Spatiotemporal organization of brain dynamics and intelligence: an EEG study in adolescents

Multimodal Brain Signal Complexity Predicts Human Intelligence

Spontaneous brain activity builds the foundation for human cognitive processing during external demands. Neuroimaging studies based on functional magnetic resonance imaging (fMRI) identified specific characteristics of spontaneous (intrinsic) brain dynamics to be associated with individual differences in general cognitive ability, i.e., intelligence. However, fMRI research is inherently limited by low temporal resolution, thus, preventing conclusions about neural fluctuations within the range of milliseconds. Here, we used resting-state electroencephalographical (EEG) recordings from 144 healthy adults to test whether individual differences in intelligence (Raven's Advanced Progressive Matrices scores) can be predicted from the complexity of temporally highly resolved intrinsic brain signals. We compared different operationalizations of brain signal complexity (multiscale entropy, Shannon entropy, Fuzzy entropy, and specific characteristics of microstates) regarding their relation to intelligence. The results indicate that associations between brain signal complexity measures and intelligence are of small effect sizes (r ∼ 0.20) and vary across different spatial and temporal scales. Specifically, higher intelligence scores were associated with lower complexity in local aspects of neural processing, and less activity in task-negative brain regions belonging to the default-mode network. Finally, we combined multiple measures of brain signal complexity to show that individual intelligence scores can be significantly predicted with a multimodal model within the sample (10-fold cross-validation) as well as in an independent sample (external replication, N = 57). In sum, our results highlight the temporal and spatial dependency of associations between intelligence and intrinsic brain dynamics, proposing multimodal approaches as promising means for future neuroscientific research on complex human traits.

INTELLIGENCE LEVEL MIGHT BE PREDICTED BY THE CHARACTERISTICS OF EEG SIGNALS AT SPECIFIC FREQUENCIES AND BRAIN REGIONS

The total energy the brain consumed and the intensities of information flows across different brain regions in an intellectual activity may help to explain an individual’s intelligence level. To verify this assumption, 43 students aged 18–25 were recruited as the research subjects. Their intelligence quotients (IQ) were scored by using Wechsler Adult Intelligence Scale (WAIS), while their electroencephalogram (EEG) signals were recorded simultaneously by using Neuroscan system. The total energy and distribution patterns of EEG signals were acquired in Curry 8.0. The intensities of information flow across different brain regions were measured by Phase Slope Index (PSI). 20 channels and 190 channel combinations were selected for data analysis. The results show that the IQ score negatively correlates to the EEG energy and positively correlates to the intensities of information flows at specific frequency bands in specific channel pairs, especially in some long distance (18–24[Formula: see text]cm) channel pairs.

Distance-based functional criticality in the human brain: intelligence and emotional intelligence

Background

Anatomical distance has been identified as a key factor in the organizational principles of the human brain. On the other hand, criticality was proposed to accommodate the multiscale properties of human brain dynamics, and functional criticality based on resting-state functional magnetic resonance imaging (rfMRI) is a sensitive neuroimaging marker for human brain dynamics. Hence, to explore the effects of anatomical distance of the human brain on behaviors in terms of functional criticality, we proposed a revised algorithm of functional criticality called the distance-based vertex-wise index of functional criticality, and assessed this algorithm compared with the original neighborhood-based functional criticality.

Results

We recruited two groups of healthy participants, including young adults and middle-aged participants, for a total of 60 datasets including rfMRI and intelligence as well as emotional intelligence to study how human brain functional criticalities at different spatial scales contribute to individual behaviors. Furthermore, we defined the average distance between the particular behavioral map and vertices with significant functional connectivity as connectivity distance. Our results demonstrated that intelligence and emotional intelligence mapped to different brain regions at different ages. Additionally, intelligence was related to a wider distance range compared to emotional intelligence.

Conclusions

For different age groups, our findings not only provided a linkage between intelligence/emotional intelligence and functional criticality but also quantitatively characterized individual behaviors in terms of anatomical distance.

Spatiotemporal complexity patterns of resting-state bioelectrical activity explain fluid intelligence: Sex matters

Neural complexity is thought to be associated with efficient information processing but the exact nature of this relation remains unclear. Here, the relationship of fluid intelligence (gf) with the resting-state EEG (rsEEG) complexity over different timescales and different electrodes was investigated. A 6-min rsEEG blocks of eyes open were analyzed. The results of 119 subjects (57 men, mean age = 22.85 ± 2.84 years) were examined using multivariate multiscale sample entropy (mMSE) that quantifies changes in information richness of rsEEG in multiple data channels at fine and coarse timescales. gf factor was extracted from six intelligence tests. Partial least square regression analysis revealed that mainly predictors of the rsEEG complexity at coarse timescales in the frontoparietal network (FPN) and the temporo-parietal complexities at fine timescales were relevant to higher gf. Sex differently affected the relationship between fluid intelligence and EEG complexity at rest. In men, gf was mainly positively related to the complexity at coarse timescales in the FPN. Furthermore, at fine and coarse timescales positive relations in the parietal region were revealed. In women, positive relations with gf were mostly observed for the overall and the coarse complexity in the FPN, whereas negative associations with gf were found for the complexity at fine timescales in the parietal and centro-temporal region. These outcomes indicate that two separate time pathways (corresponding to fine and coarse timescales) used to characterize rsEEG complexity (expressed by mMSE features) are beneficial for effective information processing.

EEG correlation during the solving of simple and complex logical-mathematical problems

Solving logical-mathematical word problems is a complex task that requires numerous cognitive operations, including comprehension, reasoning, and calculation. These abilities have been associated with activation of the parietal, temporal, and prefrontal cortices. It has been suggested that the reasoning involved in solving logical-mathematical problems requires the coordinated functionality of all these cortical areas. In this study was evaluated the activation and electroencephalographic (EEG) correlation of the prefrontal, temporal, and parietal regions in young men while solving logical-mathematical word problems with two degrees of difficulty: simple and complex. During the solving of complex problems, higher absolute power and EEG correlation of the alpha and fast bands between the left frontal and parietal cortices were observed. A temporal deactivation and functional decoupling of the right parietal-temporal cortices also were obtained. Solving complex problems probably require activation of a left prefrontal-parietal circuit to maintain and manipulate multiple pieces of information. The temporal deactivation and decreased parietal-temporal correlation could be associated to text processing and suppression of the content-dependent reasoning to focus cognitive resources on the mathematical reasoning. Together, these findings support a pivotal role for the left prefrontal and parietal cortices in mathematical reasoning and of the temporal regions in text processing required to understand and solve written mathematical problems.

Observing baby or sexual videos changes the functional synchronization between the prefrontal and parietal cortices in mothers in different postpartum periods

During the postpartum period (PP), mothers are more sensitive to sensory stimuli related to babies and less sensitive to those with sexual significance. The processing of emotional stimuli requires synchronization among different cerebral areas. This study characterized the cortical electroencephalographic (EEG) correlation in mothers from 1½ to 3 months (PP1), 4 to 5½ months (PP2) and over 6½ months, postpartum (PP3) while observing two videos: one of a baby (BV) and one with sexual content (SV). EEGs were recorded from the frontopolar, dorsolateral and parietal cortices. All three groups rated the BV as pleasant, but only PP3 reported higher sexual arousal with the SV. While watching the BV, PP1 showed a higher correlation among all cortical areas; PP2 manifested a decreased correlation between the prefrontal and parietal cortices, likely associated with the lower emotional modulation of the BV; and PP3 presented a higher synchronization among fewer cortical areas, probably related to longer maternal experience. These cortical synchronization patterns could represent adaptive mechanisms that enable the adequate processing of baby stimuli in new mothers. These data increase our knowledge of the cerebral processes associated with distinct sensitivities to the emotional stimuli that mothers experience during the PP.

Prediction of working memory ability based on EEG by functional data analysis

BACKGROUND

There is always a demand for fast and accurate algorithms for EEG signal processing. Owing to the high sample rate, EEG signals usually come with a large number of sample points, making it difficult to predict the working memory ability in cognitive research with EEG.

NEW METHOD

Following well-designed experiments, the functional linear model provides a simple framework for regressions involving EEG signal predictors. The use of a data-driven basis in a linear structure naturally extends the standard linear regression model. The proposed approach utilizes B-spline approximation of functional principal components that greatly facilitates implementation.

RESULTS

Using LASSO feature selection, critical features have been extracted from eight frontal electrodes, and the R-square of 0.72 indicates rather strong linear association of actual observations and out-of-sample predictions.

COMPARISON WITH EXISTING METHODS

There does not seem to be any existing methods of predicting working memory ability from N-back task tests via EEG signals; the data-driven functional linear regression method proposed in this work is, to the best of our knowledge, the first of its kind.

CONCLUSIONS

The data analytics suggest that a multiple functional linear regression model for the predictive relationship between working memory ability and frontal activity of the brain is both feasible and accurate via EEG signal processing.

The neural code of intelligence: From correlation to causation

Research into the neural underpinning of intelligence has mainly adopted a construct perspective: trying to find structural and functional brain characteristics that would accommodate the psychological concept of g. Few attempts have been made to explain intelligence exclusively based on brain characteristics - the brain perspective. From a methodological viewpoint the brain intelligence relation has been studied by means of correlational and interventional studies. The later providing a causal elucidation of the brain - intelligence relation. The best neuro-anatomical predictor of intelligence is brain volume showing a modest positive correlation with g, explaining between 9 to 16% of variance. The most likely explanation was that larger brains, containing more neurons, have a greater computational power and in that way allow more complex cognitive processing. Correlations with brain surface, thickness, convolution and callosal shape showed less consistent patterns. The development of diffusion tensor imaging has allowed researchers to look also into the microstructure of brain tissue. Consistently observed was a positively correlation between white matter integrity and intelligence, supporting the idea that efficient information transfer between hemispheres and brain areas is crucial for higher intellectual competence. Based on functional studies of the brain intelligence relationship three theories have been put forward: the neural efficiency, the P-FIT and the multi demand (MD) system theory. On the other hand, The Network Neuroscience Theory of g, based on methods from mathematics, physics, and computer science, is an example for the brain perspective on neurobiological underpinning of intelligence. In this framework network flexibility and dynamics provide the foundation for general intelligence. With respect to intervention studies the most promising results have been achieved with noninvasive brain stimulation and behavioral training providing tentative support for findings put forward by the correlational approach. To date the best consensus based on the diversity of results reported would be that g is predominantly determined by lateral prefrontal attentional control of structured sensory episodes in posterior brain areas. The capacity of flexible transitions between these network states represents the essence of intelligence - g.

Hypersynchronization in mild cognitive impairment: the ‘X’ model

Hypersynchronization has been considered as a biomarker of synaptic dysfunction along the Alzheimeŕs disease continuum. In a longitudinal MEG study, Pusil et al. reveal changes in functional connectivity upon progression from MCI to Alzheimer’s disease. They propose the ‘X’ model to explain their findings, and suggest that hypersynchronization predicts conversion.

An EEG Study on Emotional Intelligence and Advertising Message Effectiveness

Some electroencephalography (EEG) studies have investigated emotional intelligence (EI), but none have examined the relationships between EI and commercial advertising messages and related consumer behaviors. This study combines brain (EEG) techniques with an EI psychometric to explore the brain responses associated with a range of advertisements. A group of 45 participants (23 females, 22 males) had their EEG recorded while watching a series of advertisements selected from various marketing categories such as community interests, celebrities, food/drink, and social issues. Participants were also categorized as high or low in emotional intelligence (n = 34). The EEG data analysis was centered on rating decision-making in order to measure brain responses associated with advertising information processing for both groups. The findings suggest that participants with high and low emotional intelligence (EI) were attentive to different types of advertising messages. The two EI groups demonstrated preferences for “people” or “object,” related advertising information. This suggests that differences in consumer perception and emotions may suggest why certain advertising material or marketing strategies are effective or not.

Brain Responses to Passive Sensory Stimulation Correlate With Intelligence

This study investigates the association between intelligence and brain power responses to a passive audiovisual stimulation. We measure the power of gamma-range steady-state responses (SSRs) as well as intelligence and other aspects of neurocognitive function in 40 healthy males born in 1953. The participants are a part of a Danish birth cohort study and the data therefore include additional information measured earlier in life. Our main power measure is the difference in power between a visual stimulation and a combined audiovisual stimulation. We hypothesize and establish empirically that the power measure is associated with intelligence. In particular, we find a highly significant correlation between the power measure and present intelligence scores. The association is robust to controlling for size-at-birth measures, length of education, speed of processing as well as a range of other potentially confounding factors. Interestingly, we find that intelligence scores measured earlier in life (childhood, youth, late midlife), are also correlated with the present-day power measure, suggesting a deep connection between intelligence and the power measure. Finally, we find that the power measure has a high sensitivity for detection of an intelligence score below the average.

Lifespan Changes in Network Structure and Network Topology Dynamics During Rest and Auditory Oddball Performance

Behavioral and physiological evidence suggests that developmental changes lead to enhanced cortical differentiation and integration through maturation and learning, and that senescent changes during aging result in dedifferentiation and reduced cortical specialization of neural cell assemblies. We used electroencephalographic (EEG) recordings to evaluate network structure and network topology dynamics during rest with eyes closed and open, and during auditory oddball task across the lifespan. For this evaluation, we constructed a hyper-frequency network (HFN) based on within- and cross-frequency coupling (WFC and CFC, respectively) at 10 oscillation frequencies ranging between 2 and 20 Hz. We found that WFC increased monotonously across the lifespan, whereas CFC showed a U-shaped relationship. These changes in WFC and CFC strengths coevolve with changes in network structure and network topology dynamics, namely the magnitude of graph-theoretical topology measures increased linearly with age (except for characteristic path length, which is going shorter), while their standard deviation showed an inverse U-shaped relationship with a peak in young adults. Temporal as well as structural or nodal similarity of network topology (with some exceptions) seems to coincide with variability changes, i.e., stronger variability is related to higher similarity between consecutive time windows or nodes. Furthermore, network complexity measures showed different lifespan-related patterns, which depended on the balance of WFC and CFC strengths. Both variability and complexity of HFNs were strongly related to the perceptual speed scores. Finally, investigation of the modular organization of the networks revealed higher number of modules and stronger similarity of community structures across time in young adults as compared with children and older adults. We conclude that network variability and complexity measures reflect temporal and structural topology changes in the functional organization and reorganization of neuronal cell assemblies across the lifespan.

Characterizing synchrony patterns across cognitive task stages of associative recognition memory

Numerous studies seek to understand the role of oscillatory synchronization in cognition. This problem is particularly challenging in the context of complex cognitive behavior, which consists of a sequence of processing steps with uncertain duration. In this study, we analyzed oscillatory connectivity measures in time windows that previous computational models had associated with a specific sequence of processing steps in an associative memory recognition task (visual encoding, familiarity, memory retrieval, decision making, and motor response). The timing of these processing steps was estimated on a single‐trial basis with a novel hidden semi‐Markov model multivariate pattern analysis (HSMM‐MVPA) method. We show that different processing stages are associated with specific patterns of oscillatory connectivity. Visual encoding is characterized by a dense network connecting frontal, posterior, and temporal areas as well as frontal and occipital phase locking in the 4–9 Hz theta band. Familiarity is associated with frontal phase locking in the 9–14 Hz alpha band. Decision making is associated with frontal and temporo‐central interhemispheric connections in the alpha band. During decision making, a second network in the theta band that connects left‐temporal, central, and occipital areas bears similarity to the neural signature for preparing a motor response. A similar theta band network is also present during the motor response, with additionally alpha band connectivity between right‐temporal and posterior areas. This demonstrates that the processing stages discovered with the HSMM‐MVPA method are indeed linked to distinct synchronization patterns, leading to a closer understanding of the functional role of oscillations in cognition.

Self-guided Positive Imagery Training: Effects beyond the Emotions-A Loreta Study

Previously we demonstrated that a 12-week lasting self-guided positive imagery training had a positive effect on the psycho-emotional state of healthy subjects and was associated with an increase in functional connectivity in the brain. Here we repeated the previous project, but expanded the study, testing the hypothesis that training can also affect cognitive functions. Twenty subjects (half of them with subthreshold depression according CES-D) participated in the program of positive imagery training for 12 weeks. The schedule began with group training for 2 days, followed by training at home. Evaluations of cognitive functions and electroencephalographic (EEG) activity were conducted during three examinations as follows: E0-baseline (1 month before the training); E1-pre-training and E2-post-training. CNS Vital Signs battery was used to test the following cognitive domains: verbal and visual memory, executive functions, cognitive flexibility, social acuity, non-verbal reasoning. EEGs (19-channel) were recorded at rest with closed eyes and analyzed with Low-resolution electromagnetic tomography software. One-way repeated measures ANOVA, followed by pairwise comparison showed a significant increase after training (E2 vs. E1; E2 vs. E0) in the number of correct hits for positive emotions received during perception of emotions test (POET); after the sample was split according to the initial presence of depressive symptoms, the effect was present only in the subgroup with subthreshold depressive symptomatology. Post-training (E2 vs. E1; E2 vs. E0) the number of correct answers on non-verbal reasoning test increased; this effect was observed only in the subgroup that does have any depressive symptoms. Comparison of EEG post-training vs. pre-training demonstrated a significant reduction in current source density (CSD) after the training in the left hemisphere (insular cortex, frontal and temporal lobes in delta, theta and alpha1 bands). The observed changes were presented only in the subgroup with initial subthreshold depressive symptomatology. A negative correlation was found between POET and CSD in the left insular cortex for theta band. No significant differences were observed when data from EEG and cognitive tests obtained during pre-training were compared with baseline values. Potential use of training for the rehabilitation of various disturbances with cognitive and emotional deficits is discussed.

Changes in Dimensionality and Fractal Scaling Suggest Soft-Assembled Dynamics in Human EEG

Humans are high-dimensional, complex systems consisting of many components that must coordinate in order to perform even the simplest of activities. Many behavioral studies, especially in the movement sciences, have advanced the notion of soft-assembly to describe how systems with many components coordinate to perform specific functions while also exhibiting the potential to re-structure and then perform other functions as task demands change. Consistent with this notion, within cognitive neuroscience it is increasingly accepted that the brain flexibly coordinates the networks needed to cope with changing task demands. However, evaluation of various indices of soft-assembly has so far been absent from neurophysiological research. To begin addressing this gap, we investigated task-related changes in two distinct indices of soft-assembly using the established phenomenon of EEG repetition suppression. In a repetition priming task, we assessed evidence for changes in the correlation dimension and fractal scaling exponents during stimulus-locked event-related potentials, as a function of stimulus onset and familiarity, and relative to spontaneous non-task-related activity. Consistent with predictions derived from soft-assembly, results indicated decreases in dimensionality and increases in fractal scaling exponents from resting to pre-stimulus states and following stimulus onset. However, contrary to predictions, familiarity tended to increase dimensionality estimates. Overall, the findings support the view from soft-assembly that neural dynamics should become increasingly ordered as external task demands increase, and support the broader application of soft-assembly logic in understanding human behavior and electrophysiology.

Binet's Error: Developmental Change and Individual Differences in Intelligence Are Related to Different Mechanisms

In common with most, if not all, papers in this special issue, I will argue that understanding the nature of developmental change and individual differences in intelligence requires a theory of the mechanisms underlying both factors. Insofar as these mechanisms constitute part of the fundamental architecture of cognition, this is also an exercise in unifying the discipline and research on intelligence in both children and adults. However, I argue that a variety of data support a theory suggesting that developmental change is the province of mechanisms commonly regarded as components of executive functioning or cognitive control, whereas individual differences are constrained by the speed of information processing. Perhaps paradoxically, this leads to the conclusion that Binet's fundamental insight-that children's increasing ability to solve problems of increasing difficulty could generate a single scale of intelligence-is wrong. Compounding the paradox, this means that mental age and IQ are not simply two different ways of expressing the same thing, but are related to two different dimensions of g itself.

Effect of Sexual Arousal on Cortical Coupling During Performance of the Tower of Hanoi Task in Young Men

Sexual arousal affects cognitive processing, which depends on the coordinated functioning among cortical areas. The aim of this research was to determine whether previous observation of videos with sexual content affects the degree of cortical electroencephalographic (EEG) coupling during performance of an executive task. Cortical EEG correlations were calculated in three groups of heterosexual men under three conditions: at rest; during observation of a video with neutral, aggressive, or erotic content; and while performing the Tower of Hanoi task (TOH). Based on self-reports, it was shown that the erotic video induced general and sexual arousal, while the aggressive video affected valence and general arousal. Task performance was similar in all three groups. During performance of TOH, only the erotic group showed a decreased correlation between prefrontal areas with an increased correlation between parietal and prefrontotemporal areas, specifically in the slow bands. It is likely that these changes in the degree of cortical coupling could be associated with the cognitive strategies or functional adaptations that participants require to adequately solve the task during a state of sexual arousal. These data could contribute to improving our understanding of the central nervous mechanisms that underlie the effect of sexual arousal on the cognitive processes involved in tasks like TOH.

Genetic influences on functional connectivity associated with feedback processing and prediction error: Phase coupling of theta-band oscillations in twins

Detection and evaluation of the mismatch between the intended and actually obtained result of an action (reward prediction error) is an integral component of adaptive self-regulation of behavior. Extensive human and animal research has shown that evaluation of action outcome is supported by a distributed network of brain regions in which the anterior cingulate cortex (ACC) plays a central role, and the integration of distant brain regions into a unified feedback-processing network is enabled by long-range phase synchronization of cortical oscillations in the theta band. Neural correlates of feedback processing are associated with individual differences in normal and abnormal behavior, however, little is known about the role of genetic factors in the cerebral mechanisms of feedback processing. Here we examined genetic influences on functional cortical connectivity related to prediction error in young adult twins (age 18, n=399) using event-related EEG phase coherence analysis in a monetary gambling task. To identify prediction error-specific connectivity pattern, we compared responses to loss and gain feedback. Monetary loss produced a significant increase of theta-band synchronization between the frontal midline region and widespread areas of the scalp, particularly parietal areas, whereas gain resulted in increased synchrony primarily within the posterior regions. Genetic analyses showed significant heritability of frontoparietal theta phase synchronization (24 to 46%), suggesting that individual differences in large-scale network dynamics are under substantial genetic control. We conclude that theta-band synchronization of brain oscillations related to negative feedback reflects genetically transmitted differences in the neural mechanisms of feedback processing. To our knowledge, this is the first evidence for genetic influences on task-related functional brain connectivity assessed using direct real-time measures of neuronal synchronization.

Intelligence and eeg measures of information flow: efficiency and homeostatic neuroplasticity

The purpose of this study was to explore the relationship between the magnitude of EEG information flow and intelligence. The electroencephalogram (EEG) was recorded from 19 scalp locations from 371 subjects ranging in age from 5 years to 17.6 years. The Wechler Intelligence Scale for Children (WISC-R) was administered for individuals between 5 years of age and 16 years and the Weschler Adult Intelligence Scale revised (WAIS-R) was administered to subjects older than 16 years to estimate I.Q. The phase slope index estimated the magnitude of information flow between all electrode combinations for difference frequency bands. Discriminant analyses were performed between high I.Q. (>120) and low I.Q. groups (<90). The magnitude of information flow was inversely related to I.Q. especially in the alpha and beta frequency bands. Long distance inter-electrode distances exhibited greater information flow than short inter-electrode distances. Frontal-parietal correlations were the most significant. It is concluded that higher I.Q. is related to increased efficiency of local information processing and reduced long distance compensatory dynamics that supports a small-world model of intelligence.

Genetic influences on phase synchrony of brain oscillations supporting response inhibition

Phase synchronization of neuronal oscillations is a fundamental mechanism underlying cognitive processing and behavior, including context-dependent response production and inhibition. Abnormalities in neural synchrony can lead to abnormal information processing and contribute to cognitive and behavioral deficits in neuropsychiatric disorders. However, little is known about genetic and environmental contributions to individual differences in cortical oscillatory dynamics underlying response inhibition. This study examined heritability of event-related phase synchronization of brain oscillations in 302 young female twins including 94 MZ and 57 DZ pairs performing a cued Go/No-Go version of the Continuous Performance Test (CPT). We used the Phase Locking Index (PLI) to assess inter-trial phase clustering (synchrony) in several frequency bands in two time intervals after stimulus onset (0-300 and 301-600ms). Response inhibition (i.e., successful response suppression in No-Go trials) was characterized by a transient increase in phase synchronization of delta- and theta-band oscillations in the fronto-central midline region. Genetic analysis showed significant heritability of the phase locking measures related to response inhibition, with 30 to 49% of inter-individual variability being accounted for by genetic factors. This is the first study providing evidence for heritability of task-related neural synchrony. The present results suggest that PLI can serve as an indicator of genetically transmitted individual differences in neural substrates of response inhibition.

Changes in EEG complexity with electroconvulsive therapy in a patient with autism spectrum disorders: a multiscale entropy approach

Autism spectrum disorders (ASD) are heterogeneous neurodevelopmental disorders that are reportedly characterized by aberrant neural networks. Recently developed multiscale entropy analysis (MSE) can characterize the complexity inherent in electroencephalography (EEG) dynamics over multiple temporal scales in the dynamics of neural networks. We encountered an 18-year-old man with ASD whose refractory catatonic obsessive–compulsive symptoms were improved dramatically after electroconvulsive therapy (ECT). In this clinical case study, we strove to clarify the neurophysiological mechanism of ECT in ASD by assessing EEG complexity using MSE. Along with ECT, the frontocentral region showed decreased EEG complexity at higher temporal scales, whereas the occipital region expressed an increase at lower temporal scales. Furthermore, these changes were associated with clinical improvement associated with the elevation of brain-derived neurotrophic factor, which is a molecular hypothesis of ECT, playing key roles in ASD pathogenesis. Changes in EEG complexity in a region-specific and temporal scale-specific manner that we found might reflect atypical EEG dynamics in ASD. Although MSE is not a direct approach to measuring neural connectivity and the results are from only a single case, they might reflect specific aberrant neural network activity and the therapeutic neurophysiological mechanism of ECT in ASD.

Performance of the Towers of Hanoi task and cortical electroencephalographic power changes associated with infancy, adolescence, and early adulthood

The executive functions, which depend on the adequate maturation and functioning of the prefrontal cortex and its connection to posterior zones, follow a process of development as age increases. This work studied changes in the absolute power (AP) of EEG activity recorded in the prefrontal and parietal areas during the performance of the Tower of Hanoi task in children, adolescents, and young adults. Three groups of healthy male subjects such as G1, 11-13; G2, 18-20; and G3, 26-30, years of age were recorded at the F3, F4, P3, and P4 derivations under two conditions: basal and performance of the Towers of Hanoi task. The majority of subjects in G1 failed to complete the task in the allotted time (7 min), while those in G2 and G3 were able to resolve the task quickly and efficiently. During the Towers of Hanoi task, G1 showed an increase of AP in the delta band only in the frontal areas, with a decrease in the alpha1 and alpha2 sub-bands only at the parietal derivations, while G2 and G3 were characterized by an increase of AP in the delta band and a decreased AP in the alpha1 and alpha2 sub-bands in all derivations. These data demonstrate that during the performance of the Towers of Hanoi task, the prefrontal and parietal areas show a characteristic EEG pattern in relation to age. It is probable that the AP patterns obtained in G2 and G3 are associated with the functional changes at cortical levels that adolescents and early adults require to achieve an adequate and fast performance of the Towers of Hanoi task.

Sexual arousal decreases the functional synchronization between cortical areas in young men

The prefrontal cortex and its connections with other cortical areas participate in processing erotic stimuli and hence sexual arousal. Visual erotic stimuli elicit sexual arousal that is associated with changes in electroencephalographic activity. The electroencephalographic correlation analysis provides information on the functional synchronization among areas. This study analyzed the functional interaction among the prefrontal, parietal, and temporal cortices during sexual arousal in young men induced by observing erotic photographs. In 2 groups of heterosexual men-an erotic stimulation group and a neutral stimulation group-the authors recorded electroencephalograms at the F3, F4, T3, T4, P3, and P4 derivations under 2 conditions: baseline and visual stimulation. Heart rate was monitored as a measure of peripheral activation. Participants in the erotic stimulation group reported a moderate degree of sexual arousal and a decrease in heart rate. Decreased inter- and intrahemispheric correlations of the fast frequencies were obtained only in erotic stimulation. These data support differential hemisphere participation in modulating sexual arousal and show that decreased synchronization patterns between prefrontal and posterior cortices (parietal and temporal) favor sexual arousal in young men. The results of this study may contribute to a better understanding of the central nervous system's mechanisms that underlie sexual arousal.

Lifespan differences in nonlinear dynamics during rest and auditory oddball performance

Electroencephalographic recordings (EEG) were used to assess age-associated differences in nonlinear brain dynamics during both rest and auditory oddball performance in children aged 9.0-12.8 years, younger adults, and older adults. We computed nonlinear coupling dynamics and dimensional complexity, and also determined spectral alpha power as an indicator of cortical reactivity. During rest, both nonlinear coupling and spectral alpha power decreased with age, whereas dimensional complexity increased. In contrast, when attending to the deviant stimulus, nonlinear coupling increased with age, and complexity decreased. Correlational analyses showed that nonlinear measures assessed during auditory oddball performance were reliably related to an independently assessed measure of perceptual speed. We conclude that cortical dynamics during rest and stimulus processing undergo substantial reorganization from childhood to old age, and propose that lifespan age differences in nonlinear dynamics during stimulus processing reflect lifespan changes in the functional organization of neuronal cell assemblies.

Individual differences in EEG spectral power reflect genetic variance in gray and white matter volumes

The human electroencephalogram (EEG) consists of oscillations that reflect the summation of postsynaptic potentials at the dendritic tree of cortical neurons. The strength of the oscillations (EEG power) is a highly genetic trait that has been related to individual differences in many phenotypes, including intelligence and liability for psychopathology. Here, we investigated whether brain anatomy underlies these EEG power differences by correlating it to gray and white matter volumes (GMV, WMV), and additionally investigated whether this association can be attributed to genes or environmental factors. EEG was measured in a sample of 405 young adult twins and their siblings, and power in the theta (~4 Hz), alpha (~10 Hz), and beta (~20 Hz) frequency bands determined. A subset of 121 subjects were also scanned in a 1.5 T MRI scanner, and gray and white matter volumes defined as the total of cortical and subcortical volumes, excluding cerebellum. Both MRI-based volumes and EEG power spectra were highly heritable. GMV and WMV correlated .25 to .29 with EEG power for the slower oscillations (theta, alpha). Moreover, these phenotypic correlations largely reflected genetic covariation, irrespective of oscillation frequency and volume type. Genetic correlations (.31 < rA < .43) revealed that only moderate proportions of the heritable variance overlapped between MRI volumes and EEG power. The results suggest that MRI volumes and EEG power share genetic sources of variation, which may reflect such processes as myelination, synaptic density, and dendritic outgrowth.

DMN Operational Synchrony Relates to Self-Consciousness: Evidence from Patients in Vegetative and Minimally Conscious States

The default mode network (DMN) has been consistently activated across a wide variety of self-related tasks, leading to a proposal of the DMN’s role in self-related processing. Indeed, there is limited fMRI evidence that the functional connectivity within the DMN may underlie a phenomenon referred to as self-awareness. At the same time, none of the known studies have explicitly investigated neuronal functional interactions among brain areas that comprise the DMN as a function of self-consciousness loss. To fill this gap, EEG operational synchrony analysis [1, 2] was performed in patients with severe brain injuries in vegetative and minimally conscious states to study the strength of DMN operational synchrony as a function of self-consciousness expression. We demonstrated that the strength of DMN EEG operational synchrony was smallest or even absent in patients in vegetative state, intermediate in patients in minimally conscious state and highest in healthy fully self-conscious subjects. At the same time the process of ecoupling of operations performed by neuronal assemblies that comprise the DMN was highest in patients in vegetative state, intermediate in patients in minimally conscious state and minimal in healthy fully self-conscious subjects. The DMN’s frontal EEG operational module had the strongest decrease in operational synchrony strength as a function of selfconsciousness loss, when compared with the DMN’s posterior modules. Based on these results it is suggested that the strength of DMN functional connectivity could mediate the strength of self-consciousness expression. The observed alterations similarly occurred across EEG alpha, beta1 and beta2 frequency oscillations. Presented results suggest that the EEG operational synchrony within DMN may provide an objective and accurate measure for the assessment of signs of self-(un)consciousness in these challenging patient populations. This method therefore, may complement the current diagnostic procedures for patients with severe brain injuries and, hence, the planning of a rational rehabilitation intervention.

Neural synchrony during response production and inhibition

Inhibition of irrelevant information (conflict monitoring) and/or of prepotent actions is an essential component of adaptive self-organized behavior. Neural dynamics underlying these functions has been studied in humans using event-related brain potentials (ERPs) elicited in Go/NoGo tasks that require a speeded motor response to the Go stimuli and withholding a prepotent response when a NoGo stimulus is presented. However, averaged ERP waveforms provide only limited information about the neuronal mechanisms underlying stimulus processing, motor preparation, and response production or inhibition. In this study, we examine the cortical representation of conflict monitoring and response inhibition using time-frequency analysis of electroencephalographic (EEG) recordings during continuous performance Go/NoGo task in 50 young adult females. We hypothesized that response inhibition would be associated with a transient boost in both temporal and spatial synchronization of prefrontal cortical activity, consistent with the role of the anterior cingulate and lateral prefrontal cortices in cognitive control. Overall, phase synchronization across trials measured by Phase Locking Index and phase synchronization between electrode sites measured by Phase Coherence were the highest in the Go and NoGo conditions, intermediate in the Warning condition, and the lowest under Neutral condition. The NoGo condition was characterized by significantly higher fronto-central synchronization in the 300-600 ms window, whereas in the Go condition, delta- and theta-band synchronization was higher in centro-parietal regions in the first 300 ms after the stimulus onset. The present findings suggest that response production and inhibition is supported by dynamic functional networks characterized by distinct patterns of temporal and spatial synchronization of brain oscillations.

A strong parietal hub in the small-world network of coloured-hearing synaesthetes during resting state EEG

We investigated whether functional brain networks are different in coloured-hearing synaesthetes compared with non-synaesthetes. Based on resting state electroencephalographic (EEG) activity, graph-theoretical analysis was applied to functional connectivity data obtained from different frequency bands (theta, alpha1, alpha2, and beta) of 12 coloured-hearing synaesthetes and 13 non-synaesthetes. The analysis of functional connectivity was based on estimated intra-cerebral sources of brain activation using standardized low-resolution electrical tomography. These intra-cerebral sources of brain activity were subjected to graph-theoretical analysis yielding measures representing small-world network characteristics (cluster coefficients and path length). In addition, brain regions with strong interconnections were identified (so-called hubs), and the interconnectedness of these hubs were quantified using degree as a measure of connectedness. Our analysis was guided by the two-stage model proposed by Hubbard and Ramachandran (2005). In this model, the parietal lobe is thought to play a pivotal role in binding together the synaesthetic perceptions (hyperbinding). In addition, we hypothesized that the auditory cortex and the fusiform gyrus would qualify as strong hubs in synaesthetes. Although synaesthetes and non-synaesthetes demonstrated a similar small-world network topology, the parietal lobe turned out to be a stronger hub in synaesthetes than in non-synaesthetes supporting the two-stage model. The auditory cortex was also identified as a strong hub in these coloured-hearing synaesthetes (for the alpha2 band). Thus, our a priori hypotheses receive strong support. Several additional hubs (for which no a priori hypothesis has been formulated) were found to be different in terms of the degree measure in synaesthetes, with synaesthetes demonstrating stronger degree measures indicating stronger interconnectedness. These hubs were found in brain areas known to be involved in controlling memory processes (alpha1: hippocampus and retrosplenial area), executive functions (alpha1 and alpha2: ventrolateral prefrontal cortex; theta: inferior frontal cortex), and the generation of perceptions (theta: extrastriate cortex; beta: subcentral area). Taken together this graph-theoretical analysis of the resting state EEG supports the two-stage model in demonstrating that the left-sided parietal lobe is a strong hub region, which is stronger functionally interconnected in synaesthetes than in non-synaesthetes. The right-sided auditory cortex is also a strong hub supporting the idea that coloured-hearing synaesthetes demonstrate a specific auditory cortex. A further important point is that these hub regions are even differently operating at rest supporting the idea that these hub characteristics are predetermining factors of coloured-hearing synaesthesia.

Reduced functional connectivity between cortical sources in five meditation traditions detected with lagged coherence using EEG tomography

Brain functional states are established by functional connectivities between brain regions. In experienced meditators (13 Tibetan Buddhists, 15 QiGong, 14 Sahaja Yoga, 14 Ananda Marga Yoga, 15 Zen), 19-channel EEG was recorded before, during and after that meditation exercise which their respective tradition regards as route to the most desirable meditative state. The head surface EEG data were recomputed (sLORETA) into 19 cortical regional source model time series. All 171 functional connectivities between regions were computed as 'lagged coherence' for the eight EEG frequency bands (delta through gamma). This analysis removes ambiguities of localization, volume conduction-induced inflation of coherence, and reference-dependence. All significant differences (corrected for multiple testing) between meditation compared to no-task rest before and after meditation showed lower coherence during meditation, in all five traditions and eight (inhibitory as well as excitatory) frequency bands. Conventional coherence between the original head surface EEG time series very predominantly also showed reduced coherence during meditation. The topography of the functional connectivities was examined via PCA-based computation of principal connectivities. When going into and out of meditation, significantly different connectivities revealed clearly different topographies in the delta frequency band and minor differences in the beta-2 band. The globally reduced functional interdependence between brain regions in meditation suggests that interaction between the self process functions is minimized, and that constraints on the self process by other processes are minimized, thereby leading to the subjective experience of non-involvement, detachment and letting go, as well as of all-oneness and dissolution of ego borders during meditation.

Prefrontal-parietal correlation during performance of the towers of Hanoi task in male children, adolescents and young adults

Potential age differences in the electroencephalographic (EEG) correlation (r) between the prefrontal and parietal cortices during performance of the Tower of Hanoi task were studied. In three groups of healthy males (G1, 11-13; G2, 18-20, and G3, 26-30, years of age) EEGs were recorded at baseline and during performance of the Tower of Hanoi task. The parameters of the task showed no significant differences among groups, though the majority of younger subjects failed to complete it. The G1 group showed increases only in the interparietal r. The G2 group showed an increased interhemispheric and intrahemispheric r in almost all frequency bands, while the r in G3 increased only in selected frequency bands in the right hemisphere. These findings demonstrate that the functional coupling between these two cortices shows a characteristic pattern during performance of the Hanoi task that, while specific to each age group, was not associated with the successful performance of the task.

Developmental changes in brain connectivity assessed using the sleep EEG

Adolescence represents a time of significant cortical restructuring. Current theories posit that during this period connections between frequently utilized neural networks are strengthened while underutilized synaptic connections are discarded. The aim of the present study was to examine the developmental evolution of connectivity between brain regions using the sleep EEG. All-night sleep EEG recordings in two longitudinal cohorts (children and teens) followed at 1.5-3 year intervals and one cross-sectional cohort (adults) were analyzed. The children and teen cohorts were 9/10 and 15/16 years at the initial assessment; ages of the adults were 20 to 23 years. Intrahemispheric, interhemispheric, and diagonal coherence was measured between all six possible pairings of two central (C3/A2 and C4/A1) and two occipital (O2/A1 and O1/A2) derivations during slow wave, stage 2, and, REM sleep. Within-subjects analyses were performed for the children and teen cohorts, and a linear regression analysis was performed across every assessment of all cohorts. Within-subject analyses revealed a maturational increase in coherence for both age cohorts, though the frequencies, sleep states, and regions differed between cohorts. Regression analysis across all age cohorts showed an overall linear increase in left and right intrahemispheric coherence for all sleep states across frequencies. Furthermore, coherence between diagonal electrode pairs also increased in a linear manner for stage 2 and REM sleep. No age-related trend was found in interhemispheric coherence. Our results indicate that sleep EEG coherence increases with age and that these increases are confined to specific brain regions. This analysis highlights the utility of the sleep EEG to measure developmental changes in brain maturation.

A comparative study of pattern synchronization detection between neural signals using different cross-entropy measures

Cross-approximate entropy (X-ApEn) and cross-sample entropy (X-SampEn) have been employed as bivariate pattern synchronization measures for characterizing interdependencies between neural signals. In this study, we proposed a new measure, cross-fuzzy entropy (X-FuzzyEn), to describe the synchronicity of patterns. The performances of three statistics were first quantitatively tested using five different coupled systems including both deterministic and stochastic models, i.e., coupled broadband noises, Lorenz-Lorenz, Rossler-Rossler, Rossler-Lorenz, and neural mass model. All the measures were compared with each other with respect to their ability to distinguish between different levels of coupling and their robustness against noise. The three measures were then applied to a real-life problem, pattern synchronization analysis of left and right hemisphere rat electroencephalographic (EEG) signals. Both simulated and real EEG data analysis results showed that the X-FuzzyEn provided an improved evaluation of bivariate series pattern synchronization and could be more conveniently and powerfully applied to different neural dynamical systems contaminated by noise.

Oscillatory mechanisms of process binding in memory

A central topic in cognitive neuroscience is the question, which processes underlie large scale communication within and between different neural networks. The basic assumption is that oscillatory phase synchronization plays an important role for process binding--the transient linking of different cognitive processes--which may be considered a special type of large scale communication. We investigate this question for memory processes on the basis of different types of oscillatory synchronization mechanisms. The reviewed findings suggest that theta and alpha phase coupling (and phase reorganization) reflect control processes in two large memory systems, a working memory and a complex knowledge system that comprises semantic long-term memory. It is suggested that alpha phase synchronization may be interpreted in terms of processes that coordinate top-down control (a process guided by expectancy to focus on relevant search areas) and access to memory traces (a process leading to the activation of a memory trace). An analogous interpretation is suggested for theta oscillations and the controlled access to episodic memories.

The clinical use of quantitative EEG in cognitive disorders

The primary diagnosis of most cognitive disorders is clinically based, but the EEG plays a role in evaluating, classifying and following some of these disorders. There is an ongoing debate over routine use of qEEG. Although many findings regarding the clinical use of quantitative EEG are awaiting validation by independent investigators while confirmatory clinical follow-up studies are also needed, qEEG can be cautiously used by a skilled neurophysiologist in cognitive dysfunctions to improve the analysis of background activity, slow/fast focal activity, subtle asymmetries, spikes and waves, as well as in longitudinal follow-ups.

Increased theta coherence during Chinese reading

EEG coherence has been used extensively in the investigation of language processing of English words. In contrast, relatively less is known about the EEG coherence pattern in the language processing of Chinese characters. Given the involvement of distinct and overlapping regions in the brain during English and Chinese reading, and activation in the left and right hemisphere found in functional magnetic resonance imaging (fMRI) studies on Chinese reading, the present study aimed to investigate the EEG coherence pattern associated with Chinese reading, and to determine if higher interhemispheric coherence was found in Chinese reading. EEG coherence of 32 healthy normal participants during a resting condition, English, and Chinese reading were computed and compared. The results revealed that Chinese reading was generally associated with higher theta coherence than the resting condition and English reading. Specifically, theta coherence during Chinese reading showed an increased intrahemispheric connection in the left hemisphere and interhemispheric connections over the temporal, central and parietal/occipital regions, compared to English reading. These results suggest that interhemispheric cooperation between neuronal substrates in these regions is associated with Chinese reading, and that both hemispheres are involved in Chinese reading.

DIFFERENCES IN EEG BETWEEN GIFTED AND AVERAGE STUDENTS: NEURAL COMPLEXITY AND FUNCTIONAL CLUSTER ANALYSIS

The main aim of the present study was to assess the differences in EEG between gifted and average students. Another aim of the present study was to investigate which brain areas are related to a Rey-Osterrieth complex figure (ROCF) memorizing using a functional cluster (FC) analysis and how the complexity of cortical activities changes in both gifted and average students. The EEG was recorded from 16 electrodes in both 18 right-handed healthy gifted and age-matched average students before and during ROCF memorizing. FC was estimated to characterize the joint interactions among many brain regions and neural complexity. The study assessed the visuo-spatial memory abilities through examining EEG profiles using the measure of FC, and planning and executive function using recall score. The gifted students made a significantly high score compared to the average students during ROCF memorizing. Both groups showed very different FC patterns. ROCF memorizing is related to the visual mental process, thus simultaneous neuronal activities appears on the right central, temporal, occipital, and bilateral prefrontal regions. One of the notable characteristics of gifted students' FC map is the dominance of the right hemisphere compared with that of average students, and it is accordance with the characteristics of gifted brain.

Decreased EEG synchronization and its correlation with symptom severity in Alzheimer's disease

BACKGROUND

Global field synchronization (GFS) has recently been introduced to measure functional synchronization in frequency-domain EEG data. This study explored GFS values and its clinical significance in patients with Alzheimer's disease (AD).

METHOD

EEGs were recorded from 22 AD patients and 23 age-matched healthy controls. GFS values were computed in the delta, theta, alpha, beta1, beta2, beta3, gamma, and full frequency bands. The Mini-Mental Status Examination (MMSE) and the Clinical Dementia Rating scale (CDR) were used to assess the symptom severity in AD patients.

RESULTS

GFS values in the beta1, beta2, beta3, and full bands were lower in AD patients than in healthy controls. GFS values in the alpha, beta1, beta2, beta3, and full bands were positively correlated with the MMSE and CDR scores in combined group (AD patients and healthy controls). In AD patients, GFS values were positively correlated with MMSE scores in the beta1, beta 3, and full bands, and with CDR scores in the delta band.

CONCLUSION

GFS values were significantly lower in AD patients than in healthy controls, and they were positively correlated with MMSE and CDR scores. Our results suggest that GFS values are a useful biological correlate of cognitive decline in AD patients.

Intelligence and EEG phase reset: a two compartmental model of phase shift and lock

OBJECTIVES

The purpose of this study was to explore the relationship between EEG phase reset and performance on the Wechsler Intelligence test.

METHODS

The electroencephalogram (EEG) was recorded from 19 scalp locations from 378 subjects ranging in age from 5 years to 17.6 years. The Wechsler Intelligence test (WISC-R) was administered to the same subjects on the same day but not while the EEG was recorded. Complex demodulation was used to compute instantaneous EEG phase differences between pairs of electrodes and the 1st and 2nd derivatives were used to measure phase reset by phase shift duration and phase lock duration. The dependent variable was full scale I.Q. and the independent variables were phase shift duration (SD) and phase lock duration (LD) with age as a covariate.

RESULTS

Phase shift duration (40-90 ms) was positively related to intelligence (P<.00001) and the phase lock duration (100-800 ms) was negatively related to intelligence (P<.00001). Phase reset in short interelectrode distances (6 cm) was more highly correlated to I.Q. (P<.0001) than in long distances (>12 cm).

CONCLUSIONS

The duration of unstable phase dynamics and phase locking represent a bounded optimization process, for example, too long a duration of phase locking then less flexibility and too short of a phase shift then reduced neural resources. A two compartmental model of local field coupling and neuron synchrony to a preferred phase was developed to explain the findings.

Nonlinear analysis of electroencephalogram in schizophrenia patients with persistent auditory hallucination

OBJECTIVE

The recent nonlinear analyses of electroencephalogram (EEG) data have shown that the correlation dimension (D2) reflects the degree of integration of information processing in the brain. There is now considerable evidence that auditory hallucination (AH) reflects dysfunctional gamma and beta frequency oscillations. Gamma oscillations are thought to reflect internally driven representations of objects, and the occurrence of subsequent beta oscillations can reflect the modification of the neuronal circuitry used to encode the sensory perception. The purpose of this study was to test whether AH in schizophrenia patients is reflected in abnormalities in D2 in their EEG, especially in the gamma and beta frequency bands.

METHODS

Twenty-five schizophrenia patients with a history of treatment-refractory AH over at least the past 2 years, and 23 schizophrenia patients with no AH (N-AH) within the past 2 years were recruited for the study. Artifact-free 30-s EEG epochs during rest were examined for D2.

RESULTS

The AH patients showed significantly increased gamma frequency D2 in Fp2 and decreased beta frequency D2 in the P3 region compared with the N-AH patients. These results imply that gamma frequency D2 in the right prefrontal cortex is more chaotic and that beta frequency D2 in the left parietal cortex is more coherent (less chaotic) in AH patients than in N-AH patients.

CONCLUSION

Our study supports the previous evidence indicating that gamma and beta oscillations are pivotal to AH, and also shows the distinctive dimensional complexity between the right prefrontal and left parietal cortexes as the underlying biological correlates of AH in schizophrenia patients.

Patterns of covariant power and coherent EEG activity during rest in young adults

Functional cortical organization is not dependent on cognition only, but also reflects basic intrinsic individual characteristics stable over time and across physiological conditions that suggest the existence of self-organization patterns operating even during rest. The aim of the present study was to investigate the pattern of functional organization of EEG broad bands and coherent activity during rest by means of Principal Component Analysis (PCA) with closed and open eyes of 28 men and women. PCA revealed patterns of covariant oscillatory activity and functionally related regions with covariant coherent activity present during resting conditions, different for open and closed eyes. Different patterns of covariant oscillations within alpha frequencies were identified consistent with current notions on overlapping frequencies with different origin and behavioral context. Covariant coherent cortical networks were revealed congruent with functional systems described during cognitive processes; one, linking regions involved in visuo-motor coordination, another in language, and a third one in visuo-spatial processes suggesting that strong functional links are present during rest.