DOI 10.1007/s11055-016-0246-5 Neuroscience and Behavioral Physiology, Vol. 46, No. 4, May, 2016

Synchronization of EEG Rhythms in Baseline Conditions and during Counting in Children with Autism Spectrum Disorders E. A. Lushchekina,1 O. Yu. Khaerdinova,2 V. Yu. Novototskii-Vlasov,1 V. S. Lushchekin,2 and V. B. Strelets1

UDC 612.821+612.822.3

Translated from Zhurnal Vysshei Nervnoi Deyatel’nosti imeni I. P. Pavlova, Vol. 65, No. 1, pp. 72–81, January–February, 2015. Original article submitted June 18, 2013. Accepted October 20, 2014. EEG traces recorded in the state of calm waking from boys aged 5–7 years with autism spectrum disorders showed lower values of coherence in the δ, θ, and α ranges and higher values in the β and γ ranges as compared with healthy subjects. On performance of a cognitive task (counting), healthy children showed the greatest increases in coherence in the β and γ ranges, while changes in these ranges were minor in children with autism spectrum disorders. Children with autism spectrum disorders responded to performance of the cognitive task with significant changes in coherence in the δ range, with differently directed changes in the θ band. Keywords: cognitive tests, autism spectrum disorders, coherence, EEG, schizophrenia.

The present work is part of a series of studies seeking a set of EEG characteristics which could be used to prognosticate the transition from childhood autism to schizophrenia. In previous studies, the spectral power of EEG rhythms in children with autism spectrum disorders (ASD) was found to show a decrease in the α range as compared with healthy children, and this was regarded as a predictor of the transition of autism into schizophrenia with both positive and negative symptoms. The similarity of autism and schizophrenia with negative symptoms may be supported by the general tendency to a decrease in EEG spectral power in the γ range seen in patients with early childhood autism (ECA) and patients with schizophrenia with negative symptoms [Lushchekina et al., 2011; Magomedov et al., 2010; Strelets et al., 2005]. The decrease in θ-rhythm reac-

tivity in response to cognitive loading as compared with normal conditions observed in our studies has also been described for the fast rhythms in both early childhood autism [Lushchekina et al., 2011] and schizophrenia with negative symptoms [Magomedov et al., 2010; Strelets et al., 2005; Strelets et al., 2007; Tononi et al., 1998]. No less important information on the relationship between the mechanisms supporting cognitive functions in ASD and schizophrenia can be obtained by studies of the coherence of EEG rhythms in these diseases. The role of the topology of connections in supporting the temporospatial organization of biopotentials and the establishment of system activity in the brain has been described [Panasevich and Tsitseroshin, 2011]. Synchronization of rhythms is known to be important for the successful performance of cognitive tasks [Weiss, 2000]. Studies in adolescents with schizophrenia spectrum disorders have demonstrated reductions (from normal) in the index of “structural synchronization” of the α rhythm, i.e., the number of quasi-stationary segments of α activity at each point of baseline EEG recording summed for all leads [Borisov et al., 2005]. In adult schizophrenia

1 Institute

of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, Moscow, Russia; e-mail: [email protected]. 2 Department of Developmental Psychophysiology, Strogino Center for Psychological-Pedagogical Rehabilitation and Correction, Moscow, Russia.

382 0097-0549/16/4604-0382 ©2016 Springer Science+Business Media New York

Synchronization of EEG Rhythms in Baseline Conditions and during Counting patients with positive symptoms and with negative symptoms, the baseline EEG shows lower (than in healthy subjects) intra- and interhemisphere phase synchronization in all frequency ranges; patients with positive symptoms lack any significant interhemisphere interaction in almost all rhythms, and while interactions are somewhat greater in patients with negative symptoms than in those with positive symptoms, they remain significantly less than in normal people [Strelets et al., 2005]. On performance of cognitive tests, healthy adult subjects show increases in the numbers of significant coherence intrahemisphere and interhemisphere connections at γ frequencies. Patients with schizophrenia with positive symptoms have, at rest, have just a few longitudinal intrahemisphere connections. During task performance, the number of coherence connections decreases from the baseline level in patients with positive symptoms and does not change in those with negative symptoms [Magomedov et al., 2010]. The aims of the present work were to compare rearrangements in the synchronization of EEG rhythms in cognitive loading in healthy children and children with ASD and, in parallel, to compare the state of cognitive functions in patients and healthy children. Methods Two groups of right-handed boys aged from four years five months to seven years nine months took part in the study: a control group (24 healthy children, mean age 6.05 ± 0.86 years) and a group of children with autism spectrum disorder (International Classification of Diseases ICD F84) (27 patients, mean age 5.79 ± 1.42 years). Subjects were undergoing initial investigations at the Strogino Psychological-Pedagogical Rehabilitation and Correction Center and had not yet received medication. The parents of all the children gave written consent for their children to take part in the study. EEG recordings were made from 16 electrodes using the standard 10–20% scheme and combined ear electrodes using a CONAN 4.5 computerized electrophysiology system with a 16-channel amplifier and a personal computer. EEG was recorded in the range 0.3–70 Hz with a sampling frequency of 256 Hz, a time constant of 0.3 sec, and an analysis epoch of 60 sec. Coherence was studied in the δ (1.5–3.5 Hz), θ (4–7 Hz), α (7.5–12.5 Hz), β1 (13.5–19.5 Hz), β2 (20–29.5 Hz) and γ1 (30–40 Hz) ranges. EEG traces were recorded in two conditions – in children in the resting stage with the eyes closed and during mental loading. The cognitive task consisting of “counting” silently with the eyes closed consisted of adding or subtracting numbers up to 30. During the experiment we checked that the children did in fact perform the task. After the standard procedures for initial processing, including band filtration to remove mains interference and removal of artifacts, data were subjected to secondary processing using programs written by one of the authors [Novototskii-Vlasov et al., 2007]. Secondary processing of EEG traces included sequential filtration and factor analysis

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using the principal components method, which provides for visual identification of signals belonging to artifacts during sequential transformations. These artifact signals were then subtracted from the initial unfiltered EEG using coefficients of linear regression. Spectral characteristics were calculated using fast Fourier transformation; mean amplitudes in the various frequency ranges were analyzed statistically. Significant coherence levels were identified using a normalizing transformation. Significance thresholds (3σ, p < 0.001) for coherence, depending on group size, were determined using the Z test. The threshold for coherence in patients was 0.482 and that for controls was 0.260. The present report only discusses those changes in coherence which exceeded the significance level. Statistical analysis of coherence values was performed for paired comparisons using Student’s t test or analysis of variance. Measures of coherence had distributions close to the normal, such that the studies could use parametric statistics. EEG parameters in groups of subjects were compared by unifactorial analysis of variance (ANOVA) [Kulaichev, 2006]. Significant differences between measures of spectral characteristics were identified at a significance level of p < 0.01. Differences with significance levels of p < 0.05 were regarded as tendencies. Significant differences for intragroup comparisons (between pairs of electrodes) were identified using the paired Student’s t test. The significance criterion was used without correction for the sizes of the sets being compared. Psychodiagnostic investigations were performed using the PEP method (Psychoeducational Profile) [Schopler and Lansing, 2004] using a three-point scheme (1 point – unable to perform the task; 2 points – task performed with help from experimenter; 3 points – independent performance of task). Points for each of these measures were summed and their ratio to the age equivalent was calculated (i.e., to the points scores obtained by healthy children (of the same age as those studied here) performing the task, which was taken as unity. The data obtained for children of the age group studied here were summed. The following functions were investigated: imitation of simple actions, visual and auditory perception, fine and general motor function, visuomotor coordination, and verbal and nonverbal intellect. Results The baseline EEG of children with autism spectrum disorders showed the following differences in coherence from normal. The extents of interhemisphere connections at δ frequencies, assessed in terms of coherence, in the lead pairs FP1–FP2, FP1–F8, and O1–O2 were less than normal (p < 0.05) (Fig. 1-1). At θ frequencies, FP1–T4 (p < 0.05) and O1–T4 (p < 0.05) interhemisphere connections were less marked and the left-sided F3–P3 intrahemisphere connection (p < 0.05) was more marked (Fig. 1-2). At α frequencies, the right-sided FP2–F8 intrahemisphere connection was significantly less marked than in normal subjects (p < 0.01), and FP2–T4, FP1–T4, FP1–F7, FP1–F3, F3–F7, and F4–P3 connections were less marked (p < 0.05) (Fig. 1-3). The β1

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Fig. 1. Comparison of coherence values in the δ, θ, and α ranges in the baseline activity of children with ASD and healthy children. Black lines show connections more marked in ASD than in healthy controls; gray lines show connections less marked in ASD than in healthy controls. Thick lines show significant (p < 0.01) differences between measures of coherence in ASD and health; thin lines show tendencies (p < 0.05) to differences between measures of coherence ASD and health: 1) δ range; 2) θ range; 3) α range; 4) β1 range; 5) β2 range; 6) γ range.

range showed differently directed deviations from normal: intrahemisphere connections in the left frontal lobe (FP1–F7, FP1–T3, F3–F7, FP1–T3, and F7–C3) and the right frontal lobe (FP2–T4), as well as the left P3–T5 connections, were less marked than in normal subjects (p < 0.05, i.e., at the level of a tendency), while F8–P4 and P4–O1 connections were significantly greater than in normal subjects (p < 0.01) and F3–O2, F7–O2, FP1–O1, FP2–F4, and F4–O1 connections were somewhat more marked than in normal subjects (p < 0.05) (Fig. 1-4). In the β2 range, both intra- and interhemisphere connections were greater than normal, the C3–P4 difference being the largest (p < 0.01) (Fig. 1-5). In the γ1 range, children with ASD showed more marked (compared with normal subjects) right-sided T4–O2, left-sided C3–P3,

and interhemisphere F8–P3 connections (p < 0.05); there were also higher levels of other intra- and interhemisphere connections (p < 0.05) (Fig. 1-6). Performance of counting operations altered coherence as follows. Changes in coherence in the lower frequencies (δ, θ) in normal subjects consisted of a reduction in connections from the baseline level during counting. In the δ range, this decrease was significant for the F8–T4 lead pair (p < 0.01). For the F4–T3, F4–T4, and F4–C3 lead pairs, decreases in coherence were noted as tendencies (p < 0.05). F3–P3 and T5–C4 connections showed tendencies to increase (p < 0.05) (Fig. 2-1). In the θ frequency band, significant decreases in the right-sided F8–T4 and interhemisphere F4–C3 connections (p < 0.01) were seen, with a less marked decrease in

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Fig. 2. Comparison of coherence values in baseline conditions and during counting in healthy children. Black lines show connections with values increasing on cognitive loading compared with baseline. Gray lines show connections with decreasing values on cognitive loading compared with baseline. Thick lines show changes significant at p < 0.01. Thin lines show changes significant at p < 0.05. For further details see caption to Fig. 1.

the connection between F8 and C3 and between C3 and T4 (p < 0.05), and increases in the long right-sided connection FP2–P4 and the left-sided connection F3–P3 at the level of tendencies (p < 0.05) (Fig. 2-2). In autism, the δ range, conversely, showed increases in coherence for intra- and interhemisphere lead pairs, which were particularly marked for the interhemisphere pairs FP2–T3, T5–T6, and T4–O1 (p < 0.01) (Fig. 3-1). In the θ range, there were marked decreases in coherence on the left in F3–P3 and in the interhemisphere pair F3–O2 (p < 0.01), along with less marked decreases in the pairs F7–T4, FP1–F3, FP1–C4, and F3–F8 (p < 0.05) and a significant increase the O1–T4 connection (p < 0.01), as well as increases in the left-sided T3–P3 connection and the

interhemisphere T4–T5 and T5–T6 connections (p < 0.05) (Fig. 3-2). In healthy children during counting, coherence in the α range was minor: there were increases in the FP2–F4 and F4–O1 connections and a tendency to a decrease in the F8– F4 connection (p < 0.05) (Fig. 2-3). Changes were also minor in autism: there was a tendency to an increase in the F8–C4 connection (p < 0.05) (Fig. 3-3). In normal children, counting led to minor decreases in coherence in the β2 range in the anterior areas on the left and to increases in the posterior areas, more marked in the left hemisphere. The greatest increases in connections were seen on the right side in the F4–O2 pair and between hemispheres in the lead pairs

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Fig. 3. Comparison of coherence values in baseline conditions and during counting in children with ASD. For further details see caption to Fig. 1.

C3–O2, P3–O2, and T5–O2 (p < 0.01) (Fig. 2-4). In autism, the β1 band showed tendencies to increases in coherence in pairs T5–P4, P3–P4, and T5–O2 (p < 0.05) (Fig. 3-4). In the β2 range, counting in normal children produced an increase in the C4–P4 connection (p < 0.01) and tendencies to increases in coherence mainly in lead pairs FP1–P3, F3–P3, F8–P4, T4–O2, and C3–C4 (p < 0.05), along with a significant increase in the pair F3–F7 (p < 0.01) (Fig. 2-5). In autists, counting produced no changes in coherence in the β2 range as compared with controls (Fig. 2-5). In healthy children, counting produced the largest changes in coherence in the γ1 range. As shown in Fig. 2-6, cognitive loading led to a marked intensification of intraand interhemisphere interactions. Most changes were high-

ly significant (p < 0.01). In autism, the increase in coherence during counting as compared with baseline was minor. Increases were seen in pars FP1–C3 (p < 0.01), FP1–O1, F3–P3, C3–P3, and F3–F8 (p < 0.05) (Fig. 3-6). Children with diagnoses of ASD established by psychophysiological investigations were also studied using psychodiagnostic scales [Schopler and Lansing, 2004]. Psychodiagnostic investigations showed that all children had difficulties constructing action programs and different levels of decreases in the monitoring of the results. In some children, impairments were so severe that they were unable to perform the task even with the experimenter’s assistance. Patients with ECA, as compared with age norms, have been shown to have decreases in all the functions studied: imita-

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Fig. 4. Averaged results of psychodiagnostic investigations of children with ASD compared with healthy children. Unity on the scales corresponds to values in healthy children of the same age. The diagram includes the following measures: perception; fine motor skills; general motor skills; visual-motor coordination; nonverbal intellect; verbal intellect.

tion, perception, motor functions, visuomotor coordination, and verbal and nonverbal intellect. The smallest deviations from normal in children with ECA were seen in nonverbal thought (Fig. 4). Discussion Schizophrenia and autism are heterogeneous diseases, for which genetic factors and external influences operating at the early stages of development are responsible [Andreasen, 2001; Mathewson et al., 2012; Uhlhaas and Singer, 2010]. Knowledge of the similarities and differences between these diseases may help with their early diagnosis and treatment. Comparison of EEG characteristics in children with ASD with the EEG features of adult patients with schizophrenia has shown that as compared with healthy subjects, both diseases are associated with right-sided predominance of the α rhythm in baseline conditions and on cognitive loading, decreases in the spectral power of the θ rhythm in baseline conditions, and a reduction in the reactivity of the spectral power of the fast rhythms on cognitive loading [Lushchekina et al., 2011, 2013]. It should be emphasized that these characteristics are more typical of schizophrenia with a predominance of negative symptoms and the fact that these changes affect the characteristics of the spectral power of EEG rhythms. The present study sought to analyze the features of intra- and interhemisphere interactions in ASD with the aim of identifying changes in coherence from normal common to ASD and schizophrenia. Coherence is an informative measure of the functioning of the nervous system, reflecting the extent of intracor-

tical functional interactions in different parts of the brain and the presence of functional connections [Ivanitskii, 1996; Livanov, 1972]. Comparison of the extent of connections in baseline activity in children with ASD and healthy subjects showed that patients had higher values in higher frequency ranges, i.e., the β and γ ranges, as well as lower frequency ranges, i.e., at low frequencies. On performance of the cognitive task by healthy children, the high-frequency ranges showed the most significant increases in connections, while changes in these ranges were minor in children with ASD, as in adults with schizophrenia with predominantly negative symptoms [Magomedov et al., 2010]. Conversely, at the lower frequencies (δ, θ), performance of the cognitive task produced minor changes from baseline in healthy children, while children with ASD produced marked but differently directed changes in coherence. This fact supports the tendency to reciprocity between the low- and high-frequency rhythms identified in our previous studies for the baseline spectral power of the θ and γ rhythms, which was lower for the θ rhythm and higher for the γ rhythm in autism as compared with normal [Lushchekina et al., 2013]. Thus, at low frequencies (from δ to α), the baseline showed lower coherence values in ASD than in normal controls. Going from lower to higher frequencies, changes in baseline activity in patients with ASD, as compared with normal, were in different directions (at the β frequency), and then (at the γ band) there were higher values of coherence in ASD than normal subjects. A similar trend in the transition from changes in one direction to the opposite with

388 increases in the frequency of oscillations was seen in healthy children on cognitive loading as compared with baseline. At low frequencies, performance of counting operations produced changes in different directions compared with baseline, with a predominant trend to decreased coherence; the higher the frequency of the rhythm, the more marked the changes, i.e., increases, in coherence. In ASD, cognitive loading, conversely, led to increased coherence at δ frequencies and differently directed changes at θ frequencies (mainly reductions) and minor changes in the α, β, and γ ranges, as compared with baseline. Particular attention should be paid to changes in coherence on counting as compared with baseline, in health and ASD, in the β and γ ranges. While in healthy children these frequencies, which are evidently involved in supporting performance of the cognitive task, showed the marked changes also described in healthy adult subjects, changes at these frequencies in patients with ASD were minor, as in adult patients with schizophrenia with mainly negative symptoms [Magomedov et al., 2010; Strelets and Garakh, 2009]. In patients with ASD, the main changes in coherence on performance of the cognitive task, as compared with baseline, were seen at the δ and θ frequencies. It may follow from this that counting activity in normal children and in those with ASD are supported by different mechanisms, including synchronization of the different rhythms. ASD involves impairment to perceptive-cognitive processing of information, which prevents appropriate behavior [Dinstein et al., 2010]. The fact that cognitive loading in ASD did not lead to rearrangements of the γ rhythm either in terms of spectral power [Lushchekina et al., 2011] or synchronization, may play a key role in behavioral impairments, as high-frequency activity is the main interaction between the neuron ensembles forming the network of neuron populations supporting mental activity [Basar et al., 2001]. This “inaction” of the γ rhythm on cognitive loading may reflect loss of connections between specific neural networks, which is typical of both autism [Rippon et al., 2007] and schizophrenia [Moran and Hong, 2011]. The absence of rearrangements to spectral power and synchronization at the high rhythms on performance of the cognitive task may be the cause of the fact that autists are unable to link details together and pay attention not to the overall object but only to parts of objects [Wang et al., 2010]. It is possible that hindrance to neurophysiological rearrangements is also due to the anatomical characteristics typical of autism – narrow cortical columns [Mathewson et al., 2012]. Why is the γ rhythm inactive on cognitive loading in autists? It would seem that this is because even baseline conditions show those characteristics of high-frequency activity (increased levels of spectral power and coherence) which occur in health during task performance. From this point of view, the absence of an increase in coherence in the γ frequencies in patients may reflect a predominance of local connections in baseline conditions and inertness in relation to γ rearrange-

Lushchekina, Khaerdinova, Novototskii-Vlasov, et al. ments during activity, preventing appropriate processing of information. The following needs to be emphasized in answering the question of the extent to which the characteristics of rhythm synchronization in children with ASD are similar to those in adults with schizophrenia. Lower coherence values at low frequencies in baseline conditions in ASD as compared with normal have also been described for adults with schizophrenia with both positive and negative symptoms [Magomedov et al., 2010; Strelets and Garakh, 2009; Strelets et al., 2005]. In contrast to healthy children and adults, in whom performance of the cognitive task produced, as compared with baseline, significant activation of connections at high frequencies, children with ASD, like adults with schizophrenia with mainly negative symptoms [Magomedov et al., 2010; Strelets and Garakh, 2009], showed only minor changes. Thus, these studies support the common nature of impairments to cerebral activity in patients with ASD and schizophrenia and provide evidence of a rather high degree of similarity of these impairments in ASD and schizophrenia with mainly negative symptoms. Conclusions 1. The baseline EEG activity of children with autism spectrum disorders show lower levels of coherence in the δ, θ, and α ranges and higher values in the β and γ ranges than healthy subjects. 2. On performance of a cognitive task (counting), healthy children showed the most significant increases in connections in the β and γ ranges, while changes in these ranges were minor in children with ASD. 3. On performance of the cognitive task by children with autism spectrum disorders, increases in connections occurred in the δ range and were accompanied by differently directed changes in the θ range. This study was supported by the Russian Humanities Scientific Foundation (Grant No. 14-06-00444a) and the “Basic Sciences – Medicine” Program of the Presidium of the Russian Academy of Sciences. REFERENCES Andreasen, N. C., Brave New Brain, Oxford University Press, Oxford (2001). Basar, E., Basar-Eroglu, C., Karakas, S., and Schurmann, M., “Gamma, alpha, delta, and theta oscillations govern cognitive processes,” Int. J. Psychophysiol., 39, 241–248 (2001). Borisov, S. V., Kaplan, A. Ya., Gorbachevskaya, N. L, and Kozlova, I. A., “Analysis of the structural synchronicity of the EEG in adolescents with schizophrenia spectrum disorder,” Fiziol. Cheloveka, 31, No. 3, 1–8 (2005). Dinstein, I., Thomas, C., Humphreys, K, et al., “Normal movement selectivity in autism,” Neuron, 66, No. 3, 461–469 (2010). Ivanitskii, A. M., “The cerebral basis of subjective experiences: an information synthesis hypothesis,” Zh. Vyssh. Nerv. Deyat., 46, No. 1, 124–141 (1996). Kulaichev, A. P., Methods and Means of Complex Data Analysis, Forum, Infa-M, Moscow (2006). Livanov, M. N., Spatial Organization of Processes in the Brain, Nauka, Moscow (1972).

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