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Summary: Using electroencephalographic (EEG) signals and a novel methodology based on wavelet measures in the time-scale domain, we evaluated cortex reactions during mathematical thinking. Our purpose was to extract more precise information from the cortex reactions during this cognitive task. Initially, the brain areas (lobes) of significant activation during the task are extracted using time-averaged wavelet power spectrum estimation. Then, a refinement step makes use of statistical significance-based criteria for comparing wavelet power spectra between the task and the rest condition. EEG signals are recorded from 15 young normal volunteers using 30 scalp electrodes as participants performed one difficult arithmetic task and the results are compared with a rest situation. The results are in accordance with similar previous studies, showing activations of frontal and central regions. Compared with the alternative spectral-based techniques, the method we propose achieves higher task discrimination on the same dataset and provides additional detail-signal information to evaluate cortical reactivity during local cortical activation.

The purpose of the present study was threefold: First, to replicate previous findings of changes in local gamma band power as a function of the complexity of a visuo-semantic processing task, second, to extend these findings in tasks delivered in the auditory modality, and third to explore the use of non-linear algorithms as indices of complexity and distant synchronization in the EEG signal. EEG was recorded from 28 scalp locations as participants performed three visual discrimination tasks designed to tap into increasingly more complex operations regularly involved in the recognition of living animate objects. Two auditory processing tasks involving the same stimuli, but requiring no semantic processing, served as controls. The degree of complexity of the semantic decision was associated with the predicted changes in local gamma power, as well as with broadband changes in the non-linear predictability of the signal (an index derived using an artificial neural network algorithm). These changes were observed at all scalp regions, a finding consistent with the wide cortical distribution of component processes involved in the tasks. In addition, the synchronization between temporal and parieto-occipital electrodes and the remaining recording sites was highest in the gamma bands and lowest in the alpha bands for the task that required the most complex visuo-semantic decision. This trend reversed with reduced task complexity, consistent with the view that multidimensional semantic decisions require the involvement of distributed cortical networks in auditory and visual association areas and in the frontal lobes.

This study evaluated the utility of electroencephalographic (EEG) measures as indices of regional cerebral activation during performance of visuo-semantic analysis tasks in neurologically intact adult volunteers. EEG was recorded from 28 scalp locations as participants performed three visual discrimination tasks designed to tap into increasingly more complex operations regularly involved in the recognition of living animate objects. In addition, data from a control task involving the same stimuli, but requiring no cognitive decision or response, was included. EEG records were quantified using power spectrum measures in five frequency bands (delta, theta, alpha, beta, and gamma). Results showed a significant linear increase in absolute power in the gamma band with increasing task complexity over left hemisphere frontal and occipital regions, and over right temporoparietal regions.

Summary We studied the change of EEG alpha-band coherence between resting and motor performance (“reactivity of EEG coherence”) in 18 chronic schizophrenics and 30 normal controls, examining differences between left and right hemisphere tasks. Three coherences were examined for each hemisphere. The central-parietal coherence in normals increased on the left for left hemisphere tasks, and decreased on the right for right hemisphere tasks, whereas the patients showed a decrease of both measures, more on the left side. Thus, the interhemispheric difference of central-parietal coherence reactivity was reversed for the schizophrenics (P<0.06) and suggested a left-sided deviation. The lateral-precentral and precentral-central coherences showed no left-right deviations of reactivity in the patients. Since coherence increase is considered to be a sign of the coupled function between the studied regions, our findings suggest a defective coordination of left central-parietal regions during a task in schizophrenics.

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. (J Child Neurol1996;11:309-312).