Impairment of brain functions in Parkinson’s disease reflected by alterations in neural connectivity in EEG studies: A viewpoint

Dysfunction of motor cortices in Parkinson's disease

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

Power-integrated, wireless neural recording systems on the cranium using a direct printing method for deep-brain analysis

Conventional power-integrated wireless neural recording devices suffer from bulky, rigid batteries in head-mounted configurations, hindering the precise interpretation of the subject's natural behaviors. These power sources also pose risks of material leakage and overheating. We present the direct printing of a power-integrated wireless neural recording system that seamlessly conforms to the cranium. A quasi-solid-state Zn-ion microbattery was 3D-printed as a built-in power source geometrically synchronized to the shape of a mouse skull. Soft deep-brain neural probes, interconnections, and auxiliary electronics were also printed using liquid metals on the cranium with high resolutions. In vivo studies using mice demonstrated the reliability and biocompatibility of this wireless neural recording system, enabling the monitoring of neural activities across extensive brain regions without notable heat generation. This all-printed neural interface system revolutionizes brain research, providing bio-conformable, customizable configurations for improved data quality and naturalistic experimentation.

Early cortico-muscular coherence and cortical network changes in Parkinson's patients treated with MRgFUS

Introduction

To investigate cortical network changes using Magnetoencephalography (MEG) signals in Parkinson's disease (PD) patients undergoing Magnetic Resonance-guided Focused Ultrasound (MRgFUS) thalamotomy.

Methods

We evaluated the MEG signals in 16 PD patients with drug-refractory tremor before and after 12-month from MRgFUS unilateral lesion of the ventralis intermediate nucleus (Vim) of the thalamus contralateral to the most affected body side. We recorded patients 24 h before (T0) and 24 h after MRgFUS (T1). We analyzed signal epochs recorded at rest and during the isometric extension of the hand contralateral to thalamotomy. We evaluated cortico-muscular coherence (CMC), the out-strength index from non-primary motor areas to the pre-central area and connectivity indexes, using generalized partial directed coherence. Statistical analysis was performed using RMANOVA and post hoct-tests.

Results

Most changes found at T1 compared to T0 occurred in the beta band and included: (1) a re-adjustment of CMC distribution; (2) a reduced out-strength from non-primary motor areas toward the precentral area; (3) strongly reduced clustering coefficient values. These differences mainly occurred during motor activation and with few statistically significant changes at rest. Correlation analysis showed significant relationships between changes of out-strength and clustering coefficient in non-primary motor areas and the changes in clinical scores.

Discussion

One day after MRgFUS thalamotomy, PD patients showed a topographically reordered CMC and decreased cortico-cortical flow, together with a reduced local connection between different nodes. These findings suggest that the reordered cortico-muscular and cortical-networks in the beta band may represent an early physiological readjustment related to MRgFUS Vim lesion.

Macro and micro-sleep dysfunctions as translational biomarkers for Parkinson's disease

Sleep disturbances are highly prevalent among patients with Parkinson's disease (PD) and often appear from the early-phase disease or prodromal stages. In this chapter, we will discuss the current evidence addressing the links between sleep dysfunctions in PD, focusing most closely on those data from animal and mathematical/computational models, as well as in human-based studies that explore the electrophysiological and molecular mechanisms by which PD and sleep may be intertwined, whether as predictors or consequences of the disease. It is possible to clearly state that leucine-rich repeat kinase 2 gene (LRRK2) is significantly related to alterations in sleep architecture, particularly affecting rapid eye movement (REM) sleep and non-REM sleep, thus impacting sleep quality. Also, decreases in gamma power, observed after dopaminergic lesions, correlates negatively with the degree of injury, which brings other levels of understanding the impacts of the disease. Besides, abnormal synchronized oscillations among basal ganglia nuclei can be detrimental for information processing considering both motor and sleep-related processes. Altogether, despite clear advances in the field, it is still difficult to definitely establish a comprehensive understanding of causality among all the sleep dysfunctions with the disease itself. Although, certainly, the search for biomarkers is helping in shortening this road towards a better and faster diagnosis, as well as looking for more efficient treatments.

Application of cluster repeated mini-batch training method to classify electroencephalography for grab and lift tasks

Modern deep neural network training is based on mini-batch stochastic gradient optimization. While using extensive mini-batches improves the computational parallelism, the small batch training proved that it delivers improved generalization performance and allows a significantly smaller memory, which might also improve machine throughput. However, mini-batch size and characteristics, a key factor for training deep neural networks, has not been sufficiently investigated in training correlated group features and looping with highly complex ones. In addition, the unsupervised learning method clusters the data into different groups with similar properties to make the training process more stable and faster. Then, the supervised learning algorithm was applied with the cluster repeated mini-batch training (CRMT) methods. The CRMT algorithm changed the random minibatch characteristics in the training step into training in order of clusters. Specifically, the self-organizing maps (SOM) were used to cluster the information into n groups based on the dataset's labels Then, neural network models (ANN) were trained with each cluster using the cluster repeated mini-batch training method. Experiments conducted on EEG datasets demonstrate the survey of the proposed method and optimize it. In addition, the results in our research outperform other state-of-the-art methods.

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.

Automatic detection of Parkinson's disease from power spectral density of electroencephalography (EEG) signals using deep learning model

Parkinson's disease (PD) is characterized by slowed movements, speech disorders, an inability to control muscle movements, and tremors in the hands and feet. In the early stages of PD, the changes in these motor signs are very vague, so an objective and accurate diagnosis is difficult. The disease is complex, progressive, and very common. There are more than 10 million people worldwide suffering from PD. In this study, an EEG-based deep learning model was proposed for the automatic detection of PD to support experts. The EEG dataset comprises signals recorded by the University of Iowa from 14 PD patients and 14 healthy controls. First of all, the power spectral density values (PSDs) ​​of the frequencies between 1 and 49 Hz of the EEG signals were calculated separately using periodogram, welch, and multitaper spectral analysis methods. 49 feature vectors were extracted for each of the three different experiments. Then, the performances of support vector machine, random forest, k-nearest neighbor, and bidirectional long-short-term memory (BiLSTM) algorithms were compared using the PSDs feature vectors. After the comparison, the model integrating welch spectral analysis and the BiLSTM algorithm showed the highest performance as a result of the experiments. The deep learning model achieved satisfactory performance with 0.965 specificity, 0.994 sensitivity, 0.964 precision, 0.978 f1-score, 0.958 Matthews correlation coefficient, and 97.92% accuracy. The study is a promising attempt to detect PD from EEG signals and it also provides evidence that deep learning algorithms are more effective than machine learning algorithms for EEG signal analysis.

Electrocortical Dynamics of Usual Walking and the Planning to Step over Obstacles in Parkinson's Disease

The neural correlates of locomotion impairments observed in people with Parkinson's disease (PD) are not fully understood. We investigated whether people with PD present distinct brain electrocortical activity during usual walking and the approach phase of obstacle avoidance when compared to healthy individuals. Fifteen people with PD and fourteen older adults walked overground in two conditions: usual walking and obstacle crossing. Scalp electroencephalography (EEG) was recorded using a mobile 64-channel EEG system. Independent components were clustered using a k-means clustering algorithm. Outcome measures included absolute power in several frequency bands and alpha/beta ratio. During the usual walk, people with PD presented a greater alpha/beta ratio in the left sensorimotor cortex than healthy individuals. While approaching obstacles, both groups reduced alpha and beta power in the premotor and right sensorimotor cortices (balance demand) and increased gamma power in the primary visual cortex (visual demand). Only people with PD reduced alpha power and alpha/beta ratio in the left sensorimotor cortex when approaching obstacles. These findings suggest that PD affects the cortical control of usual walking, leading to a greater proportion of low-frequency (alpha) neuronal firing in the sensorimotor cortex. Moreover, the planning for obstacle avoidance changes the electrocortical dynamics associated with increased balance and visual demands. People with PD rely on increased sensorimotor integration to modulate locomotion.

The effect of deep brain stimulation in Parkinson's disease reflected in EEG microstates

Mechanisms of deep brain stimulation (DBS) on cortical networks were explored mainly by fMRI. Advanced analysis of high-density EEG is a source of additional information and may provide clinically useful biomarkers. The presented study evaluates EEG microstates in Parkinson's disease and the effect of DBS of the subthalamic nucleus (STN). The association between revealed spatiotemporal dynamics of brain networks and changes in oscillatory activity and clinical examination were assessed. Thirty-seven patients with Parkinson's disease treated by STN-DBS underwent two sessions (OFF and ON stimulation conditions) of resting-state EEG. EEG microstates were analyzed in patient recordings and in a matched healthy control dataset. Microstate parameters were then compared across groups and were correlated with clinical and neuropsychological scores. Of the five revealed microstates, two differed between Parkinson's disease patients and healthy controls. Another microstate differed between ON and OFF stimulation conditions in the patient group and restored parameters in the ON stimulation state toward to healthy values. The mean beta power of that microstate was the highest in patients during the OFF stimulation condition and the lowest in healthy controls; sources were localized mainly in the supplementary motor area. Changes in microstate parameters correlated with UPDRS and neuropsychological scores. Disease specific alterations in the spatiotemporal dynamics of large-scale brain networks can be described by EEG microstates. The approach can reveal changes reflecting the effect of DBS on PD motor symptoms as well as changes probably related to non-motor symptoms not influenced by DBS.

The psychophysiology of "covert" goal-directed behavior

Covert behavior is defined as behavior that is not directly visible and is thus comparable to a type of behavioral silence that requires modern psychophysiological techniques to reveal. Goal-directed behavior is teleologically purposive. Fundamentally, there are two approaches to accounting for purposeful behavior. One is the cybernetic approach, which views behavior as homeostatic and largely reflexive. The other one views behavior as a cognitive process that involves an interaction between neural events representing the previous experience, the present state of the individual, and the occurrence of particular features in the environment. This review, based on published data, presents a non-invasive psychophysiological method for investigating the electrical brain activity associated with those "silent" behaviors such as intention, evaluation of results, and memorization. Movement-related potentials (MRPs) are ideal for studying these processes. The MRPs are recorded during the execution of the skilled performance task (SPT). This task requires the execution of fast ballistic movements with the thumbs of both hands, learning a precise and short time interval between the two thumb presses, and scoring the highest number of target performances. The subject receives real-time feedback about the results of his performance. The MRPs associated with this task and present during covert behavior are the Bereitschaftspotential (BP) present before the onset of movement and the Skilled Performance Positivity (SPP) after movement, which coincides with the subject's awareness of the success or failure of his performance. These potentials show a maturational trend, reaching the adult form around the age of 10 when formal and abstract thinking progress. SPT and MRPs are particularly suitable to study neurodevelopmental disorders. Children with developmental dyslexia show abnormal MRPs, both in latency and amplitude, in different brain areas.

The Increase of Theta Power and Decrease of Alpha/Theta Ratio as a Manifestation of Cognitive Impairment in Parkinson's Disease

In this study, we aim to assess and examine cognitive functions in Parkinson's Disease patients using EEG recordings, with a central focus on characteristics associated with a cognitive decline. Based on neuropsychological evaluation using Mini-Mental State Examination, Montreal Cognitive Assessment, and Addenbrooke's Cognitive Examination-III, 98 participants were divided into three cognitive groups. All the particpants of the study underwent EEG recordings with spectral analysis. The results revealed an increase in the absolute theta power in patients with Parkinson's disease dementia (PD-D) compared to cognitively normal status (PD-CogN, p=0.00997) and a decrease in global relative beta power in PD-D compared to PD-CogN (p=0.0413). An increase in theta relative power in the left temporal region (p=0.0262), left occipital region (p=0.0109), and right occipital region (p=0.0221) were observed in PD-D compared to PD-N. The global alpha/theta ratio and global power spectral ratio significantly decreased in PD-D compared to PD-N (p = 0.001). In conclusion, the increase in relative theta power and the decrease in relative beta power are characteristic changes in EEG recordings in PD patients with cognitive impairment. Identifying these changes can be a useful biomarker and a complementary tool in the neuropsychological diagnosis of cognitive impairment in Parkinson's Disease.

The effects of gallic acid and vagotomy on motor function, intestinal transit, brain electrophysiology and oxidative stress alterations in a rat model of Parkinson's disease induced by rotenone

INTRODUCTION

The neuropathology of Parkinson's disease (PD) is complex and affects multiple systems of the body beyond the central nervous system. This study examined the effects of gallic acid (GA) and gastrointestinal vagotomy (VG) on motor, cognitive, intestinal transit time, and thalamic nuclei electrical power in an animal model of PD induced by rotenone.

MATERIALS AND METHODS

Male Wistar rats were divided into 4 groups: Sham, ROT, ROT+GA, VG + ROT. Sham rats received vehicle, those in ROT received rotenone (5 mg/kg/2 ml, ig), PD rats in ROT+GA were treated with GA (100 mg/kg, gavage/once daily, for 28 days), and in VG + ROT, the vagal nerve was dissected. Stride length, motor coordination and locomotion, intestinal transit time, cognitive and pain threshold, and thalamic local EEG were evaluated. Oxidative stress indexes in striatal tissue were also measured.

RESULTS

Rotenone diminished significantly the stride length (p < 0.001), motor coordination (p < 0.001), power of thalamic EEG (p < 0.01) and pain (p < 0.001). MDA increased significantly (p < 0.001) while GPx activity decreased (p < 0.001). Intestinal transit time rose significantly (p < 0.01). PD rats treated with GA improved all above disorders (p < 0.001, p < 0.01). Vagotomy prevented significant alterations of motor and non-motor parameters by rotenone.

CONCLUSION

According to current findings, rotenone acts as a toxin in GI and plays a role in the pathogenesis of PD through gastric vagal nerve. Thus, vagotomy could prevent the severity of toxicity by rotenone. In addition, GA improved symptoms of PD induced by rotenone. Therefore, GA can be regarded as a promising therapeutic candidate for PD patients.

Ultrasound of the Biceps Muscle in Idiopathic Parkinson's Disease with Deep Brain Stimulation: Rigidity Can Be Quantified by Shear Wave Elastography

Rigidity in Parkinson’s disease (PD) is assessed by clinical scales, mostly the Unified Parkinson’s Disease Rating Scale of the Movement Disorders Society (MDS-UPDRS). While the MDS-UPDRS-III ranges on an integer from 0 to 4, we investigated whether muscle ultrasound shear wave elastography (SWE) offers a refined assessment. Ten PD patients (five treated with deep brain stimulation (DBS) and levodopa, five with levodopa only) and ten healthy controls were included. Over a period of 80 min, both the SWE value and the item 22b-c of the MDS-UPDRS-III were measured at 5 min intervals. The measurements were performed bilaterally at the biceps brachii muscle (BB) and flexor digitorum profundus muscle in flexion and passive extension. Rigidity was modified and tracked under various therapeutic conditions (with and without medication/DBS). The feasibility of SWE for objective quantification was evaluated by correlation with the UPDRS-III: considering all positions and muscles, there was already a weak correlation (r = 0.01, p < 0.001)—in a targeted analysis, the BB in passive extension showed a markedly higher correlation (r = 0.494, p < 0.001). The application of dopaminergic medication and DBS resulted in statistically significant short-term changes in both clinical rigidity and SWE measurements in the BB (p < 0.001). We conclude that rigidity is reflected in the SWE measurements, indicating that SWE is a potential non-invasive quantitative assessment tool for PD.

High Dimensional Convolutional Neural Network for EEG Connectivity-Based Diagnosis of ADHD

Background

Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in children and adults and its early detection is effective in the successful treatment of children. Electroencephalography (EEG) has been widely used for classifying ADHD and normal children. In recent years, deep learning leads to more accurate classification.

Objective

This study aims to adapt convolutional neural networks (CNNs) for classifying ADHD and normal children based on the connectivity measure of their EEG signals.

Material and Methods

In this experimental study, the dataset consisted of 61 ADHD and 60 normal children from which 13021 epochs were extracted as input for model training and evaluation. Synchronization likelihood (SL) and wavelet coherence (WC) were considered connectivity measures. The neighborhood between EEG channels was arranged in a two-dimensional matrix for better representation. Four-dimensional (4D) and six-dimensional (6D) connectivity tensors were composed as model inputs. Two architectures were developed, one 4D and 6D CNN for SL and WC-based diagnosis of ADHD, respectively.

Results

A 5-fold cross-validation was utilized to assess developed models. The average accuracy of 98.56% for 4D CNN and 98.85% for 6D CNN in epoch-based classification were obtained. In the case of subject-based classification, the accuracy was 99.17% for both models.

Conclusion

Based on the evaluation metrics of the proposed models, ADHD children can be diagnosed and ADHD and normal children can be successfully distinguished.

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+.

Altered functional connectivity of nucleus accumbens subregions associates with non-motor symptoms in Parkinson's disease

AIMS

This study aimed to identify the functional connectivity (FC) changes of nucleus accumbens (NAc) subregions and characterize the association of network changes and non-motor symptoms (NMS) in Parkinson's disease (PD).

METHODS

We enrolled 129 PD patients and 106 healthy controls from our center and the PPMI (Parkinson's Progression Markers Initiative) database. The FC of the bilateral core and shell of the NAc were measured and compared between the two groups. We further used partial least squares correlation to reveal the relationships between altered FC of NAc subregions and manifestations of NMS of PD.

RESULTS

The subregions of left core, left shell, and right core had reduced FC with extensive brain regions in PD patients compared with healthy controls. These three subregions were commonly associated with depression, anxiety, apathy, and cognitive impairment. Moreover, the left core and left shell were associated with excessive daytime sleepiness, whereas the right core was associated with olfactory impairment and rapid eye movement sleep behavior disorder.

CONCLUSION

This study for the first time identified the neural network changes of NAc subregions in PD and the associations between network changes and phenotypes of NMS. Our findings provide new insights into the pathogenesis of NMS in PD.

Brain Connectivity and Graph Theory Analysis in Alzheimer’s and Parkinson’s Disease: The Contribution of Electrophysiological Techniques

In recent years, applications of the network science to electrophysiological data have increased as electrophysiological techniques are not only relatively low cost, largely available on the territory and non-invasive, but also potential tools for large population screening. One of the emergent methods for the study of functional connectivity in electrophysiological recordings is graph theory: it allows to describe the brain through a mathematic model, the graph, and provides a simple representation of a complex system. As Alzheimer’s and Parkinson’s disease are associated with synaptic disruptions and changes in the strength of functional connectivity, they can be well described by functional connectivity analysis computed via graph theory. The aim of the present review is to provide an overview of the most recent applications of the graph theory to electrophysiological data in the two by far most frequent neurodegenerative disorders, Alzheimer’s and Parkinson’s diseases.

Spectral Electroencephalographic and Heart Rate Variability features enhance identification of medicated/non-medicated Parkinson's disease patients

Parkinson's Disease is a neuropathy that produces changes in several biomarkers, these changes could be used to evaluate even sub-clinical conditions. This paper presents an evaluation of indices extracted from electroencephalography and Heart Rate Variability (HRV), when used to classify a sample of subjects from three groups: control (healthy), medicated and non medicated subjects diagnosed with Parkinson's disease. Classification performance was measured using accuracy over these classes and a cross validation scheme was used to assess repeatability for the classification process. Results tend to prove that inclusion of an autonomic index derived from HRV analysis enhances classification, suggesting that Parkinson's disease could be related with unperceptible to mild alterations of the Autonomic Nervous System.

Graph Theory on Brain Cortical Sources in Parkinson's Disease: The Analysis of 'Small World' Organization from EEG

Parkinson’s disease (PD) is the second most common neurodegenerative disease in the elderly population. Similarly to other neurodegenerative diseases, the early diagnosis of PD is quite difficult. The current pilot study aimed to explore the differences in brain connectivity between PD and NOrmal eLDerly (Nold) subjects to evaluate whether connectivity analysis may speed up and support early diagnosis. A total of 26 resting state EEGs were analyzed from 13 PD patients and 13 age-matched Nold subjects, applying to cortical reconstructions the graph theory analyses, a mathematical representation of brain architecture. Results showed that PD patients presented a more ordered structure at slow-frequency EEG rhythms (lower value of SW) than Nold subjects, particularly in the theta band, whereas in the high-frequency alpha, PD patients presented more random organization (higher SW) than Nold subjects. The current results suggest that PD could globally modulate the cortical connectivity of the brain, modifying the functional network organization and resulting in motor and non-motor signs. Future studies could validate whether such an approach, based on a low-cost and non-invasive technique, could be useful for early diagnosis, for the follow-up of PD progression, as well as for evaluating pharmacological and neurorehabilitation treatments.

Validation of Soft Multipin Dry EEG Electrodes

Current developments towards multipin, dry electrodes in electroencephalography (EEG) are promising for applications in non-laboratory environments. Dry electrodes do not require the application of conductive gel, which mostly confines the use of gel EEG systems to the laboratory environment. The aim of this study is to validate soft, multipin, dry EEG electrodes by comparing their performance to conventional gel EEG electrodes. Fifteen healthy volunteers performed three tasks, with a 32-channel gel EEG system and a 32-channel dry EEG system: the 40 Hz Auditory Steady-State Response (ASSR), the checkerboard paradigm, and an eyes open/closed task. Within-subject analyses were performed to compare the signal quality in the time, frequency, and spatial domains. The results showed strong similarities between the two systems in the time and frequency domains, with strong correlations of the visual (ρ = 0.89) and auditory evoked potential (ρ = 0.81), and moderate to strong correlations for the alpha band during eye closure (ρ = 0.81–0.86) and the 40 Hz-ASSR power (ρ = 0.66–0.72), respectively. However, delta and theta band power was significantly increased, and the signal-to-noise ratio was significantly decreased for the dry EEG system. Topographical distributions were comparable for both systems. Moreover, the application time of the dry EEG system was significantly shorter (8 min). It can be concluded that the soft, multipin dry EEG system can be used in brain activity research with similar accuracy as conventional gel electrodes.

Electrophysiological biomarkers for deep brain stimulation outcomes in movement disorders: state of the art and future challenges

Several neurological diseases are accompanied by rhythmic oscillatory dysfunctions in various frequency ranges and disturbed cross-frequency relationships on regional, interregional, and whole brain levels. Knowledge of these disease-specific oscillopathies is important mainly in the context of deep brain stimulation (DBS) therapy. Electrophysiological biomarkers have been used as input signals for adaptive DBS (aDBS) as well as preoperative outcome predictors. As movement disorders, particularly Parkinson's disease (PD), are among the most frequent DBS indications, the current research of DBS is the most advanced in the movement disorders field. We reviewed the literature published mainly between 2010 and 2020 to identify the most important findings concerning the current evolution of electrophysiological biomarkers in DBS and to address future challenges for prospective research.

A convolutional-recurrent neural network approach to resting-state EEG classification in Parkinson's disease

BACKGROUND

Parkinson's disease (PD) is expected to become more common, particularly with an aging population. Diagnosis and monitoring of the disease typically rely on the laborious examination of physical symptoms by medical experts, which is necessarily limited and may not detect the prodromal stages of the disease.

NEW METHOD

We propose a lightweight (~20 K parameters) deep learning model to classify resting-state EEG recorded from people with PD and healthy controls (HC). The proposed CRNN model consists of convolutional neural networks (CNN) and a recurrent neural network (RNN) with gated recurrent units (GRUs). The 1D CNN layers are designed to extract spatiotemporal features across EEG channels, which are subsequently supplied to the GRUs to discover temporal features pertinent to the classification.

RESULTS

The CRNN model achieved 99.2% accuracy, 98.9% precision, and 99.4% recall in classifying PD from HC. Interrogating the model, we further demonstrate that the model is sensitive to dopaminergic medication effects and predominantly uses phase information in the EEG signals.

COMPARISON WITH EXISTING METHODS

The CRNN model achieves superior performance compared to baseline machine learning methods and other recently proposed deep learning model.

CONCLUSION

The approach proposed in this study adequately extracts spatial and temporal features in multi-channel EEG signals that enable accurate differentiation between PD and HC. The CRNN model has excellent potential for use as an oscillatory biomarker for assisting in the diagnosis and monitoring of people with PD. Future studies to further improve and validate the model's performance in clinical practice are warranted.

Computational neuronal correlation with enhanced synchronized activity in the basal ganglia and the slowing of thalamic theta and alpha rhythms in Parkinson's disease

The aim of this work is computationally to correlate the synchronized neuronal activity of basal ganglia and slowing in theta and alpha rhythms in electroencephalogram (EEG) signal in thalamic region in case of dopamine depletion and decrease of synaptic connections. The used network topology is a scale-free network with constant node degree. The dopamine-modulated type Izikhevich neuron model is used for modeling the striatal region, consisting of fast-spiking interneurons, D1 and D2 type dopamine expressing medium spiny neurons. On the other hand, the ordinary Izikhevich neuron model is used in the modeling of extrastriatal basal ganglia (BG) regions where globus pallidus (GP) subregion neurons have also dopamine-dependent parameters. The thalamic region of the network is mass modeled including inhibitory input from basal ganglia. Depending on the decrease of synaptic connections and dopamine level, the synchronization among basal ganglia neuron populations is investigated. The effect of synaptic delay on synchronization is also considered. It is observed that the decrease of dopamine neurotransmitter and decrease in the number of synaptic connections cause an increased synchronous activity in BG. Also, slowing in theta and alpha bands in thalamus EEG signals is observed. This shows the causal relation between synchronization and power shifting to lower frequency components in the case of neurodegenerative diseases such as Parkinson's disease (PD).

Cellular and synaptic mechanisms for Parkinson's disease-related chronic pain

Parkinson’s disease is the second most common neurodegenerative disorder after Alzheimer’s disease. Chronic pain is experienced by the vast majority of patients living with Parkinson’s disease. The degeneration of dopaminergic neuron acts as the essential mechanism of Parkinson’s disease in the midbrain dopaminergic pathway. The impairment of dopaminergic neurons leads to dysfunctions of the nociceptive system. Key cortical areas, such as the anterior cingulate cortex (ACC) and insular cortex (IC) that receive the dopaminergic projections are involved in pain transmission. Dopamine changes synaptic transmission via several pathway, for example the D2-adenly cyclase (AC)-cyclic AMP (cAMP)-protein kinase A (PKA) pathway and D1-G protein-coupled receptor kinase 2 (GRK2)-fragile X mental retardation protein (FMRP) pathway. The management of Parkinson’s disease-related pain implicates maintenance of stable level of dopaminergic drugs and analgesics, however a more selective drug targeting at key molecules in Parkinson’s disease-related pain remains to be investigated.

EEG-Derived Functional Connectivity Patterns Associated with Mild Cognitive Impairment in Parkinson's Disease

OBJECTIVE

To evaluate EEG-derived functional connectivity (FC) patterns associated with mild cognitive impairment (MCI) in Parkinson's disease (PD).

METHODS

A sample of 15 patients without cognitive impairment (PD-WCI), 15 with MCI (PD-MCI), and 26 healthy subjects were studied. The EEG was performed in the waking functional state with eyes closed, for the functional analysis it was used the synchronization likelihood (SL) and graph theory (GT).

RESULTS

PD-MCI patients showed decreased FC in frequencies alpha, in posterior regions, and delta with a generalized distribution. Patients, compared to the healthy people, presented a decrease in segregation (lower clustering coefficient in alpha p = 0.003 in PD-MCI patients) and increased integration (shorter mean path length in delta (p = 0.004) and theta (p = 0.002) in PD-MCI patients). There were no significant differences in the network topology between the parkinsonian groups. In PD-MCI patients, executive dysfunction correlated positively with global connectivity in beta (r = 0.47) and negatively with the mean path length at beta (r = -0.45); alterations in working memory were negatively correlated with the mean path length at beta r = -0.45.

CONCLUSIONS

PD patients present alterations in the FC in all frequencies, those with MCI show less connectivity in the alpha and delta frequencies. The neural networks of the patients show a random topology, with a similar organization between patients with and without MCI. In PD-MCI patients, alterations in executive function and working memory are related to beta integration.

An Introductory Tutorial on Brain–Computer Interfaces and Their Applications

The prospect and potentiality of interfacing minds with machines has long captured human imagination. Recent advances in biomedical engineering, computer science, and neuroscience are making brain–computer interfaces a reality, paving the way to restoring and potentially augmenting human physical and mental capabilities. Applications of brain–computer interfaces are being explored in applications as diverse as security, lie detection, alertness monitoring, gaming, education, art, and human cognition augmentation. The present tutorial aims to survey the principal features and challenges of brain–computer interfaces (such as reliable acquisition of brain signals, filtering and processing of the acquired brainwaves, ethical and legal issues related to brain–computer interface (BCI), data privacy, and performance assessment) with special emphasis to biomedical engineering and automation engineering applications. The content of this paper is aimed at students, researchers, and practitioners to glimpse the multifaceted world of brain–computer interfacing.

Resting-State Phase-Amplitude Coupling Between the Human Subthalamic Nucleus and Cortical Activity: A Simultaneous Intracranial and Scalp EEG Study

It has been suggested that slow oscillations in the subthalamic nucleus (STN) reflect top-down inputs from the medial prefrontal cortex, thus implementing behavior control. It is unclear, however, whether the STN oscillations are related to cortical activity in a bottom-up manner. To assess resting-state subcortico-cortical interactions, we recorded simultaneous scalp electroencephalographic activity and local field potentials in the STN (LFP-STN) in 11 patients with Parkinson's disease implanted with deep brain stimulation electrodes in the on-medication state during rest. We assessed the cross-structural phase-amplitude coupling (PAC) between the STN and cortical activity within a wide frequency range of 1 to 100 Hz. The PAC was dominant between the δ/θ STN phase and β/γ cortical amplitude in most investigated scalp regions and between the δ cortical phase and θ/α STN amplitude in the frontal and temporal regions. The cross-frequency linkage between the slow oscillations of the LFP-STN activity and the amplitude of the scalp-recorded cortical activity at rest was demonstrated, and similar involvement of the left and right STNs in the coupling was observed. Our results suggest that the STN plays a role in both bottom-up and top-down processes within the subcortico-cortical circuitries of the human brain during the resting state. A relative left-right symmetry in the STN-cortex functional linkage was suggested. Practical treatment studies would be necessary to assess whether unilateral stimulation of the STN might be sufficient for treatment of Parkinson's disease.

EEG, MEG and neuromodulatory approaches to explore cognition: Current status and future directions

Neural oscillations and their association with brain states and cognitive functions have been object of extensive investigation over the last decades. Several electroencephalography (EEG) and magnetoencephalography (MEG) analysis approaches have been explored and oscillatory properties have been identified, in parallel with the technical and computational advancement. This review provides an up-to-date account of how EEG/MEG oscillations have contributed to the understanding of cognition. Methodological challenges, recent developments and translational potential, along with future research avenues, are discussed.

Theta and gamma connectivity is linked with affective and cognitive symptoms in Parkinson's disease

BACKGROUND

The progression of Parkinson's disease (PD) can often exacerbate symptoms of depression, anxiety, and/or cognitive impairment. In this study, we explore the possibility that multiple brain network responses are associated with symptoms of depression, anxiety and cognitive impairment in PD. This association is likely to provide insights into a single multivariate relationship, where common affective symptoms occurring in PD cohorts are related with alterations to electrophysiological response.

METHODS

70 PD patients and 21 healthy age-matched controls (HC) participated in a high-density electroencephalography (EEG) study. Functional connectivity differences between PD and HC groups of oscillatory activity at rest and during completion of an emotion-cognition task were examined to identify key brain oscillatory activities. A canonical correlation analysis (CCA) was applied to identify a putative multivariate relationship between connectivity patterns and affective symptoms in PD groups.

RESULTS

A CCA analysis identified a single mode of co-variation linking theta and gamma connectivity with affective symptoms in PD groups. Increases in frontotemporal gamma, frontal and parietal theta connectivity were related with increased anxiety and cognitive impairment. Decreases in temporal region theta and frontoparietal gamma connectivity were associated with higher depression ratings and PD patient age.

LIMITATIONS

This study only reports on optimal dosage of dopaminergic treatment ('on' state) in PD and did not investigate at "off" medication".

CONCLUSIONS

Theta and gamma connectivity during rest and task-states are linked to affective and cognitive symptoms within fronto-temporo-parietal networks, suggesting a potential assessment avenue for understanding brain-behaviour associations in PD with electrophysiological task paradigms.

Identifying controllable cortical neural markers with machine learning for adaptive deep brain stimulation in Parkinson's disease

The identification of oscillatory neural markers of Parkinson's disease (PD) can contribute not only to the understanding of functional mechanisms of the disorder, but may also serve in adaptive deep brain stimulation (DBS) systems. These systems seek online adaptation of stimulation parameters in closed-loop as a function of neural markers, aiming at improving treatment's efficacy and reducing side effects. Typically, the identification of PD neural markers is based on group-level studies. Due to the heterogeneity of symptoms across patients, however, such group-level neural markers, like the beta band power of the subthalamic nucleus, are not present in every patient or not informative about every patient's motor state. Instead, individual neural markers may be preferable for providing a personalized solution for the adaptation of stimulation parameters. Fortunately, data-driven bottom-up approaches based on machine learning may be utilized. These approaches have been developed and applied successfully in the field of brain-computer interfaces with the goal of providing individuals with means of communication and control. In our contribution, we present results obtained with a novel supervised data-driven identification of neural markers of hand motor performance based on a supervised machine learning model. Data of 16 experimental sessions obtained from seven PD patients undergoing DBS therapy show that the supervised patient-specific neural markers provide improved decoding accuracy of hand motor performance, compared to group-level neural markers reported in the literature. We observed that the individual markers are sensitive to DBS therapy and thus, may represent controllable variables in an adaptive DBS system.

Is Cortical Activation During Walking Different Between Parkinson’s Disease Motor Subtypes?

Parkinson’s disease (PD) is often classified into tremor dominant (TD) and postural instability gait disorder (PIGD) subtypes. Degeneration of subcortical/cortical pathways is different between PD subtypes, which leads to differences in motor behavior. However, the influence of PD subtype on cortical activity during walking remains poorly understood. Therefore, we aimed to investigate the influence of PD motor subtypes on cortical activity during unobstructed walking and obstacle avoidance. Seventeen PIGD and 19 TD patients performed unobstructed walking and obstacle avoidance conditions. Brain activity was measured using a mobile functional near-infrared spectroscopy–electroencephalography (EEG) systems, and gait parameters were analyzed using an electronic carpet. Concentrations of oxygenated hemoglobin (HbO2) of the prefrontal cortex (PFC) and EEG absolute power from alpha, beta, and gamma bands in FCz, Cz, CPz, and Oz channels were calculated. These EEG channels correspond to supplementary motor area, primary motor cortex, posterior parietal cortex, and visual cortex, respectively. Postural instability gait disorder patients presented higher PFC activity than TD patients, regardless of the walking condition. Tremor dominant patients presented reduced beta power in the Cz channel during obstacle avoidance compared to unobstructed walking. Both TD and PIGD patients decreased alpha and beta power in the FCz and CPz channels. In conclusion, PIGD patients need to recruit additional cognitive resources from the PFC for walking. Both TD and PIGD patients presented changes in the activation of brain areas related to motor/sensorimotor areas in order to maintain balance control during obstacle avoidance, being that TD patients presented further changes in the motor area (Cz channel) to avoid obstacles.

A Population Model of Deep Brain Stimulation in Movement Disorders From Circuits to Cells

For more than 30 years, deep brain stimulation (DBS) has been used to target the symptoms of a number of neurological disorders and in particular movement disorders such as Parkinson’s disease (PD) and essential tremor (ET). It is known that the loss of dopaminergic neurons in the substantia nigra leads to PD, while the exact impact of this on the brain dynamics is not fully understood, the presence of beta-band oscillatory activity is thought to be pathological. The cause of ET, however, remains uncertain, however pathological oscillations in the thalamocortical-cerebellar network have been linked to tremor. Both of these movement disorders are treated with DBS, which entails the surgical implantation of electrodes into a patient’s brain. While DBS leads to an improvement in symptoms for many patients, the mechanisms underlying this improvement is not clearly understood, and computational modeling has been used extensively to improve this. Many of the models used to study DBS and its effect on the human brain have mainly utilized single neuron and single axon biophysical models. We have previously shown in separate models however, that the use of population models can shed much light on the mechanisms of the underlying pathological neural activity in PD and ET in turn, and on the mechanisms underlying DBS. Together, this work suggested that the dynamics of the cerebellar-basal ganglia thalamocortical network support oscillations at frequency range relevant to movement disorders. Here, we propose a new combined model of this network and present new results that demonstrate that both Parkinsonian oscillations in the beta band and oscillations in the tremor frequency range arise from the dynamics of such a network. We find regions in the parameter space demonstrating the different dynamics and go on to examine the transition from one oscillatory regime to another as well as the impact of DBS on these different types of pathological activity. This work will allow us to better understand the changes in brain activity induced by DBS, and allow us to optimize this clinical therapy, particularly in terms of target selection and parameter setting.

Effects of motor imagery training of Parkinson's disease: a protocol for a randomized clinical trial

Background

Gait disorders in individuals with Parkinson’s disease (PD) may be associated with alterations in the motor control system and aggravated by psychoemotional and cognitive issues. Therapeutic strategies aimed at self-perception and motor regulation seem to be promising. Motor imagery (MI) has been shown to be one of these strategies, but there is still no clear evidence of its applicability in this population. The aim of this trial is to determine the effects of motor-imagery training on the gait and electroencephalographic activity of individuals with PD.

Methods/design

The sample will consist of 40 individuals, aged between 45 and 75 years, in the mild and moderate phase of the disease, with the ability to generate voluntary mental images. They will be assessed for cognitive level, degree of physical disability, mental-image clarity, kinematic gait variables, electroencephalographic activity and mobility. Next, subjects will be randomly assigned to an experimental group (EG) and a control group (CG). The EG will perform motor imagery and gait, while the CG will only engage in gait exercises. Twelve training sessions will be conducted lasting up to 90 min each, three times a week, for 4 weeks. The subjects will be reassessed on the kinematic variables of gait, electroencephalographic activity and mobility at 1, 7 and 30 days after the final training session.

Discussion

The results may provide an important advance in neurological rehabilitation where an easy-access and low-cost intervention may help to improve gait, electroencephalographic activity and mobility in individuals with PD.

Trial registration

Clinicaltrials.gov, ID: NCT03439800. Registered on 15 November 2017.

Electronic supplementary material

The online version of this article (10.1186/s13063-019-3694-8) contains supplementary material, which is available to authorized users.