EEG theta and beta power spectra in adolescents with ADHD versus adolescents with 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.

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.

Biobehavioral mechanisms underlying testosterone and mood relationships in peripubertal female adolescents

The pubertal transition is characterized by pronounced sex hormone fluctuation, refinement of affective neural circuitry, and an increased risk of depression in female adolescents. Sex hormones, including testosterone, exert modulatory effects on frontal-limbic brain networks and are associated with emotion dysregulation and depressive symptoms. Weekly changes in hormones predict affective symptoms in peripubertal female adolescents, particularly in the context of stress; however, the biobehavioral mechanisms underlying hormone change and mood relationships during the pubertal transition have yet to be determined and was the objective of the present study. Forty-three peripubertal female adolescents (ages 11-14) collected 8-weekly salivary hormone (estrone, testosterone) samples and mood assessments to evaluate hormone-mood relationships, followed by a biobehavioral testing session with psychosocial stress and EEG. Within-person correlations between weekly hormone changes and corresponding mood were performed to determine individual differences in mood sensitivity to weekly hormone change. Increased frontal theta activity indexing emotion reactivity, reduced cortisol reactivity, and reduced vagal efficiency predicted the strength of the relationship between testosterone and mood. Further, testosterone-sensitivity strength was associated with the enhancement of negative affect following stress testing. Results identify divergent frontal theta and stress responses as potential biobehavioral mechanisms underlying mood sensitivity to peripubertal testosterone fluctuation.

Motor cortex facilitation: a marker of attention deficit hyperactivity disorder co-occurrence in autism spectrum disorder

The neural correlates distinguishing youth with Autism Spectrum Disorder (ASD-) and ASD with co-occurring Attention Deficit Hyperactivity Disorder (ASD+) are poorly understood despite significant phenotypic and prognostic differences. Paired-pulse transcranial magnetic stimulation (TMS) measures, including intracortical facilitation (ICF), short interval cortical inhibition (SICI), and cortical silent period (CSP) were measured in an age matched cohort of youth with ASD- (n = 20), ASD + (n = 29), and controls (TDC) (n = 24). ASD- and ASD+ groups did not differ by IQ or social functioning; however, ASD+ had significantly higher inattention and hyperactivity ratings. ICF (higher ratio indicates greater facilitation) in ASD+ (Mean 1.0, SD 0.19) was less than ASD- (Mean 1.3, SD 0.36) or TDC (Mean 1.2, SD 0.24) (F2,68 = 6.5, p = 0.003; post-hoc tests, ASD+ vs either TDC or ASD-, p ≤ 0.05). No differences were found between groups for SICI or age corrected active/resting motor threshold (AMT/RMT). Across all ASD youth (ASD- and ASD+), ICF was inversely correlated with worse inattention (Conners-3 Inattention (r = -0.41; p < 0.01) and ADHDRS-IV Inattention percentile (r = -0.422, p < 0.01) scores. ICF remains intact in ASD- but is impaired in ASD+. Lack of ICF is associated with inattention and executive function across ASD. Taken with the present findings, ADHD may have a distinct electrophysiological "signature" in ASD youth. ICF may constitute an emerging biomarker to study the physiology of ADHD in ASD, which may align with disease prognosis or treatment response.

EEG characteristics of children with attention-deficit/hyperactivity disorder

The electroencephalogram (EEG) is an informative neuroimaging tool for studying attention-deficit/hyperactivity disorder (ADHD); one main goal is to characterize the EEG of children with ADHD. In this study, we employed the power spectrum, complexity and bicoherence, biomarker candidates for identifying ADHD children in a machine learning approach, to characterize resting-state EEG (rsEEG). We built support vector machine classifiers using a single type of feature, all features from a method (relative spectral power, spectral power ratio, complexity or bicoherence), or all features from all four methods. We evaluated effectiveness and performance of the classifiers using the permutation test and the area under the receiver operating characteristic curve (AUC). We analyzed the rsEEG from 50 ADHD children and 58 age-matched controls. The results show that though spectral features can be used to build a convincing model, the prediction accuracy of the model was unfortunately unstable. Bicoherence features had significant between-group differences, but classifier performance was sensitive to brain region used. rsEEG complexity of ADHD children was significantly lower than controls and may be a suitable biomarker candidate. Through a machine learning approach, 14 features from various brain regions using different methods were selected; the classifier based on these features had an AUC of 0.9158 and an accuracy of 84.59%. These findings strongly suggest that the combination of rsEEG characteristics obtained by various methods may be a tool for identifying ADHD.

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

Altered power of resting-state neurophysiological activity has been associated with autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), which commonly co-occur. We compared resting-state neurophysiological power in children with ASD, ADHD, co-occurring ASD + ADHD, and typically developing controls. Children with ASD (ASD/ASD + ADHD) showed reduced theta and alpha power compared to children without ASD (controls/ADHD). Children with ADHD (ADHD/ASD + ADHD) displayed decreased delta power compared to children without ADHD (ASD/controls). Children with ASD + ADHD largely presented as an additive co-occurrence with deficits of both disorders, although reduced theta compared to ADHD-only and reduced delta compared to controls suggested some unique markers. Identifying specific neurophysiological profiles in ASD and ADHD may assist in characterising more homogeneous subgroups to inform treatment approaches and aetiological investigations.

Intellectual performance profi le of a sample of children and adolescents from Brazil with 22q11.2 Deletion Syndrome (22q11.2DS) based on the Wechsler Scale

Abstract The 22q11.2 Deletion Syndrome (22q11.2DS), the most common human chromosome microdeletion syndrome, is associated with a very heterogeneous neurocognitive phenotype. One of the main characteristics of the syndrome spectrum is the intellectual variability, which encompasses average performance and intellectual disability and discrepancies between Verbal Intelligence Quotient and Performance Verbal Intelligence Quotient, with greater impairment in nonverbal tasks. The present study aimed at investigating the intellectual performance aspects of a 21children and adolescents sample from Brazil who had been diagnosed with 22q11.2DS, based on the Wechsler Intelligence Scale for Children - 4th edition. The samples were reviewed considering the differences between indices. The results revealed an Full Scale Intelligence Quotient predominant in the borderline range (42 to 104) and a significant discrepancy between the indices of Verbal Comprehension and Perceptual Reasoning in 42% of the sample. With regard to the performance in the subtests alone, a better performance was found in Similarities, whereas block design, matrix reasoning, digit span and letter-number sequencing subtests were the most challenging. These findings indicate that a comprehensive assessment of intellectual performance aspects covering the different measures of the Wechsler Intelligence Scale may contribute to a broader understanding of the neurocognitive phenotype associated with 22q11.2DS.

Relationship Between Executive Functioning and Symptoms of Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder in 6-8 Year Old Children

This study examined relationships between executive functioning (EF) and ADHD/ASD symptoms in 339 6-8 year-old children to characterise EF profiles associated with ADHD and ADHD + ASD. ADHD status was assessed using screening surveys and diagnostic interviews. ASD symptoms were measured using the Social Communication Questionnaire, and children completed assessments of EF. We found the EF profile of children with ADHD + ASD did not differ from ADHD-alone and that lower-order cognitive skills contributed significantly to EF. Dimensionally, ASD and inattention symptoms were differentially associated with EF, whereas hyperactivity symptoms were unrelated to EF. Differences between categorical and dimensional findings suggest it is important to use both diagnostic and symptom based approaches in clinical settings when assessing these children's functional abilities.

1-year follow-up of neurofeedback treatment in adolescents with attention-deficit hyperactivity disorder: randomised controlled trial

BACKGROUND

Estimates of the effectiveness of neurofeedback as a treatment for attention-deficit hyperactivity disorder (ADHD) are mixed.

AIMS

To investigate the long-term additional effects of neurofeedback (NFB) compared with treatment as usual (TAU) for adolescents with ADHD.

METHOD

Using a multicentre parallel-randomised controlled trial design, 60 adolescents with a DSM-IV-TR diagnosis of ADHD receiving NFB+TAU (n=41) or TAU (n=19) were followed up. Neurofeedback treatment consisted of approximately 37 sessions of theta/sensorimotor rhythm (SMR)-training on the vertex (Cz). Outcome measures included behavioural self-reports and neurocognitive measures. Allocation to the conditions was unmasked.

RESULTS

At 1-year follow-up, inattention as reported by adolescents was decreased (range η_p²=0.23-0.36, P<0.01) and performance on neurocognitive tasks was faster (range η_p²=0.20-0.67, P<0.005) irrespective of treatment group.

CONCLUSIONS

Overall, NFB+TAU was as effective as TAU. Given the absence of robust additional effects of neurofeedback in the current study, results do not support the use of theta/SMR neurofeedback as a treatment for adolescents with ADHD and comorbid disorders in clinical practice.

DECLARATION OF INTEREST

None.

COPYRIGHT AND USAGE

© The Royal College of Psychiatrists 2016. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) licence.