Shifted coupling of EEG driving frequencies and fMRI resting state networks in schizophrenia spectrum disorders

Spectral decomposition of resting state electroencephalogram reveals unique theta/alpha activity in schizophrenia

Resting state electroencephalographic (EEG) activity in schizophrenia (SZ) is frequently characterised by increased power at slow frequencies and/or a reduction of peak alpha frequency. Here we investigated the nature of these effects. As most studies to date have been limited by reliance on a priori frequency bands which impose an assumed structure on the data, we performed a data-driven analysis of resting EEG recorded in SZ patients and healthy controls (HC). The sample consisted of 39 chronic SZ and 36 matched HC. The EEG was recorded with a dense electrode array. Power spectral densities were decomposed via Varimax-rotated principal component analysis (PCA) over all participants and for each group separately. Spectral PCA was repeated at the cortical level on cortical current source density computed from standardised low resolution brain electromagnetic tomography. There was a trend for power in the theta/alpha range to be increased in SZ compared to HC, and peak alpha frequency was significantly reduced in SZ. PCA revealed that this frequency shift was because of the presence of a spectral component in the theta/alpha range (6-9 Hz) that was unique to SZ. The source distribution of the SZ > HC theta/alpha effect involved mainly prefrontal and parahippocampal areas. Abnormal low frequency resting EEG activity in SZ was accounted for by a unique theta/alpha oscillation. Other reports have described a similar phenomenon suggesting that the neural circuits oscillating in this range are relevant to SZ pathophysiology.

Visual preference of plant features in different living environments using eye tracking and EEG

Plants play a very important role in landscape construction. In order to explore whether different living environment will affect people's preference for the structural features of plant organs, this study examined 26 villagers and 33 college students as the participants, and pictures of leaves, flowers and fruits of plants as the stimulus to conduct eye-tracking and EEG detection experiments. We found that eye movement indicators can explain people's visual preferences, but they are unable to find differences in preferences between groups. EEG indicators can make up for this deficiency, which further reveals the difference in psychological and physiological responses between the two groups when viewing stimuli. The final results show that the villagers and the students liked leaves best, preferring aciculiform and leathery leaves; solitary, purple and capitulum flowers; and medium-sized, spathulate, black and pear fruits. In addition, it was found that the overall attention of the villagers when watching stimuli was far lower than that of the students, but the degree of meditation was higher. With regard to eye movement and EEG, the total duration of fixations is highly positively correlated with the number of fixations, and the average pupil size has a weak negative correlation with attention. On the contrary, the average duration of fixations has a weak positive correlation with meditation. Generally speaking, we believe that Photinia×fraseri, Metasequoia glyptostroboides, Photinia serratifolia, Koelreuteria bipinnata and Cunninghamia lanceolata are superior landscape building plants in rural areas and on campuses; Pinus thunbergii, Myrica rubra, Camellia japonica and other plants with obvious features and bright colours are also the first choice in rural landscapes; and Yulania biondii, Cercis chinensis, Hibiscus mutabilis and other plants with simple structures are the first choice in campus landscapes. This study is of great significance for selecting plants for landscape construction and management according to different environments and local conditions.

Subcortical control of the default mode network: Role of the basal forebrain and implications for neuropsychiatric disorders

The precise interplay between large-scale functional neural systems throughout the brain is essential for performance of cognitive processes. In this review we focus on the default mode network (DMN), one such functional network that is active during periods of quiet wakefulness and believed to be involved in introspection and planning. Abnormalities in DMN functional connectivity and activation appear across many neuropsychiatric disorders, including schizophrenia. Recent evidence suggests subcortical regions including the basal forebrain are functionally and structurally important for regulation of DMN activity. Within the basal forebrain, subregions like the ventral pallidum may influence DMN activity and the nucleus basalis of Meynert can inhibit switching between brain networks. Interactions between DMN and other functional networks including the medial frontoparietal network (default), lateral frontoparietal network (control), midcingulo-insular network (salience), and dorsal frontoparietal network (attention) are also discussed in the context of neuropsychiatric disorders. Several subtypes of basal forebrain neurons have been identified including basal forebrain parvalbumin-containing or somatostatin-containing neurons which can regulate cortical gamma band oscillations and DMN-like behaviors, and basal forebrain cholinergic neurons which might gate access to sensory information during reinforcement learning. In this review, we explore this evidence, discuss the clinical implications on neuropsychiatric disorders, and compare neuroanatomy in the human vs rodent DMN. Finally, we address technological advancements which could help provide a more complete understanding of modulation of DMN function and describe newly identified BF therapeutic targets that could potentially help restore DMN-associated functional deficits in patients with a variety of neuropsychiatric disorders.

Increased cingulo-orbital connectivity is associated with violent behaviours in schizophrenia

OBJECTIVE

Although schizophrenia patients are at a heightened risk of exhibiting violent behaviours compared to the general population, few functional neuroimaging studies have explored the aberrant neurocircuitry underpinning such behaviours. This study aimed to identify disrupted resting-state activity and functional connectivity in schizophrenia patients with a history of violence.

METHODS

Resting state functional magnetic resonance imaging data was collected from 62 schizophrenia patients and 25 healthy controls. Voxel-wise analyses of fractional amplitude of low frequency fluctuations (fALFF) were implemented to investigate disrupted regional patterns of spontaneous brain activity. Brain regions which yielded significant differences between groups were subsequently used as data-driven seeds for functional connectivity analyses. Finally, significant alterations (activity and connectivity) were correlated with lifetime violent behaviours.

RESULTS

When compared to healthy controls, schizophrenia patients exhibited reduced fALFF in multiple brain regions including the (subgenual) anterior cingulate cortex (ACC), posterior cingulate cortex, precuneus cortex and left lateral orbitofrontal cortex (OFC). Seed-to-voxel analyses yielded significantly enhanced connectivity between the ACC and left OFC. The heightened functional connectivity between the latter two regions predicted the number of violent behaviours reported by schizophrenia patients.

CONCLUSION

The current study demonstrated that the functional connectivity of brain regions associated with emotion regulation is impaired in schizophrenia and associated with violent antecedents among patients. This result is consistent with predominant theoretical models proposing that the OFC plays a critical role in the neurobiology of violence.

Delta resting-state functional connectivity in the cognitive control network as a prognostic factor for maintaining abstinence: An eLORETA preliminary study

BACKGROUND

Cortical regions that support cognitive control are increasingly well recognized, but the functional mechanisms that promote such control over emotional and behavioral hyperreactivity to alcohol in recently abstinent alcohol-dependent patients are still insufficiently understood. This study aimed to identify neurophysiological biomarkers of maintaining abstinence in alcohol-dependent individuals after alcohol treatment by investigating the resting-state EEG-based functional connectivity in the cognitive control network (CCN).

METHODS

Lagged phase synchronization between CCN areas by means of eLORETA as well as the Barratt Impulsiveness Scale (BIS-11) and Beck Depression Inventory (BDI) were assessed in abstinent alcohol-dependent patients recruited from treatment centers. A preliminary prospective study design was used to classify participants into those who did and did not maintain abstinence during a follow-up period (median 12 months) after discharge from residential treatment.

RESULTS

Alcohol-dependent individuals, who maintained abstinence (N = 18), showed significantly increased lagged phase synchronization between the left dorsolateral prefrontal cortex (DLPFC) and the left posterior parietal cortex (IPL) as well as between the right anterior insula cortex/frontal operculum (IA/FO) and the right inferior frontal junction (IFJ) in the delta band compared to those who later relapsed (N = 16). Regression analysis showed that the increased left frontoparietal delta connectivity in the early period of abstinence significantly predicted maintaining abstinence over the ensuing 12 months. Furthermore, right frontoinsular delta connectivity correlated negatively with impulsivity and depression measures.

CONCLUSIONS

These results suggest that the increased delta resting-state functional connectivity in the CCN may be a promising neurophysiological predictor of maintaining abstinence in individuals with alcohol dependence.

Psychotic disorders, dopaminergic agents and EEG/MEG resting-state functional connectivity: A systematic review

Both dysconnectivity and dopamine hypotheses are two well researched pathophysiological models of psychosis. However, little is known about the association of dopamine dysregulation with brain functional connectivity in psychotic disorders, specifically through the administration of antipsychotic medication. In this systematic review, we summarize the existing evidence on the association of dopaminergic effects with electro- and magnetoencephalographic (EEG/MEG) resting-state brain functional connectivity assessed by sensor- as well as source-level measures. A wide heterogeneity of results was found amongst the 20 included studies with increased and decreased functional connectivity in medicated psychosis patients vs. healthy controls in widespread brain areas across all frequency bands. No systematic difference in results was seen between studies with medicated and those with unmedicated psychosis patients and very few studies directly investigated the effect of dopamine agents with a pre-post design. The reported evidence clearly calls for longitudinal EEG and MEG studies with large participant samples to directly explore the association of antipsychotic medication effects with neural network changes over time during illness progression and to ultimately support the development of new treatment strategies.

Vigilance Effects in Resting-State fMRI

Measures of resting-state functional magnetic resonance imaging (rsfMRI) activity have been shown to be sensitive to cognitive function and disease state. However, there is growing evidence that variations in vigilance can lead to pronounced and spatially widespread differences in resting-state brain activity. Unless properly accounted for, differences in vigilance can give rise to changes in resting-state activity that can be misinterpreted as primary cognitive or disease-related effects. In this paper, we examine in detail the link between vigilance and rsfMRI measures, such as signal variance and functional connectivity. We consider how state changes due to factors such as caffeine and sleep deprivation affect both vigilance and rsfMRI measures and review emerging approaches and methodological challenges for the estimation and interpretation of vigilance effects.

Extrinsic and default mode networks in psychiatric conditions: Relationship to excitatory-inhibitory transmitter balance and early trauma

Over the last three decades there has been an accumulation of Magnetic Resonance Imaging (MRI) studies reporting that aberrant functional networks may underlie cognitive deficits and other symptoms across a range of psychiatric diagnoses. The use of pharmacological MRI and ¹H-Magnetic Resonance Spectroscopy (¹H-MRS) has allowed researchers to investigate how changes in network dynamics are related to perturbed excitatory and inhibitory neurotransmission in individuals with psychiatric conditions. More recently, changes in functional network dynamics and excitatory/inhibitory (E/I) neurotransmission have been linked to early childhood trauma, a major antecedents for psychiatric illness in adulthood. Here we review studies investigating whether perturbed network dynamics seen across psychiatric conditions are related to changes in E/I neurotransmission, and whether such changes could be linked to childhood trauma. Whilst there is currently a paucity of studies relating early traumatic experiences to altered E/I balance and network function, the research discussed here lead towards a plausible mechanistic hypothesis, linking early traumatic experiences to cognitive dysfunction and symptoms mediated by E/I neurotransmitter imbalances.

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.

Default Mode Network alterations in alexithymia: an EEG power spectra and connectivity study

Recent neuroimaging studies have shown that alexithymia is characterized by functional alterations in different brain areas [e.g., posterior cingulate cortex (PCC)], during emotional/social tasks. However, only few data are available about alexithymic cortical networking features during resting state (RS). We have investigated the modifications of electroencephalographic (EEG) power spectra and EEG functional connectivity in the default mode network (DMN) in subjects with alexithymia. Eighteen subjects with alexithymia and eighteen subjects without alexithymia matched for age and gender were enrolled. EEG was recorded during 5 min of RS. EEG analyses were conducted by means of the exact Low Resolution Electric Tomography software (eLORETA). Compared to controls, alexithymic subjects showed a decrease of alpha power in the right PCC. In the connectivity analysis, compared to controls, alexithymic subjects showed a decrease of alpha connectivity between: (i) right anterior cingulate cortex and right PCC, (ii) right frontal lobe and right PCC, and (iii) right parietal lobe and right temporal lobe. Finally, mediation models showed that the association between alexithymia and EEG connectivity values was directed and was not mediated by psychopathology severity. Taken together, our results could reflect the neurophysiological substrate of some core features of alexithymia, such as the impairment in emotional awareness.

How do abnormalities in the brain's spontaneous activity translate into symptoms in schizophrenia? From an overview of resting state activity findings to a proposed spatiotemporal psychopathology

Schizophrenia is a complex neuropsychiatric disorder with a variety of symptoms that include sensorimotor, affective, cognitive, and social changes. The exact neuronal mechanisms underlying these symptoms remain unclear though. Neuroimaging has focused mainly on the brain's extrinsic activity, specifically task-evoked or stimulus-induced activity, as related to the sensorimotor, affective, cognitive, and social functions. Recently, the focus has shifted to the brain's spontaneous activity, otherwise known as its resting state activity. While various spatial and temporal abnormalities have been observed in spontaneous activity in schizophrenia, their meaning and significance for the different psychopathological symptoms in schizophrenia, are yet to be defined. The first aim in this paper is to provide an overview of recent findings concerning changes in the spatial (e.g., functional connectivity) and temporal (e.g., couplings between different frequency fluctuations) properties of spontaneous activity in schizophrenia. The second aim is to link these spatiotemporal changes to the various psychopathological symptoms of schizophrenia, with a specific focus on basic symptoms, formal thought disorder, and ego-disturbances. Based on the various findings described, we postulate that the spatiotemporal changes on the neuronal level of the brain's spontaneous activity transform into corresponding spatiotemporal changes on the psychological level which, in turn, leads to the different kinds of psychopathological symptoms. We consequently suggest a spatiotemporal rather than cognitive or sensory approach to the condition, amounting to what we describe as "Spatiotemporal Psychopathology".

15 Years of Microstate Research in Schizophrenia - Where Are We? A Meta-Analysis

Schizophrenia patients show abnormalities in a broad range of task demands. Therefore, an explanation common to all these abnormalities has to be sought independently of any particular task, ideally in the brain dynamics before a task takes place or during resting state. For the neurobiological investigation of such baseline states, EEG microstate analysis is particularly well suited, because it identifies subsecond global states of stable connectivity patterns directly related to the recruitment of different types of information processing modes (e.g., integration of top-down and bottom-up information). Meanwhile, there is an accumulation of evidence that particular microstate networks are selectively affected in schizophrenia. To obtain an overall estimate of the effect size of these microstate abnormalities, we present a systematic meta-analysis over all studies available to date relating EEG microstates to schizophrenia. Results showed medium size effects for two classes of microstates, namely, a class labeled C that was found to be more frequent in schizophrenia and a class labeled D that was found to be shortened. These abnormalities may correspond to core symptoms of schizophrenia, e.g., insufficient reality testing and self-monitoring as during auditory verbal hallucinations. As interventional studies have shown that these microstate features may be systematically affected using antipsychotic drugs or neurofeedback interventions, these findings may help introducing novel diagnostic and treatment options.

Inefficient Preparatory fMRI-BOLD Network Activations Predict Working Memory Dysfunctions in Patients with Schizophrenia

Patients with schizophrenia show abnormal dynamics and structure of temporally -coherent networks (TCNs) assessed using fMRI, which undergo adaptive shifts in preparation for a cognitively demanding task. During working memory (WM) tasks, patients with schizophrenia show persistent deficits in TCNs as well as EEG indices of WM. Studying their temporal relationship during WM tasks might provide novel insights into WM performance deficits seen in schizophrenia. Simultaneous EEG-fMRI data were acquired during the performance of a verbal Sternberg WM task with two load levels (load 2 and load 5) in 17 patients with schizophrenia and 17 matched healthy controls. Using covariance mapping, we investigated the relationship of the activity in the TCNs before the memoranda were encoded and EEG spectral power during the retention interval. We assessed four TCNs - default mode network (DMN), dorsal attention network (dAN), left and right working memory networks (WMNs) - and three EEG bands - theta, alpha, and beta. In healthy controls, there was a load-dependent inverse relation between DMN and frontal midline theta power and an anti-correlation between DMN and dAN. Both effects were not significantly detectable in patients. In addition, healthy controls showed a left-lateralized load-dependent recruitment of the WMNs. Activation of the WMNs was bilateral in patients, suggesting more resources were recruited for successful performance on the WM task. Our findings support the notion of schizophrenia patients showing deviations in their neurophysiological responses before the retention of relevant information in a verbal WM task. Thus, treatment strategies as neurofeedback -targeting prestates could be beneficial as task performance relies on the preparatory state of the brain.

Resting State EEG in Children With Learning Disabilities: An Independent Component Analysis Approach

In this study, the neurophysiological underpinnings of learning disabilities (LD) in children are examined using resting state EEG. We were particularly interested in the neurophysiological differences between children with learning disabilities not otherwise specified (LD-NOS), learning disabilities with verbal disabilities (LD-Verbal), and healthy control (HC) children. We applied 2 different approaches to examine the differences between the different groups. First, we calculated theta/beta and theta/alpha ratios in order to quantify the relationship between slow and fast EEG oscillations. Second, we used a recently developed method for analyzing spectral EEG, namely the group independent component analysis (gICA) model. Using these measures, we identified substantial differences between LD and HC children and between LD-NOS and LD-Verbal children in terms of their spectral EEG profiles. We obtained the following findings: (a) theta/beta and theta/alpha ratios were substantially larger in LD than in HC children, with no difference between LD-NOS and LD-Verbal children; (b) there was substantial slowing of EEG oscillations, especially for gICs located in frontal scalp positions, with LD-NOS children demonstrating the strongest slowing; (c) the estimated intracortical sources of these gICs were mostly located in brain areas involved in the control of executive functions, attention, planning, and language; and (d) the LD-Verbal children demonstrated substantial differences in EEG oscillations compared with LD-NOS children, and these differences were localized in language-related brain areas. The general pattern of atypical neurophysiological activation found in LD children suggests that they suffer from neurophysiological dysfunction in brain areas involved with the control of attention, executive functions, planning, and language functions. LD-Verbal children also demonstrate atypical activation, especially in language-related brain areas. These atypical neurophysiological activation patterns might provide a helpful guide for rehabilitation strategies to treat the deficiencies in these children with LD.

On the existence of a generalized non-specific task-dependent network

In this paper we suggest the existence of a generalized task-related cortical network that is up-regulated whenever the task to be performed requires the allocation of generalized non-specific cognitive resources, independent of the specifics of the task to be performed. We have labeled this general purpose network, the extrinsic mode network (EMN) as complementary to the default mode network (DMN), such that the EMN is down-regulated during periods of task-absence, when the DMN is up-regulated, and vice versa. We conceptualize the EMN as a cortical network for extrinsic neuronal activity, similar to the DMN as being a cortical network for intrinsic neuronal activity. The EMN has essentially a fronto-temporo-parietal spatial distribution, including the inferior and middle frontal gyri, inferior parietal lobule, supplementary motor area, inferior temporal gyrus. We hypothesize that this network is always active regardless of the cognitive task being performed. We further suggest that failure of network up- and down-regulation dynamics may provide neuronal underpinnings for cognitive impairments seen in many mental disorders, such as, e.g., schizophrenia. We start by describing a common observation in functional imaging, the close overlap in fronto-parietal activations in healthy individuals to tasks that denote very different cognitive processes. We now suggest that this is because the brain utilizes the EMN network as a generalized response to tasks that exceeds a cognitive demand threshold and/or requires the processing of novel information. We further discuss how the EMN is related to the DMN, and how a network for extrinsic activity is related to a network for intrinsic activity. Finally, we discuss whether the EMN and DMN networks interact in a common single brain system, rather than being two separate and independent brain systems.

Classifying Children with Learning Disabilities on the Basis of Resting State EEG Measures Using a Linear Discriminant Analysis

Abstract. This study examines the usefulness of easy to obtain EEG measures to discriminate learning-disabled children (LD) from healthy control children. Here the spectral power in the delta, theta, alpha, and beta EEG bands and various power ratios (theta/alpha, theta/beta, beta/alpha, beta/theta, beta/[alpha+theta], [delta+theta]/alpha, alpha/delta, and [theta+alpha]/beta) are applied. These measures were subjected to a factor analysis with varimax rotation revealing four factors explaining 90 % of the entire variance. Factor 1 represents the power of the slow EEG frequency bands delta and theta, factor 2 the relationship between fast and slow frequency bands, factor 3 the slow to fast ratios, and factor 4 the absolute power of nearly all frequency bands. Group differences were found for three factor scores (1, 3, and 4). The linear discriminant analysis with the four factor scores as dependent and the group allocation as independent variables revealed a correct classification of 86 %. Although this classification is far from being perfect it is nevertheless reasonable high and statistically significant. Thus, EEG measures like the one used in this study might support the diagnosis of this difficult to diagnose disability. In addition, the EEG measures identified provide a deeper insight into the neural underpinnings of this disability. Based on this knowledge it might be possible to design new therapeutic strategies to treat LD.

Integrating Different Aspects of Resting Brain Activity: A Review of Electroencephalographic Signatures in Resting State Networks Derived from Functional Magnetic Resonance Imaging

Electroencephalography (EEG) is an established measure in the field of brain resting state with a range of quantitative methods (qEEG) that yield unique information about neuronal activation and synchronization. Meanwhile, in the last decade, functional magnetic resonance imaging (fMRI) studies have revealed the existence of more than a dozen resting state networks (RSNs), and combined qEEG and fMRI have allowed us to gain understanding about the relationship of qEEG and fMRI-RSNs. However, the overall picture is less clear because there is no a priori hypothesis about which EEG features correspond well to fMRI-RSNs. We reviewed the associations of several types of qEEG features to four RSNs considered as neurocognitive systems central for higher brain processes: the default mode network, dorsal and ventral frontoparietal networks, and the salience network. We could identify 12 papers correlating qEEG and RSNs in adult human subjects and employing a simultaneous design under a no-task resting state condition. A systematic overview investigates which qEEG features replicably relate to the chosen RSNs. This review article leads to the conclusion that spatially delimited θ and whole/local α may be the most promising measures, but the time domain methods add important additional information. © 2015 S. Karger AG, Basel.

Resting state activity and the "stream of consciousness" in schizophrenia--neurophenomenal hypotheses

Schizophrenia is a multifaceted disorder with various symptoms including auditory hallucinations, egodisturbances, passivity phenomena, and delusions. Recent neurobiological approaches have focused on, especially, the abnormal contents of consciousness, the "substantive parts" as James said, to associate them with the neural mechanisms related to sensory, motor, and cognitive functions, and the brain's underlying stimulus-induced or task-evoked activity. This leaves open, however, the neural mechanisms that provide the temporal linkage or glue between the various contents, the transitive parts that makes possible the "stream of consciousness." Interestingly, schizophrenic patients seem to suffer from abnormalities specifically in the "transitive parts" when they experience contents as temporally disconnected or fragmented which in phenomenological psychiatry has been described as "temporal fragmentation." The aim of this article is to develop so-called neurophenomenal hypothesis about the direct relationship between phenomenal features of the "stream of consciousness," the "transitive parts," and the specific neuronal mechanisms in schizophrenia as based on healthy subjects. Rather than emphasizing stimulus-induced and task-evoked activity and sensory and lateral prefrontal cortical regions as in neurocognitive approaches with their focus on the "substantive parts," the focus shifts here to the brain's intrinsic activity, its resting state activity, which may account for the temporal linkage or glue between the contents of consciousness, the transitive parts.

Recursive approach of EEG-segment-based principal component analysis substantially reduces cryogenic pump artifacts in simultaneous EEG-fMRI data

Electroencephalography (EEG) data simultaneously acquired with functional magnetic resonance imaging (fMRI) data are preprocessed to remove gradient artifacts (GAs) and ballistocardiographic artifacts (BCAs). Nonetheless, these data, especially in the gamma frequency range, can be contaminated by residual artifacts produced by mechanical vibrations in the MRI system, in particular the cryogenic pump that compresses and transports the helium that chills the magnet (the helium-pump). However, few options are available for the removal of helium-pump artifacts. In this study, we propose a recursive approach of EEG-segment-based principal component analysis (rsPCA) that enables the removal of these helium-pump artifacts. Using the rsPCA method, feature vectors representing helium-pump artifacts were successfully extracted as eigenvectors, and the reconstructed signals of the feature vectors were subsequently removed. A test using simultaneous EEG-fMRI data acquired from left-hand (LH) and right-hand (RH) clenching tasks performed by volunteers found that the proposed rsPCA method substantially reduced helium-pump artifacts in the EEG data and significantly enhanced task-related gamma band activity levels (p=0.0038 and 0.0363 for LH and RH tasks, respectively) in EEG data that have had GAs and BCAs removed. The spatial patterns of the fMRI data were estimated using a hemodynamic response function (HRF) modeled from the estimated gamma band activity in a general linear model (GLM) framework. Active voxel clusters were identified in the post-/pre-central gyri of motor area, only from the rsPCA method (uncorrected p<0.001 for both LH/RH tasks). In addition, the superior temporal pole areas were consistently observed (uncorrected p<0.001 for the LH task and uncorrected p<0.05 for the RH task) in the spatial patterns of the HRF model for gamma band activity when the task paradigm and movement were also included in the GLM.

Relating resting-state fMRI and EEG whole-brain connectomes across frequency bands

Whole brain functional connectomes hold promise for understanding human brain activity across a range of cognitive, developmental and pathological states. So called resting-state (rs) functional MRI studies have contributed to the brain being considered at a macroscopic scale as a set of interacting regions. Interactions are defined as correlation-based signal measurements driven by blood oxygenation level dependent (BOLD) contrast. Understanding the neurophysiological basis of these measurements is important in conveying useful information about brain function. Local coupling between BOLD fMRI and neurophysiological measurements is relatively well defined, with evidence that gamma (range) frequency EEG signals are the closest correlate of BOLD fMRI changes during cognitive processing. However, it is less clear how whole-brain network interactions relate during rest where lower frequency signals have been suggested to play a key role. Simultaneous EEG-fMRI offers the opportunity to observe brain network dynamics with high spatio-temporal resolution. We utilize these measurements to compare the connectomes derived from rs-fMRI and EEG band limited power (BLP). Merging this multi-modal information requires the development of an appropriate statistical framework. We relate the covariance matrices of the Hilbert envelope of the source localized EEG signal across bands to the covariance matrices derived from rs-fMRI with the means of statistical prediction based on sparse Canonical Correlation Analysis (sCCA). Subsequently, we identify the most prominent connections that contribute to this relationship. We compare whole-brain functional connectomes based on their geodesic distance to reliably estimate the performance of the prediction. The performance of predicting fMRI from EEG connectomes is considerably better than predicting EEG from fMRI across all bands, whereas the connectomes derived in low frequency EEG bands resemble best rs-fMRI connectivity.

Aberrant EEG functional connectivity and EEG power spectra in resting state post-traumatic stress disorder: a sLORETA study

The aim of the present study was to explore the modifications of EEG power spectra and EEG connectivity of resting state (RS) condition in patients with post-traumatic stress disorder (PTSD). Seventeen patients and seventeen healthy subjects matched for age and gender were enrolled. EEG was recorded during 5min of RS. EEG analysis was conducted by means of the standardized Low Resolution Electric Tomography software (sLORETA). In power spectra analysis PTSD patients showed a widespread increase of theta activity (4.5-7.5Hz) in parietal lobes (Brodmann Area, BA 7, 4, 5, 40) and in frontal lobes (BA 6). In the connectivity analysis PTSD patients also showed increase of alpha connectivity (8-12.5Hz) between the cortical areas explored by Pz-P4 electrode. Our results could reflect the alteration of memory systems and emotional processing consistently altered in PTSD patients.