computer interface therapy. Power spectra did not change significantly over the course of the brain-computer interface therapy. The significant functional improvement in chronic stroke patients induced by brain-computer interface therapy was strongly correlated with increased theta-gamma coupling in bihemispheric motor regions. These findings support the notion that specific cross-frequency coupling dynamics in the brain likely play a mechanistic role in mediating motor recovery in the chronic phase of stroke recovery.

LFP Analysis of Brain Injured Anesthetized Animals Undergoing Closed-Loop Intracortical Stimulation

Activity dependent stimulation (ADS) is a closed loop stimulation technique whose neurophysiological effects have not been deeply investigated. Here we explored how Local field Potentials (LFP) are impacted by a focal ischemic lesion and, subsequently, by ADS treatment. Intracortical microelectrode arrays were implanted in the rostral forelimb area (RFA) and in the primary somatosensory area (S1) of anaesthetized rats. An ischemic injury was induced in the caudal forelimb area through microinjections of Endothelin-1. The lesion induced an acute depressive trend in LFP power in RFA (evaluated in 6 bands of interest: Delta (1–4Hz), Theta (4–8Hz), Alpha (8–11Hz), Beta (11–30Hz), LowGamma (30–55Hz) and HighGamma (55–80)) followed by a noticeable significant rebound in both areas. Applying ADS induced an overall decrease of power. The lesion impacted the connectivity in a frequency specific manner, resulting in widespread increase in connectivity in Delta both between and within areas. Two hours after the lesion, without stimulation, correlated activity between areas increased in Beta and Gamma. After stimulation, inter-area connectivity increased in Delta, Theta and Alpha, while considerably dropping within RFA in highGamma. By computing phase-amplitude coupling, we found that the lesion produced an incremental increase in the coupling between (Theta) Alpha phase and (lowGamma) highGamma amplitude within RFA, while S1 had a more generalized increase. Likewise, coupling between Theta phase and lowGamma/highGamma amplitudes increased between areas after lesion. ADS induced a similar increase, but greater in magnitude both within and between RFA and S1. These results have important implications on the emerging field of closed-loop adaptive stimulation promoting ADS as an innovative tool for the treatment of neurological disorders.

Cross-frequency phase-amplitude coupling in repetitive movements in patients with Parkinson's disease

Bradykinesia is a cardinal motor symptom in Parkinson’s disease (PD), the pathophysiology of which is not fully understood. We analyzed the role of cross-frequency coupling of oscillatory cortical activity in motor impairment in patients with PD and healthy controls. High-density EEG signals were recorded during various motor activities and at rest. Patients performed a repetitive finger-pressing task normally, but were slower than controls during tapping. Phase-amplitude coupling (PAC) between β (13–30 Hz) and broadband γ (50–150 Hz) was computed from individual EEG source signals in the premotor, primary motor, and primary somatosensory cortices, and the primary somatosensory complex. In all four regions, averaging the entire movement period resulted in higher PAC in patients than in controls for the resting condition and the pressing task (similar performance between groups). However, this was not the case for the tapping tasks where patients performed slower. This suggests the strength of state-related β-γ PAC does not determine Parkinsonian bradykinesia. Examination of the dynamics of oscillatory EEG signals during motor transitions revealed a distinctive motif of PAC rise and decay around press onset. This pattern was also present at press offset and slow tapping onset, linking such idiosyncratic PAC changes to transitions between different movement states. The transition-related PAC modulation in patients was similar to controls in the pressing task but flattened during slow tapping, which related to normal and abnormal performance, respectively. These findings suggest that the dysfunctional evolution of neuronal population dynamics during movement execution is an important component of the pathophysiology of Parkinsonian bradykinesia.

NEW & NOTEWORTHY Our findings using noninvasive EEG recordings provide evidence that PAC dynamics might play a role in the physiological cortical control of movement execution and may encode transitions between movement states. Results in patients with Parkinson’s disease suggest that bradykinesia is related to a deficit of the dynamic regulation of PAC during movement execution rather than its absolute strength. Our findings may contribute to the development of a new concept of the pathophysiology of bradykinesia.

Cross-Frequency Brain Network Dynamics Support Pitch Change Detection

Processing auditory sequences involves multiple brain networks and is crucial to complex perception associated with music appreciation and speech comprehension. We used time-resolved cortical imaging in a pitch change detection task to detail the underlying nature of human brain network activity, at the rapid time scales of neurophysiology. In response to tone sequence presentation to the participants, we observed slow inter-regional signaling at the pace of tone presentations (2-4 Hz) that was directed from auditory cortex toward both inferior frontal and motor cortices. Symmetrically, motor cortex manifested directed influence onto auditory and inferior frontal cortices via bursts of faster (15-35 Hz) activity. These bursts occurred precisely at the expected latencies of each tone in a sequence. This expression of interdependency between slow/fast neurophysiological activity yielded a form of local cross-frequency phase-amplitude coupling in auditory cortex, which strength varied dynamically and peaked when pitch changes were anticipated. We clarified the mechanistic relevance of these observations in relation to behavior by including a group of individuals afflicted by congenital amusia, as a model of altered function in processing sound sequences. In amusia, we found a depression of inter-regional slow signaling toward motor and inferior frontal cortices, and a chronic overexpression of slow/fast phase-amplitude coupling in auditory cortex. These observations are compatible with a misalignment between the respective neurophysiological mechanisms of stimulus encoding and internal predictive signaling, which was absent in controls. In summary, our study provides a functional and mechanistic account of neurophysiological activity for predictive, sequential timing of auditory inputs.SIGNIFICANCE STATEMENT Auditory sequences are processed by extensive brain networks, involving multiple systems. In particular, fronto-temporal brain connections participate in the encoding of sequential auditory events, but so far, their study was limited to static depictions. This study details the nature of oscillatory brain activity involved in these inter-regional interactions in human participants. It demonstrates how directed, polyrhythmic oscillatory interactions between auditory and motor cortical regions provide a functional account for predictive timing of incoming items in an auditory sequence. In addition, we show the functional relevance of these observations in relation to behavior, with data from both normal hearing participants and a rare cohort of individuals afflicted by congenital amusia, which we considered here as a model of altered function in processing sound sequences.

Frequency Nesting Interactions in the Subthalamic Nucleus Correlate With the Step Phases for Parkinson's Disease

Gait disturbance in Parkinson's disease (PD) can be ameliorated by sound stimulation. Given that excessive β synchronization in basal ganglia is linked to motor impairment in PD, whether the frequency nesting interactions are associated with the gait problem is far from clear. To this end, the masking phase-amplitude coupling (PAC) method was proposed to overcome the trade-off between intrinsic nonlinearity/non-stationarity and demand for predetermined frequencies, normally extracted by the filter. In this study, we analyzed LFPs recorded from 13 patients (one female) with PD during stepping with bilateral deep brain electrodes implanted in the subthalamic nucleus (STN). We found that not only high-frequency oscillation (100-300 Hz) was modulated by β (13-30 Hz) but also β and γ amplitude were modulated by their low-frequency components in δ/θ/α and δ/θ/α/β bands. These PAC values were suppressed by sound stimulation, along with an improvement in gait. We also showed that gait-related high-β (Hβ) modulation in the STN was sensitive to auditory cues, and Hβ gait-phase modulation increased with a metronome. Meanwhile, phase-locking values (PLVs) across all frequencies were significantly suppressed around contralateral heel strikes, manifesting the contralateral step as a critical gait phase in gait initiation for PD. Only the PLVs around contralateral steps were sensitive to auditory cues. Our results support masking PAC as an effective method in exploring frequency nesting interactions in LFPs and reveal the linkages between sound stimulation and couplings related to gait phases in the STN. These findings raise the possibility that nesting interactions in the STN work as feasible biomarkers in alleviating gait disorders.

Dopaminergic Modulation of Local Non-oscillatory Activity and Global-Network Properties in Parkinson's Disease: An EEG Study

Dopaminergic medication for Parkinson's disease (PD) modulates neuronal oscillations and functional connectivity (FC) across the basal ganglia-thalamic-cortical circuit. However, the non-oscillatory component of the neuronal activity, potentially indicating a state of excitation/inhibition balance, has not yet been investigated and previous studies have shown inconsistent changes of cortico-cortical connectivity as a response to dopaminergic medication. To further elucidate changes of regional non-oscillatory component of the neuronal power spectra, FC, and to determine which aspects of network organization obtained with graph theory respond to dopaminergic medication, we analyzed a resting-state electroencephalography (EEG) dataset including 15 PD patients during OFF and ON medication conditions. We found that the spectral slope, typically used to quantify the broadband non-oscillatory component of power spectra, steepened particularly in the left central region in the ON compared to OFF condition. In addition, using lagged coherence as a FC measure, we found that the FC in the beta frequency range between centro-parietal and frontal regions was enhanced in the ON compared to the OFF condition. After applying graph theory analysis, we observed that at the lower level of topology the node degree was increased, particularly in the centro-parietal area. Yet, results showed no significant difference in global topological organization between the two conditions: either in global efficiency or clustering coefficient for measuring global and local integration, respectively. Interestingly, we found a close association between local/global spectral slope and functional network global efficiency in the OFF condition, suggesting a crucial role of local non-oscillatory dynamics in forming the functional global integration which characterizes PD. These results provide further evidence and a more complete picture for the engagement of multiple cortical regions at various levels in response to dopaminergic medication in PD.

Deep brain stimulation for Parkinson’s Disease: A Review and Future Outlook

Parkinson's Disease (PD) is a neurodegenerative disorder that manifests as an impairment of motor and non-motor abilities due to a loss of dopamine input to deep brain structures. While there is presently no cure for PD, a variety of pharmacological and surgical therapeutic interventions have been developed to manage PD symptoms. This review explores the past, present and future outlooks of PD treatment, with particular attention paid to deep brain stimulation (DBS), the surgical procedure to deliver DBS, and its limitations. Finally, our group's efforts with respect to brain mapping for DBS targeting will be discussed.

Magnetoencephalography detects phase-amplitude coupling in Parkinson's disease

To characterize Parkinson's disease, abnormal phase-amplitude coupling is assessed in the cortico-basal circuit using invasive recordings. It is unknown whether the same phenomenon might be found in regions other than the cortico-basal ganglia circuit. We hypothesized that using magnetoencephalography to assess phase-amplitude coupling in the whole brain can characterize Parkinson's disease. We recorded resting-state magnetoencephalographic signals in patients with Parkinson's disease and in healthy age- and sex-matched participants. We compared whole-brain signals from the two groups, evaluating the power spectra of 3 frequency bands (alpha, 8-12 Hz; beta, 13-25 Hz; gamma, 50-100 Hz) and the coupling between gamma amplitude and alpha or beta phases. Patients with Parkinson's disease showed significant beta-gamma phase-amplitude coupling that was widely distributed in the sensorimotor, occipital, and temporal cortices; healthy participants showed such coupling only in parts of the somatosensory and temporal cortices. Moreover, beta- and gamma-band power differed significantly between participants in the two groups (P < 0.05). Finally, beta-gamma phase-amplitude coupling in the sensorimotor cortices correlated significantly with motor symptoms of Parkinson's disease (P < 0.05); beta- and gamma-band power did not. We thus demonstrated that beta-gamma phase-amplitude coupling in the resting state characterizes Parkinson's disease.

Novel approaches for quantifying beta synchrony in Parkinson's disease

Despite the clinical and financial burden of Parkinson's disease (PD), there is no standardized, reliable biomarker to diagnose and track PD progression. Instead, PD is primarily assessed using subjective clinical rating scales and patient self-report. Such approaches can be imprecise, hindering diagnosis and disease monitoring. An objective biomarker would be beneficial for clinical care, refining diagnosis, and treatment. Due to widespread electrophysiological abnormalities both within and between brain structures in PD, development of electrophysiologic biomarkers may be feasible. Basal ganglia recordings acquired with neurosurgical approaches have revealed elevated power in the beta frequency range (13-30 Hz) in PD, suggesting that beta power could be a putative PD biomarker. However, there are limitations to the use of beta power as a biomarker. Recent advances in analytic approaches have led to novel methods to quantify oscillatory synchrony in the beta frequency range. Here we describe some of these novel approaches in the context of PD and explore how they may serve as electrophysiological biomarkers. These novel signatures include (1) interactions between beta phase and broadband (> 50 Hz, "gamma") amplitude (i.e., phase amplitude coupling, PAC), (2) asymmetries in waveform shape, (3) beta coherence, and (4) beta "bursts." Development of a robust, reliable, and readily accessible electrophysiologic biomarker would represent a major step towards more precise and personalized care in PD.

Quantitative Electroencephalography as a Biomarker for Cognitive Dysfunction in Parkinson's Disease

Background: Quantitative electroencephalography (qEEG) has been suggested as a biomarker for cognitive decline in Parkinson's disease (PD). Objective: Determine if applying a wavelet-based qEEG algorithm to 21-electrode, resting-state EEG recordings obtained in a routine clinical setting has utility for predicting cognitive impairment in PD. Methods: PD subjects, evaluated by disease stage and motor score, were compared to healthy controls (N = 20 each). PD subjects with normal (PDN, MoCA 26-30, N = 6) and impaired (PDD, MoCA ≤ 25, N = 14) cognition were compared. The wavelet-transform based time-frequency algorithm assessed the instantaneous predominant frequency (IPF) at 60 ms intervals throughout entire recordings. We then determined the relative time spent by the IPF in the four standard EEG frequency bands (RTF) at each scalp location. The resting occipital rhythm (ROR) was assessed using standard power spectral analysis. Results: Comparing PD subjects to healthy controls, mean values are decreased for ROR and RTF-Beta, greater for RTF-Theta and similar for RTF-Delta and RTF-Alpha. In logistic regression models, arithmetic combinations of RTF values [e.g., (RTF-Alpha) + (RTF-Beta)/(RTF-Delta + RTF-Theta)] and RTF-Alpha values at occipital or parietal locations are most able to discriminate between PD and controls. A principal component (PC) from principal component analysis (PCA) using RTF-band values in all subjects is associated with PD status (p = 0.004, β = 0.31, AUC = 0.780). Its loadings show positive contribution from RTF-Theta at all scalp locations, and negative contributions from RTF-Beta at occipital, parietal, central, and temporal locations. Compared to cognitively normal PD subjects, cognitively impaired PD subjects have lower median RTF-Alpha and RTF-Beta values, greater RTF-Theta values and similar RTF-Delta values. A PC from PCA using RTF-band values in PD subjects is associated with cognitive status (p = 0.002, β = 0.922, AUC = 0.89). Its loadings show positive contributions from RTF-Theta at all scalp locations, negative contributions from RTF-Beta at central locations, and negative contributions from RTF-Delta at central, frontal and temporal locations. Age, disease duration and/or sex are not significant covariates. No PC was associated with motor score or disease stage. Significance: Analyzing standard EEG recordings obtained in a community practice setting using a wavelet-based qEEG algorithm shows promise as a PD biomarker and for predicting cognitive impairment in PD.

Brain oscillations and Parkinson disease

Brain oscillations have been associated with Parkinson's disease (PD) for a long time mainly due to the fundamental oscillatory nature of parkinsonian rest tremor. Over the years, this association has been extended to frequencies well above that of tremor, largely owing to the opportunities offered by deep brain stimulation (DBS) to record electrical activity directly from the patients' basal ganglia. This chapter reviews the results of research on brain oscillations in PD focusing on theta (4-7Hz), beta (13-35Hz), gamma (70-80Hz) and high-frequency oscillations (200-400Hz). For each of these oscillations, we describe localization and interaction with brain structures and between frequencies, changes due to dopamine intake, task-related modulation, and clinical relevance. The study of brain oscillations will also help to dissect the mechanisms of action of DBS. Overall, the chapter tentatively depicts PD in terms of "oscillopathy."

Adaptive deep brain stimulation: Retuning Parkinson's disease

A brain-machine interface represents a promising therapeutic avenue for the treatment of many neurologic conditions. Deep brain stimulation (DBS) is an invasive, neuro-modulatory tool that can improve different neurologic disorders by delivering electric stimulation to selected brain areas. DBS is particularly successful in advanced Parkinson's disease (PD), where it allows sustained improvement of motor symptoms. However, this approach is still poorly standardized, with variable clinical outcomes. To achieve an optimal therapeutic effect, novel adaptive DBS (aDBS) systems are being developed. These devices operate by adapting stimulation parameters in response to an input signal that can represent symptoms, motor activity, or other behavioral features. Emerging evidence suggests greater efficacy with fewer adverse effects during aDBS compared with conventional DBS. We address this topic by discussing the basics principles of aDBS, reviewing current evidence, and tackling the many challenges posed by aDBS for PD.

Towards guided and automated programming of subthalamic area stimulation in Parkinson’s disease

Selecting the ideal contact to apply subthalamic nucleus deep brain stimulation in Parkinson’s disease can be an arduous process, with outcomes highly dependent on clinician expertise. This study aims to assess whether neuronal signals recorded intraoperatively in awake patients, and the anatomical location of contacts, can assist programming. In a cohort of 14 patients with Parkinson’s disease, implanted with subthalamic nucleus deep brain stimulation, the four contacts on each lead in the 28 hemispheres were ranked according to proximity to a nominated ideal anatomical location and power of the following neuronal signals: evoked resonant neural activity, beta oscillations and high-frequency oscillations. We assessed how these rankings predicted, on each lead: (i) the motor benefit from deep brain stimulation applied through each contact and (ii) the ‘ideal’ contact to apply deep brain stimulation. The ranking of contacts according to each factor predicted motor benefit from subthalamic nucleus deep brain stimulation, as follows: evoked resonant neural activity; r² = 0.50, Akaike information criterion 1039.9, beta; r² = 0.50, Akaike information criterion 1041.6, high-frequency oscillations; r² = 0.44, Akaike information criterion 1057.2 and anatomy; r² = 0.49, Akaike information criterion 1048.0. Combining evoked resonant neural activity, beta and high-frequency oscillations ranking data yielded the strongest predictive model (r² = 0.61, Akaike information criterion 1021.5). The ‘ideal’ contact (yielding maximal benefit) was ranked first according to each factor in the following proportion of hemispheres; evoked resonant neural activity 18/28, beta 17/28, anatomy 16/28, high-frequency oscillations 7/28. Across hemispheres, the maximal available deep brain stimulation benefit did not differ from that yielded by contacts chosen by clinicians for chronic therapy or contacts ranked first according to evoked resonant neural activity. Evoked resonant neural activity, beta oscillations and anatomy similarly predicted how motor benefit from subthalamic nucleus deep brain stimulation varied across contacts on each lead. This could assist programming by providing a probability ranking of contacts akin to a ‘monopolar survey’. However, these factors identified the ‘ideal’ contact in only a proportion of hemispheres. More advanced signal processing and anatomical techniques may be needed for the full automation of contact selection.

Influence of inter-electrode distance on subthalamic nucleus local field potential recordings in Parkinson's disease

OBJECTIVES

To evaluate spectra and their correlations with clinical symptoms of local field potentials (LFP) acquired from wide- and close-spaced contacts (i.e. between contacts 0-3 or LFP03, and contacts 1-2 or LFP12 respectively) on the same DBS electrode within the subthalamus (STN) in Parkinson's disease (PD), before and after levodopa administration.

METHODS

LFP12 and LFP03 were recorded from 20 PD patients. We evaluated oscillatory power, local and switched phase-amplitude coupling (l- and Sw-PAC) and correlation with motor symptoms (UPDRSIII).

RESULTS

Before levodopa, both LFP03 and LFP12 power in the α band inversely correlated with UPDRSIII. Differences between contacts were found in the low-frequency bands power. After levodopa, differences in UPDRSIII were associated to changes in LFP03 low-β and LFP12 HFO (high frequency oscillations, 250-350 Hz) power, while a modulation of the low-β power and an increased β-LFO (low frequency oscillations, 15-45 Hz) PAC was found only for LFP12.

CONCLUSION

This study reveals differences in spectral pattern between LFP12 and LFP03 before and after levodopa administration, as well as different correlations with PD motor symptoms.

SIGNIFICANCE

Differences between LFP12 and LFP03 may offer an opportunity for optimizing adaptive deep brain stimulation (aDBS) protocols for PD. LFP12 can be used to detect β-HFO coupling and β power (i.e. bradykinesia), while LFP03 are optimal for low frequency oscillations (dyskinesias).

Subthalamic dynamic neural states correlate with motor symptoms in Parkinson's Disease

OBJECTIVE

This study aims to discriminate the dynamic synchronization states from the subthalamic local field potentials and investigate their correlations with the motor symptoms in Parkinson's Disease (PD).

METHODS

The resting-state local field potentials of 10 patients with PD were recorded from the subthalamic nucleus. The dynamic neural states of multiple oscillations were discriminated and analyzed. The Spearman correlation was used to investigate the correlations between occurrence rate or duration of dynamic neural states and the severity of motor symptoms.

RESULTS

The proportion of long low-beta and theta synchronized state was significantly correlated with the general motor symptom and tremor, respectively. The duration of combined low/high-beta state was significantly correlated with rigidity, and the duration of combined alpha/high-beta state was significantly correlated with bradykinesia.

CONCLUSIONS

This study provides evidence that motor symptoms are associated with the neural states coded with multiple oscillations in PD.

SIGNIFICANCE

This study may advance the understanding of the neurophysiological mechanisms of the motor symptoms and provide potential biomarkers for closed-loop deep brain stimulation in PD.

Peaks in the beta band of the human subthalamic nucleus: a case for low beta and high beta activity

OBJECTIVE

Peaks in the beta band of local field potentials (LFPs) may serve as a biological feedback signal for closed-loop deep brain stimulation (DBS) in Parkinson's disease (PD). However, the specific frequency of such peaks and their response to DBS and to different types of movement remains uncertain. In the present study, the authors examined the abundance of discernible peaks in the beta band and the effect of different types of movement and DBS on these peaks.

METHODS

Subthalamic nucleus LFPs were analyzed from 38 patients with PD in a frequency range between 10 and 35 Hz, as well as the impact of movement (gait, hand movements) and electrical stimulation on these peaks. The position of the electrode segments from which LFPs were recorded was computed.

RESULTS

The authors found a bimodal distribution of peaks in the beta band with discernible high- (27 Hz) and low-frequency (15 Hz) peaks. Movement of either hand had no significant effect on these peaks, whereas walking significantly reduced high-frequency beta peaks but not the peaks in the low beta band. Stimulation caused an amplitude-dependent suppression of both peaks.

CONCLUSIONS

DBS suppresses LFP beta peaks of different frequencies, whereas beta suppression caused by movement is dependent on the type of movement and frequency of the peak. These results will support the investigation of distinct LFP spectra for the application of closed-loop DBS.

Towards adaptive deep brain stimulation: clinical and technical notes on a novel commercial device for chronic brain sensing

Objective. Technical advances in deep brain stimulation (DBS) are crucial to improve therapeutic efficacy and battery life. We report the potentialities and pitfalls of one of the first commercially available devices capable of recording brain local field potentials (LFPs) from the implanted DBS leads, chronically and during stimulation. The aim was to provide clinicians with well-grounded tips on how to maximize the capabilities of this novel device, both in everyday practice and for research purposes.Approach. We collected clinical and neurophysiological data of the first 20 patients (14 with Parkinson's disease (PD), five with dystonia, one with chronic pain) that received the Percept™ PC in our centres. We also performed tests in a saline bath to validate the recordings quality.Main results. The Percept PC reliably recorded the LFP of the implanted site, wirelessly and in real time. We recorded the most promising clinically useful biomarkers for PD and dystonia (beta and theta oscillations) with and without stimulation. Furthermore, we provide an open-source code to facilitate export and analysis of data. Critical aspects of the system are presently related to contact selection, artefact detection, data loss, and synchronization with other devices.Significance. New technologies will soon allow closed-loop neuromodulation therapies, capable of adapting stimulation based on real-time symptom-specific and task-dependent input signals. However, technical aspects need to be considered to ensure reliable recordings. The critical use by a growing number of DBS experts will alert new users about the currently observed shortcomings and inform on how to overcome them.