Transition dynamics of EEG-based network microstates during mental arithmetic and resting wakefulness reflects task-related modulations and developmental changes.

We studied how maturation influences the organization of functional brain networks engaged during mental calculations and in resting state. Surface EEG measurements from 20 children (8-12 years) and 25 students (21-26 years) were analyzed. Interregional synchronization of brain activity was quantified by means of Phase Lag Index and for various frequency bands. Based on these pairwise estimates of functional connectivity, we formed graphs which were then characterized in terms of local structure [local efficiency (LE)] and overall integration (global efficiency). The overall data analytic scheme was applied twice, in a static and time-varying mode. Our results showed a characteristic trend: functional segregation dominates the network organization of younger brains. Moreover, in childhood, the overall functional network possesses more prominent small-world network characteristics than in early acorrect in xmldulthood in accordance with the Neural Efficiency Hypothesis. The above trends were intensified by the time-varying approach and identified for the whole set of tested frequency bands (from δ to low γ). By mapping the time-indexed connectivity patterns to multivariate timeseries of nodal LE measurements, we carried out an elaborate study of the functional segregation dynamics and demonstrated that the underlying network undergoes transitions between a restricted number of stable states, that can be thought of as "network-level microstates". The rate of these transitions provided a robust marker of developmental and task-induced alterations, that was found to be insensitive to reference montage and independent component analysis denoising.

Altered temporal correlations in resting-state connectivity fluctuations in children with reading difficulties detected via MEG.

In this study we investigate systematic patterns of rapidly changing sensor-level interdependencies in resting MEG data obtained from 23 children experiencing reading difficulties (RD) and 27 non-impaired readers (NI). Three-minute MEG time series were band-passed and subjected to blind source separation (BSS) prior to estimating sensor interdependencies using the weighted phase synchronization measure (wPLI). Dynamic sensor-level network properties were then derived for two network metrics (global and local efficiency). The temporal decay of long-range temporal correlations in network metrics (LRTC) was quantified using the scaling exponent (SE) in detrended fluctuation analysis (DFA) plots. Having established the reliability of SE estimates as robust descriptors of network dynamics, we found that RD students displayed significantly reduced (a) overall sensor-level network organization across all frequency bands (global efficiency), and (b) temporal correlations between sensors covering the left temporoparietal region and the remaining sensors in the ß3 band (local efficiency). Importantly, both groups displayed scale-free global network connectivity dynamics. The direct application of DFA to MEG signals failed to reveal significant group differences. Results are discussed in relation to prior evidence for disrupted temporoparietal functional circuits for reading in developmental reading disability.

EEG correlates of cerebral engagement in reading tasks.

This study evaluated the utility of electroencephalographic (EEG) measures as indices of regional cerebral engagement activation during reading in neurologically intact adult volunteers. EEG was recorded from 16 scalp locations as participants performed four visual detection tasks designed to tap into increasingly more complex operations regularly involved in reading, namely visual-spatial, orthographic, phonological, and semantic. EEG records were quantified using power spectrum measures in four frequency bands (delta, theta, alpha, beta1, and beta2), in addition to a non-linear estimate of signal complexity (prediction error). Results showed that (1) changes in spectral power between pairs of reading tasks, and (2) regional variations in EEG measures for each task, were restricted to signals recorded over the left hemisphere. These findings are in agreement with knowledge regarding left hemisphere involvement in higher level component processes of reading.

Use of non-linear EEG analysis to study abnormal brain dynamics in deaf human subjects.

We compared the cortical dynamics of deaf subjects to those of control subjects at rest with eyes closed and during reading with the help of a non-linear prediction statistic. This method is suitable for short-term noisy time series such as electroencephalographic signals. Furthermore, we used surrogate data to test for non-linear dynamics underlying the electroencephalographic time series recorded. Our results indicate that significant non-linearity accompanies cortical activation during reading. This is more diffuse in deaf subjects and could be due to the widespread reorganization of their cerebral cortex. Predictability was lower in deaf subjects at rest, which indicates their increased 'readiness' in the resting condition. Finally, our results indicate that normal and deaf subjects differ significantly in terms of cortical dynamics.

Usefulness of non-linear EEG analysis.

Spectral analysis methods are useful for the evaluation of EEG signals. Nevertheless, they refer only to the frequency domain and ignore any potentially interesting phase information. Analytical methods based upon the theory of nonlinear dynamics provides this and additional information. We used both methods to evaluate the EEG signals of volunteers performing two distinct mental arithmetic tasks. We extracted the power spectrum, the coherence and nonlinear parameters (dimension, the first Lyapunov exponent, the Kolmogorov entropy, the mutual dimension and the dimensions based upon spatial embedding of the original data as well as their surrogates). We found that 1) the spatial embedding dimension differed from that of the surrogates, indicating nonlinearity, 2) there were differences between the two arithmetic tasks, and 3) the spectral and nonlinear methods differ in terms of the information they provide. Our results indicate that nonlinear analysis methods can be useful despite the fact that they are still at an early stage of development.

Electroencephalographic signal analysis and desynchronization effect caused by two differing mental arithmetic skills.

Using several electroencephalographic signal analysis methods, it is possible to detect activated cortical areas during cognitive processes. These methods are of interest for neuropsychological studies and in an attempt to develop neurophysiological methods that can be used in the clinic. We used two distinct methods to study two different mental arithmetic tasks. Our purpose was to test the hypothesis that there are different desynchronization effects during the two distinct cognitive processes and to compare the two methods used. The first method concerned EEG signal band reactivity changes. The bands were obtained using spectral analysis. The second method is a new one for estimation of the whole EEG signal complexity (ASE = Acceleration Spectrum Entropy). We estimated the EEG signal bands and the ASE changes during two arithmetical tasks in 24 subjects and compared them with the values of the resting state. The ASE method and the EEG band reactivity method could distinguish between the resting condition and the tasks thus demonstrating the usefulness of the ASE method to study cognitive processes. Furthermore, the two tasks affected differently the power spectrum values of the delta, theta and the alpha bands thus indicating the involvement of different brain mechanisms.

Generators for human P300 elicited by somatosensory stimuli using multiple dipole source analysis.

Cognitive event-related potentials, such as P300, are sensitive to manipulations of psychological variables and may provide evidence to support theories of brain mechanisms involved in cognition. However, the relationship between event-related potentials and the active neural structures is not yet understood. Electrical stimulation of the index and little fingers of the left hand in the context of a somatosensory target discrimination task, performed by healthy human subjects, elicited the middle-latency component of somatosensory evoked potentials, N60, the long-latency component, N140, and the P300 component. Identification of the generators for both the earlier components and P300, using equivalent electrical dipole modeling, was performed. Individual spatiotemporal seven-dipole models were developed in order to suggest locations of the sources generating each subject's scalp-recorded wave forms. Three dipoles with fairly weak moments, located in the primary and secondary sensory areas, explained the middle- and long-latency somatosensory evoked potential components, and the remaining four dipoles (4-7), with stronger dipole moments, were active during P300. There was a clear temporal separation of dipole activity between the somatosensory evoked potential components and the P300 component. Dipoles 4 and 5 were found quite symmetrically in the parahippocampal areas of the two hemispheres, while dipoles 6 and 7 were slightly asymmetrical. Dipole 7 was found in the left hippocampal area. Dipole 6 appeared in the right insular cortex. The locations of the four dipoles implicated in the generation of the somatosensory P300 were compared with the locations of four dipoles accounting for the auditory evoked P300 described in our previous paper [Tarkka et al. (1995) Electroenceph. clin. Neurophysiol. 96, 538-545]. No substantial difference in source locations of the P300 was found between auditory and somatosensory modality other than an asymmetrical activity in the somatosensory modality contralateral to the stimulated hand.

Electroencephalographic evaluation of children without neuropsychiatric disturbances but with poor school performance.

The estimation of brain function using neurophysiologic methods is of importance because, besides neuropsychiatric diseases, a variety of systemic diseases can influence brain function. Several types of brain dysfunction, resulting from many kinds of influences on the central nervous system, can cause poor school performance. Besides these dysfunctions of organic origin, there may be others that do not have an organic basis but may reflect the adverse effects of the environment. Often, the differential diagnosis between them is difficult. We performed a neurologic examination and an evaluation using the Illinois Test of Psycholinguistic Abilities and questionnaires (Eysenck and Rutter) to assess the personality of 22 poor school performers aged 10 to 12 years and 22 controls with good school performance of similar age. In order to assess the possibility of using quantitative electroencephalographic methods to evaluate nonorganic poor school performers, we estimated the power spectrum and coherence of the electroencephalographic signals at rest and their changes during motor tasks and auditory stimulation. Our results indicate that there were no signs of organicity in the group of poor school performers.

The effect of blood transfusion on alpha EEG activity in thalassemic patients.

EEG signal analysis could be very helpful in the detection of signs of adverse reaction to the brain by a systemic disease. We performed an EEG spectral analysis in 12 young patients with thalassemia before and after their regular blood transfusion, and in 10 volunteer students. Our aim was to test if the EEG analysis could detect signs of brain dysfunction due to the cerebral hypoxia as a result of the anemia. We analyzed the EEG signals on line using a dedicated computer system estimating the power of the delta, theta, alpha, sigma and beta bands of the EEG. After transfusion, the power spectral density of the alpha band showed a significant enhancement in most areas of the brain, in the group of the thalassemia patients as compared to the normals. These differences correlated with the levels of hemoglobin of the patients, and possibly reflect the degree of oxygenation of the brain. Since the visual interpretation of the EEG is not efficient for estimating the brain dysfunction in the anemic state in thalassemia, the signal analysis of the EEG may provide a more sensitive alternative to study the effects of hypoxia on brain function.