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Background: The study of tactile perception during a childhood is extremely important for understanding the social and communicative aspects of the child's development. Tactile perception of stimuli with different valence can have different normative stages of development. Methods: In the present study, we examined changes in linear and nonlinear electroencephalogram (EEG) parameters during the presentation of pleasant (C-tactile optimal stroking), unpleasant (ice stroking), and neutral tactile stimuli in three groups of healthy volunteers: preschoolers 4 and 5 years, school-age children from 8 to 10 years, and adults from 20 to 40 years. Results: According to our findings, child maturation plays a significant role in the perception of pleasant and neutral tactile stimuli. Patterns of EEG dynamics related to tactile perception showed greater similarity between adult volunteers and school-aged children than preschoolers. Conclusions: Non-linear EEG parameters such as fractal dimension (FD), envelope mean frequency (EMF), and power spectral density (PSD) dynamics of the theta-rhythm were particularly sensitive to developmental changes in tactile perception. Hjorth complexity and peak alpha frequency (PAF) scores may serve as indicators of mature perception of C-tactile (CT)-stimuli. [ABSTRACT FROM AUTHOR]

Potocki–Lupski Syndrome (PTLS) is a rare condition associated with a duplication of 17p11.2 that may underlie a wide range of congenital abnormalities and heterogeneous behavioral phenotypes. Along with developmental delay and intellectual disability, autism-specific traits are often reported to be the most common among patients with PTLS. To contribute to the discussion of the role of autism spectrum disorder (ASD) in the PTLS phenotype, we present a case of a female adolescent with a de novo dup(17) (p11.2p11.2) without ASD features, focusing on in-depth clinical, behavioral, and electrophysiological (EEG) evaluations. Among EEG features, we found the atypical peak–slow wave patterns and a unique saw-like sharp wave of 13 Hz that was not previously described in any other patient. The power spectral density of the resting state EEG was typical in our patient with only the values of non-linear EEG dynamics: Hjorth complexity and fractal dimension were drastically attenuated compared with the patient's neurotypical peers. Here we also summarize results from previously published reports of PTLS that point to the approximately 21% occurrence of ASD in PTLS that might be biased, taking into account methodological limitations. More consistent among PTLS patients were intellectual disability and speech and language disorders. [ABSTRACT FROM AUTHOR]

Schizophrenia is a mental disorder characterized by both abnormal time perception and atypical relationships with external factors. Here we compare the influence of external photic stimulation on time production between healthy subjects (n = 24) and patients with schizophrenia (n = 22). To delve into neuropsychological mechanisms of such a relationship, the EEG was recorded during variable conditions: during production of 10 s intervals; during photic stimulation of 4, 9, 16, and 25 Hz; and during combinations of these conditions. We found that the higher frequency of photic stimulation influenced the production of time intervals in healthy volunteers, which became significantly longer and were accompanied by corresponding EEG changes. The impact of photic stimulation was absent in patients with schizophrenia. In addition, the time production was characterized by less accuracy and the absence of EEG dynamics typical for healthy controls that included an increase in alpha2 power and envelope frequency. Our findings indicated that the time perception was not adjusted by external factors in patients with schizophrenia and might have involved cognitive and mental processes different from those of healthy volunteers. [ABSTRACT FROM AUTHOR]

Rett syndrome (RTT), a severe neurodevelopmental disorder caused by MECP2 gene abnormalities, is characterized by atypical EEG activity, and its detailed examination is lacking. We combined the comparison of one-time eyes open EEG resting state activity from 32 girls with RTT and their 41 typically developing peers (age 2–16 years old) with longitudinal following of one girl with RTT to reveal EEG parameters which correspond to the RTT progression. Traditional measures, such as epileptiform abnormalities, generalized background activity, beta activity and the sensorimotor rhythm, were supplemented by a new frequency rate index measured as the ratio between high- and low-frequency power of sensorimotor rhythm. Almost all studied EEG parameters differentiated the groups; however, only the elevated generalized background slowing and decrease in our newly introduced frequency rate index which reflects attenuation in the proportion of the upper band of sensorimotor rhythm in RTT showed significant relation with RTT progression both in longitudinal case and group analysis. Moreover, only this novel index was linked to the breathing irregularities RTT symptom. The percentage of epileptiform activity was unrelated to RTT severity, confirming previous studies. Thus, resting EEG can provide information about the pathophysiological changes caused by MECP2 abnormalities and disease progression. [ABSTRACT FROM AUTHOR]

Independent Component Analysis (ICA) is a conventional approach to exclude non-brain signals such as eye movements and muscle artifacts from electroencephalography (EEG). A rejection of independent components (ICs) is usually performed in semiautomatic mode and requires experts' involvement. As also revealed by our study, experts' opinions about the nature of a component often disagree, highlighting the need to develop a robust and sustainable automatic system for EEG ICs classification. The current article presents a toolbox and crowdsourcing platform for Automatic Labeling of Independent Components in Electroencephalography (ALICE) available via link http://alice.adase.org/. The ALICE toolbox aims to build a sustainable algorithm to remove artifacts and find specific patterns in EEG signals using ICA decomposition based on accumulated experts' knowledge. The difference from previous toolboxes is that the ALICE project will accumulate different benchmarks based on crowdsourced visual labeling of ICs collected from publicly available and in-house EEG recordings. The choice of labeling is based on the estimation of IC time-series, IC amplitude topography, and spectral power distribution. The platform allows supervised machine learning (ML) model training and re-training on available data subsamples for better performance in specific tasks (i.e., movement artifact detection in healthy or autistic children). Also, current research implements the novel strategy for consentient labeling of ICs by several experts. The provided baseline model could detect noisy IC and components related to the functional brain oscillations such as alpha and mu rhythm. The ALICE project implies the creation and constant replenishment of the IC database, which will improve ML algorithms for automatic labeling and extraction of non-brain signals from EEG. The toolbox and current dataset are open-source and freely available to the researcher community. [ABSTRACT FROM AUTHOR]

We aimed to investigate the non-linear features of the electroencephalogram (EEG) findings in patients with chronic microvascular ischemia (CMI) in order to determine the brain correlates of emotional impairment that could impact the risk of developing acute ischemia. We compared the EEG responses of patients with CMI and age-matched healthy volunteers to non-verbal emotionally charged sounds. We analyzed the EEG data, the psychological assessment of the stimuli, and the results of neuropsychological and behavioral testing. We assessed the (in)stability of the envelope's amplitude by calculating its average frequency and the ratio of its standard deviation to its mean. The non-linear features were lower in the patient group in the resting state. The emotional stimulation induced a decrease in the frequency of the envelope's amplitude in all subjects. Changes in the fractal dimension during stimulation were only seen in the patient group, and they correlated with symptoms of emotional lability. The lower ratio of the alpha-rhythm envelope's standard deviation to its mean in the right hemisphere correlated with a higher sense of threat. The EEG and behavioral correlates of emotional impairment in patients with CMI were found. [ABSTRACT FROM AUTHOR]

Primary Objective: We aimed to investigate the origin of EEG response of patients in coma and after moderate TBI to emotional stimulation, whether they distinguish emotional content or only react to the physical features of an audio signal. Research Design: We registered EEG during continuous pleasant and unpleasant non-verbal stimulation and in rest. A total of 69 subjects participated in our study: healthy adults, conscious patients after moderate TBI and patients in coma after severe TBI. We analysed the distances between the stimuli according to EEG data and applied correlation analysis to the distances, acoustic parameters of stimuli, subjects' emotional assessment of the stimuli, GCS score and Doppler ultrasound data. Main Outcomes and Results: The EEG response to the stimulation in patients in coma mostly reflects the physical parameters of the stimuli, and correlated with Doppler ultrasound data. The EEG of healthy adults reflects their psychological assessment of the stimuli. Patients after moderate TBI differentiate unpleasant stimuli well and pleasant stimuli poorly, they mainly consider pitch to cope with this task like healthy adults do. Conclusions: The reactivity to the acoustic features of emotional stimulation in patients in coma should be investigated further as a marker of patients' recovery. Abbreviations: TBI: traumatic brain injurym; TBI: moderate TBI; MCA: middle cerebral artery; GCS: Glasgow Coma Scale; M: mean; SD: standard deviation [ABSTRACT FROM AUTHOR]

Concurrent EEG and fMRI acquisitions in resting state showed a correlation between EEG power in various bands and spontaneous BOLD fluctuations. However, there is a lack of data on how changes in the complexity of brain dynamics derived fromEEG reflect variations in the BOLD signal. The purpose of our study was to correlate both spectral patterns, as linear features of EEG rhythms, and nonlinear EEG dynamic complexity with neuronal activity obtained by fMRI. We examined the relationships between EEG patterns and brain activation obtained by simultaneous EEG-fMRI during the resting state condition in 25 healthy right-handed adult volunteers. Using EEG-derived regressors, we demonstrated a substantial correlation of BOLD signal changes with linear and nonlinear features of EEG. We found the most significant positive correlation of fMRI signal with delta spectral power. Beta and alpha spectral features had no reliable effect on BOLD fluctuation. However, dynamic changes of alpha peak frequency exhibited a significant association with BOLD signal increase in right-hemisphere areas. Additionally, EEG dynamic complexity as measured by the HFD of the 2-20Hz EEG frequency range significantly correlated with the activation of cortical and subcortical limbic system areas. Our results indicate that both spectral features of EEG frequency bands and nonlinear dynamic properties of spontaneous EEG are strongly associated with fluctuations of the BOLD signal during the resting state condition. [ABSTRACT FROM AUTHOR]

Paralinguistic features, such as prosody (tempo, loudness, and timbre), are an essential marker of a speaker's emotional state. Abnormal processing of emotional prosody may result in the deficient social behavior associated with autism spectrum disorder (ASD). Two groups of children participated in our study: the ASD group consisted of 30 preschoolers from 4 to 6 years of age and 24 typically developing (TD) peers. An electroencephalogram (EEG) was acquired in response to a combination of syllables uttered with the following types of emotional prosody: joy, anger, sadness, fear, and calmness. Children with ASD and TD showed a similar EEG oscillatory response to fear and anger prosodies. Significant group differences in power spectral density (PSD) were detected for sad and joy intonations. The PSD differences between pairs of intonations, such as joyful and sad, sad and neutral, or joyful and neutral, were significantly higher in the control group than in the ASD group. EEG responses to affective prosody also demonstrated less hemispheric asymmetry in the ASD than in the TD group. Our results suggest that difficulties in emotional prosody recognition in autistic children could be based on the atypical processing of specific acoustic features coding differences between sad, neutral, and joyful intonations and could underlie emotional perception deficits in individuals with ASD. • Results suggest autistic children have difficulty distinguishing speech intonations of sad and joy. • Children with ASD and TD children showed a similar EEG oscillatory response to fear and anger prosody. • EEG response to affective prosody demonstrated less hemispheric asymmetry in the ASD group. [ABSTRACT FROM AUTHOR]

Emotional dysfunction, including flat affect and emotional perception deficits, is a specific symptom of schizophrenia disorder. We used a modified multimodal odd-ball paradigm with fearful facial expressions accompanied by congruent and non-congruent emotional vocalizations (sounds of women screaming and laughing) to investigate the impairment of emotional perception and reactions to other people's emotions in schizophrenia. We compared subjective ratings of emotional state and event-related potentials (EPPs) in response to congruent and non-congruent stimuli in patients with schizophrenia and healthy controls. The results showed the altered multimodal perception of fearful stimuli in patients with schizophrenia. The amplitude of N50 was significantly higher for non-congruent stimuli than congruent ones in the control group and did not differ in patients. The P100 and N200 amplitudes were higher in response to non-congruent stimuli in patients than in controls, implying impaired sensory gating in schizophrenia. The observed decrease of P3a and P3b amplitudes in patients could be associated with less attention, less emotional arousal, or incorrect interpretation of emotional valence, as patients differed from healthy controls in the emotion scores of non-congruent stimuli. The difficulties in identifying the incoherence of facial and audial components of emotional expression could be significant in understanding the psychopathology of schizophrenia. [ABSTRACT FROM AUTHOR]

Despite widespread using electroencephalography (EEG) and Doppler ultrasound in pediatric neurology clinical practice, there are still no well-known correlations between these methods that could contribute to a better understanding of brain processes and development of neurological pathology. This study aims to reveal relationship between EEG and Doppler ultrasound methods. We compared two cohorts of adults and preschool children with no history of neurological or mental diseases. The data analysis included investigation of EEG and carotid blood flow indexes, which are significant in neurological diagnosis, as well as calculation of linear and non-linear EEG parameters and ratios between the systolic peak velocities of carotid arteries and carotid blood asymmetry. We have found age-dependent correlations between EEG and power Doppler ultrasound imaging (PDUI) data. Carotid blood flow asymmetry correlated with delta-rhythm power spectral density only in preschoolers. The ratios of blood flow velocities in the internal carotid arteries to those in the common carotid arteries correlated with higher peak alpha frequency and lower fractal dimension; moreover, they were associated with lower Epworth sleepiness scale scores. The study revealed significant correlations between EEG and PDUI imaging indexes, which are different for healthy children and adults. Despite the fact that the correlations were associated with non-clinical states such as overwork or stress, we assumed that the investigated parameters could be applicable for clinical trials. [ABSTRACT FROM AUTHOR]

In this study, we have reported a correlation between structural brain changes and electroencephalography (EEG) in response to tactile stimulation in ten comatose patients after severe traumatic brain injury (TBI). Structural morphometry showed a decrease in whole-brain cortical thickness, cortical gray matter volume, and subcortical structures in ten comatose patients compared to fifteen healthy controls. The observed decrease in gray matter volume indicated brain atrophy in coma patients induced by TBI. In resting-state EEG, the power of slow-wave activity was significantly higher (2–6 Hz), and the power of alpha and beta rhythms was lower in coma patients than in controls. During tactile stimulation, coma patients' theta rhythm power significantly decreased compared to that in the resting state. This decrease was not observed in the control group and correlated positively with better coma outcome and the volume of whole-brain gray matter, the right putamen, and the insula. It correlated negatively with the volume of damaged brain tissue. During tactile stimulation, an increase in beta rhythm power correlated with the thickness of patients' somatosensory cortex. Our results showed that slow-wave desynchronization, as a nonspecific response to tactile stimulation, may serve as a sensitive index of coma outcome and morphometric changes after brain injury. [ABSTRACT FROM AUTHOR]

Objectives Concurrent EEG and fMRI acquisitions in resting state showed a correlation between EEG power in various bands and spontaneous BOLD fluctuations. However, there is a lack of data how the EEG complexity of brain dynamic, such as instability or entropy of EEG rhythmic structure, may reflect variations in the BOLD signal. Methods We aimed to find the relationships between EEG dynamics and brain activation obtained by simultaneous EEG-fMRI during resting state condition in 25 healthy right-handed adult voluntaries. We applied nonlinear analysis in order to obtain chaotic features of EEG, which we used as regressors in GLM relating to changes in the BOLD signal. Results Fluctuations in beta rhythm were associated with the BOLD signal increase in basal ganglia and thalamic structures ( p < 0.05 , FDR-corrected). Moreover, our data showed that Higuchi fractal dimension correlated with activation of cortical areas and limbic system regions. Offset of alpha-peak frequency and envelope frequency coincided with activation of thalamus, insula, hippocampus and temporal cortex predominantly in the right hemisphere. Conclusions Previously, nonlinear EEG analysis in clinical neurophysiology pointed that decreased complexity of brain dynamics might implicate increased neuronal interaction. Our results indicate that dynamic properties of spontaneous EEG have a significant association with resting state BOLD fluctuations reflecting possible sources of rhythmic neuronal activity. In particular, the observed increase in the BOLD signal significantly correlated with deviation of beta rhythm power that supports the hypothesis of basal-thalamic origin of beta rhythm. The study was supported by a grant of the Russian Science Foundation 116-15-00300. [ABSTRACT FROM AUTHOR]

• A cluster of 67 functional connections distinguished the OCD, MDD and control groups. • Three functional connections were stronger in the OCD than in the MDD group. • DMN connectivity strength was higher in the control group than in the MDD and OCD groups in the alpha and beta bands. Major depressive disorder (MDD) and obsessive-compulsive disorder (OCD) are psychiatric disorders that often co-occur. We aimed to investigate whether their high comorbidity could be traced not only by clinical manifestations, but also at the level of functional brain activity. In this paper, we examined the differences in functional connectivity (FC) at the whole-brain level and within the default mode network (DMN). Resting-state EEG was obtained from 43 controls, 26 OCD patients, and 34 MDD patients. FC was analyzed between 68 cortical sources, and between-group differences in the 4–30 Hz range were assessed via the Network Based Statistic method. The strength of DMN intra-connectivity was compared between groups in the theta, alpha and beta frequency bands. A cluster of 67 connections distinguished the OCD, MDD and control groups. The majority of the connections, 8 of which correlated with depressive symptom severity, were found to be weaker in the clinical groups. Only 3 connections differed between the clinical groups, and one of them correlated with OCD severity. The DMN strength was reduced in the clinical groups in the alpha and beta bands. It can be concluded that the high comorbidity of OCD and MDD can be traced at the level of FC. [Display omitted] [ABSTRACT FROM AUTHOR]