Relation between task-related activity modulation and cortical inhibitory function in schizophrenia and healthy controls: a TMS-EEG study.

Schizophrenia has been associated with a reduced task-related modulation of cortical activity assessed through electroencephalography (EEG). However, to the best of our knowledge, no study so far has assessed the underpinnings of this decreased EEG modulation in schizophrenia. A possible substrate of these findings could be a decreased inhibitory function, a replicated finding in the field. In this pilot study, our aim was to explore the association between EEG modulation during a cognitive task and the inhibitory system function in vivo in a sample including healthy controls and patients with schizophrenia. We hypothesized that the replicated decreased task-related activity modulation during a cognitive task in schizophrenia would be related to a hypofunction of the inhibitory system. For this purpose, 27 healthy controls and 22 patients with schizophrenia (including 13 first episodes) performed a 3-condition auditory oddball task from which the spectral entropy modulation was calculated. In addition, cortical reactivity-as an index of the inhibitory function-was assessed by the administration of 75 monophasic transcranial magnetic stimulation single pulses over the left dorsolateral prefrontal cortex. Our results replicated the task-related cortical activity modulation deficit in schizophrenia patients. Moreover, schizophrenia patients showed higher cortical reactivity following transcranial magnetic stimulation single pulses over the left dorsolateral prefrontal cortex compared to healthy controls. Cortical reactivity was inversely associated with EEG modulation, supporting the idea that a hypofunction of the inhibitory system could hamper the task-related modulation of EEG activity.

Connectivity-based Meta-Bands: A new approach for automatic frequency band identification in connectivity analyses.

The majority of electroencephalographic (EEG) and magnetoencephalographic (MEG) studies filter and analyse neural signals in specific frequency ranges, known as "canonical" frequency bands. However, this segmentation, is not exempt from limitations, mainly due to the lack of adaptation to the neural idiosyncrasies of each individual. In this study, we introduce a new data-driven method to automatically identify frequency ranges based on the topological similarity of the frequency-dependent functional neural network. The resting-state neural activity of 195 cognitively healthy subjects from three different databases (MEG: 123 subjects; EEG1: 27 subjects; EEG2: 45 subjects) was analysed. In a first step, MEG and EEG signals were filtered with a narrow-band filter bank (1 Hz bandwidth) from 1 to 70 Hz with a 0.5 Hz step. Next, the connectivity in each of these filtered signals was estimated using the orthogonalized version of the amplitude envelope correlation to obtain the frequency-dependent functional neural network. Finally, a community detection algorithm was used to identify communities in the frequency domain showing a similar network topology. We have called this approach the "Connectivity-based Meta-Bands" (CMB) algorithm. Additionally, two types of synthetic signals were used to configure the hyper-parameters of the CMB algorithm. We observed that the classical approaches to band segmentation are partially aligned with the underlying network topologies at group level for the MEG signals, but they are missing individual idiosyncrasies that may be biasing previous studies, as revealed by our methodology. On the other hand, the sensitivity of EEG signals to reflect this underlying frequency-dependent network structure is limited, revealing a simpler frequency parcellation, not aligned with that defined by the "canonical" frequency bands. To the best of our knowledge, this is the first study that proposes an unsupervised band segmentation method based on the topological similarity of functional neural network across frequencies. This methodology fully accounts for subject-specific patterns, providing more robust and personalized analyses, and paving the way for new studies focused on exploring the frequency-dependent structure of brain connectivity.

Abnormal meta-state activation of dynamic brain networks across the Alzheimer spectrum.

The characterization of the distinct dynamic functional connectivity (dFC) patterns that activate in the brain during rest can help to understand the underlying time-varying network organization. The presence and behavior of these patterns (known as meta-states) have been widely studied by means of functional magnetic resonance imaging (fMRI). However, modalities with high-temporal resolution, such as electroencephalography (EEG), enable the characterization of fast temporally evolving meta-state sequences. Mild cognitive impairment (MCI) and dementia due to Alzheimer's disease (AD) have been shown to disrupt spatially localized activation and dFC between different brain regions, but not much is known about how they affect meta-state network topologies and their network dynamics. The main hypothesis of the study was that MCI and dementia due to AD alter normal meta-state sequences by inducing a loss of structure in their patterns and a reduction of their dynamics. Moreover, we expected that patients with MCI would display more flexible behavior compared to patients with dementia due to AD. Thus, the aim of the current study was twofold: (i) to find repeating, distinctly organized network patterns (meta-states) in neural activity; and (ii) to extract information about meta-state fluctuations and how they are influenced by MCI and dementia due to AD. To accomplish these goals, we present a novel methodology to characterize dynamic meta-states and their temporal fluctuations by capturing aspects based on both their discrete activation and the continuous evolution of their individual strength. These properties were extracted from 60-s resting-state EEG recordings from 67 patients with MCI due to AD, 50 patients with dementia due to AD, and 43 cognitively healthy controls. First, the instantaneous amplitude correlation (IAC) was used to estimate instantaneous functional connectivity with a high temporal resolution. We then extracted meta-states by means of graph community detection based on recurrence plots (RPs), both at the individual- and group-level. Subsequently, a diverse set of properties of the continuous and discrete fluctuation patterns of the meta-states was extracted and analyzed. The main novelty of the methodology lies in the usage of Louvain GJA community detection to extract meta-states from IAC-derived RPs and the extended analysis of their discrete and continuous activation. Our findings showed that distinct dynamic functional connectivity meta-states can be found on the EEG time-scale, and that these were not affected by the oscillatory slowing induced by MCI or dementia due to AD. However, both conditions displayed a loss of meta-state modularity, coupled with shorter dwell times and higher complexity of the meta-state sequences. Furthermore, we found evidence that meta-state sequencing is not entirely random; it shows an underlying structure that is partially lost in MCI and dementia due to AD. These results show evidence that AD progression is associated with alterations in meta-state switching, and a degradation of dynamic brain flexibility.

Entropy in Brain Networks.

A thorough and comprehensive understanding of the human brain ultimately depends on knowledge of large-scale brain organization[...].

Exploring the Alterations in the Distribution of Neural Network Weights in Dementia Due to Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder which has become an outstanding social problem. The main objective of this study was to evaluate the alterations that dementia due to AD elicits in the distribution of functional network weights. Functional connectivity networks were obtained using the orthogonalized Amplitude Envelope Correlation (AEC), computed from source-reconstructed resting-state eletroencephalographic (EEG) data in a population formed by 45 cognitive healthy elderly controls, 69 mild cognitive impaired (MCI) patients and 81 AD patients. Our results indicated that AD induces a progressive alteration of network weights distribution; specifically, the Shannon entropy (SE) of the weights distribution showed statistically significant between-group differences (p < 0.05, Kruskal-Wallis test, False Discovery Rate corrected). Furthermore, an in-depth analysis of network weights distributions was performed in delta, alpha, and beta-1 frequency bands to discriminate the weight ranges showing statistical differences in SE. Our results showed that lower and higher weights were more affected by the disease, whereas mid-range connections remained unchanged. These findings support the importance of performing detailed analyses of the network weights distribution to further understand the impact of AD progression on functional brain activity.

Analysis of KCNH2 and CACNA1C schizophrenia risk genes on EEG functional network modulation during an auditory odd-ball task.

A deficit in task-related functional connectivity modulation from electroencephalogram (EEG) has been described in schizophrenia. The use of measures of neuronal connectivity as an intermediate phenotype may allow identifying genetic factors involved in these deficits, and therefore, establishing underlying pathophysiological mechanisms. Genes involved in neuronal excitability and previously associated with the risk for schizophrenia may be adequate candidates in relation to functional connectivity alterations in schizophrenia. The objective was to study the association of two genes of voltage-gated ion channels (CACNA1C and KCNH2) with the functional modulation of the cortical networks measured with EEG and graph-theory parameter during a cognitive task, both in individuals with schizophrenia and healthy controls. Both CACNA1C (rs1006737) and KCNH2 (rs3800779) were genotyped in 101 controls and 50 schizophrenia patients. Small-world index (SW) was calculated from EEG recorded during an odd-ball task in two different temporal windows (pre-stimulus and response). Modulation was defined as the difference in SW between both windows. Genetic, group and their interaction effects on SW in the pre-stimulus window and in modulation were evaluated using ANOVA. The CACNA1C genotype was not associated with SW properties. KCNH2 was significantly associated with SW modulation. Healthy subjects showed a positive SW modulation irrespective of the KCNH2 genotype, whereas within patients allele-related differences were observed. Patients carrying the KCNH2 risk allele (A) presented a negative SW modulation and non-carriers showed SW modulation similar to the healthy subjects. Our data suggest that KCNH2 genotype contributes to the efficient modulation of brain electrophysiological activity during a cognitive task in schizophrenia patients.

Relationship between the Presence of the ApoE ε4 Allele and EEG Complexity along the Alzheimer's Disease Continuum.

Alzheimer's disease (AD) is the most prevalent cause of dementia, being considered a major health problem, especially in developed countries. Late-onset AD is the most common form of the disease, with symptoms appearing after 65 years old. Genetic determinants of AD risk are vastly unknown, though, ε 4 allele of the ApoE gene has been reported as the strongest genetic risk factor for AD. The objective of this study was to analyze the relationship between brain complexity and the presence of ApoE ε 4 alleles along the AD continuum. For this purpose, resting-state electroencephalography (EEG) activity was analyzed by computing Lempel-Ziv complexity (LZC) from 46 healthy control subjects, 49 mild cognitive impairment subjects, 45 mild AD patients, 44 moderate AD patients and 33 severe AD patients, subdivided by ApoE status. Subjects with one or more ApoE ε 4 alleles were included in the carriers subgroups, whereas the ApoE ε 4 non-carriers subgroups were formed by subjects without any ε 4 allele. Our results showed that AD continuum is characterized by a progressive complexity loss. No differences were observed between AD ApoE ε 4 carriers and non-carriers. However, brain activity from healthy subjects with ApoE ε 4 allele (carriers subgroup) is more complex than from non-carriers, mainly in left temporal, frontal and posterior regions (p-values < 0.05, FDR-corrected Mann-Whitney U-test). These results suggest that the presence of ApoE ε 4 allele could modify the EEG complexity patterns in different brain regions, as the temporal lobes. These alterations might be related to anatomical changes associated to neurodegeneration, increasing the risk of suffering dementia due to AD before its clinical onset. This interesting finding might help to advance in the development of new tools for early AD diagnosis.

Topography of activation deficits in schizophrenia during P300 task related to cognition and structural connectivity.

BACKGROUND: The study of cerebral underpinnings of schizophrenia may benefit from the high temporal resolution of electromagnetic techniques, but its spatial resolution is low. However, source imaging approaches such as low-resolution brain electromagnetic tomography (LORETA) allow for an acceptable compromise between spatial and temporal resolutions. METHODS: We combined LORETA with 32 channels and 3-Tesla diffusion magnetic resonance (Dmr) to study cerebral dysfunction in 38 schizophrenia patients (17 first episodes, FE), compared to 53 healthy controls. The EEG was acquired with subjects performing an odd-ball task. Analyses included an adaptive window of interest to take into account the interindividual variability of P300 latency. We compared source activation patters to distractor (P3a) and target (P3b) tones within- and between-groups. RESULTS: Patients showed a reduced activation in anterior cingulate and lateral and medial prefrontal cortices, as well as inferior/orbital frontal regions. This was also found in the FE patients alone. The activation was directly related to IQ in the patients and controls and to working memory performance in controls. Symptoms were unrelated to source activation. Fractional anisotropy in the tracts connecting lateral prefrontal and anterior cingulate regions predicted source activation in these regions in the patients. CONCLUSIONS: These results replicate the source activation deficit found in a previous study with smaller sample size and a lower number of sensors and suggest an association between structural connectivity deficits and functional alterations.

EEG Characterization of the Alzheimer's Disease Continuum by Means of Multiscale Entropies.

Alzheimer's disease (AD) is a neurodegenerative disorder with high prevalence, known for its highly disabling symptoms. The aim of this study was to characterize the alterations in the irregularity and the complexity of the brain activity along the AD continuum. Both irregularity and complexity can be studied applying entropy-based measures throughout multiple temporal scales. In this regard, multiscale sample entropy (MSE) and refined multiscale spectral entropy (rMSSE) were calculated from electroencephalographic (EEG) data. Five minutes of resting-state EEG activity were recorded from 51 healthy controls, 51 mild cognitive impaired (MCI) subjects, 51 mild AD patients (ADMIL), 50 moderate AD patients (ADMOD), and 50 severe AD patients (ADSEV). Our results show statistically significant differences (p-values < 0.05, FDR-corrected Kruskal-Wallis test) between the five groups at each temporal scale. Additionally, average slope values and areas under MSE and rMSSE curves revealed significant changes in complexity mainly for controls vs. MCI, MCI vs. ADMIL and ADMOD vs. ADSEV comparisons (p-values < 0.05, FDR-corrected Mann-Whitney U-test). These findings indicate that MSE and rMSSE reflect the neuronal disturbances associated with the development of dementia, and may contribute to the development of new tools to track the AD progression.

Relations between structural and EEG-based graph metrics in healthy controls and schizophrenia patients.

Our aim was to assess structural and functional networks in schizophrenia patients; and the possible prediction of the latter based on the former. The possible dependence of functional network properties on structural alterations has not been analyzed in schizophrenia. We applied averaged path-length (PL), clustering coefficient, and density (D) measurements to data from diffusion magnetic resonance and electroencephalography in 39 schizophrenia patients and 79 controls. Functional data were collected for the global and theta frequency bands during an odd-ball task, prior to stimulus delivery and at the corresponding processing window. Connectivity matrices were constructed from tractography and registered cortical segmentations (structural) and phase-locking values (functional). Both groups showed a significant electroencephalographic task-related modulation (change between prestimulus and response windows) in the global and theta bands. Patients showed larger structural PL and prestimulus density in the global and theta bands, and lower PL task-related modulation in the theta band. Structural network values predicted prestimulus global band values in controls and global band task-related modulation in patients. Abnormal functional values found in patients (prestimulus density in the global and theta bands and task-related modulation in the theta band) were not predicted by structural data in this group. Structural and functional network abnormalities respectively predicted cognitive performance and positive symptoms in patients. Taken together, the alterations in the structural and functional theta networks in the patients and the lack of significant relations between these alterations, suggest that these types of network abnormalities exist in different groups of schizophrenia patients.

Continuous wavelet transform in the study of the time-scale properties of intracranial pressure in hydrocephalus.

Normal pressure hydrocephalus (NPH) encompasses a heterogeneous group of disorders generally characterized by clinical symptoms, ventriculomegaly and anomalous cerebrospinal fluid (CSF) dynamics. Lumbar infusion tests (ITs) are frequently performed in the preoperatory evaluation of patients who show NPH features. The analysis of intracranial pressure (ICP) signals recorded during ITs could be useful to better understand the pathophysiology underlying NPH and to assist treatment decisions. In this study, 131 ICP signals recorded during ITs were analysed using two continuous wavelet transform (CWT)-derived parameters: Jensen divergence (JD) and spectral flux (SF). These parameters were studied in two frequency bands, associated with different components of the signal: B1(0.15-0.3 Hz), related to respiratory blood pressure oscillations; and B2 (0.67-2.5 Hz), related to ICP pulse waves. Statistically significant differences (p < 1.70 × 10-3, Bonferroni-corrected Wilcoxon signed-rank tests) in pairwise comparisons between phases of ITs were found using the mean and standard deviation of JD and SF. These differences were mainly found in B2, where a lower irregularity and variability, together with less prominent time-frequency fluctuations, were found in the hypertension phase of ITs. Our results suggest that wavelet analysis could be useful for understanding CSF dynamics in NPH.This article is part of the theme issue 'Redundancy rules: the continuous wavelet transform comes of age'.

Automated Multiclass Classification of Spontaneous EEG Activity in Alzheimer's Disease and Mild Cognitive Impairment.

The discrimination of early Alzheimer's disease (AD) and its prodromal form (i.e., mild cognitive impairment, MCI) from cognitively healthy control (HC) subjects is crucial since the treatment is more effective in the first stages of the dementia. The aim of our study is to evaluate the usefulness of a methodology based on electroencephalography (EEG) to detect AD and MCI. EEG rhythms were recorded from 37 AD patients, 37 MCI subjects and 37 HC subjects. Artifact-free trials were analyzed by means of several spectral and nonlinear features: relative power in the conventional frequency bands, median frequency, individual alpha frequency, spectral entropy, Lempel-Ziv complexity, central tendency measure, sample entropy, fuzzy entropy, and auto-mutual information. Relevance and redundancy analyses were also conducted through the fast correlation-based filter (FCBF) to derive an optimal set of them. The selected features were used to train three different models aimed at classifying the trials: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and multi-layer perceptron artificial neural network (MLP). Afterwards, each subject was automatically allocated in a particular group by applying a trial-based majority vote procedure. After feature extraction, the FCBF method selected the optimal set of features: individual alpha frequency, relative power at delta frequency band, and sample entropy. Using the aforementioned set of features, MLP showed the highest diagnostic performance in determining whether a subject is not healthy (sensitivity of 82.35% and positive predictive value of 84.85% for HC vs. all classification task) and whether a subject does not suffer from AD (specificity of 79.41% and negative predictive value of 84.38% for AD vs. all comparison). Our findings suggest that our methodology can help physicians to discriminate AD, MCI and HC.

Absolute Power Spectral Density Changes in the Magnetoencephalographic Activity During the Transition from Childhood to Adulthood.

The aim of this study was to define the pattern of reduction in absolute power spectral density (PSD) of magnetoencephalography (MEG) signals throughout development. Specifically, we wanted to explore whether the human skull's high permeability for electromagnetic fields would allow us to question whether the pattern of absolute PSD reduction observed in the human electroencephalogram is due to an increase in the skull's resistive properties with age. Furthermore, the topography of the MEG signals during maturation was explored, providing additional insights about the areas and brain rhythms related to late maturation in the human brain. To attain these goals, spontaneous MEG activity was recorded from 148 sensors in a sample of 59 subjects divided into three age groups: children/adolescents (7-14 years), young adults (17-20 years) and adults (21-26 years). Statistical testing was carried out by means of an analysis of variance (ANOVA), with "age group" as between-subject factor and "sensor group" as within-subject factor. Additionally, correlations of absolute PSD with age were computed to assess the influence of age on the spectral content of MEG signals. Results showed a broadband PSD decrease in frontal areas, which suggests the late maturation of this region, but also a mild increase in high frequency PSD with age in posterior areas. These findings suggest that the intensity of the neural sources during spontaneous brain activity decreases with age, which may be related to synaptic pruning.

Variation at NRG1 genotype related to modulation of small-world properties of the functional cortical network.

Functional brain networks possess significant small-world (SW) properties. Genetic variation relevant to both inhibitory and excitatory transmission may contribute to modulate these properties. In healthy controls, genotypic variation in Neuregulin 1 (NRG1) related to the risk of psychosis (risk alleles) would contribute to functional SW modulation of the cortical network. Electroencephalographic activity during an odd-ball task was recorded in 144 healthy controls. Then, small-worldness (SWn) was calculated in five frequency bands (i.e., theta, alpha, beta1, beta2 and gamma) for baseline (from -300 to the stimulus onset) and response (150-450 ms post-target stimulus) windows. The SWn modulation was defined as the difference in SWn between both windows. Association between SWn modulation and carrying the risk allele for three single nucleotide polymorphisms (SNP) of NRG1 (i.e., rs6468119, rs6994992 and rs7005606) was assessed. A significant association between three SNPs of NRG1 and the SWn modulation was found, specifically: NRG1 rs6468119 in alpha and beta1 bands; NRG1 rs6994992 in theta band; and NRG1 rs7005606 in theta and beta1 bands. Genetic variation at NRG1 may influence functional brain connectivity through the modulation of SWn properties of the cortical network.

Noise power associated with decreased task-induced variability of brain electrical activity in schizophrenia.

In schizophrenia, both increased baseline metabolic and electroencephalographic (EEG) activities as well as decreased task-related modulation of neural dynamics have been reported. Noise power (NP) can measure the background EEG activity during task performance, and Shannon entropy (SE) is useful for quantifying the global modulation of EEG activity with a high temporal resolution. In this study, we have assessed the possible relationship between increased NP in theta and gamma bands and decreased SE modulation in 24 patients with schizophrenia and 26 controls over the parietal and central regions during a P300 task. SE modulation was calculated as the change from baseline to the active epoch (i.e., 150-550 ms following the target stimulus onset). Patients with schizophrenia displayed statistically significant higher NP values and lower SE modulation than healthy controls. We found a significant association between gamma NP and SE in all of the participants. Specifically, a NP increase in the gamma band was followed by a decrease in SE change. These results support the notion that an excess of gamma activity, unlocked to the task being performed, is accompanied by a decreased modulation of EEG activity in schizophrenia.

Association between electroencephalographic modulation, psychotic-like experiences and cognitive performance in the general population.

AIM: An association between deficit of electroencephalographic (EEG) modulation during an odd-ball task and psychotic symptoms has been described in clinical samples, in agreement with the proposed role for altered salience in psychosis. To discard the possible influence of medication, the relationship between psychotic-like experiences and EEG modulation in the general population was explored. METHODS: EEG and psychotic-like experiences were assessed in 194 healthy subjects during a P300 paradigm. EEG modulation was assessed as changes from pre-stimulus to response windows in spectral entropy (SE, a measurement of signal irregularity), median frequency (MF, a quantifier of the frequency distribution of oscillatory activity) and theta, alpha, beta-1, beta-2 and gamma relative power (RP, a summary of the distribution of spectral components). RESULTS: A significant widespread decrease in SE and MF from baseline to response was found, with a significant increase in RP for theta and a decrease for higher frequency bands, supporting an increase in EEG regularity and a slowing of brain oscillations during the response. Furthermore, a significant association was found between SE modulation and distress of negative psychotic-like experiences, as well as between verbal memory and RP modulation for beta-1. Performance in verbal fluency was associated with the increase in theta RP during the response. CONCLUSION: EEG irregularity of healthy subjects decreased at the expense of a larger contribution of theta RP and a decreased contribution of fast frequency bands. Subjects with smaller modulation showed poorer cognitive scores and greater distress of negative psychotic-like experiences.

Decreased entropy modulation of EEG response to novelty and relevance in schizophrenia during a P300 task.

The analysis of the interaction between novelty and relevance may be of interest to test the aberrant salience hypothesis of schizophrenia (SCH). In comparison with other neuroimaging techniques, such as functional magnetic resonance imaging, electroencephalography (EEG) provides high temporal resolution. Therefore, EEG is useful to analyze transient dynamics in neural activity, even in the range of milliseconds. In this study, EEG activity from 31 patients with SCH and 38 controls was analyzed using Shannon spectral entropy (SE) and median frequency (MF). The aim of the study was to quantify differences between distractor (i.e., novelty) and target (i.e., novelty and relevance) tones in an auditory oddball paradigm. Healthy controls displayed a larger SE decrease in response to target stimulus than in response to distractor tones. SE decrease was accompanied by a significant and widespread reduction of MF (i.e., a significant slowing of EEG activity). In comparison with controls, patients showed a significant reduction of changes in SE in response to both target and distractor tones. These differences were also observed in patients that only received a minimal treatment prior to EEG recording. Furthermore, significant changes in SE were inversely correlated to positive and total symptoms severity for SCH patients. Our findings support the notion that SCH is associated with a reduced response to both novelty and relevance during an auditory P300 task.

Decreased spectral entropy modulation in patients with schizophrenia during a P300 task.

Spectral entropy (SE), also known as Shannon entropy, is a useful parameter for quantifying the global regularity of the electroencephalographic (EEG) signal. Hence, it is of interest in the assessment of the electrophysiological correlates of cognitive processing in schizophrenia. However, to date, SE has been barely used in studies comparing resting EEG recordings between patients and controls. In this work, we compared SE between resting baseline [-250 0] ms and active task [150 550] ms windows of a P300 task in 31 patients with schizophrenia and 38 controls. Moreover, we also calculated the median frequency (MF) and relative power in each frequency band for these windows to assess the correlates of the possible SE differences. Controls showed a significant (p < 0.0029) SE decrease (i.e., meaning higher signal regularity) from baseline to the active task window at parietal and central electrode sites. This SE decrease from baseline to active conditions was significantly lower in patients. In controls, this SE decrease was accompanied by a statistically significant decrease in MF (i.e., a significant slowing of the EEG activity), not observed in patients. In this latter group, the difference in SE between resting baseline and active task windows was inversely correlated to positive and total symptoms scores, as measured with the positive and negative symptoms scale. Our data support the relevance of SE in the study of cerebral processing in schizophrenia.

Tracking EEG network dynamics through transitions between eyes-closed, eyes-open, and task states.

Our study aimed to verify the possibilities of effectively applying chronnectomics methods to reconstruct the dynamic processes of network transition between three types of brain states, namely, eyes-closed rest, eyes-open rest, and a task state. The study involved dense EEG recordings and reconstruction of the source-level time-courses of the signals. Functional connectivity was measured using the phase lag index, and dynamic analyses concerned coupling strength and variability in alpha and beta frequencies. The results showed significant and dynamically specific transitions regarding processes of eyes opening and closing and during the eyes-closed-to-task transition in the alpha band. These observations considered a global dimension, default mode network, and central executive network. The decrease of connectivity strength and variability that accompanied eye-opening was a faster process than the synchronization increase during eye-opening, suggesting that these two transitions exhibit different reorganization times. While referring the obtained results to network studies, it was indicated that the scope of potential similarities and differences between rest and task-related networks depends on whether the resting state was recorded in eyes closed or open condition.

Functional network properties in schizophrenia and bipolar disorder assessed with high-density electroencephalography.

BACKGROUND: The study of the cortical functional network properties in schizophrenia (SZ) may benefit from the use of graph theory parameters applied to high-density electroencephalography (EEG). Connectivity Strength (CS) assesses global synchrony of the network, and Shannon Graph Complexity (SGC) summarizes the network distribution of link weights and allows distinguishing between primary and secondary pathways. Their joint use may help in understanding the underpinnings of the functional network hyperactivation and task-related hypomodulation previously described in psychoses. METHODS: We used 64-sensor EEG recordings during a P300 oddball task in 128 SZ patients (96 chronic, CR, and 32 first episodes, FE), as well as 46 bipolar disorder (BD) patients, and 92 healthy controls (HC). Pre-stimulus and modulation (task-response minus pre-stimulus windows values) of CS and SGC were assessed in the theta band (4-8 Hz) and the broadband (4-70 Hz). RESULTS: Compared to HC, SZ patients (CR and FE) showed significantly higher pre-stimulus CS values in the broadband, and both SZ and BD patients showed lower theta-band CS modulation. SGC modulation values, both theta-band and broadband, were also abnormally reduced in CR patients. Statistically significant relationships were found in the theta band between SGC modulation and both CS pre-stimulus and modulation values in patients. CS altered measures in patients were additionally related to their cognitive outcome and negative symptoms. A primary role of antipsychotics in these results was ruled out. CONCLUSIONS: Our results linking SGC and CS alterations in psychotic patients supported a hyperactive and hypomodulatory network mainly involving connections in secondary pathways.