Individuals with schizophrenia have heterogeneous impairments of the auditory processing system that likely mediate differences in the cognitive gains induced by auditory training (AT). Mismatch negativity (MMN) is an event-related potential component reflecting auditory echoic memory, and its amplitude reduction in schizophrenia has been linked to cognitive deficits. Therefore, MMN may predict response to AT and identify individuals with schizophrenia who have the most to gain from AT. Furthermore, to the extent that AT strengthens auditory deviance processing, MMN may also serve as a readout of the underlying changes in the auditory system induced by AT.
Although autism and epilepsy are considered to be different disorders, epileptiform EEG activity is common in people with autism even when overt seizures are not present. The relatively high comorbidity between autism and all epilepsy syndromes suggests the possibility of common underlying neurophysiological mechanisms. Although many different epilepsies may be comorbid with autism, absence epilepsy is a generalized epilepsy syndrome with seizures that appear as staring spells, with no motor signs and no focal lesions, making it more difficult to diagnose. Application of nonlinear methods for EEG signal analysis may enable characterization of brain activity that can help to delineate neurophysiological commonalities and differences between autism and epilepsy. Multiscale entropy and recurrence quantitative analysis (RQA) were computed from EEG signals derived from children with autism or absence epilepsy and compared with the goal of finding significant and potentially clinically useful biomarkers neurophysiological differences between these two childhood disorders.
Shiritori is a traditional Japanese game based on word generation to letters (WGL). This game is familiar to most Japanese people, young and old. Previous functional neuroimaging studies using magnetoencephalography and functional magnetic resonance imaging suggested that compared to conventional WGL, Shiritori could activate broader cortical areas, including the frontal lobe and the superior temporal cortex in healthy subjects. These studies were conducted under restricted experimental conditions without overt speech, that prevented the subjects from performing naturally the Shiritori task. The present study aims to determine cortical activation patterns in healthy subjects during the Shiritori task with overt speech.
BACKGROUND:With millisecond-level resolution, electroencephalographic (EEG) recording provides a sensitive tool to assay neural dynamics of human cognition. However, selection of EEG features used to answer experimental questions is typically determined a priori. The utility of machine learning was investigated as a computational framework for extracting the most relevant features from EEG data empirically.METHODS:Schizophrenia (SZ; n = 40) and healthy community (HC; n = 12) subjects completed a Sternberg Working Memory Task (SWMT) during EEG recording. EEG was analyzed to extract 5 frequency components (theta1, theta2, alpha, beta, gamma) at 4 processing stages (baseline, encoding, retention, retrieval) and 3 scalp sites (frontal-Fz, central-Cz, occipital-Oz) separately for correctly and incorrectly answered trials. The 1-norm support vector machine (SVM) method was used to build EEG classifiers of SWMT trial accuracy (correct vs. incorrect; Model 1) and diagnosis (HC vs. SZ; Model 2). External validity of SVM models was examined in relation to neuropsychological test performance and diagnostic classification using conventional regression-based analyses.RESULTS:SWMT performance was significantly reduced in SZ (p < .001). Model 1 correctly classified trial accuracy at 84 % in HC, and at 74 % when cross-validated in SZ data. Frontal gamma at encoding and central theta at retention provided highest weightings, accounting for 76 % of variance in SWMT scores and 42 % variance in neuropsychological test performance across samples. Model 2 identified frontal theta at baseline and frontal alpha during retrieval as primary classifiers of diagnosis, providing 87 % classification accuracy as a discriminant function.CONCLUSIONS:EEG features derived by SVM are consistent with literature reports of gamma's role in memory encoding, engagement of theta during memory retention, and elevated resting low-frequency activity in schizophrenia. Tests of model performance and cross-validation support the stability and generalizability of results, and utility of SVM as an analytic approach for EEG feature selection.
Brian connectivity describes the networks of functional and anatomical connections across the brain. The functional network communications across the brain networks dependent on neuronal oscillations. Detection of the synchronous activation of neurons can be used to determine the wellbeing or integrity of the functional connectivity in the human brain networks. Well-connected highly synchronous functional activity can be measured by Electroencephalography (EEG) or Magnetoencephalography (MEG) and then analyzed with several types of mathematical algorithms. Coherence is one mathematical method that can be used to determine if two or more sensors, or brain regions, have similar neuronal oscillatory activity with each other. Since the 1960’s, coherence has generally been assessed on the similarity of the frequency content across EEG sensors. Recently coherence, after it has been imaged in the brain, has been used to assess how coherent or connected specific locations in the brain are networked together in several different neurological disorders. Statistical analysis can then be performed on the coherence results to verify evidence of normal or abnormal network activity in a patient. In this review we highlight how functional brain connectivity is assessed in Source space using coherence technique measured by MEG.
Working memory (WM) deficits have been repeatedly observed in people with schizophrenia (PSZ) and their unaffected biological relatives (REL). Given the apparent association with genetic liability for schizophrenia, WM deficits have been proposed as a potential endophenotype for the disorder. Abnormal neural responses during WM performance have likewise been observed in PSZ and REL and may reflect the expression of genetic liability for schizophrenia in brain function. Relatively recent investigations have examined the role of neural oscillatory activity during visuospatial WM function in healthy people, as well as dysfunction in psychopathology. This research was in part motivated by a neural model of WM proposed by Lisman and Idiart (Science 267:1512–1515, 1995) that delineated a mechanism for representing multiple stimuli within WM through systematic interactions between neural oscillations in the theta- and gamma- frequency ranges. Aberrant oscillatory activity in theta and gamma frequency ranges has since been proposed as a potential underlying factor in WM dysfunction in PSZ and REL. The experimental evidence derived from studies of healthy people that pertains to the theta-gamma model of WM is reviewed herein. Although scarce, direct examinations of theta-gamma interactions in PSZ and REL are likewise reviewed in addition to reports of separate deficits in theta and gamma frequencies observed in PSZ during WM. The implications of theta and gamma oscillatory deficits reported in PSZ are discussed in the context of the Lisman and Idiart (Science 267:1512–1515, 1995) model, as well as how these deficits may result in aberrant theta-gamma interactions that give rise to visuospatial WM dysfunction. Given evidence supporting the Lisman and Idiart (Science 267:1512–1515, 1995) model of theta-gamma interactions in WM and the lack of direct exploration of the model in schizophrenia, there is an imperative to carry out formal testing of theta-gamma interactions in PSZ and REL during WM.
The pathophysiology of schizophrenia may fundamentally involve a disturbance in the interaction between neuronal groups that leads to impaired communication within and between brain areas. Such a “dysconnectivity syndrome” could underlie the pronounced cognitive deficits as well as the emergence of psychosis. Evidence for this hypothesis has mainly come from functional magnetic resonance imaging (fMRI) studies which have investigated connectivity anomalies during spontaneous as well as task-related activity. In the current review, we will summarize evidence from electro- and magnetoencephalography (EEG/MEG) which have employed measures, such as coherence, phase-locking and mutual information, that have tested changes in functional connectivity during task-related as well as resting-state time-frequency data. We will highlight the methodological issues associated with these measures as well as provide recommendations for future research.
It has been reported that brain stimulation such as repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) can modulate a variety of cognitions and emotions in humans. rTMS and tDCS studies provide strong possibilities of applications in the manipulation of emotion regulation and decision-making in humans.
We report on a biological artifact that is unknown in the EEG-literature, presumably due to its invisibility in the unfiltered raw EEG. On account of its probable electrogenesis, we discuss Arterial Pulse Impedance Artifact (APIA). One may also call it a virtual artifact because it only becomes manifest as the averaged mean value of power spectra from consecutive 2s epochs across a total length of 5 min (150 epochs).
Measures of the coherence of electroencephalography (EEG) time-series recorded at spatially distant points on the scalp are often used by researchers to characterize the dynamic interactions of brain regions. In dense-array EEG recordings, one or more electrode signals often contain prominent artifact necessitating replacement of the recorded data with an estimated signal using interpolation from data in valid recordings from the surrounding electrodes. Typically the signal estimation is carried out using spherical spline interpolation (SSI; Perrin et al., 1989); however, it is shown that this can introduce an erroneous increase in coherence between signals because the estimated signal is derived from elements in common recordings from other electrode sites. Although SSI performance depends on three SSI parameters, including interpolation order m, the Legendre polynomial order n, and regularization parameter λ, clear guidelines on how to optimally choose parameters have yet to be established for ensuring the temporal features of interpolated signals are accurate.
In 2009 the United States National Institute of Mental Health (NIMH) introduced the Research Domain Criteria (RDoC) project, which intends to explicate fundamental bio-behavioral dimensions that cut across heterogeneous disorder categories in psychiatry. One major research domain is defined by arousal and regulatory systems.
According to recent findings cognitive and emotional dysregulation in depression and psychiatric disorders may be related to deficits in inhibitory functions and temporo-limbic seizure-like activity due to specific changes in neural dynamics and complexity of neural networks.
Autism Spectrum Disorders (ASD) are an etiologically and clinically heterogeneous group of neurodevelopmental disorders. The pathophysiology of ASD remains largely unknown. One essential and well-documented observation is high comorbidity between ASD and epilepsy. Electroencephalography (EEG) is the most widely used tool to detect epileptic brain activity. The EEG signal is characterized by a high temporal resolution (on the order of milliseconds) allowing for precise temporal examination of cortical activity. This review addresses the main EEG findings derived from both the standard or qualitative (visually inspected) EEG and the quantitative (computer analyzed) EEG during resting state in individuals with ASD. The bulk of the evidence supports significant connectivity disturbances in ASD that are possibly widespread with two specific aspects: over-connectivity in the local networks and under-connectivity in the long-distance networks. Furthermore, the review suggested that disruptions appear more severe in later developing parts of the brain (e.g., prefrontal cortex). Based on available information, from both the qualitative and quantitative EEG literature, we postulate a preliminary hypothesis that increased cortical excitability may contribute to the significant overlap between ASD and epilepsy and may be contributing to the connectivity deviations noted. As the presence of a focal epileptic discharge is a clear indication of such hyperexcitability, we conclude that the presence of epileptic discharges is a potential biomarker at least for a subgroup of ASD.
Different levels of brain arousal can be delineated not only during sleep but also during wakefulness. Electroencephalography (EEG) is the gold standard to assess different levels of brain arousal. A novel EEG- and electrooculography (EOG)-based tool, the Vigilance Algorithm Leipzig (VIGALL 2.0), allows determining the level of EEG-vigilance (indicating brain arousal). Considering the frequency patterns and LORETA-based cortical distribution of electroencephalic activity, VIGALL 2.0 automatically attributes one out of seven vigilance stages to each EEG segment (1-sec EEG segments by default), ranging from high alertness (stage 0), to relaxed wakefulness (stage A1 to A3), to drowsiness (stage B1 to B2/3) up to sleep onset (stage C). Building on the time series of these seven vigilance stages across 20 min, two parameterizations of the temporal dynamic (brain arousal regulation) are calculated: the lability score and the slope index.
