EEG spectral changes induced by hemodialysis.
Ontology highlight
ABSTRACT: Objective
To investigate the EEG spectral changes induced during hemodialysis in patients with chronic kidney disease (CKD), and to identify the risk factors associated with changes in the Central Nervous System (CNS) during hemodialysis. Paradoxical neurological deterioration at the end of hemodialysis sessions, known as dialysis disequilibrium syndrome (DDS) has been described, but previous studies on EEG spectral changes during hemodialysis were controversial.Methods
We performed quantitative EEG spectral analysis in 56 consecutive patients who underwent hemodialysis. We compared EEG at the start and at the end of the hemodialysis, and we correlated the spectral changes with the biochemical and clinical characteristics of the patients, using multivariate analysis.Results
At the end of hemodialysis sessions, we found a significant increase in total EEG power, relative power in delta frequency band and the ratio of delta-theta/alpha-beta power. EEG spectral changes were associated with younger age, recent start of hemodialysis therapy, level of uremia and lower level of glycaemia.Conclusions
Quantitative EEG spectral analysis showed that hemodialysis induced slowing of the EEG background activity. These changes were associated with risk factors of DDS.Significance
EEG spectral changes are potential biomarkers for monitoring CNS function during hemodialysis.
SUBMITTER: Florea B
PROVIDER: S-EPMC8114056 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:IntroductionElectroencephalographic (EEG) data quality is severely compromised when recorded inside the magnetic resonance (MR) environment. Here we characterized the impact of the ballistocardiographic (BCG) artifact on resting-state EEG spectral properties and compared the effectiveness of seven common BCG correction methods to preserve EEG spectral features. We also assessed if these methods retained posterior alpha power reactivity to an eyes closure-opening (EC-EO) task and compared the results from EEG-informed fMRI analysis using different BCG correction approaches.MethodElectroencephalographic data from 20 healthy young adults were recorded outside the MR environment and during simultaneous fMRI acquisition. The gradient artifact was effectively removed from EEG-fMRI acquisitions using Average Artifact Subtraction (AAS). The BCG artifact was corrected with seven methods: AAS, Optimal Basis Set (OBS), Independent Component Analysis (ICA), OBS followed by ICA, AAS followed by ICA, PROJIC-AAS and PROJIC-OBS. EEG signal preservation was assessed by comparing the spectral power of traditional frequency bands from the corrected rs-EEG-fMRI data with the data recorded outside the scanner. We then assessed the preservation of posterior alpha functional reactivity by computing the ratio between the EC and EO conditions during the EC-EO task. EEG-informed fMRI analysis of the EC-EO task was performed using alpha power-derived BOLD signal predictors obtained from the EEG signals corrected with different methods.ResultsThe BCG artifact caused significant distortions (increased absolute power, altered relative power) across all frequency bands. Artifact residuals/signal losses were present after applying all correction methods. The EEG reactivity to the EC-EO task was better preserved with ICA-based correction approaches, particularly when using ICA feature extraction to isolate alpha power fluctuations, which allowed to accurately predict hemodynamic signal fluctuations during the EEG-informed fMRI analysis.DiscussionCurrent software solutions for the BCG artifact problem offer limited efficiency to preserve the EEG spectral power properties using this particular EEG setup. The state-of-the-art approaches tested here can be further refined and should be combined with hardware implementations to better preserve EEG signal properties during simultaneous EEG-fMRI. Existing and novel BCG artifact correction methods should be validated by evaluating signal preservation of both ERPs and spontaneous EEG spectral power.
| S-EPMC9816433 | biostudies-literature
Project description:Typical electroencephalogram (EEG) recordings often contain substantial artifact. These artifacts, often large and intermittent, can interfere with quantification of the EEG via its power spectrum. To reduce the impact of artifact, EEG records are typically cleaned by a preprocessing stage that removes individual segments or components of the recording. However, such preprocessing can introduce bias, discard available signal, and be labor-intensive. With this motivation, we present a method that uses robust statistics to reduce dependence on preprocessing by minimizing the effect of large intermittent outliers on the spectral estimates.Using the multitaper method (Thomson, 1982) as a starting point, we replaced the final step of the standard power spectrum calculation with a quantile-based estimator, and the Jackknife approach to confidence intervals with a Bayesian approach. The method is implemented in provided MATLAB modules, which extend the widely used Chronux toolbox.Using both simulated and human data, we show that in the presence of large intermittent outliers, the robust method produces improved estimates of the power spectrum, and that the Bayesian confidence intervals yield close-to-veridical coverage factors.The robust method, as compared to the standard method, is less affected by artifact: inclusion of outliers produces fewer changes in the shape of the power spectrum as well as in the coverage factor.In the presence of large intermittent outliers, the robust method can reduce dependence on data preprocessing as compared to standard methods of spectral estimation.
| S-EPMC4903894 | biostudies-literature
Project description:The aim of this study was to investigate whether a 15-minute placement of a 3G dialing mobile phone causes direct changes in EEG activity compared to the placement of a sham phone. Furthermore, it was investigated whether placement of the mobile phone on the ear or the heart would result in different outcomes. Thirty-one healthy females participated. All subjects were measured twice: on one of the two days the mobile phone was attached to the ear, the other day to the chest. In this single-blind, cross-over design, assessments in the sham phone condition were conducted directly preceding and following the mobile phone exposure. During each assessment, EEG activity and radiofrequency radiation were recorded jointly. Delta, theta, alpha, slowbeta, fastbeta, and gamma activity was computed. The association between radiation exposure and the EEG was tested using multilevel random regression analyses with radiation as predictor of main interest. Significant radiation effects were found for the alpha, slowbeta, fastbeta, and gamma bands. When analyzed separately, ear location of the phone was associated with significant results, while chest placement was not. The results support the notion that EEG alterations are associated with mobile phone usage and that the effect is dependent on site of placement. Further studies are required to demonstrate the physiological relevance of these findings.
| S-EPMC4459698 | biostudies-literature
Project description:Repeatedly performing a submaximal motor task for a prolonged period of time leads to muscle fatigue comprising a central and peripheral component, which demands a gradually increasing effort. However, the brain contribution to the enhancement of effort to cope with progressing fatigue lacks a complete understanding. The intermittent motor tasks (IMTs) closely resemble many activities of daily living (ADL), thus remaining physiologically relevant to study fatigue. The scope of this study is therefore to investigate the EEG-based brain activation patterns in healthy subjects performing IMT until self-perceived exhaustion. Fourteen participants (median age 51.5 years; age range 26-72 years; 6 males) repeated elbow flexion contractions at 40% maximum voluntary contraction by following visual cues displayed on an oscilloscope screen until subjective exhaustion. Each contraction lasted ≈5 s with a 2-s rest between trials. The force, EEG, and surface EMG (from elbow joint muscles) data were simultaneously collected. After preprocessing, we selected a subset of trials at the beginning, middle, and end of the study session representing brain activities germane to mild, moderate, and severe fatigue conditions, respectively, to compare and contrast the changes in the EEG time-frequency (TF) characteristics across the conditions. The outcome of channel- and source-level TF analyses reveals that the theta, alpha, and beta power spectral densities vary in proportion to fatigue levels in cortical motor areas. We observed a statistically significant change in the band-specific spectral power in relation to the graded fatigue from both the steady- and post-contraction EEG data. The findings would enhance our understanding on the etiology and physiology of voluntary motor-action-related fatigue and provide pointers to counteract the perception of muscle weakness and lack of motor endurance associated with ADL. The study outcome would help rationalize why certain patients experience exacerbated fatigue while carrying out mundane tasks, evaluate how clinical conditions such as neurological disorders and cancer treatment alter neural mechanisms underlying fatigue in future studies, and develop therapeutic strategies for restoring the patients' ability to participate in ADL by mitigating the central and muscle fatigue.
| S-EPMC8962200 | biostudies-literature
Project description:Alzheimer's disease (AD) and vascular dementia (VaD) present with similar clinical symptoms of cognitive decline, but the underlying pathophysiological mechanisms differ. To determine whether clinical electroencephalography (EEG) can provide information relevant to discriminate between these diagnoses, we used quantitative EEG analysis to compare the spectra between non-medicated patients with AD (n?=?77) and VaD (n?=?77) and healthy elderly normal controls (NC) (n?=?77). We use curve-fitting with a combination of a power loss and Gaussian function to model the averaged resting-state spectra of each EEG channel extracting six parameters. We assessed the performance of our model and tested the extracted parameters for group differentiation. We performed regression analysis in a multivariate analysis of covariance with group, age, gender, and number of epochs as predictors and further explored the topographical group differences with pair-wise contrasts. Significant topographical differences between the groups were found in several of the extracted features. Both AD and VaD groups showed increased delta power when compared to NC, whereas the AD patients showed a decrease in alpha power for occipital and temporal regions when compared with NC. The VaD patients had higher alpha power than NC and AD. The AD and VaD groups showed slowing of the alpha rhythm. Variability of the alpha frequency was wider for both AD and VaD groups. There was a general decrease in beta power for both AD and VaD. The proposed model is useful to parameterize spectra, which allowed extracting relevant clinical EEG key features that move toward simple and interpretable diagnostic criteria.
| S-EPMC4327579 | biostudies-literature
Project description:Oscillatory neural activities are prevalent in the brain with their phase realignment contributing to the coordination of neural communication. Phase realignments may have especially strong (or weak) impact when neural activities are strongly synchronized (or desynchronized) within the interacting populations. We report that the spatiotemporal dynamics of strong regional synchronization measured as maximal EEG spectral power-referred to as activation-and strong regional desynchronization measured as minimal EEG spectral power-referred to as suppression-are characterized by the spatial segregation of small-scale and large-scale networks. Specifically, small-scale spectral-power activations and suppressions involving only 2-7% (1-4 of 60) of EEG scalp sites were prolonged (relative to stochastic dynamics) and consistently co-localized in a frequency specific manner. For example, the small-scale networks for θ, α, β1, and β2 bands (4-30 Hz) consistently included frontal sites when the eyes were closed, whereas the small-scale network for γ band (31-55 Hz) consistently clustered in medial-central-posterior sites whether the eyes were open or closed. Large-scale activations and suppressions involving over 17-30% (10-18 of 60) of EEG sites were also prolonged and generally clustered in regions complementary to where small-scale activations and suppressions clustered. In contrast, intermediate-scale activations and suppressions (involving 7-17% of EEG sites) tended to follow stochastic dynamics and were less consistently localized. These results suggest that strong synchronizations and desynchronizations tend to occur in small-scale and large-scale networks that are spatially segregated and frequency specific. These synchronization networks may broadly segregate the relatively independent and highly cooperative oscillatory processes while phase realignments fine-tune the network configurations based on behavioral demands.
| S-EPMC8291701 | biostudies-literature
Project description:The research on neural correlates of intentional emotion communication by the music performer is still limited. In this study, we attempted to evaluate EEG patterns recorded from musicians who were instructed to perform a simple piano score while manipulating their manner of play to express specific contrasting emotions and self-rate the emotion they reflected on the scales of arousal and valence. In the emotional playing task, participants were instructed to improvise variations in a manner by which the targeted emotion is communicated. In contrast, in the neutral playing task, participants were asked to play the same piece precisely as written to obtain data for control over general patterns of motor and sensory activation during playing. The spectral analysis of the signal was applied as an initial step to be able to connect findings to the wider field of music-emotion research. The experimental contrast of emotional playing vs. neutral playing was employed to probe brain activity patterns differentially involved in distinct emotional states. The tasks of emotional and neutral playing differed considerably with respect to the state of intended-to-transfer emotion arousal and valence levels. The EEG activity differences were observed between distressed/excited and neutral/depressed/relaxed playing.
| S-EPMC8620396 | biostudies-literature
Project description:The ability to detect and inactivate spore-forming bacteria is of significance within, for example, industrial, healthcare, and defense sectors. Not only are stringent protocols necessary for the inactivation of spores but robust procedures are also required to detect viable spores after an inactivation assay to evaluate the procedure's success. UV radiation is a standard procedure to inactivate spores. However, there is limited understanding regarding its impact on spores' spectral and morphological characteristics. A further insight into these UV-induced changes can significantly improve the design of spore decontamination procedures and verification assays. This work investigates the spectral and morphological changes to Bacillus thuringiensis spores after UV exposure. Using absorbance and fluorescence spectroscopy, we observe an exponential decay in the spectral intensity of amino acids and protein structures, as well as a logistic increase in dimerized DPA with increased UV exposure on bulk spore suspensions. Additionally, using micro-Raman spectroscopy, we observe DPA release and protein degradation with increased UV exposure. More specifically, the protein backbone's 1600-1700 cm-1 amide I band decays slower than other amino acid-based structures. Last, using electron microscopy and light scattering measurements, we observe shriveling of the spore bodies with increased UV radiation, alongside the leaking of core content and disruption of proteinaceous coat and exosporium layers. Overall, this work utilized spectroscopy and electron microscopy techniques to gain new understanding of UV-induced spore inactivation relating to spore degradation and CaDPA release. The study also identified spectroscopic indicators that can be used to determine spore viability after inactivation. These findings have practical applications in the development of new spore decontamination and inactivation validation methods.
| S-EPMC10895659 | biostudies-literature
Project description:ObjectiveWe have developed and validated a novel EEG-based signal processing approach to distinguish PD and control patients: Linear-predictive-coding EEG Algorithm for PD (LEAPD). This method efficiently encodes EEG time series into features that can detect PD in a computationally fast manner amenable to real time applications.MethodsWe included a total of 41 PD patients and 41 demographically-matched controls from New Mexico and Iowa. Data for all participants from New Mexico (27 PD patients and 27 controls) were used to evaluate in-sample LEAPD performance, with extensive cross-validation. Participants from Iowa (14 PD patients and 14 controls) were used for out-of-sample tests. Our method utilized data from six EEG leads which were as little as 2 min long.ResultsFor the in-sample dataset, LEAPD differentiated PD patients from controls with 85.3 ± 0.1% diagnostic accuracy, 93.3 ± 0.5% area under the receiver operating characteristics curve (AUC), 87.9 ± 0.9% sensitivity, and 82.7 ± 1.1% specificity, with multiple cross-validations. After head-to-head comparison with state-of-the-art methods using our dataset, LEAPD showed a 13% increase in accuracy and a 15.5% increase in AUC. When the trained classifier was applied to a distinct out-of-sample dataset, LEAPD showed reliable performance with 85.7% diagnostic accuracy, 85.2% AUC, 85.7% sensitivity, and 85.7% specificity. No statistically significant effect of levodopa-ON and levodopa-OFF sessions were found.ConclusionWe describe LEAPD, an efficient algorithm that is suitable for real time application and captures spectral EEG features using few parameters and reliably differentiates PD patients from demographically-matched controls.
| S-EPMC7900258 | biostudies-literature
Project description:PurposeIn simultaneous scalp electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), blood oxygen level dependent (BOLD) changes occurring before the spike have been sometimes described but could not be explained. To characterize the origin of this prespike BOLD signal change, we looked for electrographic changes in stereo-EEG (SEEG) possibly preceding the scalp spike in patients that showed early BOLD response in EEG/fMRI.MethodsWe studied four patients with drug-resistant focal epilepsy who underwent EEG/fMRI, showed a prespike BOLD response, and were then studied with depth electrodes for presurgical localization of the epileptic generator. Early BOLD responses in the region of the spike field were analyzed using models with hemodynamic response functions (HRFs) peaking from -9 to +9 s around the spike. SEEG recordings in the period and location corresponding to the early HRF responses were analyzed to detect if electrographic changes were present in the SEEG before the scalp abnormality.Key findingsOne of the four patients presented a SEEG interictal discharge in the period corresponding to the early BOLD response. In the other three, no electrographic changes were detected in the SEEG in the period corresponding to early BOLD changes.SignificanceAlthough the early BOLD activity may sometimes be explained by a synchronized neural discharge detectable with SEEG but not visible on the scalp EEG, in most cases the early BOLD response reflects a metabolic phenomenon that does not appear to result from a synchronized neuronal discharge. Prespike metabolic responses can result from synchronized or nonsynchronized neuronal activity, or from nonneuronal mechanisms including glia.
| S-EPMC3771927 | biostudies-literature