Extracting Stress-Related EEG Patterns From Pre-Sleep EEG for Forecasting Slow-Wave Sleep Deficiency.

Sleep is vital to our daily activity. Lack of proper sleep can impair functionality and overall health. While stress is known for its detrimental impact on sleep quality, the precise effect of pre-sleep stress on subsequent sleep structure remains unknown. This study introduced a novel approach to study the pre-sleep stress effect on sleep structure, specifically slow-wave sleep (SWS) deficiency. To achieve this, we selected forehead resting EEG immediately before and upon sleep onset to extract stress-related neurological markers through power spectra and entropy analysis. These markers include beta/delta correlation, alpha asymmetry, fuzzy entropy (FuzzEn) and spectral entropy (SpEn). Fifteen subjects were included in this study. Our results showed that subjects lacking SWS often exhibited signs of stress in EEG, such as an increased beta/delta correlation, higher alpha asymmetry, and increased FuzzEn in frontal EEG. Conversely, individuals with ample SWS displayed a weak beta/delta correlation and reduced FuzzEn. Finally, we employed several supervised learning models and found that the selected neurological markers can predict subsequent SWS deficiency. Our investigation demonstrated that the classifiers could effectively predict varying levels of slow-wave sleep (SWS) from pre-sleep EEG segments, achieving a mean balanced accuracy surpassing 0.75. The SMOTE-Tomek resampling method could improve the performance to 0.77. This study suggests that stress-related neurological markers derived from pre-sleep EEG can effectively predict SWS deficiency. Such information can be integrated with existing sleep-improving techniques to provide a personalized sleep forecasting and improvement solution.

Triggers and Characteristics of Brain Zaps According to the Findings of an Internet Questionnaire.

Objective: To build on the findings of our prior study of spontaneous posts made about brain zaps on a popular lay mental health website using questions specifically targeting brain zaps in a web-based questionnaire.Methods: 3,141 responses were examined from an online questionnaire made available between June 2016 and February 2018. The questions probed the specifics of the medications taken, the temporal characteristics of medication taken, the symptoms associated with the brain zaps, the specifics of the "zap" experience itself, and their effect on quality of life. Special attention was paid to gathering data regarding the triggers of brain zaps, because in our previous study, eye movements triggering brain zaps emerged as an unexpected finding. As this was a convenience sample, qualitative analysis was primarily performed, except regarding the interaction between the half-life of antidepressants and the time to the onset of the first brain zaps, for which the numerical data appeared to be specific enough to allow such analyses.Results: The data from the targeted questionnaire showed a pattern of responses that was very similar to that obtained from analysis of the spontaneous posts. These data include the types of medications taken, the length of time these medications were taken before the onset of the zaps, the length of the zaps, the feeling quality of the zaps, and the effect of gradual versus sudden discontinuation on their onset and presence. Lateral eye movement as a trigger emerged with even more clarity than in the previous study. The positive correlation between the time from onset of the brain zaps and the half-life of the drugs strongly suggests that brain zaps are indeed associated with antidepressant discontinuation.Conclusions: Brain zaps remain a barely examined and poorly understood symptom of antidepressant discontinuation. Further studies are needed from both a prevention and treatment perspective. There is now an even stronger indication that brain zaps are typically triggered by lateral eye movements, which may open avenues for investigating this process.

Unsupervised learning of brain state dynamics during emotion imagination using high-density EEG.

This study applies adaptive mixture independent component analysis (AMICA) to learn a set of ICA models, each optimized by fitting a distributional model for each identified component process while maximizing component process independence within some subsets of time points of a multi-channel EEG dataset. Here, we applied 20-model AMICA decomposition to long-duration (1-2 h), high-density (128-channel) EEG data recorded while participants used guided imagination to imagine situations stimulating the experience of 15 specified emotions. These decompositions tended to return models identifying spatiotemporal EEG patterns or states within single emotion imagination periods. Model probability transitions reflected time-courses of EEG dynamics during emotion imagination, which varied across emotions. Transitions between models accounting for imagined "grief" and "happiness" were more abrupt and better aligned with participant reports, while transitions for imagined "contentment" extended into adjoining "relaxation" periods. The spatial distributions of brain-localizable independent component processes (ICs) were more similar within participants (across emotions) than emotions (across participants). Across participants, brain regions with differences in IC spatial distributions (i.e., dipole density) between emotion imagination versus relaxation were identified in or near the left rostrolateral prefrontal, posterior cingulate cortex, right insula, bilateral sensorimotor, premotor, and associative visual cortex. No difference in dipole density was found between positive versus negative emotions. AMICA models of changes in high-density EEG dynamics may allow data-driven insights into brain dynamics during emotional experience, possibly enabling the improved performance of EEG-based emotion decoding and advancing our understanding of emotion.

Amount of < 1Hz deep sleep correlates with melatonin dose in military veterans with PTSD.

Military veterans with posttraumatic stress disorder often complain of non-restful sleep, which further exacerbates their symptoms. Our previous study showed a deficit in Lo Deep sleep, or slow oscillations, in the PTSD population compared to healthy control sleepers. Because Lo Deep sleep is likely a stage when the brain eliminates protein debris, it is critical to find the cause and effective therapeutics to reverse Lo Deep deficiency. The current study aims to replicate and extend this finding by examining several physiological and medication factors that may be responsible for the Lo Deep deficiency. We recorded overnight sleep electroencephalogram (EEG) via a 2-channel headband device on 69 veterans in a residential treatment facility. Dried urine samples were collected at 4 time points during one day to measure melatonin, cortisol, norepinephrine and other factors. EEG data were transformed into frequency power and submitted to an automated sleep scoring algorithm. The scoring corresponds to clear spectral patterns in the overnight spectrogram but does not align exactly with traditional visual scoring stages. As expected, veterans showed decreased Lo Deep (activity < 1 Hz) and more Hi Deep sleep (1-3 Hz activity) than healthy controls, replicating our previous study. Multiple linear regressions showed that melatonin dose and morning urine melatonin correlated with more Lo Deep sleep. Buspirone dose also correlated with more Lo Deep, but only 6 subjects were taking buspirone. Also replicating the findings from our last study were independent reductions of REM sleep with prazosin and sertraline. Other findings included decreased Lo and increased Hi Deep sleep with higher caffeine dose, and less Hi Deep percentage with higher testosterone. Finally, evening cortisol levels correlated with a higher percentage of Wake after sleep onset. These results confirm Lo Deep deficiency in this PTSD population and suggests melatonin as a possible therapeutic to reverse Lo Deep deficiency. This is a critical first step to establishing a systematic sleep assessment and treatment program in this and potentially other populations to prevent future brain pathology.

In-Home Sleep Recordings in Military Veterans With Posttraumatic Stress Disorder Reveal Less REM and Deep Sleep <1 Hz.

Veterans with posttraumatic stress disorder (PTSD) often report suboptimal sleep quality, often described as lack of restfulness for unknown reasons. These experiences are sometimes difficult to objectively quantify in sleep lab assessments. Here, we used a streamlined sleep assessment tool to record in-home 2-channel electroencephalogram (EEG) with concurrent collection of electrodermal activity (EDA) and acceleration. Data from a single forehead channel were transformed into a whole-night spectrogram, and sleep stages were classified using a fully automated algorithm. For this study, 71 control subjects and 60 military-related PTSD subjects were analyzed for percentage of time spent in Light, Hi Deep (1-3 Hz), Lo Deep (<1 Hz), and rapid eye movement (REM) sleep stages, as well as sleep efficiency and fragmentation. The results showed a significant tendency for PTSD sleepers to spend a smaller percentage of the night in REM (p < 0.0001) and Lo Deep (p = 0.001) sleep, while spending a larger percentage of the night in Hi Deep (p < 0.0001) sleep. The percentage of combined Hi+Lo Deep sleep did not differ between groups. All sleepers usually showed EDA peaks during Lo, but not Hi, Deep sleep; however, PTSD sleepers were more likely to lack EDA peaks altogether, which usually coincided with a lack of Lo Deep sleep. Linear regressions with all subjects showed that a decreased percentage of REM sleep in PTSD sleepers was accounted for by age, prazosin, SSRIs and SNRIs (p < 0.02), while decreased Lo Deep and increased Hi Deep in the PTSD group could not be accounted for by any factor in this study (p < 0.005). Linear regression models with only the PTSD group showed that decreased REM correlated with self-reported depression, as measured with the Depression, Anxiety, and Stress Scales (DASS; p < 0.00001). DASS anxiety was associated with increased REM time (p < 0.0001). This study shows altered sleep patterns in sleepers with PTSD that can be partially accounted for by age and medication use; however, differences in deep sleep related to PTSD could not be linked to any known factor. With several medications [prazosin, selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs); p < 0.03], as well as SSRIs were associated with less sleep efficiency (b = -3.3 ± 0.95; p = 0.0005) and more sleep fragmentation (b = -1.7 ± 0.51; p = 0.0009). Anti-psychotics were associated with less sleep efficiency (b = -4.9 ± 1.4; p = 0.0004). Sleep efficiency was negatively impacted by SSRIs, antipsychotic medications, and depression (p < 0.008). Increased sleep fragmentation was associated with SSRIs, SNRIs, and anxiety (p < 0.009), while prazosin and antipsychotic medications correlated with decreased sleep fragmentation (p < 0.05).

Brain Zaps: An Underappreciated Symptom of Antidepressant Discontinuation.

OBJECTIVE: To describe the characteristics of the electrical phenomena of antidepressant discontinuation syndrome known as brain zaps and their effect on quality of life. METHODS: We examined 595 unsolicited posts made by individuals frequenting a popular lay mental health website. The site was accessed between December 13, 2014, and December 12, 2016, and its content was saved in a text document. The posts had been accumulating on the site since December 2014. These posts were analyzed and separated into 648 separate statements regarding antidepressant intake. Of the statements, 378 contained reference to symptoms experienced in the context of antidepressant discontinuation. These posts were further analyzed for specifics of the medications involved, temporal characteristics of the medication intake, associated symptoms, specifics of the "zap" experience itself, and effect of the zaps on quality of life. As this was a convenience sample, only qualitative analysis was performed. RESULTS: Venlafaxine and paroxetine were reported more frequently, and fluoxetine less frequently, in the sample compared to their frequency of prescription in clinical practice. This finding mirrors the frequency distribution of all withdrawal effects versus antidepressant prescriptions written as reported in the literature. The most likely cause of brain zaps was abrupt discontinuation of the medication, but gradual tapering had only a partial mitigating effect. An unexpected finding was the frequent association of brain zaps with lateral eye movements. The presence of brain zaps was typically transitory, but in a small number of cases it caused significant disability lasting for months or years with no treatment available. Patients' inability to obtain effective help from prescribers and the perceived lack of interest in this symptom on the part of the medical profession risks fueling antipsychiatry attitudes among patients. CONCLUSIONS: Brain zaps are a poorly understood symptom of antidepressant discontinuation, which require further study for both better prevention and treatment. The apparent association of brain zaps with lateral eye movements may open avenues for investigation of this process.

Investigation of vagal afferent functioning using the Heartbeat Event Related Potential.

Although there has been much support for HRV Biofeedback as an effective intervention for various disorders, there is a lack of comprehension of the underlying mechanisms. The predominant theories of increased vagal efferents and baroreflex gain are insufficient in explaining the frequent observations that HRV Biofeedback impacts changes in constructs beyond ANS mediation, such as emotion regulation, attentional control, and self-regulatory reserve. It has been suspected that vagal afferent functioning may be the underlying mechanism, but little research has explored this. Previously, researchers measured cortical evoked potentials contingent to the heart, or an indication of vagal afferent functioning (Schandry et al., 1986). Twenty-five participants were randomly stratified to HRV Biofeedback or EMG Biofeedback for four sessions. Repeated measures ANOVAs revealed that the HRV group exhibited statistically significantly increased baseline Heartbeat Event-Related Potentials (updated term for 'evoked potential') while the relaxation control group did not. The results of this study provide initial support to the premise that HRV Biofeedback stimulates changes in the vagal afferent pathway that are longer lasting than simply the short term effects of breathing.

Visualization of Whole-Night Sleep EEG From 2-Channel Mobile Recording Device Reveals Distinct Deep Sleep Stages with Differential Electrodermal Activity.

Brain activity during sleep is a powerful marker of overall health, but sleep lab testing is prohibitively expensive and only indicated for major sleep disorders. This report demonstrates that mobile 2-channel in-home electroencephalogram (EEG) recording devices provided sufficient information to detect and visualize sleep EEG. Displaying whole-night sleep EEG in a spectral display allowed for quick assessment of general sleep stability, cycle lengths, stage lengths, dominant frequencies and other indices of sleep quality. By visualizing spectral data down to 0.1 Hz, a differentiation emerged between slow-wave sleep with dominant frequency between 0.1-1 Hz or 1-3 Hz, but rarely both. Thus, we present here the new designations, Hi and Lo Deep sleep, according to the frequency range with dominant power. Simultaneously recorded electrodermal activity (EDA) was primarily associated with Lo Deep and very rarely with Hi Deep or any other stage. Therefore, Hi and Lo Deep sleep appear to be physiologically distinct states that may serve unique functions during sleep. We developed an algorithm to classify five stages (Awake, Light, Hi Deep, Lo Deep and rapid eye movement (REM)) using a Hidden Markov Model (HMM), model fitting with the expectation-maximization (EM) algorithm, and estimation of the most likely sleep state sequence by the Viterbi algorithm. The resulting automatically generated sleep hypnogram can help clinicians interpret the spectral display and help researchers computationally quantify sleep stages across participants. In conclusion, this study demonstrates the feasibility of in-home sleep EEG collection, a rapid and informative sleep report format, and novel deep sleep designations accounting for spectral and physiological differences.

Combat veterans with comorbid PTSD and mild TBI exhibit a greater inhibitory processing ERP from the dorsal anterior cingulate cortex.

Posttraumatic stress disorder (PTSD) is common among combat personnel with mild traumatic brain injury (mTBI). While patients with either PTSD or mTBI share abnormal activation of multiple frontal brain areas, anterior cingulate cortex (ACC) activity during inhibitory processing may be particularly affected by PTSD. To further test this hypothesis, we recorded electroencephalography from 32 combat veterans with mTBI-17 of whom were also comorbid for PTSD (mTBI+PTSD) and 15 without PTSD (mTBI-only). Subjects performed the Stop Task, a validated inhibitory control task requiring inhibition of initiated motor responses. We observed a larger inhibitory processing eventrelated potential (ERP) in veterans with mTBI+PTSD, including greater N200 negativity. Furthermore, greater N200 negativity correlated with greater PTSD severity. This correlation was most dependent on contributions from the dorsal ACC. Support vector machine analysis demonstrated that N200 and P300 amplitudes objectively classified veterans into mTBI-only or mTBI+PTSD groups with 79.4% accuracy. Our results support a model where, in combat veterans with mTBI, larger ERPs from cingulate areas are associated with greater PTSD severity and likely related to difficulty controlling ongoing brain processes, including trauma-related thoughts and feelings.

Combat veterans with PTSD after mild TBI exhibit greater ERPs from posterior-medial cortical areas while appraising facial features.

Posttraumatic stress disorder (PTSD) worsens prognosis following mild traumatic brain injury (mTBI). Combat personnel with histories of mTBI exhibit abnormal activation of distributed brain networks-including emotion processing and default mode networks. How developing PTSD further affects these abnormalities has not been directly examined. We recorded electroencephalography in combat veterans with histories of mTBI, but without active PTSD (mTBI only, n=16) and combat veterans who developed PTSD after mTBI (mTBI+PTSD, n=16)-during the Reading the Mind in the Eyes Test (RMET), a validated test of empathy requiring emotional appraisal of facial features. Task-related event related potentials (ERPs) were identified, decomposed using independent component analysis (ICA) and localized anatomically using dipole modeling. We observed larger emotional face processing ERPs in veterans with mTBI+PTSD, including greater N300 negativity. Furthermore, greater N300 negativity correlated with greater PTSD severity, especially avoidance/numbing and hyperarousal symptom clusters. This correlation was dependent on contributions from the precuneus and posterior cingulate cortex (PCC). Our results support a model where, in combat veterans with histories of mTBI, larger ERPs from over-active posterior-medial cortical areas may be specific to PTSD, and is likely related to negative self-referential activity.

Independent EEG sources are dipolar.

Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition 'dipolarity' defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison).

Human brain dynamics accompanying use of egocentric and allocentric reference frames during navigation.

Maintaining spatial orientation while travelling requires integrating spatial information encountered from an egocentric viewpoint with accumulated information represented within egocentric and/or allocentric reference frames. Here, we report changes in high-density EEG activity during a virtual tunnel passage task in which subjects respond to a postnavigation homing challenge in distinctly different ways--either compatible with a continued experience of the virtual environment from a solely egocentric perspective or as if also maintaining their original entrance orientation, indicating use of a parallel allocentric reference frame. By spatially filtering the EEG data using independent component analysis, we found that these two equal subject subgroups exhibited differences in EEG power spectral modulation during tunnel passages in only a few cortical areas. During tunnel turns, stronger alpha blocking occurred only in or near right primary visual cortex of subjects whose homing responses were compatible with continued use of an egocentric reference frame. In contrast, approaching and during tunnel turns, subjects who responded in a way compatible with use of an allocentric reference frame exhibited stronger alpha blocking of occipito-temporal, bilateral inferior parietal, and retrosplenial cortical areas, all areas implicated by hemodynamic imaging and neuropsychological observation in construction and maintenance of an allocentric reference frame. We conclude that in these subjects, stronger activation of retrosplenial and related cortical areas during turns support a continuous translation of egocentrically experienced visual flow into an allocentric model of their virtual position and movement.

High-frequency Broadband Modulations of Electroencephalographic Spectra.

High-frequency cortical potentials in electroencephalographic (EEG) scalp recordings have low amplitudes and may be confounded with scalp muscle activities. EEG data from an eyes-closed emotion imagination task were linearly decomposed using independent component analysis (ICA) into maximally independent component (IC) processes. Joint decomposition of IC log spectrograms into source- and frequency-independent modulator (IM) processes revealed three distinct classes of IMs that separately modulated broadband high-frequency ( approximately 15-200 Hz) power of brain, scalp muscle, and likely ocular motor IC processes. Multi-dimensional scaling revealed significant but spatially complex relationships between mean broadband brain IM effects and the valence of the imagined emotions. Thus, contrary to prevalent assumption, unitary modes of spectral modulation of frequencies encompassing the beta, gamma, and high gamma frequency ranges can be isolated from scalp-recorded EEG data and may be differentially associated with brain sources and cognitive activities.

Information-based modeling of event-related brain dynamics.

We discuss the theory and practice of applying independent component analysis (ICA) to electroencephalographic (EEG) data. ICA blindly decomposes multi-channel EEG data into maximally independent component processes (ICs) that typically express either particularly brain generated EEG activities or some type of non-brain artifacts (line or other environmental noise, eye blinks and other eye movements, or scalp or heart muscle activity). Each brain and non-brain IC is identified with an activity time course (its 'activation') and a set of relative strengths of its projections (by volume conduction) to the recording electrodes (its 'scalp map'). Many non-articraft IC scalp maps strongly resemble the projection of a single dipole, allowing the location and orientation of the best-fitting equivalent dipole (or other source model) to be easily determined. In favorable circumstances, ICA decomposition of high-density scalp EEG data appears to allow concurrent monitoring, with high time resolution, of separate EEG activities in twenty or more separate cortical EEG source areas. We illustrate the differences between ICA and traditional approaches to EEG analysis by comparing time courses and mean event related spectral perturbations (ERSPs) of scalp channel and IC data. Comparing IC activities across subjects necessitates clustering of similar Ics based on common dynamic and/or spatial features. We discuss and illustrate such a component clustering strategy. In sum, continued application of ICA methods in EEG research should continue to yield new insights into the nature and role of the complex macroscopic cortical dynamics captured by scalp electrode recordings.

Imaging human EEG dynamics using independent component analysis.

This review discusses the theory and practical application of independent component analysis (ICA) to multi-channel EEG data. We use examples from an audiovisual attention-shifting task performed by young and old subjects to illustrate the power of ICA to resolve subtle differences between evoked responses in the two age groups. Preliminary analysis of these data using ICA suggests a loss of task specificity in independent component (IC) processes in frontal and somatomotor cortex during post-response periods in older as compared to younger subjects, trends not detected during examination of scalp-channel event-related potential (ERP) averages. We discuss possible approaches to component clustering across subjects and new ways to visualize mean and trial-by-trial variations in the data, including ERP-image plots of dynamics within and across trials as well as plots of event-related spectral perturbations in component power, phase locking, and coherence. We believe that widespread application of these and related analysis methods should bring EEG once again to the forefront of brain imaging, merging its high time and frequency resolution with enhanced cm-scale spatial resolution of its cortical sources.

Mapping single-trial EEG records on the cortical surface through a spatiotemporal modality.

Event-related potentials (ERPs) induced by visual perception and cognitive tasks have been extensively studied in neuropsychological experiments. ERP activities time-locked to stimulus presentation and task performance are often observed separately at individual scalp channels based on averaged time series across epochs and experimental subjects. An analysis using averaged EEG dynamics could discount information regarding interdependency between ongoing EEG and salient ERP features. Advanced tools such as independent component analysis (ICA) have been developed for decomposing collections of single-trial EEG records into separate features. Those features (or independent components) can then be mapped onto the cortical surface using source localization algorithms to visualize brain activation maps and to study between-subject consistency. In this study, we propose a statistical framework for estimating the time course of spatiotemporally independent EEG components simultaneously with their cortical distributions. Within this framework, we implemented Bayesian spatiotemporal analysis for imaging the sources of EEG features on the cortical surface. The framework allows researchers to include prior knowledge regarding spatial locations as well as spatiotemporal independence of different EEG sources. The use of the Electromagnetic Spatiotemporal ICA (EMSICA) method is illustrated by mapping event-related EEG dynamics induced by events in a visual two-back continuous performance task. The proposed method successfully identified several interesting components with plausible corresponding cortical activation topographies, including processes contributing to the late positive complex (LPC) located in central parietal, frontal midline, and anterior cingulate cortex, to atypical mu rhythms associated with the precentral gyrus, and to the central posterior alpha activity in the precuneus.

Frontal midline EEG dynamics during working memory.

We show that during visual working memory, the electroencephalographic (EEG) process producing 5-7 Hz frontal midline theta (fmtheta) activity exhibits multiple spectral modes involving at least three frequency bands and a wide range of amplitudes. The process accounting for the fmtheta increase during working memory was separated from 71-channel data by clustering on time/frequency transforms of components returned by independent component analysis (ICA). Dipole models of fmtheta component scalp maps were consistent with their generation in or near dorsal anterior cingulate cortex. From trial to trial, theta power of fmtheta components varied widely but correlated moderately with theta power in other frontal and left temporal processes. The weak mean increase in frontal midline theta power with increasing memory load, produced entirely by the fmtheta components, largely reflected progressively stronger theta activity in a relatively small proportion of trials. During presentations of letter series to be memorized or ignored, fmtheta components also exhibited 12-15 Hz low-beta activity that was stronger during memorized than during ignored letter trials, independent of letter duration. The same components produced a brief 3-Hz burst 500 ms after onset of the Probe letter following each letter sequence. A new decomposition method, log spectral ICA, applied to normalized log time/frequency transforms of fmtheta component Memorize-letter trials, showed that their low-beta activity reflected harmonic energy in continuous, sharp-peaked theta wave trains as well as independent low-beta bursts. Possibly, the observed fmtheta process variability may index dynamic adjustments in medial frontal cortex to trial-specific behavioral context and task demands.

Mining event-related brain dynamics.

This article provides a new, more comprehensive view of event-related brain dynamics founded on an information-based approach to modeling electroencephalographic (EEG) dynamics. Most EEG research focuses either on peaks 'evoked' in average event-related potentials (ERPs) or on changes 'induced' in the EEG power spectrum by experimental events. Although these measures are nearly complementary, they do not fully model the event-related dynamics in the data, and cannot isolate the signals of the contributing cortical areas. We propose that many ERPs and other EEG features are better viewed as time/frequency perturbations of underlying field potential processes. The new approach combines independent component analysis (ICA), time/frequency analysis, and trial-by-trial visualization that measures EEG source dynamics without requiring an explicit head model.

Reduced G(i) and G(o) protein function in the rat nucleus accumbens attenuates sensorimotor gating deficits.

Prepulse inhibition of the acoustic startle response (PPI) is a cross-species measure of sensorimotor gating, which is severely disrupted in patients with schizophrenia. PPI deficits can be produced in experimental animals by administration of selective D(2)-like dopamine receptor agonists in the nucleus accumbens (NAc). G proteins coupled to these receptors reportedly are altered in the NAc of patients with schizophrenia. Therefore, we sought to determine whether experimental inactivation of intracellular G proteins in the NAc alters PPI. In adult male Sprague-Dawley rats, baseline PPI was determined by presenting acoustic pulse stimuli (120 dB) alone or preceded 100 ms earlier by prepulse stimuli (3, 6 or 12 dB above 70 dB ambient noise). PPI disruption was assessed in the presence of quinpirole (0.0, 0.05, 0.1, 0.5 mg/kg, sc), and pertussis toxin (PTX; 0.05 microg/side) was then infused into the NAc bilaterally. Ten days later, quinpirole-mediated disruption of PPI was significantly reduced; neither PTX alone, nor heat-inactivated PTX had any effect on quinpirole-induced PPI reductions. PPI was significantly higher after PTX infusion upon moderate quinpirole challenge, suggesting that D(2)-like receptors were less effective. PTX treatment significantly reduced basal and dopamine-stimulated [35S]GTPgammaS binding in the NAc core and shell, and reduced G(i)(alpha) protein immunoreactivity in the NAc. The results suggest that PPI disruption mediated by D(2)-like receptor activation in the NAc depends on coupling to G(i) and G(o) proteins, alteration of which could cause sensorimotor gating deficits in schizophrenia.