Alpha connectivity and inhibitory control in adults with autism spectrum disorder

Effect of Infra Low Frequency (ILF) neurofeedback training on EEG in children with autism spectrum disorders

Objective

To investigate whether Infra-low frequency Neurofeedback (ILF-NFB) training can improve brain electrical activity in children with autism spectrum disorders ASD.

Method

This single arm pre and post intervention study was carried out at IBMS (Institute of Basic Medical Sciences), Khyber Medical University, Peshawar and Shaheed Zulfiqar Ali Bhutto Medical University (SZABMU), Islamabad from January 2021 to December 2022. A purposive sampling technique was used. Thirty-five ASD children (male=24; female=11; 7-17 years) were provided with 30 sessions of infra low frequency (ILF) neurofeedback training for 15-20 minutes, during 10 weeks. Childhood Autism Rating Scale (CARS) scoring was done and electroencephalogram (EEG) activity was compared before and after ILF-NF training sessions.

Results

Around 62.9% participants had mild-moderate autism and 37.1% had severe autism. Wilcoxon Signed rank test revealed a significant decline in delta (Pre-test=47.31±19.22, Post-test=22.07±6.83; p=<0.001), theta (Pre-test=24.75±16.62, Post-test=12.37±3.59; p=<0.001) and alpha (Pre-test=12.01±9.81, Post-test=4.03±1.61; p=< 0.001) waves. Mann Whitney U test exhibited no significant gender differences in EEG pattern before and after neurofeedback except in theta waves (p=0.03) before the intervention.

Conclusion

Decline in delta, theta, beta and alpha waves propose that ILF-NF training can be effective in improving the EEG activity. ILF-NFB can be perceived as a valuable non-invasive, non-pharmacological intervention for improving EEG pattern via reintegration of brain activity resulting in increased the attention and focus, enhanced mental stability and cognitive engagement.

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.

Linear and Non-linear Analyses of EEG in a Group of ASD Children During Resting State Condition

This study analyses the spontaneous electroencephalogram (EEG) brain activity of 14 children diagnosed with Autism Spectrum Disorder (ASD) compared to 18 children with normal development, aged 5-11 years. (i) Power Spectral Density (PSD), (ii) variability across trials (coefficient of variation: CV), and (iii) complexity (multiscale entropy: MSE) of the brain signal analysis were computed on the resting state EEG. PSD (0.5-45 Hz) and CV were averaged over different frequency bands (low-delta, delta, theta, alpha, low-beta, high-beta and gamma). MSE were calculated with a coarse-grained procedure on 67 time scales and divided into fine, medium and coarse scales. In addition, significant neurophysiological variables were correlated with behavioral performance data (Kaufman Brief Intelligence Test (KBIT) and Autism Spectrum Quotient (AQ)). Results show increased PSD fast frequency bands (high-beta and gamma), higher variability (CV) and lower complexity (MSE) in children with ASD when compared to typically developed children. These results suggest a more variable, less complex and, probably, less adaptive neural networks with less capacity to generate optimal responses in ASD children.

Training and Transfer Effects of Combining Inhibitory Control Training With Transcutaneous Vagus Nerve Stimulation in Healthy Adults

Inhibitory control training (ICT) is a promising method to improve individual performance of inhibitory control (IC). Recent studies have suggested transcutaneous vagus nerve stimulation (tVNS) as a novel approach to affect cognitive function owing to its ability to modulate the locus coeruleus-noradrenaline system. To examine the synergistic effects of combining ICT with tVNS, 58 young males in college were randomly assigned to four groups: ICT + tVNS, ICT + sham tVNS, sham ICT + tVNS, and sham ICT + sham tVNS. Participants were instructed to complete three sessions that comprised pre-training tests, a training session, and post-training tests sequentially. Results showed that the ICT + tVNS group significantly improved training and near-transfer effects on the stop-signal and Go/No-go tasks, and these effects were larger than those of the other groups. However, none of the groups exhibited the far-transfer effect on the color-word Stroop task. These results suggest that tVNS augments the intervention effects of training and similar inhibition tasks to achieve the synergistic effect; however, it does not modulate the effects of non-training tasks and obtain the far-transfer effect. ICT combined with tVNS may be a valuable intervention for improving IC in healthy individuals in certain industries and offers novel research ideas for using tVNS for cognitive improvement.

Microstructural profiles of thalamus and thalamocortical connectivity in patients with disorder of consciousness

Thalamus and thalamocortical connectivity are crucial for consciousness; however, their microstructural changes in patients with a disorder of consciousness (DOC) have not yet been thoroughly characterized. In the present study, we applied the novel fixel-based analysis to comprehensively investigate the thalamus-related microstructural abnormalities in 10 patients with DOC using 7-T diffusion-weighted imaging data. We found that compared to healthy controls, patients with DOC showed reduced fiber density (FD) and fiber density and cross-section (FDC) in the mediodorsal, anterior, and ventral anterior thalamic nuclei, while fiber-bundle cross-section (FC) was not significantly altered in the thalamus. Impaired thalamocortical connectivity in the DOC cohort was mainly connected to the middle frontal gyrus, anterior cingulate gyrus, fusiform gyrus, and sensorimotor cortices, including the precentral gyrus and postcentral gyrus, with predominant microstructural abnormalities in FD and FDC. Correlation analysis showed that FC of the right mediodorsal thalamus was negatively correlated with the level of consciousness. Our results suggest that microstructural abnormalities of thalamus and thalamocortical connectivity in DOC were mainly attributed to axonal injury. In particular, the microstructural integrity of the thalamus is a vital factor in consciousness generation.

Predictive waves in the autism-schizophrenia continuum: A novel biobehavioral model

The brain is a predictive machine. Converging data suggests a diametric predictive strategy from autism spectrum disorders (ASD) to schizophrenic spectrum disorders (SSD). Whereas perceptual inference in ASD is rigidly shaped by incoming sensory information, the SSD population is prone to overestimate the precision of their priors' models. Growing evidence considers brain oscillations pivotal biomarkers to understand how top-down predictions integrate bottom-up input. Starting from the conceptualization of ASD and SSD as oscillopathies, we introduce an integrated perspective that ascribes the maladjustments of the predictive mechanism to dysregulation of neural synchronization. According to this proposal, disturbances in the oscillatory profile do not allow the appropriate trade-off between descending predictive signal, overweighted in SSD, and ascending prediction errors, overweighted in ASD. These opposing imbalances both result in an ill-adapted reaction to external challenges. This approach offers a neuro-computational model capable of linking predictive coding theories with electrophysiological findings, aiming to increase knowledge on the neuronal foundations of the two spectra features and stimulate hypothesis-driven rehabilitation/research perspectives.

The Very Preterm Brain at Rest: Longitudinal Social-Cognitive Network Connectivity During Childhood

Very preterm (VPT: ≤32 weeks of gestational age) birth poses an increased risk for social and cognitive morbidities that persist throughout life. Resting-state functional network connectivity studies provide information about the intrinsic capacity for cognitive processing. We studied the following four social–cognitive resting-state networks: the default mode, salience, frontal-parietal and language networks. We examined functional connectivity using magnetoencephalography with individual head localization using each participant’s MRI at 6 (n = 40) and 8 (n = 40) years of age compared to age- and sex-matched full-term (FT) born children (n = 38 at 6 years and n = 43 at 8 years). VPT children showed increased connectivity compared to FT children in the gamma band (30–80 Hz) at 6 years within the default mode network (DMN), and between the DMN and the salience, frontal-parietal and language networks, pointing to more diffuse, less segregated processing across networks at this age. At 8 years, VPT children had more social and academic difficulties. Increased DMN connectivity at 6 years was associated with social and working memory difficulties at 8 years. Therefore, we suggest that increased DMN connectivity contributes to the observed emerging social and cognitive morbidities in school age.

A review of executive functioning challenges and strengths in autistic adults

Objective: There has been a steady rise in research characterizing executive functioning (EF) impairments in autistic individuals but limited research investigating EF strengths. This review provides a summary of current EF research in autistic adults with a focus on EF challenges and strengths and potential sources of heterogeneity in research findings. New avenues for addressing gaps in our understanding of EF strengths are proposed.Method: A review of the EF literature was conducted. One hundred twenty-four studies of inhibition, working memory, cognitive flexibility, fluency, planning, decision-making, and subjective measures of EF in autistic adults were included.Results: Autistic adults with average intellectual functioning demonstrate difficulties with cognitive flexibility, phonemic fluency, and working memory. Strengths in planning, decision-making, and semantic verbal fluency were evident in some but not all studies. Findings regarding inhibition are inconclusive. Key findings across each EF domain are discussed and sources of potential heterogeneity across studies were evaluated. The type of measure used appears to contribute to heterogeneous findings. Subjective EF measures revealed more consistent findings of deficits in autistic adults than objective EF measures.Conclusions: Research reveals areas of EF weaknesses as well as strengths in autistic adults. Unlike EF challenges, EF strengths are not well understood. Future research identifying EF strengths is needed to improve services and supports for autistic adults. Further investigation of potential factors that interact with or constrain EF such as comorbid disorders, verbal ability, sensory processing, and other factors specific to autism will be critical to move the field forward and increase understanding of how EF is related to everyday functioning in autistic adults.

Volitional modification of brain activity in adolescents with Autism Spectrum Disorder: A Bayesian analysis of Slow Cortical Potential neurofeedback

Autism spectrum disorder is (ASD) characterized by a persisting triad of impairments of social interaction, language as well as inflexible, stereotyped and ritualistic behaviors. Increasingly, scientific evidence suggests a neurobiological basis of these emotional, social and cognitive deficits in individuals with ASD. The aim of this randomized controlled brain self-regulation intervention study was to investigate whether the core symptomatology of ASD could be reduced via an electroencephalography (EEG) based brain self-regulation training of Slow Cortical Potentials (SCP). 41 male adolescents with ASD were recruited and allocated to a) an experimental group undergoing 24 sessions of EEG-based brain training (n1 = 21), or to b) an active control group undergoing conventional treatment (n2 = 20), that is, clinical counseling during a 3-months intervention period. We employed real-time neurofeedback training recorded from a fronto-central electrode intended to enable participants to volitionally regulate their brain activity. Core autistic symptomatology was measured at six time points during the intervention and analyzed with Bayesian multilevel approach to characterize changes in core symptomatology. Additional Bayesian models were formulated to describe the neural dynamics of the training process as indexed by SCP (time-domain) and power density (PSD, frequency-domain) measures. The analysis revealed a substantial improvement in the core symptomatology of ASD in the experimental group (reduction of 21.38 points on the Social Responsiveness Scale, SD = 5.29), which was slightly superior to that observed in the control group (evidence Ratio = 5.79). Changes in SCP manifested themselves as different trajectories depending on the different feedback conditions and tasks. Further, the model of PSD revealed a continuous decrease in delta power, parallel to an increase in alpha power. Most notably, a non-linear (quadratic) model turned out to be better at predicting the data than a linear model across all analyses. Taken together, our analyses suggest that behavioral and neural processes of change related to neurofeedback training are complex and non-linear. Moreover, they have implications for the design of future trials and training protocols.