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EEG Coherence Patterns in Autism: An Updated Review
... In growth disorders such as autism, this process may be stimulated and lead to an abnormal neural connection. A detailed analysis of EEG patterns can be a way to examine the differences in brain and nervous functions between healthy and autistic individuals [9]. This biological signal can also be applied in other diagnosis tasks such as Alzheimer disease [1]. ...
... Various types of classifiers such as artificial neural network, support vector machine (SVM), Naïve Bayes, k-nearest neighbors (KNN), linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) are some instances of them. In fact, each of them has their own strategy [9]. Tuning of the parameters in each category has a direct impact on its performance. ...
... Since the main goal of the approach is to diagnose this disorder in early ages, in order to validate the proposed method, we used the children older than 5 years old to train the system and the children younger than 5 years old to test [3,6,1,4,21,2,17,5,14,9,19,11,20] Channel 2 (C4) [1,6,3,4,18,5,2,21,9,17,14,19,20] mRmR Channel 1 (C3) [6,3,1,4,21,2,8,11,20,5,9,19,7,15] Channel 2 (C4) [1,6,4,9,3,5,20,8,18,19,11,17,10] IG Channel 1 (C3) [19,5,4,1,3,9,2,6,8,10,7,21,20,16] Channel 2 (C4) [18,19,9, [21,20,3,5,17,19,9,4,18,6,2] Channel 2 (C4) [21,19,3,17,20,5,18,9,4,6] mRmR Channel 1 (C3) [19,21,3,2,11,8,13,6,17,14] Channel 2 (C4) [19,3,11,2,20,16,17,9] IG Channel 1 (C3) [19,9,2,3,5,13,1,4,21,8,18] Channel 2 (C4) [18,19,2,9,13 [1,4,7,10,11,13,[18][19][20] the framework. Therefore, the main dataset is divided into 2 parts as denoted in Table 5. ...
Electroencephalogram (EEG) is one of the most important signals for diagnosis of Autism Spectrum Disorder (ASD). There are different challenges such as feature selection and the existence of artifacts in EEG signals. This article aims to present a robust method for early diagnosis of ASD from EEG signal. The study population consists of 34 children with ASD between 3–12 years and 11 healthy children in the same ranges of age. The proposed approach uses linear and nonlinear features such as Power Spectrum, Wavelet Transform, Fast Fourier Transform (FFT), Fractal Dimension, Correlation Dimension, Lyapunov Exponent, Entropy, Detrended Fluctuation Analysis and Synchronization Likelihood for describing the EEG signal. In addition Density Based Clustering is utilized for artifact removal and robustness. Besides, features selection is applied based on different criterions such as Mutual Information (MI), Information Gain (IG), Minimum-Redundancy Maximum-Relevancy (mRmR) and Genetic Algorithm (GA). Finally, the K-Nearest-Neighbor (KNN) and Support Vector Machines (SVM) classifiers are used for final decision. As a result, the investigation indicates that the classification accuracy of the approach using SVM is 90.57% while for KNN it is 72.77%. Moreover, the sensitivity of the proposed method is 99.91% for SVM and 91.96% for KNN. Also, experiments show that DFA, LE, Entropy and SL features have considerable influence in promoting the classification accuracy.
... The results on the functional connectivity in ASD are currently controversial [19,51]. The abnormal neural couplings change during the development of the nervous system. ...
... Several studies have reached similar conclusions, i.e., cognitive performance is associated with neural connectivity in TD participant. Therefore we should also consider the association of functional development with connectivity in clinical participants [19,53]. Schwartz et al. [19] claimed that including participants with a wide age range when performing a comparison made it difficult to probe the developmental trajectory. ...
... Therefore we should also consider the association of functional development with connectivity in clinical participants [19,53]. Schwartz et al. [19] claimed that including participants with a wide age range when performing a comparison made it difficult to probe the developmental trajectory. This is because studies typically attempt to examine the developmental trajectory by seeking out the difference between groups at different developmental stages. ...
Autism spectrum disorder (ASD) is a developmental disorder that involves developmental delays. It has been hypothesized that aberrant neural connectivity in ASD may cause atypical brain network development. Brain graphs not only describe the differences in brain networks between clinical and control groups, but also provide information about network development within each group. In the present study, graph indices of brain networks were estimated in children with ASD and in typically developing (TD) children using magnetoencephalography performed while the children viewed a cartoon video. We examined brain graphs from a developmental point of view, and compared the networks between children with ASD and TD children. Network development patterns (NDPs) were assessed by examining the association between the graph indices and the raw scores on the achievement scale or the age of the children. The ASD and TD groups exhibited different NDPs at both network and nodal levels. In the left frontal areas, the nodal degree and efficiency of the ASD group were negatively correlated with the achievement scores. Reduced network connections were observed in the temporal and posterior areas of TD children. These results suggested that the atypical network developmental trajectory in children with ASD is associated with the development score rather than age.
... Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with social, communicative, and motor deficits [1]. While the deficits may have been defined fairly well, the neural processes underlying the disorder have yet to be determined [2]. Few studies have employed task-based experiments to analyze the brain functional connectivity of ASD [2] [3], though motor deficits were one of the most common symptoms. ...
... While the deficits may have been defined fairly well, the neural processes underlying the disorder have yet to be determined [2]. Few studies have employed task-based experiments to analyze the brain functional connectivity of ASD [2] [3], though motor deficits were one of the most common symptoms. ...
... Disrupted functional connectivity was identified as one of the underlying features of ASD which were reported in previous EEG studies [2] [3]. While the studies mainly supported the long-range underconnectivity in ASD [6], the others have reported varying results [2]. ...
Abnormal functional connectivity was reported as one of the underlying characteristics of autism spectrum disorder (ASD). Considering the motor deficits in ASD, we utilized praxis to investigate the neural mechanisms of ASD during motor task. Since the previous functional connectivity studies reported divergent results, we explored the properties of the functional connectivity using graph metrics to address brain organization alterations of ASD. We proposed the use of eLORETA to investigate the cortical connectivity during praxis based on a cohort of 45 high-functioning ASD (HFA) children and 45 typically developing (TD) children. The between-group comparison revealed higher clustering coefficient and lower global efficiency for HFA relative to TD while the between-phase comparison suggested decreasing global efficiency, increasing characteristic path length for TD. Nodal metrics exhibited significant differences between groups in frontal and occipital regions. These regions also showed significant changes of nodal metrics and connection strengths between baseline and praxis execution for TD. However, there were no significant changes in global, nodal metrics and connection strengths between phases for HFA. Our study suggested that cortical connectivity in ASD exhibited lower overall efficiency and a deficit in reorganization, which deepens the understanding of abnormal brain organization in ASD.
... Coherence is a measure of how two simultaneously recorded EEG signals are correlated and represents a noninvasive approach to assess functional connectivity between brain areas [8]. We were motivated to study coherence in AS by the observation that individuals with autism show altered coherence patterns [9][10][11][12][13][14][15][16][17]. Autism has been recognized as a component feature of AS [18][19][20][21][22], and copy number increases in the 15q11-13 chromosomal region including UBE3A are also associated with syndromic autism [23,24]. ...
... Some estimates suggest that up to~50-80% of individuals with AS meet diagnostic criteria for autism [18]; however, these estimates vary greatly due to the difficulties assessing autism with standardized clinical tests in AS individuals. Traditionally, individuals with autism were thought to have comparatively high coherence between nearby electrode pairs (local hyperconnectivity) and low coherence between long-distance signals (global hypoconnectivity) [9][10][11][12][13], but this view has been challenged and become more nuanced in recent years [14][15][16][17]25]. Thus, although specific connectivity patterns remain unclear, there is widespread consensus that EEG coherence is altered in autism. ...
... Increased long-range coherence in AS was seen throughout the brain and was not driven by altered coherence in a spatially restricted subset of connections (Fig. 2e, Additional file 1: Figure S1). There is general consensus that functional connectivity is widely disrupted in autism [9][10][11][12][13][14][15][16][17]25], and our findings confirm that coherence is also dysregulated in AS, a disorder with some autistic features. However, increased long-range functional connectivity may be surprising given prior studies of decreased structural connectivity in AS, both in mouse models [65] and patient populations [66,67]. ...
Background:
Angelman syndrome (AS) is a neurodevelopmental disorder characterized by intellectual disability, speech and motor impairments, epilepsy, abnormal sleep, and phenotypic overlap with autism. Individuals with AS display characteristic EEG patterns including high-amplitude rhythmic delta waves. Here, we sought to quantitatively explore EEG architecture in AS beyond known spectral power phenotypes. We were motivated by studies of functional connectivity and sleep spindles in autism to study these EEG readouts in children with AS.
Methods:
We analyzed retrospective wake and sleep EEGs from children with AS (age 4-11) and age-matched neurotypical controls. We assessed long-range and short-range functional connectivity by measuring coherence across multiple frequencies during wake and sleep. We quantified sleep spindles using automated and manual approaches.
Results:
During wakefulness, children with AS showed enhanced long-range EEG coherence across a wide range of frequencies. During sleep, children with AS showed increased long-range EEG coherence specifically in the gamma band. EEGs from children with AS contained fewer sleep spindles, and these spindles were shorter in duration than their neurotypical counterparts.
Conclusions:
We demonstrate two quantitative readouts of dysregulated sleep composition in children with AS-gamma coherence and spindles-and describe how functional connectivity patterns may be disrupted during wakefulness. Quantitative EEG phenotypes have potential as biomarkers and readouts of target engagement for future clinical trials and provide clues into how neural circuits are dysregulated in children with AS.
... Findings on global EEG connectivity in ASD have consistently demonstrated reduced coherence in children and adolescents with ASD compared with TD controls (Wang et al., 2013;O'Reilly et al., 2017;Schwartz et al., 2017). Global under-connectivity has been generally found in these populations for lower frequencies from delta to beta bands, with over-connectivity found in the gamma band (O'Reilly et al., 2017). ...
... Some recent studies have suggested that these inconsistencies in EEG connectivity could be due to methodological factors, such as age, brain regions examined, method of analysis of time intervals and frequency bands, limited statistical power, volume conduction and experimental focus (Mohammad-Rezazadeh et al., 2016;O'Reilly et al., 2017;Schwartz et al., 2017). However, although methodology may have an impact on these inconsistent results, the heterogeneity of ASD may also be a factor (O'Reilly et al., 2017;Schwartz et al., 2017). ...
... Some recent studies have suggested that these inconsistencies in EEG connectivity could be due to methodological factors, such as age, brain regions examined, method of analysis of time intervals and frequency bands, limited statistical power, volume conduction and experimental focus (Mohammad-Rezazadeh et al., 2016;O'Reilly et al., 2017;Schwartz et al., 2017). However, although methodology may have an impact on these inconsistent results, the heterogeneity of ASD may also be a factor (O'Reilly et al., 2017;Schwartz et al., 2017). ...
Autism spectrum disorder (ASD) is a neurodevelopmental condition affecting about 1 in 100 children and is currently incurable. ASD represents a challenge to traditional methods of assessment and diagnosis, and it has been suggested that direct measures of brain activity and connectivity between brain regions during demanding tasks represents a potential pathway to building more accurate models of underlying brain function and ASD. One of the key behavioural diagnostic indicators of ASD consists of sensory features (SF), often characterised by over- or under-reactivity to environmental stimuli. SF are associated with behavioural difficulties that impede social and education success in these children as well as anxiety and depression. This review examines the previous literature on the measurement of EEG connectivity and SF observed in individuals with ASD.
... However, the increase in coherence coincides with longer dwell times in a globally-disconnected state ( Rashid et al., 2018), and increased variability in connectivity over time (Mash et al., 2019). Analyses of EEG and MEG recordings, which provide higher temporal resolution, indicate longrange functional underconnectivity (O'Reilly et al., 2017), and decreased synchrony in short-and medium-range connections in ASD participants ( Schwartz et al., 2017), particularly in higher frequency bands. EEG data also point to changes in brain network organization developmentally: e.g. between 3 and 11 years of age decreased synchronization among brain regions persists, but in addition to it, increases in within-regional synchronization are observed ( Han et al., 2017;Kang et al., 2019). ...
... Although the relationships between behavior and oscillatory activity were not tested directly in the present study, some relationships might be hypothesized from prior work, where decreased theta and alpha coherence were shown to lead to impairment in working memory and between-network binding (particularly as related to executive processing, inhibition, and conscious attention), while beta frequency synchrony has been related to successful higher-order cognitive processing (cf. Schwartz et al., 2017). Additionally, atypical pattern of synchronization in the left hemisphere might be related to leftlateralized microstructural abnormalities in ASD ( Peterson et al., 2015) Our analysis of fronto-parietal temporal dysregulation suggests a possible underlying mechanism whereby normal functional organization of brain networks in ASD fails to emerge. ...
Autism spectrum disorder is increasingly understood to be based on atypical signal transfer among multiple interconnected networks in the brain. Relative temporal patterns of neural activity have been shown to underlie both the altered neurophysiology and the altered behaviors in a variety of neurogenic disorders. We assessed brain network dynamics variability in Autism Spectrum Disorders (ASD) using measures of synchronization (phase-locking) strength, and timing of synchronization and desynchronization of neural activity (desynchronization ratio) across frequency bands of resting state EEG. Our analysis indicated that fronto-parietal synchronization is higher in ASD, but with more short periods of desynchronization. It also indicates that the relationship between the properties of neural synchronization and behavior is different in ASD and typically developing populations. Recent theoretical studies suggest that neural networks with high desynchronization ratio have increased sensitivity to inputs. Our results point to the potential significance of this phenomenon to autistic brain. This sensitivity may disrupt production of an appropriate neural and behavioral responses to external stimuli. Cognitive processes dependent on integration of activity from multiple networks may be, as a result, particularly vulnerable to disruption.
Lay Summary
Parts of the brain can work together by synchronizing activity of the neurons. We recorded electrical activity of the brain in adolescents with autism spectrum disorder, and then compared the recording to that of their peers without the diagnosis. We found that in participants with autism, there were a lot of very short time periods of non-synchronized activity between frontal and parietal parts of the brain. Mathematical models show that the brain system with this kind of activity is very sensitive to external events.
... Atypical cortical connectivity has been commonly observed in ASD [4]. Given that the integration and transfer of information between and within neural networks is essential for cognition, altered connectivity may be a mechanism contributing to the expression of the core diagnostic characteristics of ASD, including impairments in FER. ...
... To date the majority of coherency investigations in individuals diagnosed with ASD have used resting state conditions, with a dearth of connectivity studies during cognitive tasks [4]. Further, drawing conclusions regarding atypical connectivity in ASD has been hindered by the use of differing tasks, participant groups and measures [5]. ...
Difficulties in Facial Emotion Recognition (FER) are commonly associated with individuals diagnosed with Autism Spectrum Disorder (ASD). However, the mechanisms underlying these impairments remain inconclusive. While atypical cortical connectivity has been observed in autistic individuals, there is a paucity of investigation during cognitive tasks such as FER. It is possible that atypical cortical connectivity may underlie FER impairments in this population. Electroencephalography (EEG) Imaginary Coherence was examined in 22 autistic adults and 23 typically developing (TD) matched controls during a complex, dynamic FER task. Autistic adults demonstrated reduced coherence between both short and long range inter-hemispheric electrodes. By contrast, short range intra-hemispheric connectivity was increased in frontal and occipital regions during FER. These findings suggest altered network functioning in ASD.
... 34 For ASD, the ongoing changes of the pruning and synaptogenesis disturbed the normal brain development, thus leading to the abnormal neural connectivity. 35 Therefore, differences in EEG signals can be used to compare the children with autism with the typical developmental children. 36 Peak alpha frequency PAF) is an electroencephalographic measure of cognitive preparedness 37 and might be a neural marker of cognitive function for the autism. ...
... Coherence provides a measure of the degree of synchronization between two signals, which means that the two signals with the same frequency have the consistent phase relationship over time, and we could also assume there is a high degree of the coordinated brain activity between the underlying brain areas where those two signals come from. 35 EEG coherence is one way to assess the brain functional connectivity, which has proven abnormal in previous studies for ASD children. 43,44 In this study, we calculated the coherence of 62 channels at the alpha frequency band. ...
Background:
Autism spectrum disorder (ASD) is a very complex neurodevelopmental disorder, characterized by social difficulties and stereotypical or repetitive behavior. Some previous studies using low-frequency repetitive transcranial magnetic stimulation (rTMS) have proven of benefit in ASD children.
Methods:
In this study, 32 children (26 males and six females) with low-function autism were enrolled, 16 children (three females and 13 males; mean ± SD age: 7.8 ± 2.1 years) received rTMS treatment twice every week, while the remaining 16 children (three females and 13 males; mean ± SD age: 7.2 ± 1.6 years) served as waitlist group. This study investigated the effects of rTMS on brain activity and behavioral response in the autistic children.
Results:
Peak alpha frequency (PAF) is an electroencephalographic measure of cognitive preparedness and might be a neural marker of cognitive function for the autism. Coherence is one way to assess the brain functional connectivity of ASD children, which has proven abnormal in previous studies. The results showed significant increases in the PAF at the frontal region, the left temporal region, the right temporal region and the occipital region and a significant increase of alpha coherence between the central region and the right temporal region. Autism Behavior Checklist (ABC) scores were also compared before and after receiving rTMS with positive effects shown on behavior.
Conclusion:
These findings supported our hypothesis by demonstration of positive effects of combined rTMS neurotherapy in active treatment group as compared to the waitlist group, as the rTMS group showed significant improvements in behavioral and functional outcomes as compared to the waitlist group.
... Several studies analyzing electrophysiological patterns across the brain have collected temporally precise information on global network processing; frequency domain measures in EEG have also indicated atypical activation of cortical regions during facial-emotion processing in ASD. Whereas increased neural connectivity reflected by theta frequency band (4-7 Hz) synchronization has been associated with information encoding and episodic memory in TD groups (Klimesch, 1999), ASD participants have shown a reduction in theta synchronization and reduced right frontal theta coherence, perhaps reflecting atypical connectivity between neural networks and less efficient encoding and memory retrieval of facial expressions (Schwartz et al., 2017). A phasic suppression of alpha (8-12 Hz) during cognitive task performance has been reported in TD individuals, reflecting an increase in attentional demands (Klimesch, 1999). ...
... Similarly, individuals with ASD have shown greater alpha de-synchronization during face tasks, suggesting increased concentration or attention (Yang et al., 2011). One hypothesis is that a reduction in the lower frequency bands in ASD may reflect impaired automatic processing of emotion while increased alpha desynchronization may indicate increased conscious control of visual processing (Schwartz et al., 2017). In effect, ASD individuals may compensate for weaknesses in the automatic processes involved in emotion perception by directing more cognitive effort to the task presented. ...
Few studies have used task-based functional connectivity (FC) magnetic resonance imaging to examine emotion-processing during the critical neurodevelopmental period of adolescence in Autism Spectrum Disorders (ASDs). Moreover, task designs with pervasive confounds (e.g., lack of appropriate controls) persist because they activate neural circuits of interest reliably. As an alternative approach to “subtracting” activity from putative control conditions, we propose examining FC across an entire task run. By pivoting our analysis and interpretation of existing paradigms we may better understand neural response to non-focal instances of socially-relevant stimuli that approximate real-world experiences more closely. Hence, using two well-established affective tasks (face-viewing, face-matching) with diverging social-cognitive demands, we investigated extrinsic FC from amygdala (AMG) and fusiform gyrus (FG) seeds in typically-developing (TD; N = 17) and ASD (N = 17) male adolescents (10–18 yo) and clinical correlations (Social Communication Questionnaire; SCQ) of group FC differences. Participant data (4TD, 6ASD) with excessive head-motion were excluded from final analysis. Direct between-group comparisons revealed significant differences between groups for neural response but not task performance (accuracy, reaction time). During face-viewing, we found greater FC from AMG and FG seeds for ASD participants (ASD > TD) in regions involved in the Default Mode and Fronto-Parietal Task Control Networks. During face-matching, we found greater FC from AMG and FG seeds for TD participants (TD > ASD), in regions associated with the Salience, Dorsal Attention, and Somatosensory Networks. SCQ scores correlated positively with regions with group differences on the face-viewing task and negatively with regions identified for the face-matching task. Task-dependent group differences in FC despite comparable behavioral performance suggest that high-functioning ASD may wield compensatory strategies; clinically-correlated FC patterns may associate with differential task-demands, ecological validity, and context-dependent processing. Employing this novel approach may further the development of targeted therapeutic interventions informed by individual differences in the highly heterogeneous ASD population.
... In addition, EEG coherence has been utilized to discover abnormalities of functional connectivity between specific locations in the brain in several neurological and neuropsychological disorders; examples include epilepsy (Song et al., 2013), bipolar disorder (Özerdem et al., 2011), Alzheimer disease (Adler et al., 2003), schizophrenia (Uhlhaas & Singer, 2010), attention deficit hyperactivity disorder (Duffy et al., 2017), traumatic brain injury (Sponheim et al., 2011), and post-traumatic stress disorder (Modarres et al., 2019). In neurodevelopmental disorders, such as autism spectrum disorder (ASD), EEG coherence has been utilized to evaluate abnormal neural connectivity in several studies reviewed in Schwartz et al. (2017). For example, in schooled-aged children, group differences between ASD and typically developing children have been reported in EEG coherences particularly in higher frequency bands of alpha, beta, and gamma (Coben et al., 2008;Machado et al., 2015;Elhabashy et al., 2015;Lazarev et al., 2015;Clarke et al., 2016;Lushchekina et al., 2016). ...
... Several previous studies have shown the presence of abnormal synchronous patterns of neural activity within and across brain networks, as measured by electroencephalography (EEG) coherence, during resting state or cognitive tasks (Schwartz et al., 2017). We believe that the social motor coordination paradigm is an informative test case of our proposed approach since incorporation of EEG coherence measurement will allow the discovery of abnormal neural mechanisms associated with social motor coordination abnormalities and their relationship with social deficits. ...
Electroencephalography (EEG) coherence analysis, based on measurement of synchronous oscillations of neuronal clusters, has been used extensively to evaluate functional connectivity in brain networks. EEG coherence studies have used a variety of analysis variables (e.g., time and frequency resolutions corresponding to the analysis time period and frequency bandwidth), regions of the brain (e.g., connectivity within and between various cortical lobes and hemispheres) and experimental paradigms (e.g., resting state with eyes open or closed; performance of cognitive tasks). This variability in study designs has resulted in difficulties in comparing the findings from different studies and assimilating a comprehensive understanding of the underlying brain activity and regions with abnormal functional connectivity in a particular disorder. In order to address the variability in methods across studies and to facilitate the comparison of research findings between studies, this paper presents the structure and utilization of a comprehensive hierarchical electroencephalography (EEG) coherence analysis that allows for formal inclusion of analysis duration, EEG frequency band, cortical region, and experimental test condition in the computation of the EEG coherences. It further describes the method by which this EEG coherence analysis can be utilized to derive biomarkers related to brain (dys)function and abnormalities. In order to document the utility of this approach, the paper describes the results of the application of this method to EEG and behavioral data from a social synchrony paradigm in a small cohort of adolescents with and without Autism Spectral Disorder.
... However, the increase in coherence coincides with longer dwell times in a globally disconnected state [Rashid et al., 2018], and increased variability in connectivity over time [Mash et al., 2019]. Analyses of EEG and MEG recordings, which provide higher temporal resolution, indicate long-range functional underconnectivity [O'Reilly, Lewis, & Elsabbagh, 2017], and decreased synchrony in short-and medium-range connections in ASD participants [Schwartz, Kessler, Gaughan, & Buckley, 2017], particularly in higher frequency bands. EEG data also point to changes in brain network organization developmentally, for example, between 3 and 11 years of age decreased synchronization among brain regions persists, but in addition to it, increases in withinregional synchronization are observed [Han et al., 2017;Kang, Chen, Li, & Li, 2019]. ...
... Although the relationships between behavior and oscillatory activity were not tested directly in the present study, some relationships might be hypothesized from prior work, where decreased theta and alpha coherence were shown to lead to impairment in working memory and between-network binding (particularly as related to executive processing, inhibition, and conscious attention), while beta frequency synchrony has been related to successful higher-order cognitive processing (cf. Schwartz et al., 2017). Additionally, atypical pattern of synchronization in the left hemisphere might be related to left-lateralized microstructural abnormalities in ASD [Peterson, Mahajan, Crocetti, Mejia, & Mostofsky, 2015] Our analysis of frontoparietal temporal dysregulation suggests a possible underlying mechanism whereby normal functional organization of brain networks in ASD fails to emerge. ...
Autism spectrum disorder is increasingly understood to be based on atypical signal transfer among multiple interconnected networks in the brain. Relative temporal patterns of neural activity have been shown to underlie both the altered neurophysiology and the altered behaviors in a variety of neurogenic disorders. We assessed brain network dynamics variability in autism spectrum disorders (ASD) using measures of synchronization (phase‐locking) strength, and timing of synchronization and desynchronization of neural activity (desynchronization ratio) across frequency bands of resting‐state electroencephalography (EEG). Our analysis indicated that frontoparietal synchronization is higher in ASD but with more short periods of desynchronization. It also indicates that the relationship between the properties of neural synchronization and behavior is different in ASD and typically developing populations. Recent theoretical studies suggest that neural networks with a high desynchronization ratio have increased sensitivity to inputs. Our results point to the potential significance of this phenomenon to the autistic brain. This sensitivity may disrupt the production of an appropriate neural and behavioral responses to external stimuli. Cognitive processes dependent on the integration of activity from multiple networks maybe, as a result, particularly vulnerable to disruption. Autism Res 2020, 13: 24–31. © 2019 International Society for Autism Research, Wiley Periodicals, Inc.
Lay Summary
Parts of the brain can work together by synchronizing the activity of the neurons. We recorded the electrical activity of the brain in adolescents with autism spectrum disorder and then compared the recording to that of their peers without the diagnosis. We found that in participants with autism, there were a lot of very short time periods of non‐synchronized activity between frontal and parietal parts of the brain. Mathematical models show that the brain system with this kind of activity is very sensitive to external events.
... Relative power reflects the relationship between frequency bands, but does not yield an indication of the degree to which abnormal electrophysiological activity is present in a specific frequency band [13]. Absolute and relative power indices are divided with 4 frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz) and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). The relative power at each frequency band is calculated as the power in each frequency band divided by total power across all frequency bands. ...
... EEG coherence is one way to noninvasively evaluate this functional connectivity. Coherence provides a measure of the degree of synchronization between two signals, when two signals in the same frequency are active with a consistent phase relationship over time, they are considered coherent and we assume there is a high degree of coordinated activity between the underlying brain regions producing those two signals [25]. In this paper, phase lag index (PLI) is applied as a coherence measure, because PLI is slightly influenced by volume condition [26]. ...
Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder which affects the developmental trajectory in several behavioral domains, including impairments of social communication, cognitive and language abilities. In this paper, multi-feature fusion method based on EEG signal is used to extract as many as possible features including power spectrum analysis, bicoherence, entropy and coherence methods, then we use minimum redundancy maximum correlation (mRMR) algorithm to choose the features, which are applied to input to three classifiers to obtain accuracy classification results. We try to find some key biomarkers of ASD by examining the accuracy of classifier, using different models which use the combination of multiplex features. The results show when nine features are selected by SVM-linear classifier, the accuracy is up to 91.38%. This method might provide objective basis for clinical diagnosis of autism.
... Indeed, some studies report findings of decreased intra-hemispheric connectivity (linear coherence) in the gamma band in 12month-old high-risk infants with a later diagnosis of ASD (relative to other high-risk or low-risk infants), and decreased connectivity (phase lag index) in toddlers with ASD (versus TD) for the beta band 29,30 . The lack of consistency across findings of functional connectivity may depend on age, task and length of the EEG recordings, frequency band of interest, the selected index of functional connectivity, and small sample sizes, among others 12,32 . Moreover, the heterogeneity in ASD and the possibility of subtypes of ASD might underlie the inconsistent findings. ...
We conducted a replication study of our prior report that increased alpha EEG connectivity at 14-months associates with later autism spectrum disorder (ASD) diagnosis, and dimensional variation in restricted interests/repetitive behaviours. 143 infants at high and low familial risk for ASD watched dynamic videos of spinning toys and women singing nursery rhymes while high-density EEG was recorded. Alpha functional connectivity (7–8 Hz) was calculated using the debiased weighted phase lag index. The final sample with clean data included low-risk infants (N = 20), and high-risk infants who at 36 months showed either typical development (N = 47), atypical development (N = 21), or met criteria for ASD (N = 13). While we did not replicate the finding that global EEG connectivity associated with ASD diagnosis, we did replicate the association between higher functional connectivity at 14 months and greater severity of restricted and repetitive behaviours at 36 months in infants who met criteria for ASD. We further showed that this association is strongest for the circumscribed interests subdomain. We propose that structural and/or functional abnormalities in frontal-striatal circuits underlie the observed association. This is the first replicated infant neural predictor of dimensional variation in later ASD symptoms.
... Indeed, some studies report findings of decreased intra-hemispheric connectivity (linear coherence) in the gamma band in 12month-old high-risk infants with a later diagnosis of ASD (relative to other high-risk or low-risk infants), and decreased connectivity (phase lag index) in toddlers with ASD (versus TD) for the beta band 29,30 . The lack of consistency across findings of functional connectivity may depend on age, task and length of the EEG recordings, frequency band of interest, the selected index of functional connectivity, and small sample sizes, among others 12,32 . Moreover, the heterogeneity in ASD and the possibility of subtypes of ASD might underlie the inconsistent findings. ...
We conducted a replication study of our prior report that increased alpha EEG connectivity at 14-months associates with later autism spectrum disorder (ASD) diagnosis, and dimensional variation in restricted interests/repetitive behaviours. 143 infants at high and low familial risk for ASD watched dynamic videos of spinning toys and women singing nursery rhymes while high-density EEG was recorded. Alpha functional connectivity (7–8 Hz) was calculated using the debiased weighted phase lag index. The final sample with clean data included low-risk infants (N = 20), and high-risk infants who at 36 months showed either typical development (N = 47), atypical development (N = 21), or met criteria for ASD (N = 13). While we did not replicate the finding that global EEG connectivity associated with ASD diagnosis, we did replicate the association between higher functional connectivity at 14 months and greater severity of restricted and repetitive behaviours at 36 months in infants who met criteria for ASD. We further showed that this association is strongest for the circumscribed interests subdomain. We propose that structural and/or functional abnormalities in frontal-striatal circuits underlie the observed association. This is the first replicated infant neural predictor of dimensional variation in later ASD symptoms.
... In recent years, utilising EEG analysis in studying neurodevelopmental disorders has been thriving. For example, several studies have been conducted for investigating the ASD (Abdolzadegan et al. 2020, Lavanga et al. 2021, Peters et al. 2013, Righi et al. 2014, Schwartz et al. 2017, ADHD (Barry et al. 2003, Janssen et al. 2017, Mahmoud et al. 2021, Moghaddari et al. 2020, and learning disorders (Kaisar 2020, Suchetha et al. 2021, Xue et al. 2020). ...
This dissertation aims to identify the neurological biomarkers that could assist in providing reliable, automated and objective prediction of neurodevelopmental disorders (NDDs) in early infancy. Quantitative electroencephalography analysis (qEEG), mainly phase synchronisation-based functional brain connectivity estimated using phase locking value (PLV) and weighted phase lag index (WPLI), were investigated to deduce whether it can be used for the early prediction of such disorders. The resulting connectivity network was quantitatively characterised using complex graph-theoretical features, namely transitivity, global efficiency, radius, diameter, and characteristic path length. These features were then fed into the machine learning algorithms such as linear discriminant analysis (LDA), support vector machine (SVM), decision tree and k-nearest neighbour to examine their discriminant capability in classifying /predicting NDDs. The proposed framework has gained initial validation in classifying autism spectrum disorders (ASD) from an experimentally obtained EEG data set of 24 children. Then, the framework was utilised to predict the appearance of cerebral palsy (CP) at two years of age. The EEG data were recorded within the first week after birth from a cohort of infants born with hypoxic-ischaemic encephalopathy (HIE). The exploration results revealed that the proposed analytical methodology successfully predicted the infants that would develop CP with a performance of 84.6% accuracy, 83% sensitivity, 85% specificity, 84% balanced accuracy and 0.85 area under the curve (AUC) in the delta band, with a close result also obtained in the theta and alpha bands. The WPLI and graph parameters were then used to predict the cognitive scores of infants born with HIE by developing the regression framework correlating these EEG features and a cognitive profile completed in a follow-up assessment at two years of age. The regression analysis showed that the radius feature yielded the best performance (root mean square error (RMSE)= 16.78, mean absolute error (MAE)= 12.07 and R-squared= 0.24). Although this study has successfully demonstrated that the qEEG features could be considered potential biomarkers for identifying the brain deficits causing the NDDs, it has a certain limitation due to the size of the data set. It needs to be validated on large trials with a statistically significant population.
... This would explain why high information transfer was also found in the alpha frequency band. EEG experiments in individuals with ASD show a reduction or an increase in coherence patterns in the theta and/or alpha frequency bands compared to their TD peers at various ages and under different experimental conditions ( Schwartz et al., 2017). However, most of the available EEG experiments performed on very young children with ASD and analysis thereof were so far restricted to the scalp surface. ...
Social impairments are a hallmark of Autism Spectrum Disorders (ASD), but empirical
evidence for early brain network alterations in response to social stimuli is scant in ASD. We
recorded the gaze patterns and brain activity of toddlers with ASD and their typically developing
peers while they explored dynamic social scenes. Directed functional connectivity analyses based
on electrical source imaging revealed frequency specific network atypicalities in the theta and alpha
frequency bands, manifesting as alterations in both the driving and the connections from key nodes
of the social brain associated with autism. Analyses of brain-behavioural relationships within the
ASD group suggested that compensatory mechanisms from dorsomedial frontal, inferior temporal
and insular cortical regions were associated with less atypical gaze patterns and lower clinical
impairment. Our results provide strong evidence that directed functional connectivity alterations of
social brain networks is a core component of atypical brain development at early stages of ASD.
... Disruption of typical patterns of coherence between the EEG measured at scalp sites is observed in human patients with disconnection syndromes (Leocani et al., 2000;Spencer et al., 2003;Babiloni et al., 2016;Duffy et al., 2017;Schwartz et al., 2017;Shou et al., 2017). In experiments with non-human primates, coherence in local field potentials (LFPs) between brain areas measured with microelectrodes positioned intracranially near cell bodies has been linked to a number of cognitive functions such as selective attention (Gregoriou et al., 2009), working memory (Salazar et al., 2012), and decision-making (Nacher et al., 2013). ...
Long-range interactions between cortical areas are undoubtedly key to the computational power of the brain. For healthy human subjects the premier method for measuring brain activity on fast timescales is EEG, and coherence between EEG signals is often used to assay functional connectivity between different brain regions. However, the nature of the underlying brain activity that is reflected in EEG coherence is currently the realm of speculation, because seldom have EEG signals been recorded simultaneously with intracranial recordings near cell bodies in multiple brain areas. Here we take the early steps towards narrowing this gap in our understanding of EEG coherence by measuring local field potentials with microelectrode arrays in two brain areas (extrastriate visual area V4 and dorsolateral prefrontal cortex) simultaneously with EEG at the nearby scalp in rhesus macaque monkeys. Although we found inter-area coherence at both scales of measurement, we did not find that scalp-level coherence was reliably related to coherence between brain areas measured intracranially on a trial-to-trial basis, despite that scalp-level EEG was related to other important features of neural oscillations, such as trial-to-trial variability in overall amplitudes. This suggests that caution must be exercised when interpreting EEG coherence effects, and new theories devised about what aspects of neural activity long-range coherence in the EEG reflects.
... Research with humans has also seen growing interest in inter-area correlations and coherence of late, using non-invasive neuroimaging methodologies such as EEG/MEG and fMRI. For example, atypical coherence between locations on the scalp measured with EEG has been observed in several neurological disorders suspected of involving deficient inter-area communication, including Alzheimer's disease (71), attention deficit disorders (72), and autism spectrum disorders (73,74). The relationship between these neuroimaging signals and the spiking activity of individual neurons remains largely unknown, however. ...
Purpose of review:
The computational power of the brain arises from the complex interactions between neurons. One straightforward method to quantify the strength of neuronal interactions is by measuring correlation and coherence. Efforts to measure correlation have been advancing rapidly of late, spurred by the development of advanced recording technologies enabling recording from many neurons and brain areas simultaneously. This review highlights recent results that provide clues into the principles of neural coordination, connections to cognitive and neurological phenomena, and key directions for future research.
Recent findings:
The correlation structure of neural activity in the brain has important consequences for the encoding properties of neural populations. Recent studies have shown that this correlation structure is not fixed, but adapts in a variety of contexts in ways that appear beneficial to task performance. By studying these changes in biological neural networks and computational models, researchers have improved our understanding of the principles guiding neural communication.
Summary:
Correlation and coherence are highly informative metrics for studying coding and communication in the brain. Recent findings have emphasized how the brain modifies correlation structure dynamically in order to improve information-processing in a goal-directed fashion. One key direction for future research concerns how to leverage these dynamic changes for therapeutic purposes.
... Theta band activity is associated with sustained anticipatory attention, memory encoding, emotional processing and cognitive performance during infancy, toddlerhood and preschool years (Saby & Marshall, 2012). EEG experiments in individuals with ASD show a reduction or an increase in the coherence patterns in the theta frequency band compared to their TD peers at various ages and under different experimental conditions with sensor-space based analysis (Schwartz et al., 2016). In very young children, theta band activity is linked to the development of the social brain. ...
Social impairments are a hallmark of Autism Spectrum Disorders (ASD), but empirical evidence for early brain network alterations in response to social stimuli is scant in ASD. Here, we recorded the gaze patterns and brain activity of toddlers and preschoolers with ASD and their typically developing (TD) peers while they explored dynamic social scenes. Source-space directed functional connectivity analyses revealed the presence of network alterations in the theta frequency band, manifesting as increased driving (hyper-activity) and stronger connections (hyper-connectivity) from key nodes of the social brain associated with autism. Further analyses of brain-behavioural relationships within the ASD group suggested that compensatory mechanisms from dorsomedial frontal, inferior temporal and insular cortical regions were associated with lower clinical impairment and less atypical gaze patterns. Our results provide strong evidence that directed functional connectivity alterations of social brain networks is a core component of atypical brain development at early stages of ASD.
... Given that some studies show that ASD patients and controls have similar FRN amplitudes to reward and punishment feedback (Larson et al., 2011;McPartland et al., 2012), these data underscore the importance of also measuring the consistency of trial-to-trial phase alignment as a measure of neural synchrony. In a reward prediction task we show a unique difference, lower trial-to-trial phase locking in ASD, consistent with several studies highlighting a lack of neural synchrony as an endophenotype in ASD (Catarino et al., 2013;David et al., 2016;Dinstein et al., 2011;Lushchekina et al., 2016;Schwartz et al., 2016). Our findings point to further evidence for reduced ITC in ASD and the benefit of examining more nuanced measures in EEG studies that can differentiate ASD from neurotypical controls. ...
Background:
Impairment in prediction and appreciation for choice outcomes could contribute to several core symptoms of ASD. We examined electroencephalography (EEG) oscillations in 27 youth and young adults diagnosed with autism spectrum disorder (ASD) and 22 IQ-matched neurotypical controls while they performed a chance-based reward prediction task.
Method:
We re-analyzed our previously published ERP data (Larson et al., 2011) and examined theta band oscillations (4-8 Hz) at frontal midline sites, within a timing window that overlaps with the feedback-related negativity (FRN). We focused on event-related changes after presentation of feedback for reward (WIN) and punitive (LOSE) outcomes, both for spectral power and inter-trial phase coherence.
Results:
In our reward prediction task, for both groups, medial frontal theta power and phase coherence were greater following LOSE compared to WIN feedback. However, compared to controls, inter-trial coherence of medial frontal theta was significantly lower overall (across both feedback types) for individuals with ASD. Our results indicate that while individuals with ASD are sensitive to the valence of reward feedback, comparable to their neurotypical peers, they have reduced synchronization of medial frontal theta activity during feedback processing.
Conclusions:
This finding are consistent with previous studies showing neural variability in ASD and suggest that the processes underlying decision-making and reinforcement learning may be atypical and less efficient in ASD.
... Non-instantaneous or lagged coherence is a methodological approach to frequency domain connectivity that removes the effects of volume conduction in EEG co-spectra (Pascual-Marqui, 2007;Pascual-Marqui et al., 2011). Lagged coherence has been used to investigate functional connectivity in resting-state brain networks in several disorders, e.g., Olbrich et al. (2013), Mohan et al. (2016a), Schwartz et al. (2016). However, to our knowledge, brain connectivity in AWS has not yet been investigated with lagged coherence. ...
Neural network-based investigations of stuttering have begun to provide a possible integrative account for the large number of brain-based anomalies associated with stuttering. Here we used resting-state EEG to investigate functional brain networks in adults who stutter (AWS). Participants were 19 AWS and 52 age-, and gender-matched normally fluent speakers. EEGs were recorded and connectivity matrices were generated by LORETA in the theta (4–8 Hz), alpha (8–12 Hz), beta1 (12–20 Hz), and beta2 (20–30 Hz) bands. Small-world propensity (SWP), shortest path, and clustering coefficients were computed for weighted graphs. Minimum spanning tree analysis was also performed and measures were compared by non-parametric permutation test. The results show that small-world topology was evident in the functional networks of all participants. Three graph indices (diameter, clustering coefficient, and shortest path) exhibited significant differences between groups in the theta band and one [maximum betweenness centrality (BC)] measure was significantly different between groups in the beta2 band. AWS show higher BC than control in right temporal and inferior frontal areas and lower BC in the right primary motor cortex. Abnormal functional networks during rest state suggest an anomaly of DMN activity in AWS. Furthermore, functional segregation/integration deficits in the theta network are evident in AWS. These deficits reinforce the hypothesis that there is a neural basis for abnormal executive function in AWS. Increased beta2 BC in the right speech–motor related areas confirms previous evidence that right audio–speech areas are over-activated in AWS. Decreased beta2 BC in the right primary motor cortex is discussed in relation to abnormal neural mechanisms associated with time perception in AWS.
... Here, connectivity is studied through the coherence analysis, because is a widely used methodology in different fields of Neuroscience [23][24][25] , when functional connectivity between regions of the brain must be evaluated, and it can be useful to identify neuroanatomical and neurophysiological factors in EEG signals [26] . Also, the connectivity dynamics during locomotion is evaluated via the graphs associated to the respective coherence matrices. ...
One of the most interesting brain machine interface (BMI) applications, is the control of assistive devices for rehabilitation of neuromotor pathologies. This means that assistive devices (prostheses, orthoses, or exoskeletons) are able to detect user motion intention, by the acquisition and interpretation of electroencephalographic (EEG) signals. Such interpretation is based on the time, frequency or space features of the EEG signals. For this reason, in this paper a coherence-based EEG study is proposed during locomotion that along with the graph theory allows to establish spatio-temporal parameters that are characteristic in this study. The results show that along with the temporal features of the signal it is possible to find spatial patterns in order to classify motion tasks of interest. In this manner, the connectivity analysis alongside graphs provides reliable information about the spatio-temporal characteristics of the neural activity, showing a dynamic pattern in the connectivity during locomotions tasks.
... EEGs have been widely used across various fields, such as clinical neurology [34], psychology [35]- [36], education [37], [26], [17], and so on. Two flow state indicatorsattention and engagement-have been commonly measured. ...
Although game-based interactive technology has long enhanced flow experiences crucial for learning, its effects have been unclear. Thus, this study gathered students’ electroencephalogram (EEG) information during their work in game-based learning environments with different levels of technological interactivity (LTIs; low, mid, and high LTIs). Multiple measurements were used in a relatively small sample, and 3 9th graders (age 15) of different learning environments worked on 360 test items. The EEG data were analyzed with the LTI, balance of challenge and skill (BCS), and sense of control (SC) to establish the flow state construct. A chi-square test showed a significant association between flow states and the LTI, whereas a J48 decision tree analysis and logistic regression demonstrated that inflow experiences would likely emerge in students with high short-term SC (ST-SC), high BCS, and high-LTI learning environments. Furthermore, in high ST-SC and high BCS cases, the odds ratio (OR) of emerging inflow experiences with a high LTI is eight times more than the rest, suggesting that instructional designers (and teachers) use high-LTI game-based learning environments while ensuring students’ learning with adequate SC and BCS.
... We reasoned that one potential source might be disruption of synchronous neuronal activity at the neural circuitry level, a neural mechanism that has been implicated strongly in the formation of anticipatory predictive processes (Calderone et al., 2014) and in the representation of rhythmic environmental structures (Thut et al., 2012). The impairments observed in children with autism in tasks that involve adaptation to stimuli and/or anticipatory processing raise the possibility that impairment in the coherence of underlying neural activity might play a role (Coben et al., 2008;Schwartz et al., 2017;Foxe et al., 2018). ...
Anticipating near-future events is fundamental to adaptive behavior, whereby neural processing of predictable stimuli is significantly facilitated relative to non-predictable inputs. Neural oscillations appear to be a key anticipatory mechanism by which processing of upcoming stimuli is modified, and they often entrain to rhythmic environmental sequences. Clinical and anecdotal observations have led to the hypothesis that people with Autism Spectrum Disorder (ASD) may have deficits in generating predictions in daily life, and as such, a candidate neural mechanism may be failure to adequately entrain neural activity to repetitive environmental patterns. Here, we tested this hypothesis by interrogating rhythmic entrainment both behaviorally and electrophysiologically. We recorded high-density electroencephalography in children with ASD (n=31) and Typically Developing (TD) age- and IQ-matched controls (n=20), while they reacted to an auditory target as quickly as possible. This auditory event was either preceded by predictive rhythmic visual cues, or not. Results showed that while both groups presented highly comparable evoked responses to the visual stimuli, children with ASD showed reduced neural entrainment to the rhythmic visual cues, and altered anticipation of the occurrence of these stimuli. Further, in both groups, neuro-oscillatory phase coherence correlated with behavior. These results describe neural processes that may underlie impaired event anticipation in children with ASD, and support the notion that their perception of events is driven more by instantaneous sensory inputs and less by their temporal predictability.
... Despite a large number of EEG connectivity studies in children and adults with ASD during rest, sleep, and the performance of nonsocial tasks, relatively few ASD studies have examined EEG connectivity during tasks involving social engagement (see O'Reilly et al. 2017;Schwartz et al. 2017 for a review on EEG connectivity studies in ASD). Given that one of the core deficits of ASD is social functioning, there is knowledge to be gained from EEG connectivity studies that examine the ASD-affected brain while engaged within a social context. ...
... There is evidence that synaptic disruption occurs in ASD at both the local level of single axons and the broader level of brain networks (70,71). Using EEG coherence to examine electrical connection patterns, researchers may be able to analyze the resulting differences in brain function between persons with and without ASD (72,73). ...
Electroencephalography (EEG) can further out our understanding of autistic spectrum disorders (ASD) neurophysiology. Epilepsy and ASD comorbidity range between 5 and 46%, but its temporal relationship, causal mechanisms and interplay with intellectual disability are still unknown. Epileptiform discharges with or without seizures go as high as 60%, and associate with epileptic encephalopathies, conceptual term suggesting that epileptic activity can lead to cognitive and behavioral impairment beyond the underlying pathology. Seizures and ASD may be the result of similar mechanisms, such as abnormalities in GABAergic fibers or GABA receptor function. Epilepsy and ASD are caused by a number of genetic disorders and variations that induce such dysregulation. Similarly, initial epilepsy may influence synaptic plasticity and cortical connection, predisposing a growing brain to cognitive delays and behavioral abnormalities. The quantitative EEG techniques could be a useful tool in detecting and possibly measuring dysfunctions in specific brain regions and neuronal regulation in ASD. Power spectra analysis reveals a U-shaped pattern of power abnormalities, with excess power in the low and high frequency bands. These might be the consequence of a complicated network of neurochemical changes affecting the inhibitory GABAergic interneurons and their regulation of excitatory activity in pyramidal cells. EEG coherence studies of functional connectivity found general local over-connectivity and long-range under-connectivity between different brain areas. GABAergic interneuron growth and connections are presumably impaired in the prefrontal and temporal cortices in ASD, which is important for excitatory/inhibitory balance. Recent advances in quantitative EEG data analysis and well-known epilepsy ASD co-morbidity consistently indicate a role of aberrant GABAergic transmission that has consequences on neuronal organization and connectivity especially in the frontal cortex.
... Based on the version presented by Brovelli et al. (2004), FieldTrip was used to calculate spectrally resolved GC, due to its wide usage in EEG effective (i.e., bidirectional) connectivity research and in research on the autistic population (Nolte et al., 2010;O'Reilly et al., 2017;Pollonini et al., 2010;Schwartz et al., 2017). GC determines if one stochastic process has a causal influence on another stochastic process; if the autoregressive prediction error of one time series is reduced by previous information acquired from another time series, it can be considered that the second process has a causal influence on the first process (Brovelli et al., 2004;Ding et al., 2006). ...
Several lines of research suggest that autism is a neurological phenomenon, but the precise associations between neurological activity and the key diagnostic symptoms of autism are yet to be completely clarified. This study examined EEG connectivity and Sensory Features (SF) in a sample of young autistic males by examining bi-directional neural connectivity between separate brain regions as the key potential correlate of SF. Forty male autistic participants aged between 6 and 17 years, with an IQ of at least 70, underwent EEG measurements of their Frontal, Occipital and Temporal region responses to low-, medium-, and high-intensity audiovisual stimulus conditions. EEG connectivity data were analysed via Granger Causality. SF was measured via parent responses about their sons on the Child Sensory Profile (2nd ed.) (CSP-2). There were significant (p < .05) correlations between right hemisphere Frontal and Temporal connectivity and CSP-2 dominant scores, largely due to lower Temporal-to-Frontal than Frontal-to-Temporal connectivity. There were no significant correlations between general CSP-2 scores and EEG connectivity data collected during audiovisual stimuli. These results confirm and extend previous findings by adding bi-directional connectivity as an index of brain activity to other studies that used only uni-directional connectivity data when measuring SF. Although there may be a discrepancy between the kinds of information collected via instruments such as the CSP-2 and actual brain electrical connectivity across major regions, these results hold implications for the use of brain-training interventions with autistic boys.
... However, despite comparable accuracy and signal quality between traditional and new active electrodes, traditional electrodes EEG is regarded as the golden standard [23]. Furthermore, EEG signals in people with ASD have been found to have different patterns compared to neurotypical individuals [38,44]. ...
Autism Spectrum Disorder (ASD) is one of the most common developmental conditions with one in 160 children worldwide being diagnosed. Both Virtual Reality (VR) and Brain-Computer Interfaces (BCI) are believed to be beneficial to enhance communication for people with ASD. However, BCI solutions for ASD are not yet commercially available. This is partly due to the current challenge with long and fatiguing calibration sessions with conventional gel based BCI. BCI using active electrodes hold the potentials to resolve part of this issue but might increase the challenges to classification of tasks due to reduced signal quality. The dataset considered in this paper, available from the IFMBE Scientific Challenge (IFMBE SC) of 15 participants with ASD, contained data captured using electroencephalogram (EEG) headsets, from g.Nautilus system, with active electrodes in a VR environment. Known approaches, such as the Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) have demonstrated potential solutions to enhancing current algorithms. Nevertheless, in this paper, a novel Recurrent Neural Network (RNN) solution with several pre-processing methods was introduced. Our results show that our novel RNN solution achieved 92.59% accuracy, an improvement with 0.61 percentage point from the previously best reported algorithm during the IFMBE SC. Furthermore, with a standard 80%-20% initial separation strategy, our solution also generated a compatible accuracy at 89.92%.
... However, resting state quantitative EEG (qEEG) spectral power across the entire frequency spectrum from youth and adults with ASD versus unaffected controls has been summarized as an inverted-U shaped curve, with excessive power in low-and high-frequency bands and reduced power in the alpha band, hypothesized as consistent with dysfunctional gamma-aminobutyric acid (GABAergic) inhibitory tone and its effects on connectivity ( Wang et al., 2013 ). Advances in analytic approaches have revived interest in EEG as a possible diagnostic/risk biomarker and a window into ASD's neurobiology ( Strzelecka, 2014 ;Schwartz et al., 2016 ;Heunis et al., 2016Heunis et al., , 2018Shou et al., 2018 ). but with much variability across studies (Lefebvre et al., 2018). ...
In 2017, facing lack of progress and failures encountered in targeted drug development for Autism Spectrum Disorder (ASD) and related neurodevelopmental disorders, the ISCTM with the ECNP created the ASD Working Group charged to identify barriers to progress and recommending research strategies for the field to gain traction. Working Group international academic, regulatory and industry representatives held multiple in-person meetings, teleconferences, and subgroup communications to gather a wide range of perspectives on lessons learned from extant studies, current challenges, and paths for fundamental advances in ASD therapeutics. This overview delineates the barriers identified, and outlines major goals for next generation biomedical intervention development in ASD. Current challenges for ASD research are many: heterogeneity, lack of validated biomarkers, need for improved endpoints, prioritizing molecular targets, comorbidities, and more. The Working Group emphasized cautious but unwavering optimism for therapeutic progress for ASD core features given advances in the basic neuroscience of ASD and related disorders. Leveraging genetic data, intermediate phenotypes, digital phenotyping, big database discovery, refined endpoints, and earlier intervention, the prospects for breakthrough treatments are substantial. Recommendations include new priorities for expanded research funding to overcome challenges in translational clinical ASD therapeutic research.
... To date, few studies have examined EEG connectivity in infants with elevated risk of developing ASD. While there are many measures of EEG functional connectivity, the measures of power and coherence have been most commonly examined in the ASD literature (Wang et al., 2013;Luckhardt et al., 2014;Schwartz et al., 2017). Power is calculated in terms of the amplitude of a signal (the amount of EEG activity within a frequency band) and reflects baseline synchronization of underlying neural oscillations (Wang et al., 2013). ...
While autism spectrum disorder (ASD) is diagnosed based on behavioral symptoms at 3 years of age, the infant sibling study design has enabled the detection and characterization of atypical neural development during the first year of life, prior to the emergence of behavioral symptoms. Infants who have older siblings with ASD are at increased risk for ASD, language delay, and other neurodevelopmental delays. As such, it is important to identify as early as possible if an infant is on a trajectory towards atypical development in order to help guide close monitoring and implement targeted behavioral interventions. The body of work in this dissertation contributes to the field of infant sibling research by showing that with robust methods, electroencephalography (EEG) can be used to detect altered functional connectivity during the first year of life, starting as early as 3 months of age. Chapter 1 introduces known deficits in behaviors and neural connectivity in infants at risk for ASD, highlights methodological gaps in the field of EEG infant research, and outlines the goals of this dissertation. Chapter 2 addresses methodological considerations in the development of an EEG pre-processing pipeline, designed to maximize data quality and data retention for infant EEG. Chapters 3 through 5 present different aspects of a comprehensive study of functional connectivity during language processing in infants at risk for ASD, with focus on theta (4-6 Hz) and alpha (6-12 Hz) spectral power and phase coherence within putative language networks. Chapter 3 describes differences in coherence at 3-months of age in infants who show ASD symptoms at 18-months of age. Chapter 4 highlights altered trajectories in coherence development over the first year of life in infants who later have ASD symptoms at 18-months. At the same left fronto-central network that differentiated risk groups at 3-months of age, reduced average coherence over the first year of life is maintained in infants who showed ASD symptoms at 18 months. Chapter 5 characterizes connectivity as an endophenotype of ASD in familial risk infants using both the 3-month cross-sectional study design and the 3-12-month longitudinal study design. Connectivity measures that differentiate risk groups in Chapters 3-5 also relate to language ability and ASD symptoms at 18-months of age. Taken together, the body of work in this dissertation support the hypothesis that early differences in neural connectivity lay a foundation for and precede behavioral signs of neurodevelopmental disabilities in infants at risk for ASD.
Lay abstract:
This study investigates the effects of a probiotic on preschoolers' brain electrical activity with autism spectrum disorder. Autism is a disorder with an increasing prevalence characterized by an enormous individual, family, and social cost. Although the etiology of autism spectrum disorder is unknown, an interaction between genetic and environmental factors is implicated, converging in altered brain synaptogenesis and, therefore, connectivity. Besides deepening the knowledge on the resting brain electrical activity that characterizes this disorder, this study allows analyzing the positive central effects of a 6-month therapy with a probiotic through a randomized, double-blind placebo-controlled study and the correlations between electroencephalography activity and biochemical and clinical parameters. In subjects treated with probiotics, we observed a decrease of power in frontopolar regions in beta and gamma bands, and increased coherence in the same bands together with a shift in frontal asymmetry, which suggests a modification toward a typical brain activity. Electroencephalography measures were significantly correlated with clinical and biochemical measures. These findings support the importance of further investigations on probiotics' benefits in autism spectrum disorder to better elucidate mechanistic links between probiotics supplementation and changes in brain activity.
A growing body of research suggests that consistency in cortical activity may be a promising neurophysiological marker of autism spectrum disorder (ASD). In the current study we examined inter-trial coherence, a measure of phase consistency across trials, in the theta range (t-ITC: 3-6 Hz), as theta has been implicated in the processing of social and emotional stimuli in infants and adults. The sample included infants who had an older sibling with a confirmed ASD diagnosis and typically developing (TD) infants with no family history of ASD. The data were collected as part of the British Autism Study of Infant Siblings (BASIS) study. Infants between 6 and 10 months of age (Mage = 7.34, SDage = 1.21) performed a visual face processing task that included faces and scrambled, "face noise", stimuli. Follow-up assessments in higher likelihood infants were completed at 24 and again at 36 months to determine diagnostic outcomes. Analysis focused on posterior t-ITC during early (0-200 ms) and late (200-500 ms) visual processing stages commonly investigated in infant studies. t-ITC over posterior scalp regions during late stage face processing was significantly higher in TD and higher likelihood infants without ASD (HRA-), indicating reduced consistency in theta-band responses in higher likelihood infants who eventually receive a diagnosis of ASD (HRA+). These findings indicate that the temporal dynamics of theta during face processing relate to ASD outcomes. Reduced consistency of oscillatory dynamics at basic levels of infant sensory processing could have downstream effects on learning and social communication. LAY SUMMARY: We examined the consistency in brain responses to faces in infants at lower or higher familial likelihood for autism. Our results show that the consistency of EEG responses was lower during face processing in higher likelihood infants who eventually received a diagnosis of autism. These findings highlight that reduced consistency in brain activity during face processing in the first year of life is related to emerging autism.
In recent years complexity of the brain structure in healthy and disordered subjects has been studied increasingly. But to the best of the authors’ knowledge, researchers so far have investigated the structural complexity only in the context of two restricted networks known as Small-World and Scale-free networks; whereas other aspects of the structural complexity of brain activities may be affected by aging and neurodegenerative disorders such as the Alzheimer’s disease and autism spectrum disorder. In this study, two general complexity metrics of graphs, Graph Index Complexity and Offdiagonal Complexity are proposed as general measures of complexity, not restricted to SWN only. They are adopted to measure the structural complexity of the weighted graphs instead of the common binary graphs. Fuzzy Synchronization Likelihood is applied to the EEGs and their sub-bands, as a functional connectivity metric of the brain, to construct the functional connectivity graphs. Two applications are used to evaluate the efficacy of the complexity measures: diagnosis of autism and aging, both based on EEG. It was discovered that the Graph Index Complexity of gamma band is discriminative in distinguishing autistic children from non-autistic children. Also, Offdiagonal Complexity of theta band in young subjects was observed to be significantly different than old subjects. This study shows that changes in the structure of functional connectivity of brain in disorders and different healthy states can be revealed by unrestricted metrics of graph complexity. While the applications presented in this paper are based on EEG, the approach is general and can be used with other modalities such as fMRI, MEG, etc. Further, it can be used to study every other neurological and psychiatric disorder.
During the recent decade, there is a growing interest in the use of neuroimaging methods and different data analysis approaches to recognize and understand neuropsychiatric disorders. In this study, we investigated resting-state Electroencephalography (EEG) data of children with autism and healthy children. The direct Directed Transfer Function (dDTF) method was used to estimate the effective connectivity. We introduced and applied the directed temporal network measures for quantifying the effective brain connections in frequency bands of Alpha, Beta1, Beta2, Delta, Theta, and Gamma. Our results showed that each of the global measures was able to demonstrate a significant distinction at least in one frequency band, between the healthy and Autistic Spectrum Disorder (ASD) groups. The burstiness properties of edges and the directed temporal centrality properties of nodes were different in all the frequency bands in both groups. Also, the significant edges and nodes were determined in each group. The number of significant bursty edges in ASD was less than the healthy group, in Alpha, Delta, Beta1, and Theta bands. Finally, we could show how autism changes the pattern of the brain network across time.
Although EEG connectivity data are often used to build models of the association between overt behavioural signs of Autism Spectrum Disorder (ASD) and underlying brain connectivity indices, use of a large number of possible connectivity methods across studies has produced a fairly inconsistent set of results regarding this association. To explore the level of agreement between results from five commonly-used EEG connectivity models (i.e., Coherence, Weighted Phased Lag Index- Debiased, Phase Locking Value, Phase Slope Index, Granger Causality), a sample of 41 young males with ASD provided EEG data under eyes-opened and eyes-closed conditions. There were relatively few statistically significant and/or meaningful correlations between the results obtained from the five connectivity methods, arguing for a re-estimation of the methodology used in such studies so that specific connectivity methods may be matched to particular research questions regarding the links between neural connectivity and overt behaviour within this population.
Previous studies measured flow states using students’ self-reported experiences, resulting in issues regarding nonobjective and nonreal-time data. Thus, this study used an electroencephalogram (EEG) to measure the EEG-detected real-time flow states (EEG-Fs) of 30 students from the 4th and 5th grades. Their EEG measurements, self-reported reflective flow experiences (SR-Fs), grade levels (GLs), balance of challenge and skill (BCS), and sense of control, represented by their overall test performance (OA-tp) and momentary test performance (MOM-tp), were analyzed to establish their EEG-F’s construct. Based on the results of a chi-square test, the EEG-F correlates significantly with SR-F, BCS, OA-tp, and MOM-tp. A J48 decision tree analysis and logistic regression further revealed that in-flow experiences (in-EEG-F) were detected when students had high SR-Fs, where the BCS contributed to flow states. In particular, students with a low-challenge/high-skill BCS demonstrated an in-EEG-F state upon having a high OA-tp. For high-challenge/high-skill, the in-EEG-F state was determined through their MOM-tp. Through the EEG and flow state construct, this study revealed a whole-part association between students’ momentary and overall reflective flow experiences and identified viable paths for inducing students’ EEG-Fs, which can contribute to future e-learning development when integrated with a brain-computer interface for e-learning or e-evaluation systems.
Tuberous sclerosis complex (TSC) is a neurocutaneous disorder caused by mutations of either the TSC1 or TSC2 gene. Various neuropsychiatric features, including autism, are prevalent in TSC. Recently, significant progress has been possible with the prospective calculation of the prevalence of autism in TSC, identification of early clinical and neurophysiological biomarkers to predict autism, and investigation of different therapies to prevent autism in this high-risk population. The author provides a narrative review of recent findings related to biomarkers for diagnosis of autism in TSC, as well as recent studies related to the management of TSC-associated autism. Further sophisticated modeling and analysis are required to understand the role of different models—tuber models, seizures and related neurophysiological factors models, genotype models, and brain connectivity models—to unravel the neurobiological basis of autism in TSC. Early neuropsychologic assessments may be beneficial in this high-risk group. Targeted intervention to improve visual skill, cognition, and fine motor skills with later addition of social skill training can be helpful. Multicenter, prospective studies are ongoing to identify if presymptomatic treatment with vigabatrin in patients with TSC can improve outcomes, including autism. Several studies indicated reasonable safety of everolimus in young children, and its potential application in high-risk infants with TSC, before the closure of the temporal window of permanent changes, maybe undertaken shortly.
We present a review of the state of the art of the techniques and algorithms most used in the selection and detection of characteristics of electroencephalographic signals of people when consciously performing activities. These features are numeric parameters that describe the behavior of the signal and are the basis of patterns. In addition, previous experiences in the acquisition of electroencephalographic signals using the Epoc brain-computer interface manufactured by Emotiv are presented. First, some techniques used to eliminate artifacts (disturbances) present in the signal generated by blinking, strong breathing or other movements that contaminate the signal are presented. Later, the algorithms most frequently used in the processing of electroencephalographic signals are shown for the extraction of characteristics that describe the behavior of these patterns and that can be used to detect and recognize patterns in other signals. Finally, we present the lessons that we have acquired as a work team in the recording of electroencephalographic signals in order to be helpful for beginners.
Recent studies aiming to facilitate mathematical skill development in primary school children have explored the electrophysiological characteristics associated with different levels of arithmetic achievement. The present work introduces an alternative EEG signal characterization using graph metrics and, based on such features, a classification analysis using a decision tree model. This proposal aims to identify group differences in brain connectivity networks with respect to mathematical skills in elementary school children. The methods of analysis utilized were signal-processing (EEG artifact removal, Laplacian filtering, and magnitude square coherence measurement) and the characterization (Graph metrics) and classification (Decision Tree) of EEG signals recorded during performance of a numerical comparison task. Our results suggest that the analysis of quantitative EEG frequency-band parameters can be used successfully to discriminate several levels of arithmetic achievement. Specifically, the most significant results showed an accuracy of 80.00% (α band), 78.33% (δ band), and 76.67% (θ band) in differentiating high-skilled participants from low-skilled ones, averaged-skilled subjects from all others, and averaged-skilled participants from low-skilled ones, respectively. The use of a decision tree tool during the classification stage allows the identification of several brain areas that seem to be more specialized in numerical processing.
We studied autistics by quantitative EEG spectral and coherence analysis during three experimental conditions: basal, watching a cartoon with audio (V-A), and with muted audio band (VwA). Significant reductions were found for the absolute power spectral density (PSD) in the central region for delta and theta, and in the posterior region for sigma and beta bands, lateralized to the right hemisphere. When comparing VwA versus the V- A in the midline regions, we found significant decrements of absolute PSD for delta, theta and alpha, and increments for the beta and gamma bands. In autistics, VwA versus V-A tended to show lower coherence values in the right hemisphere. An impairment of visual and auditory sensory integration in autistics might explain our resells.
There is a growing body of evidence suggesting that altered brain connectivity may be a defining feature of disorders such as autism spectrum disorder (ASD), anxiety, and ADHD. This study investigated whether resting state functional connectivity, measured by 128-channel EEG oscillation coherence, differs between developmental disorders. Analyses were conducted separately on groups with and without comorbid conditions. Analyses revealed increased coherence across central electrodes over the primary motor cortex and decreased coherence in the frontal lobe networks in those with ASD compared to neurotypical controls. There was increased coherence in occipital lobe networks in the ADHD group compared to other groups. Symptoms of generalised anxiety were positively correlated with both frontal-occipital intrahemispheric (alpha only) coherence and occipital interhemispheric coherence (alpha, approaching theta band). The patterns of coherence in the ASD pure group were different when comorbid conditions were included in the analyses, suggesting that aberrant coherence in the frontal and central areas of the brain is specifically associated with ASD. Our findings support the idea that comorbid conditions are additive, rather than being symptoms of the same disorder.
Although prior studies have demonstrated reduced resting state EEG coherence in adults with autism spectrum disorder (ASD), no studies have explored the nature of EEG coherence during joint attention. We examined the EEG coherence of the joint attention network in adolescents with and without ASD during congruent and incongruent joint attention perception and an eyes-open resting condition. Across conditions, adolescents with ASD showed reduced right hemisphere temporal-central alpha coherence compared to typically developing adolescents. Greater right temporal-central alpha coherence during joint attention was positively associated with social cognitive performance in typical development but not in ASD. These results suggest that, in addition to a resting state, EEG coherence during joint attention perception is reduced in ASD.
Background: While there is increasing evidence of altered brain connectivity in autism, the degree and direction of these alterations in connectivity and their uniqueness to autism has not been established. The aim of the present study was to compare connectivity in children with autism to that of typically developing controls and children with developmental delay without autism.
Methods: We assessed EEG spectral power, coherence, phase lag, Pearson and partial correlations, and epileptiform activity during the awake, slow wave sleep, and REM sleep states in 137 children aged 2 to 6 years with autism (n = 87), developmental delay without autism (n = 21), or typical development (n = 29).
Findings: We found that brain connectivity, as measured by coherence, phase lag, and Pearson and partial correlations distinguished children with autism from both neurotypical and developmentally delayed children. In general, children with autism had increased coherence which was most prominent during slow wave sleep.
Interpretation: Functional connectivity is distinctly different in children with autism compared to samples with typical development and developmental delay without autism. Differences in connectivity in autism are state and region related. In this study, children with autism were characterized by a dynamically evolving pattern of altered connectivity.
Autism spectrum disorder (ASD) is a behaviorally defined and heterogeneous disorder. Biomarkers for ASD offer the opportunity to improve prediction, diagnosis, stratification by severity and subtype, monitoring over time and in response to interventions, and overall understanding of the underlying biology of this disorder. A variety of potential biomarkers, from the level of genes and proteins to network-level interactions, is currently being examined. Many of these biomarkers relate to inhibition, which is of particular interest because in many cases ASD is thought to be a disorder of imbalance between excitation and inhibition. Abnormalities in inhibition at the cellular level lead to emergent properties in networks of neurons. These properties take into account a more complete genetic and cellular background than findings at the level of individual genes or cells, and are able to be measured in live humans, offering additional potential as diagnostic biomarkers and predictors of behaviors. In this review we provide examples of how altered inhibition may inform the search for ASD biomarkers at multiple levels, from genes to cells to networks.
Among the many experimental findings that tend to distinguish those with and without autism spectrum disorder (ASD) are face processing deficits, reduced hemispheric specialization, and atypical neurostructural and functional connectivity. To investigate the earliest manifestations of these features, we examined lateralization of event-related gamma-band coherence to faces during the first year of life in infants at high risk for autism (HRA; defined as having an older sibling with ASD) who were compared with low-risk comparison (LRC) infants, defined as having no family history of ASD. Participants included 49 HRA and 46 LRC infants who contributed a total of 127 data sets at 6 and 12 months. Electroencephalography was recorded while infants viewed images of familiar/unfamiliar faces. Event-related gamma-band (30-50 Hz) phase coherence between anterior-posterior electrode pairs for left and right hemispheres was computed. Developmental trajectories for lateralization of intra-hemispheric coherence were significantly different in HRA and LRC infants: by 12 months, HRA infants showed significantly greater leftward lateralization compared with LRC infants who showed rightward lateralization. Preliminary results indicate that infants who later met criteria for ASD were those that showed the greatest leftward lateralization. HRA infants demonstrate an aberrant pattern of leftward lateralization of intra-hemispheric coherence by the end of the first year of life, suggesting that the network specialized for face processing may develop atypically. Further, infants with the greatest leftward asymmetry at 12 months where those that later met criteria for ASD, providing support to the growing body of evidence that atypical hemispheric specialization may be an early neurobiological marker for ASD. Autism Res 2015, ●●: ●●-●●. © 2015 International Society for Autism Research, Wiley Periodicals, Inc.
© 2015 International Society for Autism Research, Wiley Periodicals, Inc.
It has been previously reported that structural and functional brain connectivity in individuals with autism spectrum disorders (ASD) is atypical and may vary with age. However, to date, no measures of functional connectivity measured within the first 2 years have specifically associated with a later ASD diagnosis.
In the present study, we analyzed functional brain connectivity in 14-month-old infants at high and low familial risk for ASD using electroencephalography (EEG). EEG was recorded while infants attended to videos. Connectivity was assessed using debiased weighted phase lag index (dbWPLI). At 36 months, the high-risk infants were assessed for symptoms of ASD.
As a group, high-risk infants who were later diagnosed with ASD demonstrated elevated phase-lagged alpha-range connectivity as compared to both low-risk infants and high-risk infants who did not go on to ASD. Hyper-connectivity was most prominent over frontal and central areas. The degree of hyper-connectivity at 14 months strongly correlated with the severity of restricted and repetitive behaviors in participants with ASD at 3 years. These effects were not attributable to differences in behavior during the EEG session or to differences in spectral power.
The results suggest that early hyper-connectivity in the alpha frequency range is an important feature of the ASD neurophysiological phenotype.
The genetic architecture of autism spectrum disorder involves the interplay of common and rare variants and their impact on hundreds of genes. Using exome sequencing, here we show that analysis of rare coding variation in 3,871 autism cases and 9,937 ancestry-matched or parental controls implicates 22 autosomal genes at a false discovery rate (FDR) < 0.05, plus a set of 107 autosomal genes strongly enriched for those likely to affect risk (FDR < 0.30). These 107 genes, which show unusual evolutionary constraint against mutations, incur de novo loss-of-function mutations in over 5% of autistic subjects. Many of the genes implicated encode proteins for synaptic formation, transcriptional regulation and chromatin-remodelling pathways. These include voltage-gated ion channels regulating the propagation of action potentials, pacemaking and excitability-transcription coupling, as well as histone-modifying enzymes and chromatin remodellers-most prominently those that mediate post-translational lysine methylation/demethylation modifications of histones.
In the field of autism research, recent work has been devoted to studying both behavioral and neural markers that may aide in early identification of autism spectrum disorder (ASD). These studies have often tested infants who have a significant family history of autism spectrum disorder, given the increased prevalence observed among such infants. In the present study we tested infants at high- and low-risk for ASD (based on having an older sibling diagnosed with the disorder or not) at 6- and 12-months-of-age. We computed intrahemispheric linear coherence between anterior and posterior sites as a measure of neural functional connectivity derived from electroencephalography while the infants were listening to speech sounds. We found that by 12-months-of-age infants at risk for ASD showed reduced functional connectivity compared to low risk infants. Moreover, by 12-months-of-age infants later diagnosed with ASD showed reduced functional connectivity, compared to both infants at low risk for the disorder and infants at high risk who were not later diagnosed with ASD. Significant differences in functional connectivity were also found between low-risk infants and high-risk infants who did not go onto develop ASD. These results demonstrate that reduced functional connectivity appears to be related to genetic vulnerability for ASD. Moreover, they provide further evidence that ASD is broadly characterized by differences in neural integration that emerge during the first year of life.
The purpose of this paper is to present our original and multidisciplinary approach to study multimodal social-emotional behaviors in children with autism spectrum disorders. Our goal is to conduct fundamental and applied research regarding the reception and production of social signals involved in human interactions. To achieve this aim, we try to understand and model cognitive and multimodal emotional integration (e.g., auditory, visual, postural) during infancy and to analyze dysfunctions in pathologies that affect the dynamics of social interactions such as autism spectrum disorders. More specifically, we study the characterization of multimodal social-emotional signals (speech, prosody, faces, postures) and the dynamics of communication (e.g., synchrony, engagement). The fields of application covered are the improvement of differential diagnosis, interactive robotics, assisting people with autism spectrum disorders and their caregivers, and objectification in psychopathology.
Electroencephalogram coherence was measured in children with autism spectrum disorders (ASD) and control children at baseline and while watching videos of a familiar and unfamiliar person reading a story. Coherence was measured between the left and right hemispheres of the frontal, parietal, and temporal-parietal lobes (interhemispheric) and between the frontal and parietal lobes in each hemisphere (intrahemispheric). A data-reduction technique was employed to identify the frequency (alpha) that yielded significant differences in video conditions. Children with ASD displayed reduced coherence at the alpha frequency between the left and right temporal-parietal lobes in all conditions and reduced coherence at the alpha frequency between left and right frontal lobes during baseline. No group differences in intrahemispheric coherence at the alpha frequency emerged at the chosen statistical threshold. Results suggest decreased interhemispheric connectivity in frontal and temporal-parietal regions in children with ASD compared to controls. Autism Res 2014, ●●: ●●–●●. © 2014 International Society for Autism Research, Wiley Periodicals, Inc.
Sleep has beneficial effects on brain function and learning, which are reflected in plastic changes in the cortex. Early childhood is a time of rapid maturation in fundamental skills-e.g., language, cognitive control, working memory-that are predictive of future functioning. Little is currently known about the interactions between sleep and brain maturation during this developmental period. We propose coherent electroencephalogram (EEG) activity during sleep may provide unique insight into maturational processes of functional brain connectivity. Longitudinal sleep EEG assessments were performed in eight healthy subjects at ages 2, 3 and 5 years. Sleep EEG coherence increased across development in a region- and frequency-specific manner. Moreover, although connectivity primarily decreased intra-hemispherically across a night of sleep, an inter-hemispheric overnight increase occurred in the frequency range of slow waves (0.8-2 Hz), theta (4.8-7.8 Hz) and sleep spindles (10-14 Hz), with connectivity changes of up to 20% across a night of sleep. These findings indicate sleep EEG coherence reflects processes of brain maturation-i.e., programmed unfolding of neuronal networks-and moreover, sleep-related alterations of brain connectivity during the sensitive maturational window of early childhood.
Cognition arises from the transient integration and segregation of activity across functionally distinct brain areas. Autism Spectrum Disorders (ASD), which encompass a wide range of developmental disabilities, have been presumed to be associated with a problem in cortical and sub-cortical dynamics of coordinated activity, often involving enhanced local but decreased long range coordination over areas of integration. In this paper we challenge this idea by presenting results from a relatively large population of ASD children and age-matched controls during a face-processing task. Over most of the explored domain, children with ASD exhibited enhanced synchronization, although finer detail reveals specific enhancement/reduction of synchrony depending on time, frequency and brain site. Our results are derived from the use of the imaginary part of coherency, a measure which is not susceptible to volume conduction artifacts and therefore presents a credible picture of coordinated brain activity. We also present evidence that this measure is a good candidate to provide features in building a classifier to be used as a potential biomarker for autism.
We studied autistics by quantitative EEG spectral and coherence analysis during three experimental conditions: basal, watching a cartoon with audio (V-A), and with muted audio band (VwA). Significant reductions were found for the absolute power spectral density (PSD) in the central region for delta and theta, and in the posterior region for sigma and beta bands, lateralized to the right hemisphere. When comparing VwA versus the V-A in the midline regions, we found significant decrements of absolute PSD for delta, theta and alpha, and increments for the beta and gamma bands. In autistics, VwA versus V-A tended to show lower coherence values in the right hemisphere. An impairment of visual and auditory sensory integration in autistics might explain our results.
Under noisy listening conditions, visualizing a speaker's articulations substantially improves speech intelligibility. This multisensory speech integration ability is crucial to effective communication, and the appropriate development of this capacity greatly impacts a child's ability to successfully navigate educational and social settings. Research shows that multisensory integration abilities continue developing late into childhood. The primary aim here was to track the development of these abilities in children with autism, since multisensory deficits are increasingly recognized as a component of the autism spectrum disorder (ASD) phenotype. The abilities of high-functioning ASD children (n = 84) to integrate seen and heard speech were assessed cross-sectionally, while environmental noise levels were systematically manipulated, comparing them with age-matched neurotypical children (n = 142). Severe integration deficits were uncovered in ASD, which were increasingly pronounced as background noise increased. These deficits were evident in school-aged ASD children (5-12 year olds), but were fully ameliorated in ASD children entering adolescence (13-15 year olds). The severity of multisensory deficits uncovered has important implications for educators and clinicians working in ASD. We consider the observation that the multisensory speech system recovers substantially in adolescence as an indication that it is likely amenable to intervention during earlier childhood, with potentially profound implications for the development of social communication abilities in ASD children.
It has long been debated whether Asperger's Syndrome (ASP) should be considered part of the Autism Spectrum Disorders (ASD) or whether it constitutes a unique entity. The Diagnostic and Statistical Manual, fourth edition (DSM-IV) differentiated ASP from high functioning autism. However, the new DSM-5 umbrellas ASP within ASD, thus eliminating the ASP diagnosis. To date, no clear biomarkers have reliably distinguished ASP and ASD populations. This study uses EEG coherence, a measure of brain connectivity, to explore possible neurophysiological differences between ASP and ASD.
Voluminous coherence data derived from all possible electrode pairs and frequencies were previously reduced by principal components analysis (PCA) to produce a smaller number of unbiased, data-driven coherence factors. In a previous study, these factors significantly and reliably differentiated neurotypical controls from ASD subjects by discriminant function analysis (DFA). These previous DFA rules are now applied to an ASP population to determine if ASP subjects classify as control or ASD subjects. Additionally, a new set of coherence based DFA rules are used to determine whether ASP and ASD subjects can be differentiated from each other.
Using prior EEG coherence based DFA rules that successfully classified subjects as either controls or ASD, 96.2% of ASP subjects are classified as ASD. However, when ASP subjects are directly compared to ASD subjects using new DFA rules, 92.3% ASP subjects are identified as separate from the ASD population. By contrast, five randomly selected subsamples of ASD subjects fail to reach significance when compared to the remaining ASD populations. When represented by the discriminant variable, both the ASD and ASD populations are normally distributed.
Within a control-ASD dichotomy, an ASP population falls closer to ASD than controls. However, when compared directly with ASD, an ASP population is distinctly separate. The ASP population appears to constitute a neurophysiologically identifiable, normally distributed entity within the higher functioning tail of the ASD population distribution. These results must be replicated with a larger sample given their potentially immense clinical, emotional and financial implications for affected individuals, their families and their caregivers.
Background
Graph theory has been recently introduced to characterize complex brain networks, making it highly suitable to investigate altered connectivity in neurologic disorders. A current model proposes autism spectrum disorder (ASD) as a developmental disconnection syndrome, supported by converging evidence in both non-syndromic and syndromic ASD. However, the effects of abnormal connectivity on network properties have not been well studied, particularly in syndromic ASD. To close this gap, brain functional networks of electroencephalographic (EEG) connectivity were studied through graph measures in patients with Tuberous Sclerosis Complex (TSC), a disorder with a high prevalence of ASD, as well as in patients with non-syndromic ASD.
Methods
EEG data were collected from TSC patients with ASD (n = 14) and without ASD (n = 29), from patients with non-syndromic ASD (n = 16), and from controls (n = 46). First, EEG connectivity was characterized by the mean coherence, the ratio of inter- over intra-hemispheric coherence and the ratio of long- over short-range coherence. Next, graph measures of the functional networks were computed and a resilience analysis was conducted. To distinguish effects related to ASD from those related to TSC, a two-way analysis of covariance (ANCOVA) was applied, using age as a covariate.
Results
Analysis of network properties revealed differences specific to TSC and ASD, and these differences were very consistent across subgroups. In TSC, both with and without a concurrent diagnosis of ASD, mean coherence, global efficiency, and clustering coefficient were decreased and the average path length was increased. These findings indicate an altered network topology. In ASD, both with and without a concurrent diagnosis of TSC, decreased long- over short-range coherence and markedly increased network resilience were found.
Conclusions
The altered network topology in TSC represents a functional correlate of structural abnormalities and may play a role in the pathogenesis of neurological deficits. The increased resilience in ASD may reflect an excessively degenerate network with local overconnection and decreased functional specialization. This joint study of TSC and ASD networks provides a unique window to common neurobiological mechanisms in autism.
Autism Spectrum Conditions (ASC) are a set of pervasive neurodevelopmental conditions characterized by a wide range of lifelong signs and symptoms. Recent explanatory models of autism propose abnormal neural connectivity and are supported by studies showing decreased interhemispheric coherence in individuals with ASC. The first aim of this study was to test the hypothesis of reduced interhemispheric coherence in ASC, and secondly to investigate specific effects of task performance on interhemispheric coherence in ASC.
We analyzed electroencephalography (EEG) data from 15 participants with ASC and 15 typical controls, using Wavelet Transform Coherence (WTC) to calculate interhemispheric coherence during face and chair matching tasks, for EEG frequencies from 5 to 40 Hz and during the first 400 ms post-stimulus onset.
Results demonstrate a reduction of interhemispheric coherence in the ASC group, relative to the control group, in both tasks and for all electrode pairs studied. For both tasks, group differences were generally observed after around 150 ms and at frequencies lower than 13 Hz. Regarding within-group task comparisons, while the control group presented differences in interhemispheric coherence between faces and chairs tasks at various electrode pairs (FT7-FT8, TP7-TP8, P7-P8), such differences were only seen for one electrode pair in the ASC group (T7-T8). No significant differences in EEG power spectra were observed between groups.
Interhemispheric coherence is reduced in people with ASC, in a time and frequency specific manner, during visual perception and categorization of both social and inanimate stimuli and this reduction in coherence is widely dispersed across the brain.
Results of within-group task comparisons may reflect an impairment in task differentiation in people with ASC relative to typically developing individuals.
Overall, the results of this research support the value of WTC in examining the time-frequency microstructure of task-related interhemispheric EEG coherence in people with ASC.
Communication and integration of information between brain regions plays a key role in healthy brain function. Conversely, disruption in brain communication may lead to cognitive and behavioral problems. Autism is a neurodevelopmental disorder that is characterized by impaired social interactions and aberrant basic information processing. Aberrant brain connectivity patterns have indeed been hypothesized to be a key neural underpinning of autism. In this study, graph analytical tools are used to explore the possible deviant functional brain network organization in autism at a very early stage of brain development. Electroencephalography (EEG) recordings in 12 toddlers with autism (mean age 3.5 years) and 19 control subjects were used to assess interregional functional brain connectivity, with functional brain networks constructed at the level of temporal synchronization between brain regions underlying the EEG electrodes. Children with autism showed significantly increased normalized path length and reduced normalized clustering, suggesting a reduced global communication capacity already during early brain development. In addition, whole brain connectivity was found to be significantly reduced in these young patients suggesting an overall under-connectivity of functional brain networks in autism. Our findings support the hypothesis of abnormal neural communication in autism, with deviating effects already present at the early stages of brain development.
The autism rate has recently increased to 1 in 100 children. Genetic studies demonstrate poorly understood complexity. Environmental factors apparently also play a role. Magnetic resonance imaging (MRI) studies demonstrate increased brain sizes and altered connectivity. Electroencephalogram (EEG) coherence studies confirm connectivity changes. However, genetic-, MRI- and/or EEG-based diagnostic tests are not yet available. The varied study results likely reflect methodological and population differences, small samples and, for EEG, lack of attention to group-specific artifact.
Of the 1,304 subjects who participated in this study, with ages ranging from 1 to 18 years old and assessed with comparable EEG studies, 463 children were diagnosed with autism spectrum disorder (ASD); 571 children were neuro-typical controls (C). After artifact management, principal components analysis (PCA) identified EEG spectral coherence factors with corresponding loading patterns. The 2- to 12-year-old subsample consisted of 430 ASD- and 554 C-group subjects (n = 984). Discriminant function analysis (DFA) determined the spectral coherence factors' discrimination success for the two groups. Loading patterns on the DFA-selected coherence factors described ASD-specific coherence differences when compared to controls.
Total sample PCA of coherence data identified 40 factors which explained 50.8% of the total population variance. For the 2- to 12-year-olds, the 40 factors showed highly significant group differences (P < 0.0001). Ten randomly generated split half replications demonstrated high-average classification success (C, 88.5%; ASD, 86.0%). Still higher success was obtained in the more restricted age sub-samples using the jackknifing technique: 2- to 4-year-olds (C, 90.6%; ASD, 98.1%); 4- to 6-year-olds (C, 90.9%; ASD 99.1%); and 6- to 12-year-olds (C, 98.7%; ASD, 93.9%). Coherence loadings demonstrated reduced short-distance and reduced, as well as increased, long-distance coherences for the ASD-groups, when compared to the controls. Average spectral loading per factor was wide (10.1 Hz).
Classification success suggests a stable coherence loading pattern that differentiates ASD- from C-group subjects. This might constitute an EEG coherence-based phenotype of childhood autism. The predominantly reduced short-distance coherences may indicate poor local network function. The increased long-distance coherences may represent compensatory processes or reduced neural pruning. The wide average spectral range of factor loadings may suggest over-damped neural networks.
The Autism Diagnostic Observation Schedule—Generic (ADOS-G) is a semistructured, standardized assessment of social interaction, communication, play, and imaginative use of materials for individuals suspected of having autism spectrum disorders. The observational schedule consists of four 30-minute modules, each designed to be administered to different individuals according to their level of expressive language. Psychometric data are presented for 223 children and adults with Autistic Disorder (autism), Pervasive Developmental Disorder Not Otherwise Specified (PDDNOS) or nonspectrum diagnoses. Within each module, diagnostic groups were equivalent on expressive language level. Results indicate substantial interrater and test—retest reliability for individual items, excellent interrater reliability within domains and excellent internal consistency. Comparisons of means indicated consistent differentiation of autism and PDDNOS from nonspectrum individuals, with some, but less consistent, differentiation of autism from PDDNOS. A priori operationalization of DSM-IV/ICD-10 criteria, factor analyses, and ROC curves were used to generate diagnostic algorithms with thresholds set for autism and broader autism spectrum/PDD. Algorithm sensitivities and specificities for autism and PDDNOS relative to nonspectrum disorders were excellent, with moderate differentiation of autism from PDDNOS.
Currently there are no brief, self-administered instruments for measuring the degree to which an adult with normal intelligence has the traits associated with the autistic spectrum. In this paper, we report on a new instrument to assess this: the Autism-Spectrum Quotient (AQ). Individuals score in the range 0–50. Four groups of subjects were assessed: Group 1: 58 adults with Asperger syndrome (AS) or high-functioning autism (HFA); Group 2: 174 randomly selected controls. Group 3: 840 students in Cambridge University; and Group 4: 16 winners of the UK Mathematics Olympiad. The adults with AS/HFA had a mean AQ score of 35.8 (SD = 6.5), significantly higher than Group 2 controls (M = 16.4, SD = 6.3). 80% of the adults with AS/HFA scored 32+, versus 2% of controls. Among the controls, men scored slightly but significantly higher than women. No women scored extremely highly (AQ score 34+) whereas 4% of men did so. Twice as many men (40%) as women (21%) scored at intermediate levels (AQ score 20+). Among the AS/HFA group, male and female scores did not differ significantly. The students in Cambridge University did not differ from the randomly selected control group, but scientists (including mathematicians) scored significantly higher than both humanities and social sciences students, confirming an earlier study that autistic conditions are associated with scientific skills. Within the sciences, mathematicians scored highest. This was replicated in Group 4, the Mathematics Olympiad winners scoring significantly higher than the male Cambridge humanities students. 6% of the student sample scored 327plus; on the AQ. On interview, 11 out of 11 of these met three or more DSM-IV criteria for AS/HFA, and all were studying sciences/mathematics, and 7 of the 11 met threshold on these criteria. Test—retest and interrater reliability of the AQ was good. The AQ is thus a valuable instrument for rapidly quantifying where any given individual is situated on the continuum from autism to normality. Its potential for screening for autism spectrum conditions in adults of normal intelligence remains to be fully explored.
Evolutionary interpretation of brain oscillations and empirical evidence link delta oscillations with reward motivation and
alpha with anxiety. It is hypothesised that a balance of activity in these two oscillatory systems underlies the dimension
of behavioural control. Overcontrollers could be characterised as individuals with a relatively high activity of the alpha
oscillatory system, undercontrollers as individuals with excessive activity of the delta oscillatory system, and resilients
as individuals with balanced activity of these systems. This hypothesis was tested in a sample of 78 adolescents aged 10-16
years. The predictions were generally confirmed. Relative prevalence of alpha oscillations, particularly in the parietal zone,
predicted overcontrolled prototype whereas relative prevalence of delta oscillations, mostly in the frontal region, predicted
undercontrolled prototype. Resilients were characterised by balanced activity of alpha and delta oscillations. (Netherlands Journal of Psychology, 62, 81-90.)
Keywords EEG-personality types-alpha oscillations-delta oscillations
Successful integration of auditory and visual inputs is crucial for both basic perceptual functions and for higher-order processes related to social cognition. Autism spectrum disorders (ASD) are characterized by impairments in social cognition and are associated with abnormalities in sensory and perceptual processes. Several groups have reported that individuals with ASD are impaired in their ability to integrate socially relevant audiovisual (AV) information, and it has been suggested that this contributes to the higher-order social and cognitive deficits observed in ASD. However, successful integration of auditory and visual inputs also influences detection and perception of nonsocial stimuli, and integration deficits may impair earlier stages of information processing, with cascading downstream effects. To assess the integrity of basic AV integration, we recorded high-density electrophysiology from a cohort of high-functioning children with ASD (7-16 years) while they performed a simple AV reaction time task. Children with ASD showed considerably less behavioral facilitation to multisensory inputs, deficits that were paralleled by less effective neural integration. Evidence for processing differences relative to typically developing children was seen as early as 100 ms poststimulation, and topographic analysis suggested that children with ASD relied on different cortical networks during this early multisensory processing stage.
Autism Spectrum Conditions: Low, Medium, and High-functioning Subgroups The Mindblindness/Empathizing Theory The Empathizing-Systemizing Theory The Extreme Male Brain Theory Other Models of Cognitive Development in Autism Summary
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.
Purpose of review:
Many studies have reported that individuals with autism spectrum disorder (ASD) have different brain connectivity patterns compared with typically developing individuals. However, the results of more recent studies do not unanimously support the traditional view in which individuals with ASD have lower connectivity between distant brain regions and increased connectivity within local brain regions. In this review, we discuss different methods for measuring brain connectivity and how the use of different metrics may contribute to the lack of convergence of investigations of connectivity in ASD.
Recent findings:
The discrepancy in brain connectivity results across studies may be due to important methodological factors, such as the connectivity measure applied, the age of patients studied, the brain region(s) examined, and the time interval and frequency band(s) in which connectivity was analyzed.
Summary:
We conclude that more sophisticated electroencephalography analytic approaches should be utilized to more accurately infer causation and directionality of information transfer between brain regions, which may show dynamic changes of functional connectivity in the brain. Moreover, further investigations of connectivity with respect to behavior and clinical phenotype are needed to probe underlying brain networks implicated in core deficits of ASD.
Objective
This study investigated differences in EEG coherence measures between two groups of children with Attention-Deficit/Hyperactivity Disorder (AD/HD) – one with the more common EEG profile (increased theta), and a group with excess alpha activity as the dominant EEG abnormality.
Methods
26 children (aged 9–13 years) with AD/HD were included in each of the excess-theta and excess-alpha groups, and were age- and sex-matched with 26 control subjects. EEG was recorded from 19 electrode sites during an eyes-closed resting condition. Wave-shape coherence was calculated for eight intrahemispheric and eight interhemispheric electrode pairs, for the delta, theta, alpha and beta bands.
Results
In comparison with the controls, the excess-theta AD/HD group had increased theta intrahemispheric coherences at short-medium inter-electrode distances. Frontally, the excess-theta AD/HD group had increased interhemispheric theta and reduced beta coherences. The excess-alpha group primarily showed increased slow wave (delta and theta) intrahemispheric coherence at short-medium inter-electrode distances, and reduced alpha coherence at longer inter-electrode distances, compared with controls. An increase in frontal interhemispheric theta coherence was also found.
Conclusions
These results suggest that AD/HD children with excess alpha power have an underlying connectivity dysfunction in the frontal lobes, which is found in common with other subjects with the excess-theta EEG profile. However, a number of qualitative differences exist that could be associated with other aspects of the AD/HD diagnosis. The excess-alpha group appeared to have fewer frontal-lobe abnormalities than the excess-theta AD/HD group.
Significance
This is the first study to investigate coherence in AD/HD children who have the atypical profile of increased alpha power in their EEG.
Autism Spectrum Disorders (ASDs) are a clinically and genetically heterogeneous set of conditions that share deficits in the core domains of social interaction, restricted interests/repetitive behaviors, and language. Patients with ASDs frequently present with medical comorbidities. Twin and family studies have demonstrated that there is a strong genetic component to the etiologies of ASDs, although environmental factors are likely to play a role as well. Over the past two decades, our understanding of the genetic basis for ASDs has improved dramatically. Genome Wide Association Studies have identified a few variants with small effects, and no clear common variants underlying the few replicated linkage peaks have been identified, suggesting that common variation may not play a large role in the etiologies of ASDs. In contrast, more than a dozen rare genetic syndromes have shown to have high penetrance for ASDs and studies of chromosomal copy number variation have consistently identified rare de novo mutations contributing to ASDs. In total, over 100 candidate genes have been implicated in ASDs with varying degrees of evidence and common pathophysiological pathways are beginning to be identified. In this chapter, we review these recent advances in the genetics of ASDs. We also discuss ideas for future genetic studies and how these studies will impact clinical practice.
The triad of characteristics that defines the autistic disorder includes the following: social and communication impairments, and restricted, stereotypical patterns of behavior and interests (American Psychiatric Association [APA], 1994, 2000, for all symptoms see Table 8.1). There are different classic autism-like conditions, and these other pervasive developmental disorders (PDD), such as Asperger syndrome and PDD not otherwise specified (PDDNOS), are part of the broader phenotype of autism. In the current classification system, DSM-IV (APA, 1994, 2000), also Rett syndrome and the Disintegration disorder are considered autism-like conditions. However, in the current chapter we will focus solely on classic autism, Asperger syndrome, and PDDNOS. The combination of these three disorders is referred to as an autism spectrum disorder (ASD), which is the term we will use throughout this chapter.
Background Autism spectrum disorder is a neurodevelopmental disorder that is characterized mainly by difficulties in social interaction and communication. Studies have suggested abnormal neural connectivity patterns in the brains of patients with autism. Objective The current work aimed to study the quantitative electroencephalography (EEG) findings in autistic children and compare it with those of normal controls. Methods The EEG recordings of 21 autistic children between 4 and 12 years of age were compared with those of 21 age-matched and sex-matched controls under an eyes-opened condition. Differences in cerebral functioning were examined using measurements of absolute and relative power and intrahemispheric and interhemispheric coherence. Results There were statistically significant differences in EEG power between the autistic and control groups, with greater absolute of delta and theta power especially at the frontal region in autistic children. There was also global reduction in relative alpha and beta power especially in the frontal, central, and posterior regions in autistic children. In addition, there was a pattern of underconnectivity and overconnectivity when measuring the intrahemispheric and interhemispheric coherence in the autistic compared with the control group. Conclusion These results suggested regional dysfunction of the brain in autistic children, along with a pattern of abnormal neural connectivity, which could explain the autistic symptomatology. © 2015 The Egyptian Journal of Neurology, Psychiatry and Neurosurgery.
The heterogeneity in clinical presentation and outcome in neurodevelopmental disorders such as attention deficit hyperactivity disorder (ADHD) autism spectrum disorder (ASD) necessitates the identification and validation of biomarkers that can guide diagnosis, predict developmental outcomes, and monitor treatment response. Electrophysiology holds both practical and theoretical advantages as a clinical biomarker in neurodevelopmental disorders, and considerable effort has been invested in the search for electroencephalography (EEG) biomarkers in ADHD and ASD.
Here, we discuss the major themes in the evaluation of biomarkers and then review studies that have applied EEG to better inform diagnosis, focusing on the controversy surrounding the theta:beta ratio in ADHD; prediction of risk, highlighting recent studies of infants at high risk for ASD; and treatment monitoring, presenting new efforts in the redefinition of outcome measures in clinical trials of ASD treatment.
We conclude that insights gained from EEG studies will contribute significantly to a more mechanistic understanding of these disorders and to the development of biomarkers that can assist with diagnosis, prognosis, and intervention. There is a need, however, to utilize approaches that accommodate, rather than ignore, diagnostic heterogeneity and individual differences.