scholarly journals Electroencephalographic Microstates in Schizophrenia and Bipolar Disorder

2021 ◽  
Vol 12 ◽  
Author(s):  
Fanglan Wang ◽  
Khamlesh Hujjaree ◽  
Xiaoping Wang
Keyword(s):  
Bipolar Disorder ◽  
Transition Probability ◽  
Mental States ◽  
Brain Dysfunction ◽  
Eyes Closed ◽  
Eeg Data ◽  
Frequent Switching ◽  
Physiological Explanation ◽  
Microstate Analysis ◽  
Brain Connections

Schizophrenia (SCH) and bipolar disorder (BD) are characterized by many types of symptoms, damaged cognitive function, and abnormal brain connections. The microstates are considered to be the cornerstones of the mental states shown in EEG data. In our study, we investigated the use of microstates as biomarkers to distinguish patients with bipolar disorder from those with schizophrenia by analyzing EEG data measured in an eyes-closed resting state. The purpose of this article is to provide an electron directional physiological explanation for the observed brain dysfunction of schizophrenia and bipolar disorder patients.Methods: We used microstate resting EEG data to explore group differences in the duration, coverage, occurrence, and transition probability of 4 microstate maps among 20 SCH patients, 26 BD patients, and 35 healthy controls (HCs).Results: Microstate analysis revealed 4 microstates (A–D) in global clustering across SCH patients, BD patients, and HCs. The samples were chosen to be matched. We found the greater presence of microstate B in BD patients, and the less presence of microstate class A and B, the greater presence of microstate class C, and less presence of D in SCH patients. Besides, a greater frequent switching between microstates A and B and between microstates B and A in BD patients than in SCH patients and HCs and less frequent switching between microstates C and D and between microstates D and C in BD patients compared with SCH patients.Conclusion: We found abnormal features of microstate A, B in BD patients and abnormal features of microstate A, B, C, and D in SCH patients. These features may indicate the potential abnormalities of SCH patients and BD patients in distributing neural resources and influencing opportune transitions between different states of activity.

scholarly journals EEG Data Quality: Determinants and Impact in a Multicenter Study of Children, Adolescents, and Adults with Attention-Deficit/Hyperactivity Disorder (ADHD)

2021 ◽  
Vol 11 (2) ◽  
pp. 214
Author(s):  
Anna Kaiser ◽  
Pascal-M. Aggensteiner ◽  
Martin Holtmann ◽  
Andreas Fallgatter ◽  
Marcel Romanos ◽  
...  
Keyword(s):  
Data Quality ◽  
Large Scale ◽  
Linear Models ◽  
Spectral Power ◽  
Control Group ◽  
Continuous Performance Task ◽  
Related Data ◽  
Eyes Closed ◽  
Eeg Data ◽  
Eyes Open

Electroencephalography (EEG) represents a widely established method for assessing altered and typically developing brain function. However, systematic studies on EEG data quality, its correlates, and consequences are scarce. To address this research gap, the current study focused on the percentage of artifact-free segments after standard EEG pre-processing as a data quality index. We analyzed participant-related and methodological influences, and validity by replicating landmark EEG effects. Further, effects of data quality on spectral power analyses beyond participant-related characteristics were explored. EEG data from a multicenter ADHD-cohort (age range 6 to 45 years), and a non-ADHD school-age control group were analyzed (ntotal = 305). Resting-state data during eyes open, and eyes closed conditions, and task-related data during a cued Continuous Performance Task (CPT) were collected. After pre-processing, general linear models, and stepwise regression models were fitted to the data. We found that EEG data quality was strongly related to demographic characteristics, but not to methodological factors. We were able to replicate maturational, task, and ADHD effects reported in the EEG literature, establishing a link with EEG-landmark effects. Furthermore, we showed that poor data quality significantly increases spectral power beyond effects of maturation and symptom severity. Taken together, the current results indicate that with a careful design and systematic quality control, informative large-scale multicenter trials characterizing neurophysiological mechanisms in neurodevelopmental disorders across the lifespan are feasible. Nevertheless, results are restricted to the limitations reported. Future work will clarify predictive value.

scholarly journals States Transitions Inference of Postpartum Depression Based on Multi-State Markov Model

2021 ◽  
Vol 18 (14) ◽  
pp. 7449
Author(s):  
Juan Xiong ◽  
Qiyu Fang ◽  
Jialing Chen ◽  
Yingxin Li ◽  
Huiyi Li ◽  
...  
Keyword(s):  
Postpartum Depression ◽  
Markov Model ◽  
Normal State ◽  
Sojourn Time ◽  
Transition Probabilities ◽  
Transition Probability ◽  
Public Health Problem ◽  
Mental States ◽  
Hazard Ratio ◽  
Multiple Time

Background: Postpartum depression (PPD) has been recognized as a severe public health problem worldwide due to its high incidence and the detrimental consequences not only for the mother but for the infant and the family. However, the pattern of natural transition trajectories of PPD has rarely been explored. Methods: In this research, a quantitative longitudinal study was conducted to explore the PPD progression process, providing information on the transition probability, hazard ratio, and the mean sojourn time in the three postnatal mental states, namely normal state, mild PPD, and severe PPD. The multi-state Markov model was built based on 912 depression status assessments in 304 Chinese primiparous women over multiple time points of six weeks postpartum, three months postpartum, and six months postpartum. Results: Among the 608 PPD status transitions from one visit to the next visit, 6.2% (38/608) showed deterioration of mental status from the level at the previous visit; while 40.0% (243/608) showed improvement at the next visit. A subject in normal state who does transition then has a probability of 49.8% of worsening to mild PPD, and 50.2% to severe PPD. A subject with mild PPD who does transition has a 20.0% chance of worsening to severe PPD. A subject with severe PPD is more likely to improve to mild PPD than developing to the normal state. On average, the sojourn time in the normal state, mild PPD, and severe PPD was 64.12, 6.29, and 9.37 weeks, respectively. Women in normal state had 6.0%, 8.5%, 8.7%, and 8.8% chances of progress to severe PPD within three months, nine months, one year, and three years, respectively. Increased all kinds of supports were associated with decreased risk of deterioration from normal state to severe PPD (hazard ratio, HR: 0.42–0.65); and increased informational supports, evaluation of support, and maternal age were associated with alleviation from severe PPD to normal state (HR: 1.46–2.27). Conclusions: The PPD state transition probabilities caused more attention and awareness about the regular PPD screening for postnatal women and the timely intervention for women with mild or severe PPD. The preventive actions on PPD should be conducted at the early stages, and three yearly; at least one yearly screening is strongly recommended. Emotional support, material support, informational support, and evaluation of support had significant positive associations with the prevention of PPD progression transitions. The derived transition probabilities and sojourn time can serve as an importance reference for health professionals to make proactive plans and target interventions for PPD.

SIMULATION AND MODELING OF EVOKED RESPONSE ELECTROENCEPHALOGRAPH SIGNAL

1999 ◽  
Vol 10 (04) ◽  
pp. 759-776
Author(s):  
D. R. KULKARNI ◽  
J. C. PARIKH ◽  
R. PRATAP
Keyword(s):  
Small Range ◽  
Eeg Signal ◽  
Simulation And Modeling ◽  
State Space Reconstruction ◽  
Eyes Closed ◽  
Normal Individuals ◽  
Eeg Data ◽  
Closed Data ◽  
Simulation Results ◽  
Low Dimensional

Electroencephalograph (EEG) data for normal individuals with eyes-closed and under stimuli is analyzed. The stimuli consisted of photo, audio, motor and mental activity. We use several measures from nonlinear dynamics to analyze and characterize the data. We find that the dynamics of the EEG signal is deterministic and chaotic but it is not a low dimensional chaotic system. The evoked responses lead to a redistribution of strengths relative to eyes-closed data. Basically, strength in α waves decreases whereas that in β wave increases. We also carried out simulations separately and in combination for δ, θ, α and β waves to understand the data. From the simulation results, it appears that the characteristics of EEG data are consequences of filtering the data with a relatively small range of frequency (0.5–32 Hz). In view of this, we believe that calculation of known nonlinear measures is not likely to be very useful for studying the dynamics of EEG data. We have also successfully modeled the EEG time series using the concept of state space reconstruction in the framework of artificial neural network. It gives us confidence that one would be able to understand, in a more basic way, how collectivity in EEG signal arises.

Thought overactivation as a marker of bipolar disorder

2016 ◽  
Vol 33 (S1) ◽  
pp. s222-s223 ◽  
Author(s):  
M. Ferrari ◽  
P. Ossola ◽  
V. Lucarini ◽  
V. Accardi ◽  
C. De Panfilis ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Mood Disorders ◽  
Subjective Experience ◽  
Small Sample Size ◽  
Mental States ◽  
Small Sample ◽  
Bipolar Spectrum ◽  
Global Functioning ◽  
Competing Interest ◽  
Bipolar Patients

IntroductionRecent studies have underlined the importance of considering the form of thoughts, beyond their content, in order to achieve a better phenomenological comprehension of mental states in mood disorders. The subjective experience of thought overactivation is an important feature of mood disorders that could help in identifying, among patients with a depressive episode, those who belong to the bipolar spectrum.ObjectivesPatients with a diagnosis of bipolar disorder (BD) were compared with matched healthy controls (HC) on a scale that evaluates thought overactivation.AimsValidate the Italian version of a scale for thought overactivation (i.e. STOQ) in a sample of bipolar patients.MethodsThirty euthymic BD and 30 HC completed the Subjective Thought Overactivation Questionnaire (STOQ), the Ruminative Responses Scale (RRS), the Beck Depression Inventory-II (BDI-II) and global functioning (VGF).ResultsThe 9-items version of the STOQ has been back translated and its internal consistency in this sample was satisfactory (alpha = .91). Both the brooding subscore of RRS (b-RRS) (r = .706; P < .001) and STOQ (r = .664; P < .001) correlate significantly with depressive symptoms whereas only the first correlate with VGF (r = –.801; P < .001). The two groups did not differed in the b-RRS (HC = 8.41 vs BD = 9.72; P = .21), whereas BD where significantly higher in the STOQ total score (HC = 6.62 vs. BD = 14.9; P = .007).ConclusionOur results, although limited by the small sample size, confirm the validity of the STOQ and suggest that this scale could grasp a feature characteristic of BD, independently from their tendency to ruminate. The latter seems to impact more on global functioning.Disclosure of interestThe authors have not supplied their declaration of competing interest.

scholarly journals BSoniq: A 3-D EEG Sound Installation

2017 ◽  
Author(s):  
Marlene Mathew ◽  
Mert Cetinkaya ◽  
Agnieszka Roginska
Keyword(s):  
Brain Activity ◽  
Spatial Location ◽  
Data Access ◽  
Mental States ◽  
Brain Wave ◽  
Sound Installation ◽  
Eeg Data ◽  
Additional Process ◽  
Event Based ◽  
Exciting Possibility

Brain Computer Interface (BCI) methods have received a lot of attention in the past several decades, owing to the exciting possibility of computer-aided communication with the outside world. Most BCIs allow users to control an external entity such as games, prosthetics, musical output etc. or are used for offline medical diagnosis processing. Most BCIs that provide neurofeedback, usually categorize the brainwaves into mental states for the user to interact with. Raw brainwave interaction by the user is not usually a feature that is readily available for a lot of popular BCIs. If there is, the user has to pay for or go through an additional process for raw brain wave data access and interaction. BSoniq is a multi-channel interactive neurofeedback installation which, allows for real-time sonification and visualization of electroencephalogram (EEG) data. This EEG data provides multivariate information about human brain activity. Here, a multivariate event-based sonification is proposed using 3D spatial location to provide cues about these particular events. With BSoniq, users can listen to the various sounds (raw brain waves) emitted from their brain or parts of their brain and perceive their own brainwave activities in a 3D spatialized surrounding giving them a sense that they are inside their own heads.

scholarly journals Prediction of Individual User’s Dynamic Ranges of EEG Features from Resting-State EEG Data for Evaluating Their Suitability for Passive Brain–Computer Interface Applications

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 988
Author(s):  
Ho-Seung Cha ◽  
Chang-Hee Han ◽  
Chang-Hwan Im
Keyword(s):  
Resting State ◽  
Brain Computer Interface ◽  
Low Cost ◽  
Mental States ◽  
Computer Interface ◽  
Practical Applications ◽  
Eeg Data ◽  
Brain States ◽  
Electroencephalogram Eeg ◽  
Eeg Features

With the recent development of low-cost wearable electroencephalogram (EEG) recording systems, passive brain–computer interface (pBCI) applications are being actively studied for a variety of application areas, such as education, entertainment, and healthcare. Various EEG features have been employed for the implementation of pBCI applications; however, it is frequently reported that some individuals have difficulty fully enjoying the pBCI applications because the dynamic ranges of their EEG features (i.e., its amplitude variability over time) were too small to be used in the practical applications. Conducting preliminary experiments to search for the individualized EEG features associated with different mental states can partly circumvent this issue; however, these time-consuming experiments were not necessary for the majority of users whose dynamic ranges of EEG features are large enough to be used for pBCI applications. In this study, we tried to predict an individual user’s dynamic ranges of the EEG features that are most widely employed for pBCI applications from resting-state EEG (RS-EEG), with the ultimate goal of identifying individuals who might need additional calibration to become suitable for the pBCI applications. We employed a machine learning-based regression model to predict the dynamic ranges of three widely used EEG features known to be associated with the brain states of valence, relaxation, and concentration. Our results showed that the dynamic ranges of EEG features could be predicted with normalized root mean squared errors of 0.2323, 0.1820, and 0.1562, respectively, demonstrating the possibility of predicting the dynamic ranges of the EEG features for pBCI applications using short resting EEG data.

Capturing the Forest but Missing the Trees: Microstates Inadequate for Characterizing Shorter-Scale EEG Dynamics

2019 ◽  
Vol 31 (11) ◽  
pp. 2177-2211 ◽  
Author(s):  
Saurabh Bhaskar Shaw ◽  
Kiret Dhindsa ◽  
James P. Reilly ◽  
Suzanna Becker
Keyword(s):  
Systems Analysis ◽  
Temporal Dynamics ◽  
Field Potential ◽  
Stable States ◽  
Dynamical Systems Analysis ◽  
Eeg Data ◽  
Show Evidence ◽  
Winner Takes All ◽  
Microstate Analysis ◽  
The Brain

The brain is known to be active even when not performing any overt cognitive tasks, and often it engages in involuntary mind wandering. This resting state has been extensively characterized in terms of fMRI-derived brain networks. However, an alternate method has recently gained popularity: EEG microstate analysis. Proponents of microstates postulate that the brain discontinuously switches between four quasi-stable states defined by specific EEG scalp topologies at peaks in the global field potential (GFP). These microstates are thought to be “atoms of thought,” involved with visual, auditory, salience, and attention processing. However, this method makes some major assumptions by excluding EEG data outside the GFP peaks and then clustering the EEG scalp topologies at the GFP peaks, assuming that only one microstate is active at any given time. This study explores the evidence surrounding these assumptions by studying the temporal dynamics of microstates and its clustering space using tools from dynamical systems analysis, fractal, and chaos theory to highlight the shortcomings in microstate analysis. The results show evidence of complex and chaotic EEG dynamics outside the GFP peaks, which is being missed by microstate analysis. Furthermore, the winner-takes-all approach of only one microstate being active at a time is found to be inadequate since the dynamic EEG scalp topology does not always resemble that of the assigned microstate, and there is competition among the different microstate classes. Finally, clustering space analysis shows that the four microstates do not cluster into four distinct and separable clusters. Taken collectively, these results show that the discontinuous description of EEG microstates is inadequate when looking at nonstationary short-scale EEG dynamics.

A Numerical Study of Information Entropy in EEG Wavelet Analysis

2016 ◽  
Author(s):  
Parham Ghorbanian ◽  
Hashem Ashrafiuon
Keyword(s):  
Information Entropy ◽  
Signal Analysis ◽  
Numerical Study ◽  
Window Size ◽  
Normal Subjects ◽  
Sample Entropy ◽  
Eyes Closed ◽  
Eeg Data ◽  
Eeg Signal Analysis ◽  
The Ideal

The purpose of this study is to numerically evaluate the performance of information entropy in electroencephalography (EEG) signal analysis. In particular, we use EEG data from an Alzheimer’s disease (AD) pilot study and apply several wavelet functions to determine the signals’ time and frequency characteristics. The wavelet entropy and wavelet sample entropy of the continuous wavelet transformed data are then determined at various scale ranges corresponding to major brain frequency bands. Non-parametric statistical analysis is then used to compare the entropy features of the EEG data obtained in trials with AD patients and age-matched healthy normal subjects under resting eyes-closed (EC) and eye-open (EO) conditions. The effectiveness and reliability of both choice of wavelet functions and the parameters used in wavelet sample entropy calculations are discussed and the ideal choices are identified. The result shows that, when applied to wavelet transformed filtered data, information entropy can be effective in determining EEG discriminant features, after selecting the best wavelet functions and window size of the sample entropy.

SOURCE RECONSTRUCTION AND SYNCHRONY MEASUREMENTS FOR REVEALING FUNCTIONAL BRAIN NETWORKS AND CLASSIFYING MENTAL STATES

2010 ◽  
Vol 20 (06) ◽  
pp. 1703-1721 ◽  
Author(s):  
FRANÇOIS LAURENT ◽  
MICHEL BESSERVE ◽  
LINE GARNERO ◽  
MATTHIEU PHILIPPE ◽  
GENEVIÈVE FLORENCE ◽  
...  
Keyword(s):  
Phase Locking ◽  
Brain Networks ◽  
Mental States ◽  
Source Reconstruction ◽  
Functional Brain ◽  
High Dimension Data ◽  
Eeg Data ◽  
Functional Brain Networks ◽  
Phase Locking Value ◽  
The Relationship

We classified performance-related mental states from EEG-derived measurements. We investigated the usefulness of massively distributed source reconstruction, comparing scalp and cortical scales. This approach provides a more detailed picture of the functional brain networks underlying the changes related to the mental state of interest. Local and distant synchrony measurements (coherence, phase locking value) were used for both scalp measurements and cortical current density sources, and were fed into a SVM-based classifier. We designed two simulations where classification scores increased when our 60-electrode scalp measurements were reconstructed on 60 sources and on a 500-source cortex. Source reconstruction appeared to be most useful in these simulations, in particular, when distant synchronies were involved and local synchronies did not prevail. Despite the simplicity of the model used, certain flaws in accuracy were observed in the localization of informative activities, due to the relationship between amplitude and phase for mixed signals. Our results with real EEG data suggested that the phenomenon of interest was characterized merely by modulations in local amplitudes, but also in strength of distant couplings. After source reconstruction, classification rates also increased for real EEG data when seeking distant phase-related couplings. When reconstructing a large number of sources, the regularization coefficient should be carefully selected on a subject-by-subject basis. We showed that training classifiers using such high-dimension data is useful for localizing discriminating patterns of activity.

Social Cognition in Bipolar Illness

2009 ◽  
Vol 24 (S1) ◽  
pp. 1-1
Author(s):  
G. Lahera
Keyword(s):  
Bipolar Disorder ◽  
Executive Function ◽  
Social Network ◽  
Social Cognition ◽  
Cognitive Deficits ◽  
Mental States ◽  
Control Group ◽  
Cognitive Mechanisms ◽  
Bipolar Illness ◽  
Multiple Factors

Theory of Mind (ToM) is defined as the cognitive ability to infer mental states to oneself and to others, in terms of thought, emotion and intention. There are many studies about ToM in schizophrenia, but a paucity of them about ToM in bipolar disorder, despite the suggesting relationship between ToM and emotions. Some affective patients were included as control group in schizophrenia studies, but these samples were small and heterogeneous. Some authors have found ToM deficit in manic and depressed patients, but there is also some evidence of a ToM deficit even in a state of euthymia, associated to other cognitive deficits, mainly in executive function. Multiple factors could be involved in this ToM deficit, but these studies open the way for a line of research about the cognitive mechanisms underlying the psychosocial disadjustment that these patients present. Mentalization skills could be more decisive for keeping a job or a social network than other neurocognitive variables, and BD remains a very important cause of psychosocial disadvantage. In this workshop we will debate the relevance of these findings in BD and the potential therapeutic consecuences.

Sign in / Sign up

Export Citation Format

Share Document