Neural networks involved in mathematical thinking: evidence from linear and non-linear analysis of electroencephalographic activity

Using linear and non-linear methods, electroencephalographic (EEG) signals were measured at various brain regions to provide information regarding patterns of local and coordinated activity during performance of three arithmetic tasks (number comparison, single-digit multiplication, and two-digit multiplication) and two control tasks that did not require arithmetic operations. It was hypothesized that these measures would reveal the engagement of local and increasingly complex cortical networks as a function of task specificity and complexity. Results indicated regionally increased neuronal signalling as a function of task complexity at frontal, temporal and parietal brain regions, although more robust task-related changes in EEG-indices of activation were derived over the left hemisphere. Both linear and non-linear indices of synchronization among EEG signals recorded from over different brain regions were consistent with the notion of more "local" processing for the number comparison task. Conversely, multiplication tasks were associated with a widespread pattern of distant signal synchronizations, which could potentially indicate increased demands for neural networks cooperation during performance of tasks that involve a greater number of cognitive operations.     

Decision support system for classification of epilepsies in childhood

Diagnosis of epilepsy in childhood is often difficult as the symptoms are often atypical and the epilepsy syndromes are multiform. Methods from the domain of artificial intelligence give the opportunity to formalize medical knowledge and standardize various diagnostic procedures in specific domains of medicine. We developed a decision support system using artificial intelligence techniques for the classification and ultimately the diagnosis of epilepsies and epilepsy syndromes in children. The system incorporates knowledge from the International Classification of Epilepsies and Epileptic Syndromes. It was assessed using clinical data and the system's conclusions were compared with the diagnoses proposed by an experienced doctor. The system and the physician reached identical diagnoses in 85.2% of the cases. In an additional 8.2% of the cases, the system's diagnosis was similar to that of the physician, thus raising its overall success rate to 93.4%. The system can be helpful, especially for trainees, since it only needs to import the clinical and laboratory data. Decision making and differential diagnosis are then performed automatically.     

Detection of Signs of Brain Dysfunction in Epileptic Children by Recognition of Transient Changes in the Correlation of Seizure-Free EEG

Seizure-free EEG signals recorded from epileptic children were compared with EEG signals recorded from normal children. The comparison was based on the detection of transient events characterized by decrease in the correlation between different traces. For this purpose, a conceptually and mathematically simple method was applied. Two clear and remarkable phenomena, able to quantitatively discriminate between the two groups of subjects, were evidenced, with high statistical significance. In fact, it was observed that: (a) The number of events for the epileptic group was larger; (b) Applying restrictive criteria for event definition, the number of subjects in the epileptic group presenting events was larger. The results support the hypothesis of a decrease in brain correlation in children with epilepsy under treatment. This confirms the efficacy of the EEG signal in evaluating cortical functional differences not visible by visual inspection, independently of the cause (epilepsy or drugs), and demonstrate the specific effectiveness of the analysis method applied.     

Graph-based network analysis in schizophrenia

Over the last few years, many studies have been published using modern network analysis of the brain. Researchers and practical doctors alike should understand this method and its results on the brain evaluation at rest, during activation and in brain disease. The studies are noninvasive and usually performed with elecroencephalographic, magnetoencephalographic, magnetic resonance imaging and diffusion tensor imaging brain recordings. Different tools for analysis have been developed, although the methods are in their early stages. The results of these analyses are of special value. Studies of these tools in schizophrenia are important because widespread and local network disturbances can be evaluated by assessing integration, segregation and several structural and functional properties. With the help of network analyses, the main findings in schizophrenia are lower optimum network organization, less efficiently wired networks, less local clustering, less hierarchical organization and signs of disconnection. There are only about twenty five relevant papers on the subject today. Only a few years of study of these methods have produced interesting results and it appears promising that the development of these methods will present important knowledge for both the preclinical signs of schizophrenia and the methods' therapeutic effects.     

Small-world networks and disturbed functional connectivity in schizophrenia

Disturbances in "functional connectivity" have been proposed as a major pathophysiological mechanism for schizophrenia, and in particular, for cognitive disorganization. Detection and estimation of these disturbances would be of clinical interest. Here we characterize the spatial pattern of functional connectivity by computing the "synchronization likelihood" (SL) of EEG at rest and during performance of a 2Back working memory task using letters of the alphabet presented on a PC screen in subjects with schizophrenia and healthy controls. The spatial patterns of functional connectivity were then characterized with graph theoretical measures to test whether a disruption of an optimal spatial pattern ("small-world") of the functional connectivity network underlies schizophrenia. Twenty stabilized patients with schizophrenia, who were able to work, and 20 healthy controls participated in the study. During the working memory (WM) task healthy subjects exhibited small-world properties (a combination of local clustering and high overall integration of the functional networks) in the alpha, beta and gamma bands. These properties were not present in the schizophrenia group. These findings are in accordance with a partially inadequate organization of neuronal networks in subjects with schizophrenia. This method could be helpful for diagnosis and evaluation of the severity of the disease, as well as understanding the pathophysiologic mechanisms underlying cognitive dysfunction in schizophrenia.     

What does delta band tell us about cognitive processes: A mental calculation study

Multichannel EEG recordings from 18 healthy subjects were used to investigate brain activity in four delta subbands during two mental arithmetic tasks (number comparison and two-digit multiplication) and a control condition. The spatial redistribution of signal-power (SP) was explored based on four consecutives subbands of the delta rhythm. Additionally, network analysis was performed, independently for each subband, and the related graphs reflecting functional connectivity were characterized in terms of local structure (i.e. the clustering coefficient), overall integration (i.e. the path length) and the optimality of network organization (i.e. the “small-worldness”). EEG delta activity showed a widespread increase in all subbands during the performance of both arithmetic tasks. The inter-task comparison of the two arithmetic tasks revealed significant differences, in terms of signal-power, for the two subbands of higher frequency over left hemisphere (frontal, temporal, parietal and occipital) regions. The estimated brain networks exhibited small-world characteristics in the case of all subbands. On the contrary, lower frequency subbands were found to operate differently than the higher frequency subbands, with the latter featuring nodal organization and poor remote interconnectivity. These findings possibly reflect the deactivation of default mode network and could be attributed to inhibitory mechanisms activated during mental tasks.     

Variability of EEG synchronization during a working memory task in healthy subjects.

Working memory is associated with an increase in EEG theta synchronization and a decrease in lower alpha band synchronization. We investigated whether such changes in mean synchronization level are accompanied by changes in small scale fluctuations of synchronization. EEGs (19 channels; average reference; sample frequency 250 Hz) were recorded in 21 healthy subjects (12 males; mean age 62.5 years; S.D. 2.1) at rest and during a visual working memory condition. EEG synchronization was computed in six frequency bands (2-6; 6-10; 10-14; 14-18; 18-22; 22-50 Hz) using the synchronization likelihood. Variability of the synchronization was quantified with synchronization entropy. During the working memory condition synchronization increased in the 2-6 Hz band, and decreased in the 6-10, 14-18 and 18-22 Hz bands. Working memory was associated with increased variability in the 2-6 Hz band, and decreased variability in the 6-10 Hz band and, to a lesser extent, in the 14-18 and 18-22 Hz bands. Working memory is accompanied not only by characteristic changes in the mean level of interactions between neural networks, but also by changes in small scale fluctuations in such interactions. Strong, but rapidly fluctuating coupling between neural systems might provide a mechanism to optimize the balance between local differentiation and global integration of brain activity.     

Changes in linear and nonlinear EEG measures as a function of task complexity: evidence for local and distant signal synchronization

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.     

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.     

Giant somatosensory evoked potentials in children without myoclonic epilepsy

Sixteen of 1093 children 5-14 years of age with various neurological problems were detected showing giant somatosensory evoked potentials (GSSEP). These potentials were analysed and their enlarged components described. None of the 16 children had evidence of myoclonic epileptic seizures. Nine children had epileptic seizures, but 7 did not. The characteristics of GSSEPs in patients without myoclonic seizures are described. We conclude that in patients without myoclonic seizures GSSEPs occur and bear some similarity with those elicited in patients with myoclonic seizures. They may represent a form of hyperexcitability of the CNS.