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

Multiscale Functional Clustering Reveals Frequency Dependent Brain Organization in Type II Focal Cortical Dysplasia With Sleep Hypermotor Epilepsy

OBJECTIVE

A multiscale functional clustering approach is proposed to investigate the organization of the epileptic networks during different sleep stages and in relation with the occurrence of seizures.

METHOD

Stereo-electroencephalographic signals from seven pharmaco-resistant epileptic patients (focal cortical dysplasia type II) were analyzed. The discrete wavelet transform provided a multiscale framework on which a data-driven functional clustering procedure was applied, based on multivariate measures of integration and mutual information. The most interacting functional clusters (FCs) within the sampled brain areas were extracted.

RESULTS

FCs characterized by strongly integrated activity were observed mostly in the beta and alpha frequency bands, immediately before seizure onset and in deep sleep stages. These FCs generally included the electrodes from the epileptogenic zone. Furthermore, repeatable patterns were found across ictal events in the same patient.

CONCLUSION

In line with previous studies, our findings provide evidence of the important role of beta and alpha activity in seizures generation and support the relation between epileptic activity and sleep stages.

SIGNIFICANCE

Despite the small number of subjects included in the study, the present results suggest that the proposed multiscale functional clustering approach is a useful tool for the identification of the frequency-dependent mechanisms underlying seizure generation.

K-th Nearest Neighbor (KNN) Entropy Estimates of Complexity and Integration from Ongoing and Stimulus-Evoked Electroencephalographic (EEG) Recordings of the Human Brain

Information-theoretic measures for quantifying multivariate statistical dependence have proven useful for the study of the unity and diversity of the human brain. Two such measures–integration, I(X), and interaction complexity, C_I(X)–have been previously applied to electroencephalographic (EEG) signals recorded during ongoing wakeful brain states. Here, I(X) and C_I(X) were computed for empirical and simulated visually-elicited alpha-range (8–13 Hz) EEG signals. Integration and complexity of evoked (stimulus-locked) and induced (non-stimulus-locked) EEG responses were assessed using nonparametric k-th nearest neighbor (KNN) entropy estimation, which is robust to the nonstationarity of stimulus-elicited EEG signals. KNN-based I(X) and C_I(X) were also computed for the alpha-range EEG of ongoing wakeful brain states. I(X) and C_I(X) patterns differentiated between induced and evoked EEG signals and replicated previous wakeful EEG findings obtained using Gaussian-based entropy estimators. Absolute levels of I(X) and C_I(X) were related to absolute levels of alpha-range EEG power and phase synchronization, but stimulus-related changes in the information-theoretic and other EEG properties were independent. These findings support the hypothesis that visual perception and ongoing wakeful mental states emerge from complex, dynamical interaction among segregated and integrated brain networks operating near an optimal balance between order and disorder.

Functional Clustering approach for the analysis of Stereo-EEG activity patterns in correspondence of epileptic seizures

In this study, a functional clustering approach is proposed and tested for the identification of brain functional networks emerging during sleep-related seizures. Stereo-EEG signals recorded in patients with Type II Focal Cortical Dysplasia (FCD type II), were analyzed. This novel approach is able to identify the network configuration changes in pre-ictal and early ictal periods, by grouping Stereo-EEG signals on the basis of the Cluster Index, after wavelet multiscale decomposition. Results showed that the proposed method is able to detect clusters of interacting leads, mainly overlapped on the Epileptogenic Zone (EZ) identified by a clinical expert, with distinctive configurations related to analyzed frequency ranges. This suggested the presence of coupling activities between the elements of the epileptic system at different frequency scales.

Mathematically gifted adolescents have deficiencies in social valuation and mentalization

Many mathematically gifted adolescents are characterized as being indolent, underachieving and unsuccessful despite their high cognitive ability. This is often due to difficulties with social and emotional development. However, research on social and emotional interactions in gifted adolescents has been limited. The purpose of this study was to observe differences in complex social strategic behaviors between gifted and average adolescents of the same age using the repeated Ultimatum Game. Twenty-two gifted adolescents and 24 average adolescents participated in the Ultimatum Game. Two adolescents participate in the game, one as a proposer and the other as a responder. Because of its simplicity, the Ultimatum Game is an apt tool for investigating complex human emotional and cognitive decision-making in an empirical setting. We observed strategic but socially impaired offers from gifted proposers and lower acceptance rates from gifted responders, resulting in lower total earnings in the Ultimatum Game. Thus, our results indicate that mathematically gifted adolescents have deficiencies in social valuation and mentalization.

Different gain/loss sensitivity and social adaptation ability in gifted adolescents during a public goods game

Gifted adolescents are considered to have high IQs with advanced mathematical and logical performances, but are often thought to suffer from social isolation or emotional mal-adaptation to the social group. The underlying mechanisms that cause stereotypic portrayals of gifted adolescents are not well known. We aimed to investigate behavioral performance of gifted adolescents during social decision-making tasks to assess their affective and social/non-social cognitive abilities. We examined cooperation behaviors of 22 gifted and 26 average adolescents during an iterative binary public goods (PG) game, a multi-player social interaction game, and analyzed strategic decision processes that include cooperation and free-riding. We found that the gifted adolescents were more cooperative than average adolescents. Particularly, comparing the strategies for the PG game between the two groups, gifted adolescents were less sensitive to loss, yet were more sensitive to gain. Additionally, the behavioral characteristics of average adolescents, such as low trust of the group and herding behavior, were not found in gifted adolescents. These results imply that gifted adolescents have a high cognitive ability but a low ability to process affective information or to adapt in social groups compared with average adolescents. We conclude that gain/loss sensitivity and the ability to adapt in social groups develop to different degrees in average and gifted adolescents.