Characteristics of the Spectral Power of EEG Rhythms in Children with Early Childhood Autism and Their Association with the Development of Different Symptoms of Schizophrenia

Lushchekina, E. A.; Podreznaya, E. D.; Lushchekin, V. S.; Novototskii-Vlasov, V. Yu.; Стрелец, В. Б.

doi:10.1007/s11055-012-9688-6

Cited by 9 publications

(9 citation statements)

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“…The great similarity between these two disorders may also be indicated by the common tendency to decreased power in the gamma range seen in patients with ECA and in patients with schizophrenia with negative symptomatology on cognitive loading. The decrease in theta-rhythm reactivity in response to cognitive loading as compared with normal children seen by ourselves has also been described for the fast rhythms in both early childhood autism [7,8] and in schizophrenia with negative symptomatology [9,[13][14][15]26]. Of interest was the reciprocal relationship between the low-and high-frequency rhythms seen in our studies.…”

Section: Discussionsupporting

confidence: 79%

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confidence: 99%

“…Despite the specific features of cognitive impairments in ECA associated with the characteristics of information processing [19] and interhemisphere interactions [25], similarities in the mechanisms of these two types of pathology can be suggested [22]. Studies have shown [7,8] that cognitive loading in healthy children produces increases in the spectral power and coherence of fast rhythms from baseline levels. In early childhood autism, the spectral power of the alpha rhythm decreases, while the spectral power of the gamma rhythm is higher than normal, including in baseline conditions.…”

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confidence: 99%

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Comparative Studies of EEG Theta and Gamma Rhythms in Normal Children and Children with Early Childhood Autism

Lushchekina

Podreznaya²,

Lushchekin³

et al. 2014

Neurosci Behav Physi

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Previous studies [7,8] have addressed the question of the extent to which EEG characteristics typical of adult schizophrenia patients apply to children with early childhood autism (ECA). It has become possible to state the problem in these terms since clinical studies of ECA demonstrated that positive symptoms could arise and that ECA could develop into a processual disorder of the schizophrenia type [1]. Many children with ECA are diagnosed with "schizophrenia" at age 7-8 years. We were interested to determine whether EEG data could be used to obtain early information regarding the probability of this change in diagnosis. Published views on this question are contradictory, while solution of the problem is very important for the timely provision of corrective and rehabilitation services. Definition of the possible criteria for the development of ECA into schizophrenia based on EEG data are of great scientific and social significance. Despite the specific features of cognitive impairments in ECA associated with the characteristics of information processing [19] and interhemisphere interactions [25], similarities in the mechanisms of these two types of pathology can be suggested [22]. Studies have shown [7,8] that cognitive loading in healthy children produces increases in the spectral power and coherence of fast rhythms from baseline levels. In early childhood autism, the spectral power of the alpha rhythm decreases, while the spectral power of the gamma rhythm is higher than normal, including in baseline conditions. Cognitive loading of patients produces smaller changes in the spectral power of fast rhythms than seen in normal subjects. Decreased alpha-rhythm power is seen in children with autism and in adults with schizophrenia, with both positive and negative symptoms [13]. Similar changes have also been described in adolescents with schizophrenia spectrum disorders [3]. The increased spectral power of the We present here a comparative analysis of spectral power and mean coherence of the theta and gamma rhythms in healthy boys and boys with early childhood autism aged 5-7 years (mean age 6 years 1 month) in the state of rest and on cognitive loading (counting). In contrast to the situation in patients, the background theta rhythm in healthy children showed a marked fronto-occipital gradient. In early childhood autism, the spectral power of the theta rhythm in baseline conditions had lower values than in healthy children, while baseline gamma rhythm power was greater. In healthy children, cognitive loading led to decreases in spectral power and mean coherence in the theta rhythm and increases in these measures of the gamma rhythm. In patients with early childhood autism, cognitive loading produced no marked changes from baseline in either rhythm. In healthy children, counting shifted the focus of theta-rhythm activity to the right occipital area, which did not occur in patients with early childhood autism.

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Section: Discussionsupporting

confidence: 79%

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confidence: 99%

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confidence: 99%

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Comparative Studies of EEG Theta and Gamma Rhythms in Normal Children and Children with Early Childhood Autism

Lushchekina

Podreznaya²,

Lushchekin³

et al. 2014

Neurosci Behav Physi

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“…Just as in the case of behavioural studies, most research on (spontaneous resting‐state) EEG in autism focuses on children. This is also true for many of the studies from which the hypotheses as postulated in the current study were derived (Cornew et al., ; Lushchekina et al., , ; Orekhova et al., ; Van Diessen et al., ; Vaughan Van Hecke et al., ). Deriving hypotheses about adults from studies carried out on children requires the assumption of no major discrepancies between those two groups on the variable of interest.…”

Section: Discussionsupporting

confidence: 58%

Spontaneous resting‐state gamma oscillations are not predictive of autistic traits in the general population

Groot

Strien

2018

Eur J of Neuroscience

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The autism spectrum hypothesis states that not only diagnosed individuals but also individuals from the general population exhibit a certain amount of autistic traits. While this idea is supported by neuroimaging studies, there have been few electrophysiological studies. In particular, there have been no spontaneous resting‐state studies yet. In order to examine the autism spectrum hypothesis, the present study tried to predict the level of autistic traits typically developing young adults (n = 93) exhibit from spontaneous resting‐state gamma power, a measure that has been linked to social functioning impairments seen in autism. The influence of age and gender was controlled for by employing regression. It was expected that enhanced gamma activity would be predictive of self‐reported autistic traits. The model with only age and gender included reached significance, with higher age within this student population being related to more autistic traits. However, no relationship between either low (30–50 Hz) or high (50–70 Hz) gamma power and autistic traits was found. Models with eyes closed low gamma asymmetry and eyes closed high gamma asymmetry included did reach significance, but these findings were not robust, and the gamma asymmetry explained very little additional variance above age and gender. In addition, exploratory correlation analyses showed no relationship between the other power spectra (delta, theta, alpha and beta) on the one hand and autistic traits on the other hand, suggesting that any relationship between spontaneous resting‐state brain electrophysiology and autistic traits might not be strong enough to be detected in the general population.

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“…The recorded EEG signals had bandwidth from 0.2 Hz to 45 Hz, covering five frequency bands, which are delta (frequency<4Hz), theta (4Hz<frequency<8Hz), alpha (8Hz<frequency<13Hz), beta (13Hz<frequency<30Hz), and gamma (frequency> 30 Hz). The theta, alpha, beta, and gamma frequency band activities have been reported to be sensitive for measuring cognitive load for ASD populations [45]. The delta band was less informative and was susceptible to movement artifacts during driving.…”

Section: Feature Extractionmentioning

confidence: 99%

Cognitive Load Measurement in a Virtual Reality-Based Driving System for Autism Intervention

Zhang

Wade

Bian

et al. 2017

IEEE Trans. Affective Comput.

107

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Autism Spectrum Disorder (ASD) is a highly prevalent neurodevelopmental disorder with enormous individual and social cost. In this paper, a novel virtual reality (VR)-based driving system was introduced to teach driving skills to adolescents with ASD. This driving system is capable of gathering eye gaze, electroencephalography, and peripheral physiology data in addition to driving performance data. The objective of this paper is to fuse multimodal information to measure cognitive load during driving such that driving tasks can be individualized for optimal skill learning. Individualization of ASD intervention is an important criterion due to the spectrum nature of the disorder. Twenty adolescents with ASD participated in our study and the data collected were used for systematic feature extraction and classification of cognitive loads based on five well-known machine learning methods. Subsequently, three information fusion schemes—feature level fusion, decision level fusion and hybrid level fusion—were explored. Results indicate that multimodal information fusion can be used to measure cognitive load with high accuracy. Such a mechanism is essential since it will allow individualization of driving skill training based on cognitive load, which will facilitate acceptance of this driving system for clinical use and eventual commercialization.

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Characteristics of the Spectral Power of EEG Rhythms in Children with Early Childhood Autism and Their Association with the Development of Different Symptoms of Schizophrenia

Cited by 9 publications

References 8 publications

Comparative Studies of EEG Theta and Gamma Rhythms in Normal Children and Children with Early Childhood Autism

Comparative Studies of EEG Theta and Gamma Rhythms in Normal Children and Children with Early Childhood Autism

Spontaneous resting‐state gamma oscillations are not predictive of autistic traits in the general population

Cognitive Load Measurement in a Virtual Reality-Based Driving System for Autism Intervention

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