Resting-State Neurophysiological Activity Patterns in Young People with ASD, ADHD, and ASD + ADHD

Specific EEG resting state biomarkers in FXS and ASD

BACKGROUND

Fragile X syndrome (FXS) and autism spectrum disorder (ASD) are neurodevelopmental conditions that often have a substantial impact on daily functioning and quality of life. FXS is the most common cause of inherited intellectual disability (ID) and the most common monogenetic cause of ASD. Previous literature has shown that electrophysiological activity measured by electroencephalogram (EEG) during resting state is perturbated in FXS and ASD. However, whether electrophysiological profiles of participants with FXS and ASD are similar remains unclear. The aim of this study was to compare EEG alterations found in these two clinical populations presenting varying degrees of cognitive and behavioral impairments.

METHODS

Resting state EEG signal complexity, alpha peak frequency (APF) and power spectral density (PSD) were compared between 47 participants with FXS (aged between 5-20), 49 participants with ASD (aged between 6-17), and 52 neurotypical (NT) controls with a similar age distribution using MANCOVAs with age as covariate when appropriate. MANCOVAs controlling for age, when appropriate, and nonverbal intelligence quotient (NVIQ) score were subsequently performed to determine the impact of cognitive functioning on EEG alterations.

RESULTS

Our results showed that FXS participants manifested decreased signal complexity and APF compared to ASD participants and NT controls, as well as altered power in the theta, alpha and low gamma frequency bands. ASD participants showed exaggerated beta power compared to FXS participants and NT controls, as well as enhanced low and high gamma power compared to NT controls. However, ASD participants did not manifest altered signal complexity or APF. Furthermore, when controlling for NVIQ, results of decreased complexity in higher scales and lower APF in FXS participants compared to NT controls and ASD participants were not replicated.

CONCLUSIONS

These findings suggest that signal complexity and APF might reflect cognitive functioning, while altered power in the low gamma frequency band might be associated with neurodevelopmental conditions, particularly FXS and ASD.

Relationships between peak alpha frequency, age, and autistic traits in young children with and without autism spectrum disorder

Background

Atypical peak alpha frequency (PAF) has been reported in children with autism spectrum disorder (ASD); however, the relationships between PAF, age, and autistic traits remain unclear. This study was conducted to investigate and compare the resting-state PAF of young children with ASD and their typically developing (TD) peers using magnetoencephalography (MEG).

Methods

Nineteen children with ASD and 24 TD children, aged 5-7 years, underwent MEG under resting-state conditions. The PAFs in ten brain regions were calculated, and the associations between these findings, age, and autistic traits, measured using the Social Responsiveness Scale (SRS), were examined.

Results

There were no significant differences in PAF between the children with ASD and the TD children. However, a unique positive association between age and PAF in the cingulate region was observed in the ASD group, suggesting the potential importance of the cingulate regions as a neurophysiological mechanism underlying distinct developmental trajectory of ASD. Furthermore, a higher PAF in the right temporal region was associated with higher SRS scores in TD children, highlighting the potential role of alpha oscillations in social information processing.

Conclusions

This study emphasizes the importance of regional specificity and developmental factors when investigating neurophysiological markers of ASD. The distinct age-related PAF patterns in the cingulate regions of children with ASD and the association between right temporal PAF and autistic traits in TD children provide novel insights into the neurobiological underpinnings of ASD. These findings pave the way for future research on the functional implications of these neurophysiological patterns and their potential as biomarkers of ASD across the lifespan.

Reduced Visual Evoked Potential Amplitude in Autistic Children with Co-Occurring Features of Attention-Deficit/Hyperactivity Disorder

Provided the significant overlap in features of autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), there is a critical need to identify transdiagnostic markers that could meaningfully stratify subgroups. The objective of this study was to compare the visual evoked potential (VEP) between 30 autistic children, 17 autistic children with co-occurring ADHD presentation (ASD + ADHD), and 21 neurotypical children (NTC). Electroencephalography was recorded while children passively viewed a pattern-reversal stimulus. Mean amplitude of the P1 event-related potential was extracted from a midline occipital channel and compared between groups. P1 mean amplitude was reduced in the ASD + ADHD group compared to the ASD and NTC groups, indicating a distinct pattern of brain activity in autistic children with co-occurring ADHD features.

Role of visual brainwave entrainment on the resting state brainwaves of children with and without attention-deficit/hyperactivity disorder

The relationship between brainwave oscillations and Attention-Deficit/Hyperactivity Disorder (ADHD)-related cognitive challenges is a trending proposition in the field of Cognitive Neuroscience. Studies suggest the role of brainwave oscillations in the symptom expressions of ADHD-diagnosed children. Intervention studies have further suggested the scope of brain stimulation techniques in improving cognition. The current manuscript explored the effect of changes in the brainwaves post-sensory entrainment on cognitive performance of children. We calculated each participant's brainwave difference and ratios of theta, alpha, and beta power after the entrainment sessions. Further, we explored possible correlations between these values and the psychometric scores. The beta resting state showed the strongest association with selective attention performance of all participants. Theta-beta ratio (TBR) showed an inverse correlation with selective attention and working memory performances. The theta frequency was associated with decreased working performance in children without ADHD. Our findings also suggest a predominant role of TBR than the theta-alpha ratio in determining the cognitive performance of children with ADHD. The individual differences in the entrainment reception were attributed to the participant's age, IQ, and their innate baseline frequencies. The implications of our findings can initiate substantiating brainwave-based entrainment sessions as a therapeutic modality to improve cognition among children.

Abnormalities in both stimulus-induced and baseline MEG alpha oscillations in the auditory cortex of children with Autism Spectrum Disorder

The neurobiology of Autism Spectrum Disorder (ASD) is hypothetically related to the imbalance between neural excitation (E) and inhibition (I). Different studies have revealed that alpha-band (8-12 Hz) activity in magneto- and electroencephalography (MEG and EEG) may reflect E and I processes and, thus, can be of particular interest in ASD research. Previous findings indicated alterations in event-related and baseline alpha activity in different cortical systems in individuals with ASD, and these abnormalities were associated with core and co-occurring conditions of ASD. However, the knowledge on auditory alpha oscillations in this population is limited. This MEG study investigated stimulus-induced (Event-Related Desynchronization, ERD) and baseline alpha-band activity (both periodic and aperiodic) in the auditory cortex and also the relationships between these neural activities and behavioral measures of children with ASD. Ninety amplitude-modulated tones were presented to two groups of children: 20 children with ASD (5 girls, Mage = 10.03, SD = 1.7) and 20 typically developing controls (9 girls, Mage = 9.11, SD = 1.3). Children with ASD had a bilateral reduction of alpha-band ERD, reduced baseline aperiodic-adjusted alpha power, and flattened aperiodic exponent in comparison to TD children. Moreover, lower raw baseline alpha power and aperiodic offset in the language-dominant left auditory cortex were associated with better language skills of children with ASD measured in formal assessment. The findings highlighted the alterations of E / I balance metrics in response to basic auditory stimuli in children with ASD and also provided evidence for the contribution of low-level processing to language difficulties in ASD.

Resting-State Activity in Children: Replicating and Extending Findings of Early Maturation of Alpha Rhythms in Autism Spectrum Disorder

Resting-state alpha brain rhythms provide a foundation for basic as well as higher-order brain processes. Research suggests atypical maturation of the peak frequency of resting-state alpha activity (= PAF) in autism spectrum disorder (ASD). The present study examined resting-state alpha activity in young school-aged children, obtaining magnetoencephalographic (MEG) eyes-closed resting-state data from 47 typically developing (TD) males and 45 ASD males 6.0 to 9.3 years old. Results confirmed a higher PAF in ASD versus TD, and demonstrated that alpha power differences between groups were linked to the shift of PAF in ASD. Additionally, a higher PAF was associated with better cognitive performance in TD but not ASD. Finding thus suggested functional consequences of group differences in resting-state alpha activity.

Microstate dynamics and spectral components as markers of persistent and remittent attention-deficit/hyperactivity disorder

OBJECTIVE

We leveraged microstate characteristics and power features to examine temporal and spectral deviations underlying persistent and remittent attention-deficit/hyperactivity disorder (ADHD).

METHODS

50 young adults with childhood ADHD (28 persisters, 22 remitters) and 28 demographically similar healthy controls (HC) were compared on microstates features and frequency principal components (f-PCs) of eye-closed resting state. Support vector machine model with sequential forward selection (SVM-SFS) was utilized to discriminate three groups.

RESULTS

Four microstates and four comparable f-PCs were identified. Compared to HC, ADHD persisters showed prolonged duration in microstate C, elevated power of the delta component (D), and compromised amplitude of the two alpha components (A1 and A2). Remitters showed increased duration and coverage of microstate C, together with decreased activity of D, relatively intact amplitude of A1, and amplitude reduction in A2. The SVM-SFS algorithm achieved an accuracy of 93.59% in classifying persisters, remitters and controls. The most discriminative features selected were those exhibiting group differences.

CONCLUSIONS

We found widespread anomalies in ADHD persisters in brain dynamics and intrinsic EEG components. Meanwhile, the neural features in remitters exhibited multiple patterns.

SIGNIFICANCE

This study underlines the use of microstate dynamics and spectral components as potential markers of persistent and remittent ADHD.

Word-Finding Difficulties as a Prominent Early Finding in a Later Diagnosis of Attention Deficit Hyperactivity Disorder

OBJECTIVE

 Attention deficit hyperactivity disorder (ADHD) is a common neuropsychological disorder primarily diagnosed in childhood. Early intervention was found to significantly improve developmental outcomes, implicating on the role of early identification of ADHD markers. In the current study, we explored the developmental history of children referred to neurological assessment to identify early ADHD predictors.

METHODS

 A total of 92 children and adolescents (41 females) recruited at a pediatric neurology clinic, with suspected ADHD (n = 39) or other neurological difficulties (n = 53) such as headaches, seizures, tic disorders, orthostatic hypotension, postischemic stroke, intermittent pain, and vasovagal syncope. Developmental history information was obtained from caregivers, and evaluation for possible ADHD was performed. Developmental details were compared between children with and without current ADHD diagnosis.

RESULTS

 Word-finding difficulties (WFDs) in preschool age was reported in 30.4% of the sample. Among children diagnosed with ADHD, 43% had WFDs history, compared with only 5% in children without ADHD. Among children with WFDs history, 93% were later diagnosed with ADHD compared with 42% in children without WFDs history. The relationship between WFDs and ADHD was significant (chi-square test [1, N = 92] = 20.478, p < 0.0001), and a logistic regression model demonstrated that asides from a family history of ADHD, the strongest predictor for ADHD in school age children was a history of WFDs.

CONCLUSION

 Preliminary evidence supports a predictive link between preschool WFDs and later ADHD diagnosis, highlighting the importance of early WFDs clinical attention.

Evaluating early EEG correlates of restricted and repetitive behaviors for toddlers with or without autism

Background

Restricted and repetitive behaviors (RRB) are among the primary characteristics of autism spectrum disorder (ASD). Despite the potential impact on later developmental outcomes, our understanding of the neural underpinnings of RRBs is limited. Alterations in EEG alpha activity have been observed in ASD and implicated in RRBs, however, developmental changes within the alpha band requires careful methodological considerations when studying its role in brain-behavior relationships during infancy and early childhood. Novel approaches now enable the parameterization of the power spectrum into periodic and aperiodic components. This study aimed to characterize the neural correlates of RRBs in infancy by (1) comparing infant resting-state measures (periodic alpha and aperiodic activity) between infants who develop ASD, elevated likelihood infants without ASD, and low likelihood infants without ASD, and (2) evaluate whether these infant EEG measures are associated with frequency of RRBs measured at 24 months.

Methods

Baseline non-task related EEG data were collected from 12-to-14-month-old infants with and without elevated likelihood of autism (N=160), and periodic alpha activity (periodic alpha power, individual peak alpha frequency and amplitude), and aperiodic activity measures (aperiodic exponent) were calculated. Parent-reported RRBs were obtained at 24 months using the Repetitive Behavior Scale-Revised questionnaire. Group differences in EEG measures were evaluated using ANCOVA, and multiple linear regressions were conducted to assess relationships between EEG and RRB measures.

Results

No group-level differences in infant EEG measures were observed. Marginal effects analysis of linear regressions revealed significant associations within the ASD group, such that higher periodic alpha power, lower peak alpha frequency, and lower aperiodic exponent, were associated with elevated RRBs at 24 months. No significant associations were observed for non-ASD outcome groups.

Limitations

The sample size for ASD (N=19) was modest for examining brain-behavior relations. Larger sample sizes are needed to increase statistical power.

Conclusion

For infants with later ASD diagnoses, measures of alpha and aperiodic activity measured at 1-year of age were associated with later manifestation of RRBs at 2-years. Longitudinal studies are needed to elucidate whether the early trajectory of these EEG measures and their dynamic relations in development influence manifestations of RRBs in ASD.

A multi-task learning model with reinforcement optimization for ASD comorbidity discrimination

How to discriminate the comorbidities in autism spectrum disorder (ASD) population has long been an intriguing and challenging issue in neuroscience and neurology practices. Taking attention deficit hyperactivity disorder (ADHD) for example, electroencephalogram (EEG) analysis has alleviated the problem caused by the task of evaluation of similar behaviors of subjects with ASD, ADHD and ASD+ADHD, which requires a very high expertise to reach any concrete conclusions. However, the performance of ASD comorbidity discrimination is still limited by two major difficulties 1) crucial EEG features regarding ASD and ASD+ADHD largely overlap, and 2) reliable data for model training are routinely insufficient. This study proposes a multi-task learning method with "reinforcement optimization" (namely RO-MLT) working in a two-fold manner: 1)Modeling for Discrimination: a multi-task CNN model maintains the target discrimination task (ASD vs. ASD+ADHD) with the aid of the auxiliary task (ASD vs. Typically Developed (TD)), which is designed to mitigate the aforementioned difficulties on model training; and 2) Reinforcement Optimization: a reinforcement learning algorithm enhances the model's feature extraction and fusion capabilities by optimizing its shared structure. Experimental results based on resting-state EEG that collected from 150 ASD, ASD+ADHD or TD children with the RO-MLT method against the state-of-the-art counterparts indicate that RO-MLT is far superior in terms of all performance indicators (e.g., accuracy). Ablation experiments also show that introduction of multi-task learning and reinforcement optimization can achieve a performance boost-up by 11.07%, a gain even higher than the sums of introduction of two individual techniques to the model design.

Autism Spectrum Disorder with Epilepsy: A Research Protocol for a Clinical and Genetic Study

Autism spectrum disorder (ASD) is a common neurodevelopmental condition affecting ~1% of people worldwide. Core ASD features present with impaired social communication abilities, repetitive and stereotyped behaviors, and atypical sensory responses and are often associated with a series of comorbidities. Among these, epilepsy is frequently observed. The co-occurrence of ASD and epilepsy is currently thought to result from common abnormal neurodevelopmental pathways, including an imbalanced excitation/inhibition ratio. However, the pathological mechanisms involved in ASD-epilepsy co-morbidity are still largely unknown. Here, we propose a research protocol aiming to investigate electrophysiological and genetic features in subjects with ASD and epilepsy. This study will include a detailed electroencephalographic (EEG) and blood transcriptomic characterization of subjects with ASD with and without epilepsy. The combined approach of EEG and transcriptomic studies in the same subjects will contribute to a novel stratification paradigm of the heterogeneous ASD population based on quantitative gene expression and neurophysiological biomarkers. In addition, our protocol has the potential to indicate new therapeutic options, thus amending the current condition of absence of data and guidelines for the treatment of ASD with epilepsy.

Resting-state EEG power differences in autism spectrum disorder: a systematic review and meta-analysis

Narrative reviews have described various resting-state EEG power differences in autism across all five canonical frequency bands, with increased power for low and high frequencies and reduced power for middle frequencies. However, these differences have yet to be quantified using effect sizes and probed robustly for consistency, which are critical next steps for clinical translation. Following PRISMA guidelines, we conducted a systematic review of published and gray literature on resting-state EEG power in autism. We performed 10 meta-analyses to synthesize and quantify differences in absolute and relative resting-state delta, theta, alpha, beta, and gamma EEG power in autism. We also conducted moderator analyses to determine whether demographic characteristics, methodological details, and risk-of-bias indicators might account for heterogeneous study effect sizes. Our literature search and study selection processes yielded 41 studies involving 1,246 autistic and 1,455 neurotypical individuals. Meta-analytic models of 135 effect sizes demonstrated that autistic individuals exhibited reduced relative alpha (g = -0.35) and increased gamma (absolute: g = 0.37, relative: g = 1.06) power, but similar delta (absolute: g = 0.06, relative: g = 0.10), theta (absolute: g = -0.03, relative: g = -0.15), absolute alpha (g = -0.17), and beta (absolute: g = 0.01, relative: g = 0.08) power. Substantial heterogeneity in effect sizes was observed across all absolute (I2: 36.1-81.9%) and relative (I2: 64.6-84.4%) frequency bands. Moderator analyses revealed that age, biological sex, IQ, referencing scheme, epoch duration, and use of gold-standard autism diagnostic instruments did not moderate study effect sizes. In contrast, resting-state paradigm type (eyes-closed versus eyes-open) moderated absolute beta, relative delta, and relative alpha power effect sizes, and resting-state recording duration moderated relative alpha power effect sizes. These findings support further investigation of resting-state alpha and gamma power as potential biomarkers for autism.

Development of peak alpha frequency reflects a distinct trajectory of neural maturation in autistic children

Electroencephalographic peak alpha frequency (PAF) is a marker of neural maturation that increases with age throughout childhood. Distinct maturation of PAF is observed in children with autism spectrum disorder such that PAF does not increase with age and is instead positively associated with cognitive ability. The current study clarifies and extends previous findings by characterizing the effects of age and cognitive ability on PAF between diagnostic groups in a sample of children and adolescents with and without autism spectrum disorder. Resting EEG data and behavioral measures were collected from 45 autistic children and 34 neurotypical controls aged 8 to 18 years. Utilizing generalized additive models to account for nonlinear relations, we examined differences in the joint effect of age and nonverbal IQ by diagnosis as well as bivariate relations between age, nonverbal IQ, and PAF across diagnostic groups. Age was positively associated with PAF among neurotypical children but not among autistic children. In contrast, nonverbal IQ but not age was positively associated with PAF among autistic children. Models accounting for nonlinear relations revealed different developmental trajectories as a function of age and cognitive ability based on diagnostic status. Results align with prior evidence indicating that typical age-related increases in PAF are absent in autistic children and that PAF instead increases with cognitive ability in these children. Findings suggest the potential of PAF to index distinct trajectories of neural maturation in autistic children.

EEG Correlates of Cognitive Functions in a Child with ASD and White Matter Signal Abnormalities: A Case Report with Two-and-a-Half-Year Follow-Up

This research aimed to examine the EEG correlates of different stimuli processing instances in a child with ASD and white matter signal abnormalities and to investigate their relationship to the results of behavioral tests. The prospective case study reports two and a half years of follow-up data from a child aged 38 to 66 months. Cognitive, speech-language, sensory, and EEG correlates of auditory-verbal and auditory-visual-verbal information processing were recorded during five test periods, and their mutual interrelation was analyzed. EEG findings revealed no functional theta frequency range redistribution in the frontal regions favoring the left hemisphere during speech processing. The results pointed to a positive linear trend in the relative theta frequency range and a negative linear trend in the relative alpha frequency range when listening to and watching the cartoon. There was a statistically significant correlation between EEG signals and behavioral test results. Based on the obtained results, it may be concluded that EEG signals and their association with the results of behavioral tests should be evaluated with certain restraints considering the characteristics of the stimuli during EEG recording.

A scoping review of physiological biomarkers in autism

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by pervasive deficits in social interaction, communication impairments, and the presence of restricted and repetitive behaviors. This complex disorder is a significant public health concern due to its escalating incidence and detrimental impact on quality of life. Currently, extensive investigations are underway to identify prospective susceptibility or predictive biomarkers, employing a physiological biomarker-based framework. However, knowledge regarding physiological biomarkers in relation to Autism is sparse. We performed a scoping review to explore putative changes in physiological activities associated with behaviors in individuals with Autism. We identified studies published between January 2000 and June 2023 from online databases, and searched keywords included electroencephalography (EEG), magnetoencephalography (MEG), electrodermal activity markers (EDA), eye-tracking markers. We specifically detected social-related symptoms such as impaired social communication in ASD patients. Our results indicated that the EEG/ERP N170 signal has undergone the most rigorous testing as a potential biomarker, showing promise in identifying subgroups within ASD and displaying potential as an indicator of treatment response. By gathering current data from various physiological biomarkers, we can obtain a comprehensive understanding of the physiological profiles of individuals with ASD, offering potential for subgrouping and targeted intervention strategies.

Exploration of electroencephalogram response to MPH treatment in ADHD patients

Attention-deficit/hyperactivity disorder (ADHD) is known to be associated with several diagnostic resting-state electroencephalography (EEG) patterns, including the theta/beta ratio, but no objective predictive markers for each medication. In this study, we explored EEG markers with which the therapeutic efficacy of medications could be estimated at the 1st clinical visit. Thirty-two ADHD patients and thirty-one healthy subjects participated in this study. EEG was recorded during eyes-closed resting conditions, and ADHD symptoms were scored before and after the therapeutic intervention (8 ± 2 weeks). Although comparing EEG patterns between ADHD patients and healthy subjects showed significant differences, EEG dynamics, e.g., theta/beta ratio, in ADHD patients before and after MPH treatment were not significantly different despite improvements in ADHD symptoms. We demonstrated that MPH good responders and poor responders, defined by the efficacy of MPH, had significantly different theta band power in right temporal areas, alpha in left occipital and frontal areas, and beta in left frontal areas. Moreover, we showed that MPH good responders had significant improvements toward normalization in several coherence measures after MPH treatment. Our study implies the possibility of these EEG indices as predictive markers for ADHD therapeutic efficacy.

Repetitive transcranial magnetic stimulation modulates long-range functional connectivity in autism spectrum disorder

Repetitive transcranial magnetic stimulation (rTMS) is growingly applied in autism spectrum disorder (ASD) due to its potential therapeutic value, however, its effects on functional network configuration and the mechanism underlying clinical improvement are still unclear. In this study, we examined the alternations of functional connectivity induced by rTMS using resting-state electroencephalogram (EEG) in children with ASD. Resting-state EEG was obtained from 24 children with ASD before and after rTMS intervention and from 24 age- and gender-matched typically developing (TD) children. The rTMS intervention course consisted of five 5-s trains at 15 Hz, with 10-min inter-train intervals, on the left parietal lobe each consecutive weekday for 3 weeks (15 sessions in total). Children with ASD showed significantly hypo-connected networks and sub-optimal network properties at both global and local levels, compared with TD peers. After rTMS intervention, long-range intra- and inter-hemispheric connections were significantly promoted, especially those within the alpha band. Meanwhile, network properties at both local and global levels were greatly promoted in the delta, theta, and alpha bands. Consistent with the changes in the network connectivities and properties, the core symptoms in ASD were also relieved measured by clinical scales after treatment. The findings of this study demonstrate that high-frequency rTMS over the parietal lobe is potentially an effective strategy to improve core symptoms by enhancing long-range connectivity reorganization in ASD.

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.

Environmentally relevant uptake, elimination, and metabolic changes following early embryonic exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin in zebrafish

Dioxin and dioxin-like compounds are ubiquitous environmental contaminants that induce toxicity by binding to the aryl hydrocarbon receptor (AHR), a ligand activated transcription factor. The zebrafish model has been used to define the developmental toxicity observed following exposure to exogenous AHR ligands such as the potent agonist 2,3,7,8-tetrachlorodibenzo-p-dioxin (dioxin, TCDD). While the model has successfully identified cellular targets of TCDD and molecular mechanisms mediating TCDD-induced phenotypes, fundamental information such as the body burden produced by standard exposure models is still unknown. We performed targeted gas chromatography (GC) high-resolution mass spectrometry (HRMS) in tandem with non-targeted liquid chromatography (LC) HRMS to quantify TCDD uptake, model the elimination dynamics of TCDD, and determine how TCDD exposure affects the zebrafish metabolome. We found that 50 ppt, 10 ppb, and 1 ppb waterborne exposures to TCDD during early embryogenesis produced environmentally relevant body burdens: 38 ± 4.34, 26.6 ± 1.2, and 8.53 ± 0.341 pg/embryo, respectively, at 24 hours post fertilization. TCDD exposure was associated with the dysregulation of metabolic pathways that are associated with the AHR signaling pathway as well as pathways shown to be affected in mammals following TCDD exposure. In addition, we discovered that TCDD exposure affected several metabolic pathways that are critical for brain development and function including glutamate metabolism, chondroitin sulfate biosynthesis, and tyrosine metabolism. Together, these data demonstrate that existing exposure methods produce environmentally relevant body burdens of TCDD in zebrafish and provide insight into the biochemical pathways impacted by toxicant-induced AHR activation.

The development of peak alpha frequency from infancy to adolescence and its role in visual temporal processing: A meta-analysis

While it has been shown that alpha frequency increases over development (Stroganova et al., 1999), a precise trajectory has not yet been specified, making it challenging to constrain theories linking alpha rhythms to perceptual development. We conducted a comprehensive review of studies measuring resting-state occipital peak alpha frequency (PAF, the frequency exhibiting maximum power) from birth to 18 years of age. From 889 potentially relevant studies, we identified 40 reporting PAF (109 samples; 3882 subjects). A nonlinear regression revealed that PAF increases quickly in early childhood (from 6.1 Hz at 6 months to 8.4 Hz at 5 years) and levels off in adolescence (9.7 Hz at 13 years), with an asymptote at 10.1 Hz. We found no effect of resting state procedure (eyes-open versus eyes-closed) or biological sex. PAF has been implicated as a clock on visual temporal processing, with faster frequencies associated with higher visual temporal resolution. Psychophysical studies have shown that temporal resolution reaches adult levels by 5 years of age (Freschl et al., 2019, 2020). The fact that PAF reaches the adult range of 8-12 Hz by that age strengthens the link between PAF and temporal resolution.

Altered theta-beta ratio in infancy associates with family history of ADHD and later ADHD-relevant temperamental traits

BACKGROUND

Uncovering the neural mechanisms that underlie symptoms of attention deficit hyperactivity disorder (ADHD) requires studying brain development prior to the emergence of behavioural difficulties. One new approach to this is prospective studies of infants with an elevated likelihood of developing ADHD.

METHODS

We used a prospective design to examine an oscillatory electroencephalography profile that has been widely studied in both children and adults with ADHD - the balance between lower and higher frequencies operationalised as the theta-beta ratio (TBR). In the present study, we examined TBR in 136 10-month-old infants (72 male and 64 female) with/without an elevated likelihood of developing ADHD and/or a comparison disorder (Autism Spectrum Disorder; ASD).

RESULTS

Infants with a first-degree relative with ADHD demonstrated lower TBR than infants without a first-degree relative with ADHD. Further, lower TBR at 10 months was positively associated with temperament dimensions conceptually related to ADHD at 2 years. TBR was not altered in infants with a family history of ASD.

CONCLUSIONS

This is the first demonstration that alterations in TBR are present prior to behavioural symptoms of ADHD. However, these alterations manifest differently than those sometimes observed in older children with an ADHD diagnosis. Importantly, altered TBR was not seen in infants at elevated likelihood of developing ASD, suggesting a degree of specificity to ADHD. Taken together, these findings demonstrate that there are brain changes associated with a family history of ADHD observable in the first year of life.

EEG resting-state functional connectivity: evidence for an imbalance of external/internal information integration in autism

Background

Autism spectrum disorder (ASD) is associated with atypical neural activity in resting state. Most of the studies have focused on abnormalities in alpha frequency as a marker of ASD dysfunctions. However, few have explored alpha synchronization within a specific interest in resting-state networks, namely the default mode network (DMN), the sensorimotor network (SMN), and the dorsal attention network (DAN). These functional connectivity analyses provide relevant insight into the neurophysiological correlates of multimodal integration in ASD.

Methods

Using high temporal resolution EEG, the present study investigates the functional connectivity in the alpha band within and between the DMN, SMN, and the DAN. We examined eyes-closed EEG alpha lagged phase synchronization, using standardized low-resolution brain electromagnetic tomography (sLORETA) in 29 participants with ASD and 38 developing (TD) controls (age, sex, and IQ matched).

Results

We observed reduced functional connectivity in the ASD group relative to TD controls, within and between the DMN, the SMN, and the DAN. We identified three hubs of dysconnectivity in ASD: the posterior cingulate cortex, the precuneus, and the medial frontal gyrus. These three regions also presented decreased current source density in the alpha band.

Conclusion

These results shed light on possible multimodal integration impairments affecting the communication between bottom-up and top-down information. The observed hypoconnectivity between the DMN, SMN, and DAN could also be related to difficulties in switching between externally oriented attention and internally oriented thoughts.

Supplementary Information

The online version contains supplementary material available at 10.1186/s11689-022-09456-8.

Alpha oscillatory activity during attentional control in children with Autism Spectrum Disorder (ASD), Attention-Deficit/Hyperactivity Disorder (ADHD), and ASD+ADHD

BACKGROUND

Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) share impairments in top-down and bottom-up modulation of attention. However, it is not yet well understood if co-occurrence of ASD and ADHD reflects a distinct or additive profile of attention deficits. We aimed to characterise alpha oscillatory activity (stimulus-locked alpha desynchronisation and prestimulus alpha) as an index of integration of top-down and bottom-up attentional processes in ASD and ADHD.

METHODS

Children with ASD, ADHD, comorbid ASD+ADHD, and typically-developing children completed a fixed-choice reaction-time task ('Fast task') while neurophysiological activity was recorded. Outcome measures were derived from source-decomposed neurophysiological data. Main measures of interest were prestimulus alpha power and alpha desynchronisation (difference between poststimulus and prestimulus alpha). Poststimulus activity linked to attention allocation (P1, P3), attentional control (N2), and cognitive control (theta synchronisation, 100-600 ms) was also examined. ANOVA was used to test differences across diagnostics groups on these measures. Spearman's correlations were used to investigate the relationship between attentional control processes (alpha oscillations), central executive functions (theta synchronisation), early visual processing (P1), and behavioural performance.

RESULTS

Children with ADHD (ADHD and ASD+ADHD) showed attenuated alpha desynchronisation, indicating poor integration of top-down and bottom-up attentional processes. Children with ADHD showed reduced N2 and P3 amplitudes, while children with ASD (ASD and ASD+ADHD) showed greater N2 amplitude, indicating atypical attentional control and attention allocation across ASD and ADHD. In the ASD group, prestimulus alpha and theta synchronisation were negatively correlated, and alpha desynchronisation and theta synchronisation were positively correlated, suggesting an atypical association between attentional control processes and executive functions.

CONCLUSIONS

ASD and ADHD are associated with disorder-specific impairments, while children with ASD+ADHD overall presented an additive profile with attentional deficits of both disorders. Importantly, these findings may inform the improvement of transdiagnostic procedures and optimisation of personalised intervention approaches.

A randomized controlled study of remote computerized cognitive, neurofeedback, and combined training in the treatment of children with attention-deficit/hyperactivity disorder

There is an increasing interest in non-pharmacological treatments for children with attention-deficit/hyperactivity disorder (AD/HD), especially digital techniques that can be remotely delivered, such as neurofeedback (NFT) and computerized cognitive training (CCT). In this study, a randomized controlled design was used to compare training outcomes between remotely delivered NFT, CCT, and combined NFT/CCT training approaches. A total of 121 children with AD/HD were randomly assigned to the NFT, CCT, or NFT/CCT training groups, with 80 children completing the training program. Pre- and post-training symptoms (primary outcome), executive and daily functions were measured using questionnaires as well as resting EEG during eyes-closed (EC) and eyes-open (EO) conditions. After 3 months of training, the inattentive and hyperactive/impulsive symptoms, inhibition, working memory, learning and life skills of the three groups of children were significantly improved. The objective EEG activity showed a consistent increase in the relative alpha power in the EO condition among the three training groups. Training differences were not observed between groups. There was a positive correlation between pre-training EO relative alpha power and symptom improvement scores of inattention and hyperactivity/impulsivity, as well as a negative correlation between pre-training inattention scores and change in EO relative alpha. This study verified the training effects of NFT, CCT, and combined NFT/CCT training in children with AD/HD and revealed an objective therapeutic role for individual relative alpha activity. The verified feasibility and effectiveness of home-based digital training support promotion and application of digital remote training.

Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials

Accurate diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) is a significant challenge. Misdiagnosis has significant negative medical side effects. Due to the complex nature of this disorder, there is no computational expert system for diagnosis. Recently, automatic diagnosis of ADHD by machine learning analysis of brain signals has received an increased attention. This paper aimed to achieve an accurate model to discriminate between ADHD patients and healthy controls by pattern discovery. Event-Related Potentials (ERP) data were collected from ADHD patients and healthy controls. After pre-processing, ERP signals were decomposed and features were calculated for different frequency bands. The classification was carried out based on each feature using seven machine learning algorithms. Important features were then selected and combined. To find specific patterns for each model, the classification was repeated using the proposed patterns. Results indicated that the combination of complementary features can significantly improve the performance of the predictive models. The newly developed features, defined based on band power, were able to provide the best classification using the Generalized Linear Model, Logistic Regression, and Deep Learning with the average accuracy and Receiver operating characteristic curve > %99.85 and > 0.999, respectively. High and low frequencies (Beta, Delta) performed better than the mid, frequencies in the discrimination of ADHD from control. Altogether, this study developed a machine learning expert system that minimises misdiagnosis of ADHD and is beneficial for the evaluation of treatment efficacy.

Signal complexity indicators of health status in clinical EEG

Brain signal variability changes across the lifespan in both health and disease, likely reflecting changes in information processing capacity related to development, aging and neurological disorders. While signal complexity, and multiscale entropy (MSE) in particular, has been proposed as a biomarker for neurological disorders, most observations of altered signal complexity have come from studies comparing patients with few to no comorbidities against healthy controls. In this study, we examined whether MSE of brain signals was distinguishable across patient groups in a large and heterogeneous set of clinical-EEG data. Using a multivariate analysis, we found unique timescale-dependent differences in MSE across various neurological disorders. We also found MSE to differentiate individuals with non-brain comorbidities, suggesting that MSE is sensitive to brain signal changes brought about by metabolic and other non-brain disorders. Such changes were not detectable in the spectral power density of brain signals. Our findings suggest that brain signal complexity may offer complementary information to spectral power about an individual's health status and is a promising avenue for clinical biomarker development.

Resting state EEG in youth with ASD: age, sex, and relation to phenotype

BACKGROUND

Identification of ASD biomarkers is a key priority for understanding etiology, facilitating early diagnosis, monitoring developmental trajectories, and targeting treatment efforts. Efforts have included exploration of resting state encephalography (EEG), which has a variety of relevant neurodevelopmental correlates and can be collected with minimal burden. However, EEG biomarkers may not be equally valid across the autism spectrum, as ASD is strikingly heterogeneous and individual differences may moderate EEG-behavior associations. Biological sex is a particularly important potential moderator, as females with ASD appear to differ from males with ASD in important ways that may influence biomarker accuracy.

METHODS

We examined effects of biological sex, age, and ASD diagnosis on resting state EEG among a large, sex-balanced sample of youth with (N = 142, 43% female) and without (N = 138, 49% female) ASD collected across four research sites. Absolute power was extracted across five frequency bands and nine brain regions, and effects of sex, age, and diagnosis were analyzed using mixed-effects linear regression models. Exploratory partial correlations were computed to examine EEG-behavior associations in ASD, with emphasis on possible sex differences in associations.

RESULTS

Decreased EEG power across multiple frequencies was associated with female sex and older age. Youth with ASD displayed decreased alpha power relative to peers without ASD, suggesting increased neural activation during rest. Associations between EEG and behavior varied by sex. Whereas power across various frequencies correlated with social skills, nonverbal IQ, and repetitive behavior for males with ASD, no such associations were observed for females with ASD.

CONCLUSIONS

Research using EEG as a possible ASD biomarker must consider individual differences among participants, as these features influence baseline EEG measures and moderate associations between EEG and important behavioral outcomes. Failure to consider factors such as biological sex in such research risks defining biomarkers that misrepresent females with ASD, hindering understanding of the neurobiology, development, and intervention response of this important population.

Disrupted Functional Rich-Club Organization of the Brain Networks in Children with Attention-Deficit/Hyperactivity Disorder, a Resting-State EEG Study

Growing evidence indicates that disruptions in the brain's functional connectivity play an important role in the pathophysiology of ADHD. The present study investigates alterations in resting-state EEG source connectivity and rich-club organization in children with inattentive (ADHDI) and combined (ADHDC) ADHD compared with typically developing children (TD) under the eyes-closed condition. EEG source analysis was performed by eLORETA in different frequency bands. The lagged phase synchronization (LPS) and graph theoretical metrics were then used to examine group differences in the topological properties and rich-club organization of functional networks. Compared with the TD children, the ADHDI children were characterized by a widespread significant decrease in delta and beta LPS, as well as increased theta and alpha LPS in the left frontal and right occipital regions. The ADHDC children displayed significant increases in LPS in the central, temporal and posterior areas. Both ADHD groups showed small-worldness properties with significant increases and decreases in the network degree in the θ and β bands, respectively. Both subtypes also displayed reduced levels of network segregation. Group differences in rich-club distribution were found in the central and posterior areas. Our findings suggest that resting-state EEG source connectivity analysis can better characterize alterations in the rich-club organization of functional brain networks in ADHD patients.

12-Month peak alpha frequency is a correlate but not a longitudinal predictor of non-verbal cognitive abilities in infants at low and high risk for autism spectrum disorder

Although studies of PAF in individuals with autism spectrum disorder (ASD) report group differences and associations with non-verbal cognitive ability, it is not known how PAF relates to familial risk for ASD, and whether similar associations with cognition in are present in infancy. Using a large multi-site prospective longitudinal dataset of infants with low and high familial risk for ASD, metrics of PAF at 12 months were extracted and growth curves estimated for cognitive development between 12-36 months. Analyses tested whether PAF 1) differs between low and high risk infants, 2) is associated with concurrent non-verbal/verbal cognitive ability and 3) predicts developmental change in non-verbal/verbal ability. Moderation of associations between PAF and cognitive ability by familial risk status was also tested. No differences in 12-month PAF were found between low and high risk infants. PAF was associated with concurrent non-verbal cognitive ability, but did not predict change in non-verbal cognitive over development. No associations were found between PAF and verbal ability, along with no evidence of moderation. PAF is not related to familial risk for ASD, and is a neural marker of concurrent non-verbal cognitive ability, but not verbal ability, in young infants at low and high risk for ASD.

Objective Assessments in Diagnoses and Treatment: A Proposed Change in Paradigm

For patients with psychiatric disorders, current diagnostic and treatment approaches are far from optimal. The clinical interview drives the standard approach-matching symptoms to diagnostic criteria-and results in standardized pharmacological and behavioral treatments, often, with inadequate outcome; but now, recent imaging advances can correlate behavioral assessments with brain function and measure them against normative databases to provide data critical for the reevaluation of patient diagnosis and treatment. This article addresses the data that support a redefinition of our current paradigm. We believe a neurobehavioral approach provides for more personalized treatment approaches unbound from classically defined diagnostic biases.

Associations between sensory processing and electrophysiological and neurochemical measures in children with ASD: an EEG-MRS study

Background

Autism spectrum disorder (ASD) is associated with hyper- and/or hypo-sensitivity to sensory input. Spontaneous alpha power, which plays an important role in shaping responsivity to sensory information, is reduced across the lifespan in individuals with ASD. Furthermore, an excitatory/inhibitory imbalance has also been linked to sensory dysfunction in ASD and has been hypothesized to underlie atypical patterns of spontaneous brain activity. The present study examined whether resting-state alpha power differed in children with ASD as compared to TD children, and investigated the relationships between alpha levels, concentrations of excitatory and inhibitory neurotransmitters, and atypical sensory processing in ASD.

Methods

Participants included thirty-one children and adolescents with ASD and thirty-one age- and IQ-matched typically developing (TD) participants. Resting-state electroencephalography (EEG) was used to obtain measures of alpha power. A subset of participants (ASD = 16; TD = 16) also completed a magnetic resonance spectroscopy (MRS) protocol in order to measure concentrations of excitatory (glutamate + glutamine; Glx) and inhibitory (GABA) neurotransmitters.

Results

Children with ASD evidenced significantly decreased resting alpha power compared to their TD peers. MRS estimates of GABA and Glx did not differ between groups with the exception of Glx in the temporal-parietal junction. Inter-individual differences in alpha power within the ASD group were not associated with region-specific concentrations of GABA or Glx, nor were they associated with sensory processing differences. However, atypically decreased Glx was associated with increased sensory impairment in children with ASD.

Conclusions

Although we replicated prior reports of decreased alpha power in ASD, atypically reduced alpha was not related to neurochemical differences or sensory symptoms in ASD. Instead, reduced Glx in the temporal-parietal cortex was associated with greater hyper-sensitivity in ASD. Together, these findings may provide insight into the neural underpinnings of sensory processing differences present in ASD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s11689-020-09351-0.

EEG Signal Complexity Is Reduced During Resting-State in Fragile X Syndrome

Introduction: Fragile X syndrome (FXS) is a genetic disorder caused by a mutation of the fragile X mental retardation 1 gene (FMR1). FXS is associated with neurophysiological abnormalities, including cortical hyperexcitability. Alterations in electroencephalogram (EEG) resting-state power spectral density (PSD) are well-defined in FXS and were found to be linked to neurodevelopmental delays. Whether non-linear dynamics of the brain signal are also altered remains to be studied. Methods: In this study, resting-state EEG power, including alpha peak frequency (APF) and theta/beta ratio (TBR), as well as signal complexity using multi-scale entropy (MSE) were compared between 26 FXS participants (ages 5-28 years), and 7 neurotypical (NT) controls with a similar age distribution. Subsequently a replication study was carried out, comparing our cohort to 19 FXS participants independently recorded at a different site. Results: PSD results confirmed the increased gamma, decreased alpha power and APF in FXS participants compared to NT controls. No alterations in TBR were found. Importantly, results revealed reduced signal complexity in FXS participants, specifically in higher scales, suggesting that altered signal complexity is sensitive to brain alterations in this population. The replication study mostly confirmed these results and suggested critical points of stagnation in the neurodevelopmental curve of FXS. Conclusion: Signal complexity is a powerful feature that can be added to the electrophysiological biomarkers of brain maturation in FXS.

Examining the Overlap Between ADHD and Autism Spectrum Disorder (ASD) Using Candidate Endophenotypes of ADHD

Objective: Recent discussions of aetiological overlap between ADHD and Autism Spectrum Disorder (ASD) require comparative studying of these disorders.

METHOD

We examined performance of ASD patients with (ASD+) and without (ASD-) comorbid ADHD, ADHD patients, and controls for selected putative endophenotypes of ADHD: Intrasubject Variability (ISV) of reaction times, working memory (WM), inhibition, and temporal processing.

RESULTS

We found that patients with ADHD or ASD+, but not ASD-, had elevated ISV across the entire task battery and temporal processing deficits, and that none of the groups were impaired in WM or inhibition. High levels of ISV and generally poor performance in ASD+ patients were only partially due to additive effects of the pure disorders.

CONCLUSION

Overall, we conclude that, within our limited but heterogeneous task battery, ISV and temporal processing deficits are most sensitive to ADHD symptomatology and that controlling for ADHD comorbidity is mandatory when assessing ISV in autism.

A study on EEG feature extraction and classification in autistic children based on singular spectrum analysis method

INTRODUCTION

The clinical diagnosis of Autism spectrum disorder (ASD) depends on rating scale evaluation, which introduces subjectivity. Thus, objective indicators of ASD are of great interest to clinicians. In this study, we sought biomarkers from resting-state electroencephalography (EEG) data that could be used to accurately distinguish children with ASD and typically developing (TD) children.

METHODS

We recorded resting-state EEG from 46 children with ASD and 63 age-matched TD children aged 3 to 5 years. We applied singular spectrum analysis (SSA) to the EEG sequences to eliminate noise components and accurately extract the alpha rhythm.

RESULTS

When we used individualized alpha peak frequency (iAPF) and individualized alpha absolute power (iABP) as features for a linear support vector machine, ASD versus TD classification accuracy was 92.7%.

CONCLUSION

This study suggested that our methods have potential to assist in clinical diagnosis.

Increased EEG coherence in long-distance and short-distance connectivity in children with autism spectrum disorders

INTRODUCTION

Autism spectrum disorder (ASD) is a complex and prevalent neurodevelopmental disorder characterized by deficits in social communication and social interaction as well as repetitive behaviors. Alterations in function connectivity are widely recognized in recent electroencephalogram (EEG) studies. However, most studies have not reached consistent conclusions, which could be due to the developmental nature and the heterogeneity of ASD.

METHODS

Here, EEG coherence analysis was used in a cohort of children with ASD (n = 13) and matched typically developing controls (TD, n = 15) to examine the functional connectivity characteristics in long-distance and short-distance electrode pairs. Subsequently, we explore the association between the connectivity strength of coherence and symptom severity in children with ASD.

RESULTS

Compared with TD group, individuals with ASD showed increased coherence in short-distance electrode pairs in the right temporal-parietal region (delta, alpha, beta bands), left temporal-parietal region (all frequency bands), occipital region (theta, alpha, beta bands), right central-parietal region (delta, alpha, beta bands), and the prefrontal region (only beta band). In the long-distance coherence analysis, the ASD group showed increased coherence in bilateral frontal region, temporal region, parietal region, and frontal-occipital region in alpha and beta bands. The strength of such connections was associated with symptom severity.

DISCUSSION

Our study indicates that abnormal connectivity patterns in neuroelectrophysiology may be of critical importance to acknowledge the underlying brain mechanism.

Atypical Electrophysiological Indices of Eyes-Open and Eyes-Closed Resting-State in Children and Adolescents with ADHD and Autism

Investigating electrophysiological measures during resting-state might be useful to investigate brain functioning and responsivity in individuals under diagnostic assessment for attention deficit hyperactivity disorder (ADHD) and autism. EEG was recorded in 43 children with or without ADHD and autism, during a 4-min-long resting-state session which included an eyes-closed and an eyes-open condition. We calculated and analyzed occipital absolute and relative spectral power in the alpha frequency band (8-12 Hz), and alpha reactivity, conceptualized as the difference in alpha power between eyes-closed and eyes-open conditions. Alpha power was increased during eyes-closed compared to eyes-open resting-state. While absolute alpha power was reduced in children with autism, relative alpha power was reduced in children with ADHD, especially during the eyes-closed condition. Reduced relative alpha reactivity was mainly associated with lower IQ and not with ADHD or autism. Atypical brain functioning during resting-state seems differently associated with ADHD and autism, however further studies replicating these results are needed; we therefore suggest involving research groups worldwide by creating a shared and publicly available repository of resting-state EEG data collected in people with different psychological, psychiatric, or neurodevelopmental conditions, including ADHD and autism.

Multiplex temporal measures reflecting neural underpinnings of brain functional connectivity under cognitive load in Autism Spectrum Disorder

Methods: Thirty individuals with ASD (11-18years) and thirty Typically Developing (TD) individuals (11-16years) were recruited to perform the mental task. The participants were instructed to flip the shown geometric images mentally and mark their response on a scale. The task-related multivariate EEG activations were analyzed using multiplex temporal Visibility Graphs (VGs) to compute local and global brain network functional connectivity dynamics.Results: With cognitive load (0-back to the 2-back task), the behavioral performance (d' index and Reaction Time) has reduced in ASD. The brain network has become more segregated and less integrated, reflecting more involvement of intra-regions over inter-regions in ASD. The frequent rerouting in hubs measured by Eigenvector Centrality (EC) indicated the progression of brain trajectory towards ASD. Overall, the neural mechanisms involving hyperactive response in frontal regions, frequent rewiring, and strengthened brain connectivity as a result of learning-induced performance reflected the adaptation to cognitive demands in ASD.Discussion: The correlation between complex graph measures and behavioral domain further reflected that neural metrics could predict the behavioral performance of the individuals in the task. In the future, modeling functional connectivity-based markers have the potential to reflect the brain trajectory alterations, which can detect ASD even before the behavioral manifestations become apparent.

Longitudinal Neuropsychological Assessment in Two Elderly Adults With Attention-Deficit/Hyperactivity Disorder: Case Report

The neuropsychological deficits in attention-deficit/hyperactivity disorder (ADHD) may present clinical features similar to mild and/or major neurocognitive disorder and may act as a confounding factor, making it difficult to detect cognitive decline. In this paper, we present the results of longitudinal neuropsychological evaluations in two elderly women with ADHD. Three neuropsychological assessments were performed in two women with ADHD (60 and 77 years old) between 2010 and 2013 at intervals varying from 12 to 15 months. We used structural magnetic resonance imaging to rule out significant abnormalities that could account for cognitive impairment. The results showed two different cognitive profiles with fluctuations in performance over these 2 years, sometimes with improvement and sometimes with decline of some functions such as attention, memory, inhibitory control, and reaction time. To minimize confounding aspects of these fluctuations in clinical practice, we used a longer follow-up with the application of a reliable change index and a minimum of three spaced assessments to provide a more consistent baseline cognitive profile. Our findings did not indicate a consistent cognitive decline, suggesting a less pessimistic perspective about cognitive impairments that could be a prodrome of ADHD-related dementia.

Oscillatory neural networks underlying resting-state, attentional control and social cognition task conditions in children with ASD, ADHD and ASD+ADHD

Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are common and impairing neurodevelopmental disorders that frequently co-occur. The neurobiological mechanisms involved in ASD and ADHD are not fully understood. However, alterations in large-scale neural networks have been proposed as core deficits in both ASD and ADHD and may help to disentangle the neurobiological basis of these disorders and their co-occurrence. In this study, we examined similarities and differences in large-scale oscillatory neural networks between boys aged 8-13 years with ASD (n = 19), ADHD (n = 18), ASD + ADHD (n = 29) and typical development (Controls, n = 26). Oscillatory neural networks were computed using graph-theoretical methods from electroencephalographic (EEG) data collected during an eyes-open resting-state and attentional control and social cognition tasks in which we previously reported disorder-specific atypicalities in oscillatory power and event-related potentials (ERPs). We found that children with ASD showed significant hypoconnectivity in large-scale networks during all three task conditions compared to children without ASD. In contrast, children with ADHD showed significant hyperconnectivity in large-scale networks during the attentional control and social cognition tasks, but not during the resting-state, compared to children without ADHD. Children with co-occurring ASD + ADHD did not differ from children with ASD when paired with this group and vice versa when paired with the ADHD group, indicating that these children showed both ASD-like hypoconnectivity and ADHD-like hyperconnectivity. Our findings suggest that ASD and ADHD are associated with distinct alterations in large-scale oscillatory networks, and these atypicalities present together in children with both disorders. These alterations appear to be task-independent in ASD but task-related in ADHD, and may underlie other neurocognitive atypicalities in these disorders.

EEG Frequency Bands in Psychiatric Disorders: A Review of Resting State Studies

A significant proportion of the electroencephalography (EEG) literature focuses on differences in historically pre-defined frequency bands in the power spectrum that are typically referred to as alpha, beta, gamma, theta and delta waves. Here, we review 184 EEG studies that report differences in frequency bands in the resting state condition (eyes open and closed) across a spectrum of psychiatric disorders including depression, attention deficit-hyperactivity disorder (ADHD), autism, addiction, bipolar disorder, anxiety, panic disorder, post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD) and schizophrenia to determine patterns across disorders. Aggregating across all reported results we demonstrate that characteristic patterns of power change within specific frequency bands are not necessarily unique to any one disorder but show substantial overlap across disorders as well as variability within disorders. In particular, we show that the most dominant pattern of change, across several disorder types including ADHD, schizophrenia and OCD, is power increases across lower frequencies (delta and theta) and decreases across higher frequencies (alpha, beta and gamma). However, a considerable number of disorders, such as PTSD, addiction and autism show no dominant trend for spectral change in any direction. We report consistency and validation scores across the disorders and conditions showing that the dominant result across all disorders is typically only 2.2 times as likely to occur in the literature as alternate results, and typically with less than 250 study participants when summed across all studies reporting this result. Furthermore, the magnitudes of the results were infrequently reported and were typically small at between 20% and 30% and correlated weakly with symptom severity scores. Finally, we discuss the many methodological challenges and limitations relating to such frequency band analysis across the literature. These results caution any interpretation of results from studies that consider only one disorder in isolation, and for the overall potential of this approach for delivering valuable insights in the field of mental health.

Neurodevelopmental Disorders and Adaptive Functions: A Study of Children With Autism Spectrum Disorders (ASD) and/or Attention Deficit and Hyperactivity Disorder (ADHD)

Introduction: Autism spectrum disorder (ASD) and attention deficit and hyperactivity disorder (ADHD) are the two most common neurodevelopmental disorders observed in childhood. The DSM-5 accepts a combined diagnosis of ADHD and ASD, while the DSM-IV did not. The aim of this study was to identify and evaluate the adaptive profile of children and adolescents with a diagnosis of comorbid ADHD and ASD, in comparison with adaptive functioning in subjects with a diagnosis of only ASD or ADHD. Materials and Methods: Ninety-one children (77 boys, 14 girls), aging from 3.1 to 13.4 years (mean age: 8.3 ± 7.2), who met the criteria for a diagnosis of ASD and/or ADHD were enrolled. A neuropsychological evaluation involving cognitive and adaptive assessment was conducted using the Autism Diagnostic Observation Schedule - Second Edition (ADOS-2), the Conners' Parent Rating Scale - Revised: Long Version (CPRS-R), the Wechsler Intelligence Scale - Fourth Edition or the Griffiths Mental Developmental Scales - Extended Revised, the Vineland Adaptive Behaviour Scale - Second Edition (VABS-II). Conclusion: As to the adaptive skills in the three groups evaluated, a worse general profile was ascertained in the ASD and in ASD plus ADHD groups in comparison with respect to the ADHD-only group. With VABS-II evaluation, we found significant differences among the three groups across all domains and combined scores: Communication (F = 18.960; p < 0.001), Socialization (F = 25.410; p < 0.001), Daily Living Skills (F = 19.760; p < 0.001), Motor (F = 9.615; p < 0.001), and Adaptive behavior composite [ABC] (F = 29.370; p < 0.001). Implications of neurodevelopmental double diagnosis such as ASD plus ADHD are discussed.

Alpha Waves as a Neuromarker of Autism Spectrum Disorder: The Challenge of Reproducibility and Heterogeneity

Background: There is no consensus in the literature concerning the presence of abnormal alpha wave profiles in patients with autism spectrum disorder (ASD). This may be due to phenotypic heterogeneity among patients as well as the limited sample sizes utilized. Here we present our results of alpha wave profile analysis based on a sample larger than most of those in the field, performed using a robust processing pipeline. Methods: We compared the alpha waves profiles at rest in children with ASD to those of age-, sex-, and IQ-matched control individuals. We used linear regression and non-parametric normative models using age as covariate forparsing the clinical heterogeneity. We explored the correlation between EEG profiles and the patient's brain volumes, obtained from structural MRI. We automatized the detection of the alpha peak and visually quality controled our MRI measurements. We assessed the robustness of our results by running the EEG preprocessing with two different versions of Matlab as well as Python. Results: A simple linear regression between peak power or frequency of the alpha waves and the status or age of the participants did not allow to identify any statistically significant relationship. The non-parametric normative model (which took account the non-linear effect of age on the alpha profiles) suggested that participants with ASD displayed more variability than control participants for both frequency and amplitude of the alpha peak (p < 0.05). Independent of the status of the individual, we also observed weak associations (uncorrected p < 0.05) between the alpha frequency, and the volumes of several cortical and subcortical structures (in particular the striatum), but which did not survive correction for multiple testing and changed between analysis pelines. Discussions: Our study did not find evidence for abnormal alpha wave profiles in ASD. We propose, however, an analysis pipeline to perform standardized and automatized EEG analyses on large cohorts. These should help the community to address the challenge of clinical heterogeneity of ASD and to tackle the problems of reproducibility.