Atypical alpha band microstates produced during eyes-closed resting state EEG in autism.

Electroencephalogram (EEG) microstates, which represent quasi-stable patterns of scalp topography, are a promising tool that has the temporal resolution to study atypical spatial and temporal networks in autism spectrum disorder (ASD). While current literature suggests microstates are atypical in ASD, their clinical utility, i.e., relationship with the core behavioural characteristics of ASD, is not fully understood. The aim of this study was to examine microstate parameters in ASD, and examine the relationship between these parameters and core behavioural characteristics in ASD. We compared duration, occurrence, coverage, global explained variance percentage, global field power and spatial correlation of EEG microstates between autistic and neurotypical (NT) adults. Modified k-means cluster analysis was used on eyes-closed, resting state EEG from 30 ASD (10 females, 28.97 ± 9.34 years) and 30 age-equated NT (13 females, 29.33 ± 8.88 years) adults. Five optimal microstates, A to E, were selected to best represent the data. Five microstate maps explaining 80.44% of the NT and 78.44% of the ASD data were found. The ASD group was found to have atypical parameters of microstate A, C, D, and E. Of note, all parameters of microstate C in the ASD group were found to be significantly less than NT. While parameters of microstate D, and E were also found to significantly correlate with subscales of the Ritvo Autism Asperger Diagnostic Scale - Revised (RAADS-R), these findings did not survive a Bonferroni Correction. These findings, in combination with previous findings, highlight the potential clinical utility of EEG microstates and indicate their potential value as a neurophysiologic marker that can be further studied.

EEG microstates in early-to-middle childhood show associations with age, biological sex, and alpha power.

Electroencephalographic (EEG) microstates can provide a unique window into the temporal dynamics of large-scale brain networks across brief (millisecond) timescales. Here, we analysed fundamental temporal features of microstates extracted from the broadband EEG signal in a large (N = 139) cohort of children spanning early-to-middle childhood (4-12 years of age). Linear regression models were used to examine if participants' age and biological sex could predict the temporal parameters GEV, duration, coverage, and occurrence, for five microstate classes (A-E) across both eyes-closed and eyes-open resting-state recordings. We further explored associations between these microstate parameters and posterior alpha power after removal of the 1/f-like aperiodic signal. The microstates obtained from our neurodevelopmental EEG recordings broadly replicated the four canonical microstate classes (A to D) frequently reported in adults, with the addition of the more recently established microstate class E. Biological sex served as a significant predictor in the regression models for four of the five microstate classes (A, C, D, and E). In addition, duration and occurrence for microstate E were both found to be positively associated with age for the eyes-open recordings, while the temporal parameters of microstates C and E both exhibited associations with alpha band spectral power. Together, these findings highlight the influence of age and sex on large-scale functional brain networks during early-to-middle childhood, extending understanding of neural dynamics across this important period for brain development.

Exploring the role of autistic traits and eating disorder psychopathology on mentalising ability in the general population.

BACKGROUND: This study evaluated the role of overlapping traits and characteristics related to autism spectrum disorder (autism) and anorexia nervosa (AN) in the general population, and the impact of these traits on mentalising ability. METHODS: A sample of young adults (N = 306), aged 18-25 years, was recruited to complete an online study that consisted of 4 measures: the Autism-Spectrum Quotient, Eating Disorder Examination Questionnaire, the Mentalization Scale, and the Reading the Mind in the Eyes task. RESULTS: Higher levels of autistic traits, particularly difficulty with attention switching, were associated with increased eating disorder psychopathology. Overall, autistic traits and eating disorder psychopathology were related among females, but not males. Difficulty with attention switching, however, was related to eating disorder psychopathology among both females and males. Autistic traits also appear to have a greater role in mentalising ability than does eating disorder psychopathology. CONCLUSION: The role of attention switching in overlapping traits of autism and eating disorder psychopathology needs to be more comprehensively evaluated by future research, as does the role of biological sex. Expanded knowledge in this field will help to better understand and evaluate symptoms at presentation, leading to clearer diagnoses and potentially better treatment outcomes.

An Investigation of Age-related Neuropathophysiology in Autism Spectrum Disorder Using Fixel-based Analysis of Corpus Callosum White Matter Micro- and Macrostructure.

Fixel-based analysis was used to probe age-related changes in white matter micro- and macrostructure of the corpus callosum between participants with (N = 54) and without (N = 50) autism spectrum disorder (ASD). Data were obtained from the Autism Brain Imaging Data Exchange-II (ABIDE-II). Compared to age-matched controls, young adolescents with ASD (11.19 ± 7.54 years) showed reduced macroscopic fiber cross-section (logFC) and combined fiber-density and cross-section (FDC). Reduced fiber-density (FD) and FDC was noted in a marginally older (13.87 ± 3.15 years) ASD cohort. Among the oldest ASD cohort (17.07 ± 3.56 years), a non-significant trend indicative of reduced FD was noted. White matter aberration appears greatest and most widespread among younger ASD cohorts. This supports the suggestion that some early neuropathophysiological indicators in ASD may dissipate with age.

A systematic review of the neurobiological effects of theta-burst stimulation (TBS) as measured using functional magnetic resonance imaging (fMRI).

Theta burst stimulation (TBS) is associated with the modulation of a range of clinical, cognitive, and behavioural outcomes, but specific neurobiological effects remain somewhat unclear. This systematic literature review investigated resting-state and task-based functional magnetic resonance imaging (fMRI) outcomes post-TBS in healthy human adults. Fifty studies that applied either continuous-or intermittent-(c/i) TBS, and adopted a pretest-posttest or sham-controlled design, were included. For resting-state outcomes following stimulation applied to motor, temporal, parietal, occipital, or cerebellar regions, functional connectivity generally decreased in response to cTBS and increased in response to iTBS, though there were some exceptions to this pattern of response. These findings are mostly consistent with the assumed long-term depression (LTD)/long-term potentiation (LTP)-like plasticity effects of cTBS and iTBS, respectively. Task-related outcomes following TBS were more variable. TBS applied to the prefrontal cortex, irrespective of task or state, also produced more variable responses, with no consistent patterns emerging. Individual participant and methodological factors are likely to contribute to the variability in responses to TBS. Future studies assessing the effects of TBS via fMRI must account for factors known to affect the TBS outcomes, both at the level of individual participants and of research methodology.

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

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

Examination of Cerebellar Grey-Matter Volume in Children with Neurodevelopmental Disorders: a Coordinated Analysis Using the ACAPULCO Algorithm.

Alterations in cerebellar morphology relative to controls have been identified in children with autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), and developmental coordination disorder (DCD). However, it is not clear if common cerebellar regions are affected in each neurodevelopmental disorder and whether cerebellar morphological changes reflect a generic developmental vulnerability, or disorder-specific characteristic. The present study concatenated anatomical MRI scans from five existing cohorts, resulting in data from 252 children between the age of 7 and 12 years (ASD = 58, ADHD = 86, DCD = 22, Controls = 86). The ACAPULCO processing pipeline for cerebellar segmentation was conducted on T1-weighted images. A voxel-wise approach with general linear model was used to compare grey-matter volume of the 27 cerebellar lobules between each clinical group and controls. Our findings revealed that the ADHD group showed lower grey-matter volume in the left Crus I - part of the executive/non-motor portion of the cerebellum, relative to controls (p = 0.02). This no longer remained significant after controlling for medication status. There were no regions of significant differences in volume of the cerebellar lobules in ASD or DCD compared to controls. Future work will conduct harmonisation of behavioural data (cognitive and motor outcomes) across cohorts, enabling more advanced analyses to identify symptom cluster across neurodevelopmental disorders.

Resting state electroencephalography microstates in autism spectrum disorder: A mini-review.

Atypical spatial organization and temporal characteristics, found via resting state electroencephalography (EEG) microstate analysis, have been associated with psychiatric disorders but these temporal and spatial parameters are less known in autism spectrum disorder (ASD). EEG microstates reflect a short time period of stable scalp potential topography. These canonical microstates (i.e., A, B, C, and D) and more are identified by their unique topographic map, mean duration, fraction of time covered, frequency of occurrence and global explained variance percentage; a measure of how well topographical maps represent EEG data. We reviewed the current literature for resting state microstate analysis in ASD and identified eight publications. This current review indicates there is significant alterations in microstate parameters in ASD populations as compared to typically developing (TD) populations. Microstate parameters were also found to change in relation to specific cognitive processes. However, as microstate parameters are found to be changed by cognitive states, the differently acquired data (e.g., eyes closed or open) resting state EEG are likely to produce disparate results. We also review the current understanding of EEG sources of microstates and the underlying brain networks.

Cross-frequency coupling in psychiatric disorders: A systematic review.

Cross-frequency coupling (CFC), an electrophysiologically derived measure of oscillatory coupling in the brain, is believed to play a critical role in neuronal computation, learning and communication. It has received much recent attention in the study of both health and disease. We searched for literature that studied CFC during resting state and task-related activities during electroencephalography and magnetoencephalography in psychiatric disorders. Thirty-eight studies were identified, which included attention-deficit hyperactivity disorder, Alzheimer's dementia, autism spectrum disorder, bipolar disorder, depression, obsessive compulsive disorder, social anxiety disorder and schizophrenia. The systematic review was registered with PROSPERO (ID#CRD42021224188). The current review indicates measurable differences exist between CFC in disease states vs. healthy controls. There was variance in CFC at different regions of the brain within the same psychiatric disorders, perhaps this could be explained by the mechanisms and functionality of CFC. There was heterogeneity in methodologies used, which may lead to spurious CFC analyses. Going forward, standardized methodologies need to be established and utilized in further research to understand the neuropathophysiology associated with psychiatric disorders.

Large-scale analysis of interindividual variability in single and paired-pulse TMS data.

OBJECTIVE: This study brought together over 60 transcranial magnetic stimulation (TMS) researchers to create the largest known sample of individual participant single and paired-pulse TMS data to date, enabling a more comprehensive evaluation of factors driving response variability. METHODS: Authors of previously published studies were contacted and asked to share deidentified individual TMS data. Mixed-effects regression investigated a range of individual and study level variables for their contribution to variability in response to single and paired-pulse TMS data. RESULTS: 687 healthy participant's data were pooled across 35 studies. Target muscle, pulse waveform, neuronavigation use, and TMS machine significantly predicted an individual's single-pulse TMS amplitude. Baseline motor evoked potential amplitude, motor cortex hemisphere, and motor threshold (MT) significantly predicted short-interval intracortical inhibition response. Baseline motor evoked potential amplitude, test stimulus intensity, interstimulus interval, and MT significantly predicted intracortical facilitation response. Age, hemisphere, and TMS machine significantly predicted MT. CONCLUSIONS: This large-scale analysis has identified a number of factors influencing participants' responses to single and paired-pulse TMS. We provide specific recommendations to minimise interindividual variability in single and paired-pulse TMS data. SIGNIFICANCE: This study has used large-scale analyses to give clarity to factors driving variance in TMS data. We hope that this ongoing collaborative approach will increase standardisation of methods and thus the utility of single and paired-pulse TMS.

Examining resting-state functional connectivity in key hubs of the default mode network in chronic low back pain.

OBJECTIVES: Changes in brain connectivity have been observed within the default mode network (DMN) in chronic low back pain (CLBP), however the extent of these disruptions and how they may be related to CLBP requires further examination. While studies using seed-based analysis have found disrupted functional connectivity in the medial prefrontal cortex (mPFC), a major hub of the DMN, limited studies have investigated other equally important hubs, such as the posterior cingulate cortex (PCC) in CLBP. METHODS: This preliminary study comprised 12 individuals with CLBP and 12 healthy controls who completed a resting-state functional magnetic resonance imaging (fMRI) scan. The mPFC and PCC were used as seeds to assess functional connectivity. RESULTS: Both groups displayed similar patterns of DMN connectivity, however group comparisons showed that CLBP group had reduced connectivity between the PCC and angular gyrus compared to healthy controls. An exploratory analysis examined whether the alterations observed in mPFC and PCC connectivity were related to pain catastrophizing in CLBP, but no significant associations were observed. CONCLUSIONS: These results may suggest alterations in the PCC are apparent in CLBP, however, the impact and functional role of these disruptions require further investigation.

Repetitive transcranial magnetic stimulation (rTMS) in autism spectrum disorder: protocol for a multicentre randomised controlled clinical trial.

INTRODUCTION: There are no well-established biomedical treatments for the core symptoms of autism spectrum disorder (ASD). A small number of studies suggest that repetitive transcranial magnetic stimulation (rTMS), a non-invasive brain stimulation technique, may improve clinical and cognitive outcomes in ASD. We describe here the protocol for a funded multicentre randomised controlled clinical trial to investigate whether a course of rTMS to the right temporoparietal junction (rTPJ), which has demonstrated abnormal brain activation in ASD, can improve social communication in adolescents and young adults with ASD. METHODS AND ANALYSIS: This study will evaluate the safety and efficacy of a 4-week course of intermittent theta burst stimulation (iTBS, a variant of rTMS) in ASD. Participants meeting criteria for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition ASD (n=150, aged 14-40 years) will receive 20 sessions of either active iTBS (600 pulses) or sham iTBS (in which a sham coil mimics the sensation of iTBS, but no active stimulation is delivered) to the rTPJ. Participants will undergo a range of clinical, cognitive, epi/genetic, and neurophysiological assessments before and at multiple time points up to 6 months after iTBS. Safety will be assessed via a structured questionnaire and adverse event reporting. The study will be conducted from November 2020 to October 2024. ETHICS AND DISSEMINATION: The study was approved by the Human Research Ethics Committee of Monash Health (Melbourne, Australia) under Australia's National Mutual Acceptance scheme. The trial will be conducted according to Good Clinical Practice, and findings will be written up for scholarly publication. TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trials Registry (ACTRN12620000890932).

Fixel-based Analysis of Diffusion MRI: Methods, Applications, Challenges and Opportunities.

Diffusion MRI has provided the neuroimaging community with a powerful tool to acquire in-vivo data sensitive to microstructural features of white matter, up to 3 orders of magnitude smaller than typical voxel sizes. The key to extracting such valuable information lies in complex modelling techniques, which form the link between the rich diffusion MRI data and various metrics related to the microstructural organization. Over time, increasingly advanced techniques have been developed, up to the point where some diffusion MRI models can now provide access to properties specific to individual fibre populations in each voxel in the presence of multiple "crossing" fibre pathways. While highly valuable, such fibre-specific information poses unique challenges for typical image processing pipelines and statistical analysis. In this work, we review the "Fixel-Based Analysis" (FBA) framework, which implements bespoke solutions to this end. It has recently seen a stark increase in adoption for studies of both typical (healthy) populations as well as a wide range of clinical populations. We describe the main concepts related to Fixel-Based Analyses, as well as the methods and specific steps involved in a state-of-the-art FBA pipeline, with a focus on providing researchers with practical advice on how to interpret results. We also include an overview of the scope of all current FBA studies, categorized across a broad range of neuro-scientific domains, listing key design choices and summarizing their main results and conclusions. Finally, we critically discuss several aspects and challenges involved with the FBA framework, and outline some directions and future opportunities.

Neural activity during cognitive reappraisal in chronic low back pain: a preliminary study.

OBJECTIVES: Chronic pain patients often report higher levels of negative emotions, suggesting reduced ability to regulate emotions effectively, however, little is known of the underlying neural cognitive mechanisms. Therefore, the aim of this study was to explore brain activity and connectivity during cognitive reappraisal in chronic low back pain (CLBP). METHODS: This study recruited 24 female participants; 12 with CLBP and 12 healthy controls. Participants completed an emotion regulation task that involved cognitive reappraisal of negative images during functional magnetic resonance imaging. The negative affect following each image and perceived success of the task were reported. Region of interest and seed-to-voxel analyses were conducted using key regions involved in cognitive reappraisal (i.e., amygdalae and dorsomedial prefrontal cortex) as seed regions. RESULTS: During the task, there were no group differences in the behavioural measures and blood oxygen level-dependent (BOLD) brain activation in the seed regions. Functional connectivity analysis showed reduced coupling between the amygdalae and dorsolateral prefrontal cortex, orbitofrontal cortex and inferior parietal cortex in the CLBP group compared to controls. Connectivity between the amygdala and inferior parietal cortex positively correlated with the percent of reduced negative affect during reappraisal in the CLBP group. CONCLUSIONS: These preliminary findings demonstrate that individuals with CLBP exhibit similar emotion regulation abilities to healthy controls at the behavioural and BOLD level. However, altered functional connectivity observed in the CLBP group may reduce effective cognitive reappraisal. These results provide evidence for the potential clinical impact of network changes in CLBP.

Continuous theta-burst stimulation modulates language-related inhibitory processes in bilinguals: evidence from event-related potentials.

The dorsolateral prefrontal cortex (DLPFC) is activated when bilinguals switch between languages. Language switching can also elicit the N2 event-related potential (ERP). This ERP component appears to capture the cognitive control processes related to conflict monitoring, response selection and response inhibition. In the present study, continuous theta-burst stimulation (cTBS) was used to examine the role of the left DLPFC in bilingual language switching, using a picture-naming task. Participants in the study were 17 Afrikaans-English bilinguals. The picture-naming task consisted of non-switch and switch trials. On non-switch trials, participants named two consecutive pictures in the same language. On switch trials, participants named consecutive pictures in different languages (e.g., Afrikaans and then English). The participants completed three testing sessions. In each session, participants received either cTBS to the left DLPFC or the vertex, or sham stimulation, and then completed the picture-naming task. The results showed that following DLPFC stimulation, the N2 ERP was attenuated on switch trials compared to non-switch trials. Vertex or sham stimulation did not modulate the N2 ERP. cTBS did not affect language switching at the behavioural level. These results provide support for the role of the left DLPFC in the cognitive control processes underlying bilingual language switching. Furthermore, the study demonstrates that these processes can be modulated via non-invasive brain stimulation and the effects detected at the neural level.

Meta-Analysis Reveals Gait Anomalies in Autism.

Gait abnormalities are frequently reported in autism. The empirical literature, however, is characterized by inconsistent findings concerning which aspects of gait are affected. We conducted a meta-analysis to summarize study findings that examined temporal and spatial (i.e., two-dimensional) gait parameters in pediatric and adult samples comprising individuals with autism and healthy controls. After searching electronic databases, a total of 18 studies were identified and included in this review. Results from the meta-analyses revealed autism is associated with a wider step width, slower walking speed, longer gait cycle, longer stance time and longer step time. Additionally, autism appears to be associated with greater intra-individual variability on measures of stride length, stride time and walking speed. Meta-regression analyses revealed cadence and gait cycle duration differences, between autism and control groups, become more pronounced with age. Overall, this review demonstrates that autism is associated with gait abnormalities. However, assessment of the methodological quality of the studies reveal, additional research is required to understand the extent that gait abnormalities are specifically linked to autism, or whether they may be secondary to other factors commonly found in this group, such as increased weight. LAY SUMMARY: It is often noted by clinicians that individuals with autism have an awkward or unusual walking style, which is also referred to as gait. In this report, we reviewed past studies that compared gait in individuals with and without autism. Our review indicates autism is associated with an abnormal gait. However, it is not yet clear whether gait abnormalities are caused by autism, or arise due to other factors such as heavier weight, which often co-occurs in this group.

Fixel Based Analysis Reveals Atypical White Matter Micro- and Macrostructure in Adults With Autism Spectrum Disorder: An Investigation of the Role of Biological Sex.

Atypical white matter (WM) microstructure is commonly implicated in the neuropathophysiology of autism spectrum disorder (ASD). Fixel based analysis (FBA), at the cutting-edge of diffusion-weighted imaging, can account for crossing WM fibers and can provide indices of both WM micro- and macrostructure. We applied FBA to investigate WM structure between 25 (12 males, 13 females) adults with ASD and 24 (12 males, 12 females) matched controls. As the role of biological sex on the neuropathophysiology of ASD is of increasing interest, this was also explored. There were no significant differences in WM micro- or macrostructure between adults with ASD and matched healthy controls. When data were stratified by sex, females with ASD had reduced fiber density and cross-section (FDC), a combined metric comprised of micro- and macrostructural measures, in the corpus callosum, a finding not detected between the male sub-groups. We conclude that micro- and macrostructural WM aberrations are present in ASD, and may be influenced by biological sex.

Meta-Analysis Reveals a Bilingual Advantage That Is Dependent on Task and Age.

Debate continues on whether a bilingual advantage exists with respect to executive functioning. This report synthesized the results of 170 studies to test whether the bilingual advantage is dependent on the task used to assess executive functioning and the age of the participants. The results of the meta-analyses indicated that the bilingual advantage was both task- and age-specific. Bilinguals were significantly faster than monolinguals (Hedges' g values ranged from 0.23 to 0.34), and significantly more accurate than monolinguals (Hedges' g values ranged between 0.18 and 0.49) on four out of seven tasks. Also, an effect of age was found whereby the bilingual advantage was larger for studies comprising samples aged 50-years and over (Hedges' g = 0.49), compared to those undertaken with participants aged between 18 and 29 years (Hedges' g = 0.12). The extent to which the bilingual advantage might be due to publication bias was assessed using multiple methods. These were Egger's Test of Asymmetry, Duval and Tweedie's Trim and Fill, Classic Fail-Safe N, and PET-PEESE. Publication bias was only found when using Egger's Test of Asymmetry and PET-PEESE method, but not when using the other methods. This review indicates that if bilingualism does enhance executive functioning, the effects are modulated by task and age. This may arise because using multiple languages has a highly specific effect on executive functioning which is only observable in older, relative to younger, adults. The finding that publication bias was not uniformly detected across the different methods raises questions about the impact that unpublished (or undetected) studies have on meta-analyses of this literature.