Keywords

CHILDREN / TEENAGERS / AGE DEVELOPMENT/ BRAIN / EEG / NORTH / ADAPTATION

annotation scientific article on medical technologies, author of scientific work - Soroko S.I., Rozhkov Vladimir Pavlovich, Bekshaev S.S.

Using an original method for assessing the structure of the interaction of EEG components (waves), the dynamics of the formation of patterns of bioelectrical activity of the brain and age-related changes in the relationships between the main frequency components of the EEG characterizing the features of the development of the central nervous system in children and adolescents living in difficult environmental conditions of the North of the Russian Federation were studied. It has been established that the statistical structure of the interaction of EEG components undergoes significant changes with age and has its own topographical and gender differences. In the period from 7 to 18 years, the probability of interaction of waves of all frequency ranges of EEG rhythms with waves of the delta and theta ranges decreases with a simultaneous increase in interaction with waves of the beta and alpha2 ranges. To the greatest extent, the dynamics of the analyzed EEG parameters is manifested in the parietal, temporal and occipital areas of the cerebral cortex. The greatest gender differences in the analyzed EEG parameters occur in the pubertal period. By the age of 16-17, in girls, the functional core of the interaction of wave components, which supports the structure of the EEG pattern, is formed in the alpha2-beta1 range, while in boys it is in the alpha2-alpha1 range. The severity of age-related rearrangements of the EEG pattern reflects the gradual formation of electrogenesis of various brain structures and has individual characteristics due to both genetic and environmental factors. The obtained quantitative indicators of the formation of dynamic relationships of the main rhythms with age make it possible to identify children with impaired or delayed development of the central nervous system.

The text of the scientific work on the topic "Features of the frequency-temporal organization of the EEG pattern in children and adolescents in the North in different age periods"

UDC 612.821-053.4/.7(470.1/.2)

FEATURES OF THE FREQUENCY AND TIME ORGANIZATION OF THE EEG PATTERN IN CHILDREN AND ADOLESCENTS IN THE NORTH IN DIFFERENT AGE PERIODS

S. I. Soroko, V. P. Rozhkov, and S. S. Bekshaev

Institute of Evolutionary Physiology and Biochemistry. I. M. Sechenov of the Russian Academy of Sciences,

St. Petersburg

Using an original method for evaluating the structure of the interaction of EEG components (waves), the dynamics of the formation of patterns of brain bioelectrical activity and age-related changes in the relationships between the main frequency components of the EEG characterizing the features of the development of the central nervous system in children and adolescents living in difficult environmental conditions of the North of the Russian Federation was studied. It has been established that the statistical structure of the interaction of EEG components undergoes significant changes with age and has its own topographical and gender differences. In the period from 7 to 18 years, the probability of the interaction of waves of all frequency ranges of EEG rhythms with the waves of the delta and theta ranges decreases with a simultaneous increase in interaction with the waves of the beta and alpha2 ranges. To the greatest extent, the dynamics of the analyzed EEG parameters is manifested in the parietal, temporal, and occipital areas of the cerebral cortex. The greatest gender differences in the analyzed EEG parameters occur in the pubertal period. By the age of 16-17, in girls, the functional core of the interaction of wave components, which supports the structure of the EEG pattern, is formed in the alpha2-beta1 range, while in boys it is in the alpha2-alpha1 range. The severity of age-related rearrangements of the EEG pattern reflects the gradual formation of electrogenesis of various brain structures and has individual characteristics due to both genetic and environmental factors. The obtained quantitative indicators of the formation of dynamic relationships of the main rhythms with age make it possible to identify children with impaired or delayed development of the central nervous system.

Keywords: children, adolescents, age development, brain, EEG, North, adaptation

CARACTERISTICS OF TIME AND FREQUENCY EEG PATTERN IN CHILDREN AND ADOLESCENTS LIVING IN THE NORTH IN DIFFERENT AGE PERIODS

S. I. Soroko, V. P., Rozhkov, S. S. Bekshaev

I. M. Sechenov Institute of Evolutionary Physiology and Biochemistry of the Russian Academy of Sciences,

St. Petersburg, Russia

Features of CNS development have been investigated in children and adolescents living under the severe ecological conditions in the North of Russia. The original method for estimating a time structure of the EEG frequency components interrelations was used to study the dynamics of maturation of bioelectrical brain activity pattern and age-related changes of the interplay between the main EEG rhythms. It was found that the statistical structure of the interaction of the frequency components of EEG is undergoing a significant restructuring with age and has certain topography and gender differences. The period from 7 to 18 years of age is marked by a decrease in the probability of interaction of wave components of the main EEG frequency bands with components of delta and theta bands while simultaneously increasing interaction with the components of beta and alpha2 frequency bands. The dynamics of the studied EEG indices manifested in the parietal, temporal and occipital regions of the cerebral cortex to the greatest extent. The largest sex-related differences in the EEG parameters occur in puberty. Functional core of the wave components interaction that maintain the structure of the frequency-temporal EEG pattern is formed to 16-18 years in girls in alpha2-beta1 range, while in boys - in alpha1-alpha2 range. The intensity of age-related rearrangements of the EEG pattern reflects the gradual maturation of electrogenesis in different brain structures and has individual features due to both genetic and environmental factors. Obtained quantitative indicators of formation with age of dynamic relationships between basic EEG rhythms permit to reveal children with disturbed or delayed development of the central nervous system.

Keywords: children, adolescents, brain development, EEG, the North, adaptation

Soroko S.I., Rozhkov V.P., Bekshaev S.S. Peculiarities of the time-frequency organization of the EEG pattern in children and adolescents in the North in different age periods // Human Ecology. 2016. No. 5. S. 36-43.

Soroko S. I., Rozhkov V. P., Bekshaev S. S. Caracteristics of Time and Frequency EEG Pattern in Children and Adolescents Living in the North in Different Age Periods. Ekologiya cheloveka. 2016, 5, pp. 36-43.

The socio-economic development of the Arctic zone is defined as one of the priority areas of the state policy of the Russian Federation. In this regard, a comprehensive study of the medical and socio-economic problems of the population of the North, health protection and improving the quality of life is very relevant.

It is known that the complex of extreme environmental factors of the North (natural, technogenic,

social) has a pronounced stressful effect on the human body, while the greatest stress is experienced by the children's population. Increased loads on physiological systems and tension of the central mechanisms of regulation of functions in children living in adverse climatic conditions of the North cause the development of two types of negative reactions: a reduction in reserve capacity and delay

pace of age development. These negative reactions are based on an increased level of costs for homeostatic regulation and provision of metabolism with the formation of a deficit of a bioenergetic substrate. In addition, through higher-order genes that control age-related development, unfavorable environmental factors can have epigenetic effects on the rate of age-related development by temporarily stopping or shifting one or another stage of development. Deviations from normal development not identified in childhood can subsequently lead to a violation of certain functions or to pronounced defects already in adulthood, significantly reducing the quality of human life.

There is a huge number of works in the literature devoted to the study of the age-related development of the CNS in children and adolescents, nosological forms in developmental disorders. Under the conditions of the North, the impact of complex natural and social factors can determine the characteristics of age-related maturation of the EEG of children. However, there are still no sufficiently reliable methods for early detection of abnormalities in brain development at different stages of postnatal ontogenesis. It is necessary to carry out in-depth fundamental research in order to search for local and spatial EEG markers that make it possible to control the individual morpho-functional development of the brain at different age periods in specific living conditions.

The purpose of this study was to study the features of the dynamics of the formation of rhythmic patterns of bioelectrical activity and age-related changes in the relationships between the main EEG frequency components that characterize the maturation of both individual cortical and subcortical structures and regulatory subcortical-cortical interactions in healthy children living in the conditions of the European North of Russia.

The contingent of the examined. 44 boys and 42 girls from 7 to 17 years old - students of grades 1 - 11 of the rural comprehensive school of the Konoshsky district of the Arkhangelsk region took part in the study of the age formation of the bioelectrical activity of the brain. The studies were carried out in compliance with the requirements of the Declaration of Helsinki, approved by the Biomedical Research Ethics Commission of the Institute of Evolutionary Physiology and Biochemistry. I. M. Sechenov of the Russian Academy of Sciences protocol. Parents of the students were informed about the purpose of the survey and agreed to conduct it. The students participated in the research voluntarily.

EEG procedure. EEG was recorded on a computer electroencephalograph EEGA 21/26 "Encephalan-131-03" (NPKF "Medikom" MTD, Russia) in 21 leads according to the international

system "10-20" in the band 0.5-70 Hz with a sampling frequency of 250 Hz. A monopolar lead was used with a combined reference electrode on the earlobes. The EEG was recorded in the sitting position. The results for the state of calm wakefulness with closed eyes are presented.

EEG analysis. Digital filtering was preliminarily applied with the limitation of the EEG frequency range from 1.6 to 30 Hz. EEG fragments containing oculomotor and muscle artifacts were excluded. To analyze the EEG, original methods were used to study the dynamic structure of the temporal sequence of EEG waves. The EEG was converted into a sequence of periods (EEG waves), each of which, depending on the duration, belongs to one of the six EEG frequency ranges (P2: 17.5-30 Hz; P1: 12.5-17.5 Hz; a2: 9, 5-12.5 Hz; a1: 7-9.5 Hz; 0: 4-7 Hz and 5: 1.5-4 Hz). The conditional probability of the appearance of any frequency component of the EEG under the condition of its direct precedence by any other was estimated; this probability is equal to the probability of the transition from the previous frequency component to the next one. Based on the numerical values of the transition probabilities between all the indicated frequency ranges, a 6 x 6 transition probability matrix was compiled. For a visual representation of the transition probability matrices, oriented probability graphs were constructed. The above frequency components of the EEG serve as vertices, the edges of the graph connect the EEG components of different frequency ranges, the thickness of the edge is proportional to the probability of the corresponding transition.

Statistical data analysis. To identify the relationship between changes in EEG parameters with age, Pearson correlation coefficients were calculated, and multiple linear regression analysis was used with ridge estimates of regression parameters with stepwise inclusion of predictors. When analyzing the topical features of age-related changes in EEG parameters, the predictors were estimates of the probability of transitions between all 6 frequency ranges (36 parameters for each EEG derivation). Multiple correlation coefficients r, regression coefficients, and determination coefficients (r2) were analyzed.

To assess the age patterns of EEG pattern formation, all schoolchildren (86 people) were divided into three age groups: the youngest - from 7 to 10.9 years old (n = 24), the middle one - from 11 to 13.9 years old (n = 25), the eldest - from 14 to 17.9 years (n = 37). Two-way analysis of variance (ANOVA) was used to assess the influence of the factors "Gender" (2 grades), "Age" (3 grades), as well as the effect of their interaction on EEG parameters. The effects (values of the F-test) were analyzed with a significance level p< 0,01. Для оценки возможности возрастной классификации детей по описанным выше матрицам вероятностей переходов в 21-м отведении использовали классический дискриминантный анализ

with stepwise inclusion of predictors. Statistical processing of the obtained data was carried out using the $1a software package.<лз1лса-Ш.

results

For 86 students, matrices of transition probabilities from one EEG frequency component to another were calculated, on which the corresponding transition graphs were constructed in 21 EEG derivations. Examples of such graphs for a schoolboy aged 7 and 16 are shown in Fig. 1. The graphs show a repeating structure of transitions in many leads, which characterizes a certain algorithm for changing one EEG frequency components by others in their time sequence. Lines (edges) on each of the graphs coming from most of the vertices (vertices correspond to the main EEG frequency ranges) of the left column of the graph converge on the right column to 2-3 vertices (EEG ranges). Such convergence of lines to individual ranges reflects the formation of a "functional core" of the interaction of EEG wave components, which plays the main role in maintaining this structure of the bioelectrical activity pattern. The core of such interaction in children from elementary grades (7-10 years old) is theta- and alpha1-frequency ranges, in adolescents from senior classes (14-17 years old) - alpha1- and alpha2-frequency ranges, that is, there is a "change" of functional the cores of the low-frequency (theta) range by the high-frequency (alpha1 and alpha2).

In elementary school students, a stable structure of transition probabilities is characteristic of

occipital, parietal and central leads. In most adolescents aged 14-17, probabilistic transitions are already well structured not only in the occipital-parietal and central, but also in the temporal (T5, T6, T3, T4) areas.

Correlation analysis makes it possible to quantify the dependence of changes in the probabilities of interfrequency transitions on the age of the student. On fig. 2 in the cells of the matrices (constructed in the similarity of transition probability matrices, each matrix corresponds to a certain EEG derivation), triangles display only significant correlation coefficients: the top of the triangle up characterizes an increase in the probability, the top down - a decrease in the probability of a given transition. Attention is drawn to the presence of a regular structure in the matrices for all EEG leads. Thus, in the columns marked 9 and 5, there are only signs with a top pointing downward, which reflects a decrease with age in the probability of the transition of a wave of any range (indicated vertically in the matrix) to the waves of the EEG delta and theta ranges. In the columns marked a2, p1, p2, there are only icons with the vertex pointing upwards, which reflects an increase in the probability of transition of a wave of any range to the waves of the beta1-, beta2-, and especially the alpha2-range of EEG frequencies with age. It can be seen that the most pronounced age-related changes, while oppositely directed, are associated with transitions to the alpha2 and theta ranges. A special place is occupied by the alpha 1 frequency range. The probability of transitions to this range in all EEG leads shows an age dependence

Fig.1. Topical features of the structure of mutual transitions of waves of different EEG frequency ranges in a student of 7 (I) and 16 (II) years p1, p2 - beta-, a1, a2 - alpha, 9 - theta, 5 - delta components (waves) of the EEG. Shown are transitions whose conditional probability is greater than 0.2. Fp1 ... 02 - EEG leads.

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Rice. Fig. 2. Changes in the probabilities of transitions between the wave components of the main EEG rhythms in different leads with age in schoolchildren (86 people)

5 ... p2 - EEG frequency ranges, Fp1 ... 02 - EEG derivations. Triangle in a cell: point down - decrease, point up - increase with age in the probability of transitions between EEG components of different frequency ranges. Significance level: p< 0,05 - светлый треугольник, р < 0,01 - темный треугольник.

only in isolated cases. However, if we follow the filling of the lines, then the alpha 1-range of EEG frequencies with age in schoolchildren reduces the connection with the slow-wave bands and increases the connection with the alpha 2-range, thereby acting as a factor regulating the stability of the EEG wave pattern.

For a comparative assessment of the degree of relationship between the age of children and changes in the wave pattern in each EEG derivation, we used the multiple regression method, which made it possible to evaluate the effect of combined rearrangements of mutual transitions between the components of all EEG frequency ranges, taking into account their mutual correlation (in order to reduce the redundancy of predictors, we used ridge regression). Determination coefficients characterizing the share of variability of the studied

EEG parameters, which can be explained by the influence of the age factor, vary in different leads from 0.20 to 0.49 (Table 1). Changes in the structure of transitions with age have certain topical features. Thus, the highest coefficients of determination between the analyzed parameters and age are detected in the occipital (01, 02), parietal (P3, Pr, P4) and posterior temporal (T6, T5) leads, decreasing in the central and temporal (T4, T3) leads, and also in F8 and F3, reaching the lowest values in the frontal leads (^p1, Fpz, Fp2, F7, F4, Fz). Based on the absolute values of the coefficients of determination, it can be assumed that at school age, the neuronal structures of the occipital, temporal, and parietal regions develop most dynamically. At the same time, changes in the structure of transitions in the parietal-temporal areas in

in the right hemisphere (P4, T6, T4) are more closely associated with age than in the left hemisphere (P3, T5, T3).

Table 1

Multiple regression results between student age and transition probabilities

between all EEG frequency components (36 variables) separately for each lead

EEG derivation r F df r2

Fp1 0.504 5.47* 5.80 0.208

Fpz 0.532 5.55* 5.70 0.232

Fp2 0.264 4.73* 6.79 0.208

F7 0.224 7.91* 3.82 0.196

F3 0.383 6.91** 7.78 0.327

Fz 0.596 5.90** 7.75 0.295

F4 0.524 4.23* 7.78 0.210

F8 0.635 5.72** 9.76 0.333

T3 0.632 5.01** 10.75 0.320

C3 0.703 7.32** 10.75 0.426

Cz 0.625 6.90** 7.75 0.335

C4 0.674 9.29** 7.78 0.405

T4 0.671 10.83** 6.79 0.409

T5 0.689 10.07** 7.78 0.427

P3 0.692 12.15** 6.79 0.440

Pz 0.682 13.40** 5.77 0.430

P4 0.712 11.46** 7.78 0.462

T6 0.723 9.26** 9.76 0.466

O1 0.732 12.88** 7.78 0.494

Oz 0.675 6.14** 9.66 0.381

O2 0.723 9.27** 9.76 0.466

Note. r - multiple correlation coefficient

between the variable "schoolchild's age" and independent variables, F - the corresponding value of the F-criterion, significance levels: * p< 0,0005, ** p < 0,0001; r2 - скорректированный на число степеней свободы (df) коэффициент детерминации.

The multiple correlation coefficient between the age of schoolchildren and the values of transition probabilities, calculated for the entire set of leads (in this case, transitions whose correlation with age did not reach a significance level of 0.05 were previously excluded from the complete list of transitions) amounted to 0.89, adjusted r2 = 0, 72 (F(21.64) = 11.3, p< 0,0001). То есть 72 % от исходной изменчивости зависимой переменной (возраст) могут быть объяснены в рамках модели множественной линейной регрессии, где предикторами являются вероятности переходов в определенном наборе отведений ЭЭГ. В числе предикторов оказались: P3 (t/t) = -0,21; O2 (b2/t) = -0,18; C3 (b 1 /t) = -0,16; F7 (a1/t) = 0,25; T6 (d/t) = -0,20; P4 (b2/a1) = -0,21; O1 (t/ t) = -0,21; T5 (a1/a2) = -0,20; F8 (t/d) = -0,18; O1 (d/t) = -0,08; F8 (t/t) = 0,22; T6 (a1/t) = -0,26; C3 (d/t) = -0,19; C3 (b2/b1) = 0,16; F8 (b2/t) = 0,19; Fp1 (a1/a2) = -0,17; P4 (t/t) = -0,15; P3 (a2/d) = 0,11; C4 (a2/a2) = 0,16;

Fp2 (b2/b1) = 0.11; 02 (1/а2) = -0.11 (in brackets 1/ - transition from component 1 to component ]). The sign of the regression coefficient characterizes the direction of the relationship between the variables: if the sign is positive, the probability of this transition increases with age, if the sign is negative, the probability of this transition decreases with age.

With the help of discriminant analysis according to the values of EEG transition probabilities, schoolchildren were divided into age groups. Of the entire set of transition probabilities, only 26 parameters were used for classification - according to the number of predictors obtained from the results of multiple linear regression analysis with ridge estimates of regression parameters. The separation results are shown in fig. 3. It can be seen that the obtained sets for different age groups overlap slightly. According to the degree of deviation from the center of the cluster of a particular student or his falling into another age group, one can judge the delay or advance in the rate of formation of the EEG wave pattern.

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Rice. Fig. 3. Distribution of schoolchildren of different age groups (j - junior, av - middle, st - senior) in the discriminant field Transition probabilities of EEG components (waves) significant according to the results of multiple regression were selected as predictors in the discriminant analysis.

Peculiarities in the age-related dynamics of the formation of the EEG wave pattern in girls and boys are revealed (Table 2). According to the analysis of variance, the main effect of the Gender factor is more pronounced in the parietal-temporal areas than in the fronto-central ones and has an accent in the leads of the right hemisphere. The effect of the Gender factor is that boys have a more pronounced relationship between alpha2- and the low-frequency alpha 1-range, and girls have a more pronounced relationship between alpha2- and high-frequency beta frequency ranges.

The effect of the interaction of factors associated with age-related dynamics is better manifested in the EEG parameters of the frontal and temporal (also predominantly on the right) areas. It is mainly associated with a decrease with increasing age of schoolchildren

table 2

Differences in transition probabilities between EEG frequency components and their age-related dynamics in girls and boys (ANOVA data for EEG derivations)

Transition between EEG frequency components

EEG derivation Main effect of factor Gender Effect of interaction of factors Gender*Age

Fp1 ß1-0 a1-5 0-0

Fp2 ß2-0 a1-0 0-ß1

T4 ß2-a1 0-a1 ß2-0 a2-0 a1-0 a1-5

T6 a2-a1 a2-ß1 a1-ß1 a2-0 a1-0

P4 a2-a1 ß2-a1 a1-0 a1-5

O2 a2-a1 a2-ß1 a1-ß2 a1-a1 0-0

Note. p2 ... 5 - EEG components Probabilities of transitions are presented with the level of significance of the influence of the Gender factor (interaction of Gender and Age factors) p< 0,01. Отведения Fpz, F7, F8, F3, F4, Т3, С2, 02 в таблице не представлены из-за отсутствия значимых эффектов влияния фактора Пол и взаимодействия факторов.

transitions from the alpha and beta frequency bands to the theta band. At the same time, a faster decrease in the probability of transition from the beta and alpha bands to the theta frequency band in boys is observed between the younger and middle school age groups, while in girls it is between the middle and older age groups.

The discussion of the results

Thus, based on the analysis performed, the frequency components of the EEG were identified, which determine the age-related reorganization and specificity of patterns of brain bioelectrical activity in northern schoolchildren. Quantitative indicators of the formation of dynamic relationships between the main EEG rhythms in children and adolescents with age in children and adolescents, taking into account gender characteristics, have been obtained, which make it possible to control the rate of age-related development and possible deviations in the dynamics of development.

Thus, in primary school children, a stable structure of the temporal organization of EEG rhythms was found in the occipital, parietal, and central leads. In most adolescents aged 14-17 years, the EEG pattern is well structured not only in the occipital-parietal and central, but also in the temporal regions. The data obtained confirm the ideas about the sequential development of brain structures and the staged formation of rhythmogenesis and integrative functions of the corresponding brain areas. It is known that the sensory and motor areas of the cortex

mature by the primary school period, later polymodal and associative zones mature, and the formation of the frontal cortex continues until adulthood. At a younger age, the wave structure of the EEG pattern is less organized (diffuse). Gradually, with age, the structure of the EEG pattern begins to acquire an organized character, and by the age of 17–18 it approaches that of adults.

The core of the functional interaction of EEG wave components in children of primary school age is theta and alpha1 frequency ranges, in senior school age - alpha1 and alpha2 frequency ranges. In the period from 7 to 18 years, the probability of the interaction of waves of all frequency ranges of EEG rhythms with the waves of the delta and theta ranges decreases with a simultaneous increase in interaction with the waves of the beta and alpha2 ranges. To the greatest extent, the dynamics of the analyzed EEG parameters is manifested in the parietal and temporo-occipital regions of the cerebral cortex. The greatest gender differences in the analyzed EEG parameters occur in the pubertal period. By the age of 16-17, in girls, the functional core of the interaction of wave components, which supports the structure of the EEG pattern, is formed in the alpha2-beta1 range, while in boys it is in the alpha2-alpha1 range. However, it should be noted that the age-related formation of the EEG pattern in different areas of the cerebral cortex proceeds heterochronously, undergoing some disorganization with an increase in theta activity during puberty. These deviations from the general dynamics are most pronounced in the pubertal period in girls.

Studies have shown that children in the Arkhangelsk region, in comparison with children living in the Moscow region, have a lag in puberty by one to two years. This may be due to the influence of climatic and geographical conditions of the habitat, which determine the characteristics of the hormonal development of children in the northern regions.

One of the factors of the ecological troubles of the human environment in the North is the lack or excess of chemical elements in soil and water. Residents of the Arkhangelsk region have a lack of calcium, magnesium, phosphorus, iodine, fluorine, iron, selenium, cobalt, copper and other elements. Violations of the micro- and macroelemental balance were also detected in children and adolescents, whose EEG data are presented in this paper. This can also affect the nature of the age-related morphofunctional development of various body systems, including the central nervous system, since essential and other chemical elements are an integral part of many proteins and are involved in the most important molecular biochemical processes, and some of them are toxic.

The nature of adaptive rearrangements and the degree

their severity is largely determined by the adaptive capabilities of the body, depending on individual typological characteristics, sensitivity and resistance to certain influences. The study of the developmental features of the child's body and the formation of the EEG structure is an important basis for the formation of ideas about the different stages of ontogeny, early detection of disorders and the development of possible methods for their correction.

The work was carried out under the Program of Fundamental Research No. 18 of the Presidium of the Russian Academy of Sciences.

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Renko G. V., Sergeeva E. G., Khovanskikh A. E., Kormilitsyn B. N., Moralev S. N., Yagodina O. V., Dobrodeeva L. K., Maksimova I. A., Protasova O V. Features of the formation of systemic brain activity in children in the conditions of the European North (problem article) // Russian Physiological Journal. I. M. Sechenov. 2006. V. 92, No. 8. S. 905-929.

13. Soroko S. I., Maksimova I. A., Protasova O. V. Age and sex characteristics of the content of macro- and microelements in the body of children in the European North // Human Physiology. 2014. V. 40. No. 6. S. 23-33.

14. Tkachev A. V. Influence of natural factors of the North on the human endocrine system // Problems of human ecology. Arkhangelsk, 2000. S. 209-224.

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16. Baars, B. J. The conscious access hypothesis: Origins and recent evidence // Trends in Cognitive Sciences. 2002 Vol. 6, No. 1. P. 47-52.

17. Clarke A. R., Barry R. J., Dupuy F. E., McCarthy R., Selikowitz M., Heaven P. C. L. Childhood EEG as a predictor of adult attention-deficit/hyperactivity disorder // Clinical Neurophysiology. 2011 Vol. 122. P. 73-80.

18. Loo S. K., Makeig S. Clinical utility of EEG in attention-deficit/hyperactivity disorder: a research update // Neurotherapeutics. 2012. Vol. 9, No. 3. P. 569-587.

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3. Gudkov A. B., Lukmanova I. B., Ramenskaya E. B. Chelovek v Pripolyarnom regione Evropejskogo Severa. Ecologo-fiziologicheskie aspekty. Arkhangelsk, 2013, 184 p.

4. Demin D. B., Poskotinova L. V., Krivonogova E. V. Variants of EEG Formation in Adolescents Living in Subpolar and Polar Regions of the Northern Russia. Vestnik Severnogo (Arkticheskogo) federalnogo universiteta, seriya "Mediko-biologicheskie nauki" . 2013, 1, pp. 41-45.

5. Jos Yu. S., Nekhoroshkova A. N., Gribanov A. V. Peculiarities of EEG and DC-potential of the Brain in Northern Schoolchildren. Ekologiya cheloveka. 2014, 12, pp. 15-20.

6. Kubasov R. V., Demin D. B., Tipisova E. V, Tkachev A. V. Hormonal provision of pituitary-thyroid-gonad gland system in boys during puberty living in Konosha District of the Arkhangelsk Region. Ekologiya cheloveka. 2004, 1 (4), pp. 265-268.

7. Kudrin A. V., Gromova O. A. Mikroelementyi v nevro-logii. Moscow, 2006, 304 p.

8. Lukmanova N. B., Volokitina T. V., Gudkov A. B., Safonova O. A. Changes of Psychomotor development parameters in 7-9 y. o. children. Ekologiya cheloveka. 2014, 8, pp. 13-19.

9. Nifontova O. L., Gudkov A. B., Shherbakova A. Je. Description of parameters of cardiac rhythm in indigenous children in Khanty-Mansiisky autonomous area. Ekologiya cheloveka. 2007, 1 1, pp. 41-44.

10. Novikova L. A., Farber D. A. Funkcionalnoe sozrevanie kory i podkorkovych struktur v razlichnye periody po dannym elektroencefalograficheskich issledovanij. Rukovodstvo po fiziologii. Ed. V. N. Chernigovsky. Leningrad, 1975, pp. 491-522.

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12. Soroko S. I., Burykh E. A., Bekshaev S. S., Sidorenko G. V., Sergeeva E. G., Khovanskich A. E., Kormilicyn B. N., Moralev S. N., Yagodina O. V., Dobrodeeva L. K., Maksimova I. A., Protasova O. V. Characteristics of the brain system activity and vegetative function formation in children under children conditions of the European north (a problem study). Rossiiskii fiziologicheskii jurnal imeni I. M. Sechenova / Rossiiskaia akademiia nauk. 2006, 92 (8), pp. 905-929.

13. Soroko S. I., Maksimova I. A., Protasova O. V Age and gender characteristics of the content of macro- and trace elements in the organisms of the children from the European North. Fiziologiya cheloveka. 2014, 40 (6), pp. 23-33.

14. Tkachev A. V. Vliyanie prirodnych faktorov Severa na endokrinnuyu sistemu cheloveka. Problemy ekologii cheloveka. Arkhangelsk. 2000, pp. 209-224.

15. Ciceroshin M. N., Shepovalnikov A. N. Stanovlenie integrativnojfunkcii mozga. St. Petersburg, 2009, 250 p.

16. Baars B. J. The conscious access hypothesis: Origins and recent evidence. Trends in Cognitive Sciences. 2002, 6(1), pp. 47-52.

17. Clarke A. R., Barry R. J., Dupuy F. E., McCarthy R., Selikowitz M., Heaven P. C. L. Childhood EEG as a predictor of adult attention-deficit/hyperactivity disorder. clinical neurophysiology. 2011, 122, pp. 73-80.

18. Loo S. K., Makeig S. Clinical utility of EEG in attention-deficit/hyperactivity disorder: a research update. neurotherapeutics. 2012, 9(3), pp. 569-587.

19. Sowell E. R., Trauner D. A., Gamst A., Jernigan T. L. Development of cortical and subcortical brain structures in childhood and adolescence: a structural MRI study. Developmental Medicine and Child Neurology. 2002, 44(1), pp. 4-16.

Contact Information:

Rozhkov Vladimir Pavlovich - Candidate of Biological Sciences, Leading Researcher, Institute of Evolutionary Physiology and Biochemistry named after A.I. I. M. Sechenov of the Russian Academy of Sciences

Address: 194223, St. Petersburg, Torez Ave., 44

The main feature of the EEG, which makes it an indispensable tool for age-related psychophysiology, is its spontaneous, autonomous character. Regular electrical activity of the brain can be recorded already in the fetus, and stops only with the onset of death. At the same time, age-related changes in the bioelectrical activity of the brain cover the entire period of ontogenesis from the moment of its occurrence at a certain (and not yet precisely established) stage of intrauterine development of the brain and up to the death of a person. Another important circumstance that makes it possible to use the EEG productively in the study of brain ontogeny is the possibility of a quantitative assessment of the changes taking place.

Studies of ontogenetic transformations of the EEG are very numerous. EEG age dynamics is studied at rest, in other functional states (sleep, active wakefulness, etc.), as well as under the action of various stimuli (visual, auditory, tactile). Based on many observations, indicators have been identified that judge age-related transformations throughout ontogeny, both in the process of maturation (see Chapter 12.1.1.), and during aging. First of all, these are the features of the frequency-amplitude spectrum of the local EEG, i.e. activity recorded at individual points in the cerebral cortex. In order to study the relationship of bioelectrical activity recorded from different points of the cortex, spectral-correlation analysis is used (see Chapter 2.1.1) with an assessment of the coherence functions of individual rhythmic components.

Age-related changes in the rhythmic composition of the EEG. In this regard, age-related changes in the EEG frequency-amplitude spectrum in different areas of the cerebral cortex are the most studied. Visual analysis of the EEG shows that in awake newborns, the EEG is dominated by slow irregular oscillations with a frequency of 1–3 Hz and an amplitude of 20 μV. In the spectrum of EEG frequencies, however, they have frequencies in the range from 0.5 to 15 Hz. The first manifestations of rhythmic order appear in the central zones, starting from the third month of life. During the first year of life, there is an increase in the frequency and stabilization of the main rhythm of the child's electroencephalogram. The trend towards an increase in the dominant frequency persists at further stages of development. By the age of 3, this is already a rhythm with a frequency of 7 - 8 Hz, by 6 years - 9 - 10 Hz (Farber, Alferova, 1972).

One of the most controversial is the question of how to qualify the rhythmic components of the EEG in young children, i.e. how to correlate the classification of rhythms accepted for adults by frequency ranges (see Chapter 2.1.1) with those rhythmic components that are present in the EEG of children of the first years of life. There are two alternative approaches to solving this issue.

The first comes from the fact that the delta, theta, alpha and beta frequency ranges have a different origin and functional significance. In infancy, slow activity turns out to be more powerful, and in further ontogenesis, a change in the dominance of activity from slow to fast frequency rhythmic components occurs. In other words, each EEG frequency band dominates in ontogeny one after the other (Garshe, 1954). According to this logic, 4 periods were identified in the formation of the bioelectrical activity of the brain: 1 period (up to 18 months) - the dominance of delta activity, mainly in the central parietal leads; 2 period (1.5 years - 5 years) - dominance of theta activity; 3 period (6 - 10 years) - dominance of alpha activity (labile phase); 4 period (after 10 years of life) dominance of alpha activity (stable phase). In the last two periods, the maximum activity falls on the occipital regions. Based on this, it was proposed to consider the ratio of alpha to theta activity as an indicator (index) of brain maturity (Matousek and Petersen, 1973).

Another approach considers the main, i.e. the dominant rhythm in the electroencephalogram, regardless of its frequency parameters, as an ontogenetic analog of the alpha rhythm. The grounds for such an interpretation are contained in the functional features of the dominant rhythm in the EEG. They found their expression in the "principle of functional topography" (Kuhlman, 1980). In accordance with this principle, the identification of the frequency component (rhythm) is carried out on the basis of three criteria: 1) the frequency of the rhythmic component; 2) the spatial location of its maximum in certain areas of the cerebral cortex; 3) EEG reactivity to functional loads.

Applying this principle to the analysis of the EEG of infants, T.A. Stroganova showed that the frequency component of 6-7 Hz, recorded in the occipital region, can be considered as a functional analogue of the alpha rhythm or as the alpha rhythm itself. Since this frequency component has a low spectral density in the state of visual attention, but becomes dominant with a uniform dark field of vision, which, as is known, characterizes the alpha rhythm of an adult (Stroganova et al., 1999).

The stated position seems convincingly argued. Nevertheless, the problem as a whole remains unresolved, because the functional significance of the remaining rhythmic components of the EEG of infants and their relationship with the EEG rhythms of an adult: delta, theta, and beta are not clear.

From the foregoing, it becomes clear why the problem of the ratio of theta and alpha rhythms in ontogeny is the subject of discussion. The theta rhythm is still often regarded as a functional precursor of the alpha rhythm, and thus it is recognized that the alpha rhythm is virtually absent in the EEG of young children. Researchers adhering to this position do not consider it possible to consider the rhythmic activity that dominates in the EEG of young children as an alpha rhythm (Shepovalnikov et al., 1979).

However, regardless of how these frequency components of the EEG are interpreted, age-related dynamics, indicating a gradual shift in the frequency of the dominant rhythm towards higher values in the range from theta rhythm to high-frequency alpha, is an indisputable fact (for example, Fig. 13.1).

Heterogeneity of the alpha rhythm. It has been established that the alpha range is heterogeneous, and depending on the frequency, a number of subcomponents can be distinguished in it, which apparently have different functional significance. The ontogenetic dynamics of their maturation serves as a significant argument in favor of distinguishing narrow-band alpha subranges. Three subranges include: alpha-1 - 7.7 - 8.9 Hz; alpha-2 - 9.3 - 10.5 Hz; alpha-3 - 10.9 - 12.5 Hz (Alferova, Farber, 1990). From 4 to 8 years, alpha-1 dominates, after 10 years - alpha-2, and at 16-17 years, alpha-3 predominates in the spectrum.

The components of the alpha rhythm also have different topography: the alpha-1 rhythm is more pronounced in the posterior cortex, mainly in the parietal. It is considered local in contrast to alpha-2, which is widely distributed in the cortex, with a maximum in the occipital region. The third alpha component, the so-called murhythm, has a focus of activity in the anterior regions: the sensorimotor cortex. It also has a local character, since its thickness sharply decreases with distance from the central zones.

The general trend of changes in the main rhythmic components is manifested in a decrease with age in the severity of the slow component of alpha-1. This component of the alpha rhythm behaves like theta and delta bands, the power of which decreases with age, while the power of the alpha-2 and alpha-3 components, as well as the beta band, increases. However, beta activity in normal healthy children is low in amplitude and power, and in some studies this frequency range is not even processed due to its relatively rare occurrence in a normal sample.

EEG features in puberty. The progressive dynamics of the frequency characteristics of the EEG in adolescence disappears. At the initial stages of puberty, when the activity of the hypothalamic-pituitary region in the deep structures of the brain increases, the bioelectrical activity of the cerebral cortex changes significantly. In the EEG, the power of slow-wave components, including alpha-1, increases, and the power of alpha-2 and alpha-3 decreases.

During puberty, there are noticeable differences in biological age, especially between the sexes. For example, in girls aged 12-13 years (experiencing stages II and III of puberty), the EEG is characterized by a greater intensity of the theta-rhythm and alpha-1 component compared to boys. At 14-15 years old, the opposite picture is observed. Girls have final ( TU and Y) the stage of puberty, when the activity of the hypothalamic-pituitary region decreases, and negative trends in the EEG gradually disappear. In boys at this age, stages II and III of puberty predominate, and the signs of regression listed above are observed.

By the age of 16, these differences between the sexes practically disappear, since most adolescents enter the final stage of puberty. The progressive direction of development is being restored. The frequency of the main EEG rhythm increases again and acquires values close to the adult type.

Features of the EEG during aging. In the process of aging, there are significant changes in the nature of the electrical activity of the brain. It has been established that after 60 years there is a slowdown in the frequency of the main EEG rhythms, primarily in the range of the alpha rhythm. In persons aged 17-19 years and 40-59 years, the frequency of the alpha rhythm is the same and is approximately 10 Hz. By the age of 90, it drops to 8.6 Hz. Deceleration of the frequency of the alpha rhythm is called the most stable "EEG symptom" of brain aging (Frolkis, 1991). Along with this, slow activity (delta and theta rhythms) increases, and the number of theta waves is greater in individuals at risk of developing vascular psychology.

Along with this, in persons over 100 years old - centenarians with a satisfactory state of health and preserved mental functions - the dominant rhythm in the occipital region is in the range of 8-12 Hz.

Regional dynamics of maturation. Until now, when discussing the age-related dynamics of the EEG, we have not specifically analyzed the problem of regional differences, i.e. differences existing between the EEG parameters of different cortical zones in both hemispheres. Meanwhile, such differences exist, and it is possible to single out a certain sequence of maturation of individual cortical zones according to EEG parameters.

This, for example, is evidenced by the data of the American physiologists Hudspeth and Pribram, who traced the maturation trajectories (from 1 to 21 years) of the EEG frequency spectrum of different areas of the human brain. According to EEG indicators, they identified several stages of maturation. So, for example, the first covers the period from 1 to 6 years, is characterized by a fast and synchronous rate of maturation of all zones of the cortex. The second stage lasts from 6 to 10.5 years, and the peak of maturation is reached in the posterior sections of the cortex at 7.5 years, after which the anterior sections of the cortex begin to develop rapidly, which are associated with the implementation of voluntary regulation and control of behavior.

After 10.5 years, the synchrony of maturation is broken, and 4 independent trajectories of maturation are distinguished. According to EEG indicators, the central areas of the cerebral cortex are ontogenetically the earliest maturing zone, while the left frontal area, on the contrary, matures the latest, with its maturation being associated with the formation of the leading role of the anterior sections of the left hemisphere in the organization of information processing processes (Hudspeth and Pribram, 1992). Comparatively late terms of maturation of the left frontal zone of the cortex were also repeatedly noted in the works of D. A. Farber et al.

Age-related changes in the bioelectrical activity of the brain cover a significant period of ontogeny from birth to adolescence. On the basis of many observations, signs have been identified that can be used to judge the maturity of the bioelectrical activity of the brain. These include: 1) features of the EEG frequency-amplitude spectrum; 2) the presence of stable rhythmic activity; 3) average frequency of dominant waves; 4) EEG features in different areas of the brain; 5) features of generalized and local evoked brain activity; 6) features of the spatio-temporal organization of brain biopotentials.

In this regard, age-related changes in the EEG frequency-amplitude spectrum in different areas of the cerebral cortex are the most studied. Newborns are characterized by non-rhythmic activity with an amplitude of about 20 uV and frequency 1-6 Hz. The first signs of rhythmic order appear in the central zones starting from the third month of life. During the first year of life, there is an increase in the frequency and stabilization of the main EEG rhythm of the child. The trend towards an increase in the dominant frequency persists at further stages of development. By the age of 3, this is already a rhythm with a frequency of 7-8 Hz, by 6 years - 9-10 Hz etc. . At one time, it was believed that each EEG frequency band dominates in ontogeny one after the other. According to this logic, 4 periods were distinguished in the formation of the bioelectrical activity of the brain: the 1st period (up to 18 months) - the dominance of delta activity, mainly in the central parietal leads; 2nd period (1.5 years - 5 years) - dominance of theta activity; 3rd period (6-10 years) - dominance of alpha activity (labile

naya phase); 4th period (after 10 years of life) - dominance of alpha activity (stable phase). In the last two periods, the maximum activity falls on the occipital regions. Based on this, it was proposed to consider the ratio of alpha and theta activity as an indicator (index) of brain maturity.

However, the problem of the relationship between theta and alpha rhythms in ontogeny is a subject of discussion. According to one view, the theta rhythm is considered as a functional precursor of the alpha rhythm, and thus it is recognized that the alpha rhythm is virtually absent in the EEG of young children. Researchers adhering to this position consider it unacceptable to consider the rhythmic activity dominant in the EEG of young children as alpha rhythm; from the point of view of others, the rhythmic activity of infants in the range of 6-8 Hz in terms of its functional properties, it is an analogue of the alpha rhythm.

In recent years, it has been established that the alpha range is inhomogeneous, and, depending on the frequency, a number of subcomponents can be distinguished in it, which apparently have different functional significance. The ontogenetic dynamics of their maturation serves as a significant argument in favor of distinguishing narrow-band alpha subranges. Three subranges include: alpha-1 - 7.7-8.9 Hz; alpha-2 - 9.3-10.5 Hz; alpha-3 - 10.9-12.5 Hz. From 4 to 8 years, alpha-1 dominates, after 10 years - alpha-2, and by 16-17 years alpha-3 dominates the spectrum.

Studies of EEG age dynamics are carried out at rest, in other functional states (soy, active wakefulness, etc.), as well as under the action of various stimuli (visual, auditory, tactile).

The study of sensory-specific reactions of the brain to stimuli of different modalities, i.e. VP shows that local responses of the brain in the projection zones of the cortex are recorded from the moment the child is born. However, their configuration and parameters indicate a different degree of maturity and inconsistency with those of an adult in different modalities. For example, in the projection zone of a functionally more significant and morphologically more mature somatosensory analyzer at the time of birth, EPs contain the same components as in adults, and their parameters reach maturity already in the first weeks of life. At the same time, visual and auditory EPs are much less mature in newborns and infants.

The visual EP of newborns is a positive-negative fluctuation recorded in the projection occipital region. The most significant changes in the configuration and parameters of such EPs occur in the first two years of life. During this period, EPs for the flash are converted from positive-negative fluctuations with a latency of 150-190 ms into a multicomponent reaction, which, in general terms, is preserved in further ontogenesis. The final stabilization of the component composition of such EP

occurs by the age of 5-6, when the main parameters of all visual EP components for a flash are within the same limits as in adults. The age-related dynamics of EP to spatially structured stimuli (chessboards, grids) differs from responses to a flash. The final design of the component composition of these EPs occurs up to 11-12 years.

Endogenous, or "cognitive" components of EP, reflecting the provision of more complex aspects of cognitive activity, can be registered in children of all ages, starting from infancy, but at each age they have their own specifics. The most systematic facts were obtained in the study of age-related changes in the P3 component in decision-making situations. It has been established that in the age range from 5-6 years to adulthood, the latent period decreases and the amplitude of this component decreases. It is assumed that the continuous nature of the changes in these parameters is due to the fact that at all ages there are common generators of electrical activity.

Thus, the study of EP ontogenesis opens up opportunities for studying the nature of age-related changes and continuity in the work of the brain mechanisms of perceptual activity.

ONTOGENETIC STABILITY OF EEG AND EP PARAMETERS

The variability of the bioelectrical activity of the brain, like other individual traits, has two components: intra-individual and inter-individual. Intra-individual variability characterizes the reproducibility (retest reliability) of EEG and EP parameters in repeated studies. Under constant conditions, the reproducibility of EEG and EP in adults is quite high. In children, the reproducibility of the same parameters is lower; they are distinguished by a significantly greater intra-individual variability of the EEG and EP.

Individual differences between adult subjects (interindividual variability) reflect the work of stable nerve formations and are largely determined by genotype factors. In children, interindividual variability is due not only to individual differences in the work of already established nerve formations, but also to individual differences in the rate of CNS maturation. Therefore, in children it is closely related to the concept of ontogenetic stability. This concept implies not the absence of changes in the absolute values of maturation indicators, but the relative constancy of the rate of age-related transformations. It is possible to assess the degree of ontogenetic stability of one or another indicator only in longitudinal studies, in the course of which the same indicators are compared in the same children at different stages of ontogeny. Evidence of ontogenetic stability

The constancy of the rank place that the child occupies in the group during repeated examinations can serve as a feature of the trait. To assess ontogenetic stability, Spearman's rank correlation coefficient is often used, preferably adjusted for age. Its value does not indicate the invariance of the absolute values of one or another attribute, but about the preservation by the subjects of their ranking place in the group.

Thus, the individual differences in the EEG and EP parameters in children and adolescents compared to the individual differences in adults have, relatively speaking, a “double” nature. They reflect, firstly, individually stable features of the work of nerve formations and, secondly, differences in the rate of maturation of the brain substrate and psychophysiological functions.

There are few experimental data indicating the ontogenetic stability of the EEG. However, some information about this can be obtained from works devoted to the study of age-related changes in the EEG. In the well-known work of Lindsley [op. by: 33] studied children from 3 months to 16 years, and the EEG of each child was monitored for three years. Although the stability of individual characteristics was not specifically assessed, the analysis of the data allows us to conclude that, despite the natural age-related changes, the subject's ranking position is approximately preserved.

Some EEG characteristics have been shown to be stable over long periods of time, regardless of the EEG maturation process. In the same group of children (13 people), the EEG was recorded twice, with an interval of 8 years, and its changes during the orienting and conditioned reflex reactions in the form of depression of the alpha rhythm. During the first registration, the average age of the subjects in the group was 8.5 years; during the second - 16.5 years, the coefficients of rank correlation for total energies were: in the bands of delta and theta rhythms - 0.59 and 0.56; in the alpha rhythm band -0.36, in the beta rhythm band -0.78. Similar correlations for frequencies were not lower, however, the highest stability was found for the frequency of the alpha rhythm (R = 0.84).

In another group of children, the assessment of the ontogenetic stability of the same baseline EEG parameters was carried out with a break of 6 years - at 15 years and 21 years. In this case, the most stable were the total energies of slow rhythms (delta and theta) and alpha rhythm (correlation coefficients for all - about 0.6). In terms of frequency, the alpha rhythm again showed the maximum stability (R = 0.47).

Thus, judging by the rank correlation coefficients between the two data series (1st and 2nd surveys) obtained in these studies, it can be stated that such parameters as the frequency of the alpha rhythm, the total energies of the delta and theta rhythms, and a number of other indicators, EEG are individually stable.

Interindividual and intraindividual variability of EP in ontogeny has been relatively little studied. However, one fact is beyond doubt: with age, the variability of these reactions decreases.

The individual specificity of the configuration and parameters of the EP is increasing and increasing. The available estimates of the retest reliability of the amplitudes and latent periods of visual EPs, the endogenous P3 component, as well as the brain potentials associated with movement, in general, indicate a relatively low level of reproducibility of the parameters of these reactions in children compared to adults. The corresponding correlation coefficients vary over a wide range, but do not rise above 0.5-0.6. This circumstance significantly increases the measurement error, which, in turn, can affect the results of genetic and statistical analysis; as already noted, the measurement error is included in the assessment of the individual environment. Nevertheless, the use of certain statistical techniques makes it possible in such cases to introduce the necessary corrections and increase the reliability of the results.

2.1.3. Topographic mapping of the electrical activity of the brain

2.1.4. CT scan

2.1.5. neural activity

2.1.6. Methods of influencing the brain

2.2. Electrical activity of the skin

2.3. Indicators of the cardiovascular system

2.4. Indicators of the activity of the muscular system

2.5. Indicators of activity of the respiratory system (pneumography)

2.6. Eye reactions

2.7. Lie detector

2.8. Choice of methods and indicators

Conclusion

Recommended reading

Section II. Psychophysiology of functional states and emotions Chapter. 3. Psychophysiology of functional states

3.1. Problems of determining functional states

3.1.1. Different approaches to the definition of fs

3.1.2. Neurophysiological mechanisms of wakefulness regulation

Main Differences in the Effects of Brainstem and Thalamus Activation

3.1.3. Methods for diagnosing functional states

Effects of the action of the sympathetic and parasympathetic systems

3.2. Psychophysiology of sleep

3.2.1. Physiological features of sleep

3.2.2. Theories of sleep

3.3. Psychophysiology of stress

3.3.1. conditions for stress

3.3.2. General adaptation syndrome

3.4. Pain and its physiological mechanisms

3.5. Feedback in the regulation of functional states

3.5.1. Types of artificial feedback in psychophysiology

3.5.2. The value of feedback in the organization of behavior

Chapter 4

4.1. Psychophysiology of needs

4.1.1. Definition and classification of needs

4.1.2. Psychophysiological mechanisms of the emergence of needs

4.2. Motivation as a factor in the organization of behavior

4.3. Psychophysiology of emotions

4.3.1. Morphofunctional substratum of emotions

4.3.2. Theories of emotion

4.3.3. Methods for studying and diagnosing emotions

Recommended reading

Section III. Psychophysiology of the Cognitive Sphere Chapter 5. Psychophysiology of Perception

5.1. Coding information in the nervous system

5.2. Neural Models of Perception

5.3. Electroencephalographic studies of perception

5.4. Topographic aspects of perception

Differences between the hemispheres in visual perception (L. Ileushina et al., 1982)

Chapter 6

6.1. Approximate reaction

6.2. Neurophysiological mechanisms of attention

6.3. Methods for studying and diagnosing attention

Chapter 7

7.1. Classification of types of memory

7.1.1. Elementary types of memory and learning

7.1.2. Specific types of memory

7.1.3. Temporal organization of memory

7.1.4. Imprinting mechanisms

7.2. Physiological theories of memory

7.3. Biochemical studies of memory

Chapter 8. Psychophysiology of speech processes

8.1. Non-verbal forms of communication

8.2. Speech as a system of signals

8.3. Peripheral speech systems

8.4. Brain centers of speech

8.5. Speech and interhemispheric asymmetry

8.6. Development of speech and specialization of the hemispheres in ontogeny

8.7. Electrophysiological correlates of speech processes

Chapter 9

9.1. Electrophysiological correlates of thinking

9.1.1. Neural correlates of thinking

9.1.2. Electroencephalographic correlates of thinking

9.2. Psychophysiological aspects of decision making

9.3. Psychophysiological approach to intelligence

Chapter 10

10.1. Psychophysiological approach to the definition of consciousness

10.2. Physiological conditions for awareness of stimuli

10.3. Brain centers and consciousness

10.4. Altered States of Consciousness

10.5. Information approach to the problem of consciousness

Chapter 11

11.1. The structure of the propulsion system

11.2. Classification of movements

11.3. Functional organization of voluntary movement

11.4. Electrophysiological correlates of movement organization

11.5. Complex of brain potentials associated with movements

11.6. neural activity

Recommended reading

SectionIy. Age-related psychophysiology Chapter 12. Basic concepts, ideas and problems

12.1. General concept of maturation

12.1.1. Ripening Criteria

12.1.2. Age norm

12.1.3. The problem of periodization of development

12.1.4. Continuity of maturation processes

12.2. Plasticity and sensitivity of the CNS in ontogenesis

12.2.1. Enrichment and depletion effects

12.2.2. Critical and sensitive periods of development

Chapter 13 Main methods and directions of research

13.1. Assessing the effects of age

13.2. Electrophysiological methods for studying the dynamics of mental development

13.2.1. Electroencephalogram changes in ontogeny

13.2.2. Age-related changes in evoked potentials

13.3. Eye reactions as a method for studying cognitive activity in early ontogeny

13.4. The main types of empirical research in developmental psychophysiology

Chapter 14

14.1. Maturation of the nervous system in embryogenesis

14.2. Maturation of the main blocks of the brain in postnatal ontogenesis

14.2.1. Evolutionary approach to the analysis of brain maturation

14.2.2. Corticolization of functions in ontogenesis

14.2.3. Lateralization of functions in ontogeny

14.3. Brain maturation as a condition for mental development

Chapter 15

15.1. Biological age and aging

15.2. Body changes with aging

15.3. Theories of aging

15.4. Vitaukt

Recommended reading

Cited Literature

Content

13.2. Electrophysiological methods for studying the dynamics of mental development

In age-related psychophysiology, practically all the methods that are used when working with a contingent of adult subjects are used (see Chapter 2). However, in the application of traditional methods there is an age specificity, which is determined by a number of circumstances. First, the indicators obtained using these methods have large age differences. For example, the electroencephalogram and, accordingly, the indicators obtained with its help change significantly in the course of ontogenesis. Secondly, these changes (in their qualitative and quantitative terms) can act in parallel both as a subject of research, and as a way to assess the dynamics of brain maturation, and as a tool/means for studying the emergence and functioning of the physiological conditions of mental development. Moreover, it is the latter that is of the greatest interest for age-related psychophysiology.

All three aspects of the study of EEG in ontogeny are certainly related to each other and complement each other, but they differ quite significantly in content, and, therefore, they can be considered separately from each other. For this reason, both in specific scientific research and in practice, the emphasis is often placed on only one or two aspects. However, despite the fact that the third aspect is of the greatest importance for the developmental psychophysiology, i.e. how EEG indicators can be used to assess the physiological prerequisites and/or conditions of mental development, the depth of study and understanding of this problem depends decisively on the degree of elaboration of the first two aspects of EEG study.

13.2.1. Electroencephalogram changes in ontogeny

Quantitative assessment of maturation dynamics by indicators

EEG. Repeated attempts have been made to quantitatively analyze the EEG parameters in order to identify the patterns of their ontogenetic dynamics that have a mathematical expression. As a rule, various variants of regression analysis (linear, non-linear and multiple regressions) were used, which were used to estimate the age dynamics of the power density spectra of individual spectral ranges (from delta to beta) (for example, Gasser et al., 1988). The results obtained generally indicate that changes in the relative and absolute power of the spectra and the severity of individual EEG rhythms in ontogeny are non-linear. The most adequate description of the experimental data is obtained by using polynomials of the second - fifth degree in the regression analysis.

The use of multidimensional scaling appears to be promising. For example, in one of the recent studies, an attempt was made to improve the method for quantifying age-related EEG changes in the range from 0.7 to 78 years. Multidimensional scaling of spectral data from 40 cortical points made it possible to detect the presence of a special “age factor”, which turned out to be non-linearly related to chronological age. As a result of the analysis of age-related changes in the spectral composition of the EEG, the Scale of Maturation of the Electrical Activity of the Brain was proposed, which is determined on the basis of the logarithm of the ratio of age predicted from EEG data and chronological age (Wackerman, Matousek, 1998).

In general, the assessment of the level of maturity of the cortex and other brain structures using the EEG method has a very important clinical and diagnostic aspect, and visual analysis of individual EEG records still plays a special role in this, irreplaceable by statistical methods. For the purpose of standardized and unified evaluation of the EEG in children, a special method for EEG analysis was developed, based on the structuring of expert knowledge in the field of visual analysis (Machinskaya et al., 1995).

Figure 13.2 is a general diagram showing its main components. Created on the basis of the structural organization of knowledge of specialist experts, this EEG description scheme can

be used for individual diagnosis of the state of the central nervous system of children, as well as for research purposes in determining the characteristic features of the EEG of various groups of subjects.

Age features of the spatial organization of the EEG. These features are less studied than the age-related dynamics of individual EEG rhythms. Meanwhile, the importance of studies of the spatial organization of biocurrents is very great for the following reasons.

Back in the 70s, the outstanding Russian physiologist M.N. Livanov formulated a position on a high level of synchronism (and coherence) of oscillations of brain biopotentials as a condition that favors the emergence of a functional connection between brain structures that are directly involved in systemic interaction. The study of the features of the spatial synchronization of the biopotentials of the cerebral cortex during different types of activity in adults showed that the degree of distant synchronization of the biopotentials of various cortical zones under the conditions of activity increases, but rather selectively. The synchronism of the biopotentials of those cortical zones that form functional associations involved in the provision of a specific activity increases.

Consequently, the study of the indicators of distant synchronization, which reflect the age-related features of interzonal interaction in ontogenesis, can provide new grounds for understanding the systemic mechanisms of brain functioning, which undoubtedly play an important role in mental development at each stage of ontogenesis.

Quantification of spatial synchronization, i.e. the degree of coincidence of the dynamics of the biocurrents of the brain recorded in different zones of the cortex (taken in pairs) makes it possible to judge how the interaction between these zones is carried out. The study of spatial synchronization (and coherence) of brain biopotentials in newborns and infants showed that the level of interzonal interaction at this age is very low. It is assumed that the mechanism that provides the spatial organization of the field of biopotentials in young children is not yet developed and is gradually formed as the brain matures (Shepovalnikov et al., 1979). It follows from this that the possibilities of systemic unification of the cerebral cortex at an early age are relatively small and gradually increase with age.

At present, the degree of interzonal synchrony of biopotentials is estimated by calculating the coherence functions of the biopotentials of the corresponding cortical zones, and the assessment is usually carried out for each frequency range separately. For example, in 5-year-old children, coherence is calculated in the theta band, since the theta rhythm at this age is the dominant EEG rhythm. At school age and older, coherence is calculated in the alpha rhythm band as a whole or separately for each of its components. As the interzonal interaction is formed, the general distance rule begins to clearly manifest itself: the level of coherence is relatively high between close points of the crust and decreases with increasing distance between zones.

However, against this general background, there are some peculiarities. The average level of coherence increases with age, but unevenly. The non-linear nature of these changes is illustrated by the following data: in the anterior cortex, the level of coherence increases from 6 to 9–10 years of age, then it decreases by 12–14 years (during puberty) and increases again by 16–17 years (Alferova, Farber , 1990). The above, however, do not exhaust all the features of the formation of interzonal interaction in ontogeny.

The study of distant synchronization and coherence functions in ontogenesis has many problems, one of them is that the synchronization of brain potentials (and the level of coherence) depends not only on age, but also on a number of other factors: 1) the functional state of the subject; 2) the nature of the activity performed; 3) individual features of interhemispheric asymmetry (profile of lateral organization) of a child and an adult. Research in this direction is scarce, and so far there is no clear picture describing the age dynamics in the formation of distant synchronization and intercentral interaction of the cerebral cortex zones in the course of a particular activity. However, the available data are sufficient to assert that the systemic mechanisms of intercentral interaction necessary to ensure any mental activity go through a long path of formation in ontogenesis. Its general line consists in the transition from relatively poorly coordinated regional manifestations of activity, which, due to the immaturity of the conduction systems of the brain, are characteristic of children as early as the age of 7–8 years, to an increase in the degree of synchronization and specific (depending on the nature of the task) consistency in the intercentral interaction of zones cerebral cortex in adolescence.

When studying neurophysiological processes

the following methods are used:

Conditioned reflex method,

The method of recording the activity of brain formations (EEG),

evoked potential: optical and electrophysiological

methods of registration of multicellular activity of groups of neurons.

The study of brain processes that provide

behavior of mental processes through

electronic computing technology.

Neurochemical methods to determine

changes in the rate of formation and amount of neurohormones,

entering the blood.

1. Electrode implantation method,

2. Split brain method,

3. The method of observing people with

organic lesions of the central nervous system,

4. Testing,

5. Observation.

Currently, the study method is used

activity of functional systems, which provides

a systematic approach to the study of GNI. Content way

GNI - the study of conditioned reflex activity

in the interaction of + and - conditioned reflexes with each other

Since in defining the conditions for this

interactions go from normal

to a pathological state of the functions of the nervous system:

the balance between nervous processes is disturbed and then

impaired ability to adequately respond to stimuli

external environment or internal processes, which provokes

mental attitude and behaviour.

Age features of the EEG.

Electrical activity of the fetal brain

appears at the age of 2 months, it is low-amplitude,

is intermittent and irregular.

Interhemispheric EEG asymmetry is observed.

The EEG of a newborn is

arrhythmic fluctuations, there is a reaction

activation to sufficiently strong stimuli - sound, light.

The EEG of infants and toddlers is characterized by

the presence of phi-rhythms, gamma-rhythms.

The amplitude of the waves reaches 80 μV.

The EEG of preschool children is dominated by

two types of waves: alpha and phi rhythm, the latter is registered

in the form of groups of high-amplitude oscillations.

EEG of schoolchildren from 7 to 12 years old. Stabilization and acceleration

the main rhythm of the EEG, the stability of the alpha rhythm.

By the age of 16-18, the EEG of children is identical to the EEG of adults No. 31. Medulla oblongata and bridge: structure, functions, age features.

The medulla oblongata is a direct continuation of the spinal cord. Its lower boundary is considered to be the exit point of the roots of the 1st cervical spinal nerve or the intersection of the pyramids, the upper boundary is the posterior edge of the bridge. The length of the medulla oblongata is about 25 mm, its shape approaches a truncated cone, with its base turned upwards. The medulla oblongata is built of white and gray matter. The gray matter of the medulla oblongata is represented by the nuclei of the IX, X, XI, XII pairs of cranial nerves, olives, the reticular formation, centers of respiration and blood circulation. White matter is formed by nerve fibers that make up the corresponding pathways. The motor pathways (descending) are located in the anterior sections of the medulla oblongata, the sensory pathways (ascending) lie more dorsally. The reticular formation is a collection of cells, cell clusters and nerve fibers that form a network located in the brain stem (medulla oblongata, pons and midbrain). The reticular formation is connected with all sense organs, motor and sensitive areas of the cerebral cortex, the thalamus and hypothalamus, and the spinal cord. It regulates the level of excitability and tone of various parts of the nervous system, including the cerebral cortex, is involved in the regulation of the level of consciousness, emotions, sleep and wakefulness, autonomic functions, purposeful movements. Above the medulla oblongata is the bridge, and behind it is the cerebellum. Bridge (Varoliev bridge) has the appearance of a transversely thickened roller, from the lateral side of which the middle cerebellar peduncles extend to the right and left. The posterior surface of the bridge, covered by the cerebellum, is involved in the formation of the rhomboid fossa. In the back of the bridge (tire) there is a reticular formation, where the nuclei of the V, VI, VII, VIII pairs of cranial nerves lie, the ascending pathways of the bridge pass. The anterior part of the bridge consists of nerve fibers that form pathways, among which are the nuclei of gray matter. The pathways of the anterior part of the bridge connect the cerebral cortex with the spinal cord, with the motor nuclei of the cranial nerves and the cerebellar cortex. The medulla oblongata and the bridge perform the most important functions. The sensitive nuclei of the cranial nerves located in these parts of the brain receive nerve impulses from the scalp, mucous membranes of the mouth and nasal cavity, pharynx and larynx, from the digestive and respiratory organs, from the organ of vision and the organ of hearing, from the vestibular apparatus, heart and blood vessels. . Along the axons of the cells of the motor and autonomic (parasympathetic) nuclei of the medulla oblongata and the pons, impulses follow not only the skeletal muscles of the head (chewing, facial, tongue and pharynx), but also to the smooth muscles of the digestive, respiratory and cardiovascular systems, to the salivary and numerous other glands. Through the nuclei of the medulla oblongata, many reflex acts are performed, including protective ones (coughing, blinking, tearing, sneezing). The nerve centers (nucleus) of the medulla oblongata are involved in the reflex acts of swallowing, the secretory function of the digestive glands. The vestibular (pre-door) nuclei, in which the pre-door-spinal path originates, perform complex reflex acts of redistribution of skeletal muscle tone, balance, and provide a “standing posture”. These reflexes are called locating reflexes. The most important respiratory and vasomotor (cardiovascular) centers located in the medulla oblongata are involved in the regulation of respiratory function (pulmonary ventilation), the activity of the heart and blood vessels. Damage to these centers leads to death. In case of damage to the medulla oblongata, respiratory disorders, cardiac activity, vascular tone, and swallowing disorders can be observed - bulbar disorders that can lead to death. At the time of birth, the medulla oblongata is fully developed and functionally mature. Its mass together with the bridge in a newborn is 8 g, which is 2℅ of the mass of the brain. The nerve cells of a newborn have long processes, their cytoplasm contains a tigroid substance. Cell pigmentation is intensely manifested from the age of 3-4 and increases until the period of puberty. By the age of one and a half years of a child's life, the number of cells of the center of the vagus nerve increases and the cells of the medulla oblongata are well differentiated. The length of the processes of neurons increases significantly. By the age of 7, the nuclei of the vagus nerve are formed in the same way as in an adult.
The bridge in a newborn is located higher compared to its position in an adult, and by the age of 5 it is located at the same level as in an adult. The development of the bridge is associated with the formation of the cerebellar peduncles and the establishment of connections between the cerebellum and other parts of the central nervous system. The internal structure of the bridge in a child does not have any distinctive features compared to its structure in an adult. The nuclei of the nerves located in it are formed by the time of birth.

Age features of the EEG of healthy children - clinical electroencephalography. EEG, its age-related features Features of the EEG in children norm and violations

annotation scientific article on medical technologies, author of scientific work - Soroko S.I., Rozhkov Vladimir Pavlovich, Bekshaev S.S.

Related Topics scientific works on medical technologies, author of scientific work - Soroko S.I., Rozhkov Vladimir Pavlovich, Bekshaev S.S.

The text of the scientific work on the topic "Features of the frequency-temporal organization of the EEG pattern in children and adolescents in the North in different age periods"

13.2. Electrophysiological methods for studying the dynamics of mental development

13.2.1. Electroencephalogram changes in ontogeny