Eyes-closed Electroencephalography Research Articles

EEG-based clusters differentiate psychological distress, sleep quality and cognitive function in adolescents

IntroductionTo better understand the relationships between neurophysiology, cognitive function and psychopathology risk in adolescence there is value in identifying data-driven subgroups based on measurements of brain activity and function, and then comparing cognition and mental health between such subgroups. MethodsWe developed a flexible and scaleable multi-stage analysis pipeline to identify data-driven clusters of 12-year-olds (M = 12.64, SD = 0.32) based on frequency characteristics calculated from resting state, eyes-closed electroencephalography (EEG) recordings. For this preliminary cross-sectional study, EEG data was collected from 59 individuals in the Longitudinal Adolescent Brain Study (LABS) being undertaken in Queensland, Australia. Applying multiple unsupervised clustering algorithms to these EEG features, we identified well-separated subgroups of individuals. To study patterns of difference in cognitive function and mental health symptoms between clusters, we applied Bayesian regression models to probabilistically identify differences in these measures between clusters. ResultsWe identified 5 core clusters associated with distinct subtypes of resting state EEG frequency content. Bayesian models demonstrated substantial differences in psychological distress, sleep quality and cognitive function between clusters. By examining associations between neurophysiology and health measures across clusters, we have identified preliminary risk and protective profiles linked to EEG characteristics. ConclusionThis method provides the potential to identify neurophysiological subgroups of adolescents in the general population based on resting state EEG, and associated patterns of health and cognition that are not observed at the whole group level. This approach offers potential utility in clinical risk prediction for mental and cognitive health outcomes throughout adolescent development.

Quantitative Electroencephalography Findings in Patients With Diabetes Mellitus

Objective. Diabetes mellitus (DM) causes structural central nervous system (CNS) impairment, and this situation can be detected by quantitative electroencephalography (QEEG) findings before cognitive impairment is clinically observed. The main aim of this study is to uncover the effect of DM on brain function. Since QEEG reflects the CNS functioning, particularly in cognitive aspects, we expected electrophysiological clues to be found for prevention and follow-up in DM-related cognitive decline. Since a majority of the psychiatric population have cognitive dysfunction, we have given particular attention to those people. It was stated that a decrease was observed in the posterior cortical alpha power due to the hippocampal atrophy by several previous studies and we hypothesize that decreased alpha power will be observed also in DM. Methods. This study included 2094 psychiatric patients, 207 of whom were diagnosed with DM and 1887 of whom were not diagnosed with DM, and QEEG recordings were performed. Eyes-closed electroencephalography data were segmented into consecutive 2 s epochs. Fourier analysis was performed by averaging across 2 s epochs without artifacts. The absolute alpha power in the occipital regions (O1 and O2) of patients with and without DM was compared. Results. In the DM group, a decrease in the absolute alpha, alpha 1, and alpha 2 power in O1 and O2 was observed in comparison with the control group. It was determined that the type of psychiatric diagnosis did not affect QEEG findings. Conclusion. The decrease in absolute alpha power observed in patients diagnosed with DM may be related to the CNS impairment in DM. QEEG findings in DM can be useful while monitoring the CNS impairment, diagnosing DM-related dementia, in the follow-up of the cognitive process, constructing the protocols for electrophysiological interventions like neurofeedback and transcranial magnetic stimulation and monitoring the response to treatment.

Late-Onset Epilepsy With Unknown Etiology: A Pilot Study on Neuropsychological Profile, Cerebrospinal Fluid Biomarkers, and Quantitative EEG Characteristics.

Introduction: Despite the fact that epilepsy has been associated with cognitive decline, neuropsychological, neurobiological, and neurophysiological features in patients with late-onset epilepsy of unknown etiology (LOEU) are still unknown. This cross-sectional study aims to investigate the neuropsychological profile, cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease (AD), and resting-state quantitative electroencephalographic (qEEG) cortical rhythms in LOEU patients with mild cognitive impairment (LOEU-MCI) and with normal cognition (LOEU-CN), compared to non-epileptic MCI (NE-MCI) and cognitively normal (CN) controls.Methods: Consecutive patients in two clinical Units diagnosed with LOEU-CN (19), LOEU-MCI (27), and NE-MCI (21) were enrolled, and compared to age and sex-matched cognitively normal subjects CN (11). Patients underwent standardized comprehensive neuropsychological evaluation and CSF core AD biomarkers assessment (i.e., CSF Aβ42, phospho-tau and total tau, classified through A/T/(N) system). Recordings of resting-state eyes-closed electroencephalographic (EEG) rhythms were collected and cortical source estimation of delta (<4 Hz) to gamma (>30 Hz) bands with exact Low Resolution Electromagnetic Tomography (eLORETA) was performed.Results: Most LOEU patients had an MCI status at seizure onset (59%). Patients with LOEU-MCI performed significantly worse on measures of global cognition, visuo-spatial abilities, and executive functions compared to NE-MCI patients (p < 0.05). Regarding MCI subtypes, multiple-domain MCI was 3-fold more frequent in LOEU-MCI than in NE-MCI patients (OR 3.14, 95%CI 0.93–10.58, p = 0.06). CSF Aβ42 levels were lower in the LOEU-MCI compared with the LOEU-CN group. Finally, parietal and occipital sources of alpha (8–12 Hz) rhythms were less active in the LOEU-MCI than in the NE-MCI and CN groups, while the opposite was true for frontal and temporal cortical delta sources.Discussion: MCI status was relatively frequent in LOEU patients, involved multiple cognitive domains, and might have been driven by amyloidosis according to CSF biomarkers. LOEU-MCI status was associated with abnormalities in cortical sources of EEG rhythms related to quiet vigilance. Future longitudinal studies should cross-validate our findings and test the predictive value of CSF and EEG variables.

Cognitive deficits in patients with obsessive&amp;ndash;compulsive disorder &amp;ndash; electroencephalography correlates

BackgroundObsessive–compulsive disorder (OCD) is associated with cognitive dysfunction. Although there are several studies focused on the neurobiology of OCD, little is known about the biological correlates of the cognitive deficit linked to this disorder. The aim of our study was to examine the association between cognitive impairment and current source density markers in patients with OCD.MethodsResting-state eyes-closed electroencephalography (EEG) data were recorded in 20 patients with OCD and 15 healthy controls who were involved in the study. Cortical EEG sources were estimated by standardized low-resolution electromagnetic tomography in seven frequency bands: delta (1.5–6 Hz), theta (6.5–8 Hz), alpha-1 (8.5–10 Hz), alpha-2 (10.5–12 Hz), beta-1 (12.5–18 Hz), beta-2 (18.5–21 Hz), and beta-3 (21.5–30 Hz). Cognitive performance was measured by the Trail-Making Test (versions A and B), Stroop CW Test, and D2 Test.ResultsFrontal delta and theta EEG sources showed significantly higher activity in the whole group of patients with OCD (N=20) than in control subjects (N=15). Subsequent analysis revealed that this excess of low-frequency activity was present only in the subgroup of eleven patients with cognitive impairment (based on the performance in the Trail-Making Test – A). The subgroup of patients with normal cognitive functions (N=9) did not differ in cortical EEG sources from healthy controls.ConclusionThe present results suggest that frontal low-frequency cortical sources of resting-state EEG rhythms can distinguish groups of cognitively impaired and cognitively intact patients with OCD. Based on our results, future studies should consider whether the present methodological approach provides clinically useful information for the revelation of cognitive impairment in patients with OCD.

Electroencephalographic Fractal Dimension in Healthy Ageing and Alzheimer's Disease.

Brain activity is complex; a reflection of its structural and functional organization. Among other measures of complexity, the fractal dimension is emerging as being sensitive to neuronal damage secondary to neurological and psychiatric diseases. Here, we calculated Higuchi’s fractal dimension (HFD) in resting-state eyes-closed electroencephalography (EEG) recordings from 41 healthy controls (age: 20–89 years) and 67 Alzheimer’s Disease (AD) patients (age: 50–88 years), to investigate whether HFD is sensitive to brain activity changes typical in healthy aging and in AD. Additionally, we considered whether AD-accelerating effects of the copper fraction not bound to ceruloplasmin (also called “free” copper) are reflected in HFD fluctuations. The HFD measure showed an inverted U-shaped relationship with age in healthy people (R2 = .575, p < .001). Onset of HFD decline appeared around the age of 60, and was most evident in central-parietal regions. In this region, HFD decreased with aging stronger in the right than in the left hemisphere (p = .006). AD patients demonstrated reduced HFD compared to age- and education-matched healthy controls, especially in temporal-occipital regions. This was associated with decreasing cognitive status as assessed by mini-mental state examination, and with higher levels of non-ceruloplasmin copper. Taken together, our findings show that resting-state EEG complexity increases from youth to maturity and declines in healthy, aging individuals. In AD, brain activity complexity is further reduced in correlation with cognitive impairment. In addition, elevated levels of non-ceruloplasmin copper appear to accelerate the reduction of neural activity complexity. Overall, HDF appears to be a proper indicator for monitoring EEG-derived brain activity complexity in healthy and pathological aging.

Resting state Rolandic mu rhythms are related to activity of sympathetic component of autonomic nervous system in healthy humans

We tested the hypothesis of a relationship between heart rate variability (HRV) and Rolandic mu rhythms in relaxed condition of resting state. Resting state eyes-closed electroencephalographic (EEG) and electrocardiographic (ECG) data were recorded (10–20 System) in 42 healthy adults. EEG rhythms of interest were high-frequency alpha (10.5–13Hz) and low-frequency beta (13–20Hz), which are supposed to form Rolandic mu rhythms. Rolandic and occipital (control) EEG sources were estimated by LORETA software. Results showed a statistically significant (p<0.05, corrected) negative correlation across all subjects between Rolandic cortical sources of low-frequency beta rhythms and the low-frequency band power (LF, 0.04–0.15Hz) of tachogram spectrum as an index of HRV. The lower the amplitude of Rolandic sources of low-frequency beta rhythms (as a putative sign of activity of somatomotor cortex), the higher the LF band power of tachogram spectrum (as a putative sign of sympathetic activity). This effect was specific as there was neither a similar correlation between these EEG rhythms and high-frequency band power of tachogram spectrum (as a putative sign of parasympathetic vagal activity) neither between occipital sources of low-frequency beta rhythms (as a putative sign of activity of visual cortex) and LF band power of tachogram spectrum. These results suggest that Rolandic low-frequency beta rhythms are related to sympathetic activity regulating heart rate, as a dynamic neurophysiologic oscillatory mechanism sub-serving the interaction between brain neural populations involved in somatomotor control and brain neural populations regulating ANS signals to heart for on-going homeostatic adaptations.

P.1.i.011 The effects of the VRK2 (rs2312147) gene on white matter connectivity in patients with schizophrenia

High power of pre-stimulus cortical alpha rhythms (about 8–12 Hz) underlies conscious perception in normal subjects. Here we tested the hypothesis that these rhythms are abnormal in persistent vegetative state (PVS) patients, who are awake but not aware of self and environment.Clinical and resting-state, eyes-closed electroencephalographic (EEG) data were taken from a clinical archive. These data were recorded in 50 PVS subjects (level of cognitive functioning – LCF score: I–II) and in 30 cognitively normal subjects. Rhythms of interest were delta (2–4 Hz), theta (4–8 Hz), alpha 1 (8–10.5 Hz), alpha 2 (10.5–13 Hz), beta 1 (13–20 Hz), and beta 2 (20–30 Hz). Cortical sources were estimated by low-resolution electromagnetic tomography (LORETA). Based on LCF score at 3-months follow-up, PVS patients were retrospectively divided into three groups: 30 subjects who did not recover (NON-REC patients; follow-up LCF: I–II), 8 subjects classified as minimally conscious state patients (MCS patients; follow-up LCF: III–IV), and 12 subjects who recovered (REC patients; follow-up LCF: V–VIII).Occipital source power of alpha 1 and alpha 2 was high in normal subjects, low in REC patients, and practically null in NON-REC patients. A Cox regression analysis showed that the power of alpha source predicted the rate of the follow up recovery, namely the higher its power, the higher the chance to recover consciousness. Furthermore, the MCS patients showed intermediate values of occipital alpha source power between REC and NON-REC patients.These results suggest that cortical sources of alpha rhythms are related to the chance of recovery at a 3-months follow-up in patients in persistent vegetative state.Cortical sources of resting alpha rhythms might predict recovery in PVS patients.

Cortical neural synchronization mechanisms in Alzheimer's disease as revealed by resting state EEG rhythms: A window on cholinergic function?

Current pharmacological treatments of Alzheimer's disease (AD) include the use of acetylcholinesterase inhibitors (AChEIs), based on the cholinergic hypothesis of the disease. However, converging evidence has shown a strong and unpredictable inter-individual variation in response to AChEI treatment. This motivates a better understanding of the neurophysiological role played by cholinergic neurophysiological systems in subjects with prodromal and overt AD. The present Consortium has probed neurophysiological mechanisms of cortical neural synchronization in pathological aging as revealed by an advanced spectral analysis of resting state eyes-closed electroencephalographic (EEG) rhythms in groups of normal elderly (Nold), amnesic mild cognitive impairment (MCI), cerebrovascular dementia (CVD), Parkinson disease with dementia (PDD), covert hepatic encephalopathy (CHE), and AD subjects. (1) dominant alpha frequencies (8–10 Hz) of EEG rhythms were specifically abnormal in the AD group when compared to the Nold, MCI, CVD, PPD, and CHE groups; (2) delta (2–4 Hz) and alpha rhythms were related to attention and global cognitive status in both MCI and AD groups; (3) power and functional coupling of alpha rhythms were related to white-matter lesions along the cholinergic tracts in the MCI group; (4) long-term (1 year) cholinergic therapy was just able to slow the derangement of alpha rhythms in the AD group. These findings suggest that resting state EEG rhythms may reflect the dysfunction of cholinergic neuromodulation of cortical neural synchronization in the MCI and in the AD group. Future research will have to evaluate the clinical impact of this methodological approach in individual patients.

The influence of serotonin transporter polymorphisms on cortical activity: A resting EEG study

BackgroundThe serotonin transporter gene (5-HTT) is a key regulator of serotonergic neurotransmission and has been linked to various psychiatric disorders. Among the genetic variants, polymorphisms in the 5-HTT gene-linked polymorphic region (5-HTTLPR) and variable-number-of-tandem-repeat in the second intron (5-HTTVNTR) have functional consequences. However, their genetic impact on cortical oscillation remains unclear. This study examined the modulatory effects of 5-HTTLPR (L-allele carriers vs. non-carriers) and 5-HTTVNTR (10-repeat allele carriers vs. non-carriers) polymorphism on regional neural activity in a young female population.MethodsBlood samples and resting state eyes-closed electroencephalography (EEG) signals were collected from 195 healthy women and stratified into 2 sets of comparisons of 2 groups each: L-allele carriers (N = 91) vs. non-carriers for 5-HTTLPR and 10-repeat allele carriers (N = 25) vs. non-carriers for 5-HTTVNTR. The mean power of 18 electrodes across theta, alpha, beta, gamma, gamma1, and gamma2 frequencies was analyzed. Between-group statistics were performed by an independent t-test, and global trends of regional power were quantified by non-parametric analyses.ResultsAmong 5-HTTVNTR genotypes, 10-repeat allele carriers showed significantly low regional power at gamma frequencies across the brain. We noticed a consistent global trend that carriers with low transcription efficiency of 5-HTT possessed low regional powers, regardless of frequency bands. The non-parametric analyses confirmed this observation, with P values of 3.071 × 10-8 and 1.459 × 10-12 for 5-HTTLPR and 5-HTTVNTR, respectively.Conclusions and LimitationsOur analyses showed that genotypes with low 5-HTT activity are associated with less local neural synchronization during relaxation. The implication with respect to genetic vulnerability of 5-HTT across a broad range of psychiatric disorders is discussed. Given the low frequency of 10-repeat allele of 5-HTTVNTR in our research sample, the possibility of false positive findings should also be considered.

Network analysis of resting state EEG in the developing young brain: structure comes with maturation.

During childhood, brain structure and function changes substantially. Recently, graph theory has been introduced to model connectivity in the brain. Small-world networks, such as the brain, combine optimal properties of both ordered and random networks, i.e., high clustering and short path lengths. We used graph theoretical concepts to examine changes in functional brain networks during normal development in young children. Resting-state eyes-closed electroencephalography (EEG) was recorded (14 channels) from 227 children twice at 5 and 7 years of age. Synchronization likelihood (SL) was calculated in three different frequency bands and between each pair of electrodes to obtain SL-weighted graphs. Mean normalized clustering index, average path length and weight dispersion were calculated to characterize network organization. Repeated measures analysis of variance tested for time and gender effects. For all frequency bands mean SL decreased from 5 to 7 years. Clustering coefficient increased in the alpha band. Path length increased in all frequency bands. Mean normalized weight dispersion decreased in beta band. Girls showed higher synchronization for all frequency bands and a higher mean clustering in alpha and beta bands. The overall decrease in functional connectivity (SL) might reflect pruning of unused synapses and preservation of strong connections resulting in more cost-effective networks. Accordingly, we found increases in average clustering and path length and decreased weight dispersion indicating that normal brain maturation is characterized by a shift from random to more organized small-world functional networks. This developmental process is influenced by gender differences early in development.

Cortical sources of resting-state alpha rhythms are abnormal in persistent vegetative state patients

High power of pre-stimulus cortical alpha rhythms (about 8-12 Hz) underlies conscious perception in normal subjects. Here we tested the hypothesis that these rhythms are abnormal in persistent vegetative state (PVS) patients, who are awake but not aware of self and environment. Clinical and resting-state, eyes-closed electroencephalographic (EEG) data were taken from a clinical archive. These data were recorded in 50 PVS subjects (level of cognitive functioning--LCF score: I-II) and in 30 cognitively normal subjects. Rhythms of interest were delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), and beta 2 (20-30 Hz). Cortical sources were estimated by low-resolution electromagnetic tomography (LORETA). Based on LCF score at 3-months follow-up, PVS patients were retrospectively divided into three groups: 30 subjects who did not recover (NON-REC patients; follow-up LCF: I-II), 8 subjects classified as minimally conscious state patients (MCS patients; follow-up LCF: III-IV), and 12 subjects who recovered (REC patients; follow-up LCF: V-VIII). Occipital source power of alpha 1 and alpha 2 was high in normal subjects, low in REC patients, and practically null in NON-REC patients. A Cox regression analysis showed that the power of alpha source predicted the rate of the follow up recovery, namely the higher its power, the higher the chance to recover consciousness. Furthermore, the MCS patients showed intermediate values of occipital alpha source power between REC and NON-REC patients. These results suggest that cortical sources of alpha rhythms are related to the chance of recovery at a 3-months follow-up in patients in persistent vegetative state. Cortical sources of resting alpha rhythms might predict recovery in PVS patients.

Hippocampal volume and cortical sources of EEG alpha rhythms in mild cognitive impairment and Alzheimer disease

Atrophy of hippocampus and alteration of resting eyes-closed electroencephalographic (EEG) rhythms represent important features of mild cognitive impairment (MCI) and Alzheimer's disease (AD). Here we evaluated linear and non-linear aspects of the relationship between these features in the continuum along MCI and AD conditions, as a reflection of neurodegenerative processes. Eyes-closed resting EEG data were recorded in 60 healthy elderly (Nold), 88 MCI, and 35 Alzheimer's disease (AD) patients. Hippocampal volume was measured in magnetic resonance imaging of the MCI and AD subjects. Based on the normalized hippocampal volume, selected MCI subjects could be divided into two demographically paired sub-groups: those with larger hippocampal volume (MCI + h; N = 40; mini mental state evaluation – MMSE – score = 27.5 ± 0.26 SE) and those with smaller hippocampal volume (MCI − h; N = 40; h; MMSE = 26.5 ± 0.34 SE); the normalized hippocampal volume was statistically greater in the MCI + h than in the MCI − h and AD subjects ( p < 0.0001). EEG rhythms of interest were delta (2–4 Hz), theta (4–8 Hz), alpha 1 (8–10.5 Hz), alpha 2 (10.5–13 Hz), beta 1 (13–20 Hz), and beta 2 (20–30 Hz). Cortical EEG generators were estimated by LORETA software. Results showed that the power of occipital, parietal, and temporal alpha 1 sources was maximum in MCI + h, intermediate in MCI − h, and low in AD patients. Furthermore, the power of these sources was linearly and non-linearly correlated with the normalized hippocampal volume. These 3 EEG sources were given as input for evaluating correlations (linear, exponential, logarithmic and power) with hippocampal volume. When subjects were considered as a unique group, there was a significant linear correlation of hippocampal volume with the magnitude of alpha 1 sources in the parietal, occipital and temporal areas. In general, the EEG sources showing significant linear correlation with hippocampal volume also supported a non-linear correlation with hippocampal volume strongly for the logarithmic one. The present results suggest that progressive atrophy of hippocampus correlates with decreased cortical alpha power, as estimated by using LORETA source modeling, in the continuum along MCI and AD conditions.

Heroin abusers' performance on the Tower of London Test relates to the baseline EEG alpha2 mean frequency shifts

Planning function deficit is the most consistent finding in neuropsychological studies of heroin addicts. The performance on the Tower of London Test (TLT) correlated with the duration of daily heroin abuse (DDHA) in our previous study. Alpha2 mean frequency in anterior/central derivations was also predicted by DDHA in the same patient population. This retrospective study was undertaken in order to understand better the relationships between observed neurological deviations in heroin abusers. Thirty-three heroin addicts and 12 healthy males were evaluated with 14 neuropsychological tests and resting eyes-closed electroencephalography (EEG). Multivariate tests showed that performance on the difficult (five-move) problems of TLT was strongly predicted by the EEG alpha2 mean frequency shifts, and these relationships were generally mediated by chronic heroin length. However, post-hoc analyses at separate leads demonstrated that the relationships between cognitive variables and alpha2 mean frequencies in the left hemisphere were independent of chronic heroin effects, whereas elevation of alpha2 frequency in the right hemisphere was strongly predicted by chronic heroin intake length. The patients with extremely high alpha2 mean frequency at the left central region were especially prone to failure in TLT due to the inability of the hypothesized alpha2-generating network, which normally projects to the central and temporal derivations bilaterally and to the right posterior temporal derivation to function appropriately. Hence, it was concluded that planning dysfunction in heroin abusers is related to alpha2 mean frequency shifts predominantly at the central regions.