EEG resting-state networks in Alzheimer's disease associated with clinical symptoms.

Alzheimer's disease (AD) is a progressive neuropsychiatric disease affecting many elderly people and is characterized by progressive cognitive impairment of memory, visuospatial, and executive functions. As the elderly population is growing, the number of AD patients is increasing considerably. There is currently growing interest in determining AD's cognitive dysfunction markers. We used exact low-resolution-brain-electromagnetic-tomography independent-component-analysis (eLORETA-ICA) to assess activities of five electroencephalography resting-state-networks (EEG-RSNs) in 90 drug-free AD patients and 11 drug-free patients with mild-cognitive-impairment due to AD (ADMCI). Compared to 147 healthy subjects, the AD/ADMCI patients showed significantly decreased activities in the memory network and occipital alpha activity, where the age difference between the AD/ADMCI and healthy groups was corrected by linear regression analysis. Furthermore, the age-corrected EEG-RSN activities showed correlations with cognitive function test scores in AD/ADMCI. In particular, decreased memory network activity showed correlations with worse total cognitive scores for both Mini-Mental-State-Examination (MMSE) and Alzheimer's Disease-Assessment-Scale-cognitive-component-Japanese version (ADAS-J cog) including worse sub-scores for orientation, registration, repetition, word recognition and ideational praxis. Our results indicate that AD affects specific EEG-RSNs and deteriorated network activity causes symptoms. Overall, eLORETA-ICA is a useful, non-invasive tool for assessing EEG-functional-network activities and provides better understanding of the neurophysiological mechanisms underlying the disease.

Normalized Power Variance: A new Field Orthogonal to Power in EEG Analysis.

To date, electroencephalogram (EEG) has been used in the diagnosis of epilepsy, dementia, and disturbance of consciousness via the inspection of EEG waves and identification of abnormal electrical discharges and slowing of basic waves. In addition, EEG power analysis combined with a source estimation method like exact-low-resolution-brain-electromagnetic-tomography (eLORETA), which calculates the power of cortical electrical activity from EEG data, has been widely used to investigate cortical electrical activity in neuropsychiatric diseases. However, the recently developed field of mathematics "information geometry" indicates that EEG has another dimension orthogonal to power dimension - that of normalized power variance (NPV). In addition, by introducing the idea of information geometry, a significantly faster convergent estimator of NPV was obtained. Research into this NPV coordinate has been limited thus far. In this study, we applied this NPV analysis of eLORETA to idiopathic normal pressure hydrocephalus (iNPH) patients prior to a cerebrospinal fluid (CSF) shunt operation, where traditional power analysis could not detect any difference associated with CSF shunt operation outcome. Our NPV analysis of eLORETA detected significantly higher NPV values at the high convexity area in the beta frequency band between 17 shunt responders and 19 non-responders. Considering our present and past research findings about NPV, we also discuss the advantage of this application of NPV representing a sensitive early warning signal of cortical impairment. Overall, our findings demonstrated that EEG has another dimension - that of NPV, which contains a lot of information about cortical electrical activity that can be useful in clinical practice.

Mindfulness augmentation for anxiety through concurrent use of transcranial direct current stimulation: a randomized double-blind study.

Transcranial direct current stimulation (tDCS) have revealed the capability to augment various types of behavioural interventions. We aimed to augment the effects of mindfulness, suggested for reducing anxiety, with concurrent use of tDCS. We conducted a double-blind randomized study with 58 healthy individuals. We introduced treadmill walking for focused meditation and active or sham tDCS on the left dorsolateral prefrontal cortex for 20 min. We evaluated outcomes using State-Trait Anxiety Inventory-State Anxiety (STAI) before the intervention as well as immediately, 60 min, and 1 week after the intervention, and current density from electroencephalograms (EEG) before and after the intervention. The linear mixed-effect models demonstrated that STAI-state anxiety showed a significant interaction effect between 1 week after the intervention and tDCS groups. As for alpha-band EEG activity, the current density in the rostral anterior cingulate cortex (rACC) was significantly reduced in the active compared with the sham stimulation group, and a significant correlation was seen between changes in STAI-trait anxiety and the current density of the rACC in the active stimulation group. Our study provided that despite this being a one-shot and short intervention, the reduction in anxiety lasts for one week, and EEG could potentially help predict its anxiolytic effect.

Fundamentally altered global- and microstate EEG characteristics in Huntington's disease.

OBJECTIVE: Huntington's disease (HD) is characterized by psychiatric, cognitive, and motor disturbances. The study aimed to determine electroencephalography (EEG) global state and microstate changes in HD and their relationship with cognitive and behavioral impairments. METHODS: EEGs from 20 unmedicated HD patients and 20 controls were compared using global state properties (connectivity and dimensionality) and microstate properties (EEG microstate analysis). For four microstate classes (A, B, C, D), three parameters were computed: duration, occurrence, coverage. Global- and microstate properties were compared between groups and correlated with cognitive test scores for patients. RESULTS: Global state analysis showed reduced connectivity in HD and an increasing dimensionality with increasing HD severity. Microstate analysis revealed parameter increases for classes A and B (coverage), decreases for C (occurrence) and D (coverage and occurrence). Disease severity and poorer test performances correlated with parameter increases for class A (coverage and occurrence), decreases for C (coverage and duration) and a dimensionality increase. CONCLUSIONS: Global state changes may reflect higher functional dissociation between brain areas and the complex microstate changes possibly the widespread neuronal death and corresponding functional deficits in brain regions associated with HD symptomatology. SIGNIFICANCE: Combining global- and microstate analyses can be useful for a better understanding of progressive brain deterioration in HD.

Normalized power variance of eLORETA at high-convexity area predicts shunt response in idiopathic normal pressure hydrocephalus.

Idiopathic normal pressure hydrocephalus (iNPH) is a neuropsychiatric disease characterized by gait disturbance, cognitive deterioration and urinary incontinence associated with excessive accumulation of cerebrospinal fluid (CSF) in the brain ventricles. These symptoms, in particular gait disturbance, can be potentially improved by shunt operation in the early stage of the disease, and the intervention associates with a worse outcome when performed late during the course of the disease. Despite the variable outcome of shunt operation, noninvasive presurgical prediction methods of shunt response have not been established yet. In the present study, we used normalized power variance (NPV), a sensitive measure of the instability of cortical electrical activity, to analyze cortical electrical activity derived from EEG data using exact-low-resolution-electromagnetic-tomography (eLORETA) in 15 shunt responders and 19 non-responders. We found that shunt responders showed significantly higher NPV values at high-convexity areas in beta frequency band than non-responders. In addition, using this difference, we could discriminate shunt responders from non-responders with leave-one-subject-out cross-validation accuracy of 67.6% (23/34) [positive predictive value of 61.1% (11/18) and negative predictive value of 75.0% (12/16)]. Our findings indicate that eLORETA-NPV can be a useful tool for noninvasive prediction of clinical response to shunt operation in patients with iNPH.

Short-term meditation modulates EEG activity in subjects with post-traumatic residual disabilities.

OBJECTIVE: Neurophysiological changes related to meditation have recently attracted scientific attention. We aimed to detect changes in electroencephalography (EEG) parameters induced by a meditative intervention in subjects with post-traumatic residual disability (PTRD), which has been confirmed for effectiveness and safety in a previous study. This will allow us to estimate the objective effect of this intervention at the neurophysiological level. METHODS: Ten subjects with PTRD were recruited and underwent psychological assessment and EEG recordings before and after the meditative intervention. Furthermore, 10 additional subjects were recruited as normal controls. Source current density as an EEG parameter was estimated by exact Low Resolution Electromagnetic Tomography (eLORETA). Comparisons of source current density in PTRD subjects after the meditative intervention with normal controls were investigated. Additionally, we compared source current density in PTRD subjects between before and after meditative intervention. Correlations between psychological assessments and source current density were also explored. RESULTS: After meditative intervention, PTRD subjects exhibited increased gamma activity in the left inferior parietal lobule relative to normal controls. In addition, changes of delta activity in the right precuneus correlated with changes in the psychological score on role physical item, one of the quality of life scales reflecting the work or daily difficulty due to physical problems. CONCLUSIONS: These results show that the meditative intervention used in this study produces neurophysiological changes, in particular the modulation of oscillatory activity of the brain. SIGNIFICANCE: Our meditative interventions might induce the neurophysiological changes associated with the improvement of psychological symptoms in the PTRD subjects.

Automated Source Estimation of Scalp EEG Epileptic Activity Using eLORETA Kurtosis Analysis.

OBJECTIVES: eLORETA (exact low-resolution brain electromagnetic tomography) is a technique created by Pascual-Marqui et al. [Int J Psychophysiol. 1994 Oct; 18(1): 49-65] for the 3-dimensional representation of current source density in the brain by electroencephalography (EEG) data. Kurtosis analysis allows for the identification of spiky activity in the brain. In this study, we focused on the evaluation of the reliability of eLORETA kurtosis analysis. For this purpose, the results of eLORETA kurtosis source localization of paroxysmal activity in EEG were compared with those of eLORETA current source density (CSD) analysis of EEG data in 3 epilepsy patients with partial seizures. METHODS: EEG was measured using a digital EEG system with 19 channels. We set the bandpass filter at traditional frequency band settings (1-4, 4-8, 8-15, 15-30, and 30-60 Hz) and 5-10 and 20-70 Hz and performed eLORETA kurtosis to compare the source localization of paroxysmal activity with that of visual interpretation of EEG data and CSD analysis of eLORETA in focal epilepsy patients. RESULTS: The eLORETA kurtosis analysis of EEG data preprocessed by bandpass filtering from 20 to 70 Hz and traditional frequency band settings did not show any discrete paroxysmal source activity compatible with the results of CSD analysis of eLORETA. In all 3 cases, eLORETA kurtosis analysis filtered at 5-10 Hz showed paroxysmal activities in the theta band, which were all consistent with the visual inspection results and the CSD analysis results. DISCUSSION: Our findings suggested that eLORETA kurtosis analysis of EEG data might be useful for the identification of spiky paroxysmal activity sources in epilepsy patients. Since EEG is widely used in the clinical practice of epilepsy, eLORETA kurtosis analysis is a promising method that can be applied to epileptic activity mapping.

Hyperactivation of the Frontal Control Network Revealed by Symptom Provocation in Obsessive-Compulsive Disorder Using EEG Microstate and sLORETA Analyses.

The aim of this study was to investigate the changes of brain electric field induced by symptom provocation in patients with obsessive-compulsive disorder (OCD) in comparison to healthy controls in the resting state. For this purpose, EEG recordings in conditions of initial rest, clean control, symptom provocation by imaginal exposure, and final rest were used for computing spatiotemporal activity characteristics based on microstate segmentation. Within-group comparisons were significant for the symptom provocation condition: OCD showed high global field power (GFP) and transition rates into a medial frontal microstate, whereas healthy controls showed high frequency of occurrence and high percent of dwelling time for a medial occipitoparietal microstate. Between-group comparisons demonstrated significantly lower GFP and dwelling time for the medial occipitoparietal microstate in OCD in several conditions including initial rest and symptom provocation. In addition, OCD compared to healthy controls showed significant instability of the medial occipitoparietal microstate, with high preference for transitions into the medial frontal microstate. In conclusion, during rest and symptom provocation, OCD patients make preferential use of a medial frontal brain network, with concomitant reduction of use of a medial occipitoparietal network, as shown by dwelling times, explained variance, and dynamic transition rates. These findings support the idea of a possible biological marker for OCD, which might correspond to pathological hyperactivation of the frontal control network.

EEG Resting-State Networks Responsible for Gait Disturbance Features in Idiopathic Normal Pressure Hydrocephalus.

Idiopathic normal pressure hydrocephalus (iNPH) is a neuropsychiatric disease characterized by gait disturbance, cognitive dysfunction, and urinary incontinence that affects a large population of elderly people. These symptoms, especially gait disturbance, can potentially be improved by cerebrospinal fluid (CSF) drainage, which is more effective if performed at an early stage of the disease. However, the neurophysiological mechanisms of these symptoms and their recovery by CSF drainage are poorly understood. In this study, using exact low-resolution brain electromagnetic tomography-independent component analysis (eLORETA-ICA) with electroencephalography (EEG) data, we assessed activities of five EEG resting-state networks (EEG-RSNs) in 58 iNPH patients before and after drainage of CSF by lumbar puncture (CSF tapping). In addition, we assessed correlations of changes in these five EEG-RSNs activities with CSF tapping-induced changes in iNPH symptoms. The results reveal that compared with 80 healthy controls, iNPH patients had significantly decreased activities in the occipital alpha rhythm, visual perception network, and self-referential network before CSF tapping. Furthermore, CSF tapping-induced changes in occipital alpha activity correlated with changes in postural sway and frontal lobe function. Changes in visual perception network activity correlated with changes in gait speed. In addition, changes in memory perception network activity correlated with changes in Parkinsonian gait features. These results indicate a recruitment of cognitive networks in gait control, and involvement of the occipital alpha activity in cognitive dysfunction in iNPH patients. Based on these findings, eLORETA-ICA with EEG data can be considered a noninvasive, useful tool for detection of EEG-RSN activities and for understanding the neurophysiological mechanisms underlying this disease.

Evaluation of Electroencephalogram Using Exact Low-Resolution Electromagnetic Tomography During Photic Driving Response in Patients with Migraine.

BACKGROUND: Photophobia is a common feature of migraine, which may involve abnormal cortical information processing. In electroencephalograms (EEG), photic driving is known as a reaction to visual stimulation. Both photophobia and photic driving response are present during light stimulation. We hypothesized that cortical response to photic stimulation would differ between migraine patients with and without aura. METHODS: We recruited 50 migraine patients (migraine with aura [MWA] = 21; migraine without aura [MWOA] = 29). Spontaneous eyes-closed resting EEG from 20 electrodes on the scalp during the interictal phase was recorded. After recording, each photic stimulation was separately selected. We analyzed EEG by fast Fourier transform and observed the spectrum frequency peaks and topographies in response to photic stimulation. Exact low-resolution electromagnetic tomography (eLORETA) was used to compute the 3-dimensional intracerebral distribution of EEG activity. RESULTS: Photic stimulation at frequencies 5, 8, 15, and 20 Hz showed significant differences between migraine patients with and without aura. MWOA patients consistently had a stronger response to photic stimulation than MWA patients. In all patients, the differential response was located in the visual cortex, except for the stimulation at 20 Hz, where the difference at subharmonic 10 Hz was located in the parietal cortex (Brodmann Area 7). CONCLUSION: We confirmed high incidences of photic hypersensitivity and photic driving responses in migraine patients. We suggest that repeated occurrences of cortical spreading depression in MWA may suppress cortical function, thus contributing to a weaker visual cortical response to photic stimulation in MWA patients compared with MWOA patients.

Unmixing EEG Inverse Solutions Based on Brain Segmentation.

Due to its low resolution, any EEG inverse solution provides a source estimate at each voxel that is a mixture of the true source values over all the voxels of the brain. This mixing effect usually causes notable distortion in estimates of source connectivity based on inverse solutions. To lessen this shortcoming, an unmixing approach is introduced for EEG inverse solutions based on piecewise approximation of the unknown source by means of a brain segmentation formed by specified Regions of Interests (ROIs). The approach is general and flexible enough to be applied to any inverse solution with any specified family of ROIs, including point, surface and 3D brain regions. Two of its variants are elaborated in detail: arbitrary piecewise constant sources over arbitrary regions and sources with piecewise constant intensity of known direction over cortex surface regions. Numerically, the approach requires just solving a system of linear equations. Bounds for the error of unmixed estimates are also given. Furthermore, insights on the advantages and of variants of this approach for connectivity analysis are discussed through a variety of designed simulated examples.

Mapping Slow Waves by EEG Topography and Source Localization: Effects of Sleep Deprivation.

Slow waves are a salient feature of the electroencephalogram (EEG) during non-rapid eye movement (non-REM) sleep. The aim of this study was to assess the topography of EEG power and the activation of brain structures during slow wave sleep under normal conditions and after sleep deprivation. Sleep EEG recordings during baseline and recovery sleep after 40 h of sustained wakefulness were analyzed (eight healthy young men, 27 channel EEG). Power maps were computed for the first non-REM sleep episode (where sleep pressure is highest) in baseline and recovery sleep, at frequencies between 0.5 and 2 Hz. Power maps had a frontal predominance at all frequencies between 0.5 and 2 Hz. An additional occipital focus of activity was observed below 1 Hz. Power maps ≤ 1 Hz were not affected by sleep deprivation, whereas an increase in power was observed in the maps ≥ 1.25 Hz. Based on the response to sleep deprivation, low-delta (0.5-1 Hz) and mid-delta activity (1.25-2 Hz) were dissociated. Electrical sources within the cortex of low- and mid-delta activity were estimated using eLORETA. Source localization revealed a predominantly frontal distribution of activity for low-delta and mid-delta activity. Sleep deprivation resulted in an increase in source strength only for mid-delta activity, mainly in parietal and frontal regions. Low-delta activity dominated in occipital and temporal regions and mid-delta activity in limbic and frontal regions independent of the level of sleep pressure. Both, power maps and electrical sources exhibited trait-like aspects.

Source estimation of epileptic activity using eLORETA kurtosis analysis.

Exact low-resolution brain electromagnetic tomography (eLORETA) is a technique for three-dimensional representation of the distribution of sources of electrical activity in the brain. Kurtosis analysis allows for identification of spiky activity in the brain. To evaluate the reliability of eLORETA kurtosis analysis, the results of the analysis were compared with those of equivalent current dipole (ECD) and synthetic aperture magnetometry (SAM) kurtosis analysis of magnetoencephalography (MEG) data in a patient with epilepsy with elementary visual seizures in a 6-year follow-up.The results of electroencephalography (EEG) eLORETA kurtosis analysis indicative of a right superior temporal spike source partially overlapped with MEG ECD/SAM kurtosis results in all recordings, with a total overlapping at the end of the follow-up period. Overall findings suggest that eLORETA kurtosis analysis of EEG data may aid in the localisation of spike activity sources in patients with epilepsy.

Functional localization and effective connectivity of cortical theta and alpha oscillatory activity during an attention task.

OBJECTIVES: The aim of this paper is to investigate cortical electric neuronal activity as an indicator of brain function, in a mental arithmetic task that requires sustained attention, as compared to the resting state condition. The two questions of interest are the cortical localization of different oscillatory activities, and the directional effective flow of oscillatory activity between regions of interest, in the task condition compared to resting state. In particular, theta and alpha activity are of interest here, due to their important role in attention processing. METHODS: We adapted mental arithmetic as an attention ask in this study. Eyes closed 61-channel EEG was recorded in 14 participants during resting and in a mental arithmetic task ("serial sevens subtraction"). Functional localization and connectivity analyses were based on cortical signals of electric neuronal activity estimated with sLORETA (standardized low resolution electromagnetic tomography). Functional localization was based on the comparison of the cortical distributions of the generators of oscillatory activity between task and resting conditions. Assessment of effective connectivity was based on the iCoh (isolated effective coherence) method, which provides an appropriate frequency decomposition of the directional flow of oscillatory activity between brain regions. Nine regions of interest comprising nodes from the dorsal and ventral attention networks were selected for the connectivity analysis. RESULTS: Cortical spectral density distribution comparing task minus rest showed significant activity increase in medial prefrontal areas and decreased activity in left parietal lobe for the theta band, and decreased activity in parietal-occipital regions for the alpha1 band. At a global level, connections among right hemispheric nodes were predominantly decreased during the task condition, while connections among left hemispheric nodes were predominantly increased. At more detailed level, decreased flow from right inferior frontal gyrus to anterior cingulate cortex for theta, and low and high alpha oscillations, and increased feedback (bidirectional flow) between left superior temporal gyrus and left inferior frontal gyrus, were observed during the arithmetic task. CONCLUSIONS: Task related medial prefrontal increase in theta oscillations possibly corresponds to frontal midline theta, while parietal decreased alpha1 activity indicates the active role of this region in the numerical task. Task related decrease of intracortical right hemispheric connectivity support the notion that these nodes need to disengage from one another in order to not interfere with the ongoing numerical processing. The bidirectional feedback between left frontal-temporal-parietal regions in the arithmetic task is very likely to be related to attention network working memory function. SIGNIFICANCE: The methods of analysis and the results presented here will hopefully contribute to clarify the roles of the different EEG oscillations during sustained attention, both in terms of their functional localization and in terms of how they integrate brain function by supporting information flow between different cortical regions. The methodology presented here might be clinically relevant in evaluating abnormal attention function.

Cerebrospinal Fluid Biomarkers of Alzheimer's Disease Correlate With Electroencephalography Parameters Assessed by Exact Low-Resolution Electromagnetic Tomography (eLORETA).

Recently, cerebrospinal fluid (CSF) biomarkers related to Alzheimer's disease (AD) have garnered a lot of clinical attention. To explore neurophysiological traits of AD and parameters for its clinical diagnosis, we examined the association between CSF biomarkers and electroencephalography (EEG) parameters in 14 probable AD patients. Using exact low-resolution electromagnetic tomography (eLORETA), artifact-free 40-sesond EEG data were estimated with current source density (CSD) and lagged phase synchronization (LPS) as the EEG parameters. Correlations between CSF biomarkers and the EEG parameters were assessed. Patients with AD showed significant negative correlation between CSF beta-amyloid (Aß)-42 concentration and the logarithms of CSD over the right temporal area in the theta band. Total tau concentration was negatively correlated with the LPS between the left frontal eye field and the right auditory area in the alpha-2 band in patients with AD. Our study results suggest that AD biomarkers, in particular CSF Aß42 and total tau concentrations are associated with the EEG parameters CSD and LPS, respectively. Our results could yield more insights into the complicated pathology of AD.

Modalities of Thinking: State and Trait Effects on Cross-Frequency Functional Independent Brain Networks.

Functional states of the brain are constituted by the temporally attuned activity of spatially distributed neural networks. Such networks can be identified by independent component analysis (ICA) applied to frequency-dependent source-localized EEG data. This methodology allows the identification of networks at high temporal resolution in frequency bands of established location-specific physiological functions. EEG measurements are sensitive to neural activity changes in cortical areas of modality-specific processing. We tested effects of modality-specific processing on functional brain networks. Phasic modality-specific processing was induced via tasks (state effects) and tonic processing was assessed via modality-specific person parameters (trait effects). Modality-specific person parameters and 64-channel EEG were obtained from 70 male, right-handed students. Person parameters were obtained using cognitive style questionnaires, cognitive tests, and thinking modality self-reports. EEG was recorded during four conditions: spatial visualization, object visualization, verbalization, and resting. Twelve cross-frequency networks were extracted from source-localized EEG across six frequency bands using ICA. RMANOVAs, Pearson correlations, and path modelling examined effects of tasks and person parameters on networks. Results identified distinct state- and trait-dependent functional networks. State-dependent networks were characterized by decreased, trait-dependent networks by increased alpha activity in sub-regions of modality-specific pathways. Pathways of competing modalities showed opposing alpha changes. State- and trait-dependent alpha were associated with inhibitory and automated processing, respectively. Antagonistic alpha modulations in areas of competing modalities likely prevent intruding effects of modality-irrelevant processing. Considerable research suggested alpha modulations related to modality-specific states and traits. This study identified the distinct electrophysiological cortical frequency-dependent networks within which they operate.

Functional connectivity assessed by resting state EEG correlates with cognitive decline of Alzheimer's disease - An eLORETA study.

OBJECTIVE: To explore neurophysiological biomarkers of Alzheimer's disease (AD), we investigated electroencephalography (EEG) of AD patients, and assessed lagged phase synchronization, a measure of brain functional connectivity. METHODS: Twenty-eight probable AD patients and 30 healthy controls (HC) were enrolled. Forty seconds of artifact-free EEG data were selected and compared between patients with AD and HC. Current source density (CSD) and lagged phase synchronization were analyzed by using eLORETA. RESULTS: Patients with AD showed significantly decreased lagged phase synchronization between most cortical regions in delta band relative to controls. There also was a decrease in lagged phase synchronization between the right dorsolateral prefrontal cortex (DLPFC) and the right posterior-inferior parietal lobule (pIPL) in theta band. In addition, some connections in delta band were found to be associated with cognitive function, measured by MMSE. This involved specifically interhemispheric temporal connections as well as left inferior parietal connectivity with the left hippocampus, lateral frontal regions, and the anterior cingulate cortex (aCC). Right temporal connections in delta band were related to global function, as estimated by CDR. No differences were found in CSD analysis between patients and HC. CONCLUSIONS: Functional connectivity disruptions between certain brain regions, as measured with lagged phase synchronization, may potentially represent a neurophysiological biomarker of AD. SIGNIFICANCE: Our study indicated that AD and healthy elderly could have the different patterns of lagged phase synchronization.

Emotion Regulation of Neuroticism: Emotional Information Processing Related to Psychosomatic State Evaluated by Electroencephalography and Exact Low-Resolution Brain Electromagnetic Tomography.

Emotion regulation is the process that adjusts the type or amount of emotion when we experience an emotional situation. The aim of this study was to reveal quantitative changes in brain activity during emotional information processing related to psychosomatic states and to determine electrophysiological features of neuroticism. Twenty-two healthy subjects (mean age 25 years, 14 males and 8 females) were registered. Electroencephalography (EEG) was measured during an emotional audiovisual memory task under three conditions (neutral, pleasant and unpleasant sessions). We divided the subjects into two groups using the Cornell Medical Index (CMI): (CMI-I: control group, n = 10: CMI-II, III or IV: neuroticism group, n = 12). We analyzed the digital EEG data using exact low-resolution brain electromagnetic tomography (eLORETA) current source density (CSD) and functional connectivity analysis in several frequency bands (δ, θ, α, ß, γ and whole band). In all subjects, bilateral frontal α CSD in the unpleasant session increased compared to the pleasant session, especially in the control group (p < 0.05). CSD of the neuroticism group was significantly higher than that of the control group in the full band at the amygdala and inferior temporal gyrus, and in the α band at the right temporal lobe (p < 0.05). Additionally, we found an increase in functional connectivity between the left insular cortex and right superior temporal gyrus in all subjects during the unpleasant session compared to the pleasant session (p < 0.05). In this study, using EEG analysis, we could find a novel cortical network related to brain mechanisms underlying emotion regulation. Overall findings indicate that it is possible to characterize neuroticism electrophysiologically, which may serve as a neurophysiological marker of this personality trait. © 2015 S. Karger AG, Basel.

Detection of EEG-resting state independent networks by eLORETA-ICA method.

Recent functional magnetic resonance imaging (fMRI) studies have shown that functional networks can be extracted even from resting state data, the so called "Resting State independent Networks" (RS-independent-Ns) by applying independent component analysis (ICA). However, compared to fMRI, electroencephalography (EEG) and magnetoencephalography (MEG) have much higher temporal resolution and provide a direct estimation of cortical activity. To date, MEG studies have applied ICA for separate frequency bands only, disregarding cross-frequency couplings. In this study, we aimed to detect EEG-RS-independent-Ns and their interactions in all frequency bands. We applied exact low resolution brain electromagnetic tomography-ICA (eLORETA-ICA) to resting-state EEG data in 80 healthy subjects using five frequency bands (delta, theta, alpha, beta and gamma band) and found five RS-independent-Ns in alpha, beta and gamma frequency bands. Next, taking into account previous neuroimaging findings, five RS-independent-Ns were identified: (1) the visual network in alpha frequency band, (2) dual-process of visual perception network, characterized by a negative correlation between the right ventral visual pathway (VVP) in alpha and beta frequency bands and left posterior dorsal visual pathway (DVP) in alpha frequency band, (3) self-referential processing network, characterized by a negative correlation between the medial prefrontal cortex (mPFC) in beta frequency band and right temporoparietal junction (TPJ) in alpha frequency band, (4) dual-process of memory perception network, functionally related to a negative correlation between the left VVP and the precuneus in alpha frequency band; and (5) sensorimotor network in beta and gamma frequency bands. We selected eLORETA-ICA which has many advantages over the other network visualization methods and overall findings indicate that eLORETA-ICA with EEG data can identify five RS-independent-Ns in their intrinsic frequency bands, and correct correlations within RS-independent-Ns.