State-Dependent Differences in Functional Connectivity in Young Children With Autism Spectrum Disorder

Differences in Sleep EEG Coherence and Spindle Metrics in Toddlers With and Without Language Delay: A Prospective Observational Study

Background

Sleep plays a crucial role in early language development, and sleep disturbances are common in children with neurodevelopmental disorders. Examining sleep microarchitecture in toddlers with and without language delays can offer key insights into neurophysiological abnormalities associated with atypical neurodevelopmental trajectories and potentially aid in early detection and intervention.

Methods

Here, we investigated electroencephalogram (EEG) coherence and sleep spindles in 16 toddlers with language delay (LD) compared with a group of 39 typically developing (TD) toddlers. The sample was majority male (n = 34, 62%). Participants were aged 12-to-22 months at baseline, and 34 (LD, n=11; TD, n=23) participants were evaluated again at 36 months of age.

Results

LD toddlers demonstrated increased EEG coherence compared to TD toddlers, with differences most prominent during slow-wave sleep. Within the LD group, lower expressive language skills were associated with higher coherence in REM sleep. Within the TD group, lower expressive language skills were associated with higher coherence in slow-wave sleep. Sleep spindle density, duration, and frequency changed between baseline and follow-up for both groups, with the LD group demonstrating a smaller magnitude of change than the TD group. The direction of change was frequency-dependent for both groups.

Conclusions

These findings indicate that atypical sleep EEG connectivity and sleep spindle development can be detected in toddlers between 12 and 36 months and offers insights into neurophysiological mechanisms underlying the etiology of neurodevelopmental disorders.

Trial registration

https://clinicaltrials.gov/study/NCT01339767; Registration date: 4/20/2011.

Functional networks of working memory abilities in children with complex congenital heart disease: a sleep EEG study

Working memory is frequently impaired in children with complex congenital heart disease (CHD), but little is known about the functional neuronal correlates. Sleep slow wave activity (SWA; 1-4.5 Hz EEG power) has previously been shown to reliably map neurofunctional networks of cognitive abilities in children with and without neurodevelopmental impairments. This study investigated whether functional networks of working memory abilities are altered in children with complex CHD using EEG recordings during sleep. Twenty-one children with complex CHD (aged 10.9 [SD: 0.3] years) and 17 typically-developing peers (10.5 [0.7] years) completed different working memory tasks and an overnight high-density sleep EEG recording (128 electrodes). The combined working memory score tended to be lower in children with complex CHD (CHD group: -0.44 [1.12], typically-developing group: 0.55 [1.24], d = 0.59, p = .06). The working memory score and sleep SWA of the first hour of deep sleep were correlated over similar brain regions in both groups: Strong positive associations were found over prefrontal and fronto-parietal brain regions - known to be part of the working memory network - and strong negative associations were found over central brain regions. Within these working memory networks, the associations between working memory abilities and sleep SWA (r between -.36 and .58, all p < .03) were not different between the two groups (no interactions, all p > .05). The current findings suggest that sleep SWA reliably maps working memory networks in children with complex CHD and that these functional networks are generally preserved in these patients.

Identification of diagnostic markers for ASD: a restrictive interest analysis based on EEG combined with eye tracking

Electroencephalography (EEG) functional connectivity (EFC) and eye tracking (ET) have been explored as objective screening methods for autism spectrum disorder (ASD), but no study has yet evaluated restricted and repetitive behavior (RRBs) simultaneously to infer early ASD diagnosis. Typically developing (TD) children (n = 27) and ASD (n = 32), age- and sex-matched, were evaluated with EFC and ET simultaneously, using the restricted interest stimulus paradigm. Network-based machine learning prediction (NBS-predict) was used to identify ASD. Correlations between EFC, ET, and Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) were performed. The Area Under the Curve (AUC) of receiver-operating characteristics (ROC) was measured to evaluate the predictive performance. Under high restrictive interest stimuli (HRIS), ASD children have significantly higher α band connectivity and significantly more total fixation time (TFT)/pupil enlargement of ET relative to TD children (p = 0.04299). These biomarkers were not only significantly positively correlated with each other (R = 0.716, p = 8.26e-4), but also with ADOS total scores (R = 0.749, p = 34e-4) and RRBs sub-score (R = 0.770, p = 1.87e-4) for EFC (R = 0.641, p = 0.0148) for TFT. The accuracy of NBS-predict in identifying ASD was 63.4%. ROC curve demonstrated TFT with 91 and 90% sensitivity, and 78.7% and 77.4% specificity for ADOS total and RRB sub-scores, respectively. Simultaneous EFC and ET evaluation in ASD is highly correlated with RRB symptoms measured by ADOS-2. NBS-predict of EFC offered a direct prediction of ASD. The use of both EFC and ET improve early ASD diagnosis.

Why is everyone talking about brain state?

The rapid and coordinated propagation of neural activity across the brain provides the foundation for complex behavior and cognition. Technical advances across neuroscience subfields have advanced understanding of these dynamics, but points of convergence are often obscured by semantic differences, creating silos of subfield-specific findings. In this review we describe how a parsimonious conceptualization of brain state as the fundamental building block of whole-brain activity offers a common framework to relate findings across scales and species. We present examples of the diverse techniques commonly used to study brain states associated with physiology and higher-order cognitive processes, and discuss how integration across them will enable a more comprehensive and mechanistic characterization of the neural dynamics that are crucial to survival but are disrupted in disease.

Machine learning approaches for electroencephalography and magnetoencephalography analyses in autism spectrum disorder: A systematic review

There are growing application of machine learning models to study the intricacies of non-linear and non-stationary characteristics of electroencephalography (EEG) and magnetoencephalography (MEG) data in neurobiologically complex and heterogeneous conditions such as autism spectrum disorder (ASD). Such tools have potential diagnostic applications, and given the highly heterogeneous presentation of ASD, might prove fruitful in early detection and therefore could facilitate very early intervention. We conducted a systematic review (PROSPERO ID#CRD42021257438) by searching PubMed, EMBASE, and PsychINFO for machine learning approaches for EEG and MEG analyses in ASD. Thirty-nine studies were identified, of which the majority (18) used support vector machines for classification; other successful methods included deep learning. Thirty-seven studies were found to employ EEG and two were found to employ MEG. This systematic review indicate that machine learning methods can be used to classify ASD, predict ASD diagnosis in high-risk infants as early as 3 months of age, predict ASD symptom severity, and classify states of cognition in ASD with high accuracy. Replication studies testing validity, reproducibility and generalizability in tandem with randomized controlled trials in ASD populations will likely benefit the field.

Systematic Review on EEG Analysis to Diagnose and Treat Autism by Evaluating Functional Connectivity and Spectral Power

An abnormality in neural connectivity is linked to autism spectrum disorder (ASD). There is no way to test the concept of neural connectivity empirically. According to recent network theory and time series analysis findings, electroencephalography (EEG) can assess neural network architecture, a sign of activity in the brain. This systematic review aims to evaluate functional connectivity and spectral power using EEG signals. EEG records the brain activity of an individual by displaying wavy lines that depict brain cells' communication through electrical impulses. EEG can diagnose various brain disorders, including epilepsy and related seizure illness, brain dysfunction, tumors, and damage. We found 21 studies using two of the most common EEG analysis methods: functional connectivity and spectral power. ASD and non-ASD individuals were found to differ significantly in all selected papers. Due to high heterogeneity in the outcomes, generalizations cannot be drawn, and no single method is currently beneficial as a diagnostic tool. For ASD subtype delineation, the lack of research prevented the evaluation of these techniques as diagnostic tools. These findings confirm the presence of abnormalities in the EEG in ASD, but they are insufficient to diagnose. Our study suggests that EEG is useful in diagnosing ASD by evaluating entropy in the brain. Researchers may be able to develop new diagnostic methods for ASD which focuses on particular stimuli and brainwaves if they conduct more extensive studies with higher numbers and more rigorous study designs.

Electroencephalographic assessment of infant spinal anesthesia: A pilot prospective observational study

INTRODUCTION

Spinal anesthesia is utilized as an alternative to general anesthesia in infants for some surgeries. After spinal anesthesia, infants often become less conscious without administration of sedative medications. The aim of this study was to assess electroencephalographic (EEG) correlates after spinal anesthesia in a cohort of infants.

PATIENTS AND METHODS

This pilot study included 12 infants who underwent spinal anesthesia. Unprocessed electroencephalography was recorded. The electroencephalogram was interpreted by four neurologists. Processed analyses compared electroencephalogram changes 30 min after spinal anesthesia to baseline.

RESULTS

Following spinal anesthesia, all 12 infants became sedated. Electroencephalography in all 12 demonstrated Stage 2 sleep with the appearance of sleep spindles (12-14 Hz) in the frontal and central leads in 8/12 (67%) of subjects. The median time to onset of sleep spindles was 24.7 interquartile range (21.2, 29.9) min. The duration of sleep spindles was 25.1 interquartile range (5.8, 99.8) min. Voltage attenuation and background slowing were the most common initial changes. Compared to baseline, the electroencephalogram 30 min after spinal anesthesia showed significantly increased absolute delta power (p = 0.02) and gamma power (p < 0.0001); decreases in beta (p = 0.0006) and higher beta (p < 0.0001) were also observed. The Fast Fourier Transform power ratio difference for delta/beta was increased (p = 0.03). Increased coherence was noted in the delta (p = 0.02) and theta (p = 0.04) bandwidths.

DISCUSSION

Spinal anesthesia in infants is associated with increased electroencephalographic slow wave activity and decreased beta activity compared to the awake state, with appearance of sleep spindles suggestive of normal sleep. The etiology and significance of the observed voltage attenuation and background slowing remains unclear.

CONCLUSIONS

The EEG signature of infant spinal anesthesia is distinct from that seen with general anesthesia and is consistent with normal sleep. Further investigation is required to better understand the etiology of these findings. Our preliminary findings contribute to the understanding of the brain effects of spinal anesthesia in early development.

Evoked and intrinsic brain network dynamics in children with autism spectrum disorder

OBJECTIVE

Brain dynamics underlie flexible cognition and behavior, yet little is known regarding this relationship in autism spectrum disorder (ASD). We examined time-varying changes in functional co-activation patterns (CAPs) across rest and task-evoked brain states to characterize differences between children with ASD and typically developing (TD) children and identify relationships with severity of social behaviors and restricted and repetitive behaviors.

METHOD

17 children with ASD and 27 TD children ages 7-12 completed a resting-state fMRI scan and four runs of a non-cued attention switching task. Metrics indexing brain dynamics were generated from dynamic CAPs computed across three major large-scale brain networks: midcingulo-insular (M-CIN), medial frontoparietal (M-FPN), and lateral frontoparietal (L-FPN).

RESULTS

Five time-varying CAPs representing dynamic co-activations among network nodes were identified across rest and task fMRI datasets. Significant Diagnosis × Condition interactions were observed for the dwell time of CAP 3, representing co-activation between nodes of the M-CIN and L-FPN, and the frequency of CAP 1, representing co-activation between nodes of the L-FPN. A significant brain-behavior association between dwell time of CAP 5, representing co-activation between nodes of the M-FPN, and social abilities was also observed across both groups of children.

CONCLUSION

Analysis of brain co-activation patterns reveals altered dynamics among three core networks in children with ASD, particularly evident during later stages of an attention task. Dimensional analyses demonstrating relationships between M-FPN dwell time and social abilities suggest that metrics of brain dynamics may index individual differences in social cognition and behavior.

Environmental Factors Shape Sleep EEG Connectivity During Early Adolescence

Quantifying the degree to which genetic and environmental factors shape brain network connectivity is critical to furthering our understanding of the developing human brain. Sleep, a state of sensory disengagement, provides a unique opportunity to study brain network activity noninvasively by means of sleep electroencephalography (EEG) coherence. We conducted a high-density sleep EEG study in monozygotic (MZ; n = 38; mean age = 12.46; 20 females) and dizygotic (DZ; n = 24; mean age = 12.50; 12 females) twins to assess the heritability of sleep EEG coherence in early adolescence—a period of significant brain rewiring. Structural equation modeling was used to estimate three latent factors: genes, environmental factors shared between twins and environmental factors unique to each twin. We found a strong contribution of unique environmental factors (66% of the variance) and moderate genetic influence (19% of the variance) on sleep EEG coherence across frequencies and sleep states. An exception to this was sleep spindle activity, an index of the thalamocortical network, which showed on average a genetic contribution of 48% across connections. Furthermore, we observed high intraindividual stability of coherence across two consecutive nights suggesting that despite only a modest genetic contribution, sleep EEG coherence is like a trait. Our findings in adolescent humans are in line with earlier findings in animals that show the primordial cerebral map and its connections are plastic and it is through interaction with the environment that the pattern of brain network connectivity is shaped. Therefore, even in twins living together, small differences in the environment may cascade into meaningful differences in brain connectivity.

Dysregulation of temporal dynamics of synchronous neural activity in adolescents on autism spectrum

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.

Functional brain connectivity in a rodent seizure model of autistic-like behavior

OBJECTIVE

There is increasing evidence that Autism Spectrum Disorder (ASD) is a disorder of functional connectivity with both human and rodent studies demonstrating alterations in connectivity. Here, we hypothesized that early-life seizures (ELS) in rats would interrupt normal brain connectivity and result in autistic-like behavior (ALB).

METHODS

Following 50 seizures, adult rats were tested in the social interaction and social novelty tests and then underwent qualitative and quantitative intracranial electroencephalography (EEG) monitoring in the medial prefrontal cortex (PFC) and the hippocampal subfields, CA3 and CA1.

RESULTS

Rats with ELS showed deficits in social interaction and novelty, and compared with control, rats had marked increases in coherence within the hippocampus (CA3-CA1) and between the hippocampus and PFC during the awake and sleep states indicating hyperconnectivity. In addition, sleep spindle density was significantly reduced in rats with ELS. There were no differences in voltage correlations and power spectral densities between the ELS and control rats in any bandwidths.

CONCLUSION

Taken together, these findings indicate that ELS can result in ALB and alter functional connectivity as measured by coherence and sleep spindle density. These findings implicate altered connectivity as a robust neural signature for ALB following ELS.

Functional Connectivity in Term Neonates With Hypoxic-Ischemic Encephalopathy Undergoing Therapeutic Hypothermia

BACKGROUND

We investigated whether therapeutic hypothermia and rewarming impact functional connectivity using electroencephalography (EEG) as a measure in neonates with hypoxic-ischemic encephalopathy. We hypothesized that EEG coherence and voltage correlations would be lower and phase lag greater in infants with hypoxic-ischemic encephalopathy than control subjects and that functional connectivity would evolve during therapeutic hypothermia with the greatest improvement occurring during rewarming.

METHODS

This study was a retrospective study of 14 term neonates (greater than 37 weeks) with moderate hypoxic-ischemic encephalopathy who underwent therapeutic hypothermia and rewarming. Continuous EEG and video monitoring was conducted for 96 hours during therapeutic hypothermia and rewarming. The primary quantitative EEG measures of functional connectivity were coherence, phase lag, and voltage correlations. These EEG parameters were compared with a cohort of normal age-matched neonates.

RESULTS

Neonates with hypoxic-ischemic encephalopathy had marked decreases in power, coherences, and voltage correlation and increases in phase lag when compared with control neonates. However, there were no significant changes in these measures between therapeutic hypothermia and rewarming.

CONCLUSIONS

Neonates with hypoxic-ischemic encephalopathy demonstrate significant abnormalities in functional connectivity compared with control subjects. These abnormalities persist through therapeutic hypothermia and rewarming and are not altered after rewarming. Although hypoxic-ischemic encephalopathy is associated with impaired functional brain connectivity, there is no evidence, using quantitative EEG measures, that therapeutic hypothermia or rewarming either improves or exacerbates these abnormalities in connectivity.

Unsupervised identification of states from voltage recordings of neural networks

BACKGROUND

Modern techniques for multi-neuronal recording produce large amounts of data. There is no automatic procedure for the identification of states in recurrent voltage patterns.

NEW METHOD

We propose NetSAP (Network States And Pathways), a data-driven analysis method that is able to recognize multi-neuron voltage patterns (states). To capture the subtle differences between snapshots in voltage recordings, NetSAP infers the underlying functional neural network in a time-resolved manner with a sliding window approach. Then NetSAP identifies states from a reordering of the time series of inferred networks according to a user-defined metric. The procedure for unsupervised identification of states was developed originally for the analysis of molecular dynamics simulations of proteins.

RESULTS

We tested NetSAP on neural network simulations of GABAergic inhibitory interneurons. Most simulation parameters are chosen to reproduce literature observations, and we keep noise terms as control parameters to regulate the coherence of the simulated signals. NetSAP is able to identify multiple states even in the case of high internal noise and low signal coherence. We provide evidence that NetSAP is robust for networks with up to about 50% of the neurons spiking randomly. NetSAP is scalable and its code is open source.

COMPARISON WITH EXISTING METHODS

NetSAP outperforms common analysis techniques, such as PCA and k-means clustering, on a simulated recording of voltage traces of 50 neurons.

CONCLUSIONS

NetSAP analysis is an efficient tool to identify voltage patterns from neuronal recordings.

Functional EEG connectivity in infants associates with later restricted and repetitive behaviours in autism; a replication study

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.

Functional brain connectivity in electrical status epilepticus in sleep

Electrical status epilepticus in sleep (ESES) is an age-related, self-limited epileptic encephalopathy. The syndrome is characterized by cognitive and behavioral abnormalities and a specific EEG pattern of continuous spikes and waves during slow-wave sleep. While spikes and sharp waves are known to result in transient cognitive impairment during learning and memory tasks performed during the waking state, the effect of epileptiform discharges during sleep on cognition and behavior is unclear. There is increasing evidence that abnormalities of coherence, a measure of the consistency of the phase difference between two EEG signals when compared over time, is an important feature of brain oscillations and plays a role in cognition and behavior. The objective of this study was to determine whether coherence of EEG activity is altered during slow-wave sleep in children with ESES when compared to typically developing children. We examined coherence during epochs of ESES versus epochs when ESES was not present. In addition, we compared coherence during slow-wave sleep between typically developing children and children with ESES. ESES was associated with remarkably high coherences at all bandwidths and most electrode pairs. While the high coherence was largely attributed to the spikes and spike-and-wave discharge, activity between spikes and spike-and-wave discharge also demonstrated high coherence. This study indicates that EEG coherence during ESES is relatively high. Whether these increases in coherence correlate with the cognitive and behavioral abnormalities seen in children with this EEG pattern remains to be determined.

Validity of Actigraphy Compared to Polysomnography for Sleep Assessment in Children With Autism Spectrum Disorder

Actigraphy (ACT) is a non-invasive objective assessment tool for the study of sleep-wake rhythms. It is of particular interest in children with autism spectrum disorder (ASD), as sleep disorders are highly prevalent and have a significant impact on both cognitive and behavioral functions. As polysomnography (PSG), the gold standard for the assessment of sleep, is difficult to perform in children with ASD, ACT has become a tool of choice but has not yet been validated against PSG using state-of-the-art methodology. The main objective of this study was to assess, for the first time, the validity of ACT compared to PSG for the measurement of sleep in children with ASD. During the same night of hospitalization, PSG and ACT were conducted in 26 children (6 girls and 20 boys; mean age 5.4 years ± 1.6) diagnosed with ASD according to DSM-5 criteria and standardized diagnostic scales. Sleep parameters were total sleep time (TST), sleep latency (SL), wake after sleep onset (WASO), and sleep efficiency (SE). To compare PSG and ACT, we conducted sleep parameter agreement analyses including: intraclass correlation coefficient (ICC), Bland-Altman plots, and equivalence tests. The comparison also included an epoch-by-epoch (EBE) agreement analysis to determine sensitivity (ability to detect sleep) and specificity (ability to detect wake). According to equivalence tests, the difference between ACT and PSG measures was clinically acceptable for TST (<30 min, p < 0.01), SL (<15 min, p < 0.001), and SE (10%, p < 0.01), but not for WASO (<15 min, p = 0.13). There was a good agreement between methods for SL (ICC = 0.79) and TST (ICC = 0.85) and a moderate agreement for WASO (ICC = 0.73) and SE (ICC = 0.68). The EBE agreement analysis revealed a high sensitivity (0.94 ± 0.06) and moderate specificity (0.5 ± 0.2). Since sleep disorders are one of the most common comorbidities within the ASD population and are highly prevalent, it is essential to validate objective tools of assessment. To our knowledge, our study is the first to validate ACT compared to PSG, using a state-of-the-art methodology, in children with ASD. The results suggest ACT to be a valid method to evaluate sleep within this population, with a good reliability for most sleep parameters.

Interhemispheric alpha-band hypoconnectivity in children with autism spectrum disorder

Diverse genetic and environmental etiologies converge onto circuit level brain dysfunction in autism spectrum disorder (ASD), manifesting at a macroscopic level as aberrant neural connectivity. Previous studies have described atypical patterns of decreased short range and increased long range connectivity in ASD [1 ]. However, it remains unclear whether group level features of circuit dysfunction are consistently present across the range of cognitive function seen in the autism spectrum.

The dynamics of neural oscillations in the alpha range (6-12 Hz) are exquisitely sensitive to healthy development of functional and structural connectivity. Alpha-band coherence, measured with high temporal-precision electroencephalography (EEG) therefore represents an ideal tool for studying neural connectivity in developmental populations.

Here we examined spontaneous alpha phase coherence in a heterogeneous sample of 59 children with ASD and 39 age matched typically developing children. Using a data driven approach, we conducted an unbiased examination of all possible atypical connectivity patterns across all cortical regions.

Long-range hypoconnectivity was present in children with ASD compared to typically developing children, with temporal interhemispheric connectivity showing the largest difference between the two groups. Decreased long range alpha coherence distinguishes a heterogeneous group of ASD children from typically developing children. Interhemispheric temporal hypoconnectivity represents a fundamental functional difference in children with ASD across a wide cognitive and age range that may reflect white matter disturbances or increased signal variability at temporal sites in ASD.

Spindle activity in young children with autism, developmental delay, or typical development

OBJECTIVE

To determine whether spindle activity differs in young children with and without autism.

METHODS

We investigated differences in spindle density, duration, and oscillatory features in 135 young children with autism, developmental delay without autism (DD), or typical development (TD) and secondarily assessed the dimensional relationship between spindle density and both cognitive ability and social functioning.

RESULTS

Compared to TD, both spindle density (Cohen d 0.93, 95% confidence interval [CI] 0.49-1.37) and duration (Cohen d 0.58, 95% CI 0.15-1.01) were significantly decreased in autism. Spindle density was also significantly reduced in autism compared to DD (Cohen d 0.61, 95% CI 0.13-1.09). Decreased spindle frequency in autism compared to both TD (Cohen d 0.47, 95% CI 0.04-0.90) and DD (Cohen d 0.58, 95% CI 0.10-1.06) did not survive correction. The DD group did not differ significantly from the TD group on any spindle parameter. These results, suggesting a relationship between spindle density and autism but not DD, were further illustrated in exploratory analyses, wherein nonverbal ratio IQ (RIQ) and the Vineland Socialization domain standard score were strongly correlated with spindle density in the full sample (r = 0.33, p ≤ 001 and r = 0.41, p ≤ 001, respectively) but not within group. After nonverbal RIQ was accounted for, the relationship between spindle density and Vineland Socialization remained statistically significant (r = 0.23, p < 0.01). However, Vineland Socialization scores accounted for the relationship between spindle density and nonverbal RIQ (r = 0.04, p = 0.67).

CONCLUSION

In a large cohort of young children with autism, spindle density was reduced compared to groups of age-matched children with DD or TD. Alterations in the maturational trajectory of spindles may provide valuable insight into the neurophysiologic differences related to behavior in disorders of neurodevelopment.

Increased overall cortical connectivity with syndrome specific local decreases suggested by atypical sleep-EEG synchronization in Williams syndrome

Williams syndrome (7q11.23 microdeletion) is characterized by specific alterations in neurocognitive architecture and functioning, as well as disordered sleep. Here we analyze the region, sleep state and frequency-specific EEG synchronization of whole night sleep recordings of 21 Williams syndrome and 21 typically developing age- and gender-matched subjects by calculating weighted phase lag indexes. We found broadband increases in inter- and intrahemispheric neural connectivity for both NREM and REM sleep EEG of Williams syndrome subjects. These effects consisted of increased theta, high sigma, and beta/low gamma synchronization, whereas alpha synchronization was characterized by a peculiar Williams syndrome-specific decrease during NREM states (intra- and interhemispheric centro-temporal) and REM phases of sleep (occipital intra-area synchronization). We also found a decrease in short range, occipital connectivity of NREM sleep EEG theta activity. The striking increased overall synchronization of sleep EEG in Williams syndrome subjects is consistent with the recently reported increase in synaptic and dendritic density in stem-cell based Williams syndrome models, whereas decreased alpha and occipital connectivity might reflect and underpin the altered microarchitecture of primary visual cortex and disordered visuospatial functioning of Williams syndrome subjects.

How Useful Is Electroencephalography in the Diagnosis of Autism Spectrum Disorders and the Delineation of Subtypes: A Systematic Review

Autism spectrum disorders (ASD) are thought to be associated with abnormal neural connectivity. Presently, neural connectivity is a theoretical construct that cannot be easily measured. Research in network science and time series analysis suggests that neural network structure, a marker of neural activity, can be measured with electroencephalography (EEG). EEG can be quantified by different methods of analysis to potentially detect brain abnormalities. The aim of this review is to examine evidence for the utility of three methods of EEG signal analysis in the ASD diagnosis and subtype delineation. We conducted a review of literature in which 40 studies were identified and classified according to the principal method of EEG analysis in three categories: functional connectivity analysis, spectral power analysis, and information dynamics. All studies identified significant differences between ASD patients and non-ASD subjects. However, due to high heterogeneity in the results, generalizations could not be inferred and none of the methods alone are currently useful as a new diagnostic tool. The lack of studies prevented the analysis of these methods as tools for ASD subtypes delineation. These results confirm EEG abnormalities in ASD, but as yet not sufficient to help in the diagnosis. Future research with larger samples and more robust study designs could allow for higher sensitivity and consistency in characterizing ASD, paving the way for developing new means of diagnosis.

Is functional brain connectivity atypical in autism? A systematic review of EEG and MEG studies

BACKGROUND

Although it is well recognized that autism is associated with altered patterns of over- and under-connectivity, specifics are still a matter of debate. Little has been done so far to synthesize available literature using whole-brain electroencephalography (EEG) and magnetoencephalography (MEG) recordings.

OBJECTIVES

1) To systematically review the literature on EEG/MEG functional and effective connectivity in autism spectrum disorder (ASD), 2) to synthesize and critically appraise findings related with the hypothesis that ASD is characterized by long-range underconnectivity and local overconnectivity, and 3) to provide, based on the literature, an analysis of tentative factors that are likely to mediate association between ASD and atypical connectivity (e.g., development, topography, lateralization).

METHODS

Literature reviews were done using PubMed and PsychInfo databases. Abstracts were screened, and only relevant articles were analyzed based on the objectives of this paper. Special attention was paid to the methodological characteristics that could have created variability in outcomes reported between studies.

RESULTS

Our synthesis provides relatively strong support for long-range underconnectivity in ASD, whereas the status of local connectivity remains unclear. This observation was also mirrored by a similar relationship with lower frequencies being often associated with underconnectivity and higher frequencies being associated with both under- and over-connectivity. Putting together these observations, we propose that ASD is characterized by a general trend toward an under-expression of lower-band wide-spread integrative processes compensated by more focal, higher-frequency, locally specialized, and segregated processes. Further investigation is, however, needed to corroborate the conclusion and its generalizability across different tasks. Of note, abnormal lateralization in ASD, specifically an elevated left-over-right EEG and MEG functional connectivity ratio, has been also reported consistently across studies.

CONCLUSIONS

The large variability in study samples and methodology makes a systematic quantitative analysis (i.e. meta-analysis) of this body of research impossible. Nevertheless, a general trend supporting the hypothesis of long-range functional underconnectivity can be observed. Further research is necessary to more confidently determine the status of the hypothesis of short-range overconnectivity. Frequency-band specific patterns and their relationships with known symptoms of autism also need to be further clarified.

Electroencephalogram Coherence Patterns in Autism: An Updated Review

Electrophysiologic studies suggest that autism spectrum disorder is characterized by aberrant anatomic and functional neural circuitry. During normal brain development, pruning and synaptogenesis facilitate ongoing changes in both short- and long-range neural wiring. In developmental disorders such as autism, this process may be perturbed and lead to abnormal neural connectivity. Careful analysis of electrophysiologic connectivity patterns using EEG coherence may provide a way to probe the resulting differences in neurological function between people with and without autism. There is general consensus that electroencephalogram coherence patterns differ between individuals with and without autism spectrum disorders; however, the exact nature of the differences and their clinical significance remain unclear. Here we review recent literature comparing electroencephalogram coherence patterns between patients with autism spectrum disorders or at high risk for autism and their nonautistic or low-risk for autism peers.

Brain connectivity in autism spectrum disorder

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.