Changes in theta activities in the left posterior temporal region, left occipital region and right frontal region related to mild cognitive impairment in Parkinson's disease patients

TMS and tDCS as potential tools for the treatment of cognitive deficits in Parkinson's disease: a meta-analysis

BACKGROUND

Cognitive deficits are common nonmotor symptoms in Parkinson's disease (PD). Non-Invasive Brain Stimulation (NIBS) could be a potential aid to prevent or delay dementia progression in this clinical population. However, previous studies reported controversial results concerning their efficacy on cognitive symptoms of PD. Hence, the present meta-analysis aims to systematically examine the effects of NIBS as possible treatments for PD cognitive impairments. Understanding NIBS' impact on these symptoms may be of outstanding importance to implement new therapeutic strategies and improve the patients' quality of life.

METHODS

EMBASE, Scopus, and PubMed databases were systematically searched for consecutive studies published from 2000 to March 2023 describing Randomized Controlled Trials studies evaluating the effect of NIBS on PD cognitive symptoms. From the included studies, data concerning neuropsychological tests were extracted and grouped into six cognitive domains, separately analyzed. Hedge's method was computed as the effect size measure of the extracted data; heterogeneity among studies and publication bias were also assessed. The Cochrane's RoB2 tool was used to evaluate the risk of bias for each of the included studies.

RESULTS

After database searching and screening of texts, sixteen studies met the inclusion criteria. No significant results emerged from any investigated cognitive domain when comparing NIBS and sham treatments.

CONCLUSION

Several factors may have contributed to the lack of effects; among these, methodological choices, the small sample of studies, the high heterogeneity of data and stimulation protocols pose the need for more controlled studies to highlight the potentiality of NIBS as a future treatment for PD cognitive impairments.

Analysis of microstate features for Parkinson's disease based on reliability validation

BACKGROUND

Parkinson's disease (PD) is a disorder with abnormal changes in brain activity. The lack of objective indicators makes the assessment of PD progression difficult. Assessment of brain activity changes in PD may offer a potential solution.

NEW METHOD

Electroencephalogram (EEG) microstates reflect global dynamic changes in the brain. Therefore, we utilized microstates to assess changes in PD brain activity. However, the effect of epoch duration on the reliability of microstate analyses in PD is unclear. Thus, we first assessed the effect of data duration on the reliability of microstate topography and temporal features in PD and older healthy individuals. According to the reliability assessment, EEG epochs with high reliability were selected for microstate analysis in PD. Finally, we investigated the correlation between microstate features and clinical scales to determine whether these features could serve as objective indicators to evaluate PD progression.

RESULTS

Microstate analysis features that show high reliability for 3 min and above epoch durations. The topology of microstate D was significantly changed in PD compared to healthy controls, as well as the temporal features of microstates C and D. Additionally, the occurrence of C was negatively correlated with MoCA, and the duration of D was positively correlated with UPDRS.

COMPARISON WITH EXISTING METHOD(S)

High reliability of PD microstate features obtained by our approach.

CONCLUSION

EEG for PD microstate analysis should be at least 3 min. Microstate analysis is expected to provide new ideas and objective indicators for assessing Parkinson's disease progression in the clinical setting.

Normative brain mapping using scalp EEG and potential clinical application

A normative electrographic activity map could be a powerful resource to understand normal brain function and identify abnormal activity. Here, we present a normative brain map using scalp EEG in terms of relative band power. In this exploratory study we investigate its temporal stability, its similarity to other imaging modalities, and explore a potential clinical application. We constructed scalp EEG normative maps of brain dynamics from 17 healthy controls using source-localised resting-state scalp recordings. We then correlated these maps with those acquired from MEG and intracranial EEG to investigate their similarity. Lastly, we use the normative maps to lateralise abnormal regions in epilepsy. Spatial patterns of band powers were broadly consistent with previous literature and stable across recordings. Scalp EEG normative maps were most similar to other modalities in the alpha band, and relatively similar across most bands. Towards a clinical application in epilepsy, we found abnormal temporal regions ipsilateral to the epileptogenic hemisphere. Scalp EEG relative band power normative maps are spatially stable across time, in keeping with MEG and intracranial EEG results. Normative mapping is feasible and may be potentially clinically useful in epilepsy. Future studies with larger sample sizes and high-density EEG are now required for validation.

Diagnosis and classification of Parkinson's disease using ensemble learning and 1D-PDCovNN

In this paper, we proposed a novel approach to diagnose and classify Parkinson's Disease (PD) using ensemble learning and 1D-PDCovNN, a novel deep learning technique. PD is a neurodegenerative disorder; early detection and correct classification are essential for better disease management. The primary aim of this study is to develop a robust approach to diagnosing and classifying PD using EEG signals. As the dataset, we have used the San Diego Resting State EEG dataset to evaluate our proposed method. The proposed method mainly consists of three stages. In the first stage, the Independent Component Analysis (ICA) method has been used as the pre-processing method to filter out the blink noises from the EEG signals. Also, the effect of the band showing motor cortex activity in the 7-30 Hz frequency band of EEG signals in diagnosing and classifying Parkinson's disease from EEG signals has been investigated. In the second stage, the Common Spatial Pattern (CSP) method has been used as the feature extraction to extract useful information from EEG signals. Finally, an ensemble learning approach, Dynamic Classifier Selection (DCS) in Modified Local Accuracy (MLA), has been employed in the third stage, consisting of seven different classifiers. As the classifier method, DCS in MLA, XGBoost, and 1D-PDCovNN classifier has been used to classify the EEG signals as the PD and healthy control (HC). We first used dynamic classifier selection to diagnose and classify Parkinson's disease (PD) from EEG signals, and promising results have been obtained. The performance of the proposed approach has been evaluated using the classification accuracy, F-1 score, kappa score, Jaccard score, ROC curve, recall, and precision values in the classification of PD with the proposed models. In the classification of PD, the combination of DCS in MLA achieved an accuracy of 99,31%. The results of this study demonstrate that the proposed approach can be used as a reliable tool for early diagnosis and classification of PD.

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.

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

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

Quantitative Electroencephalogram Standardization: A Sex- and Age-Differentiated Normative Database

We describe the utility of a standardized index (Z-score) in quantitative EEG (QEEG) capable of when referenced to a resting-state, sex- and age-differentiated QEEG normative database (ISB-NormDB). Our ISB-NormDB comprises data for 1,289 subjects (553 males, 736 females) ages 4.5 to 81 years that met strict normative data criteria. A de-noising process allowed stratification based on QEEG variability between normal healthy men and women at various age ranges. The ISB-NormDB data set that is stratified by sex provides a unique, highly accurate ISB-NormDB model (ISB-NormDB: ISB-NormDB-Male, ISB-NormDB-Female). To evaluate the trends and accuracy of the ISB-NormDB, we used actual data to compare Z-scores obtained through the ISB-NormDB with those obtained through a traditional QEEG normative database to confirm that basic trends are maintained in most bands and are sensitive to abnormal test data. Finally, we demonstrate the value of our standardized index of QEEG, and highlight it's capacity to minimize the confounding variables of sex and age in any analysis.

Abnormal Functional Brain Network in Parkinson's Disease and the Effect of Acute Deep Brain Stimulation

Objective: The objective of this study was to use functional connectivity and graphic indicators to investigate the abnormal brain network topological characteristics caused by Parkinson's disease (PD) and the effect of acute deep brain stimulation (DBS) on those characteristics in patients with PD. Methods: We recorded high-density EEG (256 channels) data from 21 healthy controls (HC) and 20 patients with PD who were in the DBS-OFF state and DBS-ON state during the resting state with eyes closed. A high-density EEG source connectivity method was used to identify functional brain networks. Power spectral density (PSD) analysis was compared between the groups. Functional connectivity was calculated for 68 brain regions in the theta (4-8 Hz), alpha (8-13 Hz), beta1 (13-20 Hz), and beta2 (20-30 Hz) frequency bands. Network estimates were measured at both the global (network topology) and local (inter-regional connection) levels. Results: Compared with HC, PSD was significantly increased in the theta (p = 0.003) frequency band and was decreased in the beta1 (p = 0.009) and beta2 (p = 0.04) frequency bands in patients with PD. However, there were no differences in any frequency bands between patients with PD with DBS-OFF and DBS-ON. The clustering coefficient and local efficiency of patients with PD showed a significant decrease in the alpha, beta1, and beta2 frequency bands (p < 0.001). In addition, edgewise statistics showed a significant difference between the HC and patients with PD in all analyzed frequency bands (p < 0.005). However, there were no significant differences between the DBS-OFF state and DBS-ON state in the brain network, except for the functional connectivity in the beta2 frequency band (p < 0.05). Conclusion: Compared with HC, patients with PD showed the following characteristics: slowed EEG background activity, decreased clustering coefficient and local efficiency of the brain network, as well as both increased and decreased functional connectivity between different brain areas. Acute DBS induces a local response of the brain network in patients with PD, mainly showing decreased functional connectivity in a few brain regions in the beta2 frequency band.

Regional Neural Activity Changes in Parkinson's Disease-Associated Mild Cognitive Impairment and Cognitively Normal Patients

PURPOSE

The aim of this study was to compare regional homogeneity (ReHo) changes in Parkinson's disease mild cognitive impairment (PD-MCI) patients with respect to normal controls (NC) and those with cognitively normal PD (PD-CN). Further, the study investigated the relationship between ReHo changes in PD patients and neuropsychological variation.

PATIENTS AND METHODS

Thirty PD-MCI, 19 PD-CN, and 21 NC subjects were enrolled. Resting state functional magnetic resonance imaging data of all subjects were collected, and regional brain activity was measured for ReHo. Analysis of covariance for ReHo was determined between the PD-MCI, PD-CN, and NC groups. Spearman rank correlations were assessed using the ReHo maps and data from the neuropsychological tests.

RESULTS

In comparison with NC, PD-CN patients showed significantly higher ReHo values in the right middle frontal gyrus (MFG) and lower ReHo values in the left supramarginal gyrus, bilateral inferior parietal lobule (IPL), and the right postcentral gyrus (PCG). In comparison with PD-CN patients, PD-MCI patients displayed significantly higher ReHo values in the right PCG, left middle occipital gyrus (MOG) and IPL. No significant correlation between ReHo indices and the neuropsychological scales was observed.

CONCLUSION

Our finding revealed that decreases in ReHo in the default mode network (DMN) may appear before PD-related cognitive impairment. In order to preserve executive attention capacity, ReHo in the right MFG in PD patients lacking cognition impairment increased for compensation. PD-MCI showed increased ReHo in the left MOG, which might have been caused by visual and visual-spatial dysfunction, and increased ReHo in the left IPL, which might reflect network disturbance and induce cognition deficits.

Patterns of Intrahemispheric EEG Asymmetry in Insomnia Sufferers: An Exploratory Study

Individuals with insomnia present unique patterns of electroencephalographic (EEG) asymmetry between homologous regions of each brain hemisphere, yet few studies have assessed asymmetry within the same hemisphere. Increase in intrahemispheric asymmetry during rapid eye movement (REM) sleep in good sleepers (GS) and disruption of REM sleep in insomnia sufferers (INS) both point out that this activity may be involved in the pathology of insomnia. The objective of the present exploratory study was to evaluate and quantify patterns of fronto-central, fronto-parietal, fronto-occipital, centro-parietal, centro-occipital and parieto-occipital intrahemispheric asymmetry in GS and INS, and to assess their association with sleep-wake misperception, daytime anxiety and depressive symptoms, as well as insomnia severity. This paper provides secondary analysis of standard EEG recorded in 43 INS and 19 GS for three nights in a sleep laboratory. Asymmetry measures were based on EEG power spectral analysis within 0.3–60 Hz computed between pairs of regions at frontal, central, parietal and occipital derivations. Repeated-measures ANOVAs were performed to assess group differences. Exploratory correlations were then performed on asymmetry and sleep-wake misperception, as well as self-reported daytime anxiety and depressive symptoms, and insomnia severity. INS presented increased delta and theta F3/P3 asymmetry during REM sleep compared with GS, positively associated with depressive and insomnia complaints. INS also exhibited decreased centro-occipital (C3/O1, C4/O2) and parieto-occipital (P3–O1, P4/O2) theta asymmetry during REM. These findings suggest that INS present specific patterns of intrahemispheric asymmetry, partially related to their clinical symptoms. Future studies may investigate the extent to which asymmetry is related to sleep-wake misperception or memory impairments.

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.

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.

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.

Novel Effective Connectivity Inference Using Ultra-Group Constrained Orthogonal Forward Regression and Elastic Multilayer Perceptron Classifier for MCI Identification

Mild cognitive impairment (MCI) detection is important, such that appropriate interventions can be imposed to delay or prevent its progression to severe stages, including Alzheimer's disease (AD). Brain connectivity network inferred from the functional magnetic resonance imaging data has been prevalently used to identify the individuals with MCI/AD from the normal controls. The capability to detect the causal or effective connectivity is highly desirable for understanding directed functional interactions between brain regions and further helping the detection of MCI. In this paper, we proposed a novel sparse constrained effective connectivity inference method and an elastic multilayer perceptron classifier for MCI identification. Specifically, a ultra-group constrained structure detection algorithm is first designed to identify the parsimonious topology of the effective connectivity network, in which the weak derivatives of the observable data are considered. Second, based on the identified topology structure, an effective connectivity network is then constructed by using an ultra-orthogonal forward regression algorithm to minimize the shrinking effect of the group constraint-based method. Finally, the effective connectivity network is validated in MCI identification using an elastic multilayer perceptron classifier, which extracts lower to higher level information from initial input features and hence improves the classification performance. Relatively high classification accuracy is achieved by the proposed method when compared with the state-of-the-art classification methods. Furthermore, the network analysis results demonstrate that MCI patients suffer a rich club effect loss and have decreased connectivity among several brain regions. These findings suggest that the proposed method not only improves the classification performance but also successfully discovers critical disease-related neuroimaging biomarkers.

A systematic review of MEG-based studies in Parkinson's disease: The motor system and beyond

Parkinson's disease (PD) is accompanied by functional changes throughout the brain, including changes in the electromagnetic activity recorded with magnetoencephalography (MEG). An integrated overview of these changes, its relationship with clinical symptoms, and the influence of treatment is currently missing. Therefore, we systematically reviewed the MEG studies that have examined oscillatory activity and functional connectivity in the PD‐affected brain. The available articles could be separated into motor network‐focused and whole‐brain focused studies. Motor network studies revealed PD‐related changes in beta band (13–30 Hz) neurophysiological activity within and between several of its components, although it remains elusive to what extent these changes underlie clinical motor symptoms. In whole‐brain studies PD‐related oscillatory slowing and decrease in functional connectivity correlated with cognitive decline and less strongly with other markers of disease progression. Both approaches offer a different perspective on PD‐specific disease mechanisms and could therefore complement each other. Combining the merits of both approaches will improve the setup and interpretation of future studies, which is essential for a better understanding of the disease process itself and the pathophysiological mechanisms underlying specific PD symptoms, as well as for the potential to use MEG in clinical care.

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.