Frontal theta and beta oscillations during lower-limb movement in Parkinson's disease

Cortical modulations before lower limb motor blocks are associated with freezing of gait in Parkinson's disease: an EEG source localization study

BACKGROUND

Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease (PD) characterized by paroxysmal episodes in which patients are unable to step forward. A research priority is identifying cortical changes before freezing in PD-FOG.

METHODS

We tested 19 patients with PD who had been assessed for FOG (n=14 with FOG and 5 without FOG). While seated, patients stepped bilaterally on pedals to progress forward through a virtual hallway while 64-channel EEG was recorded. We assessed cortical activities before and during lower limb motor blocks (LLMB), defined as a break in rhythmic pedaling, and stops, defined as movement cessation following an auditory stop cue. This task was selected because LLMB correlates with FOG severity in PD and allows recording of high-quality EEG. Patients were tested after overnight withdrawal from dopaminergic medications ("off" state) and in the "on" medications state. EEG source activities were evaluated using individual MRI and standardized low resolution brain electromagnetic tomography (sLORETA). Functional connectivity was evaluated by phase lag index between seeds and pre-defined cortical regions of interest.

RESULTS

EEG source activities for LLMB vs. cued stops localized to right posterior parietal area (Brodmann area 39), lateral premotor area (Brodmann area 6), and inferior frontal gyrus (Brodmann area 47). In these areas, PD-FOG (n=14) increased alpha rhythms (8-12 Hz) before LLMB vs. typical stepping, whereas PD without FOG (n=5) decreased alpha power. Alpha rhythms were linearly correlated with LLMB severity, and the relationship became an inverted U-shape when assessing alpha rhythms as a function of percent time in LLMB in the "off" medication state. Right inferior frontal gyrus and supplementary motor area connectivity was observed before LLMB in the beta band (13-30 Hz). This same pattern of connectivity was seen before stops. Dopaminergic medication improved FOG and led to less alpha synchronization and increased functional connections between frontal and parietal areas.

CONCLUSIONS

Right inferior parietofrontal structures are implicated in PD-FOG. The predominant changes were in the alpha rhythm, which increased before LLMB and with LLMB severity. Similar connectivity was observed for LLMB and stops between the right inferior frontal gyrus and supplementary motor area, suggesting that FOG may be a form of "unintended stopping." These findings may inform approaches to neurorehabilitation of PD-FOG.

Quantifying instability in neurological disorders EEG based on phase space DTM function

Classifying individuals with neurological disorders and healthy subjects using EEG is a crucial area of research. The current feature extraction approach focuses on the frequency domain features in each of the EEG frequency bands and functional brain networks. In recent years, researchers have discovered and extensively studied stability differences in the electroencephalograms (EEG) of patients with neurological disorders. Based on this, this paper proposes a feature descriptor to characterize EEG instability. The proposed method starts by forming a signal point cloud through Phase Space Reconstruction (PSR). Subsequently, a pseudo-metric space is constructed, and pseudo-distances are calculated based on the consistent measure of the point cloud. Finally, Distance to Measure (DTM) Function are generated to replace the distance function in the original metric space. We calculated the relative distances in the point cloud by measuring signal similarity and, based on this, summarized the point cloud structures formed by EEG with different stabilities after PSR. This process demonstrated that Multivariate Kernel Density Estimation (MKDE) based on a Gaussian kernel can effectively separate the mappings of different stable components within the signal in the phase space. The two average DTM values are then proposed as feature descriptors for EEG instability.In the validation phase, the proposed feature descriptor is tested on three typical neurological disorders: epilepsy, Alzheimer's disease, and Parkinson's disease, using the Bonn dataset, CHB-MIT, the Florida State University dataset, and the Iowa State University dataset. DTM values are used as feature inputs for four different machine learning classifiers, and The results show that the best classification accuracy of the proposed method reaches 98.00 %, 96.25 %, 96.71 % and 95.34 % respectively, outperforming commonly used nonlinear descriptors. Finally, the proposed method is tested and analyzed using noisy signals, demonstrating its robustness compared to other methods.

Multi-Scale Feature and Multi-Channel Selection toward Parkinson's Disease Diagnosis with EEG

OBJECTIVE

Motivated by Health Care 4.0, this study aims to reducing the dimensionality of traditional EEG features based on manual extracted features, including statistical features in the time and frequency domains.

METHODS

A total of 22 multi-scale features were extracted from the UNM and Iowa datasets using a 4th order Butterworth filter and wavelet packet transform. Based on single-channel validation, 29 channels with the highest R2 scores were selected from a pool of 59 common channels. The proposed channel selection scheme was validated on the UNM dataset and tested on the Iowa dataset to compare its generalizability against models trained without channel selection.

RESULTS

The experimental results demonstrate that the proposed model achieves an optimal classification accuracy of 100%. Additionally, the generalization capability of the channel selection method is validated through out-of-sample testing based on the Iowa dataset Conclusions: Using single-channel validation, we proposed a channel selection scheme based on traditional statistical features, resulting in a selection of 29 channels. This scheme significantly reduced the dimensionality of EEG feature vectors related to Parkinson's disease by 50%. Remarkably, this approach demonstrated considerable classification performance on both the UNM and Iowa datasets. For the closed-eye state, the highest classification accuracy achieved was 100%, while for the open-eye state, the highest accuracy reached 93.75%.

Investigating the Brain Mechanisms of Externally Cued Sit-to-Stand Movement in Parkinson's Disease

BACKGROUND

One of the more challenging daily-life actions for Parkinson's disease patients is starting to stand from a sitting position. Parkinson's disease patients are known to have difficulty with self-initiated movements and benefit from external cues. However, the brain processes underlying external cueing as an aid remain unknown. The advent of mobile electroencephalography (EEG) now enables the investigation of these processes in dynamic sit-to-stand movements.

OBJECTIVE

To identify cortical correlates of the mechanisms underlying auditory cued sit-to-stand movement in Parkinson's disease.

METHODS

Twenty-two Parkinson's disease patients and 24 healthy age-matched participants performed self-initiated and externally cued sit-to-stand movements while cortical activity was recorded through 32-channel mobile EEG.

RESULTS

Overall impaired integration of sensory and motor information can be seen in the Parkinson's disease patients exhibiting less modulation in the θ band during movement compared to healthy age-matched controls. How Parkinson's disease patients use external cueing of sit-to-stand movements can be seen in larger high β power over sensorimotor brain areas compared to healthy controls, signaling sensory integration supporting the maintenance of motor output. This appears to require changes in cognitive processing to update the motor plan, reflected in frontal θ power increases in Parkinson's disease patients when cued.

CONCLUSION

These findings provide the first neural evidence for why and how cueing improves motor function in sit-to-stand movement in Parkinson's disease. The Parkinson's disease patients' neural correlates indicate that cueing induces greater activation of motor cortical areas supporting the maintenance of a more stable motor output, but involves the use of cognitive resources to update the motor plan. © 2024 International Parkinson and Movement Disorder Society.

Neural oscillations guiding action during effects imagery

Goal-directed acting requires the integration of sensory information but can also be performed without direct sensory input. Examples of this can be found in sports and can be conceptualized by feedforward processes. There is, however, still a lack of understanding of the temporal neural dynamics and neuroanatomical structures involved in such processes. In the current study, we used EEG beamforming methods and examined 37 healthy participants in two well-controlled experiments varying the necessity of anticipatory processes during goal-directed action. We found that alpha and beta activity in the medial and posterior cingulate cortex enabled feedforward predictions about the position of an object based on the latest sensorimotor state. On this basis, theta band activity seems more related to sensorimotor representations, while beta band activity would be more involved in setting up the structure of the neural representations themselves. Alpha band activity in sensory cortices reflects an intensified gating of the anticipated perceptual consequences of the to-be-executed action. Together, the findings indicate that goal-directed acting through the anticipation of the predicted state of an effector is based on accompanying processes in multiple frequency bands in midcingulate and sensory brain regions.

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.

Neurophysiological processes reflecting the effects of the immediate past during the dynamic management of actions

In recent years, there has been many efforts to establish a comprehensive theoretical framework explaining the working mechanisms involved in perception-action integration. This framework stresses the importance of the immediate past on mechanisms supporting perception-action integration. The present study investigates the neurophysiological principles of dynamic perception-action bindings, particularly considering the influence of the immediate history on action control mechanisms. For this purpose, we conducted an established stimulus-response binding paradigm during EEG recording. The SR-task measures stimulus-response binding in terms of accuracy and reaction time differences depending on the degree of feature overlap between conditions. Alpha, beta and theta band activity in distinct time domains as well as associated brain regions were investigated applying time-frequency analyses, a beamforming approach as well as correlation analyses. We demonstrate, for the first time, interdependencies of neuronal processes relying on the immediate past. The reconfiguration of an action seems to overwrite immediately preceding processes. The analyses revealed modulations of theta (TBA), alpha (ABA) and beta band activity (BBA) in connection with fronto-temporal structures supporting the theoretical assumptions of the considered conceptual framework. The close interplay of attentional modulation by gating irrelevant information (ABA) and binding and retrieval processes (TBA) is reflected by the correlation of ABA in all pre-probe-intervals with post-probe TBA. Likewise, the role of BBA in maintaining the event file until retrieval is corroborated by BBA preceding the TBA-associated retrieval of perception-action codes. Following action execution, TBA shifted towards visual association cortices probably reflecting preparation for upcoming information, while ABA and BBA continue to reflect processes of attentional control and information selection for goal-directed behavior. The present work provides the first empirical support for concepts about the neurophysiological mechanisms of dynamic management of perception and action.

Executive function elevated by long term high-intensity physical activity and the regulation role of beta-band activity in human frontal region

The importance of physical activity (PA) to people's health has become a consensus around the world, and regular long-term PA has been accepted as an alternative preventive measure for many chronic medical conditions. Although the daily PA have several benefits for the public, the systematic research on its effect in human physiology, cognition and cerebral nerve level is not fully studied. Hence, in this study, we aim to investigate this question in several specific aspects: basal heart rate, executive function, and neural oscillatory activity in the brain. A total of 146 subjects participated in this study and they were divided into two groups. One group (SG) is the long-term training (more than 8 years) subjects in soccer (n = 31), and the other group (CG) is a normal control group (n = 115). The heart rate was monitored with a portable equipment. Besides, 24 subjects (14 in SG and 10 in CG) participated the Go/No-Go task and EEG recording before and after exercise fatigue task. In the physiology level, we found that in the non-training time, the heart rate in CG group is significantly higher than that of the SG group (P < 0.001). In the cognition level, we found that the SG group has a faster reaction time that that of CG group (P < 0.01), while for the accuracy, two groups did show significant difference. In the neural level in the brain, we found a significant abnormal increased beta-band (around 25 Hz) activity in CG group after the exercise fatigue task immediately. Long-term high-intensity physical activity reduces basal heart rate, improves executive function, and improve the central tolerance of the body under the stimulation of fatigue and stress. These benefits of long-term activity could be used as a manual to guide people's healthy life.

Characterizing neurocognitive impairments in Parkinson's disease with mobile EEG when walking and stepping over obstacles

The neural correlates that help us understand the challenges that Parkinson's patients face when negotiating their environment remain under-researched. This deficit in knowledge reflects the methodological constraints of traditional neuroimaging techniques, which include the need to remain still. As a result, much of our understanding of motor disorders is still based on animal models. Daily life challenges such as tripping and falling over obstacles represent one of the main causes of hospitalization for individuals with Parkinson's disease. Here, we report the neural correlates of naturalistic ambulatory obstacle avoidance in Parkinson's disease patients using mobile EEG. We examined 14 medicated patients with Parkinson's disease and 17 neurotypical control participants. Brain activity was recorded while participants walked freely, and while they walked and adjusted their gait to step over expected obstacles (preset adjustment) or unexpected obstacles (online adjustment) displayed on the floor. EEG analysis revealed attenuated cortical activity in Parkinson's patients compared to neurotypical participants in theta (4-7 Hz) and beta (13-35 Hz) frequency bands. The theta power increase when planning an online adjustment to step over unexpected obstacles was reduced in Parkinson's patients compared to neurotypical participants, indicating impaired proactive cognitive control of walking that updates the online action plan when unexpected changes occur in the environment. Impaired action planning processes were further evident in Parkinson's disease patients' diminished beta power suppression when preparing motor adaptation to step over obstacles, regardless of the expectation manipulation, compared to when walking freely. In addition, deficits in reactive control mechanisms in Parkinson's disease compared to neurotypical participants were evident from an attenuated beta rebound signal after crossing an obstacle. Reduced modulation in the theta frequency band in the resetting phase across conditions also suggests a deficit in the evaluation of action outcomes in Parkinson's disease. Taken together, the neural markers of cognitive control of walking observed in Parkinson's disease reveal a pervasive deficit of motor-cognitive control, involving impairments in the proactive and reactive strategies used to avoid obstacles while walking. As such, this study identified neural markers of the motor deficits in Parkinson's disease and revealed patients' difficulties in adapting movements both before and after avoiding obstacles in their path.

Rhythmic temporal prediction enhances neural representations of movement intention for brain-computer interface

Objective.Detecting movement intention is a typical use of brain-computer interfaces (BCI). However, as an endogenous electroencephalography (EEG) feature, the neural representation of movement is insufficient for improving motor-based BCI. This study aimed to develop a new movement augmentation BCI encoding paradigm by incorporating the cognitive function of rhythmic temporal prediction, and test the feasibility of this new paradigm in optimizing detections of movement intention.Methods.A visual-motion synchronization task was designed with two movement intentions (left vs. right) and three rhythmic temporal prediction conditions (1000 ms vs. 1500 ms vs. no temporal prediction). Behavioural and EEG data of 24 healthy participants were recorded. Event-related potentials (ERPs), event-related spectral perturbation induced by left- and right-finger movements, the common spatial pattern (CSP) and support vector machine, Riemann tangent space algorithm and logistic regression were used and compared across the three temporal prediction conditions, aiming to test the impact of temporal prediction on movement detection.Results.Behavioural results showed significantly smaller deviation time for 1000 ms and 1500 ms conditions. ERP analyses revealed 1000 ms and 1500 ms conditions led to rhythmic oscillations with a time lag in contralateral and ipsilateral areas of movement. Compared with no temporal prediction, 1000 ms condition exhibited greater beta event-related desynchronization (ERD) lateralization in motor area (P< 0.001) and larger beta ERD in frontal area (P< 0.001). 1000 ms condition achieved an averaged left-right decoding accuracy of 89.71% using CSP and 97.30% using Riemann tangent space, both significantly higher than no temporal prediction. Moreover, movement and temporal information can be decoded simultaneously, achieving 88.51% four-classification accuracy.Significance.The results not only confirm the effectiveness of rhythmic temporal prediction in enhancing detection ability of motor-based BCI, but also highlight the dual encodings of movement and temporal information within a single BCI paradigm, which is promising to expand the range of intentions that can be decoded by the BCI.

Evoked mid-frontal activity predicts cognitive dysfunction in Parkinson's disease

BACKGROUND

Cognitive dysfunction is a major feature of Parkinson's disease (PD), but the pathophysiology remains unknown. One potential mechanism is abnormal low-frequency cortical rhythms which engage cognitive functions and are deficient in PD. We tested the hypothesis that mid-frontal delta/theta rhythms predict cognitive dysfunction in PD.

METHOD

We recruited 100 patients with PD and 49 demographically similar control participants who completed a series of cognitive control tasks, including the Simon, oddball and interval-timing tasks. We focused on cue-evoked delta (1-4 Hz) and theta (4-7 Hz) rhythms from a single mid-frontal EEG electrode (cranial vertex (Cz)) in patients with PD who were either cognitively normal, with mild-cognitive impairments (Parkinson's disease with mild-cognitive impairment) or had dementia (Parkinson's disease dementia).

RESULTS

We found that PD-related cognitive dysfunction was associated with increased response latencies and decreased mid-frontal delta power across all tasks. Within patients with PD, the first principal component of evoked electroencephalography features from a single electrode (Cz) strongly correlated with clinical metrics such as the Montreal Cognitive Assessment score (r=0.34) and with National Institutes of Health Toolbox Executive Function score (r=0.46).

CONCLUSIONS

These data demonstrate that cue-evoked mid-frontal delta/theta rhythms directly relate to cognition in PD. Our results provide insight into the nature of low-frequency frontal rhythms and suggest that PD-related cognitive dysfunction results from decreased delta/theta activity. These findings could facilitate the development of new biomarkers and targeted therapies for cognitive symptoms of PD.

Parkinson Disease Recognition Using a Gamified Website: Machine Learning Development and Usability Study

BACKGROUND

Parkinson disease (PD) affects millions globally, causing motor function impairments. Early detection is vital, and diverse data sources aid diagnosis. We focus on lower arm movements during keyboard and trackpad or touchscreen interactions, which serve as reliable indicators of PD. Previous works explore keyboard tapping and unstructured device monitoring; we attempt to further these works with structured tests taking into account 2D hand movement in addition to finger tapping. Our feasibility study uses keystroke and mouse movement data from a remotely conducted, structured, web-based test combined with self-reported PD status to create a predictive model for detecting the presence of PD.

OBJECTIVE

Analysis of finger tapping speed and accuracy through keyboard input and analysis of 2D hand movement through mouse input allowed differentiation between participants with and without PD. This comparative analysis enables us to establish clear distinctions between the two groups and explore the feasibility of using motor behavior to predict the presence of the disease.

METHODS

Participants were recruited via email by the Hawaii Parkinson Association (HPA) and directed to a web application for the tests. The 2023 HPA symposium was also used as a forum to recruit participants and spread information about our study. The application recorded participant demographics, including age, gender, and race, as well as PD status. We conducted a series of tests to assess finger tapping, using on-screen prompts to request key presses of constant and random keys. Response times, accuracy, and unintended movements resulting in accidental presses were recorded. Participants performed a hand movement test consisting of tracing straight and curved on-screen ribbons using a trackpad or mouse, allowing us to evaluate stability and precision of 2D hand movement. From this tracing, the test collected and stored insights concerning lower arm motor movement.

RESULTS

Our formative study included 31 participants, 18 without PD and 13 with PD, and analyzed their lower limb movement data collected from keyboards and computer mice. From the data set, we extracted 28 features and evaluated their significances using an extra tree classifier predictor. A random forest model was trained using the 6 most important features identified by the predictor. These selected features provided insights into precision and movement speed derived from keyboard tapping and mouse tracing tests. This final model achieved an average F1-score of 0.7311 (SD 0.1663) and an average accuracy of 0.7429 (SD 0.1400) over 20 runs for predicting the presence of PD.

CONCLUSIONS

This preliminary feasibility study suggests the possibility of using technology-based limb movement data to predict the presence of PD, demonstrating the practicality of implementing this approach in a cost-effective and accessible manner. In addition, this study demonstrates that structured mouse movement tests can be used in combination with finger tapping to detect PD.

Distinct effects of different neurofeedback protocols on the neural mechanisms of response inhibition in ADHD

OBJECTIVE

In attention deficit/hyperactivity disorder (ADHD), impaired response inhibition is frequently observed. A promising non-pharmacological treatment is electroencephalography (EEG)-neurofeedback (NF) training. However, the widely used theta-down/beta-up regulation (↓θ↑β) NF protocol may not be optimal for targeting these deficits. We examined how neurofeedback protocols training the upregulation of theta and/or beta power affect inhibitory control in children and adolescents with ADHD.

METHODS

64 patients with ADHD took part in the three NF trainings. Aside from parent-reported ADHD symptoms and behavioural performance data, neurophysiological parameters collected via a Go/Nogo task and corrected to account for intraindividual variability were compared in a pre-post design and to an ADHD (n = 20) as well as a typically developing control group (n = 24).

RESULTS

The examined NF protocols resulted in similar improvements in response inhibition with the neurophysiological mechanisms differing substantially. The upregulation of theta led to a specific Nogo-P3 increase, while training beta upregulation as well as the combined protocol resulted in less specific effects.

CONCLUSIONS

This study shows distinct effects of different theta/beta-neurofeedback protocols on the neural mechanisms underlying improvements in response inhibition in patients with ADHD.

SIGNIFICANCE

These effects shed further light on the oscillatory dynamics underlying cognitive control in ADHD and how these may be targeted in neurofeedback treatments.

Identification of Parkinson's Disease Subtypes from Resting-State Electroencephalography

BACKGROUND

Parkinson's disease (PD) patients present with a heterogeneous clinical phenotype, including motor, cognitive, sleep, and affective disruptions. However, this heterogeneity is often either ignored or assessed using only clinical assessments.

OBJECTIVES

We aimed to identify different PD sub-phenotypes in a longitudinal follow-up analysis and their electrophysiological profile based on resting-state electroencephalography (RS-EEG) and to assess their clinical significance over the course of the disease.

METHODS

Using electrophysiological features obtained from RS-EEG recordings and data-driven methods (similarity network fusion and source-space spectral analysis), we have performed a clustering analysis to identify disease sub-phenotypes and we examined whether their different patterns of disruption are predictive of disease outcome.

RESULTS

We showed that PD patients (n = 44) can be sub-grouped into three phenotypes with distinct electrophysiological profiles. These clusters are characterized by different levels of disruptions in the somatomotor network (Δ and β band), the frontotemporal network (α2 band) and the default mode network (α1 band), which consistently correlate with clinical profiles and disease courses. These clusters are classified into either moderate (only-motor) or mild-to-severe (diffuse) disease. We showed that EEG features can predict cognitive evolution of PD patients from baseline, when the cognitive clinical scores were overlapped.

CONCLUSIONS

The identification of novel PD subtypes based on electrical brain activity signatures may provide a more accurate prognosis in individual patients in clinical practice and help to stratify subgroups in clinical trials. Innovative profiling in PD can also support new therapeutic strategies that are brain-based and designed to modulate brain activity disruption. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Adaptation strategies and neurophysiological response in early-stage Parkinson's disease: BioVRSea approach

Introduction

There is accumulating evidence that many pathological conditions affecting human balance are consequence of postural control (PC) failure or overstimulation such as in motion sickness. Our research shows the potential of using the response to a complex postural control task to assess patients with early-stage Parkinson's Disease (PD).

Methods

We developed a unique measurement model, where the PC task is triggered by a moving platform in a virtual reality environment while simultaneously recording EEG, EMG and CoP signals. This novel paradigm of assessment is called BioVRSea. We studied the interplay between biosignals and their differences in healthy subjects and with early-stage PD.

Results

Despite the limited number of subjects (29 healthy and nine PD) the results of our work show significant differences in several biosignals features, demonstrating that the combined output of posturography, muscle activation and cortical response is capable of distinguishing healthy from pathological.

Discussion

The differences measured following the end of the platform movement are remarkable, as the induced sway is different between the two groups and triggers statistically relevant cortical activities in α and θ bands. This is a first important step to develop a multi-metric signature able to quantify PC and distinguish healthy from pathological response.

Spectral features of resting-state EEG in Parkinson's Disease: A multicenter study using functional data analysis

OBJECTIVE

This study aims 1) To analyse differences in resting-state electroencephalogram (rs-EEG) spectral features of Parkinson's Disease (PD) and healthy subjects (non-PD) using Functional Data Analysis (FDA) and 2) To explore, in four independent cohorts, the external validity and reproducibility of the findings using both epoch-to-epoch FDA and averaged-epochs approach.

METHODS

We included 169 subjects (85 non-PD; 84 PD) from four centres. Rs-EEG signals were preprocessed with a combination of automated pipelines. Sensor-level relative power spectral density (PSD), dominant frequency (DF), and DF variability (DFV) features were extracted. Differences in each feature were compared between PD and non-PD on averaged epochs and using FDA to model the epoch-to-epoch change of each feature.

RESULTS

For averaged epochs, significantly higher theta relative PSD in PD was found across all datasets. Also, higher pre-alpha relative PSD was observed in three of four datasets in PD patients. For FDA, similar findings were achieved in theta, but all datasets showed consistently significant posterior pre-alpha differences across multiple epochs.

CONCLUSIONS

Increased generalised theta, with posterior pre-alpha relative PSD, was the most reproducible finding in PD.

SIGNIFICANCE

Rs-EEG theta and pre-alpha findings are generalisable in PD. FDA constitutes a reliable and powerful tool to analyse epoch-to-epoch the rs-EEG.

Translating Pathological Brain Activity Primers in Parkinson's Disease Research

Translational experimental approaches that help us better trace Parkinson's disease (PD) pathophysiological mechanisms leading to new therapeutic targets are urgently needed. In this article, we review recent experimental and clinical studies addressing abnormal neuronal activity and pathological network oscillations, as well as their underlying mechanisms and modulation. Our aim is to enhance our knowledge about the progression of Parkinson's disease pathology and the timing of its symptom's manifestation. Here, we present mechanistic insights relevant for the generation of aberrant oscillatory activity within the cortico-basal ganglia circuits. We summarize recent achievements extrapolated from available PD animal models, discuss their advantages and limitations, debate on their differential applicability, and suggest approaches for transferring knowledge on disease pathology into future research and clinical applications.

A dissociable functional relevance of theta- and beta-band activities during complex sensorimotor integration

Sensorimotor integration processes play a central role in daily life and require that different sources of sensory information become integrated: i.e. the information related to the object being under control of the agent (i.e. indicator) and the information about the goal of acting. Yet, how this is accomplished on a neurophysiological level is contentious. We focus on the role of theta- and beta-band activities and examine which neuroanatomical structures are involved. Healthy participants (n = 41) performed 3 consecutive pursuit-tracking EEG experiments in which the source of visual information available for tracking was varied (i.e. that of the indicator and the goal of acting). The initial specification of indicator dynamics is determined through beta-band activity in parietal cortices. When information about the goal was not accessible, but operating the indicator was required nevertheless, this incurred increased theta-band activity in the superior frontal cortex, signaling a higher need for control. Later, theta- and beta-band activities encode distinct information within the ventral processing stream: Theta-band activity is affected by the indicator information, while beta-band activity is affected by the information about the action goal. Complex sensorimotor integration is realized through a cascade of theta- and beta-band activities in a ventral-stream-parieto-frontal network.

TMS Bursts Can Modulate Local and Networks Oscillations During Lower-Limb Movement

PURPOSE

Lower-limb motor functions involve processing information via both motor and cognitive control networks. Measuring oscillations is a key element in communication within and between cortical networks during high-order motor functions. Increased midfrontal theta oscillations are related to improved lower-limb motor performances in patients with movement disorders. Noninvasive neuromodulation approaches have not been explored extensively to understand the oscillatory mechanism of lower-limb motor functions. This study aims to examine the effects of repetitive transcranial magnetic stimulation on local and network EEG oscillations in healthy elderly subjects.

METHODS

Eleven healthy elderly subjects (67-73 years) were recruited via advertisements, and they underwent both active and sham stimulation procedures in a random, counterbalanced design. Transcranial magnetic stimulation bursts (θ-transcranial magnetic stimulation; 4 pulses/second) were applied over the midfrontal lead (vertex) before a GO-Cue pedaling task, and signals were analyzed using time-frequency methods.

RESULTS

Transcranial magnetic stimulation bursts increase the theta activity in the local ( p = 0.02) and the associated network during the lower-limb pedaling task ( p = 0.02). Furthermore, after task-related transcranial magnetic stimulation burst sessions, increased resting-state alpha activity was observed in the midfrontal region ( p = 0.01).

CONCLUSIONS

This study suggests the ability of midfrontal transcranial magnetic stimulation bursts to directly modulate local and network oscillations in a frequency manner during lower-limb motor task. Transcranial magnetic stimulation burst-induced modulation may provide insights into the functional roles of oscillatory activity during lower-limb movement in normal and disease conditions.

Effects of Very Low- and High-Frequency Subthalamic Stimulation on Motor Cortical Oscillations During Rhythmic Lower-Limb Movements in Parkinson's Disease Patients

BACKGROUND

Standard high-frequency deep brain stimulation (HF-DBS) at the subthalamic nucleus (STN) is less effective for lower-limb motor dysfunctions in Parkinson's disease (PD) patients. However, the effects of very low frequency (VLF; 4 Hz)-DBS on lower-limb movement and motor cortical oscillations have not been compared.

OBJECTIVE

To compare the effects of VLF-DBS and HF-DBS at the STN on a lower-limb pedaling motor task and motor cortical oscillations in patients with PD and with and without freezing of gait (FOG).

METHODS

Thirteen PD patients with bilateral STN-DBS performed a cue-triggered lower-limb pedaling motor task with electroencephalography (EEG) in OFF-DBS, VLF-DBS (4 Hz), and HF-DBS (120-175 Hz) states. We performed spectral analysis on the preparatory signals and compared GO-cue-triggered theta and movement-related beta oscillations over motor cortical regions across DBS conditions in PD patients and subgroups (PDFOG-and PDFOG+).

RESULTS

Both VLF-DBS and HF-DBS decreased the linear speed of the pedaling task in PD, and HF-DBS decreased speed in both PDFOG-and PDFOG+. Preparatory theta and beta activities were increased with both stimulation frequencies. Both DBS frequencies increased motor cortical theta activity during pedaling movement in PD patients, but this increase was only observed in PDFOG + group. Beta activity was not significantly different from OFF-DBS at either frequency regardless of FOG status.

CONCLUSION

Results suggest that VL and HF DBS may induce similar effects on lower-limb kinematics by impairing movement speed and modulating motor cortical oscillations in the lower frequency band.

How Intermittent Brain States Modulate Neurophysiological Processes in Cognitive Flexibility

Cognitive flexibility is an essential facet of everyday life, for example, when switching between different tasks. Neurophysiological accounts on cognitive flexibility have often focused on the task switch itself, disregarding preceding processes and the possible impact of "brain states" before engaging in cognitive flexibility. In a combined working memory/task-switching paradigm, we examined how neuronal processes during cognitive flexibility are interrelated to preceding neuronal processes across time and brain regions in a sample of n = 42 healthy adults. The interrelation of alpha- and theta-band-related processes over brain states ahead and during response selection was investigated on a functional neuroanatomical level using EEG-beamforming. The results showed that response selection processes (reflected by theta-band activity) seem to be strongly connected to "idling" and preparatory brain activity states (in both the theta- and alpha-band). Notably, the superior parietal cortex seems to play a crucial role by assembling alpha-band-related inhibitory processes from the rule- and goal-based actions during "idling" brain states, namely, short-term maintenance of rules (temporal cortex), task-set reconfiguration (superior frontal/precentral regions), and perceptual control (occipital cortex). This information is further relayed to response selection processes associated with theta-band activity. Notably, when the task has to be switched, theta-band activity in the superior frontal gyrus indicates a need for cognitive control in the "idling" brain state, which also seems to be relayed by BA7. The results indicate the importance of brain activity states ahead of response selection processes for cognitive flexibility.

Cognition and freezing of gait in Parkinson's disease: A systematic review and meta-analysis

Freezing of gait (FOG) is a common and disabling symptom in people with Parkinson's Disease (PwPD). Although cognition is thought to be worse in PwPD who freeze, a comprehensive analysis of this relationship will inform future research and clinical care. This systematic review and meta-analysis compared cognition between PwPD who do and do not exhibit FOG across a range of cognitive domains and assessed the impact of disease severity and medication status on this relationship. 145 papers (n = 9010 participants) were included in the analysis, with 144 and 138 articles meeting the criteria to assess moderating effects of disease severity and medication status, respectively. PwPD who freeze exhibited worse cognition than PwPD without FOG across global cognition, executive function/attention, language, memory, and visuospatial domains. Greater disease severity and "ON" levodopa medication status moderated the FOG status-cognition relationship in global cognitive performance but not in other cognitive domains. This meta-analysis confirmed that cognition is worse in PwPD with FOG and highlights the importance of disease severity and medication status in this relationship.

Cerebellar oscillatory dysfunction during lower-limb movement in Parkinson's disease with freezing of gait

Studies have demonstrated dysfunctional connectivity between the cortico-basal ganglia and cerebellar networks in Parkinson's disease (PD). These networks are critical for appropriate motor and cognitive functions, specifically to control gait and postural tasks in PD. Our recent reports have shown abnormal cerebellar oscillations during rest, motor, and cognitive tasks in people with PD compared to healthy individuals, however, the role of cerebellar oscillations in people with PD and freezing of gait (PDFOG+) during lower-limb movements has not been examined. Here, we evaluated cerebellar oscillations using electroencephalography (EEG) electrodes during cue-triggered lower-limb pedaling movement in 13 PDFOG+, 13 PDFOG-, and 13 age-matched healthy subjects. We focused analyses on the mid-cerebellar Cbz as well as lateral cerebellar Cb1 and Cb2 electrodes. PDFOG+ performed the pedaling movement with reduced linear speed and higher variation compared to healthy subjects. PDFOG+ exhibited attenuated theta power during pedaling motor tasks in the mid-cerebellar location compared to PDFOG- or healthy subjects. Cbz theta power was also associated with FOG severity. No significant differences between groups were seen in Cbz beta power. In the lateral cerebellar electrodes, lower theta power was seen between PDFOG+ and healthy subjects. Our cerebellar EEG data demonstrate the occurrence of reduced theta oscillations in PDFOG+ during lower-limb movement and suggest a potential cerebellar biosignature for neurostimulation therapy to improve gait dysfunctions.

Functional near-infrared spectroscopy measures of frontal hemodynamic responses in Parkinson's patients and controls performing the Timed-Up-and-Go test

Using functional near-infrared spectroscopy (fNIRS), hemodynamic responses (i.e., changes in oxygenated and deoxygenated hemoglobin) were measured while participants with Parkinson's disease (PD) and healthy controls performed the Timed-Up-and-Go test (TUGT), and differences in cortical activity at baseline and three different intervals were examined between the two groups. Seventeen PD patients and twenty-two controls participated in the study, but two PD patients were excluded from statistical analysis due to the presence of freezing of gait and using walking aids during the TUGT. During the TUGT, activity in the front, left, right and total frontal cortices initially decreased significantly, then significantly increased in PD participants and low-risk faller PD participants, compared to when in a sitting position. ΔHbO (HbO change from baseline) over the front, left and total frontal cortices in the PD group was significantly lower than the control group in interval 1 (P = 0.019, P = 0.014 and P = 0.031, respectively), while significantly higher than the control group in interval 2 over the left frontal cortex (P = 0.010). No significant differences were observed between the high-risk faller and low-risk faller subgroups of PD participants in ΔHbO and ΔHbR in the three intervals (P > 0.05). In the high-risk faller subgroup, ΔHbO over the left frontal cortex was significantly higher than the right frontal cortex in interval 2 and interval 3 (P = 0.015, P = 0.030, respectively). There was a strong positive correlation between education and HbR concentration over the right frontal cortex in PD participants (rho = 0.557, P = 0.031), while there were strong negative correlations between PD duration and HbR concentration over the right and total frontal cortices in the high-risk faller subgroup of PD participants (rho = -0.854, P = 0.014 for the right; rho = -0.784, P = 0.037 for the total). The falls prediction cutoff TUGT time for PD participants was 14.2 s. These results suggest that frontal cognition training, along with exercise training, could be used as an effective training method to improve motor performance in PD patients, especially for those at high-risk for falls.

Quantitative High Density EEG Brain Connectivity Evaluation in Parkinson's Disease: The Phase Locking Value (PLV)

INTRODUCTION

The present study explores brain connectivity in Parkinson's disease (PD) and in age matched healthy controls (HC), using quantitative EEG analysis, at rest and during a motor tasks. We also evaluated the diagnostic performance of the phase locking value (PLV), a measure of functional connectivity, in differentiating PD patients from HCs.

METHODS

High-density, 64-channels, EEG data from 26 PD patients and 13 HC were analyzed. EEG signals were recorded at rest and during a motor task. Phase locking value (PLV), as a measure of functional connectivity, was evaluated for each group in a resting state and during a motor task for the following frequency bands: (i) delta: 2-4 Hz; (ii) theta: 5-7 Hz; (iii) alpha: 8-12 Hz; beta: 13-29 Hz; and gamma: 30-60 Hz. The diagnostic performance in PD vs. HC discrimination was evaluated.

RESULTS

Results showed no significant differences in PLV connectivity between the two groups during the resting state, but a higher PLV connectivity in the delta band during the motor task, in HC compared to PD. Comparing the resting state versus the motor task for each group, only HCs showed a higher PLV connectivity in the delta band during motor task. A ROC curve analysis for HC vs. PD discrimination, showed an area under the ROC curve (AUC) of 0.75, a sensitivity of 100%, and a negative predictive value (NPV) of 100%.

CONCLUSIONS

The present study evaluated the brain connectivity through quantitative EEG analysis in Parkinson's disease versus healthy controls, showing a higher PLV connectivity in the delta band during the motor task, in HC compared to PD. This neurophysiology biomarkers showed the potentiality to be explored in future studies as a potential screening biomarker for PD patients.

Greater prefrontal activation during sitting toe tapping predicts severer freezing of gait in Parkinson's disease: an fNIRS study

OBJECTIVE

Previous studies have revealed that, compared with Parkinson's disease (PD) patients without freezing of gait (FoG), the ones with FoG showed greater prefrontal activation while doing lower-limb movements involving standing, walking and turning, which require both locomotor and balance control. However, the relation between FoG and pure locomotor control as well as its underlying mechanism remain unclear.

METHODS

A total of 56 PD subjects were recruited and allocated to PD-FoG and PD-noFoG subgroups, and 34 age-matched heathy adults were included as heathy control (HC). Functional near-infrared spectroscopy was used to measure their prefrontal activation in a sitting lower-limb movement task, wherein subjects were asked to sit and tap their right toes as big and as fast as possible.

RESULTS

Result of one-way ANOVA (Group: PD-FoG vs. PD-noFoG vs. HC) revealed greater activation in the right prefrontal cortex in the PD-FoG group than in the other 2 groups. Linear mixed-effects model showed consistent result. Furthermore, the right prefrontal activation positively correlated with the severity of FoG symptoms in PD-FoG patients.

CONCLUSION

These findings suggested that PD patients with FoG require additional cognitive resources to compensate their damaged automaticity in locomotor control, which is more pronounced in severe FoG patients than milder ones.

The mechanism of Parkinson oscillation in the cortex: Possible evidence in a feedback model projecting from the globus pallidus to the cortex

The origin, location and cause of Parkinson's oscillation are not clear at present. In this paper, we establish a new cortex-basal ganglia model to study the origin mechanism of Parkinson beta oscillation. Unlike many previous models, this model includes two direct inhibitory projections from the globus pallidus external (GPe) segment to the cortex. We first obtain the critical calculation formula of Parkinson's oscillation by using the method of Quasilinear analysis. Different from previous studies, the formula obtained in this paper can include the self-feedback connection of GPe. Then, we use the bifurcation analysis method to systematically explain the influence of some key parameters on the oscillation. We find that the bifurcation principle of different cortical nuclei is different. In general, the increase of the discharge capacity of the nuclei will cause oscillation. In some special cases, the sharp reduction of the discharge rate of the nuclei will also cause oscillation. The direction of bifurcation simulation is consistent with the critical condition curve. Finally, we discuss the characteristics of oscillation amplitude. At the beginning of the oscillation, the amplitude is relatively small; with the evolution of oscillation, the amplitude will gradually strengthen. This is consistent with the experimental phenomenon. In most cases, the amplitude of cortical inhibitory nuclei (CIN) is greater than that of cortical excitatory nuclei (CEX), and the two direct inhibitory projections feedback from GPe can significantly reduce the amplitude gap between them. We calculate the main frequency of the oscillation generated in this model, which basically falls between 13 and 30 Hz, belonging to the typical beta frequency band oscillation. Some new results obtained in this paper can help to better understand the origin mechanism of Parkinson's disease and have guiding significance for the development of experiments.

Novelty-induced frontal-STN networks in Parkinson's disease

Novelty detection is a primitive subcomponent of cognitive control that can be deficient in Parkinson's disease (PD) patients. Here, we studied the corticostriatal mechanisms underlying novelty-response deficits. In participants with PD, we recorded from cortical circuits with scalp-based electroencephalography (EEG) and from subcortical circuits using intraoperative neurophysiology during surgeries for implantation of deep brain stimulation (DBS) electrodes. We report three major results. First, novel auditory stimuli triggered midfrontal low-frequency rhythms; of these, 1-4 Hz "delta" rhythms were linked to novelty-associated slowing, whereas 4-7 Hz "theta" rhythms were specifically attenuated in PD. Second, 32% of subthalamic nucleus (STN) neurons were response-modulated; nearly all (94%) of these were also modulated by novel stimuli. Third, response-modulated STN neurons were coherent with midfrontal 1-4 Hz activity. These findings link scalp-based measurements of neural activity with neuronal activity in the STN. Our results provide insight into midfrontal cognitive control mechanisms and how purported hyperdirect frontobasal ganglia circuits evaluate new information.

Phase-adaptive brain stimulation of striatal D1 medium spiny neurons in dopamine-depleted mice

Brain rhythms are strongly linked with behavior, and abnormal rhythms can signify pathophysiology. For instance, the basal ganglia exhibit a wide range of low-frequency oscillations during movement, but pathological "beta" rhythms at ~ 20 Hz have been observed in Parkinson's disease (PD) and in PD animal models. All brain rhythms have a frequency, which describes how often they oscillate, and a phase, which describes the precise time that peaks and troughs of brain rhythms occur. Although frequency has been extensively studied, the relevance of phase is unknown, in part because it is difficult to causally manipulate the instantaneous phase of ongoing brain rhythms. Here, we developed a phase-adaptive, real-time, closed-loop algorithm to deliver optogenetic stimulation at a specific phase with millisecond latency. We combined this Phase-Adaptive Brain STimulation (PABST) approach with cell-type-specific optogenetic methods to stimulate basal ganglia networks in dopamine-depleted mice that model motor aspects of human PD. We focused on striatal medium spiny neurons expressing D1-type dopamine receptors because these neurons can facilitate movement. We report three main results. First, we found that our approach delivered PABST within system latencies of 13 ms. Second, we report that closed-loop stimulation powerfully influenced the spike-field coherence of local brain rhythms within the dorsal striatum. Finally, we found that both 4 Hz PABST and 20 Hz PABST improved movement speed, but we found differences between phase only with 4 Hz PABST. These data provide causal evidence that phase is relevant for brain stimulation, which will allow for more precise, targeted, and individualized brain stimulation. Our findings are applicable to a broad range of preclinical brain stimulation approaches and could also inform circuit-specific neuromodulation treatments for human brain disease.

Classification of Parkinson's disease motor phenotype: a machine learning approach

To assess the cortical activity in people with Parkinson's disease (PwP) with different motor phenotype (tremor-dominant-TD and postural instability and gait difficulty-PIGD) and to compare with controls. Twenty-four PwP (during OFF and ON medication) and twelve age-/sex-/handedness-matched healthy controls underwent electrophysiological assessment of spectral ratio analysis through electroencephalography (EEG) at resting state and during the hand movement. We performed a machine learning method with 35 attributes extracted from EEG. To verify the efficiency of the proposed phenotype-based EEG classification the random forest and random tree were tested (performed 30 times, using a tenfolds cross validation in Weka environment). The analyses based on phenotypes indicated a slowing down of cortical activity during OFF medication state in PwP. PD with TD phenotype presented this characteristic at resting and the individuals with PIGD presented during the hand movement. During the ON state, there is no difference between phenotypes at resting nor during the hand movement. PD phenotypes may influence spectral activity measured by EEG. Random forest machine learning provides a slightly more accurate, sensible and specific approach to distinguish different PD phenotypes. The phenotype of PD might be a clinical characteristic that could influence cortical activity.

Functional Connectivity in Patients With Parkinson’s Disease and Freezing of Gait Using Resting-State EEG and Graph Theory

Background

Although many studies have shown abnormalities in brain structure and function in people with Parkinson’s disease (PD), we still have a poor understanding of how brain structure and function relates to freezing of gait (FOG). Graph theory analysis of electroencephalography (EEG) can explore the relationship between brain network structure and gait function in PD.

Methods

Scalp EEG signals of 83 PD (42 PDFOG+ and 41 PDFOG−) and 42 healthy controls were recorded in an eyes-opened resting-state. The phase lag index was calculated for each electrode pair in different frequency bands, but we focused our analysis on the theta-band and performed global analyses along with nodal analyses over a midfrontal channel. The resulting connectivity matrices were converted to weighted graphs, whose structure was characterized using strength and clustering coefficient measurements, our main outcomes.

Results

We observed increased global strength and increased global clustering coefficient in people with PD compared to healthy controls in the theta-band, though no differences were observed in midfrontal nodal strength and midfrontal clustering coefficient. Furthermore, no differences in global nor midfrontal nodal strength nor global clustering coefficients were observed between PDFOG+ and PDFOG− in the theta-band. However, PDFOG+ exhibited a significantly diminished midfrontal nodal clustering coefficient in the theta-band compared to PDFOG−. Furthermore, FOG scores were negatively correlated with midfrontal nodal clustering coefficient in the theta-band.

Conclusion

The present findings support the involvement of midfrontal theta oscillations in FOG symptoms in PD and the sensitivity of graph metrics to characterize functional networks in PDFOG+.

A pilot study of machine learning of resting-state EEG and depression in Parkinson's disease

Introduction

Depression is a non-motor symptom of Parkinson's disease (PD). PD-related depression is difficult to diagnose, and the neurophysiological basis is poorly understood. Depression can markedly affect cortical function, which suggests that scalp electroencephalography (EEG) may be able to distinguish depression in PD. We conducted a pilot study of depression and resting-state EEG in PD.

Methods

We recruited 18 PD patients without depression, 18 PD patients with depression, and 12 demographically similar non-PD patients with clinical depression. All patients were on their usual medications. We collected resting-state EEG in all patients and compared cortical brain signal features between patients with and without depression. We used a machine learning algorithm that harnesses the entire power spectrum (linear predictive coding of EEG Algorithm for PD: LEAPD) to distinguish between groups.

Results

We found differences between PD patients with and without depression in the alpha band (8-13 Hz) globally and in the beta (13-30 Hz) and gamma (30-50 Hz) bands in the central electrodes. From two minutes of resting-state EEG, we found that LEAPD-based machine learning could robustly distinguish between PD patients with and without depression with 97 % accuracy and between PD patients with depression and non-PD patients with depression with 100 % accuracy. We verified the robustness of our finding by confirming that the classification accuracy gracefully declines as data are randomly truncated.

Conclusions

Our results suggest that resting-state EEG power spectral analysis has the potential to distinguish depression in PD accurately. We demonstrated the efficacy of the LEAPD algorithm in identifying PD patients with depression from PD patients without depression and controls with depression. Our data provide insight into cortical mechanisms of depression and could lead to novel neurophysiological markers for non-motor symptoms of PD.

Abnormal neural oscillations during gait and dual-task in Parkinson’s disease

Gait dysfunctions are debilitating motor symptoms of Parkinson’s disease (PD) and may result in frequent falling with health complications. The contribution of the motor-cognitive network to gait disturbance can be studied more thoroughly by challenging motor-cognitive dual-task gait performances. Gait is a complex motor task that requires an appropriate contribution from motor and cognitive networks, reflected in frequency modulations among several cortical and subcortical networks. Electrophysiological recordings by scalp electroencephalography and implanted deep brain stimulation (DBS) electrodes have unveiled modulations of specific oscillatory patterns in the cortical-subcortical circuits in PD. In this review, we summarize oscillatory contributions of the cortical, basal ganglia, mesencephalic locomotor, and cerebellar regions during gait and dual-task activities in PD. We detail the involvement of the cognitive network in dual-task settings and compare how abnormal oscillations in the specific frequency bands in the cortical and subcortical regions correlate with gait deficits in PD, particularly freezing of gait (FOG). We suggest that altered neural oscillations in different frequencies can cause derangements in broader brain networks, so neuromodulation and pharmacological therapies should be considered to normalize those network oscillations to improve challenged gait and dual-task motor functions in PD. Specifically, the theta and beta bands in premotor cortical areas, subthalamic nucleus, as well as alpha band activity in the brainstem prepontine nucleus, modulate under clinically effective levodopa and DBS therapies, improving gait and dual-task performance in PD with FOG, compared to PD without FOG and age-matched healthy control groups.

Processing of embedded response plans is modulated by an interplay of fronto-parietal theta and beta activity

Even simple actions like opening a door require integration/binding and flexible re-activation of different motor elements. Yet, the neural mechanisms underlying the processing of such 'embedded response plans' are largely elusive, despite theoretical frameworks, such as the Theory of Event Coding, describing the involved cognitive processes. In a sample of N = 40 healthy participants we combine time-frequency decomposition and various beamforming methods to examine neurophysiological dynamics of such action plans - with special emphasis on the interplay of theta and beta frequency activity during the processing of these plans. We show that the integration and rule-guided reactivation of embedded response plans is modulated by a complex interplay of theta and beta activity. Pre-trial BBA is related to different functional neuroanatomical structures which are activated in a consecutive fashion. Enhanced preparatory activity is positively associated with higher binding-related BBA in the precuneus/parietal areas, indicating that activity in the precuneus/parietal cortex facilitates the execution of an embedded action sequence. Increased preparation subsequently leads to reduced working memory retrieval demands. A cascading pattern of interactions between pre-trial and within-trial activity indicates the importance of preparatory brain activity. The study shows that there are multiple roles of beta and theta oscillations associated with different functional neuroanatomical structures during the integration and reactivation of motor elements during actions.

Betweenness Centrality in Resting-State Functional Networks Distinguishes Parkinson's Disease

The goal of this paper is to use graph theory network measures derived from non-invasive electroencephalography (EEG) to develop neural decoders that can differentiate Parkinson's disease (PD) patients from healthy controls (HC). EEG signals from 27 patients and 27 demographically matched controls from New Mexico were analyzed by estimating their functional networks. Data recorded from the patients during ON and OFF levodopa sessions were included in the analysis for comparison. We used betweenness centrality of estimated functional networks to classify the HC and PD groups. The classifiers were evaluated using leave-one-out cross-validation. We observed that the PD patients (on and off medication) could be distinguished from healthy controls with 89% accuracy - approximately 4% higher than the state-of-the-art on the same dataset. This work shows that brain network analysis using extracranial resting-state EEG can discover patterns of interactions indicative of PD. This approach can also be extended to other neurological disorders.

Rodent models for gait network disorders in Parkinson's disease - a translational perspective

Gait impairments in Parkinson's disease remain a scientific and therapeutic challenge. The advent of new deep brain stimulation (DBS) devices capable of recording brain activity from chronically implanted electrodes has fostered new studies of gait in freely moving patients. The hope is to identify gait-related neural biomarkers and improve therapy using closed-loop DBS. In this context, animal models offer the opportunity to investigate gait network activity at multiple biological scales and address unresolved questions from clinical research. Yet, the contribution of rodent models to the development of future neuromodulation therapies will rely on translational validity. In this review, we summarize the most effective strategies to model parkinsonian gait in rodents. We discuss how clinical observations have inspired targeted brain lesions in animal models, and whether resulting motor deficits and network oscillations match recent findings in humans. Gait impairments with hypo-, bradykinesia and altered limb rhythmicity were successfully modelled in rodents. However, clear evidence for the presence of freezing of gait was missing. The identification of reliable neural biomarkers for gait impairments has remained challenging in both animals and humans. Moving forward, we expect that the ongoing investigation of circuit specific neuromodulation strategies in animal models will lead to future optimizations of gait therapy in Parkinson's disease.

Cognitive task-related oscillations in human internal globus pallidus and subthalamic nucleus

Recently it has been acknowledged that the basal ganglia nuclei play a major role in cognitive control; however, the contribution by their network remains unclear. Previous studies have demonstrated the role of the subthalamic nucleus (STN) in cognitive processing and suggested that its connections to cortical and other associated regions regulate response inhibition during conflict conditions. By contrast, the role of the internal globus pallidus (GPi) as the output nucleus before the thalamic relay has not yet been investigated during cognitive processing. We recorded local field potentials (LFPs) from externalized deep brain stimulation (DBS) electrodes implanted bilaterally in the GPi (n=9 participants with dystonia) and STN (n=8 participants with Parkinson's disease (PD)) during a primed flanker task. Both dystonia (GPi group) and PD participants (STN group) responded faster to the congruent trials than the incongruent trials. Overall, the dystonic GPi group was significantly faster than the PD STN group. LFPs showed elevated cue-triggered theta (3-7Hz) power in GPi and STN groups in a similar way. Response-triggered LFP beta power (13-25Hz) was significantly increased in the GPi group compared to the STN group. Results demonstrate that GPi activity appears to be critical in the cognitive processing of action selection and response during the presence of conflict tasks similar to the STN group.

EEG theta and beta bands as brain oscillations for different knee osteoarthritis phenotypes according to disease severity

This study aims to investigate the multivariate relationship between different sociodemographic, clinical, and neurophysiological variables with resting-state, high-definition, EEG spectral power in subjects with chronic knee osteoarthritis (OA) pain. This was a cross-sectional study. Sociodemographic and clinical data were collected from 66 knee OA subjects. To identify associated factors, we performed independent univariate and multivariate regression models by frequency bands (delta, theta, alpha, beta, low-beta, and high-beta) and by pre-defined regions (frontal, central, and parietal). From adjusted multivariate models, we found that: (1) increased frontocentral high-beta power and reduced central theta activity are positively correlated with pain intensity (β = 0.012, 95% CI 0.004-0.020; and β = - 0.008; 95% CI 0.014 to - 0.003; respectively); (2) delta and alpha oscillations have a direct relationship with higher cortical inhibition; (3) diffuse increased power at low frequencies (delta and theta) are associated with poor cognition, aging, and depressive symptoms; and (4) higher alpha and beta power over sensorimotor areas seem to be a maladaptive compensatory mechanism to poor motor function and severe joint degeneration. Subjects with higher pain intensity and higher OA severity (likely subjects with maladaptive compensatory mechanisms to severe OA) have higher frontocentral beta power and lower theta activity. On the other hand, subjects with less OA severity and less pain have higher theta oscillations power. These associations showed the potential role of brain oscillations as a marker of pain intensity and clinical phenotypes in chronic knee OA patients. Besides, they suggest a potential compensatory mechanism of these two brain oscillators according to OA severity.

Predictive accuracy of CNN for cortical oscillatory activity in an acute rat model of parkinsonism

In neurological and neuropsychiatric disorders neuronal oscillatory activity between basal ganglia and cortical circuits are altered, which may be useful as biomarker for adaptive deep brain stimulation. We investigated whether changes in the spectral power of oscillatory activity in the motor cortex (MCtx) and the sensorimotor cortex (SMCtx) of rats after injection of the dopamine (DA) receptor antagonist haloperidol (HALO) would be similar to those observed in Parkinson disease. Thereafter, we tested whether a convolutional neural network (CNN) model would identify brain signal alterations in this acute model of parkinsonism. A sixteen channel surface micro-electrocorticogram (ECoG) recording array was placed under the dura above the MCtx and SMCtx areas of one hemisphere under general anaesthesia in rats. Seven days after surgery, micro ECoG was recorded in individual free moving rats in three conditions: (1) basal activity, (2) after injection of HALO (0.5 mg/kg), and (3) with additional injection of apomorphine (APO) (1 mg/kg). Furthermore, a CNN-based classification consisting of 23,530 parameters was applied on the raw data. HALO injection decreased oscillatory theta band activity (4-8 Hz) and enhanced beta (12-30 Hz) and gamma (30-100 Hz) in MCtx and SMCtx, which was compensated after APO injection (P ¡ 0.001). Evaluation of classification performance of the CNN model provided accuracy of 92%, sensitivity of 90% and specificity of 93% on one-dimensional signals. The CNN proposed model requires a minimum of sensory hardware and may be integrated into future research on therapeutic devices for Parkinson disease, such as adaptive closed loop stimulation, thus contributing to more efficient way of treatment.

Movement-related EEG signatures associated with freezing of gait in Parkinson's disease: an integrative analysis

Freezing of gait is the most severe gait deficit associated with Parkinson's disease and significantly affects patients' independence and consequently their quality of life. The lack of a clear understanding of its underlying neurophysiological mechanism has resulted in limited effectiveness of the current treatment options. In this study, we investigated EEG features over (pre-)supplementary motor area and primary motor cortex during a simple cue-based ankle dorsiflexion movement. These features include movement-related cortical potentials (0.05-5 Hz) and brain oscillations (1-50 Hz). Electromyogram signal from the tibialis anterior muscle of the dominant foot was used to determine the movement onset. The EEG features before, during and following the onset of the movement were compared among three groups of participants: patients with freezing (N = 14, 11 males), patients without freezing (N = 14, 13 males) and healthy age-matched controls (N = 13, 10 males) with 15 recorded trials for each individual. Additionally, Parkinson's disease patients with freezing of gait were separated into mild (N = 7) and severe cases (N = 5), so that EEG features associated with freezing severity could be investigated. The results indicated significant differences between patients with severe freezing of gait compared to healthy controls and patients without freezing of gait. In addition, patients with mild and severe freezing represented cortical activity differences. For patients with freezing, the initial component of movement-related cortical potential is significantly lower than that of the healthy controls (P = 0.002) and is affected by the severity of freezing. Furthermore, a striking absence of beta frequency band (12-35 Hz) desynchronization was observed in patients with freezing, especially low-beta frequency band over Cz, before the movement, which was also associated with the severity of the freezing of gait. Low-beta (13-20 Hz) and high-beta (21-35 Hz) frequency band activities represented unique features for each group. Beta event-related desynchronization over Cz present in healthy controls prior to movement onset, was partially replaced by the theta band (4-8 Hz) synchrony in patients with freezing. Patients with severe freezing also represented some level of theta band synchronization over contralateral supplementary motor area. This suggests the involvement of cognitive processing over the motor cortex in controlling cue-based voluntary movement as a compensatory mechanism associated with freezing of gait. The EEG features identified in this study are indicative of important freezing of gait clinical characteristics such as severity and contribute to a better understanding of the underlying neurophysiology of the mysterious phenomenon of freezing of gait.

Cortical and Cerebellar Oscillatory Responses to Postural Instability in Parkinson's Disease

Introduction: Posture and balance dysfunctions critically impair activities of daily living of patients with progressing Parkinson's disease (PD). However, the neural mechanisms underlying postural instability in PD are poorly understood, and specific therapies are lacking. Previous electrophysiological studies have shown distinct cortical oscillations with a significant contribution of the cerebellum during postural control tasks in healthy individuals.

Methods: We investigated cortical and mid-cerebellar oscillatory activity via electroencephalography (EEG) during a postural control task in 10 PD patients with postural instability (PDPI+), 11 PD patients without postural instability (PDPI–), and 15 age-matched healthy control participants. Relative spectral power was analyzed in the theta (4–7 Hz) and beta (13–30 Hz) frequency bands.

Results: Time-dependent postural measurements computed by accelerometer signals showed poor performance in PDPI+ participants. EEG results revealed that theta power was profoundly lower in mid-frontal and mid-cerebellar regions during the postural control task in PDPI+, compared to PDPI– and control participants. In addition, theta power was correlated with postural control performance in PD subjects. No significant changes in beta power were observed. Additionally, oscillatory changes during the postural control task differed from the resting state.

Conclusion: This study underlines the involvement of mid-frontal and mid-cerebellar regions in postural stability during a balance task and emphasizes the important role of theta oscillations therein for postural control in PD.

Relationships between Freezing of Gait Severity and Cognitive Deficits in Parkinson's Disease

Freezing of gait (FOG) is one of the most debilitating motor symptoms experienced by patients with Parkinson’s disease (PD), as it can lead to falls and a reduced quality of life. Evidence supports an association between FOG severity and cognitive functioning; however, results remain debatable. PD patients with (PDFOG+, n = 41) and without FOG (PDFOG–, n = 39) and control healthy subjects (n = 41) participated in this study. The NIH toolbox cognition battery, the Montreal Cognitive Assessment (MoCA), and the interval timing task were used to test cognitive domains. Measurements were compared between groups using multivariable models and adjusting for covariates. Correlation analyses, linear regression, and mediation models were applied to examine relationships among disease duration and severity, FOG severity, and cognitive functioning. Significant differences were observed between controls and PD patients for all cognitive domains. PDFOG+ and PDFOG– exhibited differences in Dimensional Change Card Sort (DCCS) test, interval timing task, and MoCA scores. After adjusting for covariates in two different models, PDFOG+ and PDFOG– differed in both MoCA and DCCS scores. In addition, significant relationships between FOG severity and cognitive function (MoCA, DCCS, and interval timing) were also found. Regression models suggest that FOG severity may be a predictor of cognitive impairment, and mediation models show the effects of cognitive impairment on the relationship between disease severity and FOG severity. Overall, this study provides insight into the relationship between cognitive and FOG severity in patients with PD, which could aid in the development of therapeutic interventions to manage both.

Altered Cerebellar Oscillations in Parkinson's Disease Patients during Cognitive and Motor Tasks

Structural and functional abnormalities in the cerebellar region have been shown in patients with Parkinson's disease (PD). Since the cerebellar region has been associated with cognitive and lower-limb motor functions, it is imperative to study cerebellar oscillations in PD. Here, we evaluated cerebellar electroencephalography (EEG) during cognitive processing and lower-limb motor performances in PD. Cortical and cerebellar EEG were collected from 74 PD patients and 37 healthy control subjects during a 7-second interval timing task, 26 PD patients and 13 controls during a lower-limb pedaling task, and 23 PD patients during eyes-open/closed resting conditions. Analyses were focused on the mid-cerebellar Cbz electrode and further compared to the mid-occipital Oz and mid-frontal Cz electrodes. Increased alpha-band power was observed during the eyes-closed resting-state condition over Oz, but no change in alpha power was observed over Cbz. PD patients showed higher dispersion when performing the 7-second interval timing cognitive task and executed the pedaling motor task with reduced speed compared to controls. PD patients exhibited attenuated cue-triggered theta-band power over Cbz during both the interval timing and pedaling motor tasks. Connectivity measures between Cbz and Cz showed theta-band differences, but only during the pedaling motor task. Cbz oscillatory activity also differed from Oz across multiple frequency bands in both groups during both tasks. Our cerebellar EEG data along with previous magnetoencephalography and animal model studies clearly show alterations in cerebellar oscillations during cognitive and motor processing in PD.

Interval timing and midfrontal delta oscillations are impaired in Parkinson's disease patients with freezing of gait

Gait abnormalities and cognitive dysfunction are common in patients with Parkinson's disease (PD) and get worse with disease progression. Recent evidence has suggested a strong relationship between gait abnormalities and cognitive dysfunction in PD patients and impaired cognitive control could be one of the causes for abnormal gait patterns. However, the pathophysiological mechanisms of cognitive dysfunction in PD patients with gait problems are unclear. Here, we collected scalp electroencephalography (EEG) signals during a 7-s interval timing task to investigate the cortical mechanisms of cognitive dysfunction in PD patients with (PDFOG +, n = 34) and without (PDFOG-, n = 37) freezing of gait, as well as control subjects (n = 37). Results showed that the PDFOG +  group exhibited the lowest maximum response density at around 7 s compared to PDFOG- and control groups, and this response density peak correlated with gait abnormalities as measured by FOG scores. EEG data demonstrated that PDFOG +  had decreased midfrontal delta-band power at the onset of the target cue, which was also correlated with maximum response density and FOG scores. In addition, our classifier performed better at discriminating PDFOG + from PDFOG- and controls with an area under the curve of 0.93 when midfrontal delta power was chosen as a feature. These findings suggest that abnormal midfrontal activity in PDFOG + is related to cognitive dysfunction and describe the mechanistic relationship between cognitive and gait functions in PDFOG + . Overall, these results could advance the development of novel biosignatures and brain stimulation approaches for PDFOG + .

Distinct Brain-Oscillatory Neuroanatomical Architecture of Perception-Action Integration in Adolescents With Tourette Syndrome

Background

Gilles de la Tourette Syndrome (GTS) is a neurodevelopmental disorder with a peak of symptom severity around late childhood and early adolescence. Previous findings in adult GTS suggest that changes in perception-action integration, as conceptualized in the theory of event coding framework, are central for the understanding of GTS. However, the neural mechanisms underlying these processes in adolescence are elusive.

Methods

A total of 59 children/adolescents aged 9 to 18 years (n = 32 with GTS, n = 27 typically developing youths) were examined using a perception-action integration task (event file task) derived from the theory of event coding. Event-related electroencephalogram recordings (theta and beta band activity) were analyzed using electroencephalogram–beamforming methods.

Results

Behavioral data showed robust event file binding effects in both groups without group differences. Neurophysiological data showed that theta and beta band activity were involved in event file integration in both groups. However, the functional neuroanatomical organization was markedly different for theta band activity between the groups. The typically developing group mainly relied on superior frontal regions, whereas the GTS group engaged parietal and inferior frontal regions. A more consistent functional neuroanatomical activation pattern was observed for the beta band, engaging inferior parietal and temporal regions in both groups.

Conclusions

Perception-action integration processes lag behind in persisting GTS but not in the GTS population as a whole, underscoring differences in developmental trajectories and the importance of longitudinal investigations for the understanding of GTS. The findings corroborate known differences in the functional/structural brain organization in GTS and suggest an important role of theta band activity in these patients.

Event-related synchronization/desynchronization and functional neuroanatomical regions associated with fatigue effects on cognitive flexibility

Cognitive flexibility is an essential prerequisite for goal-directed behavior, and daily observations already show that it deteriorates when one is engaged in a task for a (too) long time. Yet, the neural mechanisms underlying such fatigability effect in cognitive flexibility are poorly understood. We examined how theta, alpha, and beta frequency event-related synchronization and desynchronization processes during cued memory-based task switching are modulated by time-on-task effects. We put special emphasis on the examination of functional neuroanatomical regions being associated with these modulations, using EEG beamforming. We show clear declines in task switching performance (increased switch costs) with time on task. For processes occurring before rule switching or repetition processes, we show that anticipatory attentional sampling and selection mechanisms associated with fronto-parietal structures are modulated by time-on-task effects but sensory areas (occipital cortex) also show fatigability-dependent modulations. After target stimulus presentation, the allocation of processing resources for response selection as reflected by theta-related activity in parietal cortices is compromised with time on task and similarly a concomitant increase in alpha and beta band-related attentional processing or gating mechanisms in frontal and occipital regions. Yet, considering the behavioral data showing an apparent decline in performance, this probably compensatory increase is still insufficient to allow reasonable performance. The same is likely the case for processes occurring before rule switching or repetition processes. Comparative analyses show that modulations of alpha band activity are as strongly modulated by fatigability as theta band activity. Implications of these findings for theoretical concepts on fatigability are discussed.NEW & NOTEWORTHY We examine the neurophysiological and functional neuroanatomical basis of fatigability in cognitive flexibility. We show that alpha and theta modulations in fronto-parietal and primary sensory areas are central for the understanding of fatigability effects in cognitive flexibility.

Timing variability and midfrontal ~4 Hz rhythms correlate with cognition in Parkinson's disease

Patients with Parkinson's disease (PD) can have significant cognitive dysfunction; however, the mechanisms for these cognitive symptoms are unknown. Here, we used scalp electroencephalography (EEG) to investigate the cortical basis for PD-related cognitive impairments during interval timing, which requires participants to estimate temporal intervals of several seconds. Time estimation is an ideal task demand for investigating cognition in PD because it is simple, requires medial frontal cortical areas, and recruits basic executive processes such as working memory and attention. However, interval timing has never been systematically studied in PD patients with cognitive impairments. We report three main findings. First, 71 PD patients had increased temporal variability compared to 37 demographically matched controls, and this variability correlated with cognitive dysfunction as measured by the Montreal Cognitive Assessment (MOCA). Second, PD patients had attenuated ~4 Hz EEG oscillatory activity at midfrontal electrodes in response to the interval-onset cue, which was also predictive of MOCA. Finally, trial-by-trial linear mixed-effects modeling demonstrated that cue-triggered ~4 Hz power predicted subsequent temporal estimates as a function of PD and MOCA. Our data suggest that impaired cue-evoked midfrontal ~4 Hz activity predicts increased timing variability that is indicative of cognitive dysfunction in PD. These findings link PD-related cognitive dysfunction with cortical mechanisms of cognitive control, which could advance novel biomarkers and neuromodulation for PD.

A Single Case Feasibility Study of Sensorimotor Rhythm Neurofeedback in Parkinson's Disease

Electroencephalographic activity over the sensorimotor cortex has been one of the best studied targets for neurofeedback therapy. Parkinson’s disease patients display abnormal brain rhythms in the motor cortex caused by increased synchrony in the basal ganglia-cortical pathway. Few studies have examined the effects of sensorimotor-based neurofeedback therapy in humans with PD. In this pilot study, one patient, diagnosed with Parkinson’s disease 10 years prior, participated in two consecutive days of EEG neurofeedback training to increase sensorimotor rhythm (SMR) power over the motor cortex. Using a visual display connected to ongoing EEG, the patient voluntarily manipulated SMR power, and he/she was awarded with points to positively reinforce successful increases over a predefined threshold. Recorded EEG data were source localized and analyzed for the occurrence of high amplitude bursts of SMR activity as well as bursts in the beta frequency band in the precentral cortex. The rate of SMR bursts increased with each subsequent training session, while the rate of beta bursts only increased on the final session. Relative power in the beta band, a marker of PD symptom severity, decreased over the motor cortex in the later session. These results provide first evidence for the feasibility of SMR neurofeedback training as a non-invasive therapy for reducing Parkinson’s disease related activity and upregulating SMR in the human motor cortex.

Characterization of EEG Data Revealing Relationships With Cognitive and Motor Symptoms in Parkinson's Disease: A Systematic Review

Recent research regards the electroencephalogram (EEG) as a promising method to study real-time brain dynamic changes in patients with Parkinson's disease (PD), but a deeper understanding is needed to discern coincident pathophysiology, patterns of changes, and diagnosis. This review summarized recent research on EEG characterization related to the cognitive and motor functions in PD patients and discussed its potential to be used as diagnostic biomarkers. Thirty papers out of 220 published from 2010 to 2020 were reviewed. Movement abnormalities and cognitive decline are related to changes in EEG spectrum and event-related potentials (ERPs) during typical oddball paradigms and/or combined motor tasks. Abnormalities in β and δ frequency bands are, respectively the main manifestation of dyskinesia and cognitive decline in PD. The review showed that PD patients have noteworthy changes in specific EEG characterizations, however, the underlying mechanism of the interrelation between gait and cognitive is still unclear. Understanding the specific nature of the relationship is essential for development of novel invasive clinical diagnostic and therapeutic methods.