Bayesian estimation of group event-related potential components (BEGEP): testing a model for synthetic and real datasets.

Objective.The spatial resolution of event-related potentials (ERPs) recorded on the head surface is quite low, since the sensors located on the scalp register mixtures of signals from several cortical sources. Bayesian models for multi-channel ERPs obtained from a group of subjects under multiple task conditions can aid in recovering signals from these sources.Approach.This study introduces a novel model that captures several important characteristics of ERP, including person-to-person variability in the magnitude and latency of source signals. Furthermore, the model takes into account that ERP noise, the main source of which is the background electroencephalogram, has the following properties: it is spatially correlated, spatially heterogeneous, and varies over time and from person to person. Bayesian inference algorithms have been developed to estimate the parameters of this model, and their performance has been evaluated through extensive experiments using synthetic data and real ERPs records in a large number of subjects (N= 351).Main results.The signal estimates obtained using these algorithms were compared with the results of the analysis of ERPs by conventional methods. This comparison showed that the use of this model is suitable for the analysis of ERPs and helps to reveal some features of source signals that are difficult to observe in their mixture signals recorded on the scalp.Significance.This study shown that the proposed method is a potentially useful tool for analyzing ERPs collected from groups of subjects in various cognitive neuroscience experiments.

The P300 wave is decomposed into components reflecting response selection and automatic reactivation of stimulus-response links.

The parietal P300 wave of event-related potentials (ERPs) has been associated with various psychological operations in numerous laboratory tasks. This study aims to decompose the P3 wave of ERPs into subcomponents and link them with behavioral parameters, such as the strength of stimulus-response (S-R) links and GO/NOGO responses. EEGs (31 channels), referenced to linked ears, were recorded from 172 healthy adults (107 women) who participated in two cued GO/NOGO tasks, where the strength of S-R links was manipulated through instructions. P300 waves were observed in active conditions in response to cues, GO/NOGO stimuli, and in passive conditions when no manual response was required. Utilizing a combination of current source density transformation and blind source separation methods, we decomposed the P300 wave into two distinct components, purportedly originating from different parts of the parietal lobules. The amplitude of the parietal midline component (with current sources around Pz) closely mirrored the strength of the S-R link across proactive, reactive, and passive conditions. The amplitude of the lateral parietal component (with current sources around P3 and P4) resembled the push-pull activity of the output nuclei of the basal ganglia in action selection-inhibition operations. These findings provide insights into the neural mechanisms underlying action selection processes and the reactivation of S-R links.

Second Order Blind Identification of Event Related Potentials Sources.

Event-related potentials (ERPs) recorded on the surface of the head are a mixture of signals from many sources in the brain due to volume conductions. As a result, the spatial resolution of the ERPs is quite low. Blind source separation can help to recover source signals from multichannel ERP records. In this study, we present a novel implementation of a method for decomposing multi-channel ERP into components, which is based on the modeling of second-order statistics of ERPs. We also report a new implementation of Bayesian Information Criteria (BIC), which is used to select the optimal number of hidden signals (components) in the original ERPs. We tested these methods using both synthetic datasets and real ERPs data arrays. Testing has shown that the ERP decomposition method can reconstruct the source signals from their mixture with acceptable accuracy even when these signals overlap significantly in time and the presence of noise. The use of BIC allows us to determine the correct number of source signals at the signal-to-noise ratio commonly observed in ERP studies. The proposed approach was compared with conventionally used methods for the analysis of ERPs. It turned out that the use of this new method makes it possible to observe such phenomena that are hidden by other signals in the original ERPs. The proposed method for decomposing a multichannel ERP into components can be useful for studying cognitive processes in laboratory settings, as well as in clinical studies.

Event-related EEG synchronization and desynchronization in patients with obsessive-compulsive disorder.

Symptoms in patients with obsessive-compulsive disorder (OCD) are associated with impairment in cognitive control, attention, and action inhibition. We investigated OCD group differences relative to healthy subjects in terms of event-related alpha and beta range synchronization (ERS) and desynchronization (ERD) during a visually cued Go/NoGo task. Subjects were 62 OCD patients and 296 healthy controls (HC). The OCD group in comparison with HC, showed a changed value of alpha/beta oscillatory power over the central cortex, in particular, an increase in the alpha/beta ERD over the central-parietal cortex during the interstimulus interval (Cue condition) as well as changes in the postmovement beta synchronization topography and frequency. Over the frontal cortex, the OCD group showed an increase in magnitude of the beta ERS in NoGo condition. Within the parietal-occipital ERS/ERD modulations, the OCD group showed an increase in the alpha/beta ERD over the parietal cortex after the presentation of the visual stimuli as well as a decrease in the beta ERD over the occipital cortex after the presentation of the Cue and Go stimuli. The specific properties in the ERS/ERD patterns observed in the OCD group may reflect high involvement of the frontal and central cortex in action preparation and action inhibition processes and, possibly, in maintaining the motor program, which might be a result of the dysfunction of the cortico-striato-thalamo-cortical circuits involving prefrontal cortex. The data about enhanced involvement of the parietal cortex in the evaluation of the visual stimuli are in line with the assumption about overfocused attention in OCD.

Latent ERP components of cognitive dysfunctions in ADHD and schizophrenia.

OBJECTIVE: The main goal was to assess common and specific deficits of cognitive control in (Attention Deficit Hyperactivity Disorder) ADHD and schizophrenia (SZ) using event-related potentials (ERPs). METHOD: Behavioral and EEG data in cued GO/NOGO task were recorded in 132 healthy controls (HC) and age, gender and education matched 63 ADHD adults, and 68 SZ patients. RESULTS: N2d wave in NOGO-GO contrast of ERPs did not differ between the groups while the P3d wave discriminated SZ group from two other groups. Latent components of ERPs were extracted by blind source separation method based on second-order statistics Kropotov et al. (2017) and compared between the groups. A counterpart of N2d wave of a frontally distributed latent component was smaller in SZ indicating a specific frontal dysfunction of conflict detection in SZ. Two centrally distributed P3 sub-components were reduced in both groups indicating a non-specific dysfunction of action inhibition operations in ADHD and SZ. CONCLUSION: A pattern of specific and common dysfunctions in terms of latent ERP components shows a more complex picture of functional impairment in schizophrenia and ADHD in comparison to conventional N2/P3 ERP description. SIGNIFICANCE: The latent component approach shows a functionally different pattern of cognitive control impairment in comparison to the conventional ERP analysis.

Specificity of spontaneous EEG associated with different levels of cognitive and communicative dysfunctions in children.

This study aimed to reveal electrophysiological markers of communicative and cognitive dysfunctions of different severity in children with autism spectrum disorder (ASD). Eyes-opened electroencephalograms (EEGs) of 42 children with ASD, divided into two groups according to the severity of their communicative and cognitive dysfunctions (24 with severe and 18 children with less severe ASD), and 70 age-matched controls aged 4-9 years were examined by means of spectral and group independent component (gIC) analyses. A predominance of theta and beta EEG activity in both groups of children with ASD compared to the activity in the control group was found in the global gIC together with a predominance of beta EEG activity in the right occipital region. The quantity of local gICs with enhanced slow and high-frequency EEG activity (within the frontal, temporal, and parietal cortex areas) in children 4-9 years of age might be considered a marker of cognitive and communicative dysfunction severity.

Functional indexes of reactive cognitive control: ERPs in cued go/no-go tasks.

We aimed to determine the functional meaning of latent (hidden) components decomposed from ERPs, in the context of a go/no-go paradigm. To accomplish this, we used a new group blind source separation method, based on joint diagonalization of covariance matrices of ERPs. Four variants of a frequently used go/no-go paradigm were designed, in which operations of reactive cognitive control, such as conflict detection and action inhibition, were independently manipulated. The results showed that a latent component, generated in the anterior cingulate cortex, induced N2/P3 fluctuation only in conditions in which the prepotent model was violated, and thus can be associated with conflict detection operations. In contrast, the two latent components generated in the vicinity of the central sulcus induced P3-like fluctuations in conditions in which the prepared action was suppressed, and thus can be associated with action inhibition operations. The advantages and limitations of the new blind source separation method in relation to ERP research are also discussed.

Local and Widely Distributed EEG Activity in Schizophrenia With Prevalence of Negative Symptoms.

We evaluated EEG frequency abnormalities in resting state (eyes closed and eyes open) EEG in a group of chronic schizophrenia patients as compared with healthy subjects. The study included 3 methods of analysis of deviation of EEG characteristics: genuine EEG, current source density (CSD), and group independent component (gIC). All 3 methods have shown that the EEG in schizophrenia patients is characterized by enhanced low-frequency (delta and theta) and high-frequency (beta) activity in comparison with the control group. However, the spatial pattern of differences was dependent on the type of method used. Comparative analysis has shown that increased EEG power in schizophrenia patients apparently concerns both widely spatially distributed components and local components of signal. Furthermore, the observed differences in the delta and theta range can be described mainly by the local components, and those in the beta range mostly by spatially widely distributed ones. The possible nature of the widely distributed activity is discussed.

Differentiation of neuronal operations in latent components of event-related potentials in delayed match-to-sample tasks.

The goal of this study was to decompose ERPs into latent components associated with hypothetical processes of category discrimination, comparison to working memory and action-related operations. In five variants of the delayed match-to-sample s1-s2 task, instructions were varied for manipulation of the processes. The blind source separation was applied to the collection of ERPs. The category discrimination operation is attributed to three latent components with peak latencies of 130 to 170 ms, which are generated in different parts of the prestriate cortex. The comparison to working memory operation is attributed to a latent component that is generated in the temporal cortex and manifested in a positive deflection with a peak latency of 250 ms after s2. The category discrimination and comparison to working memory effects were dissociated spatially and temporally from attention and action selection effects.

Group Independent Component Analysis (gICA) and Current Source Density (CSD) in the study of EEG in ADHD adults.

OBJECTIVE: To investigate the performance of the spectral analysis of resting EEG, Current Source Density (CSD) and group independent components (gIC) in diagnosing ADHD adults. METHODS: Power spectra of resting EEG, CSD and gIC (19 channels, linked ears reference, eyes open/closed) from 96 ADHD and 376 healthy adults were compared between eyes open and eyes closed conditions, and between groups of subjects. RESULTS: Pattern of differences in gIC and CSD spectral power between conditions was approximately similar, whereas it was more widely spatially distributed for EEG. Size effect (Cohen's d) of differences in gIC and CSD spectral power between groups of subjects was considerably greater than in the case of EEG. Significant reduction of gIC and CSD spectral power depending on conditions was found in ADHD patients. Reducing power in a wide frequency range in the fronto-central areas is a common phenomenon regardless of whether the eyes were open or closed. CONCLUSIONS: Spectral power of local EEG activity isolated by gICA or CSD in the fronto-central areas may be a suitable marker for discrimination of ADHD and healthy adults. SIGNIFICANCE: Spectral analysis of gIC and CSD provides better sensitivity to discriminate ADHD and healthy adults.

Discriminating between ADHD adults and controls using independent ERP components and a support vector machine: a validation study.

BACKGROUND: There are numerous event-related potential (ERP) studies in relation to attention-deficit hyperactivity disorder (ADHD), and a substantial number of ERP correlates of the disorder have been identified. However, most of the studies are limited to group differences in children. Independent component analysis (ICA) separates a set of mixed event-related potentials into a corresponding set of statistically independent source signals, which are likely to represent different functional processes. Using a support vector machine (SVM), a classification method originating from machine learning, this study aimed at investigating the use of such independent ERP components in differentiating adult ADHD patients from non-clinical controls by selecting a most informative feature set. A second aim was to validate the predictive power of the SVM classifier by means of an independent ADHD sample recruited at a different laboratory. METHODS: Two groups of age-matched adults (75 ADHD, 75 controls) performed a visual two stimulus go/no-go task. ERP responses were decomposed into independent components, and a selected set of independent ERP component features was used for SVM classification. RESULTS: Using a 10-fold cross-validation approach, classification accuracy was 91%. Predictive power of the SVM classifier was verified on the basis of the independent ADHD sample (17 ADHD patients), resulting in a classification accuracy of 94%. The latency and amplitude measures which in combination differentiated best between ADHD patients and non-clinical subjects primarily originated from independent components associated with inhibitory and other executive operations. CONCLUSIONS: This study shows that ERPs can substantially contribute to the diagnosis of ADHD when combined with up-to-date methods.

Dissociating action inhibition, conflict monitoring and sensory mismatch into independent components of event related potentials in GO/NOGO task.

The anterior N2 and P3 waves of event related potentials (ERPs) in the GO/NOGO paradigm in trials related to preparatory set violations in previous studies were inconsistently associated either with action inhibition or conflict monitoring operations. In the present study a paired stimulus GO/NOGO design was used in order to experimentally control the preparatory sets. Three variants of the same stimulus task manipulated sensory mismatch, action inhibition and conflict monitoring operations by varying stimulus-response associations. The anterior N2 and P3 waves were decomposed into components by means of independent component analysis (ICA). The ICA was performed on collection of 114 individual ERPs in the three experimental conditions. Three of the independent components were selectively affected by the task manipulations indicating association of these components with sensory mismatch, action inhibition and conflict monitoring operations. According to sLORETA the sensory mismatch component was generated in the left and right temporal areas, the action suppression component was generated in the supplementary motor cortex, and the conflict monitoring component was generated in the anterior cingulate cortex.

Classification of ADHD patients on the basis of independent ERP components using a machine learning system.

BACKGROUND: In the context of sensory and cognitive-processing deficits in ADHD patients, there is considerable evidence of altered event related potentials (ERP). Most of the studies, however, were done on ADHD children. Using the independent component analysis (ICA) method, ERPs can be decomposed into functionally different components. Using the classification method of support vector machine, this study investigated whether features of independent ERP components can be used for discrimination of ADHD adults from healthy subjects. METHODS: Two groups of age- and sex-matched adults (74 ADHD, 74 controls) performed a visual two stimulus GO/NOGO task. ERP responses were decomposed into independent components by means of ICA. A feature selection algorithm defined a set of independent component features which was entered into a support vector machine. RESULTS: The feature set consisted of five latency measures in specific time windows, which were collected from four different independent components. The independent components involved were a novelty component, a sensory related and two executive function related components. Using a 10-fold cross-validation approach, classification accuracy was 92%. CONCLUSIONS: This study was a first attempt to classify ADHD adults by means of support vector machine which indicates that classification by means of non-linear methods is feasible in the context of clinical groups. Further, independent ERP components have been shown to provide features that can be used for characterizing clinical populations.

Independent component approach to the analysis of EEG recordings at early stages of depressive disorders.

OBJECTIVE: A modern approach for blind source separation of electrical activity represented by Independent Components Analysis (ICA) was used for QEEG analysis in depression. METHODS: The spectral characteristics of the resting EEG in 111 adults in the early stages of depression and 526 non-depressed subjects were compared between groups of patients and healthy controls using a combination of ICA and sLORETA methods. RESULTS: Comparison of the power of independent components in depressed patients and healthy controls have revealed significant differences between groups for three frequency bands: theta (4-7.5Hz), alpha (7.5-14Hz), and beta (14-20Hz) both in Eyes closed and Eyes open conditions. An increase in slow (theta and alpha) activity in depressed patients at parietal and occipital sites may reflect a decreased cortical activation in these brain regions, and a diffuse enhancement of beta power may correlate with anxiety symptoms playing an important role on the onset of depressive disorder. CONCLUSIONS: ICA approach used in the present study allowed us to localize the EEG spectra differences between the two groups. SIGNIFICANCE: A relatively rare approach which uses the ICA spectra for comparison of the quantitative parameters of EEG in different groups of patients/subjects allows to improve an accuracy of measurement.

EEG power spectra at early stages of depressive disorders.

In previous quantitative EEG studies of depression, mostly patients with a lifetime history of depressive disorders were reported. This study examined quantitative EEG parameters obtained in the early stages of depression in comparison with age-matched healthy controls. EEG was recorded using two different montages in eyes closed and eyes open resting states. A significant increase in spectrum power in theta (4-7.5 Hz), alpha (7.5-14 Hz), and beta (14-20 Hz) frequency bands was found in depressed patients at parietal and occipital sites, both in eyes closed and eyes open conditions. These results suggest that an increase in slow (theta and alpha) activity in the EEG pattern may reflect a decreased cortical activation in these brain regions. Enhancement of beta power may correlate with anxiety symptoms that most likely play an important role on the onset of depressive disorder.

Decomposing N2 NOGO wave of event-related potentials into independent components.

Inconsistencies in previous attempts to localize the N2 wave in the GO/NOGO task led to the present investigation. The inconsistencies were probably because of heterogeneity of psychological operations involved in GO/NOGO tasks. We applied the independent component analysis to a collection of individual event-related potentials in response to GO and NOGO cues in the two stimulus visual GO/NOGO task. The selected six independent components with different topographies and time courses constituted 87% of the artifact-free signal variance. Three of them were loaded into the frontally distributed N2 wave. According to standardized low-resolution electromagnetic tomography these three independent components were generated in the supplementary motor cortex, left angular gyrus and anterior cingulate cortex.

ERPs correlates of EEG relative beta training in ADHD children.

Eighty-six children (ages 9-14) with attention deficit hyperactivity disorder (ADHD) participated in this study. Event-related potentials (ERPs) were recorded in auditory GO/NOGO task before and after 15-22 sessions of EEG biofeedback. Each session consisted of 20 min of enhancing the ratio of the EEG power in 15-18 Hz band to the EEG power in the rest of spectrum, and 7-10 min of enhancing of the ratio of the EEG power in 12-15 Hz to the EEG power in the rest of spectrum with C3-Fz electrodes' placements for the first protocol and C4-Pz for the second protocol. On the basis of quality of performance during training sessions, the patients were divided into two groups: good performers and bad performers. ERPs of good performers to GO and NOGO cues gained positive components evoked within 180-420 ms latency. At the same time, no statistically significant differences between pre- and post-training ERPs were observed for bad performers. The ERP differences between post- and pretreatment conditions for good performers were distributed over frontal-central areas and appear to reflect an activation of frontal cortical areas associated with beta training.