Contribution of Quantitative EEG to the Diagnosis of Early Cognitive Impairment in Patients With Idiopathic Parkinson's Disease

Synchronous monitoring of brain-heart electrophysiology using heart rate variability coupled with rapid quantitative electroencephalography in orthostatic hypotension patients with α-synucleinopathies: Rapid prediction of orthostatic hypotension and preliminary exploration of brain stimulation therapy

BACKGROUND

In α-synucleinopathies, the dysfunction of the autonomic nervous system which typically manifests as orthostatic hypotension (OH) often leads to severe consequences and poses therapeutic challenges. This study aims to discover the brain-cardiac electrophysiological changes in OH patients with α-synucleinopathies using the rapid quantitative electroencephalography (qEEG) coupled with heart rate variability (HRV) technique to identify rapid, noninvasive biomarkers for early warning and diagnosis, as well as shed new light on complementary treatment approaches such as brain stimulation targets.

METHODS

In this study, 26 subjects of α-synucleinopathies with OH (α-OH group), 21 subjects of α-synucleinopathies without OH (α-NOH group), and 34 healthy controls (control group) were included from September 2021 to August 2023 (NCT05527067). The heart rate-blood pressure variations in supine and standing positions were monitored, and synchronization parameters of seated resting-state HRV coupled with qEEG were collected. Time-domain and frequency-domain of HRV measures as well as peak frequency and power of the brainwaves were extracted. Differences between these three groups were compared, and correlations between brain-heart parameters were analyzed.

RESULTS

The research results showed that the time-domain parameters such as MxDMn, pNN50, RMSSD, and SDSD of seated resting-state HRV exhibited a significant decrease only in the α-OH group compared to the healthy control group (p < 0.05), while there was no significant difference between the α-NOH group and the healthy control group. Several time-domain and frequency-domain parameters of seated resting-state HRV were found to be correlated with the blood pressure changes within the first 5 min of transitioning from supine to standing position (p < 0.05). Differences were observed in the power of beta1 waves (F4 and Fp2) and beta2 waves (Fp2 and F4) in the seated resting-state qEEG between the α-OH and α-NOH groups (p < 0.05). The peak frequency of theta waves in the Cz region also showed a difference (p < 0.05). The power of beta2 waves in the Fp2 and F4 brain regions correlated with frequency-domain parameters of HRV (p < 0.05). Additionally, abnormal electrical activity in the alpha, theta, and beta1 waves was associated with changes in heart rate and blood pressure within the first 5 min of transitioning from supine to standing position (p < 0.05).

CONCLUSION

Rapid resting-state HRV with certain time-domain parameters below normal levels may serve as a predictive indicator for the occurrence of orthostatic hypotension (OH) in patients with α-synucleinopathies. Additionally, the deterioration of HRV parameters correlates with synchronous abnormal qEEG patterns, which can provide insights into the brain stimulation target areas for OH in α-synucleinopathy patients.

Resting-state EEG measures cognitive impairment in Parkinson's disease

Cognitive dysfunction is common in Parkinson's disease (PD). We developed and evaluated an EEG-based biomarker to index cognitive functions in PD from a few minutes of resting-state EEG. We hypothesized that synchronous changes in EEG across the power spectrum can measure cognition. We optimized a data-driven algorithm to efficiently capture these changes and index cognitive function in 100 PD and 49 control participants. We compared our EEG-based cognitive index with the Montreal cognitive assessment (MoCA) and cognitive tests across different domains from National Institutes of Health (NIH) Toolbox using cross-validations, regression models, and randomization tests. Finally, we externally validated our approach on 32 PD participants. We observed cognition-related changes in EEG over multiple spectral rhythms. Utilizing only 8 best-performing electrodes, our proposed index strongly correlated with cognition (MoCA: rho = 0.68, p value < 0.001; NIH-Toolbox cognitive tests: rho ≥ 0.56, p value < 0.001) outperforming traditional spectral markers (rho = -0.30-0.37). The index showed a strong fit in regression models (R2 = 0.46) with MoCA, yielded 80% accuracy in detecting cognitive impairment, and was effective in both PD and control participants. Notably, our approach was equally effective (rho = 0.68, p value < 0.001; MoCA) in out-of-sample testing. In summary, we introduced a computationally efficient data-driven approach for cross-domain cognition indexing using fewer than 10 EEG electrodes, potentially compatible with dynamic therapies like closed-loop neurostimulation. These results will inform next-generation neurophysiological biomarkers for monitoring cognition in PD and other neurological diseases.

Resting-state EEG measures cognitive impairment in Parkinson's disease

Background

Cognitive dysfunction is common in Parkinson's disease (PD) and is diagnosed by complex, time-consuming psychometric tests which are affected by language and education, subject to learning effects, and not suitable for continuous monitoring of cognition.

Objectives

We developed and evaluated an EEG-based biomarker to index cognitive functions in PD from a few minutes of resting-state EEG.

Methods

We hypothesized that synchronous changes in EEG across the power spectrum can measure cognition. We optimized a data-driven algorithm to efficiently capture these changes and index cognitive function in 100 PD and 49 control participants. We compared our EEG-based cognitive index with the Montreal cognitive assessment (MoCA) and cognitive tests across different domains from the National Institutes of Health (NIH) Toolbox using cross-validation schemes, regression models, and randomization tests.

Results

We observed cognition-related changes in EEG activities over multiple spectral rhythms. Utilizing only 8 best-performing EEG electrodes, our proposed index strongly correlated with cognition (rho = 0.68, p value < 0.001 with MoCA; rho ≥ 0.56, p value < 0.001 with cognitive tests from the NIH Toolbox) outperforming traditional spectral markers (rho = -0.30 - 0.37). The index showed a strong fit in regression models (R2 = 0.46) with MoCA, yielded 80% accuracy in detecting cognitive impairment, and was effective in both PD and control participants.

Conclusions

Our approach is computationally efficient for real-time indexing of cognition across domains, implementable even in hardware with limited computing capabilities, making it potentially compatible with dynamic therapies such as closed-loop neurostimulation, and will inform next-generation neurophysiological biomarkers for monitoring cognition in PD and other neurological diseases.

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.

QEEG Signatures are Associated with Nonmotor Dysfunctions in Parkinson's Disease and Atypical Parkinsonism: An Integrative Analysis

Parkinson's disease (PD) and atypical parkinsonism (AP), including progressive supranuclear palsy (PSP) and multiple system atrophy (MSA), share similar nonmotor symptoms. Quantitative electroencephalography (QEEG) can be used to examine the nonmotor symptoms. This study aimed to characterize the patterns of QEEG and functional connectivity (FC) that differentiate PD from PSP or MSA, and explore the correlation between the differential QEEG indices and nonmotor dysfunctions in PD and AP. We enrolled 52 patients with PD, 31 with MSA, 22 with PSP, and 50 age-matched health controls to compare QEEG indices among specific brain regions. One-way analysis of variance was applied to assess QEEG indices between groups; Spearman's correlations were used to examine the relationship between QEEG indices and nonmotor symptoms scale (NMSS) and mini-mental state examination (MMSE). FCs using weighted phase lag index were compared between patients with PD and those with MSA/PSP. Patients with PSP revealed higher scores on the NMSS and lower MMSE scores than those with PD and MSA, with similar disease duration. The delta and theta powers revealed a significant increase in PSP, followed by PD and MSA. Patients with PD presented a significantly lower slow-to-fast ratio than those with PSP in the frontal region, while patients with PD presented significantly higher EEG-slowing indices than patients with MSA. The frontal slow-to-fast ratio showed a negative correlation with MMSE scores in patients with PD and PSP, and a positive correlation with NMSS in the perception and mood domain in patients with PSP but not in those with PD. Compared to PD, MSA presented enhanced FC in theta and delta bands in the posterior region, while PSP revealed decreased FC in the delta band within the frontal-temporal cortex. These findings suggest that QEEG might be a useful tool for evaluating the nonmotor dysfunctions in PD and AP. Our QEEG results suggested that with similar disease duration, the cortical neurodegenerative process was likely exacerbated in patients with PSP, followed by those with PD, and lastly in patients with MSA.

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).

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.

Identification of an early-stage Parkinson's disease neuromarker using event-related potentials, brain network analytics and machine-learning

OBJECTIVE

The purpose of this study is to explore the possibility of developing a biomarker that can discriminate early-stage Parkinson's disease from healthy brain function using electroencephalography (EEG) event-related potentials (ERPs) in combination with Brain Network Analytics (BNA) technology and machine learning (ML) algorithms.

BACKGROUND

Currently, diagnosis of PD depends mainly on motor signs and symptoms. However, there is need for biomarkers that detect PD at an earlier stage to allow intervention and monitoring of potential disease-modifying therapies. Cognitive impairment may appear before motor symptoms, and it tends to worsen with disease progression. While ERPs obtained during cognitive tasks performance represent processing stages of cognitive brain functions, they have not yet been established as sensitive or specific markers for early-stage PD.

METHODS

Nineteen PD patients (disease duration of ≤2 years) and 30 healthy controls (HC) underwent EEG recording while performing visual Go/No-Go and auditory Oddball cognitive tasks. ERPs were analyzed by the BNA technology, and a ML algorithm identified a combination of features that distinguish early PD from HC. We used a logistic regression classifier with a 10-fold cross-validation.

RESULTS

The ML algorithm identified a neuromarker comprising 15 BNA features that discriminated early PD patients from HC. The area-under-the-curve of the receiver-operating characteristic curve was 0.79. Sensitivity and specificity were 0.74 and 0.73, respectively. The five most important features could be classified into three cognitive functions: early sensory processing (P50 amplitude, N100 latency), filtering of information (P200 amplitude and topographic similarity), and response-locked activity (P-200 topographic similarity preceding the motor response in the visual Go/No-Go task).

CONCLUSIONS

This pilot study found that BNA can identify patients with early PD using an advanced analysis of ERPs. These results need to be validated in a larger PD patient sample and assessed for people with premotor phase of PD.

Comparison of resting-state EEG between adults with Down syndrome and typically developing controls

Background

Down syndrome (DS) is the most common genetic cause of intellectual disability (ID) worldwide. Understanding electrophysiological characteristics associated with DS provides potential mechanistic insights into ID, helping inform biomarkers and targets for intervention. Currently, electrophysiological characteristics associated with DS remain unclear due to methodological differences between studies and inadequate controls for cognitive decline as a potential cofounder.

Methods

Eyes-closed resting-state EEG measures (specifically delta, theta, alpha, and beta absolute and relative powers, and alpha peak amplitude, frequency and frequency variance) in occipital and frontal regions were compared between adults with DS (with no diagnosis of dementia or evidence of cognitive decline) and typically developing (TD) matched controls (n = 25 per group).

Results

We report an overall ‘slower’ EEG spectrum, characterised by higher delta and theta power, and lower alpha and beta power, for both regions in people with DS. Alpha activity in particular showed strong group differences, including lower power, lower peak amplitude and greater peak frequency variance in people with DS.

Conclusions

Such EEG ‘slowing’ has previously been associated with cognitive decline in both DS and TD populations. These findings indicate the potential existence of a universal EEG signature of cognitive impairment, regardless of origin (neurodevelopmental or neurodegenerative), warranting further exploration.

Supplementary Information

The online version contains supplementary material available at 10.1186/s11689-021-09392-z.

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.

A Comprehensive Meta-analysis on Short-term and Working Memory Dysfunction in Parkinson's Disease

A previous meta-analysis demonstrated short-term memory (STM) and working memory (WM) dysfunction in patients with Parkinson's disease (PD). However, considerable research on the topic that calls into question the extent of such impairments in PD has since been published. The aim of the present quantitative review was to provide the largest statistical overview on STM and WM dysfunction in Parkinson's disease (PD), while simultaneously providing novel insights on moderating factors of effect size heterogeneity in PD. The systematic literature search in PubMed, PsycINFO, PsycArticles, Scopus and Web of Science databases allowed us to estimate 350 effect sizes from 145 empirical studies that reported STM and WM scores for patients with PD against healthy controls. The outcomes indicated general dysfunction in the visuospatial domain and poor verbal WM in PD. Subgroup analyses suggested that mild cognitive impairment is associated with STM and WM difficulties in PD. Furthermore, meta-regression analyses revealed that disease duration accounted for more than 80% of the visuospatial STM effect size variance (β = 0.136, p < .001, R² = .8272), larger daily levodopa equivalent dose was associated with WM dysfunction (verbal: β = -0.001, p = .016, R² = .1812; visuospatial: β = 0.003, p = .069, R² = .2340), and years of education partially explained the verbal STM effect size variance (β = -0.027, p = .040, R² = .1171). Collectively, these findings advance our understanding of underlying factors that influence STM and WM functioning in PD, while at the same time providing novel directions for future research.

Correlation between alpha activity and neuropsychometric tests in Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disease that leads to memory impairment and executive and visuospatial dysfunction as the disease progresses. Alpha activity on EEG has been related to cognition in previous studies. We aimed to investigate the correlation between alpha activity and neuropsychometric tests (NPTs) in PD patients. Fifty-five idiopathic PD patients and 20 healthy controls were included. The Standardized Mini-Mental Test (SMMT), Verbal Learning Memory Test (VLMT), Wechsler Memory Scale (WMS), Stroop Color-Word Test, Categorical Verbal Fluency Test (CVFT), Benton's Face Recognition Test (BFR), and Benton Line Judgment Orientation Test (BLOT) were administered to all participants. Patients were separated into four groups according to NPT results: healthy controls (HC); PD patients with normal cognition (PDNC); PD patients with MCI (PDMCI); and PD patients with dementia (PDD). Analysis of the EEG data showed that HC had the highest alpha activity, and PDD had the lowest. High SMMT scores were correlated with high alpha activity at posterior electrode locations in all PD groups. VLMT and WMS test scores were associated with alpha activity at the parietal sites in PDMCI. VLMT, WMS, and CVFT test scores were correlated with alpha activity at parietooccipital sites in PDD. Verbal and visuospatial memory dysfunction related to low alpha activity was evident in both PDMCI and PDD, whereas executive dysfunction was more strongly associated with low alpha activity in PDD. Analysis of alpha activity could help clinicians predict the progression of cognitive dysfunction in PD patients.

Facilitatory/inhibitory intracortical imbalance in REM sleep behavior disorder: early electrophysiological marker of neurodegeneration?

STUDY OBJECTIVES

Previous studies found an early impairment of the short-interval intracortical inhibition (SICI) and intracortical facilitation (ICF) to transcranial magnetic stimulation (TMS) in Parkinson's disease. However, very little is known on the TMS correlates of rapid eye movement (REM) sleep behavior disorder (RBD), which can precede the onset of a α-synucleinopathy.

METHODS

The following TMS measures were obtained from 14 de novo patients with isolated RBD and 14 age-matched healthy controls: resting motor threshold, cortical silent period, latency and amplitude of the motor evoked potentials, SICI, and ICF. A cognitive screening and a quantification of subjective sleepiness (Epworth Sleepiness Scale [ESS]) and depressive symptoms were also performed.

RESULTS

Neurological examination, global cognitive functioning, and mood status were normal in all participants. ESS score was higher in patients, although not suggestive of diurnal sleepiness. Compared to controls, patients exhibited a significant decrease of ICF (median 0.8, range 0.5-1.4 vs. 1.9, range 1.4-2.3; p < 0.01) and a clear trend, though not significant, towards a reduction of SICI (median 0.55, range 0.1-1.4 vs. 0.25, range 0.1-0.3), with a large effect size (Cohen's d: -0.848). REM Sleep Atonia Index significantly correlated with SICI.

CONCLUSIONS

In still asymptomatic patients for a parkinsonian syndrome or neurodegenerative disorder, changes of ICF and, to a lesser extent, SICI (which are largely mediated by glutamatergic and GABAergic transmission, respectively) might precede the onset of a future neurodegeneration. SICI was correlated with the muscle tone alteration, possibly supporting the proposed RBD model of retrograde influence on the cortex from the brainstem.

Clinical correlates of quantitative EEG in Parkinson disease: A systematic review

OBJECTIVE

To assess the relevance of quantitative EEG (qEEG) measures as outcomes of disease severity and progression in Parkinson disease (PD).

METHODS

Main databases were systematically searched (January 2018) for studies of sufficient methodologic quality that examined correlations between clinical symptoms of idiopathic PD and cortical (surface) qEEG metrics.

RESULTS

Thirty-six out of 605 identified studied were included. Results were classified into 4 domains: cognition (23 studies), motor function (13 studies), responsiveness to interventions (7 studies), and other (10 studies). In cross-sectional studies, EEG slowing correlated with global cognitive impairment and with diffuse deterioration in other domains. In longitudinal studies, decreased dominant frequency and increased θ power, reflecting EEG slowing, were biomarkers of cognitive deterioration at an individual level. Results on motor dysfunction and treatment yielded contrasting findings. Studies on functional connectivity at an individual level and longitudinal studies on other domains or on connectivity measures were lacking.

CONCLUSION

qEEG measures reflecting EEG slowing, particularly decreased dominant frequency and increased θ power, correlate with cognitive impairment and predict future cognitive deterioration. qEEG could provide reliable and widely available biomarkers for nonmotor disease severity and progression in PD, potentially promoting early diagnosis of nonmotor symptoms and an objective monitoring of progression. More studies are needed to clarify the role of functional connectivity and network analyses.