Enhanced frontal low and high frequency power and synchronization in the resting EEG of parkinsonian patients

Modulation of premotor cortex excitability mitigates the behavioral and electrophysiological abnormalities in a Parkinson's disease mouse model

The subthalamic nucleus (STN) plays a crucial role in suppressing prepotent response tendency. The prefrontal regions innervating the STN exhibit increased activity during the stop-signal responses, and the optogenetic activation of these neurons suppresses ongoing behavior. High-frequency electrical stimulation of the STN effectively treats the motor symptoms of Parkinson's disease (PD), yet its underlying circuit mechanisms remain unclear. Here, we investigated the involvement of STN-projecting premotor (M2) neurons in PD mouse models and the impact of deep brain stimulation targeting the STN (DBS-STN). We found that the M2 neurons exhibited enhanced burst firing and synchronous oscillations in the PD mouse model. Remarkably, high-frequency stimulation of STN-projecting M2 neurons, simulating antidromic activation during DBS-STN relieved motor symptoms and hyperexcitability. These changes were attributed to reduced firing frequency vs. current relationship through normalized hyperpolarization-activated inward current (Ih). The M2 neurons in the PD model mouse displayed increased Ih, which was reversed by high-frequency stimulation. Additionally, the infusion of ZD7288, an HCN channel blocker, into the M2 replicated the effects of high-frequency stimulation. In conclusion, our study reveals excessive excitability and suppressive motor control through M2-STN synapses in a PD mouse model. Antidromic excitation of M2 neurons during DBS-STN alleviates this suppression, thereby improving motor impairment. These findings provide insights into the circuit-level dynamics underlying deep brain stimulation's therapeutic effects in PD, suggesting that M2-STN synapses could serve as potential targets for future therapeutic strategies.

Remote Regulation of Molecular Diffusion in Extracellular Space of Parkinson's Disease Rat Model by Subthalamic Nucleus Deep Brain Stimulation

Subthalamic nucleus deep brain stimulation (STN-DBS) is an effective therapy for Parkinson's disease (PD). However, the therapeutic mechanisms remain incompletely understood, particularly regarding the extracellular space (ECS), a critical microenvironment where molecular diffusion and interstitial fluid (ISF) dynamics are essential for neural function. This study aims to explore the regulatory mechanisms of the ECS in the substantia nigra (SN) of PD rats following STN-DBS. To evaluate whether STN-DBS can modulate ECS diffusion and drainage, we conducted quantitative measurements using a tracer-based magnetic resonance imaging. Our findings indicated that, compared to the PD group, STN-DBS treatment resulted in a decreased diffusion coefficient (D*), shorted half-life (T 1/2), and increased clearance coefficient (k') in the SN. To investigate the mechanisms underlying these changes in molecular diffusion, we employed enzyme-linked immunosorbent assay (ELISA), Western blotting (WB), and microdialysis techniques. The results revealed that STN-DBS led to an increase in hyaluronic acid content, elevated expression of excitatory amino acid transporter 2 (EAAT2), and a reduction in extracellular glutamate concentration. Additionally, to further elucidate the mechanisms influencing ISF drainage, we employed immunofluorescence and immunohistochemical techniques for staining aquaporin-4 (AQP-4) and α-synuclein. The results demonstrated that STN-DBS restored the expression of AQP-4 while decreasing the expression of α-synuclein. In conclusion, our findings suggest that STN-DBS improves PD symptoms by modifying the ECS and enhancing ISF drainage in the SN regions. These results offer new insights into the mechanisms and long-term outcomes of DBS in ECS, paving the way for precision therapies.

Long-Range Temporal Correlations in Electroencephalography for Parkinson's Disease Progression

BACKGROUND

Patients with Parkinson's disease (PD) present progressive deterioration in both motor and non-motor manifestations. However, the absence of clinical biomarkers for disease progression hinders clinicians from tailoring treatment strategies effectively.

OBJECTIVES

To identify electroencephalography (EEG) biomarker that can track disease progression in PD.

METHODS

A total of 116 patients with PD were initially enrolled, whereas 63 completed 2-year follow-up evaluation. Fifty-eight age- and sex-matched healthy individuals were recruited as the control group. All participants underwent EEG and clinical assessments. Long-range temporal correlations (LRTC) of EEG data were analyzed using the detrended fluctuation analysis.

RESULTS

Patients with PD exhibited higher LRTC in left parietal θ oscillations (P = 0.0175) and lower LRTC in centro-parietal γ oscillations (P = 0.0258) compared to controls. LRTC in parietal γ oscillations inversely correlated with changes in Unified Parkinson's Disease Rating Scale (UPDRS) part III scores over 2 years (Spearman ρ = -0.34, P = 0.0082). Increased LRTC in left parietal θ oscillations were associated with rapid motor progression (P = 0.0107), defined as an annual increase in UPDRS part III score ≥3. In cognitive assessments, LRTC in parieto-occipital α oscillations exhibited a positive correlation with changes in Mini-Mental State Examination and Montreal Cognitive Assessment scores over 2 years (Spearman ρ = 0.27-0.38, P = 0.0037-0.0452).

CONCLUSIONS

LRTC patterns in EEG potentially predict rapid progression of both motor and non-motor manifestations in PD patients, enhancing clinical assessment and understanding of the disease. © 2024 International Parkinson and Movement Disorder Society.

Electrophysiological approaches to informing therapeutic interventions with deep brain stimulation

Neuromodulation therapy comprises a range of non-destructive and adjustable methods for modulating neural activity using electrical stimulations, chemical agents, or mechanical interventions. Here, we discuss how electrophysiological brain recording and imaging at multiple scales, from cells to large-scale brain networks, contribute to defining the target location and stimulation parameters of neuromodulation, with an emphasis on deep brain stimulation (DBS).

Abnormal dynamic features of cortical microstates for detecting early-stage Parkinson's disease by resting-state electroencephalography: Systematic analysis of the influence of eye condition

Resting state electroencephalography (EEG) has proved useful in studying electrophysiological changes in neurodegenerative diseases. In many neuropathologies, microstate analysis of the eyes-closed (EC) scalp EEG is a robust and highly reproducible technique for assessing topological changes with high temporal resolution. However, scalp EEG microstate maps tend to underestimate the non-occipital or non-alpha-band networks, which can also be used to detect neuropathological changes. Recent evidence has shown that the source-space microstates can characterize distinct functional connectivity patterns but its clinical ability to detect neuropathological changes has not been demonstrated so far. It should also be remembered that the eye condition may play an important role in neural activity dynamics. The aim of this study was to systematically characterize the dynamic neuropathological features of sensor-space and source-space EEG microstates in PD patients with no cognitive impairment in both EC and EO conditions with the aim of identifying potential biomarkers that could be used as a complementary clinical screening method for early PD detection. We found that the dynamic features of the source-space microstates were more sensitive in detecting PD than the sensor-space microstates, while EO was able to detect neuropathological changes in PD patients better than EC. In EO, PD disease exhibited significantly higher occurrence and coverage in visual-network related source-space microstates and abnormally high duration in sensorimotor network-related microstates. Our results suggest that the source-space microstate analysis of resting-state EEG could provide robust biomarkers to detect early-stage PD, which would allow the development of patient-oriented strategies to prevent the disease and improve the patients' quality of life.

Low-frequency EEG power and coherence differ between drug-induced parkinsonism and Parkinson's disease

OBJECTIVE

Drug-induced parkinsonism (DIP) ranks second to Parkinson's disease (PD) in causing parkinsonism. Despite sharing similar symptoms, DIP results from exposure to specific medications or substances, underscoring the need for accurate diagnosis. Here, we used resting-state electroencephalography (rsEEG) to investigate neural markers characterizing DIP and PD.

METHODS

We conducted a retrospective analysis of rsEEG recordings from 18 DIP patients, 43 de novo PD patients, and 12 healthy controls (HC). After exclusions, data from 15 DIP, 41 PD, and 12 HC participants were analyzed. EEG spectral power and inter-channel coherence were compared across the groups.

RESULTS

Our results demonstrated significant differences in rsEEG patterns among DIP, PD, and HC groups. DIP patients exhibited increased theta band power compared with PD patients and HC. Moreover, DIP patients showed higher delta band coherence compared with PD patients.

CONCLUSION

The current study highlights the differences in EEG spectral power and inter-channel coherence between DIP and PD patients.

SIGNIFICANCE

Our results suggest that rsEEG holds promise as a valuable tool for capturing differential characteristics between DIP and PD patients.

An interpretable model based on graph learning for diagnosis of Parkinson's disease with voice-related EEG

Parkinson's disease (PD) exhibits significant clinical heterogeneity, presenting challenges in the identification of reliable electroencephalogram (EEG) biomarkers. Machine learning techniques have been integrated with resting-state EEG for PD diagnosis, but their practicality is constrained by the interpretable features and the stochastic nature of resting-state EEG. The present study proposes a novel and interpretable deep learning model, graph signal processing-graph convolutional networks (GSP-GCNs), using event-related EEG data obtained from a specific task involving vocal pitch regulation for PD diagnosis. By incorporating both local and global information from single-hop and multi-hop networks, our proposed GSP-GCNs models achieved an averaged classification accuracy of 90.2%, exhibiting a significant improvement of 9.5% over other deep learning models. Moreover, the interpretability analysis revealed discriminative distributions of large-scale EEG networks and topographic map of microstate MS5 learned by our models, primarily located in the left ventral premotor cortex, superior temporal gyrus, and Broca's area that are implicated in PD-related speech disorders, reflecting our GSP-GCN models' ability to provide interpretable insights identifying distinctive EEG biomarkers from large-scale networks. These findings demonstrate the potential of interpretable deep learning models coupled with voice-related EEG signals for distinguishing PD patients from healthy controls with accuracy and elucidating the underlying neurobiological mechanisms.

Evaluation of Parkinson's disease early diagnosis using single-channel EEG features and auditory cognitive assessment

Background

Parkinson's disease (PD) often presents with subtle early signs, making diagnosis difficult. F-DOPA PET imaging provides a reliable measure of dopaminergic function and is a primary tool for early PD diagnosis. This study aims to evaluate the ability of machine-learning (ML) extracted EEG features to predict F-DOPA results and distinguish between PD and non-PD patients. These features, extracted using a single-channel EEG during an auditory cognitive assessment, include EEG feature A0 associated with cognitive load in healthy subjects, and EEG feature L1 associated with cognitive task differentiation.

Methods

Participants in this study are comprised of cognitively healthy patients who had undergone an F-DOPA PET scan as a part of their standard care (n = 32), and cognitively healthy controls (n = 20). EEG data collected using the Neurosteer system during an auditory cognitive task, was decomposed using wavelet-packet analysis and machine learning methods for feature extraction. These features were used in a connectivity analysis that was applied in a similar manner to fMRI connectivity. A preliminary model that relies on the features and their connectivity was used to predict initially unrevealed F-DOPA test results. Then, generalized linear mixed models (LMM) were used to discern between PD and non-PD subjects based on EEG variables.

Results

The prediction model correctly classified patients with unrevealed scores as positive F-DOPA. EEG feature A0 and the Delta band revealed distinct activity patterns separating between study groups, with controls displaying higher activity than PD patients. In controls, EEG feature L1 showed variations between resting state and high-cognitive load, an effect lacking in PD patients.

Conclusion

Our findings exhibit the potential of single-channel EEG technology in combination with an auditory cognitive assessment to distinguish positive from negative F-DOPA PET scores. This approach shows promise for early PD diagnosis. Additional studies are needed to further verify the utility of this tool as a potential biomarker for PD.

Early detection of Parkinson's disease: Systematic analysis of the influence of the eyes on quantitative biomarkers in resting state electroencephalography

While resting state electroencephalography (EEG) provides relevant information on pathological changes in Parkinson's disease, most studies focus on the eyes-closed EEG biomarkers. Recent evidence has shown that both eyes-open EEG and reactivity to eyes-opening can also differentiate Parkinson's disease from healthy aging, but no consensus has been reached on a discriminatory capability benchmark. The aim of this study was to determine the resting-state EEG biomarkers suitable for real-time application that can differentiate Parkinson's patients from healthy subjects under both eyes closed and open. For this, we analysed and compared the quantitative EEG analyses of 13 early-stage cognitively normal Parkinson's patients with an age and sex-matched healthy group. We found that Parkinson's disease exhibited abnormal excessive theta activity in eyes-closed, which was reflected by a significantly higher relative theta power, a higher time percentage with a frequency peak in the theta band and a reduced alpha/theta ratio, while Parkinson's patients showed a significantly steeper non-oscillatory spectral slope activity than that of healthy subjects. We also found considerably less alpha and beta reactivity to eyes-opening in Parkinson's disease plus a significant moderate correlation between these EEG-biomarkers and the MDS-UPDRS score, used to assesses the clinical symptoms of Parkinson's Disease. Both EEG recordings with the eyes open and reactivity to eyes-opening provided additional information to the eyes-closed condition. We thus strongly recommend that both eyes open and closed be used in clinical practice recording protocols to promote EEG as a complementary non-invasive screening method for the early detection of Parkinson's disease, which would allow clinicians to design patient-oriented treatment and improve the patient's quality of life.

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.

Brain Activity of Professional Dancers During Audiovisual Stimuli Exposure: A Systematic Review

Many studies have shown the effect of dance to the brain. It seems that long-term practice modulates brain plasticity and visuomotor skills, as it activates the Action Observation Network (AON). The aim of this systematic review was to evaluate potential differences in the brain activity (visuomotor skills) between professional dancers and non-dancer adults, measured by electroencephalography (EEG), during the observation of an individual who is dancing (video dance stimuli). This literature search was conducted from February to June 2022, according to the PRISMA guidelines, in the PubMed database using advanced search, mesh terms, and extensive manual search. The included articles were published in English. Specifically, case-control studies were selected, which used healthy adults, professional dancers, and non-dancers as participants, who were exposed to video dance clips and measured by EEG. The articles were excluded if they were based on different type of study, unhealthy population, control group with athletic background, different type of stimuli (rhythmic), or different type of task and procedure. The ratings of quality of evidence were conducted using the Joanna Briggs Institute's (JBI) critical appraisal tool. Five case-control studies were included with 193 participants in total, 87% females. The participating groups of professional dancers (n = 12-25) had mean age 25.14 years, with at least 9-19 years of professional training, whereas control groups had the same sample size, mean age of 24.14 years, and no experience in dancing. Most of the studies presented high methodological quality. All studies showed significant differences in dancers' brain activity, especially regarding the visuomotor skills. The results showed faster activation of AON demonstrated by higher P300 at the frontocentral regions and increased sensitivity of the occipital temporal cortex. Dancers could cope easier with familiar-unfamiliar and effortful-effortless movements. They also demonstrated faster alpha band peak frequency, stronger synchrony over the bands theta, beta, gamma during the audiovisual stimuli, and the ability to encode faster the visual information. The results demonstrate that dancers had better visuomotor skills suggesting dance-enhanced neuroplasticity, as professional dancers processed their actions easier. Dance, which includes visuomotor tasks, could help in prevention, therapy, and rehabilitation of neurodegenerative diseases or movement disorders.

Transcranial Current Stimulation as a Tool of Neuromodulation of Cognitive Functions in Parkinson’s Disease

Decrease in cognitive function is one of the most common causes of poor life quality and early disability in patients with Parkinson’s disease (PD). Existing methods of treatment are aimed at both correction of motor and non-motor symptoms. Methods of adjuvant therapy (or complementary therapy) for maintaining cognitive functions in patients with PD are of interest. A promising subject of research in this regard is the method of transcranial electric current stimulation (tES). Here we reviewed the current understanding of the pathogenesis of cognitive impairment in PD and of the effects of transcranial direct current stimulation and transcranial alternating current stimulation on the cognitive function of patients with PD-MCI (Parkinson’s Disease–Mild Cognitive Impairment).

Evaluating the Different Stages of Parkinson's Disease Using Electroencephalography With Holo-Hilbert Spectral Analysis

Electroencephalography (EEG) can reveal the abnormalities of dopaminergic subcortico-cortical circuits in patients with Parkinson's disease (PD). However, conventional time-frequency analysis of EEG signals cannot fully reveal the non-linear processes of neural activities and interactions. A novel Holo-Hilbert Spectral Analysis (HHSA) was applied to reveal non-linear features of resting state EEG in 99 PD patients and 59 healthy controls (HCs). PD patients demonstrated a reduction of β bands in frontal and central regions, and reduction of γ bands in central, parietal, and temporal regions. Compared with early-stage PD patients, late-stage PD patients demonstrated reduction of β bands in the posterior central region, and increased θ and δ2 bands in the left parietal region. θ and β bands in all brain regions were positively correlated with Hamilton depression rating scale scores. Machine learning algorithms using three prioritized HHSA features demonstrated "Bag" with the best accuracy of 0.90, followed by "LogitBoost" with an accuracy of 0.89. Our findings strengthen the application of HHSA to reveal high-dimensional frequency features in EEG signals of PD patients. The EEG characteristics extracted by HHSA are important markers for the identification of depression severity and diagnosis of PD.

Combined Subthalamic and Nigral Stimulation Modulates Temporal Gait Coordination and Cortical Gait-Network Activity in Parkinson's Disease

Background

Freezing of gait (FoG) is a disabling burden for Parkinson's disease (PD) patients with poor response to conventional therapies. Combined deep brain stimulation of the subthalamic nucleus and substantia nigra (STN+SN DBS) moved into focus as a potential therapeutic option to treat the parkinsonian gait disorder and refractory FoG. The mechanisms of action of DBS within the cortical-subcortical-basal ganglia network on gait, particularly at the cortical level, remain unclear.

Methods

Twelve patients with idiopathic PD and chronically-implanted DBS electrodes were assessed on their regular dopaminergic medication in a standardized stepping in place paradigm. Patients executed the task with DBS switched off (STIM OFF), conventional STN DBS and combined STN+SN DBS and were compared to healthy matched controls. Simultaneous high-density EEG and kinematic measurements were recorded during resting-state, effective stepping, and freezing episodes.

Results

Clinically, STN+SN DBS was superior to conventional STN DBS in improving temporal stepping variability of the more affected leg. During resting-state and effective stepping, the cortical activity of PD patients in STIM OFF was characterized by excessive over-synchronization in the theta (4-8 Hz), alpha (9-13 Hz), and high-beta (21-30 Hz) band compared to healthy controls. Both active DBS settings similarly decreased resting-state alpha power and reduced pathologically enhanced high-beta activity during resting-state and effective stepping compared to STIM OFF. Freezing episodes during STN DBS and STN+SN DBS showed spectrally and spatially distinct cortical activity patterns when compared to effective stepping. During STN DBS, FoG was associated with an increase in cortical alpha and low-beta activity over central cortical areas, while with STN+SN DBS, an increase in high-beta was prominent over more frontal areas.

Conclusions

STN+SN DBS improved temporal aspects of parkinsonian gait impairment compared to conventional STN DBS and differentially affected cortical oscillatory patterns during regular locomotion and freezing suggesting a potential modulatory effect on dysfunctional cortical-subcortical communication in PD.

“Sound” Decisions: The Combined Role of Ambient Noise and Cognitive Regulation on the Neurophysiology of Food Cravings

Our ability to evaluate long-term goals over immediate rewards is manifested in the brain’s decision circuit. Simplistically, it can be divided into a fast, impulsive, reward “system 1” and a slow, deliberate, control “system 2.” In a noisy eating environment, our cognitive resources may get depleted, potentially leading to cognitive overload, emotional arousal, and consequently more rash decisions, such as unhealthy food choices. Here, we investigated the combined impact of cognitive regulation and ambient noise on food cravings through neurophysiological activity. Thirty-seven participants were recruited for an adapted version of the Regulation of Craving (ROC) task. All participants underwent two sessions of the ROC task; once with soft ambient restaurant noise (∼50 dB) and once with loud ambient restaurant noise (∼70 dB), while data from electroencephalography (EEG), electrodermal activity (EDA), and self-reported craving were collected for all palatable food images presented in the task. The results indicated that thinking about future (“later”) consequences vs. immediate (“now”) sensations associated with the food decreased cravings, which were mediated by frontal EEG alpha power. Likewise, “later” trials also increased frontal alpha asymmetry (FAA) —an index for emotional motivation. Furthermore, loud (vs. soft) noise increased alpha, beta, and theta activity, but for theta activity, this was solely occurring during “later” trials. Similarly, EDA signal peak probability was also higher during loud noise. Collectively, our findings suggest that the presence of loud ambient noise in conjunction with prospective thinking can lead to the highest emotional arousal and cognitive load as measured by EDA and EEG, respectively, both of which are important in regulating cravings and decisions. Thus, exploring the combined effects of interoceptive regulation and exteroceptive cues on food-related decision-making could be methodologically advantageous in consumer neuroscience and entail theoretical, commercial, and managerial implications.

Multilayer Network Approach in EEG Motor Imagery with an Adaptive Threshold

The brain has been understood as an interconnected neural network generally modeled as a graph to outline the functional topology and dynamics of brain processes. Classic graph modeling is based on single-layer models that constrain the traits conveyed to trace brain topologies. Multilayer modeling, in contrast, makes it possible to build whole-brain models by integrating features of various kinds. The aim of this work was to analyze EEG dynamics studies while gathering motor imagery data through single-layer and multilayer network modeling. The motor imagery database used consists of 18 EEG recordings of four motor imagery tasks: left hand, right hand, feet, and tongue. Brain connectivity was estimated by calculating the coherence adjacency matrices from each electrophysiological band (δ, θ, α and β) from brain areas and then embedding them by considering each band as a single-layer graph and a layer of the multilayer brain models. Constructing a reliable multilayer network topology requires a threshold that distinguishes effective connections from spurious ones. For this reason, two thresholds were implemented, the classic fixed (average) one and Otsu’s version. The latter is a new proposal for an adaptive threshold that offers reliable insight into brain topology and dynamics. Findings from the brain network models suggest that frontal and parietal brain regions are involved in motor imagery tasks.

Slow-wave sleep affects synucleinopathy and regulates proteostatic processes in mouse models of Parkinson's disease

[Figure: see text].

Effective Connectivity During Rest and Music Listening: An EEG Study on Parkinson's Disease

Music-based interventions seem to enhance motor, sensory and cognitive functions in Parkinson’s disease (PD), but the underlying action mechanisms are still largely unknown. This electroencephalography (EEG) study aimed to investigate the effective connectivity patterns characterizing PD in the resting state and during music listening. EEG recordings were obtained from fourteen non-demented PD patients and 12 healthy controls, at rest and while listening to three music tracks. Theta- and alpha-band power spectral density and multivariate partial directed coherence were computed. Power and connectivity measures were compared between patients and controls in the four conditions and in music vs. rest. Compared to controls, patients showed enhanced theta-band power and slightly enhanced alpha-band power, but markedly reduced theta- and alpha-band interactions among EEG channels, especially concerning the information received by the right central channel. EEG power differences were partially reduced by music listening, which induced power increases in controls but not in patients. Connectivity differences were slightly compensated by music, whose effects largely depended on the track. In PD, music enhanced the frontotemporal inter-hemispheric communication. Our findings suggest that PD is characterized by enhanced activity but reduced information flow within the EEG network, being only partially normalized by music. Nevertheless, music capability to facilitate inter-hemispheric communication might underlie its beneficial effects on PD pathophysiology and should be further investigated.

Magnetoencephalography: physics, techniques, and applications in the basic and clinical neurosciences

Magnetoencephalography (MEG) is a technique used to measure the magnetic fields generated from neuronal activity in the brain. MEG has a high temporal resolution on the order of milliseconds and provides a more direct measure of brain activity when compared with hemodynamic-based neuroimaging methods such as magnetic resonance imaging and positron emission tomography. The current review focuses on basic features of MEG such as the instrumentation and the physics that are integral to the signals that can be measured, and the principles of source localization techniques, particularly the physics of beamforming and the techniques that are used to localize the signal of interest. In addition, we review several metrics that can be used to assess functional coupling in MEG and describe the advantages and disadvantages of each approach. Lastly, we discuss the current and future applications of MEG.

Parkinsonism Alters Beta Burst Dynamics across the Basal Ganglia-Motor Cortical Network

Elevated synchronized oscillatory activity in the beta band has been hypothesized to be a pathophysiological marker of Parkinson's disease (PD). Recent studies have suggested that parkinsonism is closely associated with increased amplitude and duration of beta burst activity in the subthalamic nucleus (STN). How beta burst dynamics are altered from the normal to parkinsonian state across the basal ganglia-thalamocortical (BGTC) motor network, however, remains unclear. In this study, we simultaneously recorded local field potential activity from the STN, internal segment of the globus pallidus (GPi), and primary motor cortex (M1) in three female rhesus macaques, and characterized how beta burst activity changed as the animals transitioned from normal to progressively more severe parkinsonian states. Parkinsonism was associated with an increased incidence of beta bursts with longer duration and higher amplitude in the low beta band (8-20 Hz) in both the STN and GPi, but not in M1. We observed greater concurrence of beta burst activity, however, across all recording sites (M1, STN, and GPi) in PD. The simultaneous presence of low beta burst activity across multiple nodes of the BGTC network that increased with severity of PD motor signs provides compelling evidence in support of the hypothesis that low beta synchronized oscillations play a significant role in the underlying pathophysiology of PD. Given its immersion throughout the motor circuit, we hypothesize that this elevated beta-band activity interferes with spatial-temporal processing of information flow in the BGTC network that contributes to the impairment of motor function in PD.SIGNIFICANCE STATEMENT This study fills a knowledge gap regarding the change in temporal dynamics and coupling of beta burst activity across the basal ganglia-thalamocortical (BGTC) network during the evolution from normal to progressively more severe parkinsonian states. We observed that changes in beta oscillatory activity occur throughout BGTC and that increasing severity of parkinsonism was associated with a higher incidence of longer duration, higher amplitude low beta bursts in the basal ganglia, and increased concurrence of beta bursts across the subthalamic nucleus, globus pallidus, and motor cortex. These data provide new insights into the potential role of changes in the temporal dynamics of low beta activity within the BGTC network in the pathogenesis of Parkinson's disease.

Depression of auditory cortex excitability by transcranial alternating current stimulation

Transcranial alternating current stimulation (tACS) is a type of noninvasive brain stimulation technique that has been shown to modulate motor, cognitive and memory function. Direct electrophysiological evidence of an interaction between tACS and the auditory cortex excitability has rarely been reported. Different stimulation parameters and areas of tACS may have different influence on the regulatory results. In this study, 11-Hz tACS was applied to the auditory cortex of 12 subjects with normal hearing in order to explore its effects on the auditory steady-state response (ASSR). The results indicate that tACS has an inhibitory effect on 40-Hz ASSR. In addition, EEG source analysis shows that 11-Hz tACS may enhance the activity of the middle temporal gyrus under both sham and real conditions, while the estimated source activity of the posterior cingulate gyrus may be reduced under real condition. The results reveal that tACS applied to the temporal lobe of humans will make the 40-Hz ASSR a tendency to decrease, and help improve the understanding of modulation of tACS-induced auditory cortex excitability changes in humans.

Alpha Power and Functional Connectivity in Cognitive Decline: A Systematic Review and Meta-Analysis

Background:

Mild cognitive impairment (MCI) is a stage between expected age-related cognitive decline and dementia. Dementias have been associated with changes in neural oscillations across the frequency spectrum, including the alpha range. Alpha is the most prominent rhythm in human EEG and is best detected during awake resting state (RS). Though several studies measured alpha power and synchronization in MCI, findings have not yet been integrated.

Objective:

To consolidate findings on power and synchronization of alpha oscillations across stages of cognitive decline.

Methods:

We included studies published until January 2020 that compared power or functional connectivity between 1) people with MCI and cognitively healthy older adults (OA) or people with a neurodegenerative dementia, and 2) people with progressive and stable MCI. Random-effects meta-analyses were performed when enough data was available.

Results:

Sixty-eight studies were included in the review. Global RS alpha power was lower in AD than in MCI (ES = –0.30; 95% CI = –0.51, –0.10; k = 6), and in MCI than in OA (ES = –1.49; 95% CI = –2.69, –0.29; k = 5). However, the latter meta-analysis should be interpreted cautiously due to high heterogeneity. The review showed lower RS alpha power in progressive than in stable MCI, and lower task-related alpha reactivity in MCI than in OA. People with MCI had both lower and higher functional connectivity than OA. Publications lacked consistency in MCI diagnosis and EEG measures.

Conclusion:

Research indicates that RS alpha power decreases with increasing impairment, and could—combined with measures from other frequency bands—become a biomarker of early cognitive decline.

A brain connectivity characterization of children with different levels of mathematical achievement based on graph metrics

Recent studies aiming to facilitate mathematical skill development in primary school children have explored the electrophysiological characteristics associated with different levels of arithmetic achievement. The present work introduces an alternative EEG signal characterization using graph metrics and, based on such features, a classification analysis using a decision tree model. This proposal aims to identify group differences in brain connectivity networks with respect to mathematical skills in elementary school children. The methods of analysis utilized were signal-processing (EEG artifact removal, Laplacian filtering, and magnitude square coherence measurement) and the characterization (Graph metrics) and classification (Decision Tree) of EEG signals recorded during performance of a numerical comparison task. Our results suggest that the analysis of quantitative EEG frequency-band parameters can be used successfully to discriminate several levels of arithmetic achievement. Specifically, the most significant results showed an accuracy of 80.00% (α band), 78.33% (δ band), and 76.67% (θ band) in differentiating high-skilled participants from low-skilled ones, averaged-skilled subjects from all others, and averaged-skilled participants from low-skilled ones, respectively. The use of a decision tree tool during the classification stage allows the identification of several brain areas that seem to be more specialized in numerical processing.

MRgFUS Pallidothalamic Tractotomy for Chronic Therapy-Resistant Parkinson's Disease in 51 Consecutive Patients: Single Center Experience

Background: There is a long history, beginning in the 1940s, of ablative neurosurgery on the pallidal efferent fibers to treat patients suffering from Parkinson's disease (PD). Since the early 1990s, we undertook a re-actualization of the approach to the subthalamic region, and proposed, on a histological basis, to target specifically the pallidothalamic tract at the level of Forel's field H1. This intervention, the pallidothalamic tractotomy (PTT), has been performed since 2011 using the MR-guided focused ultrasound (MRgFUS) technique. A reappraisal of the histology of the pallidothalamic tract was combined recently with an optimization of our lesioning strategy using thermal dose control.

Objective: This study was aimed at demonstrating the efficacy and risk profile of MRgFUS PTT against chronic therapy-resistant PD.

Methods: This consecutive case series reflects our current treatment routine and was collected between 2017 and 2018. Fifty-two interventions in 47 patients were included. Fifteen patients received bilateral PTT. The median follow-up was 12 months.

Results: The Unified Parkinson's Disease Rating Scale (UPDRS) off-medication postoperative score was compared to the baseline on-medication score and revealed percentage reductions of the mean of 84% for tremor, 70% for rigidity, and 73% for distal hypobradykinesia, all values given for the treated side. Axial items (for voice, trunk and gait) were not significantly improved. PTT achieved 100% suppression of on-medication dyskinesias as well as reduction in pain (p < 0.001), dystonia (p < 0.001) and REM sleep disorders (p < 0.01). Reduction of the mean L-Dopa intake was 55%. Patients reported an 88% mean tremor relief and 82% mean global symptom relief on the operated side and 69% mean global symptom improvement for the whole body. There was no significant change of cognitive functions. The small group of bilateral PTTs at 1 year follow-up shows similar results as compared to unilateral PTTs but does not allow to draw firm conclusions at this point.

Conclusion: MRgFUS PTT was shown to be a safe and effective intervention for PD patients, addressing all symptoms, with varying effectiveness. We discuss the need to integrate the preoperative state of the thalamocortical network as well as the psycho-emotional dimension.

Electrophysiological resting state networks of predominantly akinetic-rigid Parkinson patients: Effects of dopamine therapy

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Analysis of whole-brain frequency-specific resting state networks with EEG.

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Comparison of dopamine medication ON and OFF state in Parkinson patients.

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Parkinson patients show distinct frequency-specific network alterations.

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Motor network at beta frequencies is re-instated after dopamine medication.

Parkinson's disease (PD) causes both motor and non-motor symptoms, which can partially be reversed by dopamine therapy. These symptoms as well as the effect of dopamine may be explained by distinct alterations in whole-brain architecture. We used functional connectivity (FC) and in particular resting state network (RSN) analysis to identify such whole-brain alterations in a frequency-specific manner. In addition, we hypothesized that standard dopaminergic medication would have a normalizing effect on these whole brain alterations. We recorded resting-state EEGs of 19 PD patients in the medical OFF and ON states, and of 12 healthy age-matched controls. The PD patients were either of akinetic-rigid or mixed subtype. We extracted RSNs with independent component analysis in the source space for five frequency bands. Within the sensorimotor network (SMN) the supplementary motor area (SMA) showed decreased FC in the OFF state compared to healthy controls. This finding was reversed after dopamine administration. Furthermore, in the OFF state no stable SMN beta component could be identified. The default mode network showed alterations due to PD independent of the medication state. The visual network was altered in the OFF state, and reinstated to a pattern similar to healthy controls by medication. In conclusion, PD causes distinct RSN alterations, which are partly reversed after levodopa administration. The changes within the SMN are of particular interest, because they broaden the pathophysiological understanding of PD. Our results identify the SMA as a central network hub affected in PD and a crucial effector of dopamine therapy.

Transcranial Ultrasound Stimulation Directly Influences the Cortical Excitability of the Motor Cortex in Parkinsonian Mice

BACKGROUND

Low-intensity transcranial ultrasound stimulation is a new noninvasive brain modulation method with high spatial resolution and high penetration depth. However, until now, it was unclear whether transcranial ultrasound stimulation has a significant effect on PD.

OBJECTIVES

In order to evaluate the effect of transcranial ultrasound stimulation on PD.

METHODS

We used transcranial ultrasound stimulation to modulate parkinsonian-related activity in mice administered MPTP and recorded local field potentials in the motor cortex before and after ultrasound stimulation. We analyzed neuronal oscillatory activity known to be relevant to the pathophysiology of PD.

RESULTS

After ultrasound stimulation, mean power intensity in the beta band (13-30 Hz) significantly decreased, and the phase-amplitude coupling strength between the beta and high gamma (55-100 Hz) bands and between the beta and ripple (100-200 Hz) bands also became significantly weaker.

CONCLUSIONS

This study demonstrates that ultrasonic neuromodulation can significantly decrease parkinsonian-related activity in mice administered MPTP. © 2019 International Parkinson and Movement Disorder Society.

Brain activity during self-paced vs. fixed protocols in graded exercise testing

Electroencephalography research surrounding maximal exercise testing has been limited to male subjects. Additionally, studies have used open-looped protocols, meaning individuals do not know the exercise endpoint. Closed-loop protocols are often shown to result in optimal performance as self-pacing is permitted. The purpose of this study was to compare brain activity during open- and closed-loop maximal exercise protocols, and to determine if any sex differences are present. Twenty-seven subjects (12 males, ages 22.0 ± 2.5 years) participated in this study. A pre-assembled EEG sensor strip was used to collect brain activity from specific electrodes (F3/F4: dorsolateral prefrontal cortex, or dlPFC; and C3/Cz/C4: motor cortex, or MC). Alpha (8-12 Hz) and beta (12-30 Hz) frequency bands were analyzed. Subjects completed two maximal exercise tests on a cycle ergometer, separated by at least 48 h: a traditional, open-loop graded exercise test (GXT) and a closed-loop self-paced VO_2max (SPV) test. Mixed model ANOVAs were performed to compare power spectral density (PSD) between test protocols and sexes. A significant interaction of time and sex was shown in the dlPFC for males, during the GXT only (p = 001), where a peak was reached and then a decrease was shown. A continuous increase was shown in the SPV. Sex differences in brain activity during exercise could be associated with inhibitory control, which is a function of the dlPFC. Knowledge of an exercise endpoint could be influential towards cessation of exercise and changes in cortical brain activity.

Complex Motor Learning and Police Training: Applied, Cognitive, and Clinical Perspectives

The practices surrounding police training of complex motor skills, including the use of force, varies greatly around the world, and even over the course of an officer’s career. As the nature of policing changes with society and the advancement of science and technology, so should the training practices that officers undertake at both central (i.e., police academy basic recruit training) and local (i.e., individual agency or precinct) levels. The following review is intended to bridge the gap between scientific knowledge and applied practice to inform best practices for training complex motor skills that are unique and critical to law enforcement, including the use of lethal force. We begin by providing a basic understanding of the fundamental cognitive processes underlying motor learning, from novel skill acquisition to complex behaviors including situational awareness, and decision-making that precede and inform action. Motor learning, memory, and perception are then discussed within the context of occupationally relevant stress, with a review of evidence-based training practices that promote officer performance and physiological responses to stress during high-stakes encounters. A lack of applied research identifying the neurophysiological mechanisms underlying motor learning in police is inferred from a review of evidence from various clinical populations suffering from disorders of cognitive and motor systems, including Alzheimer’s and Parkinson’s disease and stroke. We conclude this review by identifying practical, organizational, and systemic challenges to implementing evidence-based practices in policing and provide recommendations for best practices that will promote training effectiveness and occupational safety of end-users (i.e., police trainers and officers).

Altered EEG alpha and theta oscillations characterize apathy in Parkinson's disease during incentivized movement

Apathy is a common non-motor symptom of Parkinson's disease (PD) that is difficult to quantify and poorly understood. Some studies have used incentivized motor tasks to assess apathy, as the condition is often associated with a reduction in motivated behavior. Normally event-related desynchronization, a reduction of power in specific frequency bands, is observed in the motor cortex during the peri-movement period. Also, alpha (8–12 Hz) and theta (4–7 Hz) oscillations are sensitive to rewards that are closely related to motivational states however these oscillations have not been widely investigated in relation to apathy in PD. Using EEG recordings, we investigated the neural oscillatory characteristics of apathy in PD during an incentivized motor task with interleaved rest periods. Apathetic and non-apathetic PD subjects on dopaminergic medication and healthy control subjects were instructed to squeeze a hand grip device for a monetary reward proportional to the subject's grip force and the monetary value attributed to that trial. Apathetic PD subjects exhibited higher alpha and theta powers in the pre-trial baseline rest period compared to non-apathetic PD subjects and healthy subjects. Further, we found that both resting power and relative power in alpha and theta bands during incentivized movement predicted PD subjects' apathy scores. Our results suggest that apathetic PD patients may need to overcome greater baseline alpha and theta oscillatory activity in order to facilitate incentivized movement. Clinically, resting alpha and theta power as well as alpha and theta event-related desynchronization during movement may serve as potential neural markers for apathy severity in PD.

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Apathetic patients with Parkinson's disease on dopaminergic medication have distinct neural oscillatory characteristics.

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Apathetic patients exhibit a higher resting EEG theta and alpha power compared to non-apathetic patients.

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Both resting power and relative event-related theta and alpha desynchronization during squeezing are able to predict patient apathy scores.

[Application of brain electrical activity burst analysis method for detection of EEG characteristics in the early stage of Parkinson's disease]

AIM

To develop a mathematical method of analysis and visualization of EEG based on the ROC analysis of burst electrical activity in the cerebral cortex.

MATERIAL AND METHODS

Using a new method of analysis of EEG burst activity, the frequency parameters of brain electrical activity have been investigated in patients in the first stage of Parkinson's disease (PD) defined by the Hoehn and Yahr scale. Patients were right-handed, with disease onset in either the right or the left side. The burst term is used in neurophysiology for the description of wave activity in EEG signals. Bursts are reflected in the local peaks of wavelet spectrograms, some of the parameters of which have been analyzed. Electrical activity of the left and right central cortex areas was investigated. The results were compared with those obtained from healthy volunteers.

RESULTS

In PD patients, burst activity was changed in alpha- and beta bands. Compared to healthy volunteers, it was higher in alpha band 8-9 Hz and lower in upper alpha band 11-13 Hz and beta band 18-24 Hz. With regard to asymmetry of the brain in PD patients, there was the change in burst activity in both brain hemispheres. Diagrams of burst activity showed the difference between the patients with tremor onset in the left hand and tremor onset in the right hand.

CONCLUSION

This suggests differences in brain electrical activity changes in patients with left-sided and right-sided disease onset. The initial results of the study demonstrate that the method of analysis and visualization based on the evaluation of certain parameters of EEG bursts is promising for the analysis of EEG features in PD patients.

Resting functional connectivity and mild cognitive impairment in Parkinson’s disease. An electroencephalogram study

Objective: Parkinson’s disease (PD) is characterized by cognitive deficits. There is not clarity about electroencephalogram (EEG) connectivity related to the cognitive profile of patients. Our objective was to evaluate connectivity over resting EEG in nondemented PD. Methods: PD subjects with and without mild cognitive impairment (MCI) were assessed using coherence from resting EEG for local, intra and interhemispheric connectivity. Results: PD subjects without MCI (PD-nMCI) had lower intra and interhemispheric coherence in alpha2 compared with controls. PD with MCI (PD-MCI) showed higher intra and posterior interhemispheric coherence in alpha2 and beta1, respectively, in comparison to PD-nMCI. PD-MCI presented lower frontal coherence in beta frequencies compared with PD-nMCI. Conclusion: EEG coherence measures indicate distinct cortical activity in PD with and without MCI.

Abnormal Phase Coupling in Parkinson's Disease and Normalization Effects of Subthreshold Vestibular Stimulation

The human brain is a highly dynamic structure requiring dynamic coordination between different neural systems to perform numerous cognitive and behavioral tasks. Emerging perspectives on basal ganglia (BG) and thalamic functions have highlighted their role in facilitating and mediating information transmission among cortical regions. Thus, changes in BG and thalamic structures can induce aberrant modulation of cortico-cortical interactions. Recent work in deep brain stimulation (DBS) has demonstrated that externally applied electrical current to BG structures can have multiple downstream effects in large-scale brain networks. In this work, we identified EEG-based altered resting-state cortical functional connectivity in Parkinson's disease (PD) and examined effects of dopaminergic medication and electrical vestibular stimulation (EVS), a non-invasive brain stimulation (NIBS) technique capable of stimulating the BG and thalamus through vestibular pathways. Resting EEG was collected from 16 PD subjects and 18 age-matched, healthy controls (HC) in four conditions: sham (no stimulation), EVS1 (4-8 Hz multisine), EVS2 (50-100 Hz multisine) and EVS3 (100-150 Hz multisine). The mean, variability, and entropy were extracted from time-varying phase locking value (PLV), a non-linear measure of pairwise functional connectivity, to probe abnormal cortical couplings in the PD subjects. We found the mean PLV of Cz and C3 electrodes were important for discrimination between PD and HC subjects. In addition, the PD subjects exhibited lower variability and entropy of PLV (mostly in theta and alpha bands) compared to the controls, which were correlated with their clinical characteristics. While levodopa medication was effective in normalizing the mean PLV only, all EVS stimuli normalized the mean, variability and entropy of PLV in the PD subject, with the exact extent and duration of improvement a function of stimulus type. These findings provide evidence demonstrating both low- and high-frequency EVS exert widespread influences on cortico-cortical connectivity, likely via subcortical activation. The improvement observed in PD in a stimulus-dependent manner suggests that EVS with optimized parameters may provide a new non-invasive means for neuromodulation of functional brain networks.

Towards a Mechanistic-Driven Precision Medicine Approach for Tinnitus

In this position review, we propose to establish a path for replacing the empirical classification of tinnitus with a taxonomy from precision medicine. The goal of a classification system is to understand the inherent heterogeneity of individuals experiencing and suffering from tinnitus and to identify what differentiates potential subgroups. Identification of different patient subgroups with distinct audiological, psychophysical, and neurophysiological characteristics will facilitate the management of patients with tinnitus as well as the design and execution of drug development and clinical trials, which, for the most part, have not yielded conclusive results. An alternative outcome of a precision medicine approach in tinnitus would be that additional mechanistic phenotyping might not lead to the identification of distinct drivers in each individual, but instead, it might reveal that each individual may display a quantitative blend of causal factors. Therefore, a precision medicine approach towards identifying these causal factors might not lead to subtyping these patients but may instead highlight causal pathways that can be manipulated for therapeutic gain. These two outcomes are not mutually exclusive, and no matter what the final outcome is, a mechanistic-driven precision medicine approach is a win-win approach for advancing tinnitus research and treatment. Although there are several controversies and inconsistencies in the tinnitus field, which will not be discussed here, we will give a few examples, as to how the field can move forward by exploring the major neurophysiological tinnitus models, mostly by taking advantage of the common features supported by all of the models. Our position stems from the central concept that, as a field, we can and must do more to bring studies of mechanisms into the realm of neuroscience.

Default mode network alterations in individuals with high-trait-anxiety: An EEG functional connectivity study

BACKGROUND

Although several researches investigated Default Mode Network (DMN) alterations in individuals with anxiety disorders, up to now no studies have investigated DMN functional connectivity in non-clinical individuals with high-trait-anxiety using quantitative electroencephalography (EEG). Here, the main aim was to extend previous findings investigating the association between trait anxiety and DMN EEG functional connectivity.

METHODS

Twenty-three individuals with high-trait-anxiety and twenty-four controls were enrolled. EEG was recorded during 5 min of resting state (RS). EEG analyses were conducted by means of the exact Low-Resolution Electromagnetic Tomography software (eLORETA).

RESULTS

Compared to controls, individuals with high-trait-anxiety showed a decrease of theta connectivity between right medial prefrontal cortex (mPFC) and right posterior cingulate/retrosplenial cortex. A decrease of beta connectivity was also observed between right mPFC and right anterior cingulate cortex. Furthermore, DMN functional connectivity strength was negatively related with STAI-T total score (i.e., lower connectivity was associated with higher trait anxiety), even when controlling for potential confounding variables (i.e., sex, age, and general psychopathology).

LIMITATIONS

Small sample size makes it difficult to draw definitive conclusions. Furthermore, we did not assess state variation of anxiety, which make our interpretation specific to trait anxiety.

CONCLUSIONS

Taken together, our results suggest that high-trait-anxiety individuals fail to synchronize DMN during RS, reflecting a possible top-down cognitive control deficit. These results may help in the understanding of the individual differences in functional brain networks associated with trait anxiety, a crucial aim in the prevention and in the early etiology understanding of clinical anxiety and related sequelae.

High-intensity binge drinking is associated with alterations in spontaneous neural oscillations in young adults

Heavy episodic alcohol consumption (also termed binge drinking) contributes to a wide range of health and cognitive deficits, but the associated brain-based indices are poorly understood. The current study used electroencephalography (EEG) to examine spontaneous neural oscillations in young adults as a function of quantity, frequency, and the pattern of their alcohol consumption. Sixty-one young adults (23.4 ± 3.4 years of age) were assigned to binge drinking (BD) and light drinking (LD) groups that were equated on gender, race/ethnic identity, age, educational background, and family history of alcoholism. EEG activity was recorded during eyes-open and eyes-closed resting conditions. Each participant's alpha peak frequency (APF) was used to calculate absolute power in individualized theta and alpha frequency bands, with a canonical frequency range used for beta. APF was slower by 0.7 Hz in BD, especially in individuals engaging in high-intensity drinking, but there were no changes in alpha power. BD also exhibited higher frontal theta and beta power than LD. Alpha slowing and increased theta power in BD remained after accounting for depression, anxiety, and personality characteristics, while elevated beta power covaried with sensation seeking. Furthermore, APF slowing and theta power correlated with various measures of alcohol consumption, including binge episodes and blackouts, but not with measures of working and episodic memory, cognitive flexibility, processing speed, or personality variables, suggesting that these physiological changes may be modulated by high-intensity alcohol intake. These results are consistent with studies of alcohol-use disorder (AUD) and support the hypothesis that binge drinking is a transitional stage toward alcohol dependence. The observed thalamocortical dysrhythmia may be indicative of an excitatory-inhibitory imbalance in BD and may potentially serve as an index of the progressive development of AUD, with a goal of informing possible interventions to minimize alcohol's deleterious effects on the brain.

The analysis of EEG coherence reflects middle childhood differences in mathematical achievement

Symbolic numerical magnitude processing is crucial to arithmetic development, and it is thought to be supported by the functional activation of several brain-interconnected structures. In this context, EEG beta oscillations have been recently associated with attention and working memory processing that underlie math achievement. Due to that EEG coherence represents a useful measure of brain functional connectivity, we aimed to contrast the EEG coherence in forty 8-to-9-year-old children with different math skill levels (High: HA, and Low achievement: LA) according to their arithmetic scores in the Fourth Edition of the Wide Range Achievement Test (WRAT-4) while performing a symbolic magnitude comparison task (i.e. determining which of two numbers is numerically larger). The analysis showed significantly greater coherence over the right hemisphere in the two groups, but with a distinctive connectivity pattern. Whereas functional connectivity in the HA group was predominant in parietal areas, especially involving beta frequencies, the LA group showed more extensive frontoparietal relationships, with higher participation of delta, theta and alpha band frequencies, along with a distinct time-frequency domain expression. The results seem to reflect that lower math achievements in children mainly associate with cognitive processing steps beyond stimulus encoding, along with the need of further attentional resources and cognitive control than their peers, suggesting a lower degree of numerical processing automation.

Time estimation and beta segregation: An EEG study and graph theoretical approach

Elucidation of the neural correlates of time perception constitutes an important research topic in cognitive neuroscience. The focus to date has been on durations in the millisecond to seconds range, but here we used electroencephalography (EEG) to examine brain functional connectivity during much longer durations (i.e., 15 min). For this purpose, we conducted an initial exploratory experiment followed by a confirmatory experiment. Our results showed that those participants who overestimated time exhibited lower activity of beta (18–30 Hz) at several electrode sites. Furthermore, graph theoretical analysis indicated significant differences in the beta range (15–30 Hz) between those that overestimated and underestimated time. Participants who underestimated time showed higher clustering coefficient compared to those that overestimated time. We discuss our results in terms of two aspects. FFT results, as a linear approach, are discussed within localized/dedicated models (i.e., scalar timing model). Second, non-localized properties of psychological interval timing (as emphasized by intrinsic models) are addressed and discussed based on results derived from graph theory. Results suggested that although beta amplitude in central regions (related to activity of BG-thalamocortical pathway as a dedicated module) is important in relation to timing mechanisms, the properties of functional activity of brain networks; such as the segregation of beta network, are also crucial for time perception. These results may suggest subjective time may be created by vector units instead of scalar ticks.

Functional cortical source connectivity of resting state electroencephalographic alpha rhythms shows similar abnormalities in patients with mild cognitive impairment due to Alzheimer's and Parkinson's diseases

OBJECTIVE

This study tested the hypothesis that markers of functional cortical source connectivity of resting state eyes-closed electroencephalographic (rsEEG) rhythms may be abnormal in subjects with mild cognitive impairment due to Alzheimer's (ADMCI) and Parkinson's (PDMCI) diseases compared to healthy elderly subjects (Nold).

METHODS

rsEEG data had been collected in ADMCI, PDMCI, and Nold subjects (N = 75 for any group). eLORETA freeware estimated functional lagged linear connectivity (LLC) from rsEEG cortical sources. Area under receiver operating characteristic (AUROC) curve indexed the accuracy in the classification of Nold and MCI individuals.

RESULTS

Posterior interhemispheric and widespread intrahemispheric alpha LLC solutions were abnormally lower in both MCI groups compared to the Nold group. At the individual level, AUROC curves of LLC solutions in posterior alpha sources exhibited moderate accuracies (0.70-0.72) in the discrimination of Nold vs. ADMCI-PDMCI individuals. No differences in the LLC solutions were found between the two MCI groups.

CONCLUSIONS

These findings unveil similar abnormalities in functional cortical connectivity estimated in widespread alpha sources in ADMCI and PDMCI. This was true at both group and individual levels.

SIGNIFICANCE

The similar abnormality of alpha source connectivity in ADMCI and PDMCI subjects might reflect common cholinergic impairment.

Abnormalities of resting-state functional cortical connectivity in patients with dementia due to Alzheimer's and Lewy body diseases: an EEG study

Previous evidence showed abnormal posterior sources of resting-state delta (<4 Hz) and alpha (8-12 Hz) rhythms in patients with Alzheimer's disease with dementia (ADD), Parkinson's disease with dementia (PDD), and Lewy body dementia (DLB), as cortical neural synchronization markers in quiet wakefulness. Here, we tested the hypothesis of additional abnormalities in functional cortical connectivity computed in those sources, in ADD, considered as a "disconnection cortical syndrome", in comparison with PDD and DLB. Resting-state eyes-closed electroencephalographic (rsEEG) rhythms had been collected in 42 ADD, 42 PDD, 34 DLB, and 40 normal healthy older (Nold) participants. Exact low-resolution brain electromagnetic tomography (eLORETA) freeware estimated the functional lagged linear connectivity (LLC) from rsEEG cortical sources in delta, theta, alpha, beta, and gamma bands. The area under receiver operating characteristic (AUROC) curve indexed the classification accuracy between Nold and diseased individuals (only values >0.7 were considered). Interhemispheric and intrahemispheric LLCs in widespread delta sources were abnormally higher in the ADD group and, unexpectedly, normal in DLB and PDD groups. Intrahemispheric LLC was reduced in widespread alpha sources dramatically in ADD, markedly in DLB, and moderately in PDD group. Furthermore, the interhemispheric LLC in widespread alpha sources showed lower values in ADD and DLB than PDD groups. At the individual level, AUROC curves of LLC in alpha sources exhibited better classification accuracies for the discrimination of ADD versus Nold individuals (0.84) than for DLB versus Nold participants (0.78) and PDD versus Nold participants (0.75). Functional cortical connectivity markers in delta and alpha sources suggest a more compromised neurophysiological reserve in ADD than DLB, at both group and individual levels.

Computational Stimulation of the Basal Ganglia Neurons with Cost Effective Delayed Gaussian Waveforms

Deep brain stimulation (DBS) has compelling results in the desynchronization of the basal ganglia neuronal activities and thus, is used in treating the motor symptoms of Parkinson's disease (PD). Accurate definition of DBS waveform parameters could avert tissue or electrode damage, increase the neuronal activity and reduce energy cost which will prolong the battery life, hence avoiding device replacement surgeries. This study considers the use of a charge balanced Gaussian waveform pattern as a method to disrupt the firing patterns of neuronal cell activity. A computational model was created to simulate ganglia cells and their interactions with thalamic neurons. From the model, we investigated the effects of modified DBS pulse shapes and proposed a delay period between the cathodic and anodic parts of the charge balanced Gaussian waveform to desynchronize the firing patterns of the GPe and GPi cells. The results of the proposed Gaussian waveform with delay outperformed that of rectangular DBS waveforms used in in-vivo experiments. The Gaussian Delay Gaussian (GDG) waveforms achieved lower number of misses in eliciting action potential while having a lower amplitude and shorter length of delay compared to numerous different pulse shapes. The amount of energy consumed in the basal ganglia network due to GDG waveforms was dropped by 22% in comparison with charge balanced Gaussian waveforms without any delay between the cathodic and anodic parts and was also 60% lower than a rectangular charged balanced pulse with a delay between the cathodic and anodic parts of the waveform. Furthermore, by defining a Synchronization Level metric, we observed that the GDG waveform was able to reduce the synchronization of GPi neurons more effectively than any other waveform. The promising results of GDG waveforms in terms of eliciting action potential, desynchronization of the basal ganglia neurons and reduction of energy consumption can potentially enhance the performance of DBS devices.

Altered modulation of sensorimotor rhythms with chronic paralysis

After paralysis, the disconnection between the cortex and its peripheral targets leads to neuroplasticity throughout the nervous system. However, it is unclear how chronic paralysis specifically impacts cortical oscillations associated with attempted movement of impaired limbs. We hypothesized that μ- (8-13 Hz) and β- (15-30 Hz) event-related desynchronization (ERD) would be less modulated for individuals with hand paralysis due to cervical spinal cord injury (SCI). To test this, we compared the modulation of ERD from magnetoencephalography (MEG) during attempted and imagined grasping performed by participants with cervical SCI (n = 12) and able-bodied controls (n = 13). Seven participants with tetraplegia were able to generate some electromyography (EMG) activity during attempted grasping, whereas the other five were not. The peak and area of ERD were significantly decreased for individuals without volitional muscle activity when they attempted to grasp compared with able-bodied subjects and participants with SCI,with some residual EMG activity. However, no significant differences were found between subject groups during mentally simulated tasks (i.e., motor imagery) where no muscle activity or somatosensory consequences were expected. These findings suggest that individuals who are unable to produce muscle activity are capable of generating ERD when attempting to move, but the characteristics of this ERD are altered. However, for people who maintain volitional muscle activity after SCI, there are no significant differences in ERD characteristics compared with able-bodied controls. These results provide evidence that ERD is dependent on the level of intact muscle activity after SCI.NEW & NOTEWORTHY Source space MEG was used to investigate sensorimotor cortical oscillations in individuals with SCI. This study provides evidence that individuals with cervical SCI exhibit decreased ERD when they attempt to grasp if they are incapable of generating muscle activity. However, there were no significant differences in ERD between paralyzed and able-bodied participants during motor imagery. These results have important implications for the design and evaluation of new therapies, such as motor imagery and neurofeedback interventions.

Delta coherence in resting-state EEG predicts the reduction in cigarette craving after hypnotic aversion suggestions

Cigarette craving is a key contributor of nicotine addiction. Hypnotic aversion suggestions have been used to help smoking cessation and reduce smoking relapse rates but its neural basis is poorly understood. This study investigated the underlying neural basis of hypnosis treatment for nicotine addiction with resting state Electroencephalograph (EEG) coherence as the measure. The sample consisted of 42 male smokers. Cigarette craving was measured by the Tobacco Craving Questionnaire. The 8-minute resting state EEG was recorded in baseline state and after hypnotic induction in the hypnotic state. Then a smoking disgust suggestion was performed. A significant increase in EEG coherence in delta and theta frequency, and significant decrease in alpha and beta frequency, between the baseline and the hypnotic state was found, which may reflect alterations in consciousness after hypnotic induction. More importantly, the delta coherence between the right frontal region and the left posterior region predicted cigarette craving reduction after hypnotic aversion suggestions. This suggests that the functional connectivity between these regions plays an important role in reducing cigarette cravings via hypnotic aversion suggestions. Thus, these brain regions may serve as an important target to treat nicotine addiction, such as stimulating these brain regions via repetitive transcranial magnetic stimulation.

Experience-dependent modulation of alpha and beta during action observation and motor imagery

Background

EEG studies investigating the neural networks that facilitate action observation (AO) and kinaesthetic motor imagery (KMI) have shown reduced, or desynchronized, power in the alpha (8–12 Hz) and beta (13–30 Hz) frequency bands relative to rest, reflecting efficient activation of task-relevant areas. Functional modulation of these networks through expertise in dance has been established using fMRI, with greater activation among experts during AO. While there is evidence for experience-dependent plasticity of alpha power during AO of dance, the influence of familiarity on beta power during AO, and alpha and beta activity during KMI, remain unclear. The purpose of the present study was to measure the impact of familiarity on confidence ratings and EEG activity during (1) AO of a brief ballet sequence, (2) KMI of this same sequence, and (3) KMI of non-dance movements among ballet dancers, dancers from other genres, and non-dancers.

Results

Ballet dancers highly familiar with the genre of the experimental stimulus demonstrated higher individual alpha peak frequency (iAPF), greater alpha desynchronization, and greater task-related beta power during AO, as well as faster iAPF during KMI of non-dance movements. While no between-group differences in alpha or beta power were observed during KMI of dance or non-dance movements, all participants showed significant desynchronization relative to baseline, and further desynchronization during dance KMI relative to non-dance KMI indicative of greater cognitive load.

Conclusions

These findings confirm and extend evidence for experience-dependent plasticity of alpha and beta activity during AO of dance and KMI. We also provide novel evidence for modulation of iAPF that is faster when tuned to the specific motor repertoire of the observer. By considering the multiple functional roles of these frequency bands during the same task (AO), we have disentangled the compounded contribution of familiarity and expertise to alpha desynchronization for mediating task engagement among familiar ballet dancers and reflecting task difficulty among unfamiliar non-dance subjects, respectively. That KMI of a complex dance sequence relative to everyday, non-dance movements recruits greater cognitive resources suggests it may be a more powerful tool in driving neural plasticity of action networks, especially among the elderly and those with movement disorders.

Network-wide oscillations in the parkinsonian state: alterations in neuronal activities occur in the premotor cortex in parkinsonian nonhuman primates

A number of studies suggest that Parkinson's disease (PD) is associated with alterations of neuronal activity patterns in the basal-ganglia-thalamocortical circuit. There are limited electrophysiological data, however, describing how the premotor cortex, which is involved in movement and decision-making, is likely impacted in PD. In this study, spontaneous local field potential (LFP) and single unit neuronal activity were recorded in the dorsal premotor area of nonhuman primates in both the naïve and parkinsonian state using the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) model of parkinsonism. In both animals, we observed a shift of power in LFP power spectral densities (1-350 Hz) from higher to lower frequency bands; parkinsonism resulted in increased power in frequencies <8 Hz and decreased power at frequencies >30 Hz. A comparable but not identical trend was observed in the power spectral analysis of single unit spike trains: alpha power increased in both animals and gamma power decreased in one; power in other frequency bands remaining unchanged. Although not consistent across animals, we also observed changes in discharge rates and bursting activity. Overall, the LFP and single unit analysis suggest that abnormalities in premotor neural activity are a feature of parkinsonism, although specific details of those abnormalities may differ between subjects. This study further supports the concept that PD is a network disorder that induces abnormal spontaneous neural activities across the basal-ganglia-thalamocortical circuit including the premotor cortex and provides foundational knowledge for future studies regarding the relationship between changes in neuronal activity in this region and the development of motor deficits in PD.NEW & NOTEWORTHY This study begins to fill a gap in knowledge regarding how Parkinson's disease (PD) may cause abnormal functioning of the premotor cortex. It is novel as the premotor activity is examined in both the naïve and parkinsonian states, in the same subjects, at the single unit and LFP level. It provides foundational knowledge on which to build future studies to explore the relationships between premotor activities and specific parkinsonian motor and cognitive deficits.

Basal ganglia impairments in autism spectrum disorder are related to abnormal signal gating to prefrontal cortex

Research on the biological basis of autism spectrum disorder has yielded a list of brain abnormalities that are arguably as diverse as the set of behavioral symptoms that characterize the disorder. Among these are patterns of abnormal cortical connectivity and abnormal basal ganglia development. In attempts to integrate the existing literature, the current paper tests the hypothesis that impairments in the basal ganglia's function to flexibly select and route task-relevant neural signals to the prefrontal cortex underpins patterns of abnormal synchronization between the prefrontal cortex and other cortical processing centers observed in individuals with autism spectrum disorder (ASD). We tested this hypothesis using a Dynamic Causal Modeling analysis of neuroimaging data collected from 16 individuals with ASD (mean age=25.3 years; 6 female) and 17 age- and IQ-matched neurotypical controls (mean age=25.6, 6 female), who performed a Go/No-Go test of executive functioning. Consistent with the hypothesis tested, a random-effects Bayesian model selection procedure determined that a model of network connectivity in which basal ganglia activation modulated connectivity between the prefrontal cortex and other key cortical processing centers best fit the data of both neurotypicals and individuals with ASD. Follow-up analyses suggested that the largest group differences were observed for modulation of connectivity between prefrontal cortex and the sensory input region in the occipital lobe [t(31)=2.03, p=0.025]. Specifically, basal ganglia activation was associated with a small decrease in synchronization between the occipital region and prefrontal cortical regions in controls; however, in individuals with ASD, basal ganglia activation resulted in increased synchronization between the occipital region and the prefrontal cortex. We propose that this increased synchronization may reflect a failure in basal ganglia signal gating mechanisms, resulting in a non-selective copying of signals to prefrontal cortex. Such a failure to prioritize and filter signals to the prefrontal cortex could result in the pervasive impairments in cognitive flexibility and executive functioning that characterize autism spectrum disorder, and may offer a mechanistic explanation of some of the observed abnormalities in patterns of cortical synchronization in ASD.